From 512df8f487229c1b3747270797eab064bdf1b656 Mon Sep 17 00:00:00 2001 From: haesleinhuepf Date: Sat, 1 Feb 2025 09:50:50 +0000 Subject: [PATCH] deploy: 21e20d63fcb34f91625a3d6f0fc3f0a697f47135 --- .../training_materials.csv | 54 +- _sources/authors/beatriz_serrano-solano.md | 2 +- _sources/authors/chris_allan.md | 10 +- _sources/authors/christian_schmidt.md | 24 +- _sources/authors/christian_tischer.md | 8 +- _sources/authors/constantin_pape.md | 4 +- _sources/authors/cornelia_wetzker.md | 4 +- _sources/authors/dominik_kutra.md | 10 +- _sources/authors/elisa_ferrando-may.md | 12 +- _sources/authors/elnaz_fazeli.md | 4 +- .../estibaliz_g\303\263mez-de-mariscal.md" | 8 +- _sources/authors/et_al..md | 22 +- _sources/authors/florian_jug.md | 4 +- _sources/authors/guillaume_witz.md | 2 +- _sources/authors/jean-marie_burel.md | 6 +- _sources/authors/jens_wendt.md | 2 + _sources/authors/josh_moore.md | 22 +- _sources/authors/kota_miura.md | 8 +- _sources/authors/mara_lampert.md | 16 +- "_sources/authors/martin_sch\303\244tz.md" | 2 +- _sources/authors/martin_weigert.md | 4 +- _sources/authors/michele_bortolomeazzi.md | 12 +- _sources/authors/olivier_burri.md | 4 +- _sources/authors/riccardo_massei.md | 8 +- _sources/authors/robert_haase.md | 64 +- _sources/authors/roland_nitschke.md | 2 + _sources/authors/romain_guiet.md | 2 +- _sources/authors/silke_tulok.md | 4 + _sources/authors/stefanie_weidtkamp-peters.md | 8 +- _sources/authors/susanne_kunis.md | 10 +- _sources/authors/thomas_zobel.md | 6 +- _sources/authors/toby_hodges.md | 8 +- _sources/authors/tom_boissonnet.md | 16 +- "_sources/authors/torsten_st\303\266ter.md" | 98 -- _sources/authors/ugis_sarkans.md | 6 +- .../content_types/{blog.md => blog post.md} | 122 +- _sources/content_types/code.md | 8 +- _sources/content_types/collection.md | 32 +- _sources/content_types/data.md | 2 +- _sources/content_types/documentation.md | 10 +- _sources/content_types/event.md | 2 +- _sources/content_types/github repository.md | 42 +- _sources/content_types/notebook.md | 26 +- _sources/content_types/online tutorial.md | 8 +- _sources/content_types/preprint.md | 2 +- _sources/content_types/publication.md | 26 +- _sources/content_types/slide.md | 771 ------------ _sources/content_types/slides.md | 973 +++++++++++++-- _sources/content_types/tutorial.md | 34 +- _sources/content_types/video.md | 172 ++- _sources/content_types/website.md | 2 +- _sources/content_types/workshop.md | 12 +- _sources/domain/biapol.github.io.md | 6 +- _sources/domain/docs.google.com.md | 10 +- _sources/domain/doi.org.md | 98 +- _sources/domain/f1000research.com.md | 14 +- _sources/domain/focalplane.biologists.com.md | 30 +- _sources/domain/git.mpi-cbg.de.md | 14 +- _sources/domain/github.com.md | 70 +- _sources/domain/www.biorxiv.org.md | 2 +- _sources/domain/www.ebi.ac.uk.md | 14 +- _sources/domain/www.nature.com.md | 10 +- _sources/domain/www.youtube.com.md | 18 +- _sources/domain/zenodo.org.md | 82 +- _sources/licenses/bsd-2-clause.md | 4 +- _sources/licenses/bsd-3-clause.md | 81 +- _sources/licenses/cc-by-4.0.md | 150 ++- _sources/licenses/cc-by-sa-4.0.md | 2 +- _sources/licenses/cc0-1.0.md | 67 +- _sources/licenses/gpl-2.0.md | 6 +- _sources/licenses/gpl-3.0.md | 140 +++ _sources/licenses/mit.md | 6 +- _sources/licenses/unknown.md | 76 +- _sources/tags/artificial_intelligence.md | 279 ++++- _sources/tags/bioimage_analysis.md | 313 ++++- _sources/tags/bioimage_data.md | 405 ------ _sources/tags/bioinformatics.md | 4 +- _sources/tags/deep_learning.md | 238 ---- _sources/tags/fair-principles.md | 12 +- _sources/tags/image_data_management.md | 283 ----- _sources/tags/imagej.md | 18 +- _sources/tags/large_language_models.md | 108 -- _sources/tags/licensing.md | 2 +- _sources/tags/metadata.md | 16 +- _sources/tags/microscopy_image_analysis.md | 302 ----- _sources/tags/napari.md | 12 +- _sources/tags/neubias.md | 55 +- _sources/tags/nfdi4bioimage.md | 575 ++++++++- _sources/tags/omero.md | 20 +- _sources/tags/open_science.md | 6 +- _sources/tags/open_source_software.md | 8 +- _sources/tags/python.md | 48 +- _sources/tags/research_data_management.md | 561 ++++++++- _sources/tags/segmentation.md | 96 -- _sources/tags/sharing.md | 16 +- _sources/tags/training.md | 233 ---- _sources/tags/workflow.md | 31 +- _sources/tags/workflow_engine.md | 4 +- _sources/whats_new.md | 14 +- authors/astrid_schauss.html | 31 +- authors/beatriz_serrano-solano.html | 33 +- authors/chris_allan.html | 41 +- authors/christian_schmidt.html | 48 +- authors/christian_tischer.html | 38 +- authors/constantin_pape.html | 35 +- authors/cornelia_wetzker.html | 35 +- authors/dominik_kutra.html | 40 +- authors/elisa_ferrando-may.html | 41 +- authors/elnaz_fazeli.html | 34 +- .../estibaliz_g\303\263mez-de-mariscal.html" | 39 +- authors/et_al..html | 53 +- authors/florian_jug.html | 35 +- authors/guillaume_witz.html | 33 +- authors/isabel_kemmer.html | 31 +- authors/jean-marie_burel.html | 37 +- authors/jens_wendt.html | 32 +- authors/josh_moore.html | 48 +- authors/kota_miura.html | 39 +- authors/mara_lampert.html | 47 +- "authors/martin_sch\303\244tz.html" | 33 +- authors/martin_weigert.html | 35 +- authors/michael_gerlach.html | 31 +- authors/michele_bortolomeazzi.html | 38 +- authors/nicolas_chiaruttini.html | 31 +- authors/olivier_burri.html | 35 +- authors/pia_voigt.html | 31 +- authors/readme.html | 31 +- authors/riccardo_massei.html | 37 +- authors/robert_haase.html | 91 +- authors/roland_nitschke.html | 32 +- authors/romain_guiet.html | 33 +- authors/silke_tulok.html | 33 +- authors/stefanie_weidtkamp-peters.html | 36 +- authors/susanne_kunis.html | 37 +- authors/thomas_zobel.html | 35 +- authors/toby_hodges.html | 38 +- authors/tom_boissonnet.html | 42 +- "authors/torsten_st\303\266ter.html" | 640 ---------- authors/ugis_sarkans.html | 37 +- content_types/{blog.html => blog post.html} | 135 +- content_types/book.html | 37 +- content_types/code.html | 39 +- content_types/collection.html | 62 +- content_types/data.html | 33 +- content_types/document.html | 31 +- content_types/documentation.html | 40 +- content_types/event.html | 33 +- content_types/github repository.html | 72 +- content_types/notebook.html | 57 +- content_types/online tutorial.html | 39 +- content_types/poster.html | 31 +- content_types/preprint.html | 33 +- content_types/publication.html | 63 +- content_types/readme.html | 31 +- content_types/slide.html | 1087 ----------------- content_types/slides.html | 547 ++++++++- content_types/tutorial.html | 72 +- content_types/video.html | 150 ++- content_types/website.html | 37 +- content_types/workshop.html | 43 +- contributing/format.html | 35 +- contributing/index.html | 31 +- contributing/submit_app.html | 31 +- domain/biapol.github.io.html | 37 +- domain/docs.google.com.html | 41 +- domain/doi.org.html | 107 +- domain/f1000research.com.html | 45 +- domain/focalplane.biologists.com.html | 61 +- domain/git.mpi-cbg.de.html | 45 +- domain/github.com.html | 100 +- domain/www.biorxiv.org.html | 33 +- domain/www.ebi.ac.uk.html | 45 +- domain/www.nature.com.html | 41 +- domain/www.youtube.com.html | 49 +- domain/zenodo.org.html | 91 +- export/readme.html | 31 +- genindex.html | 31 +- imprint.html | 31 +- licenses/all_rights_reserved.html | 31 +- licenses/bsd-2-clause.html | 38 +- licenses/bsd-3-clause.html | 96 +- licenses/cc-by-4.0.html | 162 +-- licenses/cc-by-sa-4.0.html | 37 +- licenses/cc0-1.0.html | 83 +- licenses/gpl-2.0.html | 47 +- .../gpl-3.0.html | 229 ++-- licenses/mit.html | 43 +- licenses/readme.html | 31 +- licenses/unknown.html | 107 +- objects.inv | Bin 2481 -> 2349 bytes readme.html | 31 +- search.html | 31 +- searchindex.js | 2 +- statistics/readme.html | 31 +- tags/artificial_intelligence.html | 231 +++- tags/bioimage_analysis.html | 292 +++-- tags/bioimage_data.html | 849 ------------- tags/bioinformatics.html | 41 +- tags/data_stewardship.html | 37 +- tags/deep_learning.html | 746 ----------- tags/fair-principles.html | 55 +- tags/image_data_management.html | 773 ------------ tags/imagej.html | 61 +- tags/large_language_models.html | 663 ---------- tags/licensing.html | 39 +- tags/metadata.html | 53 +- tags/microscopy_image_analysis.html | 785 ------------ tags/napari.html | 53 +- tags/neubias.html | 88 +- tags/nfdi4bioimage.html | 437 ++++++- tags/omero.html | 55 +- tags/open_science.html | 37 +- tags/open_source_software.html | 39 +- tags/python.html | 83 +- tags/readme.html | 31 +- tags/research_data_management.html | 448 ++++++- tags/sharing.html | 59 +- tags/training.html | 743 ----------- tags/workflow.html | 67 +- tags/workflow_engine.html | 45 +- whats_new.html | 40 +- 221 files changed, 7948 insertions(+), 12863 deletions(-) delete mode 100644 "_sources/authors/torsten_st\303\266ter.md" rename _sources/content_types/{blog.md => blog post.md} (72%) delete mode 100644 _sources/content_types/slide.md create mode 100644 _sources/licenses/gpl-3.0.md delete mode 100644 _sources/tags/bioimage_data.md delete mode 100644 _sources/tags/deep_learning.md delete mode 100644 _sources/tags/image_data_management.md delete mode 100644 _sources/tags/large_language_models.md delete mode 100644 _sources/tags/microscopy_image_analysis.md delete mode 100644 _sources/tags/segmentation.md delete mode 100644 _sources/tags/training.md delete mode 100644 "authors/torsten_st\303\266ter.html" rename content_types/{blog.html => blog post.html} (86%) delete mode 100644 content_types/slide.html rename tags/segmentation.html => licenses/gpl-3.0.html (67%) delete mode 100644 tags/bioimage_data.html delete mode 100644 tags/deep_learning.html delete mode 100644 tags/image_data_management.html delete mode 100644 tags/large_language_models.html delete mode 100644 tags/microscopy_image_analysis.html delete mode 100644 tags/training.html diff --git a/_downloads/6de7f639209cca3eaecd5d9517493106/training_materials.csv b/_downloads/6de7f639209cca3eaecd5d9517493106/training_materials.csv index a2032250..5cfad275 100644 --- a/_downloads/6de7f639209cca3eaecd5d9517493106/training_materials.csv +++ b/_downloads/6de7f639209cca3eaecd5d9517493106/training_materials.csv @@ -79,12 +79,12 @@ DEEP NAPARI : Napari as a tool for deep learning project management,"Herearii Me Open Image Data Handbook,Kevin Yamauchi,https://kevinyamauchi.github.io/open-image-data/intro.html,"Neubias, Research Data Management, Napari, Python, Bioimage Analysis",CC-BY-4.0, "Bio-image analysis, biostatistics, programming and machine learning for computational biology","Anna Poetsch, Biotec Dresden, Marcelo Leomil Zoccoler, Johannes Richard Müller, Robert Haase",https://github.com/BiAPoL/Bio-image_Analysis_with_Python,"Python, Bioimage Analysis, Napari",CC-BY-4.0, PoL Bio-Image Analysis Training School on GPU-Accelerated Image Analysis,"Stephane Rigaud, Brian Northan, Till Korten, Neringa Jurenaite, Apurv Deepak Kulkarni, Peter Steinbach, Sebastian Starke, Johannes Soltwedel, Marvin Albert, Robert Haase",https://github.com/BiAPoL/PoL-BioImage-Analysis-TS-GPU-Accelerated-Image-Analysis/,"Gpu, Clesperanto, Dask, Python",CC-BY-4.0,"This repository hosts notebooks, information and data for the GPU-Accelerated Image Analysis Track of the PoL Bio-Image Analysis Symposium." -Bio-image Data Science,Robert Haase,https://github.com/ScaDS/BIDS-lecture-2024,"Image Data Management, Deep Learning, Microscopy Image Analysis, Python",CC-BY-4.0,This repository contains training resources for Students at Uni Leipzig who want to dive into bio-image data science with Python. -Introduction to Deep Learning for Microscopy,Costantin Pape,https://github.com/computational-cell-analytics/dl-for-micro,"Deep Learning, Pytorch, Segmentation, Python",MIT,This course consists of lectures and exercises that teach the background of deep learning for image analysis and show applications to classification and segmentation analysis problems. +Bio-image Data Science,Robert Haase,https://github.com/ScaDS/BIDS-lecture-2024,"Research Data Management, Artificial Intelligence, Bioimage Analysis, Python",CC-BY-4.0,This repository contains training resources for Students at Uni Leipzig who want to dive into bio-image data science with Python. +Introduction to Deep Learning for Microscopy,Costantin Pape,https://github.com/computational-cell-analytics/dl-for-micro,"Artificial Intelligence, Python",MIT,This course consists of lectures and exercises that teach the background of deep learning for image analysis and show applications to classification and segmentation analysis problems. QM Course Lectures on Bio-Image Analysis with napari 2024,Marcelo Leomil Zoccoler,https://zoccoler.github.io/QM_Course_Bio_Image_Analysis_with_napari_2024,"Napari, Python",CC-BY-4.0,"In these lectures, we will explore ways to analyze microscopy images with Python and visualize them with napari, an nD viewer open-source software. The analysis will be done in Python mostly using the scikit-image, pyclesperanto and apoc libraries, via Jupyter notebooks. We will also explore some napari plugins as an interactive and convenient alternative way of performing these analysis, especially the napari-assistant, napari-apoc and napari-flim-phasor-plotter plugins." -QI 2024 Analysis Lab Manual,"Beth Cimini, Florian Jug, QI 2024",https://bethac07.github.io/qi_2024_analysis_lab_manual/intro.html,"Segmentation, Python",CC-BY-4.0,"This book contains the quantitative analysis labs for the QI CSHL course, 2024" -Elastix tutorial,Marvin Albert,https://m-albert.github.io/elastix_tutorial/intro.html,"Image Registration, Itk, Elastix",BSD LICENSE,Tutorial material for teaching the basics of (itk-)elastix for image registration in microscopy images. -Microscopy data analysis: machine learning and the BioImage Archive,"Andrii Iudin, Anna Foix-Romero, Anna Kreshuk, Awais Athar, Beth Cimini, Dominik Kutra, Estibalis Gomez de Mariscal, Frances Wong, Guillaume Jacquemet, Kedar Narayan, Martin Weigert, Nodar Gogoberidze, Osman Salih, Petr Walczysko, Ryan Conrad, Simone Weyend, Sriram Sundar Somasundharam, Suganya Sivagurunathan, Ugis Sarkans",https://www.ebi.ac.uk/training/materials/microscopy-data-analysis-machine-learning-and-the-bioimage-archive-materials/,"Microscopy Image Analysis, Python, Deep Learning",CC-BY-4.0,"The Microscopy data analysis: machine learning and the BioImage Archive course, which focused on introducing programmatic approaches used in the analysis of bioimage data via the BioImage Archive, ran in May 2023." +QI 2024 Analysis Lab Manual,"Beth Cimini, Florian Jug, QI 2024",https://bethac07.github.io/qi_2024_analysis_lab_manual/intro.html,Python,CC-BY-4.0,"This book contains the quantitative analysis labs for the QI CSHL course, 2024" +Elastix tutorial,Marvin Albert,https://m-albert.github.io/elastix_tutorial/intro.html,"Image Registration, Itk, Elastix",BSD-3-CLAUSE,Tutorial material for teaching the basics of (itk-)elastix for image registration in microscopy images. +Microscopy data analysis: machine learning and the BioImage Archive,"Andrii Iudin, Anna Foix-Romero, Anna Kreshuk, Awais Athar, Beth Cimini, Dominik Kutra, Estibalis Gomez de Mariscal, Frances Wong, Guillaume Jacquemet, Kedar Narayan, Martin Weigert, Nodar Gogoberidze, Osman Salih, Petr Walczysko, Ryan Conrad, Simone Weyend, Sriram Sundar Somasundharam, Suganya Sivagurunathan, Ugis Sarkans",https://www.ebi.ac.uk/training/materials/microscopy-data-analysis-machine-learning-and-the-bioimage-archive-materials/,"Bioimage Analysis, Python, Artificial Intelligence",CC-BY-4.0,"The Microscopy data analysis: machine learning and the BioImage Archive course, which focused on introducing programmatic approaches used in the analysis of bioimage data via the BioImage Archive, ran in May 2023." Euro-BioImaging's Template for Research Data Management Plans,"Isabel Kemmer, Euro-BioImaging ERIC","['https://zenodo.org/records/11473803', 'https://doi.org/10.5281/zenodo.11473803']","Bioimage Analysis, FAIR-Principles, Research Data Management",CC-BY-4.0,"Euro-BioImaging has developed a Data Management Plan (DMP) template with questions tailored to bioimaging research projects. Outlining data management practices in this way ensures traceability of project data, allowing for a continuous and unambiguous flow of information throughout the research project. This template can be used to satisfy the requirement to submit a DMP to certain funders. Regardless of the funder, Euro-BioImaging users are encouraged to provide a DMP and can use this template accordingly.  This DMP template is available as a fillable PDF with further instructions and sample responses available by hovering over the fillable fields. " Euro-BioImaging's Guide to FAIR BioImage Data - Practical Tasks,"Isabel Kemmer, Euro-BioImaging ERIC","['https://zenodo.org/records/11474407', 'https://doi.org/10.5281/zenodo.11474407']","Bioimage Analysis, FAIR-Principles, Research Data Management",CC-BY-4.0,"Hands-on exercises on FAIR Bioimage Data from the interactive online workshop ""Euro-BioImaging's Guide to FAIR BioImage Data 2024"" (https://www.eurobioimaging.eu/news/a-guide-to-fair-bioimage-data-2024/).  Types of tasks included: FAIR characteristics of a real world dataset Data Management Plan (DMP) Journal Policies on FAIR data sharing Ontology search Metadata according to REMBI scheme (Image from: Sarkans, U., Chiu, W., Collinson, L. et al. REMBI: Recommended Metadata for Biological Images—enabling reuse of microscopy data in biology. Nat Methods 18, 1418–1422 (2021). https://doi.org/10.1038/s41592-021-01166-8) Matching datasets to bioimage repositories Browsing bioimage repositories" @@ -113,53 +113,53 @@ How to get started with Jupyter and Colab,nan,https://www.youtube.com/watch?v=OH Chris Halvin YouTube channel,nan,"['https://www.youtube.com/@chrishavlin', 'https://www.youtube.com/playlist?list=PLqbhAmYZU5KxuAcnNBIxyBkivUEiKswq1']","Napari, Python, Bioimage Analysis",UNKNOWN, RDM4mic,nan,['https://www.youtube.com/@RDM4mic'],"Research Data Management, OMERO",UNKNOWN, FAIR BioImage Data,nan,['https://www.youtube.com/watch?v=8zd4KTy-oYI&list=PLW-oxncaXRqU4XqduJzwFHvWLF06PvdVm'],"Research Data Management, Fair, Bioimage Analysis",CC-BY-4.0, -Community-developed checklists for publishing images and image analyses,Beth Cimini et al.,https://quarep-limi.github.io/WG12_checklists_for_image_publishing/intro.html,"Bioimage Analysis, Research Data Management",BSD LICENSE,"This book is a companion to the Nature Methods publication Community-developed checklists for publishing images and image analyses. In this paper, members of QUAREP-LiMi have proposed 3 sets of standards for publishing image figures and image analysis - minimal requirements, recommended additions, and ideal comprehensive goals. By following this guidance, we hope to remove some of the stress non-experts may face in determining what they need to do, and we also believe that researchers will find their science more interpretable and more reproducible." +Community-developed checklists for publishing images and image analyses,Beth Cimini et al.,https://quarep-limi.github.io/WG12_checklists_for_image_publishing/intro.html,"Bioimage Analysis, Research Data Management",BSD-3-CLAUSE,"This book is a companion to the Nature Methods publication Community-developed checklists for publishing images and image analyses. In this paper, members of QUAREP-LiMi have proposed 3 sets of standards for publishing image figures and image analysis - minimal requirements, recommended additions, and ideal comprehensive goals. By following this guidance, we hope to remove some of the stress non-experts may face in determining what they need to do, and we also believe that researchers will find their science more interpretable and more reproducible." Data life cycle,ELIXIR (2021) Research Data Management Kit,https://rdmkit.elixir-europe.org/data_life_cycle,"Data Life Cycle, Research Data Management",CC-BY-4.0,"In this section, information is organised according to the stages of the research data life cycle." Data Stewardship Wizard,nan,https://ds-wizard.org/,"Data Stewardship, Open Source, Research Data Management, FAIR-Principles",UNKOWN,Leading open-source platform for collaborative and living data management plans. RDMO - Research Data Management Organiser,nan,https://rdmorganiser.github.io/,"Research Data Management, Open Source Software",UNKNOWN,"Der Research Data Management Organiser (RDMO) unterstützt Forschungsprojekte bei der Planung, Umsetzung und Verwaltung aller Aufgaben des Forschungsdatenmanagements." -Creating a Research Data Management Plan using chatGPT,Robert Haase,https://focalplane.biologists.com/2023/11/06/creating-a-research-data-management-plan-using-chatgpt/,"Research Data Management, Large Language Models, Artificial Intelligence",CC-BY-4.0,In this blog post the author demonstrates how chatGPT can be used to combine a fictive project description with a DMP specification to produce a project-specific DMP. +Creating a Research Data Management Plan using chatGPT,Robert Haase,https://focalplane.biologists.com/2023/11/06/creating-a-research-data-management-plan-using-chatgpt/,"Research Data Management, Artificial Intelligence",CC-BY-4.0,In this blog post the author demonstrates how chatGPT can be used to combine a fictive project description with a DMP specification to produce a project-specific DMP. Fiji,nan,https://omero-guides.readthedocs.io/en/latest/fiji/docs/index.html,"Imagej, OMERO",BSD-2-CLAUSE,Fiji is a popular free open-source image processing package based on ImageJ. Dokumentation und Anleitung zum elektronischen Laborbuch (eLabFTW),"Lienhard Wegewitz, F. Strauß","['https://www.fdm.tu-clausthal.de/fileadmin/FDM/documents/Manual_eLab_v0.3_20200323.pdf', 'https://www.elabftw.net/']",Research Data Management,AGPL-3.0,"Documentation for eLabFTW. With eLabFTW you get a secure, modern and compliant system to track your experiments efficiently but also manage your lab with a powerful and versatile database." Five great reasons to share your research data,nan,https://web.library.uq.edu.au/blog/2022/03/five-great-reasons-share-your-research-data,"Research Data Management, Sharing",UNKNOWN,"Sharing your data can benefit your career in some interesting ways. In this post, read why you should be making more of your research data openly available." Research data - what are the key issues to consider when publishing this kind of material?,nan,https://www.publisso.de/en/advice/publishing-advice-faqs/research-data,"Research Data Management, FAIR-Principles, Licensing",UNKNOWN,"The website offers detailed advice on publishing research data, focusing on key issues like data management, FAIR data principles, legal considerations, and repository selection." Finding and Choosing a Data Repository,Christian Schmidt,https://gerbi-gmb.de/2023/06/01/finding-and-choosing-a-repository/,"I3Dbio, Research Data Management",UNKNOWN,"Funding agencies may demand that original source data of a publication be published, too. So the question is - where should one publish the data? And how does it get there?" -Submitting data to the BioImage Archive,nan,https://www.ebi.ac.uk/bioimage-archive/submit/,"Research Data Management, Image Data Management, Bioimage Data",CC0-1.0,"To submit, you’ll need to register an account, organise and upload your data, prepare a file list, and then submit using our web submission form. These steps are explained here." +Submitting data to the BioImage Archive,nan,https://www.ebi.ac.uk/bioimage-archive/submit/,Research Data Management,CC0-1.0,"To submit, you’ll need to register an account, organise and upload your data, prepare a file list, and then submit using our web submission form. These steps are explained here." Creating Workflows and Advanced Workflow Options,nan,https://galaxyproject.org/learn/advanced-workflow/,Workflow,CC-BY-4.0, Galaxy workflows,nan,https://galaxy-au-training.github.io/tutorials/modules/workflows/,Workflow,CC0-1.0,"A workflow is a chain of analysis steps. In Galaxy, we can create a workflow from an existing analysis history, or we can create one visually by adding tools to a canvas. This tutorial covers building a workflow to analyse a bacterial genome, from input FASTQ sequencing reads to assembly, annotation, and visualization." Galaxy Training,nan,https://training.galaxyproject.org/,"Bioimage Analysis, Data Analysis",CC-BY-4.0,Collection of tutorials developed and maintained by the worldwide Galaxy community. -KNIME Image Processing,None,https://www.knime.com/community/image-processing,"Imagej, OMERO, Bioimage Data, Workflow",GPLV3,"The KNIME Image Processing Extension allows you to read in more than 140 different kinds of images and to apply well known methods on images, like preprocessing. segmentation, feature extraction, tracking and classification in KNIME." -Open Micoscropy Environment (OME) Youtube Channel,nan,https://www.youtube.com/@OpenMicroscopyEnvironment,"Open Source Software, Microscopy Image Analysis, Bioimage Data",CC-BY-4.0,OME develops open-source software and data format standards for the storage and manipulation of biological microscopy data -Erick Martins Ratamero - Expanding the OME ecosystem for imaging data management | SciPy 2024,"SciPy, Erick Martins Ratamero",https://www.youtube.com/watch?v=GmhyDNm1RsM,"Image Data Management, OMERO, Bioimage Analysis",YOUTUBE STANDARD LICENSE, +KNIME Image Processing,None,https://www.knime.com/community/image-processing,"Imagej, OMERO, Workflow",GPL-3.0,"The KNIME Image Processing Extension allows you to read in more than 140 different kinds of images and to apply well known methods on images, like preprocessing. segmentation, feature extraction, tracking and classification in KNIME." +Open Micoscropy Environment (OME) Youtube Channel,nan,https://www.youtube.com/@OpenMicroscopyEnvironment,Open Source Software,CC-BY-4.0,OME develops open-source software and data format standards for the storage and manipulation of biological microscopy data +Erick Martins Ratamero - Expanding the OME ecosystem for imaging data management | SciPy 2024,"SciPy, Erick Martins Ratamero",https://www.youtube.com/watch?v=GmhyDNm1RsM,"OMERO, Bioimage Analysis",YOUTUBE STANDARD LICENSE, OMERO - QuPath,Rémy Jean Daniel Dornier,https://wiki-biop.epfl.ch/en/data-management/omero/qupath,"Bioimage Analysis, OMERO",CC-BY-NC-SA-4.0,"OMERO-RAW extension for QuPath allows to directly access to the raw pixels of images. All types of images (RGB, fluorescence, ...) are supported with this extension." QuPath for Python programmers,"Alan O'Callaghan, Léo Leplat",https://github.com/qupath/i2k-qupath-for-python-programmers,"Python, Notebooks, Open Source Software, Bioimage Analysis",UNKNOWN,These are the notebooks and associated files for the i2k 2024 QuPath for Python programmers workshop. -"I2K2024 workshop material - Lazy Parallel Processing and Visualization of Large Data with ImgLib2, BigDataViewer, the N5-API, and Spark","Stephan Saalfeld, Tobias Pietzsch","['https://saalfeldlab.github.io/i2k2024-lazy-workshop/', 'https://github.com/saalfeldlab/i2k2024-lazy-workshop']",Training,APACHE-2.0, -Ultrack I2K 2024 Workshop Materials,"Jordão Bragantini, Teun Huijben","['https://github.com/royerlab/ultrack-i2k2024', 'https://royerlab.github.io/ultrack-i2k2024/']","Segmentation, Bioimage Analysis, Training",BSD3-CLAUSE, -Multiplexed tissue imaging - tools and approaches,"Agustín Andrés Corbat, OmFrederic, Jonas Windhager, Kristína Lidayová","['https://github.com/BIIFSweden/I2K2024-MTIWorkshop', 'https://docs.google.com/presentation/d/1R9-4lXAmTYuyFZpTMDR85SjnLsPZhVZ8/edit#slide=id.p1']","Bioimage Analysis, Microscopy Image Analysis",CC-BY-4.0,"Material for the I2K 2024 ""Multiplexed tissue imaging - tools and approaches"" workshop" +"I2K2024 workshop material - Lazy Parallel Processing and Visualization of Large Data with ImgLib2, BigDataViewer, the N5-API, and Spark","Stephan Saalfeld, Tobias Pietzsch","['https://saalfeldlab.github.io/i2k2024-lazy-workshop/', 'https://github.com/saalfeldlab/i2k2024-lazy-workshop']",Bioimage Analysis,APACHE-2.0, +Ultrack I2K 2024 Workshop Materials,"Jordão Bragantini, Teun Huijben","['https://github.com/royerlab/ultrack-i2k2024', 'https://royerlab.github.io/ultrack-i2k2024/']",Bioimage Analysis,BSD3-CLAUSE, +Multiplexed tissue imaging - tools and approaches,"Agustín Andrés Corbat, OmFrederic, Jonas Windhager, Kristína Lidayová","['https://github.com/BIIFSweden/I2K2024-MTIWorkshop', 'https://docs.google.com/presentation/d/1R9-4lXAmTYuyFZpTMDR85SjnLsPZhVZ8/edit#slide=id.p1']",Bioimage Analysis,CC-BY-4.0,"Material for the I2K 2024 ""Multiplexed tissue imaging - tools and approaches"" workshop" I2K2024(virtual) - Bio-Image Analysis Code Generation,Robert Haase,https://github.com/haesleinhuepf/i2k2024-ai-code-generation,"Bioimage Analysis, Notebooks, Biabob",BSD-3-CLAUSE,"This repository contains training materials for the Tutorial ""Bio-Image Analysis Code Generation"" at the From Images To Knowledge (I2K) Conference (virtual) October 28th-30th 2024." -Object Tracking and Track Analysis using TrackMate and CellTracksColab,Joanna Pylvänäinen,https://github.com/CellMigrationLab/I2K_2024,"Bioimage Analysis, Training",GPL-3.0,"I2K 2024 workshop materials for ""Object Tracking and Track Analysis using TrackMate and CellTracksColab""" -I2K 2024: clEsperanto - GPU-Accelerated Image Processing Library,"Stephane Rigaud, Robert Haase",https://github.com/StRigaud/clesperanto_workshop_I2K24?tab=readme-ov-file,"Clesperanto, Training, Bioimage Analysis, Notebooks, Workflow",BSD-3-CLAUSE,"Course and material for the clEsperanto workshop presented at I2K 2024 @ Human Technopol (Milan, Italy). The workshop is an hands-on demo of the clesperanto project, focussing on how to use the library for users who want use GPU-acceleration for their Image Processing pipeline." -Example Pipeline Tutorial,Tim Monko,"['https://timmonko.github.io/napari-ndev/tutorial/01_example_pipeline/', 'https://github.com/timmonko/napari-ndev']","Napari, Microscopy Image Analysis, Bioimage Analysis",BSD-3-CLAUSE,Napari-ndev is a collection of widgets intended to serve any person seeking to process microscopy images from start to finish. The goal of this example pipeline is to get the user familiar with working with napari-ndev for batch processing and reproducibility (view Image Utilities and Workflow Widget). -Glencoe Software Webinars,"Chris Allan, Emil Rozbicki",https://www.glencoesoftware.com/media/webinars/,"OMERO, Training",UNKNOWN,Example Workflows / usage of the Glencoe Software. +Object Tracking and Track Analysis using TrackMate and CellTracksColab,Joanna Pylvänäinen,https://github.com/CellMigrationLab/I2K_2024,Bioimage Analysis,GPL-3.0,"I2K 2024 workshop materials for ""Object Tracking and Track Analysis using TrackMate and CellTracksColab""" +I2K 2024: clEsperanto - GPU-Accelerated Image Processing Library,"Stephane Rigaud, Robert Haase",https://github.com/StRigaud/clesperanto_workshop_I2K24?tab=readme-ov-file,Bioimage Analysis,BSD-3-CLAUSE,"Course and material for the clEsperanto workshop presented at I2K 2024 @ Human Technopol (Milan, Italy). The workshop is an hands-on demo of the clesperanto project, focussing on how to use the library for users who want use GPU-acceleration for their Image Processing pipeline." +Example Pipeline Tutorial,Tim Monko,"['https://timmonko.github.io/napari-ndev/tutorial/01_example_pipeline/', 'https://github.com/timmonko/napari-ndev']","Napari, Bioimage Analysis",BSD-3-CLAUSE,Napari-ndev is a collection of widgets intended to serve any person seeking to process microscopy images from start to finish. The goal of this example pipeline is to get the user familiar with working with napari-ndev for batch processing and reproducibility (view Image Utilities and Workflow Widget). +Glencoe Software Webinars,"Chris Allan, Emil Rozbicki",https://www.glencoesoftware.com/media/webinars/,OMERO,UNKNOWN,Example Workflows / usage of the Glencoe Software. Virtual-I2K-2024-multiview-stitcher,nan,['https://github.com/m-albert/Virtual-I2K-2024-multiview-stitcher'],"Big Data, Bioimageanalysis",BSD-3-CLAUSE,Repository accompanying the multiview-stitcher tutorial for Virtual I2K 2024 Prompt-Engineering-LLMs-Course,nan,https://github.com/HelmholtzAI-Consultants-Munich/Prompt-Engineering-LLMs-Course,"Llms, Prompt Engineering, Code Generation",MIT, introduction-to-generative-ai,"Bruna Piereck, Alexander Botzki","['https://github.com/vibbits/introduction-to-generative-ai', 'https://liascript.github.io/course/?https://raw.githubusercontent.com/vibbits/introduction-to-generative-ai/refs/heads/main/README.md']",Artificial Intelligence,CC-BY-4.0,Course repository for Strategic Use of Generative AI nextflow-workshop,"Tuur Muyldermans, Kris Davie, Alexander, Nicolas Vannieuwkerke, Kobe Lavaerts, Marcel Ribeiro-Dantas, Bruna Piereck, Steff Taelman","['https://github.com/vibbits/nextflow-workshop', 'https://liascript.github.io/course/?https://raw.githubusercontent.com/vibbits/nextflow-workshop/main/README.md#1']","Workflow, Nextflow",CC-BY-4.0,Nextflow workshop materials March 2023 -Upcoming Image Analysis Events,"Curtis Rueden, Albane de la Villegeorges, Simon F. Nørrelykke, Romain Guiet, Olivier Burri, et al.",https://forum.image.sc/t/upcoming-image-analysis-events/60018/67,"Bioimage Analysis, Microscopy Image Analysis",UNKNOWN, +Upcoming Image Analysis Events,"Curtis Rueden, Albane de la Villegeorges, Simon F. Nørrelykke, Romain Guiet, Olivier Burri, et al.",https://forum.image.sc/t/upcoming-image-analysis-events/60018/67,Bioimage Analysis,UNKNOWN, Finding and using publicly available data,Anna Swan,https://www.ebi.ac.uk/training/online/courses/finding-using-public-data/,"Open Science, Teaching, Sharing",CC-BY-4.0,"Sharing knowledge and data in the life sciences allows us to learn from each other and built on what others have discovered. This collection of online courses brings together a variety of training, covering topics such as biocuration, open data, restricted access data and finding publicly available data, to help you discover and make the most of publicly available data in the life sciences." Lecture-materials of the DeepLife course,"Carl Herrmann, annavonbachmann, David Hoksza, Martin Schätz, Dario Malchiodi, jnguyenvan, Britta Velten, Elodie Laine, JanaBraunger, barwil",https://github.com/deeplife4eu/Lecture-materials/,Bioinformatics,UNKNOWN, cba-support-template,"Arif Khan, Christian Tischer, Sebastian Gonzalez, Dominik Kutra, Felix Schneider, et al.",https://git.embl.de/grp-cba/cba-support-template,"Workflow, Research Data Management",MIT, -Docker Mastery - with Kubernetes + Swarm from a Docker Captain,Bret Fisher,https://www.udemy.com/course/docker-mastery/?srsltid=AfmBOornR5gRqOg-4v8Nsap1z24CaPPUPxg8JzyqEGZ6MvW_dh-sf4Af&couponCode=ST2MT110724BNEW,"Docker, Training",UNKNOWN,"In this course you will learn how to use Docker, Compose and Kubernetes on your machine for better software building and testing." -SWC/GCNU Software Skills,nan,https://software-skills.neuroinformatics.dev/index.html,Training,CC-BY-4.0,"Computational skills training at the UCL Sainsbury Wellcome Centre and Gatsby Computational Neuroscience Unit, delivered by members of the Neuroinformatics Unit." +Docker Mastery - with Kubernetes + Swarm from a Docker Captain,Bret Fisher,https://www.udemy.com/course/docker-mastery/?srsltid=AfmBOornR5gRqOg-4v8Nsap1z24CaPPUPxg8JzyqEGZ6MvW_dh-sf4Af&couponCode=ST2MT110724BNEW,Docker,UNKNOWN,"In this course you will learn how to use Docker, Compose and Kubernetes on your machine for better software building and testing." +SWC/GCNU Software Skills,nan,https://software-skills.neuroinformatics.dev/index.html,nan,CC-BY-4.0,"Computational skills training at the UCL Sainsbury Wellcome Centre and Gatsby Computational Neuroscience Unit, delivered by members of the Neuroinformatics Unit." Diátaxis - A systematic approach to technical documentation authoring.,Daniele Procida,['https://www.diataxis.fr/'],Documentation,CC-BY-SA-4.0,"Diátaxis is a systematic framework for technical documentation that organizes content into four types—tutorials, how-to guides, technical reference, and explanations—to address distinct user needs, enhancing both user understanding and the documentation process." -Image Processing with Python,"Mark Meysenburg, Toby Hodges, Dominik Kutra, Erin Becker, David Palmquist, et al.",https://datacarpentry.org/image-processing/key-points.html,"Segmentation, Bioimage Analysis, Training, Python, Scikit-Image, Image Segmentation",CC-BY-4.0,This lesson shows how to use Python and scikit-image to do basic image processing. -AI ML DL in Bioimage Analysis - Webinar,Yannick KREMPP,https://www.youtube.com/watch?v=TJXNMIWtdac,"Deep Learning, Machine Learning, Artificial Intelligence, Bioimage Analysis, Large Language Models",UNKNOWN,"A review of the tools, methods and concepts useful for biologists and life scientists as well as bioimage analysts." -Dr Guillaume Jacquemet on studying cancer cell metastasis in the era of deep learning for microscopy,Guillaume Jacquemet,https://www.youtube.com/watch?v=KTdZBgSCYJQ,"Deep Learning, Microscopy Image Analysis",UNKNOWN,"Leukocyte extravasation is a critical component of the innate immune response, while circulating tumour cell extravasation is a crucial step in metastasis formation. Despite their importance, these extravasation mechanisms remain incompletely understood. In this talk, Guillaume Jacquemet presents a novel imaging framework that integrates microfluidics with high-speed, label-free imaging to study the arrest of pancreatic cancer cells (PDAC) on human endothelial layers under physiological flow conditions." -patho_prompt_injection,"JanClusmann, Tim Lenz",https://github.com/KatherLab/patho_prompt_injection,"Histopathology, Bioimage Analysis",GNU GENERAL PUBLIC LICENSE V3.0, +Image Processing with Python,"Mark Meysenburg, Toby Hodges, Dominik Kutra, Erin Becker, David Palmquist, et al.",https://datacarpentry.org/image-processing/key-points.html,"Bioimage Analysis, Python",CC-BY-4.0,This lesson shows how to use Python and scikit-image to do basic image processing. +AI ML DL in Bioimage Analysis - Webinar,Yannick KREMPP,https://www.youtube.com/watch?v=TJXNMIWtdac,"Artificial Intelligence, Bioimage Analysis",UNKNOWN,"A review of the tools, methods and concepts useful for biologists and life scientists as well as bioimage analysts." +Dr Guillaume Jacquemet on studying cancer cell metastasis in the era of deep learning for microscopy,Guillaume Jacquemet,https://www.youtube.com/watch?v=KTdZBgSCYJQ,"Artificial Intelligence, Bioimage Analysis",UNKNOWN,"Leukocyte extravasation is a critical component of the innate immune response, while circulating tumour cell extravasation is a crucial step in metastasis formation. Despite their importance, these extravasation mechanisms remain incompletely understood. In this talk, Guillaume Jacquemet presents a novel imaging framework that integrates microfluidics with high-speed, label-free imaging to study the arrest of pancreatic cancer cells (PDAC) on human endothelial layers under physiological flow conditions." +patho_prompt_injection,"JanClusmann, Tim Lenz",https://github.com/KatherLab/patho_prompt_injection,"Histopathology, Bioimage Analysis",GPL-3.0, introduction-to-image-analysis,"Dave Barry, Stefania Marcotti, Martin Jones",https://github.com/RMS-DAIM/introduction-to-image-analysis,Bioimage Analysis,CC-BY-SA-4.0, "Biologists, stop putting UMAP plots in your papers",Rafael Irizarry,https://simplystatistics.org/posts/2024-12-23-biologists-stop-including-umap-plots-in-your-papers/,"Biology, Data Analysis, Umap",UNKNOWN,"UMAP is a powerful tool for exploratory data analysis, but without a clear understanding of how it works, it can easily lead to confusion and misinterpretation." Data Visualization with Flying Colors,Joachim Goedhart,https://thenode.biologists.com/data-visualization-with-flying-colors/research/,Data Visualization,UNKNOWN,The author discusses a number of color palettes that are suitable for coloring graphical elements in plots. -Overview of the Galaxy OMERO-suite - Upload images and metadata in OMERO using Galaxy,"Riccardo Massei, Björn Grüning",https://training.galaxyproject.org/training-material/topics/imaging/tutorials/omero-suite/tutorial.html,"OMERO, Galaxy, Metadata",CC-BY-4.0, -DL4MicEverywhere – Overcoming reproducibility challenges in deep learning microscopy imaging,Iván Hidalgo-Cenalmor,https://focalplane.biologists.com/2024/07/29/dl4miceverywhere-overcoming-reproducibility-challenges-in-deep-learning-microscopy-imaging/,"Deep Learning, Microscopy, Microsycopy Image Analysis, Bio Image Analysis, Artifical Intelligence",UNKNOWN, +Overview of the Galaxy OMERO-suite - Upload images and metadata in OMERO using Galaxy,"Riccardo Massei, Björn Grüning",https://training.galaxyproject.org/training-material/topics/imaging/tutorials/omero-suite/tutorial.html,"OMERO, Galaxy, Metadata, Nfdi4Bioimage",CC-BY-4.0, +DL4MicEverywhere – Overcoming reproducibility challenges in deep learning microscopy imaging,Iván Hidalgo-Cenalmor,https://focalplane.biologists.com/2024/07/29/dl4miceverywhere-overcoming-reproducibility-challenges-in-deep-learning-microscopy-imaging/,"Bio Image Analysis, Artifical Intelligence",UNKNOWN, New report highlights the scientific impact of open source software,UNKNOWN,https://www.statnews.com/sponsor/2024/11/26/new-report-highlights-the-scientific-impact-of-open-source-software/,"Open Source, Alphafold",UNKNOWN, Tracking of mitochondria and capturing mitoflashes,"Leonid Kostrykin, Diana Chiang Jurado",https://training.galaxyproject.org/training-material/topics/imaging/tutorials/detection-of-mitoflashes/tutorial.html#tracking-of-mitochondria-and-capturing-mitoflashes,"Bioinformatics, Bioimage Analysis",CC-BY-4.0, Artificial Intelligence for Digital Pathology,"Jakob Nikolas Kather, Faisal Mahmood, Florian Jug",https://www.youtube.com/watch?v=Om9tl4Dh2yw,Artificial Intelligence,UNKNOWN,How can artificial intelligence be used for digital pathology? diff --git a/_sources/authors/beatriz_serrano-solano.md b/_sources/authors/beatriz_serrano-solano.md index 24f3ecab..e19d5d1b 100644 --- a/_sources/authors/beatriz_serrano-solano.md +++ b/_sources/authors/beatriz_serrano-solano.md @@ -90,7 +90,7 @@ Licensed UNKNOWN Tags: Bioimage Analysis -Content type: Slide +Content type: Slides [https://docs.google.com/presentation/d/1WG_4307XmKsGfWT3taxMvX2rZiG1k0SM1E7SAENJQkI/edit#slide=id.p](https://docs.google.com/presentation/d/1WG_4307XmKsGfWT3taxMvX2rZiG1k0SM1E7SAENJQkI/edit#slide=id.p) diff --git a/_sources/authors/chris_allan.md b/_sources/authors/chris_allan.md index 59021fcc..e6ebd1cb 100644 --- a/_sources/authors/chris_allan.md +++ b/_sources/authors/chris_allan.md @@ -9,9 +9,9 @@ Licensed UNKNOWN Example Workflows / usage of the Glencoe Software. -Tags: OMERO, Training +Tags: OMERO -Content type: Videos, Tutorial, Collection +Content type: Video, Tutorial, Collection [https://www.glencoesoftware.com/media/webinars/](https://www.glencoesoftware.com/media/webinars/) @@ -47,7 +47,7 @@ Licensed CC-BY-4.0 The Open Microscopy Environment (OME) defines a data model and a software implementation to serve as an informatics framework for imaging in biological microscopy experiments, including representation of acquisition parameters, annotations and image analysis results. -Tags: Microscopy Image Analysis, Bioimage Analysis +Tags: Bioimage Analysis Content type: Publication @@ -68,7 +68,7 @@ Licensed GPL-2.0 Java application to convert image file formats, including .mrxs, to an intermediate Zarr structure compatible with the OME-NGFF specification. -Tags: Open Source Software, Bioimage Data +Tags: Open Source Software Content type: Application, Github Repository @@ -87,7 +87,7 @@ Licensed GPL-2.0 Java application to convert a directory of tiles to an OME-TIFF pyramid. This is the second half of iSyntax/.mrxs => OME-TIFF conversion. -Tags: Open Source Software, Bioimage Data +Tags: Open Source Software Content type: Application, Github Repository diff --git a/_sources/authors/christian_schmidt.md b/_sources/authors/christian_schmidt.md index 87a9633a..25d58dc7 100644 --- a/_sources/authors/christian_schmidt.md +++ b/_sources/authors/christian_schmidt.md @@ -17,6 +17,8 @@ Research data management is essential in nowadays research, and one of the big o In this presentation, Stefanie Weidtkamp-Peters will introduce the challenges for bioimaging data management, and the necessary steps to achieve data FAIRification. German BioImaging - GMB e.V., together with other institutions, contributes to this endeavor. Janina Hanne will present how the network of imaging core facilities, research groups and industry partners is key to the German bioimaging community’s aligned collaboration toward FAIR bioimaging data. These activities have paved the way for two data management initiatives in Germany: I3D:bio (Information Infrastructure for BioImage Data) and NFDI4BIOIMAGE, a consortium of the National Research Data Infrastructure. Christian Schmidt will introduce the goals and measures of these initiatives to the benefit of imaging scientist’s work and everyday practice.   +Tags: Nfdi4Bioimage, Research Data Management + [https://zenodo.org/records/7890311](https://zenodo.org/records/7890311) [https://doi.org/10.5281/zenodo.7890311](https://doi.org/10.5281/zenodo.7890311) @@ -70,7 +72,7 @@ Licensed UNKNOWN A Microscopy Research Data Management Resource. -Tags: Metadata, I3Dbio, Research Data Management, Bioimage Data +Tags: Metadata, I3Dbio, Research Data Management Content type: Collection @@ -93,7 +95,7 @@ The open-source software OME Remote Objects (OMERO) is a data management softwar Tags: OMERO, Research Data Management, Nfdi4Bioimage, I3Dbio -Content type: Slide, Video +Content type: Slides, Video [https://zenodo.org/records/8323588](https://zenodo.org/records/8323588) @@ -132,6 +134,8 @@ See cross reference: 10.5281/zenodo.12547566 Key-Value pair template for annotation of datasets in OMERO (light- and electron microscopy data within the research group of Prof. Mueller-Reichert) 10.5281/zenodo.12578084 Key-Value pair template for annotation in OMERO for light microscopy data acquired with AxioScan7 - Core Facility Cellular Imaging (CFCI) +Tags: Nfdi4Bioimage, Research Data Management + [https://zenodo.org/records/12546808](https://zenodo.org/records/12546808) [https://doi.org/10.5281/zenodo.12546808](https://doi.org/10.5281/zenodo.12546808) @@ -206,9 +210,9 @@ Licensed CCY-BY-SA-4.0 Align existing and establish novel services & solutions for data management tasks throughout the bioimage data lifecycle. -Tags: Nfdi4Bioimage, Image Data Management, Bioimage Data, Research Data Management +Tags: Nfdi4Bioimage, Research Data Management -Content type: Conference Abstract, Slide +Content type: Conference Abstract, Slides [https://doi.org/10.11588/heidok.00029489](https://doi.org/10.11588/heidok.00029489) @@ -227,7 +231,7 @@ Licensed CC-BY-4.0 As an initiative within Germany's National Research Data Infrastructure, the authors conducted this community survey in summer 2021 to assess the state of the art of bioimaging RDM and the community needs. -Tags: Research Data Management, Image Data Management, Bioimage Data +Tags: Research Data Management Content type: Publication @@ -265,6 +269,8 @@ Licensed CC-BY-4.0 Research data management (RDM) in microscopy and image analysis is a challenging task. Large files in proprietary formats, complex N-dimensional array structures, and various metadata models and formats can make image data handling inconvenient and difficult. For data organization, annotation, and sharing, researchers need solutions that fit everyday practice and comply with the FAIR (Findable, Accessible, Interoperable, Reusable) principles. International community-based efforts have begun creating open data models (OME), an open file format and translation library (OME-TIFF, Bio-Formats), data management software platforms, and microscopy metadata recommendations and annotation tools. Bringing these developments into practice requires support and training. Iterative feedback and tool improvement is needed to foster practical adoption by the scientific community. The Information Infrastructure for BioImage Data (I3D:bio) project works on guidelines, training resources, and practical assistance for FAIR microscopy RDM adoption with a focus on the management platform OMERO and metadata annotations. +Tags: Nfdi4Bioimage, Research Data Management + [https://zenodo.org/records/10805204](https://zenodo.org/records/10805204) [https://doi.org/10.5281/zenodo.10805204](https://doi.org/10.5281/zenodo.10805204) @@ -284,6 +290,8 @@ Licensed CC-BY-4.0 Germany’s National Research Data Infrastructure (NFDI) aims to establish a sustained, cross-disciplinary research data management (RDM) infrastructure that enables researchers to handle FAIR (findable, accessible, interoperable, reusable) data. While FAIR principles have been adopted by funders, policymakers, and publishers, their practical implementation remains an ongoing effort. In the field of bio-imaging, harmonization of data formats, metadata ontologies, and open data repositories is necessary to achieve FAIR data. The NFDI4BIOIMAGE was established to address these issues and develop tools and best practices to facilitate FAIR microscopy and image analysis data in alignment with international community activities. The consortium operates through its Data Stewards team to provide expertise and direct support to help overcome RDM challenges. The three Helmholtz Centers in NFDI4BIOIMAGE aim to collaborate closely with other centers and initiatives, such as HMC, Helmholtz AI, and HIP. Here we present NFDI4BIOIMAGE’s work and its significance for research in Helmholtz and beyond +Tags: Nfdi4Bioimage, Research Data Management + [https://zenodo.org/records/11501662](https://zenodo.org/records/11501662) [https://doi.org/10.5281/zenodo.11501662](https://doi.org/10.5281/zenodo.11501662) @@ -370,6 +378,8 @@ Learn from each other, leverage different expertise Learn how to train users, establish sustainability strategies, and foster FAIR RDM for bioimaging at your institution +Tags: Nfdi4Bioimage, Research Data Management + [https://zenodo.org/records/14178789](https://zenodo.org/records/14178789) [https://doi.org/10.5281/zenodo.14178789](https://doi.org/10.5281/zenodo.14178789) @@ -401,6 +411,8 @@ Dr. Riccardo Massei (Helmholtz-Center for Environmental Research, UFZ, Leipzig) Dr. Michele Bortolomeazzi (DKFZ, Single cell Open Lab, bioimage data specialist, bioinformatician, staff scientist in the NFDI4BIOIMAGE project) Dr. Christian Schmidt (Science Manager for Research Data Management in Bioimaging, German Cancer Research Center, Heidelberg, Project Coordinator of the NFDI4BIOIMAGE project) +Tags: Nfdi4Bioimage, Research Data Management + [https://zenodo.org/records/11350689](https://zenodo.org/records/11350689) [https://doi.org/10.5281/zenodo.11350689](https://doi.org/10.5281/zenodo.11350689) @@ -484,6 +496,8 @@ Publishing datasets in public archives for bioimage dataKsenia Krooß /Hein Date & Venue:Thursday, Sept. 26, 5.30 p.m.Haus 22 / Paul Ehrlich Lecture Hall (H22-1)University Hospital Frankfurt +Tags: Nfdi4Bioimage, Research Data Management + [https://zenodo.org/records/13861026](https://zenodo.org/records/13861026) [https://doi.org/10.5281/zenodo.13861026](https://doi.org/10.5281/zenodo.13861026) diff --git a/_sources/authors/christian_tischer.md b/_sources/authors/christian_tischer.md index b29d387f..1e0b69f2 100644 --- a/_sources/authors/christian_tischer.md +++ b/_sources/authors/christian_tischer.md @@ -60,7 +60,7 @@ Licensed UNKNOWN -Content type: Slide +Content type: Slides [https://github.com/tischi/presentation-image-analysis](https://github.com/tischi/presentation-image-analysis) @@ -126,7 +126,7 @@ Licensed CC-BY-4.0 Tags: Bioimage Analysis -Content type: Online Tutorial, Video, Slide +Content type: Online Tutorial, Video, Slides [https://www.ebi.ac.uk/training/events/methods-bioimage-analysis/](https://www.ebi.ac.uk/training/events/methods-bioimage-analysis/) @@ -139,7 +139,7 @@ Content type: Online Tutorial, Video, Slide ## Modular training resources for bioimage analysis -Christian Tischer, Antonio Politi, Tim-Oliver Buchholz, Elnaz Fazeli, Nicola Gritti, Aliaksandr Halavatyi, Gonzalez Tirado, Sebastian, Julian Hennies, Toby Hodges, Arif Khan, Dominik Kutra, Stefania Marcotti, Bugra Oezdemir, Felix Schneider, Martin Schorb, Anniek Stokkermans, Yi Sun, Nima Vakili +Christian Tischer, Antonio Politi, Tim-Oliver Buchholz, Elnaz Fazeli, Nicola Gritti, Aliaksandr Halavatyi, Sebastian Gonzalez Tirado, Julian Hennies, Toby Hodges, Arif Khan, Dominik Kutra, Stefania Marcotti, Bugra Oezdemir, Felix Schneider, Martin Schorb, Anniek Stokkermans, Yi Sun, Nima Vakili Published 2024-12-03 @@ -149,6 +149,8 @@ Licensed CC-BY-4.0 Resources for teaching/preparing to teach bioimage analysis +Tags: Neubias, Bioimage Analysis + [https://zenodo.org/records/14264885](https://zenodo.org/records/14264885) [https://doi.org/10.5281/zenodo.14264885](https://doi.org/10.5281/zenodo.14264885) diff --git a/_sources/authors/constantin_pape.md b/_sources/authors/constantin_pape.md index 3e785b07..bea31182 100644 --- a/_sources/authors/constantin_pape.md +++ b/_sources/authors/constantin_pape.md @@ -24,7 +24,7 @@ Licensed UNKNOWN Tags: Artificial Intelligence, Bioimage Analysis -Content type: Slide, Notebook +Content type: Slides, Notebook [https://github.com/JLrumberger/DL-MBL-2021](https://github.com/JLrumberger/DL-MBL-2021) @@ -65,7 +65,7 @@ Overview of Segment Anythign for Microscopy given at the SWISSBIAS online meetin Talk about vision foundation models and Segment Anything for Microscopy given at Human Technopole as part of the EMBO Deep Learning Course in May 2024 -Tags: Image Segmentation, Bioimage Analysis, Deep Learning +Tags: Bioimage Analysis, Artificial Intelligence Content type: Slides diff --git a/_sources/authors/cornelia_wetzker.md b/_sources/authors/cornelia_wetzker.md index 77bb5384..ab9ba9bb 100644 --- a/_sources/authors/cornelia_wetzker.md +++ b/_sources/authors/cornelia_wetzker.md @@ -30,9 +30,9 @@ Licensed CC-BY-4.0 This presentation gives a short outline of the complexity of data and metadata in the bioimaging universe. It introduces NFDI4BIOIMAGE as a newly formed consortium as part of the German 'Nationale Forschungsdateninfrastruktur' (NFDI) and its goals and tools for data management including its current members on TU Dresden campus.   -Tags: Research Data Management, Tu Dresden, Bioimage Data, Nfdi4Bioimage +Tags: Research Data Management, Nfdi4Bioimage -Content type: Slide +Content type: Slides [https://zenodo.org/records/10083555](https://zenodo.org/records/10083555) diff --git a/_sources/authors/dominik_kutra.md b/_sources/authors/dominik_kutra.md index d77d7a67..ee159574 100644 --- a/_sources/authors/dominik_kutra.md +++ b/_sources/authors/dominik_kutra.md @@ -9,7 +9,7 @@ Licensed CC-BY-4.0 This lesson shows how to use Python and scikit-image to do basic image processing. -Tags: Segmentation, Bioimage Analysis, Training, Python, Scikit-Image, Image Segmentation +Tags: Bioimage Analysis, Python Content type: Tutorial, Workflow @@ -28,7 +28,7 @@ Licensed CC-BY-4.0 The Microscopy data analysis: machine learning and the BioImage Archive course, which focused on introducing programmatic approaches used in the analysis of bioimage data via the BioImage Archive, ran in May 2023. -Tags: Microscopy Image Analysis, Python, Deep Learning +Tags: Bioimage Analysis, Python, Artificial Intelligence Content type: Video, Slides @@ -39,7 +39,7 @@ Content type: Video, Slides ## Modular training resources for bioimage analysis -Christian Tischer, Antonio Politi, Tim-Oliver Buchholz, Elnaz Fazeli, Nicola Gritti, Aliaksandr Halavatyi, Gonzalez Tirado, Sebastian, Julian Hennies, Toby Hodges, Arif Khan, Dominik Kutra, Stefania Marcotti, Bugra Oezdemir, Felix Schneider, Martin Schorb, Anniek Stokkermans, Yi Sun, Nima Vakili +Christian Tischer, Antonio Politi, Tim-Oliver Buchholz, Elnaz Fazeli, Nicola Gritti, Aliaksandr Halavatyi, Sebastian Gonzalez Tirado, Julian Hennies, Toby Hodges, Arif Khan, Dominik Kutra, Stefania Marcotti, Bugra Oezdemir, Felix Schneider, Martin Schorb, Anniek Stokkermans, Yi Sun, Nima Vakili Published 2024-12-03 @@ -49,6 +49,8 @@ Licensed CC-BY-4.0 Resources for teaching/preparing to teach bioimage analysis +Tags: Neubias, Bioimage Analysis + [https://zenodo.org/records/14264885](https://zenodo.org/records/14264885) [https://doi.org/10.5281/zenodo.14264885](https://doi.org/10.5281/zenodo.14264885) @@ -85,7 +87,7 @@ Licensed CC-BY-4.0 Tags: Artificial Intelligence, Bioimage Analysis -Content type: Slide +Content type: Slides [https://zenodo.org/doi/10.5281/zenodo.4330625](https://zenodo.org/doi/10.5281/zenodo.4330625) diff --git a/_sources/authors/elisa_ferrando-may.md b/_sources/authors/elisa_ferrando-may.md index b6a8fa04..f2e4f525 100644 --- a/_sources/authors/elisa_ferrando-may.md +++ b/_sources/authors/elisa_ferrando-may.md @@ -28,7 +28,7 @@ The open-source software OME Remote Objects (OMERO) is a data management softwar Tags: OMERO, Research Data Management, Nfdi4Bioimage, I3Dbio -Content type: Slide, Video +Content type: Slides, Video [https://zenodo.org/records/8323588](https://zenodo.org/records/8323588) @@ -51,9 +51,9 @@ Licensed CCY-BY-SA-4.0 Align existing and establish novel services & solutions for data management tasks throughout the bioimage data lifecycle. -Tags: Nfdi4Bioimage, Image Data Management, Bioimage Data, Research Data Management +Tags: Nfdi4Bioimage, Research Data Management -Content type: Conference Abstract, Slide +Content type: Conference Abstract, Slides [https://doi.org/10.11588/heidok.00029489](https://doi.org/10.11588/heidok.00029489) @@ -72,7 +72,7 @@ Licensed CC-BY-4.0 As an initiative within Germany's National Research Data Infrastructure, the authors conducted this community survey in summer 2021 to assess the state of the art of bioimaging RDM and the community needs. -Tags: Research Data Management, Image Data Management, Bioimage Data +Tags: Research Data Management Content type: Publication @@ -110,6 +110,8 @@ Licensed CC-BY-4.0 Research data management (RDM) in microscopy and image analysis is a challenging task. Large files in proprietary formats, complex N-dimensional array structures, and various metadata models and formats can make image data handling inconvenient and difficult. For data organization, annotation, and sharing, researchers need solutions that fit everyday practice and comply with the FAIR (Findable, Accessible, Interoperable, Reusable) principles. International community-based efforts have begun creating open data models (OME), an open file format and translation library (OME-TIFF, Bio-Formats), data management software platforms, and microscopy metadata recommendations and annotation tools. Bringing these developments into practice requires support and training. Iterative feedback and tool improvement is needed to foster practical adoption by the scientific community. The Information Infrastructure for BioImage Data (I3D:bio) project works on guidelines, training resources, and practical assistance for FAIR microscopy RDM adoption with a focus on the management platform OMERO and metadata annotations. +Tags: Nfdi4Bioimage, Research Data Management + [https://zenodo.org/records/10805204](https://zenodo.org/records/10805204) [https://doi.org/10.5281/zenodo.10805204](https://doi.org/10.5281/zenodo.10805204) @@ -129,6 +131,8 @@ Licensed CC-BY-4.0 Germany’s National Research Data Infrastructure (NFDI) aims to establish a sustained, cross-disciplinary research data management (RDM) infrastructure that enables researchers to handle FAIR (findable, accessible, interoperable, reusable) data. While FAIR principles have been adopted by funders, policymakers, and publishers, their practical implementation remains an ongoing effort. In the field of bio-imaging, harmonization of data formats, metadata ontologies, and open data repositories is necessary to achieve FAIR data. The NFDI4BIOIMAGE was established to address these issues and develop tools and best practices to facilitate FAIR microscopy and image analysis data in alignment with international community activities. The consortium operates through its Data Stewards team to provide expertise and direct support to help overcome RDM challenges. The three Helmholtz Centers in NFDI4BIOIMAGE aim to collaborate closely with other centers and initiatives, such as HMC, Helmholtz AI, and HIP. Here we present NFDI4BIOIMAGE’s work and its significance for research in Helmholtz and beyond +Tags: Nfdi4Bioimage, Research Data Management + [https://zenodo.org/records/11501662](https://zenodo.org/records/11501662) [https://doi.org/10.5281/zenodo.11501662](https://doi.org/10.5281/zenodo.11501662) diff --git a/_sources/authors/elnaz_fazeli.md b/_sources/authors/elnaz_fazeli.md index 5b47aef1..b8d04569 100644 --- a/_sources/authors/elnaz_fazeli.md +++ b/_sources/authors/elnaz_fazeli.md @@ -39,7 +39,7 @@ Bioimaging has transformed our understanding of biological processes, yet extrac ## Modular training resources for bioimage analysis -Christian Tischer, Antonio Politi, Tim-Oliver Buchholz, Elnaz Fazeli, Nicola Gritti, Aliaksandr Halavatyi, Gonzalez Tirado, Sebastian, Julian Hennies, Toby Hodges, Arif Khan, Dominik Kutra, Stefania Marcotti, Bugra Oezdemir, Felix Schneider, Martin Schorb, Anniek Stokkermans, Yi Sun, Nima Vakili +Christian Tischer, Antonio Politi, Tim-Oliver Buchholz, Elnaz Fazeli, Nicola Gritti, Aliaksandr Halavatyi, Sebastian Gonzalez Tirado, Julian Hennies, Toby Hodges, Arif Khan, Dominik Kutra, Stefania Marcotti, Bugra Oezdemir, Felix Schneider, Martin Schorb, Anniek Stokkermans, Yi Sun, Nima Vakili Published 2024-12-03 @@ -49,6 +49,8 @@ Licensed CC-BY-4.0 Resources for teaching/preparing to teach bioimage analysis +Tags: Neubias, Bioimage Analysis + [https://zenodo.org/records/14264885](https://zenodo.org/records/14264885) [https://doi.org/10.5281/zenodo.14264885](https://doi.org/10.5281/zenodo.14264885) diff --git "a/_sources/authors/estibaliz_g\303\263mez-de-mariscal.md" "b/_sources/authors/estibaliz_g\303\263mez-de-mariscal.md" index 6a03d073..a8424a6f 100644 --- "a/_sources/authors/estibaliz_g\303\263mez-de-mariscal.md" +++ "b/_sources/authors/estibaliz_g\303\263mez-de-mariscal.md" @@ -9,7 +9,7 @@ Licensed UNKNOWN Tags: Artificial Intelligence, Bioimage Analysis -Content type: Slide +Content type: Slides [https://github.com/esgomezm/NEUBIAS_chapter_DL_2020](https://github.com/esgomezm/NEUBIAS_chapter_DL_2020) @@ -57,7 +57,7 @@ Licensed UNKNOWN Tags: Artificial Intelligence, Bioimage Analysis -Content type: Slide +Content type: Slides [https://raw.githubusercontent.com/esgomezm/esgomezm.github.io/master/assets/pdf/SPAOM2018/MachineLearning_SPAOMworkshop_public.pdf](https://raw.githubusercontent.com/esgomezm/esgomezm.github.io/master/assets/pdf/SPAOM2018/MachineLearning_SPAOMworkshop_public.pdf) @@ -74,7 +74,7 @@ Licensed UNKNOWN Tags: Neubias, Bioimage Analysis -Content type: Slide, Code, Notebook +Content type: Slides, Code, Notebook [https://github.com/miura/NEUBIAS_AnalystSchool2020](https://github.com/miura/NEUBIAS_AnalystSchool2020) @@ -91,7 +91,7 @@ Licensed UNKNOWN Tags: Artificial Intelligence, Bioimage Analysis -Content type: Slide +Content type: Slides [https://github.com/esgomezm/zidas2020_intro_DL](https://github.com/esgomezm/zidas2020_intro_DL) diff --git a/_sources/authors/et_al..md b/_sources/authors/et_al..md index 3e8d21b0..fd9167c5 100644 --- a/_sources/authors/et_al..md +++ b/_sources/authors/et_al..md @@ -85,7 +85,7 @@ Licensed CC-BY-4.0 This lesson shows how to use Python and scikit-image to do basic image processing. -Tags: Segmentation, Bioimage Analysis, Training, Python, Scikit-Image, Image Segmentation +Tags: Bioimage Analysis, Python Content type: Tutorial, Workflow @@ -157,7 +157,7 @@ Licensed CC-BY-4.0 The Brain Imaging Data Structure (BIDS) is a specification for organizing, sharing, and archiving neuroimaging data and metadata in a reusable way. -Tags: Research Data Management, Image Data Management, Bioimage Data +Tags: Research Data Management Content type: Publication @@ -189,7 +189,7 @@ Licensed UNKNOWN Tags: Neubias, Bioimage Analysis -Content type: Slide, Tutorial +Content type: Slides, Tutorial [https://github.com/miura/NEUBIAS_Bioimage_Analyst_Course2017](https://github.com/miura/NEUBIAS_Bioimage_Analyst_Course2017) @@ -206,7 +206,7 @@ Licensed UNKNOWN Tags: Neubias, Bioimage Analysis -Content type: Slide, Code, Notebook +Content type: Slides, Code, Notebook [https://github.com/miura/NEUBIAS_AnalystSchool2019](https://github.com/miura/NEUBIAS_AnalystSchool2019) @@ -223,7 +223,7 @@ Licensed UNKNOWN Tags: Neubias, Bioimage Analysis -Content type: Slide, Code, Notebook +Content type: Slides, Code, Notebook [https://github.com/miura/NEUBIAS_AnalystSchool2020](https://github.com/miura/NEUBIAS_AnalystSchool2020) @@ -280,7 +280,7 @@ Licensed UNKNOWN Bioimaging data have significant potential for reuse, but unlocking this potential requires systematic archiving of data and metadata in public databases. The authors propose draft metadata guidelines to begin addressing the needs of diverse communities within light and electron microscopy. -Tags: Metadata, Bioimage Data, Image Data Management, Research Data Management +Tags: Metadata, Research Data Management Content type: Publication @@ -305,7 +305,7 @@ Licensed CC-BY-4.0 As an initiative within Germany's National Research Data Infrastructure, the authors conducted this community survey in summer 2021 to assess the state of the art of bioimaging RDM and the community needs. -Tags: Research Data Management, Image Data Management, Bioimage Data +Tags: Research Data Management Content type: Publication @@ -326,7 +326,7 @@ Licensed CC-BY-4.0 The authors offer trainers some simple rules, to help make their training materials FAIR, enabling others to find, (re)use, and adapt them. -Tags: Metadata, Bioinformatics, FAIR-Principles, Training +Tags: Metadata, Bioinformatics, FAIR-Principles Content type: Publication @@ -347,7 +347,7 @@ Licensed UNKNOWN Rigorous record-keeping and quality control are required to ensure the quality, reproducibility and value of imaging data. The 4DN Initiative and BINA here propose light Microscopy Metadata specifications that extend the OME data model, scale with experimental intent and complexity, and make it possible for scientists to create comprehensive records of imaging experiments. -Tags: Reproducibility, Microscopy Image Analysis, Metadata, Image Data Management, Bioimage Data +Tags: Reproducibility, Bioimage Analysis, Metadata Content type: Publication @@ -383,7 +383,7 @@ Licensed UNKNOWN -Tags: Bioimage Analysis, Microscopy Image Analysis +Tags: Bioimage Analysis Content type: Collection, Event, Forum Post, Workshop @@ -440,7 +440,7 @@ Licensed GPL-2.0 Java application to convert image file formats, including .mrxs, to an intermediate Zarr structure compatible with the OME-NGFF specification. -Tags: Open Source Software, Bioimage Data +Tags: Open Source Software Content type: Application, Github Repository diff --git a/_sources/authors/florian_jug.md b/_sources/authors/florian_jug.md index 41454bb3..57dbd57b 100644 --- a/_sources/authors/florian_jug.md +++ b/_sources/authors/florian_jug.md @@ -28,7 +28,7 @@ How can artificial intelligence be used for digital pathology? Tags: Artificial Intelligence -Content type: Youtube Video +Content type: Video [https://www.youtube.com/watch?v=Om9tl4Dh2yw](https://www.youtube.com/watch?v=Om9tl4Dh2yw) @@ -81,7 +81,7 @@ Licensed CC-BY-4.0 This book contains the quantitative analysis labs for the QI CSHL course, 2024 -Tags: Segmentation, Python +Tags: Python Content type: Notebook diff --git a/_sources/authors/guillaume_witz.md b/_sources/authors/guillaume_witz.md index cddbcdb2..0152509b 100644 --- a/_sources/authors/guillaume_witz.md +++ b/_sources/authors/guillaume_witz.md @@ -113,7 +113,7 @@ Licensed UNKNOWN Tags: Neubias, Bioimage Analysis -Content type: Slide +Content type: Slides [https://docs.google.com/presentation/d/1q8q1xE-c35tvCsRXZay98s2UYWwXpp0cfCljBmMFpco/edit#slide=id.ga456d5535c_2_53](https://docs.google.com/presentation/d/1q8q1xE-c35tvCsRXZay98s2UYWwXpp0cfCljBmMFpco/edit#slide=id.ga456d5535c_2_53) diff --git a/_sources/authors/jean-marie_burel.md b/_sources/authors/jean-marie_burel.md index e8d3702b..badd25c8 100644 --- a/_sources/authors/jean-marie_burel.md +++ b/_sources/authors/jean-marie_burel.md @@ -87,7 +87,7 @@ Licensed CC-BY-4.0 The Open Microscopy Environment (OME) defines a data model and a software implementation to serve as an informatics framework for imaging in biological microscopy experiments, including representation of acquisition parameters, annotations and image analysis results. -Tags: Microscopy Image Analysis, Bioimage Analysis +Tags: Bioimage Analysis Content type: Publication @@ -100,7 +100,7 @@ Content type: Publication ## ome2024-ngff-challenge -Will Moore, Josh Moore, sherwoodf, jean-marie burel, Norman Rzepka, dependabot[bot], JensWendt, Joost de Folter, Torsten Stöter, AybukeKY, Eric Perlman, Tom Boissonnet +Will Moore, Josh Moore, sherwoodf, Jean-Marie Burel, Norman Rzepka, dependabot[bot], JensWendt, Joost de Folter, Torsten St\xF6ter, AybukeKY, Eric Perlman, Tom Boissonnet Published 2024-08-30T12:00:53+00:00 @@ -110,7 +110,7 @@ Licensed BSD-3-CLAUSE Project planning and material repository for the 2024 challenge to generate 1 PB of OME-Zarr data -Tags: Sharing +Tags: Sharing, Nfdi4Bioimage, Research Data Management Content type: Github Repository diff --git a/_sources/authors/jens_wendt.md b/_sources/authors/jens_wendt.md index c44cbff0..5168c515 100644 --- a/_sources/authors/jens_wendt.md +++ b/_sources/authors/jens_wendt.md @@ -109,6 +109,8 @@ Content: ... +Tags: Nfdi4Bioimage, Research Data Management + [https://zenodo.org/records/7018750](https://zenodo.org/records/7018750) [https://doi.org/10.5281/zenodo.7018750](https://doi.org/10.5281/zenodo.7018750) diff --git a/_sources/authors/josh_moore.md b/_sources/authors/josh_moore.md index 9bdad1ac..11609bc9 100644 --- a/_sources/authors/josh_moore.md +++ b/_sources/authors/josh_moore.md @@ -43,7 +43,7 @@ Licensed CC-BY-4.0 -Tags: Bioimage Analysis, Open Science, Microscopy Image Analysis, Microscopy +Tags: Bioimage Analysis, Open Science, Microscopy Content type: Publication @@ -100,6 +100,8 @@ Presented at the 2024 FoundingGIDE event in Okazaki, Japan: https://founding-gid Note: much of the presentation was a demonstration of the OME2024-NGFF-Challenge -- https://ome.github.io/ome2024-ngff-challenge/ especially of querying an extraction of the metadata (https://github.com/ome/ome2024-ngff-challenge-metadata)   +Tags: Nfdi4Bioimage, Research Data Management + [https://zenodo.org/records/14234608](https://zenodo.org/records/14234608) [https://doi.org/10.5281/zenodo.14234608](https://doi.org/10.5281/zenodo.14234608) @@ -138,7 +140,7 @@ Licensed CC-BY-4.0 As an initiative within Germany's National Research Data Infrastructure, the authors conducted this community survey in summer 2021 to assess the state of the art of bioimaging RDM and the community needs. -Tags: Research Data Management, Image Data Management, Bioimage Data +Tags: Research Data Management Content type: Publication @@ -232,6 +234,8 @@ Licensed CC-BY-4.0 Talk given at Georg-August-Universität Göttingen Campus Institute Data Science23rd January 2025 https://www.uni-goettingen.de/en/653203.html +Tags: Nfdi4Bioimage + [https://zenodo.org/records/14716546](https://zenodo.org/records/14716546) [https://doi.org/10.5281/zenodo.14716546](https://doi.org/10.5281/zenodo.14716546) @@ -253,6 +257,8 @@ CMCB LIFE SCIENCES SEMINARSTechnische Universität Dresden16th January 2025 https://tu-dresden.de/cmcb/crtd/news-termine/termine/cmcb-life-sciences-seminar-josh-moore-german-bioimaging-e-v-society-for-microscopy-and-image-analysis-constance   +Tags: Nfdi4Bioimage + [https://zenodo.org/records/14650434](https://zenodo.org/records/14650434) [https://doi.org/10.5281/zenodo.14650434](https://doi.org/10.5281/zenodo.14650434) @@ -311,6 +317,8 @@ Licensed CC-BY-4.0 Poster presented at the European Light Microscopy Initiative meeting in Liverpool (https://www.elmi2024.org/) +Tags: Nfdi4Bioimage, Research Data Management + [https://zenodo.org/records/11235513](https://zenodo.org/records/11235513) [https://doi.org/10.5281/zenodo.11235513](https://doi.org/10.5281/zenodo.11235513) @@ -449,7 +457,7 @@ Licensed GPL-2.0 Java application to convert image file formats, including .mrxs, to an intermediate Zarr structure compatible with the OME-NGFF specification. -Tags: Open Source Software, Bioimage Data +Tags: Open Source Software Content type: Application, Github Repository @@ -470,8 +478,6 @@ Licensed BSD-2-CLAUSE Web page for validating OME-NGFF files. -Tags: Bioimage Data - Content type: Github Repository, Application [https://ome.github.io/ome-ngff-validator/](https://ome.github.io/ome-ngff-validator/) @@ -483,7 +489,7 @@ Content type: Github Repository, Application ## ome2024-ngff-challenge -Will Moore, Josh Moore, sherwoodf, jean-marie burel, Norman Rzepka, dependabot[bot], JensWendt, Joost de Folter, Torsten Stöter, AybukeKY, Eric Perlman, Tom Boissonnet +Will Moore, Josh Moore, sherwoodf, Jean-Marie Burel, Norman Rzepka, dependabot[bot], JensWendt, Joost de Folter, Torsten St\xF6ter, AybukeKY, Eric Perlman, Tom Boissonnet Published 2024-08-30T12:00:53+00:00 @@ -493,7 +499,7 @@ Licensed BSD-3-CLAUSE Project planning and material repository for the 2024 challenge to generate 1 PB of OME-Zarr data -Tags: Sharing +Tags: Sharing, Nfdi4Bioimage, Research Data Management Content type: Github Repository @@ -512,7 +518,7 @@ Licensed GPL-2.0 Java application to convert a directory of tiles to an OME-TIFF pyramid. This is the second half of iSyntax/.mrxs => OME-TIFF conversion. -Tags: Open Source Software, Bioimage Data +Tags: Open Source Software Content type: Application, Github Repository diff --git a/_sources/authors/kota_miura.md b/_sources/authors/kota_miura.md index 58dcdc33..70745723 100644 --- a/_sources/authors/kota_miura.md +++ b/_sources/authors/kota_miura.md @@ -132,7 +132,7 @@ Licensed UNKNOWN Tags: Neubias, Bioimage Analysis -Content type: Slide, Tutorial +Content type: Slides, Tutorial [https://github.com/miura/NEUBIAS_Bioimage_Analyst_Course2017](https://github.com/miura/NEUBIAS_Bioimage_Analyst_Course2017) @@ -149,7 +149,7 @@ Licensed UNKNOWN Tags: Neubias, Bioimage Analysis -Content type: Slide, Code, Notebook +Content type: Slides, Code, Notebook [https://github.com/miura/NEUBIAS_AnalystSchool2019](https://github.com/miura/NEUBIAS_AnalystSchool2019) @@ -166,7 +166,7 @@ Licensed UNKNOWN Tags: Neubias, Bioimage Analysis -Content type: Slide, Code, Notebook +Content type: Slides, Code, Notebook [https://github.com/miura/NEUBIAS_AnalystSchool2020](https://github.com/miura/NEUBIAS_AnalystSchool2020) @@ -183,7 +183,7 @@ Licensed UNKNOWN Tags: Neubias, Bioimage Analysis -Content type: Slide +Content type: Slides [https://www.dropbox.com/s/5abw3cvxrhpobg4/20220923_DefragmentationTS.pdf?dl=0](https://www.dropbox.com/s/5abw3cvxrhpobg4/20220923_DefragmentationTS.pdf?dl=0) diff --git a/_sources/authors/mara_lampert.md b/_sources/authors/mara_lampert.md index bf2c69e0..f6b4f5f7 100644 --- a/_sources/authors/mara_lampert.md +++ b/_sources/authors/mara_lampert.md @@ -7,7 +7,7 @@ Mara Lampert Tags: Python, Napari, Bioimage Analysis -Content type: Blog +Content type: Blog Post [https://focalplane.biologists.com/2023/03/30/annotating-3d-images-in-napari/](https://focalplane.biologists.com/2023/03/30/annotating-3d-images-in-napari/) @@ -22,7 +22,7 @@ Mara Lampert Tags: Python, Napari, Bioimage Analysis -Content type: Blog +Content type: Blog Post [https://focalplane.biologists.com/2023/05/03/feature-extraction-in-napari/](https://focalplane.biologists.com/2023/05/03/feature-extraction-in-napari/) @@ -39,7 +39,7 @@ Licensed CC-BY-4.0 Tags: Python, Conda, Mamba -Content type: Blog +Content type: Blog Post [https://biapol.github.io/blog/mara_lampert/getting_started_with_mambaforge_and_python/readme.html](https://biapol.github.io/blog/mara_lampert/getting_started_with_mambaforge_and_python/readme.html) @@ -54,7 +54,7 @@ Mara Lampert Tags: Github, Python, Science Communication -Content type: Blog +Content type: Blog Post [https://focalplane.biologists.com/2024/04/03/how-to-write-a-bug-report/](https://focalplane.biologists.com/2024/04/03/how-to-write-a-bug-report/) @@ -69,7 +69,7 @@ Mara Lampert Tags: Python, Jupyter, Bioimage Analysis, Prompt Engineering, Biabob -Content type: Blog +Content type: Blog Post [https://focalplane.biologists.com/2024/07/18/prompt-engineering-in-bio-image-analysis/](https://focalplane.biologists.com/2024/07/18/prompt-engineering-in-bio-image-analysis/) @@ -84,7 +84,7 @@ Mara Lampert Tags: Python, Napari, Bioimage Analysis -Content type: Blog +Content type: Blog Post [https://focalplane.biologists.com/2023/04/13/quality-assurance-of-segmentation-results/](https://focalplane.biologists.com/2023/04/13/quality-assurance-of-segmentation-results/) @@ -99,7 +99,7 @@ Mara Lampert Tags: Python, Napari, Bioimage Analysis -Content type: Blog +Content type: Blog Post [https://focalplane.biologists.com/2023/03/02/rescaling-images-and-pixel-anisotropy/](https://focalplane.biologists.com/2023/03/02/rescaling-images-and-pixel-anisotropy/) @@ -114,7 +114,7 @@ Mara Lampert Tags: Python, Napari, Bioimage Analysis -Content type: Blog +Content type: Blog Post [https://focalplane.biologists.com/2023/06/01/tracking-in-napari/](https://focalplane.biologists.com/2023/06/01/tracking-in-napari/) diff --git "a/_sources/authors/martin_sch\303\244tz.md" "b/_sources/authors/martin_sch\303\244tz.md" index c4db9aee..895d1823 100644 --- "a/_sources/authors/martin_sch\303\244tz.md" +++ "b/_sources/authors/martin_sch\303\244tz.md" @@ -34,7 +34,7 @@ The work was funded by the Ministry of Education, Youth and Sports by grant &lsq ## Interactive Image Data Flow Graphs -Martin Schätz, Martin Schätz +Martin Schätz Published 2022-10-17 diff --git a/_sources/authors/martin_weigert.md b/_sources/authors/martin_weigert.md index 8a0aed7d..9540674e 100644 --- a/_sources/authors/martin_weigert.md +++ b/_sources/authors/martin_weigert.md @@ -111,7 +111,7 @@ Licensed CC-BY-4.0 The Microscopy data analysis: machine learning and the BioImage Archive course, which focused on introducing programmatic approaches used in the analysis of bioimage data via the BioImage Archive, ran in May 2023. -Tags: Microscopy Image Analysis, Python, Deep Learning +Tags: Bioimage Analysis, Python, Artificial Intelligence Content type: Video, Slides @@ -130,7 +130,7 @@ Licensed UNKNOWN Tags: Python, Neubias, Artificial Intelligence, Bioimage Analysis -Content type: Slide, Notebook +Content type: Slides, Notebook [https://github.com/maweigert/neubias_academy_stardist](https://github.com/maweigert/neubias_academy_stardist) diff --git a/_sources/authors/michele_bortolomeazzi.md b/_sources/authors/michele_bortolomeazzi.md index 06aad585..7275fd4d 100644 --- a/_sources/authors/michele_bortolomeazzi.md +++ b/_sources/authors/michele_bortolomeazzi.md @@ -13,7 +13,7 @@ The open-source software OME Remote Objects (OMERO) is a data management softwar Tags: OMERO, Research Data Management, Nfdi4Bioimage, I3Dbio -Content type: Slide, Video +Content type: Slides, Video [https://zenodo.org/records/8323588](https://zenodo.org/records/8323588) @@ -52,6 +52,8 @@ See cross reference: 10.5281/zenodo.12547566 Key-Value pair template for annotation of datasets in OMERO (light- and electron microscopy data within the research group of Prof. Mueller-Reichert) 10.5281/zenodo.12578084 Key-Value pair template for annotation in OMERO for light microscopy data acquired with AxioScan7 - Core Facility Cellular Imaging (CFCI) +Tags: Nfdi4Bioimage, Research Data Management + [https://zenodo.org/records/12546808](https://zenodo.org/records/12546808) [https://doi.org/10.5281/zenodo.12546808](https://doi.org/10.5281/zenodo.12546808) @@ -147,6 +149,8 @@ Licensed CC-BY-4.0 Research data management (RDM) in microscopy and image analysis is a challenging task. Large files in proprietary formats, complex N-dimensional array structures, and various metadata models and formats can make image data handling inconvenient and difficult. For data organization, annotation, and sharing, researchers need solutions that fit everyday practice and comply with the FAIR (Findable, Accessible, Interoperable, Reusable) principles. International community-based efforts have begun creating open data models (OME), an open file format and translation library (OME-TIFF, Bio-Formats), data management software platforms, and microscopy metadata recommendations and annotation tools. Bringing these developments into practice requires support and training. Iterative feedback and tool improvement is needed to foster practical adoption by the scientific community. The Information Infrastructure for BioImage Data (I3D:bio) project works on guidelines, training resources, and practical assistance for FAIR microscopy RDM adoption with a focus on the management platform OMERO and metadata annotations. +Tags: Nfdi4Bioimage, Research Data Management + [https://zenodo.org/records/10805204](https://zenodo.org/records/10805204) [https://doi.org/10.5281/zenodo.10805204](https://doi.org/10.5281/zenodo.10805204) @@ -166,6 +170,8 @@ Licensed CC-BY-4.0 Germany’s National Research Data Infrastructure (NFDI) aims to establish a sustained, cross-disciplinary research data management (RDM) infrastructure that enables researchers to handle FAIR (findable, accessible, interoperable, reusable) data. While FAIR principles have been adopted by funders, policymakers, and publishers, their practical implementation remains an ongoing effort. In the field of bio-imaging, harmonization of data formats, metadata ontologies, and open data repositories is necessary to achieve FAIR data. The NFDI4BIOIMAGE was established to address these issues and develop tools and best practices to facilitate FAIR microscopy and image analysis data in alignment with international community activities. The consortium operates through its Data Stewards team to provide expertise and direct support to help overcome RDM challenges. The three Helmholtz Centers in NFDI4BIOIMAGE aim to collaborate closely with other centers and initiatives, such as HMC, Helmholtz AI, and HIP. Here we present NFDI4BIOIMAGE’s work and its significance for research in Helmholtz and beyond +Tags: Nfdi4Bioimage, Research Data Management + [https://zenodo.org/records/11501662](https://zenodo.org/records/11501662) [https://doi.org/10.5281/zenodo.11501662](https://doi.org/10.5281/zenodo.11501662) @@ -197,6 +203,8 @@ Dr. Riccardo Massei (Helmholtz-Center for Environmental Research, UFZ, Leipzig) Dr. Michele Bortolomeazzi (DKFZ, Single cell Open Lab, bioimage data specialist, bioinformatician, staff scientist in the NFDI4BIOIMAGE project) Dr. Christian Schmidt (Science Manager for Research Data Management in Bioimaging, German Cancer Research Center, Heidelberg, Project Coordinator of the NFDI4BIOIMAGE project) +Tags: Nfdi4Bioimage, Research Data Management + [https://zenodo.org/records/11350689](https://zenodo.org/records/11350689) [https://doi.org/10.5281/zenodo.11350689](https://doi.org/10.5281/zenodo.11350689) @@ -223,6 +231,8 @@ Publishing datasets in public archives for bioimage dataKsenia Krooß /Hein Date & Venue:Thursday, Sept. 26, 5.30 p.m.Haus 22 / Paul Ehrlich Lecture Hall (H22-1)University Hospital Frankfurt +Tags: Nfdi4Bioimage, Research Data Management + [https://zenodo.org/records/13861026](https://zenodo.org/records/13861026) [https://doi.org/10.5281/zenodo.13861026](https://doi.org/10.5281/zenodo.13861026) diff --git a/_sources/authors/olivier_burri.md b/_sources/authors/olivier_burri.md index 7a19f512..b74b5d6b 100644 --- a/_sources/authors/olivier_burri.md +++ b/_sources/authors/olivier_burri.md @@ -165,7 +165,7 @@ Licensed UNKNOWN Tags: Python, Neubias, Artificial Intelligence, Bioimage Analysis -Content type: Slide, Notebook +Content type: Slides, Notebook [https://github.com/maweigert/neubias_academy_stardist](https://github.com/maweigert/neubias_academy_stardist) @@ -180,7 +180,7 @@ Licensed UNKNOWN -Tags: Bioimage Analysis, Microscopy Image Analysis +Tags: Bioimage Analysis Content type: Collection, Event, Forum Post, Workshop diff --git a/_sources/authors/riccardo_massei.md b/_sources/authors/riccardo_massei.md index 9761bc6d..fdf2f05a 100644 --- a/_sources/authors/riccardo_massei.md +++ b/_sources/authors/riccardo_massei.md @@ -158,7 +158,7 @@ Licensed CC-BY-4.0 -Tags: OMERO, Galaxy, Metadata +Tags: OMERO, Galaxy, Metadata, Nfdi4Bioimage Content type: Tutorial, Framework, Workflow @@ -179,6 +179,8 @@ Licensed CC-BY-4.0 Germany’s National Research Data Infrastructure (NFDI) aims to establish a sustained, cross-disciplinary research data management (RDM) infrastructure that enables researchers to handle FAIR (findable, accessible, interoperable, reusable) data. While FAIR principles have been adopted by funders, policymakers, and publishers, their practical implementation remains an ongoing effort. In the field of bio-imaging, harmonization of data formats, metadata ontologies, and open data repositories is necessary to achieve FAIR data. The NFDI4BIOIMAGE was established to address these issues and develop tools and best practices to facilitate FAIR microscopy and image analysis data in alignment with international community activities. The consortium operates through its Data Stewards team to provide expertise and direct support to help overcome RDM challenges. The three Helmholtz Centers in NFDI4BIOIMAGE aim to collaborate closely with other centers and initiatives, such as HMC, Helmholtz AI, and HIP. Here we present NFDI4BIOIMAGE’s work and its significance for research in Helmholtz and beyond +Tags: Nfdi4Bioimage, Research Data Management + [https://zenodo.org/records/11501662](https://zenodo.org/records/11501662) [https://doi.org/10.5281/zenodo.11501662](https://doi.org/10.5281/zenodo.11501662) @@ -217,7 +219,7 @@ Licensed MIT This repository offer access to teaching material and useful resources for the YMIA - Python-Based Event Series. -Tags: Python, Large Language Models, Prompt Engineering, Biabob, Bioimage Analysis, Microscopy Image Analysis +Tags: Python, Artifical Intelligence, Bioimage Analysis Content type: Github Repository, Slides @@ -250,6 +252,8 @@ Dr. Riccardo Massei (Helmholtz-Center for Environmental Research, UFZ, Leipzig) Dr. Michele Bortolomeazzi (DKFZ, Single cell Open Lab, bioimage data specialist, bioinformatician, staff scientist in the NFDI4BIOIMAGE project) Dr. Christian Schmidt (Science Manager for Research Data Management in Bioimaging, German Cancer Research Center, Heidelberg, Project Coordinator of the NFDI4BIOIMAGE project) +Tags: Nfdi4Bioimage, Research Data Management + [https://zenodo.org/records/11350689](https://zenodo.org/records/11350689) [https://doi.org/10.5281/zenodo.11350689](https://doi.org/10.5281/zenodo.11350689) diff --git a/_sources/authors/robert_haase.md b/_sources/authors/robert_haase.md index 3e495b3a..3ba2c9ac 100644 --- a/_sources/authors/robert_haase.md +++ b/_sources/authors/robert_haase.md @@ -35,7 +35,7 @@ Content type: Publication, Poster ## Angebote der NFDI für die Forschung im Bereich Zoologie -Birgitta König-Ries, Robert Haase, Daniel Nüst, Konrad Förstner, Engel, Judith Sophie +Birgitta König-Ries, Robert Haase, Daniel Nüst, Konrad Förstner, Judith Sophie Engel Published 2024-12-04 @@ -45,6 +45,8 @@ Licensed CC-BY-4.0 In diesem Slidedeck geben wir einen Einblick in Angebote und Dienste der Nationalen Forschungsdaten Infrastruktur (NFDI), die Relevant für die Zoologie und angrenzende Disziplinen relevant sein könnten. +Tags: Nfdi4Bioimage, Research Data Management + [https://zenodo.org/records/14278058](https://zenodo.org/records/14278058) [https://doi.org/10.5281/zenodo.14278058](https://doi.org/10.5281/zenodo.14278058) @@ -62,7 +64,7 @@ Licensed CC-BY-4.0 Training resources for Students at Uni Leipzig who want to dive into bio-image data science with Python. The material developed here between April and July 2024. -Tags: Bioimage Analysis, Deep Learning, Microscopy Image Analysis, Python +Tags: Bioimage Analysis, Artificial Intelligence, Python Content type: Github Repository @@ -126,9 +128,9 @@ Licensed CC-BY-4.0 Large Language Models (LLMs) change the way how we use computers. This also has impact on the bio-image analysis community. We can generate code that analyzes biomedical image data if we ask the right prompts. This talk outlines introduces basic principles, explains prompt engineering and how to apply it to bio-image analysis. We also compare how different LLM vendors perform on code generation tasks and which challenges are ahead for the bio-image analysis community. -Tags: Large Language Models, Python +Tags: Artificial Intelligence, Python -Content type: Slide +Content type: Slides [https://zenodo.org/records/10815329](https://zenodo.org/records/10815329) @@ -147,7 +149,7 @@ Licensed CC-BY-4.0 This repository contains training resources for Students at Uni Leipzig who want to dive into bio-image data science with Python. -Tags: Image Data Management, Deep Learning, Microscopy Image Analysis, Python +Tags: Research Data Management, Artificial Intelligence, Bioimage Analysis, Python Content type: Notebook @@ -166,7 +168,7 @@ Licensed CC-BY-4.0 These are the PPTx training resources for Students at Uni Leipzig who want to dive into bio-image data science with Python. The material developed here between April and July 2024. -Tags: Bioimage Analysis, Deep Learning, Microscopy Image Analysis, Python +Tags: Bioimage Analysis, Artificial Intelligence, Python Content type: Slides @@ -202,7 +204,7 @@ Licensed CC-BY-4.0 Tags: OMERO, Python -Content type: Blog +Content type: Blog Post [https://biapol.github.io/blog/robert_haase/browsing_idr/readme.html](https://biapol.github.io/blog/robert_haase/browsing_idr/readme.html) @@ -240,7 +242,7 @@ Licensed CC-BY-4.0 Tags: Research Data Management, Bioimage Analysis, Nfdi4Bioimage -Content type: Slide +Content type: Slides [https://f1000research.com/slides/12-1054](https://f1000research.com/slides/12-1054) @@ -297,6 +299,8 @@ Licensed CC-BY-4.0 This slide deck introduces the version control tool git, related terminology and the Github Desktop app for managing files in Git[hub] repositories. We furthermore dive into:* Working with repositories* Collaborative with others* Github-Zenodo integration* Github pages* Artificial Intelligence answering Github Issues +Tags: Nfdi4Bioimage, Globias, Research Data Management, Research Software Management + [https://zenodo.org/records/14626054](https://zenodo.org/records/14626054) [https://doi.org/10.5281/zenodo.14626054](https://doi.org/10.5281/zenodo.14626054) @@ -316,7 +320,7 @@ Introduction to version control using git for collaborative, reproducible script Tags: Sharing, Research Data Management -Content type: Blog +Content type: Blog Post [https://focalplane.biologists.com/2021/09/04/collaborative-bio-image-analysis-script-editing-with-git/](https://focalplane.biologists.com/2021/09/04/collaborative-bio-image-analysis-script-editing-with-git/) @@ -335,9 +339,9 @@ Licensed CC-BY-4.0 In this blog post the author demonstrates how chatGPT can be used to combine a fictive project description with a DMP specification to produce a project-specific DMP. -Tags: Research Data Management, Large Language Models, Artificial Intelligence +Tags: Research Data Management, Artificial Intelligence -Content type: Blog +Content type: Blog Post [https://focalplane.biologists.com/2023/11/06/creating-a-research-data-management-plan-using-chatgpt/](https://focalplane.biologists.com/2023/11/06/creating-a-research-data-management-plan-using-chatgpt/) @@ -461,7 +465,7 @@ Licensed CC-BY-4.0 Tags: Python, Bioimage Analysis, Artificial Intelligence -Content type: Slide +Content type: Slides [https://f1000research.com/slides/12-971](https://f1000research.com/slides/12-971) @@ -626,7 +630,7 @@ Overview about decision making and how to influence decisions in the bio-image a Tags: Bioimage Analysis -Content type: Slide, Presentation +Content type: Slides, Presentation [https://f1000research.com/slides/11-746](https://f1000research.com/slides/11-746) @@ -645,7 +649,7 @@ Licensed BSD-3-CLAUSE Course and material for the clEsperanto workshop presented at I2K 2024 @ Human Technopol (Milan, Italy). The workshop is an hands-on demo of the clesperanto project, focussing on how to use the library for users who want use GPU-acceleration for their Image Processing pipeline. -Tags: Clesperanto, Training, Bioimage Analysis, Notebooks, Workflow +Tags: Bioimage Analysis Content type: Github Repository, Workshop, Tutorial, Notebook @@ -685,7 +689,7 @@ Blog post about why we should license our work and what is important when choosi Tags: Licensing, Research Data Management -Content type: Blog +Content type: Blog Post [https://focalplane.biologists.com/2023/05/06/if-you-license-it-itll-be-harder-to-steal-it-why-we-should-license-our-work/](https://focalplane.biologists.com/2023/05/06/if-you-license-it-itll-be-harder-to-steal-it-why-we-should-license-our-work/) @@ -702,7 +706,7 @@ Licensed BSD-3-CLAUSE Tags: Neubias, Imagej, Bioimage Analysis -Content type: Slide +Content type: Slides [https://git.mpi-cbg.de/rhaase/lecture_imagej2_dev](https://git.mpi-cbg.de/rhaase/lecture_imagej2_dev) @@ -734,7 +738,7 @@ Licensed UNKNOWN Tags: Imagej, Bioimage Analysis -Content type: Slide +Content type: Slides [https://git.mpi-cbg.de/scicomp/bioimage_team/coursematerialimageanalysis/tree/master/ImageJMacro_24h_2017-01](https://git.mpi-cbg.de/scicomp/bioimage_team/coursematerialimageanalysis/tree/master/ImageJMacro_24h_2017-01) @@ -783,6 +787,8 @@ Licensed CC-BY-4.0 This slide deck introduces Large Language Models to an audience of life-scientists. We first dive into terminology: Different kinds of Language Models and what they can be used for. The remaining slides are optional slides to allow us to dive deeper into topics such as tools for using LLMs in Science, Quality Assurance, Techniques such as Retrieval Augmented Generation and Prompt Engineering. +Tags: Globias, Artificial Intelligence + [https://zenodo.org/records/14418209](https://zenodo.org/records/14418209) [https://doi.org/10.5281/zenodo.14418209](https://doi.org/10.5281/zenodo.14418209) @@ -800,7 +806,7 @@ Slides, scripts, data and other exercise materials of the BioImage Analysis lect Tags: Imagej, Bioimage Analysis -Content type: Slide +Content type: Slides [https://git.mpi-cbg.de/rhaase/lecture_applied_bioimage_analysis](https://git.mpi-cbg.de/rhaase/lecture_applied_bioimage_analysis) @@ -817,7 +823,7 @@ Licensed CC-BY-4.0 Tags: Python, Conda, Mamba -Content type: Blog +Content type: Blog Post [https://focalplane.biologists.com/2022/12/08/managing-scientific-python-environments-using-conda-mamba-and-friends/](https://focalplane.biologists.com/2022/12/08/managing-scientific-python-environments-using-conda-mamba-and-friends/) @@ -853,7 +859,7 @@ Lecture slides of a session on Multiview Fusion in Fiji Tags: Neubias, Imagej, Bioimage Analysis -Content type: Slide +Content type: Slides [https://git.mpi-cbg.de/rhaase/lecture_multiview_registration](https://git.mpi-cbg.de/rhaase/lecture_multiview_registration) @@ -870,7 +876,7 @@ Licensed UNKNOWN Tags: Neubias, Bioimage Analysis -Content type: Slide, Code, Notebook +Content type: Slides, Code, Notebook [https://github.com/miura/NEUBIAS_AnalystSchool2019](https://github.com/miura/NEUBIAS_AnalystSchool2019) @@ -946,7 +952,7 @@ Licensed CC-BY-4.0 -Content type: Slide +Content type: Slides [https://f1000research.com/slides/11-1171](https://f1000research.com/slides/11-1171) @@ -986,7 +992,7 @@ Introduction to sharing resources online and licensing Tags: Sharing, Research Data Management -Content type: Slide +Content type: Slides [https://f1000research.com/slides/10-519](https://f1000research.com/slides/10-519) @@ -1005,7 +1011,7 @@ Blog post about how to share data using zenodo.org Tags: Sharing, Research Data Management -Content type: Blog +Content type: Blog Post [https://focalplane.biologists.com/2023/02/15/sharing-research-data-with-zenodo/](https://focalplane.biologists.com/2023/02/15/sharing-research-data-with-zenodo/) @@ -1063,6 +1069,8 @@ Licensed CC-BY-4.0 This talk will present the initiatives of the NFDI4BioImage consortium aimed at the long-term preservation of life science data. We will discuss our efforts to establish metadata standards, which are crucial for ensuring data reusability and integrity. The development of sustainable infrastructure is another key focus, enabling seamless data integration and analysis in the cloud. We will take a look at how we manage training materials and communicate with our community. Through these actions, NFDI4BioImage seeks to enable FAIR bioimage data management for German researchers, across disciplines and embedded in the international framework. +Tags: Nfdi4Bioimage, Research Data Management + [https://zenodo.org/records/13640979](https://zenodo.org/records/13640979) [https://doi.org/10.5281/zenodo.13640979](https://doi.org/10.5281/zenodo.13640979) @@ -1101,7 +1109,7 @@ Lecture slides of a session on Cell Tracking in Fiji Tags: Neubias, Imagej, Bioimage Analysis -Content type: Slide +Content type: Slides [https://git.mpi-cbg.de/rhaase/lecture_tracking_trackmate](https://git.mpi-cbg.de/rhaase/lecture_tracking_trackmate) @@ -1118,7 +1126,7 @@ Licensed BSD-3-CLAUSE Tags: Neubias, Imagej, Bioimage Analysis -Content type: Slide +Content type: Slides [https://git.mpi-cbg.de/rhaase/lecture_working_with_objects_in_2d_and_3d](https://git.mpi-cbg.de/rhaase/lecture_working_with_objects_in_2d_and_3d) @@ -1135,7 +1143,7 @@ Licensed BSD-3-CLAUSE Tags: Neubias, Imagej, Bioimage Analysis -Content type: Slide +Content type: Slides [https://git.mpi-cbg.de/rhaase/lecture_working_with_pixels](https://git.mpi-cbg.de/rhaase/lecture_working_with_pixels) @@ -1154,7 +1162,7 @@ Licensed MIT This repository offer access to teaching material and useful resources for the YMIA - Python-Based Event Series. -Tags: Python, Large Language Models, Prompt Engineering, Biabob, Bioimage Analysis, Microscopy Image Analysis +Tags: Python, Artifical Intelligence, Bioimage Analysis Content type: Github Repository, Slides diff --git a/_sources/authors/roland_nitschke.md b/_sources/authors/roland_nitschke.md index 97fd1581..10791ea9 100644 --- a/_sources/authors/roland_nitschke.md +++ b/_sources/authors/roland_nitschke.md @@ -307,6 +307,8 @@ Licensed CC-BY-4.0 Research data management (RDM) in microscopy and image analysis is a challenging task. Large files in proprietary formats, complex N-dimensional array structures, and various metadata models and formats can make image data handling inconvenient and difficult. For data organization, annotation, and sharing, researchers need solutions that fit everyday practice and comply with the FAIR (Findable, Accessible, Interoperable, Reusable) principles. International community-based efforts have begun creating open data models (OME), an open file format and translation library (OME-TIFF, Bio-Formats), data management software platforms, and microscopy metadata recommendations and annotation tools. Bringing these developments into practice requires support and training. Iterative feedback and tool improvement is needed to foster practical adoption by the scientific community. The Information Infrastructure for BioImage Data (I3D:bio) project works on guidelines, training resources, and practical assistance for FAIR microscopy RDM adoption with a focus on the management platform OMERO and metadata annotations. +Tags: Nfdi4Bioimage, Research Data Management + [https://zenodo.org/records/10805204](https://zenodo.org/records/10805204) [https://doi.org/10.5281/zenodo.10805204](https://doi.org/10.5281/zenodo.10805204) diff --git a/_sources/authors/romain_guiet.md b/_sources/authors/romain_guiet.md index 68ef95a4..d16cee66 100644 --- a/_sources/authors/romain_guiet.md +++ b/_sources/authors/romain_guiet.md @@ -335,7 +335,7 @@ Licensed UNKNOWN -Tags: Bioimage Analysis, Microscopy Image Analysis +Tags: Bioimage Analysis Content type: Collection, Event, Forum Post, Workshop diff --git a/_sources/authors/silke_tulok.md b/_sources/authors/silke_tulok.md index 78fb2a31..7b997976 100644 --- a/_sources/authors/silke_tulok.md +++ b/_sources/authors/silke_tulok.md @@ -80,6 +80,8 @@ See cross reference: 10.5281/zenodo.12547566 Key-Value pair template for annotation of datasets in OMERO for light- and electron microscopy data within the research group of Prof. Mueller-Reichert 10.5281/zenodo.12546808 Key-Value pair template for annotation of datasets in OMERO (PERIKLES study) +Tags: Nfdi4Bioimage, Research Data Management + [https://zenodo.org/records/12578084](https://zenodo.org/records/12578084) [https://doi.org/10.5281/zenodo.12578084](https://doi.org/10.5281/zenodo.12578084) @@ -115,6 +117,8 @@ See cross reference: 10.5281/zenodo.12547566 Key-Value pair template for annotation of datasets in OMERO (light- and electron microscopy data within the research group of Prof. Mueller-Reichert) 10.5281/zenodo.12578084 Key-Value pair template for annotation in OMERO for light microscopy data acquired with AxioScan7 - Core Facility Cellular Imaging (CFCI) +Tags: Nfdi4Bioimage, Research Data Management + [https://zenodo.org/records/12546808](https://zenodo.org/records/12546808) [https://doi.org/10.5281/zenodo.12546808](https://doi.org/10.5281/zenodo.12546808) diff --git a/_sources/authors/stefanie_weidtkamp-peters.md b/_sources/authors/stefanie_weidtkamp-peters.md index 3a579286..c1883225 100644 --- a/_sources/authors/stefanie_weidtkamp-peters.md +++ b/_sources/authors/stefanie_weidtkamp-peters.md @@ -17,6 +17,8 @@ Research data management is essential in nowadays research, and one of the big o In this presentation, Stefanie Weidtkamp-Peters will introduce the challenges for bioimaging data management, and the necessary steps to achieve data FAIRification. German BioImaging - GMB e.V., together with other institutions, contributes to this endeavor. Janina Hanne will present how the network of imaging core facilities, research groups and industry partners is key to the German bioimaging community’s aligned collaboration toward FAIR bioimaging data. These activities have paved the way for two data management initiatives in Germany: I3D:bio (Information Infrastructure for BioImage Data) and NFDI4BIOIMAGE, a consortium of the National Research Data Infrastructure. Christian Schmidt will introduce the goals and measures of these initiatives to the benefit of imaging scientist’s work and everyday practice.   +Tags: Nfdi4Bioimage, Research Data Management + [https://zenodo.org/records/7890311](https://zenodo.org/records/7890311) [https://doi.org/10.5281/zenodo.7890311](https://doi.org/10.5281/zenodo.7890311) @@ -336,7 +338,7 @@ The open-source software OME Remote Objects (OMERO) is a data management softwar Tags: OMERO, Research Data Management, Nfdi4Bioimage, I3Dbio -Content type: Slide, Video +Content type: Slides, Video [https://zenodo.org/records/8323588](https://zenodo.org/records/8323588) @@ -412,6 +414,8 @@ Licensed CC-BY-4.0 Research data management (RDM) in microscopy and image analysis is a challenging task. Large files in proprietary formats, complex N-dimensional array structures, and various metadata models and formats can make image data handling inconvenient and difficult. For data organization, annotation, and sharing, researchers need solutions that fit everyday practice and comply with the FAIR (Findable, Accessible, Interoperable, Reusable) principles. International community-based efforts have begun creating open data models (OME), an open file format and translation library (OME-TIFF, Bio-Formats), data management software platforms, and microscopy metadata recommendations and annotation tools. Bringing these developments into practice requires support and training. Iterative feedback and tool improvement is needed to foster practical adoption by the scientific community. The Information Infrastructure for BioImage Data (I3D:bio) project works on guidelines, training resources, and practical assistance for FAIR microscopy RDM adoption with a focus on the management platform OMERO and metadata annotations. +Tags: Nfdi4Bioimage, Research Data Management + [https://zenodo.org/records/10805204](https://zenodo.org/records/10805204) [https://doi.org/10.5281/zenodo.10805204](https://doi.org/10.5281/zenodo.10805204) @@ -479,6 +483,8 @@ Learn from each other, leverage different expertise Learn how to train users, establish sustainability strategies, and foster FAIR RDM for bioimaging at your institution +Tags: Nfdi4Bioimage, Research Data Management + [https://zenodo.org/records/14178789](https://zenodo.org/records/14178789) [https://doi.org/10.5281/zenodo.14178789](https://doi.org/10.5281/zenodo.14178789) diff --git a/_sources/authors/susanne_kunis.md b/_sources/authors/susanne_kunis.md index 61e96292..2669bf4b 100644 --- a/_sources/authors/susanne_kunis.md +++ b/_sources/authors/susanne_kunis.md @@ -45,7 +45,7 @@ The open-source software OME Remote Objects (OMERO) is a data management softwar Tags: OMERO, Research Data Management, Nfdi4Bioimage, I3Dbio -Content type: Slide, Video +Content type: Slides, Video [https://zenodo.org/records/8323588](https://zenodo.org/records/8323588) @@ -165,6 +165,8 @@ Licensed CC-BY-4.0 Research data management (RDM) in microscopy and image analysis is a challenging task. Large files in proprietary formats, complex N-dimensional array structures, and various metadata models and formats can make image data handling inconvenient and difficult. For data organization, annotation, and sharing, researchers need solutions that fit everyday practice and comply with the FAIR (Findable, Accessible, Interoperable, Reusable) principles. International community-based efforts have begun creating open data models (OME), an open file format and translation library (OME-TIFF, Bio-Formats), data management software platforms, and microscopy metadata recommendations and annotation tools. Bringing these developments into practice requires support and training. Iterative feedback and tool improvement is needed to foster practical adoption by the scientific community. The Information Infrastructure for BioImage Data (I3D:bio) project works on guidelines, training resources, and practical assistance for FAIR microscopy RDM adoption with a focus on the management platform OMERO and metadata annotations. +Tags: Nfdi4Bioimage, Research Data Management + [https://zenodo.org/records/10805204](https://zenodo.org/records/10805204) [https://doi.org/10.5281/zenodo.10805204](https://doi.org/10.5281/zenodo.10805204) @@ -204,6 +206,8 @@ Licensed CC-BY-4.0 Overview of Activities of the Team Image Data Analysis and Management of German BioImaging e.V.   +Tags: Nfdi4Bioimage, Research Data Management + [https://zenodo.org/records/10796364](https://zenodo.org/records/10796364) [https://doi.org/10.5281/zenodo.10796364](https://doi.org/10.5281/zenodo.10796364) @@ -243,6 +247,8 @@ Licensed CC-BY-4.0 Poster presented at the European Light Microscopy Initiative meeting in Liverpool (https://www.elmi2024.org/) +Tags: Nfdi4Bioimage, Research Data Management + [https://zenodo.org/records/11235513](https://zenodo.org/records/11235513) [https://doi.org/10.5281/zenodo.11235513](https://doi.org/10.5281/zenodo.11235513) @@ -329,6 +335,8 @@ Learn from each other, leverage different expertise Learn how to train users, establish sustainability strategies, and foster FAIR RDM for bioimaging at your institution +Tags: Nfdi4Bioimage, Research Data Management + [https://zenodo.org/records/14178789](https://zenodo.org/records/14178789) [https://doi.org/10.5281/zenodo.14178789](https://doi.org/10.5281/zenodo.14178789) diff --git a/_sources/authors/thomas_zobel.md b/_sources/authors/thomas_zobel.md index 96e01ed3..6493a37e 100644 --- a/_sources/authors/thomas_zobel.md +++ b/_sources/authors/thomas_zobel.md @@ -54,7 +54,7 @@ The open-source software OME Remote Objects (OMERO) is a data management softwar Tags: OMERO, Research Data Management, Nfdi4Bioimage, I3Dbio -Content type: Slide, Video +Content type: Slides, Video [https://zenodo.org/records/8323588](https://zenodo.org/records/8323588) @@ -115,6 +115,8 @@ Content: ... +Tags: Nfdi4Bioimage, Research Data Management + [https://zenodo.org/records/7018750](https://zenodo.org/records/7018750) [https://doi.org/10.5281/zenodo.7018750](https://doi.org/10.5281/zenodo.7018750) @@ -135,6 +137,8 @@ Licensed CC-BY-4.0 Overview of Activities of the Team Image Data Analysis and Management of German BioImaging e.V.   +Tags: Nfdi4Bioimage, Research Data Management + [https://zenodo.org/records/10796364](https://zenodo.org/records/10796364) [https://doi.org/10.5281/zenodo.10796364](https://doi.org/10.5281/zenodo.10796364) diff --git a/_sources/authors/toby_hodges.md b/_sources/authors/toby_hodges.md index 94ea6291..6e71da2c 100644 --- a/_sources/authors/toby_hodges.md +++ b/_sources/authors/toby_hodges.md @@ -11,7 +11,7 @@ Licensed CC-BY-4.0 In this interactive session, Carpentries team members will guide attendees through three stages of the backward design process to create a lesson development plan for the open source tool of their choosing. Attendees will leave having identified what practical skills they aim to teach (learning objectives), an approach for designing challenge questions (formative assessment), and mechanisms to give and receive feedback. -Content type: Slide +Content type: Slides [https://zenodo.org/records/4317149](https://zenodo.org/records/4317149) @@ -30,7 +30,7 @@ Licensed CC-BY-4.0 This lesson shows how to use Python and scikit-image to do basic image processing. -Tags: Segmentation, Bioimage Analysis, Training, Python, Scikit-Image, Image Segmentation +Tags: Bioimage Analysis, Python Content type: Tutorial, Workflow @@ -41,7 +41,7 @@ Content type: Tutorial, Workflow ## Modular training resources for bioimage analysis -Christian Tischer, Antonio Politi, Tim-Oliver Buchholz, Elnaz Fazeli, Nicola Gritti, Aliaksandr Halavatyi, Gonzalez Tirado, Sebastian, Julian Hennies, Toby Hodges, Arif Khan, Dominik Kutra, Stefania Marcotti, Bugra Oezdemir, Felix Schneider, Martin Schorb, Anniek Stokkermans, Yi Sun, Nima Vakili +Christian Tischer, Antonio Politi, Tim-Oliver Buchholz, Elnaz Fazeli, Nicola Gritti, Aliaksandr Halavatyi, Sebastian Gonzalez Tirado, Julian Hennies, Toby Hodges, Arif Khan, Dominik Kutra, Stefania Marcotti, Bugra Oezdemir, Felix Schneider, Martin Schorb, Anniek Stokkermans, Yi Sun, Nima Vakili Published 2024-12-03 @@ -51,6 +51,8 @@ Licensed CC-BY-4.0 Resources for teaching/preparing to teach bioimage analysis +Tags: Neubias, Bioimage Analysis + [https://zenodo.org/records/14264885](https://zenodo.org/records/14264885) [https://doi.org/10.5281/zenodo.14264885](https://doi.org/10.5281/zenodo.14264885) diff --git a/_sources/authors/tom_boissonnet.md b/_sources/authors/tom_boissonnet.md index 6e17ff20..a4b50d4b 100644 --- a/_sources/authors/tom_boissonnet.md +++ b/_sources/authors/tom_boissonnet.md @@ -56,7 +56,7 @@ The open-source software OME Remote Objects (OMERO) is a data management softwar Tags: OMERO, Research Data Management, Nfdi4Bioimage, I3Dbio -Content type: Slide, Video +Content type: Slides, Video [https://zenodo.org/records/8323588](https://zenodo.org/records/8323588) @@ -96,6 +96,8 @@ See cross reference: 10.5281/zenodo.12547566 Key-Value pair template for annotation of datasets in OMERO for light- and electron microscopy data within the research group of Prof. Mueller-Reichert 10.5281/zenodo.12546808 Key-Value pair template for annotation of datasets in OMERO (PERIKLES study) +Tags: Nfdi4Bioimage, Research Data Management + [https://zenodo.org/records/12578084](https://zenodo.org/records/12578084) [https://doi.org/10.5281/zenodo.12578084](https://doi.org/10.5281/zenodo.12578084) @@ -131,6 +133,8 @@ See cross reference: 10.5281/zenodo.12547566 Key-Value pair template for annotation of datasets in OMERO (light- and electron microscopy data within the research group of Prof. Mueller-Reichert) 10.5281/zenodo.12578084 Key-Value pair template for annotation in OMERO for light microscopy data acquired with AxioScan7 - Core Facility Cellular Imaging (CFCI) +Tags: Nfdi4Bioimage, Research Data Management + [https://zenodo.org/records/12546808](https://zenodo.org/records/12546808) [https://doi.org/10.5281/zenodo.12546808](https://doi.org/10.5281/zenodo.12546808) @@ -226,6 +230,8 @@ Licensed CC-BY-4.0 Research data management (RDM) in microscopy and image analysis is a challenging task. Large files in proprietary formats, complex N-dimensional array structures, and various metadata models and formats can make image data handling inconvenient and difficult. For data organization, annotation, and sharing, researchers need solutions that fit everyday practice and comply with the FAIR (Findable, Accessible, Interoperable, Reusable) principles. International community-based efforts have begun creating open data models (OME), an open file format and translation library (OME-TIFF, Bio-Formats), data management software platforms, and microscopy metadata recommendations and annotation tools. Bringing these developments into practice requires support and training. Iterative feedback and tool improvement is needed to foster practical adoption by the scientific community. The Information Infrastructure for BioImage Data (I3D:bio) project works on guidelines, training resources, and practical assistance for FAIR microscopy RDM adoption with a focus on the management platform OMERO and metadata annotations. +Tags: Nfdi4Bioimage, Research Data Management + [https://zenodo.org/records/10805204](https://zenodo.org/records/10805204) [https://doi.org/10.5281/zenodo.10805204](https://doi.org/10.5281/zenodo.10805204) @@ -274,6 +280,8 @@ Learn from each other, leverage different expertise Learn how to train users, establish sustainability strategies, and foster FAIR RDM for bioimaging at your institution +Tags: Nfdi4Bioimage, Research Data Management + [https://zenodo.org/records/14178789](https://zenodo.org/records/14178789) [https://doi.org/10.5281/zenodo.14178789](https://doi.org/10.5281/zenodo.14178789) @@ -357,6 +365,8 @@ Publishing datasets in public archives for bioimage dataKsenia Krooß /Hein Date & Venue:Thursday, Sept. 26, 5.30 p.m.Haus 22 / Paul Ehrlich Lecture Hall (H22-1)University Hospital Frankfurt +Tags: Nfdi4Bioimage, Research Data Management + [https://zenodo.org/records/13861026](https://zenodo.org/records/13861026) [https://doi.org/10.5281/zenodo.13861026](https://doi.org/10.5281/zenodo.13861026) @@ -366,7 +376,7 @@ Date & Venue:Thursday, Sept. 26, 5.30 p.m.Haus 22 / Paul Ehrlich Lecture Hal ## ome2024-ngff-challenge -Will Moore, Josh Moore, sherwoodf, jean-marie burel, Norman Rzepka, dependabot[bot], JensWendt, Joost de Folter, Torsten Stöter, AybukeKY, Eric Perlman, Tom Boissonnet +Will Moore, Josh Moore, sherwoodf, Jean-Marie Burel, Norman Rzepka, dependabot[bot], JensWendt, Joost de Folter, Torsten St\xF6ter, AybukeKY, Eric Perlman, Tom Boissonnet Published 2024-08-30T12:00:53+00:00 @@ -376,7 +386,7 @@ Licensed BSD-3-CLAUSE Project planning and material repository for the 2024 challenge to generate 1 PB of OME-Zarr data -Tags: Sharing +Tags: Sharing, Nfdi4Bioimage, Research Data Management Content type: Github Repository diff --git "a/_sources/authors/torsten_st\303\266ter.md" "b/_sources/authors/torsten_st\303\266ter.md" deleted file mode 100644 index 2ccf6137..00000000 --- "a/_sources/authors/torsten_st\303\266ter.md" +++ /dev/null @@ -1,98 +0,0 @@ -# Torsten stöter (5) -## Combining the BIDS and ARC Directory Structures for Multimodal Research Data Organization - -Torsten Stöter, Tobias Gottschall, Andrea Schrader, Peter Zentis, Monica Valencia-Schneider, Niraj Kandpal, Werner Zuschratter, Astrid Schauss, Timo Dickscheid, Timo Mühlhaus, Dirk von Suchodoletz - -Licensed CC-BY-4.0 - - - -Interdisciplinary collaboration and integrating large, diverse datasets are crucial for answering complex research questions, requiring multimodal data analysis and adherence to FAIR principles. To address challenges in capturing the full research cycle and contextualizing data, DataPLANT developed the Annotated Research Context (ARC), while the neuroimaging community extended the Brain Imaging Data Structure (BIDS) for microscopic image data, both providing standardized, file system-based storage structures for organizing and sharing data with metadata. - -Tags: Research Data Management, FAIR-Principles - -Content type: Poster - -[https://zenodo.org/doi/10.5281/zenodo.8349562](https://zenodo.org/doi/10.5281/zenodo.8349562) - - ---- - -## Data stewardship and research data management tools for multimodal linking of imaging data in plasma medicine - -Mohsen Ahmadi, Robert Wagner, Philipp Mattern, Nick Plathe, Sander Bekeschus, Markus M. Becker, Torsten Stöter, Stefanie Weidtkamp-Peters - -Published 2023-11-03 - -Licensed CC-BY-4.0 - - - -A more detailed understanding of the effect of plasmas on biological systems can be fostered by combining data from different imaging modalities, such as optical imaging, fluorescence imaging, and mass spectrometry imaging. This, however, requires the implementation and use of sophisticated research data management (RDM) solutions to incorporate the influence of plasma parameters and treatment procedures as well as the effects of plasma on the treated targets. In order to address this, RDM activities on different levels and from different perspectives are started and brought together within the framework of the NFDI consortium NFDI4BIOIMAGE. - -[https://zenodo.org/records/10069368](https://zenodo.org/records/10069368) - -[https://doi.org/10.5281/zenodo.10069368](https://doi.org/10.5281/zenodo.10069368) - - ---- - -## NFDI4Bioimage - TA3-Hackathon - UoC-2023 (Cologne Hackathon) - -Mohamed M. Abdrabbou, Mehrnaz Babaki, Tom Boissonnet, Michele Bortolomeazzi, Eik Dahms, Vanessa A. F. Fuchs, Moritz Hoevels, Niraj Kandpal, Christoph Möhl, Joshua A. Moore, Astrid Schauss, Andrea Schrader, Torsten Stöter, Julia Thönnißen, Monica Valencia-S., H. Lukas Weil, Jens Wendt and Peter Zentis - -Licensed CC-BY-4.0 - - - -Tags: Arc, Dataplant, Hackathon, Nfdi4Bioimage, OMERO, Python, Research Data Management - -Content type: Event, Publication, Documentation - -[https://github.com/NFDI4BIOIMAGE/Cologne-Hackathon-2023](https://github.com/NFDI4BIOIMAGE/Cologne-Hackathon-2023) - -[https://doi.org/10.5281/zenodo.10609770](https://doi.org/10.5281/zenodo.10609770) - - ---- - -## NFDI4Bioimage - TA3-Hackathon - UoC-2023 (Cologne-Hackathon-2023, GitHub repository) - -Mohamed Abdrabbou, Mehrnaz Babaki, Tom Boissonnet, Michele Bortolomeazzi, Eik Dahms, Vanessa Fuchs, A. F. Moritz Hoevels, Niraj Kandpal, Christoph Möhl, Joshua A. Moore, Astrid Schauss, Andrea Schrader, Torsten Stöter, Julia Thönnißen, Monica Valencia-S., H. Lukas Weil, Jens Wendt, Peter Zentis - -Licensed CC-BY-4.0 - - - -This repository documents the first NFDI4Bioimage - TA3-Hackathon - UoC-2023 (Cologne Hackathon), where topics like 'Interoperability', 'REMBI / Mapping', and 'Neuroglancer (OMERO / zarr)' were explored through collaborative discussions and workflow sessions, culminating in reports that bridge NFDI4Bioimage to DataPLANT. Funded by various DFG initiatives, this event emphasized documentation and use cases, contributing preparatory work for future interoperability projects at the 2nd de.NBI BioHackathon in Bielefeld. - -Tags: Research Data Management, FAIR-Principles, Bioimage Analysis, Nfdi4Bioimage - -Content type: Github Repository - -[https://zenodo.org/doi/10.5281/zenodo.10609770](https://zenodo.org/doi/10.5281/zenodo.10609770) - - ---- - -## ome2024-ngff-challenge - -Will Moore, Josh Moore, sherwoodf, jean-marie burel, Norman Rzepka, dependabot[bot], JensWendt, Joost de Folter, Torsten Stöter, AybukeKY, Eric Perlman, Tom Boissonnet - -Published 2024-08-30T12:00:53+00:00 - -Licensed BSD-3-CLAUSE - - - -Project planning and material repository for the 2024 challenge to generate 1 PB of OME-Zarr data - -Tags: Sharing - -Content type: Github Repository - -[https://github.com/ome/ome2024-ngff-challenge](https://github.com/ome/ome2024-ngff-challenge) - - ---- - diff --git a/_sources/authors/ugis_sarkans.md b/_sources/authors/ugis_sarkans.md index ae1e8ed6..95542586 100644 --- a/_sources/authors/ugis_sarkans.md +++ b/_sources/authors/ugis_sarkans.md @@ -24,7 +24,7 @@ Licensed CC-BY-4.0 The Microscopy data analysis: machine learning and the BioImage Archive course, which focused on introducing programmatic approaches used in the analysis of bioimage data via the BioImage Archive, ran in May 2023. -Tags: Microscopy Image Analysis, Python, Deep Learning +Tags: Bioimage Analysis, Python, Artificial Intelligence Content type: Video, Slides @@ -64,7 +64,7 @@ Licensed UNKNOWN Bioimaging data have significant potential for reuse, but unlocking this potential requires systematic archiving of data and metadata in public databases. The authors propose draft metadata guidelines to begin addressing the needs of diverse communities within light and electron microscopy. -Tags: Metadata, Bioimage Data, Image Data Management, Research Data Management +Tags: Metadata, Research Data Management Content type: Publication @@ -89,7 +89,7 @@ Licensed UNKNOWN The BioImage Archive is a new archival data resource at the European Bioinformatics Institute (EMBL-EBI). -Tags: Image Data Management, Research Data Management, Bioimage Data +Tags: Research Data Management Content type: Publication diff --git a/_sources/content_types/blog.md b/_sources/content_types/blog post.md similarity index 72% rename from _sources/content_types/blog.md rename to _sources/content_types/blog post.md index 4cd5b9bc..cb806431 100644 --- a/_sources/content_types/blog.md +++ b/_sources/content_types/blog post.md @@ -1,4 +1,4 @@ -# Blog (19) +# Blog post (23) ## Annotating 3D images in napari Mara Lampert @@ -7,11 +7,32 @@ Mara Lampert Tags: Python, Napari, Bioimage Analysis -Content type: Blog +Content type: Blog Post [https://focalplane.biologists.com/2023/03/30/annotating-3d-images-in-napari/](https://focalplane.biologists.com/2023/03/30/annotating-3d-images-in-napari/) +--- + +## Biologists, stop putting UMAP plots in your papers + +Rafael Irizarry + +Published 2024-12-23 + +Licensed UNKNOWN + + + +UMAP is a powerful tool for exploratory data analysis, but without a clear understanding of how it works, it can easily lead to confusion and misinterpretation. + +Tags: Biology, Data Analysis, Umap + +Content type: Blog Post + +[https://simplystatistics.org/posts/2024-12-23-biologists-stop-including-umap-plots-in-your-papers/](https://simplystatistics.org/posts/2024-12-23-biologists-stop-including-umap-plots-in-your-papers/) + + --- ## Browsing the Open Microscopy Image Data Resource with Python @@ -24,7 +45,7 @@ Licensed CC-BY-4.0 Tags: OMERO, Python -Content type: Blog +Content type: Blog Post [https://biapol.github.io/blog/robert_haase/browsing_idr/readme.html](https://biapol.github.io/blog/robert_haase/browsing_idr/readme.html) @@ -43,7 +64,7 @@ Introduction to version control using git for collaborative, reproducible script Tags: Sharing, Research Data Management -Content type: Blog +Content type: Blog Post [https://focalplane.biologists.com/2021/09/04/collaborative-bio-image-analysis-script-editing-with-git/](https://focalplane.biologists.com/2021/09/04/collaborative-bio-image-analysis-script-editing-with-git/) @@ -62,13 +83,53 @@ Licensed CC-BY-4.0 In this blog post the author demonstrates how chatGPT can be used to combine a fictive project description with a DMP specification to produce a project-specific DMP. -Tags: Research Data Management, Large Language Models, Artificial Intelligence +Tags: Research Data Management, Artificial Intelligence -Content type: Blog +Content type: Blog Post [https://focalplane.biologists.com/2023/11/06/creating-a-research-data-management-plan-using-chatgpt/](https://focalplane.biologists.com/2023/11/06/creating-a-research-data-management-plan-using-chatgpt/) +--- + +## DL4MicEverywhere – Overcoming reproducibility challenges in deep learning microscopy imaging + +Iván Hidalgo-Cenalmor + +Published 2024-07-29 + +Licensed UNKNOWN + + + +Tags: Bio Image Analysis, Artifical Intelligence + +Content type: Blog Post + +[https://focalplane.biologists.com/2024/07/29/dl4miceverywhere-overcoming-reproducibility-challenges-in-deep-learning-microscopy-imaging/](https://focalplane.biologists.com/2024/07/29/dl4miceverywhere-overcoming-reproducibility-challenges-in-deep-learning-microscopy-imaging/) + + +--- + +## Data Visualization with Flying Colors + +Joachim Goedhart + +Published 2019-08-29 + +Licensed UNKNOWN + + + +The author discusses a number of color palettes that are suitable for coloring graphical elements in plots. + +Tags: Data Visualization + +Content type: Blog Post + +[https://thenode.biologists.com/data-visualization-with-flying-colors/research/](https://thenode.biologists.com/data-visualization-with-flying-colors/research/) + + --- ## Data handling in large-scale electron microscopy @@ -79,7 +140,7 @@ Job Fermie Tags: Research Data Management -Content type: Blog +Content type: Blog Post [https://blog.delmic.com/data-handling-in-large-scale-electron-microscopy](https://blog.delmic.com/data-handling-in-large-scale-electron-microscopy) @@ -94,7 +155,7 @@ Mara Lampert Tags: Python, Napari, Bioimage Analysis -Content type: Blog +Content type: Blog Post [https://focalplane.biologists.com/2023/05/03/feature-extraction-in-napari/](https://focalplane.biologists.com/2023/05/03/feature-extraction-in-napari/) @@ -113,7 +174,7 @@ Sharing your data can benefit your career in some interesting ways. In this post Tags: Research Data Management, Sharing -Content type: Blog +Content type: Blog Post [https://web.library.uq.edu.au/blog/2022/03/five-great-reasons-share-your-research-data](https://web.library.uq.edu.au/blog/2022/03/five-great-reasons-share-your-research-data) @@ -130,7 +191,7 @@ Licensed CC-BY-4.0 Tags: Python, Conda, Mamba -Content type: Blog +Content type: Blog Post [https://biapol.github.io/blog/mara_lampert/getting_started_with_mambaforge_and_python/readme.html](https://biapol.github.io/blog/mara_lampert/getting_started_with_mambaforge_and_python/readme.html) @@ -145,7 +206,7 @@ Mara Lampert Tags: Github, Python, Science Communication -Content type: Blog +Content type: Blog Post [https://focalplane.biologists.com/2024/04/03/how-to-write-a-bug-report/](https://focalplane.biologists.com/2024/04/03/how-to-write-a-bug-report/) @@ -164,7 +225,7 @@ Blog post about why we should license our work and what is important when choosi Tags: Licensing, Research Data Management -Content type: Blog +Content type: Blog Post [https://focalplane.biologists.com/2023/05/06/if-you-license-it-itll-be-harder-to-steal-it-why-we-should-license-our-work/](https://focalplane.biologists.com/2023/05/06/if-you-license-it-itll-be-harder-to-steal-it-why-we-should-license-our-work/) @@ -181,11 +242,30 @@ Licensed CC-BY-4.0 Tags: Python, Conda, Mamba -Content type: Blog +Content type: Blog Post [https://focalplane.biologists.com/2022/12/08/managing-scientific-python-environments-using-conda-mamba-and-friends/](https://focalplane.biologists.com/2022/12/08/managing-scientific-python-environments-using-conda-mamba-and-friends/) +--- + +## New report highlights the scientific impact of open source software + +UNKNOWN + +Published UNKNOWN + +Licensed UNKNOWN + + + +Tags: Open Source, Alphafold + +Content type: Report, Blog Post + +[https://www.statnews.com/sponsor/2024/11/26/new-report-highlights-the-scientific-impact-of-open-source-software/](https://www.statnews.com/sponsor/2024/11/26/new-report-highlights-the-scientific-impact-of-open-source-software/) + + --- ## Promoting Data Management at the Nikon Imaging Center and Cell Biology Microscopy Facility @@ -196,7 +276,7 @@ Jennifer Waters Tags: Research Data Management -Content type: Blog +Content type: Blog Post [https://datamanagement.hms.harvard.edu/news/promoting-data-management-nikon-imaging-center-and-cell-biology-microscopy-facility](https://datamanagement.hms.harvard.edu/news/promoting-data-management-nikon-imaging-center-and-cell-biology-microscopy-facility) @@ -211,7 +291,7 @@ Mara Lampert Tags: Python, Jupyter, Bioimage Analysis, Prompt Engineering, Biabob -Content type: Blog +Content type: Blog Post [https://focalplane.biologists.com/2024/07/18/prompt-engineering-in-bio-image-analysis/](https://focalplane.biologists.com/2024/07/18/prompt-engineering-in-bio-image-analysis/) @@ -226,7 +306,7 @@ Mara Lampert Tags: Python, Napari, Bioimage Analysis -Content type: Blog +Content type: Blog Post [https://focalplane.biologists.com/2023/04/13/quality-assurance-of-segmentation-results/](https://focalplane.biologists.com/2023/04/13/quality-assurance-of-segmentation-results/) @@ -241,7 +321,7 @@ Mara Lampert Tags: Python, Napari, Bioimage Analysis -Content type: Blog +Content type: Blog Post [https://focalplane.biologists.com/2023/03/02/rescaling-images-and-pixel-anisotropy/](https://focalplane.biologists.com/2023/03/02/rescaling-images-and-pixel-anisotropy/) @@ -258,7 +338,7 @@ Licensed CC-BY-4.0 Tags: Python, Artificial Intelligence, Bioimage Analysis -Content type: Blog +Content type: Blog Post [https://biapol.github.io/blog/marcelo_zoccoler/omero_scripts/readme.html](https://biapol.github.io/blog/marcelo_zoccoler/omero_scripts/readme.html) @@ -273,7 +353,7 @@ Elisabeth Kugler Tags: Sharing, Research Data Management -Content type: Blog +Content type: Blog Post [https://focalplane.biologists.com/2023/07/26/sharing-your-poster-on-figshare/](https://focalplane.biologists.com/2023/07/26/sharing-your-poster-on-figshare/) @@ -292,7 +372,7 @@ Blog post about how to share data using zenodo.org Tags: Sharing, Research Data Management -Content type: Blog +Content type: Blog Post [https://focalplane.biologists.com/2023/02/15/sharing-research-data-with-zenodo/](https://focalplane.biologists.com/2023/02/15/sharing-research-data-with-zenodo/) @@ -307,7 +387,7 @@ Mara Lampert Tags: Python, Napari, Bioimage Analysis -Content type: Blog +Content type: Blog Post [https://focalplane.biologists.com/2023/06/01/tracking-in-napari/](https://focalplane.biologists.com/2023/06/01/tracking-in-napari/) diff --git a/_sources/content_types/code.md b/_sources/content_types/code.md index ae56a548..c9b8a8aa 100644 --- a/_sources/content_types/code.md +++ b/_sources/content_types/code.md @@ -22,7 +22,7 @@ Licensed UNKNOWN Tags: Neubias, Imagej Macro, Bioimage Analysis -Content type: Slide, Code +Content type: Slides, Code [https://github.com/ahklemm/ImageJMacro_Introduction](https://github.com/ahklemm/ImageJMacro_Introduction) @@ -58,7 +58,7 @@ Licensed UNKNOWN Tags: Neubias, Bioimage Analysis -Content type: Slide, Code, Notebook +Content type: Slides, Code, Notebook [https://github.com/miura/NEUBIAS_AnalystSchool2018](https://github.com/miura/NEUBIAS_AnalystSchool2018) @@ -75,7 +75,7 @@ Licensed UNKNOWN Tags: Neubias, Bioimage Analysis -Content type: Slide, Code, Notebook +Content type: Slides, Code, Notebook [https://github.com/miura/NEUBIAS_AnalystSchool2019](https://github.com/miura/NEUBIAS_AnalystSchool2019) @@ -92,7 +92,7 @@ Licensed UNKNOWN Tags: Neubias, Bioimage Analysis -Content type: Slide, Code, Notebook +Content type: Slides, Code, Notebook [https://github.com/miura/NEUBIAS_AnalystSchool2020](https://github.com/miura/NEUBIAS_AnalystSchool2020) diff --git a/_sources/content_types/collection.md b/_sources/content_types/collection.md index 5217d6b2..468620d5 100644 --- a/_sources/content_types/collection.md +++ b/_sources/content_types/collection.md @@ -106,7 +106,7 @@ Licensed CC0-1.0 -Tags: Bioimage Analysis, Deep Learning +Tags: Bioimage Analysis, Artificial Intelligence Content type: Collection, Data @@ -308,7 +308,7 @@ Content type: Collection, Video Beth Cimini et al. -Licensed BSD LICENSE +Licensed BSD-3-CLAUSE @@ -388,7 +388,7 @@ Licensed CC-BY-4.0 Explore fundamental topics on research data management (RDM), how DataPLANT implements these aspects to support plant researchers with RDM tools and services, read guides and manuals or search for some teaching materials. -Tags: Research Data Management, Training, Dataplant +Tags: Research Data Management, Dataplant Content type: Collection @@ -426,7 +426,7 @@ Licensed CC0 (MOSTLY, BUT CAN DIFFER DEPENDING ON RESOURCE) Online tutorial and webinar library, designed and delivered by EMBL-EBI experts -Tags: Bioinformatics, Training +Tags: Bioinformatics Content type: Collection @@ -439,7 +439,7 @@ Content type: Collection Marvin Albert -Licensed BSD LICENSE +Licensed BSD-3-CLAUSE @@ -548,7 +548,7 @@ Sharing knowledge and data in the life sciences allows us to learn from each oth Tags: Open Science, Teaching, Sharing -Content type: Collection, Tutorial, Videos +Content type: Collection, Tutorial, Video [https://www.ebi.ac.uk/training/online/courses/finding-using-public-data/](https://www.ebi.ac.uk/training/online/courses/finding-using-public-data/) @@ -633,9 +633,9 @@ Licensed UNKNOWN Example Workflows / usage of the Glencoe Software. -Tags: OMERO, Training +Tags: OMERO -Content type: Videos, Tutorial, Collection +Content type: Video, Tutorial, Collection [https://www.glencoesoftware.com/media/webinars/](https://www.glencoesoftware.com/media/webinars/) @@ -695,7 +695,7 @@ Licensed UNKNOWN A Microscopy Research Data Management Resource. -Tags: Metadata, I3Dbio, Research Data Management, Bioimage Data +Tags: Metadata, I3Dbio, Research Data Management Content type: Collection @@ -823,7 +823,7 @@ Licensed UNKNOWN The mission of Metrics Reloaded is to guide researchers in the selection of appropriate performance metrics for biomedical image analysis problems, as well as provide a comprehensive online resource for metric-related information and pitfalls -Tags: Bioimage Analysis, Image Segmentation, Machine Learning +Tags: Bioimage Analysis, Quality Control Content type: Website, Collection @@ -928,7 +928,7 @@ Licensed CC-BY-4.0 OME develops open-source software and data format standards for the storage and manipulation of biological microscopy data -Tags: Open Source Software, Microscopy Image Analysis, Bioimage Data +Tags: Open Source Software Content type: Video, Collection @@ -1054,7 +1054,7 @@ Licensed CC0-1.0 Recommended Metadata for Biological Images (REMBI) provides guidelines for metadata for biological images to enable the FAIR sharing of scientific data. -Tags: FAIR-Principles, Metadata, Image Data Management, Research Data Management, Bioimage Data +Tags: FAIR-Principles, Metadata, Research Data Management Content type: Collection @@ -1073,7 +1073,7 @@ Licensed UNKNOWN This Focus issue features a series of papers offering guidelines and tools for improving the tracking and reporting of microscopy metadata with an emphasis on reproducibility and data re-use. -Tags: Reproducibility, Metadata, Bioimage Data +Tags: Reproducibility, Metadata Content type: Collection @@ -1090,9 +1090,7 @@ Licensed CC-BY-4.0 Computational skills training at the UCL Sainsbury Wellcome Centre and Gatsby Computational Neuroscience Unit, delivered by members of the Neuroinformatics Unit. -Tags: Training - -Content type: Collection, Online Course, Videos, Tutorial +Content type: Collection, Online Course, Video, Tutorial [https://software-skills.neuroinformatics.dev/index.html](https://software-skills.neuroinformatics.dev/index.html) @@ -1150,7 +1148,7 @@ Licensed UNKNOWN -Tags: Bioimage Analysis, Microscopy Image Analysis +Tags: Bioimage Analysis Content type: Collection, Event, Forum Post, Workshop diff --git a/_sources/content_types/data.md b/_sources/content_types/data.md index 780a56fe..dc6a1847 100644 --- a/_sources/content_types/data.md +++ b/_sources/content_types/data.md @@ -22,7 +22,7 @@ Licensed CC0-1.0 -Tags: Bioimage Analysis, Deep Learning +Tags: Bioimage Analysis, Artificial Intelligence Content type: Collection, Data diff --git a/_sources/content_types/documentation.md b/_sources/content_types/documentation.md index f0e6056e..9da8a4e5 100644 --- a/_sources/content_types/documentation.md +++ b/_sources/content_types/documentation.md @@ -43,7 +43,7 @@ Licensed MIT BioEngine, a Python package designed for flexible deployment and execution of bioimage analysis models and workflows using AI, accessible via HTTP API and RPC. -Tags: Workflow Engine, Deep Learning, Python +Tags: Workflow Engine, Artificial Intelligence, Python Content type: Documentation @@ -62,8 +62,6 @@ Licensed CC-BY-4.0 Bio-Formats is a standalone Java library for reading and writing life sciences image file formats. There are several scripts for using Bio-Formats on the command line, which are listed here. -Tags: Bioimage Data - Content type: Documentation [https://bio-formats.readthedocs.io/en/v8.0.0/users/comlinetools/index.html](https://bio-formats.readthedocs.io/en/v8.0.0/users/comlinetools/index.html) @@ -106,7 +104,7 @@ Licensed BSD-3-CLAUSE Napari-ndev is a collection of widgets intended to serve any person seeking to process microscopy images from start to finish. The goal of this example pipeline is to get the user familiar with working with napari-ndev for batch processing and reproducibility (view Image Utilities and Workflow Widget). -Tags: Napari, Microscopy Image Analysis, Bioimage Analysis +Tags: Napari, Bioimage Analysis Content type: Documentation, Github Repository, Tutorial @@ -170,13 +168,13 @@ Content type: Publication, Documentation None -Licensed GPLV3 +Licensed GPL-3.0 The KNIME Image Processing Extension allows you to read in more than 140 different kinds of images and to apply well known methods on images, like preprocessing. segmentation, feature extraction, tracking and classification in KNIME. -Tags: Imagej, OMERO, Bioimage Data, Workflow +Tags: Imagej, OMERO, Workflow Content type: Tutorial, Online Tutorial, Documentation diff --git a/_sources/content_types/event.md b/_sources/content_types/event.md index e3f49121..8edcf2d2 100644 --- a/_sources/content_types/event.md +++ b/_sources/content_types/event.md @@ -104,7 +104,7 @@ Licensed UNKNOWN -Tags: Bioimage Analysis, Microscopy Image Analysis +Tags: Bioimage Analysis Content type: Collection, Event, Forum Post, Workshop diff --git a/_sources/content_types/github repository.md b/_sources/content_types/github repository.md index 8c2a59c5..4576ff8e 100644 --- a/_sources/content_types/github repository.md +++ b/_sources/content_types/github repository.md @@ -9,7 +9,7 @@ Licensed CC-BY-4.0 Training resources for Students at Uni Leipzig who want to dive into bio-image data science with Python. The material developed here between April and July 2024. -Tags: Bioimage Analysis, Deep Learning, Microscopy Image Analysis, Python +Tags: Bioimage Analysis, Artificial Intelligence, Python Content type: Github Repository @@ -89,7 +89,7 @@ Licensed BSD-3-CLAUSE Napari-ndev is a collection of widgets intended to serve any person seeking to process microscopy images from start to finish. The goal of this example pipeline is to get the user familiar with working with napari-ndev for batch processing and reproducibility (view Image Utilities and Workflow Widget). -Tags: Napari, Microscopy Image Analysis, Bioimage Analysis +Tags: Napari, Bioimage Analysis Content type: Documentation, Github Repository, Tutorial @@ -131,7 +131,7 @@ Licensed BSD-3-CLAUSE Course and material for the clEsperanto workshop presented at I2K 2024 @ Human Technopol (Milan, Italy). The workshop is an hands-on demo of the clesperanto project, focussing on how to use the library for users who want use GPU-acceleration for their Image Processing pipeline. -Tags: Clesperanto, Training, Bioimage Analysis, Notebooks, Workflow +Tags: Bioimage Analysis Content type: Github Repository, Workshop, Tutorial, Notebook @@ -150,7 +150,7 @@ Licensed APACHE-2.0 -Tags: Training +Tags: Bioimage Analysis Content type: Workshop, Notebook, Github Repository @@ -209,7 +209,7 @@ Licensed CC-BY-4.0 Material for the I2K 2024 "Multiplexed tissue imaging - tools and approaches" workshop -Tags: Bioimage Analysis, Microscopy Image Analysis +Tags: Bioimage Analysis Content type: Github Repository, Slides, Workshop @@ -268,7 +268,7 @@ Licensed BSD-3-CLAUSE Tutorial for running CellPose advanced functions -Tags: Cellpose, Segmentation +Tags: Bioimage Analysis, Artificial Intelligence Content type: Github Repository @@ -308,7 +308,7 @@ Licensed GPL-3.0 I2K 2024 workshop materials for "Object Tracking and Track Analysis using TrackMate and CellTracksColab" -Tags: Bioimage Analysis, Training +Tags: Bioimage Analysis Content type: Github Repository, Tutorial, Workshop, Slides @@ -464,7 +464,7 @@ Licensed UNKNOWN This tool is intended to link different research data management platforms with each other. -Tags: Research Data Management, Image Data Management +Tags: Research Data Management Content type: Github Repository @@ -496,7 +496,7 @@ Content type: Github Repository Ziv Yaniv et al. -Licensed APACHE-2.0 LICENSE +Licensed APACHE-2.0 @@ -517,7 +517,7 @@ Richard McElreath Published 2024-03-01 -Licensed CC0-1.0 LICENSE +Licensed CC0-1.0 @@ -540,7 +540,7 @@ Licensed BSD3-CLAUSE -Tags: Segmentation, Bioimage Analysis, Training +Tags: Bioimage Analysis Content type: Workshop, Github Repository, Tutorial @@ -601,7 +601,7 @@ Licensed MIT This repository offer access to teaching material and useful resources for the YMIA - Python-Based Event Series. -Tags: Python, Large Language Models, Prompt Engineering, Biabob, Bioimage Analysis, Microscopy Image Analysis +Tags: Python, Artifical Intelligence, Bioimage Analysis Content type: Github Repository, Slides @@ -620,7 +620,7 @@ Licensed GPL-2.0 Java application to convert image file formats, including .mrxs, to an intermediate Zarr structure compatible with the OME-NGFF specification. -Tags: Open Source Software, Bioimage Data +Tags: Open Source Software Content type: Application, Github Repository @@ -725,8 +725,6 @@ Licensed BSD-2-CLAUSE Web page for validating OME-NGFF files. -Tags: Bioimage Data - Content type: Github Repository, Application [https://ome.github.io/ome-ngff-validator/](https://ome.github.io/ome-ngff-validator/) @@ -738,7 +736,7 @@ Content type: Github Repository, Application ## ome2024-ngff-challenge -Will Moore, Josh Moore, sherwoodf, jean-marie burel, Norman Rzepka, dependabot[bot], JensWendt, Joost de Folter, Torsten Stöter, AybukeKY, Eric Perlman, Tom Boissonnet +Will Moore, Josh Moore, sherwoodf, Jean-Marie Burel, Norman Rzepka, dependabot[bot], JensWendt, Joost de Folter, Torsten St\xF6ter, AybukeKY, Eric Perlman, Tom Boissonnet Published 2024-08-30T12:00:53+00:00 @@ -748,7 +746,7 @@ Licensed BSD-3-CLAUSE Project planning and material repository for the 2024 challenge to generate 1 PB of OME-Zarr data -Tags: Sharing +Tags: Sharing, Nfdi4Bioimage, Research Data Management Content type: Github Repository @@ -763,7 +761,7 @@ JanClusmann, Tim Lenz Published 2024-11-08T08:32:03+00:00 -Licensed GNU GENERAL PUBLIC LICENSE V3.0 +Licensed GPL-3.0 @@ -788,7 +786,7 @@ Licensed GPL-2.0 Java application to convert a directory of tiles to an OME-TIFF pyramid. This is the second half of iSyntax/.mrxs => OME-TIFF conversion. -Tags: Open Source Software, Bioimage Data +Tags: Open Source Software Content type: Application, Github Repository @@ -803,7 +801,7 @@ Jack Atkinson Published 2023-12-22T17:39:48+00:00 -Licensed GNU GENERAL PUBLIC LICENSE V3.0 +Licensed GPL-3.0 @@ -820,11 +818,11 @@ Content type: Github Repository, Slides ## scanpy-tutorials -Alex Wolf, pre-commit-ci[bot], Philipp A., Isaac Virshup, Ilan Gold, Giovanni Palla, Fidel Ramirez, Gökçen Eraslan, Sergei Rybakov, Abolfazl (Abe), Adam Gayoso, Dinesh Palli, Gregor Sturm, Jan Lause, Karin Hrovatin, Krzysztof Polanski, RaphaelBuzzi, Yimin Zheng, Yishen Miao, evanbiederstedt +Alex Wolf, pre-commit-ci[bot], Philipp A., Isaac Virshup, Ilan Gold, Giovanni Palla, Fidel Ramirez, G\xF6k\xE7en Eraslan, Sergei Rybakov, Abolfazl (Abe), Adam Gayoso, Dinesh Palli, Gregor Sturm, Jan Lause, Karin Hrovatin, Krzysztof Polanski, RaphaelBuzzi, Yimin Zheng, Yishen Miao, evanbiederstedt Published 2018-12-16T03:42:46+00:00 -Licensed BSD-3 +Licensed BSD-3-CLAUSE diff --git a/_sources/content_types/notebook.md b/_sources/content_types/notebook.md index 6000ce5e..ec8aa0a7 100644 --- a/_sources/content_types/notebook.md +++ b/_sources/content_types/notebook.md @@ -43,7 +43,7 @@ Licensed CC-BY-4.0 This repository contains training resources for Students at Uni Leipzig who want to dive into bio-image data science with Python. -Tags: Image Data Management, Deep Learning, Microscopy Image Analysis, Python +Tags: Research Data Management, Artificial Intelligence, Bioimage Analysis, Python Content type: Notebook @@ -158,7 +158,7 @@ Content type: Notebook, Collection Beth Cimini et al. -Licensed BSD LICENSE +Licensed BSD-3-CLAUSE @@ -234,7 +234,7 @@ Licensed UNKNOWN Tags: Artificial Intelligence, Bioimage Analysis -Content type: Slide, Notebook +Content type: Slides, Notebook [https://github.com/JLrumberger/DL-MBL-2021](https://github.com/JLrumberger/DL-MBL-2021) @@ -362,7 +362,7 @@ Content type: Notebook Marvin Albert -Licensed BSD LICENSE +Licensed BSD-3-CLAUSE @@ -421,7 +421,7 @@ Licensed BSD-3-CLAUSE Course and material for the clEsperanto workshop presented at I2K 2024 @ Human Technopol (Milan, Italy). The workshop is an hands-on demo of the clesperanto project, focussing on how to use the library for users who want use GPU-acceleration for their Image Processing pipeline. -Tags: Clesperanto, Training, Bioimage Analysis, Notebooks, Workflow +Tags: Bioimage Analysis Content type: Github Repository, Workshop, Tutorial, Notebook @@ -440,7 +440,7 @@ Licensed APACHE-2.0 -Tags: Training +Tags: Bioimage Analysis Content type: Workshop, Notebook, Github Repository @@ -567,7 +567,7 @@ Licensed MIT This course consists of lectures and exercises that teach the background of deep learning for image analysis and show applications to classification and segmentation analysis problems. -Tags: Deep Learning, Pytorch, Segmentation, Python +Tags: Artificial Intelligence, Python Content type: Notebook @@ -656,7 +656,7 @@ Licensed UNKNOWN Tags: Neubias, Bioimage Analysis -Content type: Slide, Code, Notebook +Content type: Slides, Code, Notebook [https://github.com/miura/NEUBIAS_AnalystSchool2018](https://github.com/miura/NEUBIAS_AnalystSchool2018) @@ -673,7 +673,7 @@ Licensed UNKNOWN Tags: Neubias, Bioimage Analysis -Content type: Slide, Code, Notebook +Content type: Slides, Code, Notebook [https://github.com/miura/NEUBIAS_AnalystSchool2019](https://github.com/miura/NEUBIAS_AnalystSchool2019) @@ -690,7 +690,7 @@ Licensed UNKNOWN Tags: Neubias, Bioimage Analysis -Content type: Slide, Code, Notebook +Content type: Slides, Code, Notebook [https://github.com/miura/NEUBIAS_AnalystSchool2020](https://github.com/miura/NEUBIAS_AnalystSchool2020) @@ -707,7 +707,7 @@ Licensed UNKNOWN Tags: Python, Neubias, Artificial Intelligence, Bioimage Analysis -Content type: Slide, Notebook +Content type: Slides, Notebook [https://github.com/maweigert/neubias_academy_stardist](https://github.com/maweigert/neubias_academy_stardist) @@ -839,7 +839,7 @@ Licensed CC-BY-4.0 This book contains the quantitative analysis labs for the QI CSHL course, 2024 -Tags: Segmentation, Python +Tags: Python Content type: Notebook @@ -947,7 +947,7 @@ JanClusmann, Tim Lenz Published 2024-11-08T08:32:03+00:00 -Licensed GNU GENERAL PUBLIC LICENSE V3.0 +Licensed GPL-3.0 diff --git a/_sources/content_types/online tutorial.md b/_sources/content_types/online tutorial.md index e33913fd..ef5a4fe6 100644 --- a/_sources/content_types/online tutorial.md +++ b/_sources/content_types/online tutorial.md @@ -103,13 +103,13 @@ Content type: Online Tutorial, Tutorial None -Licensed GPLV3 +Licensed GPL-3.0 The KNIME Image Processing Extension allows you to read in more than 140 different kinds of images and to apply well known methods on images, like preprocessing. segmentation, feature extraction, tracking and classification in KNIME. -Tags: Imagej, OMERO, Bioimage Data, Workflow +Tags: Imagej, OMERO, Workflow Content type: Tutorial, Online Tutorial, Documentation @@ -128,7 +128,7 @@ Licensed CC-BY-4.0 Tags: Bioimage Analysis -Content type: Online Tutorial, Video, Slide +Content type: Online Tutorial, Video, Slides [https://www.ebi.ac.uk/training/events/methods-bioimage-analysis/](https://www.ebi.ac.uk/training/events/methods-bioimage-analysis/) @@ -143,7 +143,7 @@ Content type: Online Tutorial, Video, Slide Rémy Jean Daniel Dornier -Licensed ['CC-BY-NC-SA-4.0'] +Licensed CC-BY-NC-SA-4.0 diff --git a/_sources/content_types/preprint.md b/_sources/content_types/preprint.md index c999f13b..90bebb31 100644 --- a/_sources/content_types/preprint.md +++ b/_sources/content_types/preprint.md @@ -81,7 +81,7 @@ Licensed CC-BY-4.0 Glittr.org is a platform that aggregates and indexes training materials on computational life sciences from public git repositories, making it easier for users to find, compare, and analyze these resources based on various metrics. By providing insights into the availability of materials, collaboration patterns, and licensing practices, Glittr.org supports adherence to the FAIR principles, benefiting the broader life sciences community. -Tags: Training, Bioimage Analysis, Research Data Management +Tags: Bioimage Analysis, Research Data Management Content type: Publication, Preprint diff --git a/_sources/content_types/publication.md b/_sources/content_types/publication.md index f52f23a2..bd2e9c5d 100644 --- a/_sources/content_types/publication.md +++ b/_sources/content_types/publication.md @@ -130,7 +130,7 @@ Licensed CC-BY-4.0 The authors introduce BIOMERO (bioimage analysis in OMERO), a bridge connecting OMERO, a renowned bioimaging data management platform, FAIR workflows, and high-performance computing (HPC) environments. -Tags: OMERO, Workflow, Bioimage Analysis, Image Data Management +Tags: OMERO, Workflow, Bioimage Analysis Content type: Publication @@ -373,7 +373,7 @@ Licensed CC-BY-4.0 The authors show the utility of Minimum Information for High Content Screening Microscopy Experiments (MIHCSME) for High Content Screening (HCS) data using multiple examples from the Leiden FAIR Cell Observatory, a Euro-Bioimaging flagship node for high content screening and the pilot node for implementing FAIR bioimaging data throughout the Netherlands Bioimaging network. -Tags: FAIR-Principles, Metadata, Research Data Management, Image Data Management, Bioimage Data +Tags: FAIR-Principles, Metadata, Research Data Management Content type: Publication @@ -500,7 +500,7 @@ Licensed CC-BY-4.0 -Tags: Bioimage Analysis, Open Science, Microscopy Image Analysis, Microscopy +Tags: Bioimage Analysis, Open Science, Microscopy Content type: Publication @@ -604,7 +604,7 @@ Licensed CC-BY-4.0 The Brain Imaging Data Structure (BIDS) is a specification for organizing, sharing, and archiving neuroimaging data and metadata in a reusable way. -Tags: Research Data Management, Image Data Management, Bioimage Data +Tags: Research Data Management Content type: Publication @@ -659,13 +659,13 @@ Content type: Publication, Documentation Shanghang Zhang, Gaole Dai, Tiejun Huang, Jianxu Chen -Licensed ['CC-BY-NC-SA'] +Licensed CC-BY-NC-SA Multimodal large language models have been recognized as a historical milestone in the field of artificial intelligence and have demonstrated revolutionary potentials not only in commercial applications, but also for many scientific fields. Here we give a brief overview of multimodal large language models through the lens of bioimage analysis and discuss how we could build these models as a community to facilitate biology research -Tags: Bioimage Analysis, Large Language Models, FAIR-Principles, Workflow +Tags: Bioimage Analysis, FAIR-Principles, Workflow Content type: Publication @@ -849,7 +849,7 @@ Licensed UNKNOWN Bioimaging data have significant potential for reuse, but unlocking this potential requires systematic archiving of data and metadata in public databases. The authors propose draft metadata guidelines to begin addressing the needs of diverse communities within light and electron microscopy. -Tags: Metadata, Bioimage Data, Image Data Management, Research Data Management +Tags: Metadata, Research Data Management Content type: Publication @@ -895,7 +895,7 @@ Licensed CC-BY-4.0 As an initiative within Germany's National Research Data Infrastructure, the authors conducted this community survey in summer 2021 to assess the state of the art of bioimaging RDM and the community needs. -Tags: Research Data Management, Image Data Management, Bioimage Data +Tags: Research Data Management Content type: Publication @@ -967,7 +967,7 @@ Licensed CC-BY-4.0 The authors offer trainers some simple rules, to help make their training materials FAIR, enabling others to find, (re)use, and adapt them. -Tags: Metadata, Bioinformatics, FAIR-Principles, Training +Tags: Metadata, Bioinformatics, FAIR-Principles Content type: Publication @@ -988,7 +988,7 @@ Licensed UNKNOWN The BioImage Archive is a new archival data resource at the European Bioinformatics Institute (EMBL-EBI). -Tags: Image Data Management, Research Data Management, Bioimage Data +Tags: Research Data Management Content type: Publication @@ -1074,7 +1074,7 @@ Licensed CC-BY-4.0 The Open Microscopy Environment (OME) defines a data model and a software implementation to serve as an informatics framework for imaging in biological microscopy experiments, including representation of acquisition parameters, annotations and image analysis results. -Tags: Microscopy Image Analysis, Bioimage Analysis +Tags: Bioimage Analysis Content type: Publication @@ -1097,7 +1097,7 @@ Licensed UNKNOWN Rigorous record-keeping and quality control are required to ensure the quality, reproducibility and value of imaging data. The 4DN Initiative and BINA here propose light Microscopy Metadata specifications that extend the OME data model, scale with experimental intent and complexity, and make it possible for scientists to create comprehensive records of imaging experiments. -Tags: Reproducibility, Microscopy Image Analysis, Metadata, Image Data Management, Bioimage Data +Tags: Reproducibility, Bioimage Analysis, Metadata Content type: Publication @@ -1135,7 +1135,7 @@ Licensed CC-BY-4.0 Glittr.org is a platform that aggregates and indexes training materials on computational life sciences from public git repositories, making it easier for users to find, compare, and analyze these resources based on various metrics. By providing insights into the availability of materials, collaboration patterns, and licensing practices, Glittr.org supports adherence to the FAIR principles, benefiting the broader life sciences community. -Tags: Training, Bioimage Analysis, Research Data Management +Tags: Bioimage Analysis, Research Data Management Content type: Publication, Preprint diff --git a/_sources/content_types/slide.md b/_sources/content_types/slide.md deleted file mode 100644 index ecd0d449..00000000 --- a/_sources/content_types/slide.md +++ /dev/null @@ -1,771 +0,0 @@ -# Slide (41) -## Adding a Workflow to BIAFLOWS - -Sébastien Tosi, Volker Baecker, Benjamin Pavie - -Licensed BSD-2-CLAUSE - - - -Tags: Neubias, Bioimage Analysis - -Content type: Slide - -[https://github.com/RoccoDAnt/Defragmentation_TrainingSchool_EOSC-Life_2022/blob/main/Slides/Adding_a_workflow_to_BIAFLOWS.pdf](https://github.com/RoccoDAnt/Defragmentation_TrainingSchool_EOSC-Life_2022/blob/main/Slides/Adding_a_workflow_to_BIAFLOWS.pdf) - - ---- - -## Bio Image Analysis - -Christian Tischer - -Licensed UNKNOWN - - - -Content type: Slide - -[https://github.com/tischi/presentation-image-analysis](https://github.com/tischi/presentation-image-analysis) - - ---- - -## Bio-Image Data Strudel for Workshop on Research Data Management in TU Dresden Core Facilities - -Cornelia Wetzker - -Published 2023-11-08 - -Licensed CC-BY-4.0 - - - -This presentation gives a short outline of the complexity of data and metadata in the bioimaging universe. It introduces NFDI4BIOIMAGE as a newly formed consortium as part of the German 'Nationale Forschungsdateninfrastruktur' (NFDI) and its goals and tools for data management including its current members on TU Dresden campus.   - -Tags: Research Data Management, Tu Dresden, Bioimage Data, Nfdi4Bioimage - -Content type: Slide - -[https://zenodo.org/records/10083555](https://zenodo.org/records/10083555) - -[https://doi.org/10.5281/zenodo.10083555](https://doi.org/10.5281/zenodo.10083555) - - ---- - -## Bio-image Analysis with the Help of Large Language Models - -Robert Haase - -Published 2024-03-13 - -Licensed CC-BY-4.0 - - - -Large Language Models (LLMs) change the way how we use computers. This also has impact on the bio-image analysis community. We can generate code that analyzes biomedical image data if we ask the right prompts. This talk outlines introduces basic principles, explains prompt engineering and how to apply it to bio-image analysis. We also compare how different LLM vendors perform on code generation tasks and which challenges are ahead for the bio-image analysis community. - -Tags: Large Language Models, Python - -Content type: Slide - -[https://zenodo.org/records/10815329](https://zenodo.org/records/10815329) - -[https://doi.org/10.5281/zenodo.10815329](https://doi.org/10.5281/zenodo.10815329) - - ---- - -## Building a Bioimage Analysis Workflow using Deep Learning - -Estibaliz Gómez-de-Mariscal - -Licensed UNKNOWN - - - -Tags: Artificial Intelligence, Bioimage Analysis - -Content type: Slide - -[https://github.com/esgomezm/NEUBIAS_chapter_DL_2020](https://github.com/esgomezm/NEUBIAS_chapter_DL_2020) - - ---- - -## CellProfiler Introduction - -Anna Klemm - -Licensed UNKNOWN - - - -Tags: Neubias, Cellprofiler, Bioimage Analysis - -Content type: Slide - -[https://github.com/ahklemm/CellProfiler_Introduction](https://github.com/ahklemm/CellProfiler_Introduction) - - ---- - -## Challenges and opportunities for bio-image analysis core-facilities - -Robert Haase - -Licensed CC-BY-4.0 - - - -Tags: Research Data Management, Bioimage Analysis, Nfdi4Bioimage - -Content type: Slide - -[https://f1000research.com/slides/12-1054](https://f1000research.com/slides/12-1054) - - ---- - -## Creating open computational curricula - -Kari Jordan, Zhian Kamvar, Toby Hodges - -Published 2020-12-11 - -Licensed CC-BY-4.0 - - - -In this interactive session, Carpentries team members will guide attendees through three stages of the backward design process to create a lesson development plan for the open source tool of their choosing. Attendees will leave having identified what practical skills they aim to teach (learning objectives), an approach for designing challenge questions (formative assessment), and mechanisms to give and receive feedback. - -Content type: Slide - -[https://zenodo.org/records/4317149](https://zenodo.org/records/4317149) - -[https://doi.org/10.5281/zenodo.4317149](https://doi.org/10.5281/zenodo.4317149) - - ---- - -## DL@MBL 2021 Exercises - -Jan Funke, Constantin Pape, Morgan Schwartz, Xiaoyan - -Licensed UNKNOWN - - - -Tags: Artificial Intelligence, Bioimage Analysis - -Content type: Slide, Notebook - -[https://github.com/JLrumberger/DL-MBL-2021](https://github.com/JLrumberger/DL-MBL-2021) - - ---- - -## Generative artificial intelligence for bio-image analysis - -Robert Haase - -Licensed CC-BY-4.0 - - - -Tags: Python, Bioimage Analysis, Artificial Intelligence - -Content type: Slide - -[https://f1000research.com/slides/12-971](https://f1000research.com/slides/12-971) - - ---- - -## Hitchhiking through a diverse Bio-image Analysis Software Universe - -Robert Haase - -Published 2022-07-22 - -Licensed CC-BY-4.0 - - - -Overview about decision making and how to influence decisions in the bio-image analysis software context. - -Tags: Bioimage Analysis - -Content type: Slide, Presentation - -[https://f1000research.com/slides/11-746](https://f1000research.com/slides/11-746) - -[https://doi.org/10.7490/f1000research.1119026.1](https://doi.org/10.7490/f1000research.1119026.1) - - ---- - -## I3D:bio's OMERO training material: Re-usable, adjustable, multi-purpose slides for local user training - -Christian Schmidt, Michele Bortolomeazzi, Tom Boissonnet, Carsten Fortmann-Grote, Julia Dohle, Peter Zentis, Niraj Kandpal, Susanne Kunis, Thomas Zobel, Stefanie Weidtkamp-Peters, Elisa Ferrando-May - -Published 2023-11-13 - -Licensed CC-BY-4.0 - - - -The open-source software OME Remote Objects (OMERO) is a data management software that allows storing, organizing, and annotating bioimaging/microscopy data. OMERO has become one of the best-known systems for bioimage data management in the bioimaging community. The Information Infrastructure for BioImage Data (I3D:bio) project facilitates the uptake of OMERO into research data management (RDM) practices at universities and research institutions in Germany. Since the adoption of OMERO into researchers' daily routines requires intensive training, a broad portfolio of training resources for OMERO is an asset. On top of using the OMERO guides curated by the Open Microscopy Environment Consortium (OME) team, imaging core facility staff at institutions where OMERO is used often prepare additional material tailored to be applicable for their own OMERO instances. Based on experience gathered in the Research Data Management for Microscopy group (RDM4mic) in Germany, and in the use cases in the I3D:bio project, we created a set of reusable, adjustable, openly available slide decks to serve as the basis for tailored training lectures, video tutorials, and self-guided instruction manuals directed at beginners in using OMERO. The material is published as an open educational resource complementing the existing resources for OMERO contributed by the community. - -Tags: OMERO, Research Data Management, Nfdi4Bioimage, I3Dbio - -Content type: Slide, Video - -[https://zenodo.org/records/8323588](https://zenodo.org/records/8323588) - -[https://www.youtube.com/playlist?list=PL2k-L-zWPoR7SHjG1HhDIwLZj0MB_stlU](https://www.youtube.com/playlist?list=PL2k-L-zWPoR7SHjG1HhDIwLZj0MB_stlU) - -[https://doi.org/10.5281/zenodo.8323588](https://doi.org/10.5281/zenodo.8323588) - - ---- - -## Image Data Services at Euro-BioImaging: Community efforts towards FAIR Image Data and Analysis Services - -Aastha Mathur - -Licensed UNKNOWN - - - -Content type: Slide - -[https://docs.google.com/presentation/d/1henPIDTpHT3bc1Y26AltItAHJ2C5xCOl/edit#slide=id.p1](https://docs.google.com/presentation/d/1henPIDTpHT3bc1Y26AltItAHJ2C5xCOl/edit#slide=id.p1) - - ---- - -## Image analysis in Galaxy - -Beatriz Serrano-Solano, Björn Grüning - -Licensed UNKNOWN - - - -Tags: Bioimage Analysis - -Content type: Slide - -[https://docs.google.com/presentation/d/1WG_4307XmKsGfWT3taxMvX2rZiG1k0SM1E7SAENJQkI/edit#slide=id.p](https://docs.google.com/presentation/d/1WG_4307XmKsGfWT3taxMvX2rZiG1k0SM1E7SAENJQkI/edit#slide=id.p) - - ---- - -## ImageJ Macro Introduction - -Anna Klemm - -Licensed UNKNOWN - - - -Tags: Neubias, Imagej Macro, Bioimage Analysis - -Content type: Slide, Code - -[https://github.com/ahklemm/ImageJMacro_Introduction](https://github.com/ahklemm/ImageJMacro_Introduction) - - ---- - -## ImageJ2 API-beating - -Robert Haase - -Licensed BSD-3-CLAUSE - - - -Tags: Neubias, Imagej, Bioimage Analysis - -Content type: Slide - -[https://git.mpi-cbg.de/rhaase/lecture_imagej2_dev](https://git.mpi-cbg.de/rhaase/lecture_imagej2_dev) - - ---- - -## Introduction to ImageJ macro programming, Scientific Computing Facility, MPI CBG Dresden - -Robert Haase, Benoit Lombardot - -Licensed UNKNOWN - - - -Tags: Imagej, Bioimage Analysis - -Content type: Slide - -[https://git.mpi-cbg.de/scicomp/bioimage_team/coursematerialimageanalysis/tree/master/ImageJMacro_24h_2017-01](https://git.mpi-cbg.de/scicomp/bioimage_team/coursematerialimageanalysis/tree/master/ImageJMacro_24h_2017-01) - - ---- - -## Jupyter for interactive cloud computing - -Guillaume Witz - -Licensed UNKNOWN - - - -Tags: Neubias, Bioimage Analysis - -Content type: Slide - -[https://docs.google.com/presentation/d/1q8q1xE-c35tvCsRXZay98s2UYWwXpp0cfCljBmMFpco/edit#slide=id.ga456d5535c_2_53](https://docs.google.com/presentation/d/1q8q1xE-c35tvCsRXZay98s2UYWwXpp0cfCljBmMFpco/edit#slide=id.ga456d5535c_2_53) - - ---- - -## Lecture Applied Bioimage Analysis 2020 - -Robert Haase - - - -Slides, scripts, data and other exercise materials of the BioImage Analysis lecture at CMCB TU Dresden 2020 - -Tags: Imagej, Bioimage Analysis - -Content type: Slide - -[https://git.mpi-cbg.de/rhaase/lecture_applied_bioimage_analysis](https://git.mpi-cbg.de/rhaase/lecture_applied_bioimage_analysis) - - ---- - -## Machine Learning - Deep Learning. Applications to Bioimage Analysis - -Estibaliz Gómez-de-Mariscal - -Licensed UNKNOWN - - - -Tags: Artificial Intelligence, Bioimage Analysis - -Content type: Slide - -[https://raw.githubusercontent.com/esgomezm/esgomezm.github.io/master/assets/pdf/SPAOM2018/MachineLearning_SPAOMworkshop_public.pdf](https://raw.githubusercontent.com/esgomezm/esgomezm.github.io/master/assets/pdf/SPAOM2018/MachineLearning_SPAOMworkshop_public.pdf) - - ---- - -## Machine and Deep Learning on the cloud: Segmentation - -Ignacio Arganda-Carreras - -Licensed UNKNOWN - - - -Tags: Neubias, Artificial Intelligence, Bioimage Analysis - -Content type: Slide - -[https://docs.google.com/presentation/d/1oJoy9gHmUuSmUwCkPs_InJf_WZAzmLlUNvK1FUEB4PA/edit#slide=id.ge3a24e733b_0_54](https://docs.google.com/presentation/d/1oJoy9gHmUuSmUwCkPs_InJf_WZAzmLlUNvK1FUEB4PA/edit#slide=id.ge3a24e733b_0_54) - - ---- - -## Methods in bioimage analysis - -Christian Tischer - -Licensed CC-BY-4.0 - - - -Tags: Bioimage Analysis - -Content type: Online Tutorial, Video, Slide - -[https://www.ebi.ac.uk/training/events/methods-bioimage-analysis/](https://www.ebi.ac.uk/training/events/methods-bioimage-analysis/) - -[https://doi.org/10.6019/TOL.BioImageAnalysis22-w.2022.00001.1](https://doi.org/10.6019/TOL.BioImageAnalysis22-w.2022.00001.1) - -[https://drive.google.com/file/d/1MhuqfKhZcYu3bchWMqogIybKjamU5Msg/view](https://drive.google.com/file/d/1MhuqfKhZcYu3bchWMqogIybKjamU5Msg/view) - - ---- - -## Multi-view fusion - -Robert Haase - -Licensed BSD-3-CLAUSE - - - -Lecture slides of a session on Multiview Fusion in Fiji - -Tags: Neubias, Imagej, Bioimage Analysis - -Content type: Slide - -[https://git.mpi-cbg.de/rhaase/lecture_multiview_registration](https://git.mpi-cbg.de/rhaase/lecture_multiview_registration) - - ---- - -## NEUBIAS Analyst School 2018 - -Assaf Zaritsky, Csaba Molnar, Vasja Urbancic, Richard Butler, Anna Kreshuk, Vannary Meas-Yedid - -Licensed UNKNOWN - - - -Tags: Neubias, Bioimage Analysis - -Content type: Slide, Code, Notebook - -[https://github.com/miura/NEUBIAS_AnalystSchool2018](https://github.com/miura/NEUBIAS_AnalystSchool2018) - - ---- - -## NEUBIAS Bioimage Analyst Course 2017 - -Curtis Rueden, Florian Levet, J.B. Sibarta, Alexandre Dafour, Daniel Sage, Sebastien Tosi, Michal Kozubek, Jean-Yves Tinevez, Kota Miura, et al. - -Licensed UNKNOWN - - - -Tags: Neubias, Bioimage Analysis - -Content type: Slide, Tutorial - -[https://github.com/miura/NEUBIAS_Bioimage_Analyst_Course2017](https://github.com/miura/NEUBIAS_Bioimage_Analyst_Course2017) - - ---- - -## NEUBIAS Bioimage Analyst School 2019 - -Kota Miura, Chong Zhang, Jean-Yves Tinevez, Robert Haase, Julius Hossein, Pejamn Rasti, David Rousseau, Ignacio Arganda-Carreras, Siân Culley, et al. - -Licensed UNKNOWN - - - -Tags: Neubias, Bioimage Analysis - -Content type: Slide, Code, Notebook - -[https://github.com/miura/NEUBIAS_AnalystSchool2019](https://github.com/miura/NEUBIAS_AnalystSchool2019) - - ---- - -## NEUBIAS Bioimage Analyst School 2020 - -Marion Louveaux, Stéphane Verger, Arianne Bercowsky Rama, Ignacio Arganda-Carreras, Estibaliz Gómez-de-Mariscal, Kota Miura, et al. - -Licensed UNKNOWN - - - -Tags: Neubias, Bioimage Analysis - -Content type: Slide, Code, Notebook - -[https://github.com/miura/NEUBIAS_AnalystSchool2020](https://github.com/miura/NEUBIAS_AnalystSchool2020) - - ---- - -## NFDI4BIOIMAGE - An Initiative for a National Research Data Infrastructure for Microscopy Data - -Christian Schmidt, Elisa Ferrando-May - -Published 2021-04-29 - -Licensed CCY-BY-SA-4.0 - - - -Align existing and establish novel services & solutions for data management tasks throughout the bioimage data lifecycle. - -Tags: Nfdi4Bioimage, Image Data Management, Bioimage Data, Research Data Management - -Content type: Conference Abstract, Slide - -[https://doi.org/10.11588/heidok.00029489](https://doi.org/10.11588/heidok.00029489) - - ---- - -## Neubias Academy 2020: Introduction to Nuclei Segmentation with StarDist - -Martin Weigert, Olivier Burri, Siân Culley, Uwe Schmidt - -Licensed UNKNOWN - - - -Tags: Python, Neubias, Artificial Intelligence, Bioimage Analysis - -Content type: Slide, Notebook - -[https://github.com/maweigert/neubias_academy_stardist](https://github.com/maweigert/neubias_academy_stardist) - - ---- - -## Nextflow: Scalable and reproducible scientific workflows - -Floden Evan, Di Tommaso Paolo - -Published 2020-12-17 - -Licensed CC-BY-4.0 - - - -Nextflow is an open-source workflow management system that prioritizes portability and reproducibility. It enables users to develop and seamlessly scale genomics workflows locally, on HPC clusters, or in major cloud providers’ infrastructures. Developed since 2014 and backed by a fast-growing community, the Nextflow ecosystem is made up of users and developers across academia, government and industry. It counts over 1M downloads and over 10K users worldwide. - -Tags: Workflow Engine - -Content type: Slide - -[https://zenodo.org/records/4334697](https://zenodo.org/records/4334697) - -[https://doi.org/10.5281/zenodo.4334697](https://doi.org/10.5281/zenodo.4334697) - - ---- - -## Parallelization and heterogeneous computing: from pure CPU to GPU-accelerated image processing - -Robert Haase - -Licensed CC-BY-4.0 - - - -Content type: Slide - -[https://f1000research.com/slides/11-1171](https://f1000research.com/slides/11-1171) - -[https://doi.org/10.7490/f1000research.1119154.1](https://doi.org/10.7490/f1000research.1119154.1) - - ---- - -## QuPath: Open source software for analysing (awkward) images - -Peter Bankhead - -Published 2020-12-16 - -Licensed CC-BY-4.0 - - - -Slides from the CZI/EOSS online meeting in December 2020. - -Tags: Bioimage Analysis - -Content type: Slide - -[https://zenodo.org/records/4328911](https://zenodo.org/records/4328911) - -[https://doi.org/10.5281/zenodo.4328911](https://doi.org/10.5281/zenodo.4328911) - - ---- - -## Research Data Management Seminar - Slides - -Stefano Della Chiesa - -Published 2022-05-18 - -Licensed CC-BY-4.0 - - - -This Research Data Management (RDM) Slides introduce to the multidisciplinary knowledge and competencies required to address policy compliance and research data management best practices throughout a project lifecycle, and beyond it. - - - Module 1 - Introduces the RDM giving its context in the Research Data Governance - Module 2 - Illustrates the most important RDM policies and principles - Module 3 - Provides the most relevant RDM knowledge bricks - Module 4 - Discuss the Data Management Plans (DMPs), examples, templates and guidance - - -  - -Tags: Research Data Management - -Content type: Slide - -[https://zenodo.org/record/6602101](https://zenodo.org/record/6602101) - -[https://doi.org/10.5281/zenodo.6602101](https://doi.org/10.5281/zenodo.6602101) - - ---- - -## Sharing and licensing material - -Robert Haase - -Licensed CC-BY-4.0 - - - -Introduction to sharing resources online and licensing - -Tags: Sharing, Research Data Management - -Content type: Slide - -[https://f1000research.com/slides/10-519](https://f1000research.com/slides/10-519) - - ---- - -## Thinking data management on different scales - -Susanne Kunis - -Published 2023-08-31 - -Licensed CC-BY-4.0 - - - -Presentation given at PoL BioImage Analysis Symposium Dresden 2023 - -Tags: Research Data Management, Nfdi4Bioimage - -Content type: Slide - -[https://zenodo.org/records/8329306](https://zenodo.org/records/8329306) - -[https://doi.org/10.5281/zenodo.8329306](https://doi.org/10.5281/zenodo.8329306) - - ---- - -## Tracking Theory, TrackMate, and Mastodon - -Robert Haase - -Licensed BSD-3-CLAUSE - - - -Lecture slides of a session on Cell Tracking in Fiji - -Tags: Neubias, Imagej, Bioimage Analysis - -Content type: Slide - -[https://git.mpi-cbg.de/rhaase/lecture_tracking_trackmate](https://git.mpi-cbg.de/rhaase/lecture_tracking_trackmate) - - ---- - -## What is Bioimage Analysis? An Introduction - -Kota Miura - -Licensed UNKNOWN - - - -Tags: Neubias, Bioimage Analysis - -Content type: Slide - -[https://www.dropbox.com/s/5abw3cvxrhpobg4/20220923_DefragmentationTS.pdf?dl=0](https://www.dropbox.com/s/5abw3cvxrhpobg4/20220923_DefragmentationTS.pdf?dl=0) - - ---- - -## Working with objects in 2D and 3D - -Robert Haase - -Licensed BSD-3-CLAUSE - - - -Tags: Neubias, Imagej, Bioimage Analysis - -Content type: Slide - -[https://git.mpi-cbg.de/rhaase/lecture_working_with_objects_in_2d_and_3d](https://git.mpi-cbg.de/rhaase/lecture_working_with_objects_in_2d_and_3d) - - ---- - -## Working with pixels - -Robert Haase - -Licensed BSD-3-CLAUSE - - - -Tags: Neubias, Imagej, Bioimage Analysis - -Content type: Slide - -[https://git.mpi-cbg.de/rhaase/lecture_working_with_pixels](https://git.mpi-cbg.de/rhaase/lecture_working_with_pixels) - - ---- - -## ZIDAS 2020 Introduction to Deep Learning - -Estibaliz Gómez-de-Mariscal - -Licensed UNKNOWN - - - -Tags: Artificial Intelligence, Bioimage Analysis - -Content type: Slide - -[https://github.com/esgomezm/zidas2020_intro_DL](https://github.com/esgomezm/zidas2020_intro_DL) - - ---- - -## ilastik: interactive machine learning for (bio)image analysis - -Anna Kreshuk, Dominik Kutra - -Licensed CC-BY-4.0 - - - -Tags: Artificial Intelligence, Bioimage Analysis - -Content type: Slide - -[https://zenodo.org/doi/10.5281/zenodo.4330625](https://zenodo.org/doi/10.5281/zenodo.4330625) - - ---- - diff --git a/_sources/content_types/slides.md b/_sources/content_types/slides.md index 16ab9109..32222a86 100644 --- a/_sources/content_types/slides.md +++ b/_sources/content_types/slides.md @@ -1,4 +1,4 @@ -# Slides (42) +# Slides (82) ## "ZENODO und Co." Was bringt und wer braucht ein Repositorium? Elfi Hesse, Jan-Christoph Deinert, Christian Löschen @@ -53,13 +53,30 @@ Licensed UNKNOWN A review of the tools, methods and concepts useful for biologists and life scientists as well as bioimage analysts. -Tags: Deep Learning, Machine Learning, Artificial Intelligence, Bioimage Analysis, Large Language Models +Tags: Artificial Intelligence, Bioimage Analysis -Content type: Youtube Video, Slides, Webinar +Content type: Video, Slides, Webinar [https://www.youtube.com/watch?v=TJXNMIWtdac](https://www.youtube.com/watch?v=TJXNMIWtdac) +--- + +## Adding a Workflow to BIAFLOWS + +Sébastien Tosi, Volker Baecker, Benjamin Pavie + +Licensed BSD-2-CLAUSE + + + +Tags: Neubias, Bioimage Analysis + +Content type: Slides + +[https://github.com/RoccoDAnt/Defragmentation_TrainingSchool_EOSC-Life_2022/blob/main/Slides/Adding_a_workflow_to_BIAFLOWS.pdf](https://github.com/RoccoDAnt/Defragmentation_TrainingSchool_EOSC-Life_2022/blob/main/Slides/Adding_a_workflow_to_BIAFLOWS.pdf) + + --- ## Alles meins – oder!? Urheberrechte klären für Forschungsdaten @@ -85,6 +102,67 @@ Content type: Slides [https://doi.org/10.5281/zenodo.11472148](https://doi.org/10.5281/zenodo.11472148) +--- + +## Bio Image Analysis + +Christian Tischer + +Licensed UNKNOWN + + + +Content type: Slides + +[https://github.com/tischi/presentation-image-analysis](https://github.com/tischi/presentation-image-analysis) + + +--- + +## Bio-Image Data Strudel for Workshop on Research Data Management in TU Dresden Core Facilities + +Cornelia Wetzker + +Published 2023-11-08 + +Licensed CC-BY-4.0 + + + +This presentation gives a short outline of the complexity of data and metadata in the bioimaging universe. It introduces NFDI4BIOIMAGE as a newly formed consortium as part of the German 'Nationale Forschungsdateninfrastruktur' (NFDI) and its goals and tools for data management including its current members on TU Dresden campus.   + +Tags: Research Data Management, Nfdi4Bioimage + +Content type: Slides + +[https://zenodo.org/records/10083555](https://zenodo.org/records/10083555) + +[https://doi.org/10.5281/zenodo.10083555](https://doi.org/10.5281/zenodo.10083555) + + +--- + +## Bio-image Analysis with the Help of Large Language Models + +Robert Haase + +Published 2024-03-13 + +Licensed CC-BY-4.0 + + + +Large Language Models (LLMs) change the way how we use computers. This also has impact on the bio-image analysis community. We can generate code that analyzes biomedical image data if we ask the right prompts. This talk outlines introduces basic principles, explains prompt engineering and how to apply it to bio-image analysis. We also compare how different LLM vendors perform on code generation tasks and which challenges are ahead for the bio-image analysis community. + +Tags: Artificial Intelligence, Python + +Content type: Slides + +[https://zenodo.org/records/10815329](https://zenodo.org/records/10815329) + +[https://doi.org/10.5281/zenodo.10815329](https://doi.org/10.5281/zenodo.10815329) + + --- ## Bio-image Data Science Lectures @ Uni Leipzig / ScaDS.AI @@ -97,13 +175,64 @@ Licensed CC-BY-4.0 These are the PPTx training resources for Students at Uni Leipzig who want to dive into bio-image data science with Python. The material developed here between April and July 2024. -Tags: Bioimage Analysis, Deep Learning, Microscopy Image Analysis, Python +Tags: Bioimage Analysis, Artificial Intelligence, Python Content type: Slides [https://zenodo.org/records/12623730](https://zenodo.org/records/12623730) +--- + +## Building a Bioimage Analysis Workflow using Deep Learning + +Estibaliz Gómez-de-Mariscal + +Licensed UNKNOWN + + + +Tags: Artificial Intelligence, Bioimage Analysis + +Content type: Slides + +[https://github.com/esgomezm/NEUBIAS_chapter_DL_2020](https://github.com/esgomezm/NEUBIAS_chapter_DL_2020) + + +--- + +## CellProfiler Introduction + +Anna Klemm + +Licensed UNKNOWN + + + +Tags: Neubias, Cellprofiler, Bioimage Analysis + +Content type: Slides + +[https://github.com/ahklemm/CellProfiler_Introduction](https://github.com/ahklemm/CellProfiler_Introduction) + + +--- + +## Challenges and opportunities for bio-image analysis core-facilities + +Robert Haase + +Licensed CC-BY-4.0 + + + +Tags: Research Data Management, Bioimage Analysis, Nfdi4Bioimage + +Content type: Slides + +[https://f1000research.com/slides/12-1054](https://f1000research.com/slides/12-1054) + + --- ## Crashkurs Forschungsdatenmanagement @@ -129,6 +258,27 @@ Content type: Slides [https://doi.org/10.5281/zenodo.3778431](https://doi.org/10.5281/zenodo.3778431) +--- + +## Creating open computational curricula + +Kari Jordan, Zhian Kamvar, Toby Hodges + +Published 2020-12-11 + +Licensed CC-BY-4.0 + + + +In this interactive session, Carpentries team members will guide attendees through three stages of the backward design process to create a lesson development plan for the open source tool of their choosing. Attendees will leave having identified what practical skills they aim to teach (learning objectives), an approach for designing challenge questions (formative assessment), and mechanisms to give and receive feedback. + +Content type: Slides + +[https://zenodo.org/records/4317149](https://zenodo.org/records/4317149) + +[https://doi.org/10.5281/zenodo.4317149](https://doi.org/10.5281/zenodo.4317149) + + --- ## Cultivating Open Training @@ -177,6 +327,23 @@ Content type: Slides [https://doi.org/10.5281/zenodo.11066250](https://doi.org/10.5281/zenodo.11066250) +--- + +## DL@MBL 2021 Exercises + +Jan Funke, Constantin Pape, Morgan Schwartz, Xiaoyan + +Licensed UNKNOWN + + + +Tags: Artificial Intelligence, Bioimage Analysis + +Content type: Slides, Notebook + +[https://github.com/JLrumberger/DL-MBL-2021](https://github.com/JLrumberger/DL-MBL-2021) + + --- ## Data management at France BioImaging @@ -187,7 +354,7 @@ Licensed CC-BY-SA-4.0 -Tags: Research Data Management, Bioimage Analysis, Image Data Management, Open Science +Tags: Research Data Management, Bioimage Analysis, Open Science Content type: Slides, Presentation @@ -313,9 +480,9 @@ Licensed UNKNOWN Leukocyte extravasation is a critical component of the innate immune response, while circulating tumour cell extravasation is a crucial step in metastasis formation. Despite their importance, these extravasation mechanisms remain incompletely understood. In this talk, Guillaume Jacquemet presents a novel imaging framework that integrates microfluidics with high-speed, label-free imaging to study the arrest of pancreatic cancer cells (PDAC) on human endothelial layers under physiological flow conditions. -Tags: Deep Learning, Microscopy Image Analysis +Tags: Artificial Intelligence, Bioimage Analysis -Content type: Youtube Video, Slides +Content type: Video, Slides [https://www.youtube.com/watch?v=KTdZBgSCYJQ](https://www.youtube.com/watch?v=KTdZBgSCYJQ) @@ -380,7 +547,7 @@ Licensed CC-BY-4.0 Introduction to FAIR deep learning. Furthermore, tools to deploy trained DL models (deepImageJ), easily train and evaluate them (ZeroCostDL4Mic and DeepBacs) ensure reproducibility (DL4MicEverywhere), and share this technology in an open-source and reproducible manner (BioImage Model Zoo) are introduced. -Tags: Deep Learning, FAIR-Principles, Microscopy Image Analysis +Tags: Artificial Intelligence, FAIR-Principles, Bioimage Analysis Content type: Slides @@ -430,260 +597,645 @@ Content type: Slides, Tutorial --- -## Hackaton Results - Conversion of KNIME image analysis workflows to Galaxy +## Generative artificial intelligence for bio-image analysis -Riccardo Massei +Robert Haase Licensed CC-BY-4.0 -Results of the project 'Conversion of KNIME image analysis workflows to Galaxy' during the Hackathon 'Image Analysis in Galaxy' (Freiburg 26 Feb - 01 Mar 2024) - -Tags: Research Data Management +Tags: Python, Bioimage Analysis, Artificial Intelligence Content type: Slides -[https://zenodo.org/doi/10.5281/zenodo.10793699](https://zenodo.org/doi/10.5281/zenodo.10793699) +[https://f1000research.com/slides/12-971](https://f1000research.com/slides/12-971) --- -## Intro napari slides +## Hackaton Results - Conversion of KNIME image analysis workflows to Galaxy -Peter Sobolewski +Riccardo Massei -Licensed MIT +Licensed CC-BY-4.0 -Introduction to napari workshop run at JAX (Spring 2024). +Results of the project 'Conversion of KNIME image analysis workflows to Galaxy' during the Hackathon 'Image Analysis in Galaxy' (Freiburg 26 Feb - 01 Mar 2024) -Tags: Napari +Tags: Research Data Management Content type: Slides -[https://thejacksonlaboratory.github.io/intro-napari-slides/#/section](https://thejacksonlaboratory.github.io/intro-napari-slides/#/section) +[https://zenodo.org/doi/10.5281/zenodo.10793699](https://zenodo.org/doi/10.5281/zenodo.10793699) --- -## Introduction to Research Data Management and Open Research +## Hitchhiking through a diverse Bio-image Analysis Software Universe -Shanmugasundaram +Robert Haase -Published 2024-05-17 +Published 2022-07-22 Licensed CC-BY-4.0 -Introduction to RDM primarily for researchers. Can be seen as primer to all other materials in this catalogue. +Overview about decision making and how to influence decisions in the bio-image analysis software context. -Tags: Research Data Management, Open Science +Tags: Bioimage Analysis -Content type: Slides +Content type: Slides, Presentation -[https://zenodo.org/records/4778265](https://zenodo.org/records/4778265) +[https://f1000research.com/slides/11-746](https://f1000research.com/slides/11-746) + +[https://doi.org/10.7490/f1000research.1119026.1](https://doi.org/10.7490/f1000research.1119026.1) --- -## Kollaboratives Arbeiten und Versionskontrolle mit Git +## I3D:bio's OMERO training material: Re-usable, adjustable, multi-purpose slides for local user training -Robert Haase +Christian Schmidt, Michele Bortolomeazzi, Tom Boissonnet, Carsten Fortmann-Grote, Julia Dohle, Peter Zentis, Niraj Kandpal, Susanne Kunis, Thomas Zobel, Stefanie Weidtkamp-Peters, Elisa Ferrando-May -Published 2024-04-15 +Published 2023-11-13 Licensed CC-BY-4.0 -Gemeinsames Arbeiten im Internet stellt uns vor neue Herausforderungen: Wer hat eine Datei wann hochgeladen? Wer hat zum Inhalt beigetragen? Wie kann man Inhalte zusammenfuehren, wenn mehrere Mitarbeiter gleichzeitig Aenderungen gemacht haben? Das Versionskontrollwerkzeug git stellt eine umfassende Loesung fuer solche Fragen bereit. Die Onlineplatform github.com stellt nicht nur Softwareentwicklern weltweit eine git-getriebene Platform zur Verfuegung und erlaubt ihnen effektiv zusammen zu arbeiten. In diesem Workshop lernen wir: - -Infuerung in FAIR-Prinzipien im Softwarecontext -Arbeiten mit git: Pull-requests -Aufloesen von Merge-Konflikten -Automatisiertes Archivieren von Inhalten nach Zenodo.org -Eigene Webseiten auf github.io publizieren +The open-source software OME Remote Objects (OMERO) is a data management software that allows storing, organizing, and annotating bioimaging/microscopy data. OMERO has become one of the best-known systems for bioimage data management in the bioimaging community. The Information Infrastructure for BioImage Data (I3D:bio) project facilitates the uptake of OMERO into research data management (RDM) practices at universities and research institutions in Germany. Since the adoption of OMERO into researchers' daily routines requires intensive training, a broad portfolio of training resources for OMERO is an asset. On top of using the OMERO guides curated by the Open Microscopy Environment Consortium (OME) team, imaging core facility staff at institutions where OMERO is used often prepare additional material tailored to be applicable for their own OMERO instances. Based on experience gathered in the Research Data Management for Microscopy group (RDM4mic) in Germany, and in the use cases in the I3D:bio project, we created a set of reusable, adjustable, openly available slide decks to serve as the basis for tailored training lectures, video tutorials, and self-guided instruction manuals directed at beginners in using OMERO. The material is published as an open educational resource complementing the existing resources for OMERO contributed by the community. +Tags: OMERO, Research Data Management, Nfdi4Bioimage, I3Dbio -Tags: Research Data Management, FAIR-Principles, Git, Zenodo +Content type: Slides, Video -Content type: Slides +[https://zenodo.org/records/8323588](https://zenodo.org/records/8323588) -[https://zenodo.org/records/10972692](https://zenodo.org/records/10972692) +[https://www.youtube.com/playlist?list=PL2k-L-zWPoR7SHjG1HhDIwLZj0MB_stlU](https://www.youtube.com/playlist?list=PL2k-L-zWPoR7SHjG1HhDIwLZj0MB_stlU) -[https://doi.org/10.5281/zenodo.10972692](https://doi.org/10.5281/zenodo.10972692) +[https://doi.org/10.5281/zenodo.8323588](https://doi.org/10.5281/zenodo.8323588) --- -## Lecture-materials of the DeepLife course - -Carl Herrmann, annavonbachmann, David Hoksza, Martin Schätz, Dario Malchiodi, jnguyenvan, Britta Velten, Elodie Laine, JanaBraunger, barwil +## Image Data Services at Euro-BioImaging: Community efforts towards FAIR Image Data and Analysis Services -Published 2023-12-06 +Aastha Mathur Licensed UNKNOWN -Tags: Bioinformatics - -Content type: Github Repository, Slides, Notebook +Content type: Slides -[https://github.com/deeplife4eu/Lecture-materials/](https://github.com/deeplife4eu/Lecture-materials/) +[https://docs.google.com/presentation/d/1henPIDTpHT3bc1Y26AltItAHJ2C5xCOl/edit#slide=id.p1](https://docs.google.com/presentation/d/1henPIDTpHT3bc1Y26AltItAHJ2C5xCOl/edit#slide=id.p1) --- -## MicroSam-Talks - -Constantin Pape - -Published 2024-05-23 - -Licensed CC-BY-4.0 - +## Image analysis in Galaxy +Beatriz Serrano-Solano, Björn Grüning -Talks about Segment Anything for Microscopy: https://github.com/computational-cell-analytics/micro-sam. -Currently contains slides for two talks: +Licensed UNKNOWN -Overview of Segment Anythign for Microscopy given at the SWISSBIAS online meeting in April 2024 -Talk about vision foundation models and Segment Anything for Microscopy given at Human Technopole as part of the EMBO Deep Learning Course in May 2024 -Tags: Image Segmentation, Bioimage Analysis, Deep Learning +Tags: Bioimage Analysis Content type: Slides -[https://zenodo.org/records/11265038](https://zenodo.org/records/11265038) - -[https://doi.org/10.5281/zenodo.11265038](https://doi.org/10.5281/zenodo.11265038) +[https://docs.google.com/presentation/d/1WG_4307XmKsGfWT3taxMvX2rZiG1k0SM1E7SAENJQkI/edit#slide=id.p](https://docs.google.com/presentation/d/1WG_4307XmKsGfWT3taxMvX2rZiG1k0SM1E7SAENJQkI/edit#slide=id.p) --- -## Microscopy data analysis: machine learning and the BioImage Archive - -Andrii Iudin, Anna Foix-Romero, Anna Kreshuk, Awais Athar, Beth Cimini, Dominik Kutra, Estibalis Gomez de Mariscal, Frances Wong, Guillaume Jacquemet, Kedar Narayan, Martin Weigert, Nodar Gogoberidze, Osman Salih, Petr Walczysko, Ryan Conrad, Simone Weyend, Sriram Sundar Somasundharam, Suganya Sivagurunathan, Ugis Sarkans +## ImageJ Macro Introduction -Licensed CC-BY-4.0 +Anna Klemm +Licensed UNKNOWN -The Microscopy data analysis: machine learning and the BioImage Archive course, which focused on introducing programmatic approaches used in the analysis of bioimage data via the BioImage Archive, ran in May 2023. -Tags: Microscopy Image Analysis, Python, Deep Learning +Tags: Neubias, Imagej Macro, Bioimage Analysis -Content type: Video, Slides +Content type: Slides, Code -[https://www.ebi.ac.uk/training/materials/microscopy-data-analysis-machine-learning-and-the-bioimage-archive-materials/](https://www.ebi.ac.uk/training/materials/microscopy-data-analysis-machine-learning-and-the-bioimage-archive-materials/) +[https://github.com/ahklemm/ImageJMacro_Introduction](https://github.com/ahklemm/ImageJMacro_Introduction) --- -## Multiplexed tissue imaging - tools and approaches - -Agustín Andrés Corbat, OmFrederic, Jonas Windhager, Kristína Lidayová - -Licensed CC-BY-4.0 +## ImageJ2 API-beating +Robert Haase +Licensed BSD-3-CLAUSE -Material for the I2K 2024 "Multiplexed tissue imaging - tools and approaches" workshop -Tags: Bioimage Analysis, Microscopy Image Analysis -Content type: Github Repository, Slides, Workshop +Tags: Neubias, Imagej, Bioimage Analysis -[https://github.com/BIIFSweden/I2K2024-MTIWorkshop](https://github.com/BIIFSweden/I2K2024-MTIWorkshop) +Content type: Slides -[https://docs.google.com/presentation/d/1R9-4lXAmTYuyFZpTMDR85SjnLsPZhVZ8/edit#slide=id.p1](https://docs.google.com/presentation/d/1R9-4lXAmTYuyFZpTMDR85SjnLsPZhVZ8/edit#slide=id.p1) +[https://git.mpi-cbg.de/rhaase/lecture_imagej2_dev](https://git.mpi-cbg.de/rhaase/lecture_imagej2_dev) --- -## My Journey Through Bioimage Analysis Teaching Methods From Classroom to Cloud - -Elnaz Fazeli +## Intro napari slides -Published 2024-02-19 +Peter Sobolewski -Licensed CC-BY-4.0 +Licensed MIT -In these slides I introducemy journey through teaching bioimage analysis courses in different formats, from in person courses to online material. I have an overview of different training formats and comparing these for different audiences.  +Introduction to napari workshop run at JAX (Spring 2024). -Tags: Teaching +Tags: Napari Content type: Slides -[https://zenodo.org/records/10679054](https://zenodo.org/records/10679054) - -[https://doi.org/10.5281/zenodo.10679054](https://doi.org/10.5281/zenodo.10679054) +[https://thejacksonlaboratory.github.io/intro-napari-slides/#/section](https://thejacksonlaboratory.github.io/intro-napari-slides/#/section) --- -## NFDI4BIOIMAGE - -Carsten Fortmann-Grote +## Introduction to ImageJ macro programming, Scientific Computing Facility, MPI CBG Dresden -Licensed CC-BY-4.0 +Robert Haase, Benoit Lombardot +Licensed UNKNOWN -Presentation was given at the 2nd MPG-NFDI Workshop on April 18th about e NFDI4BIOIMAGE Consortium, FAIRification of Image (meta)data, Zarr, RFC, Training (TA5), contributing. -Tags: Research Data Management, Bioimage Analysis, FAIR-Principles, Zarr, Nfdi4Bioimage +Tags: Imagej, Bioimage Analysis Content type: Slides -[https://zenodo.org/doi/10.5281/zenodo.11031746](https://zenodo.org/doi/10.5281/zenodo.11031746) +[https://git.mpi-cbg.de/scicomp/bioimage_team/coursematerialimageanalysis/tree/master/ImageJMacro_24h_2017-01](https://git.mpi-cbg.de/scicomp/bioimage_team/coursematerialimageanalysis/tree/master/ImageJMacro_24h_2017-01) --- -## NFDI4BIOIMAGE - National Research Data Infrastructure for Microscopy and BioImage Analysis - Online Kick-Off 2023 +## Introduction to Research Data Management and Open Research -Stefanie Weidtkamp-Peters +Shanmugasundaram + +Published 2024-05-17 Licensed CC-BY-4.0 -NFDI4BIOIMAGE core mission, bioimage data challenge, task areas, FAIR bioimage workflows. +Introduction to RDM primarily for researchers. Can be seen as primer to all other materials in this catalogue. -Tags: Research Data Management, FAIR-Principles, Bioimage Analysis, Nfdi4Bioimage +Tags: Research Data Management, Open Science Content type: Slides -[https://doi.org/10.5281/zenodo.8070038](https://doi.org/10.5281/zenodo.8070038) +[https://zenodo.org/records/4778265](https://zenodo.org/records/4778265) --- -## NFDI4BIOIMAGE - National Research Data Infrastructure for Microscopy and BioImage Analysis [conference talk: The Pelagic Imaging Consortium meets Helmholtz Imaging, 5.10.2023, Hamburg] +## Jupyter for interactive cloud computing -Riccardo Massei +Guillaume Witz -Licensed CC-BY-4.0 +Licensed UNKNOWN -NFDI4BIOIMAGE is a consortium within the framework of the National Research Data Infrastructure (NFDI) in Germany. In this talk, the consortium and the contribution to the work programme by the Helmholtz Centre for Environmental Research (UFZ) in Leipzig are outlined. +Tags: Neubias, Bioimage Analysis -Tags: Research Data Management, Bioimage Analysis, Nfdi4Bioimage +Content type: Slides + +[https://docs.google.com/presentation/d/1q8q1xE-c35tvCsRXZay98s2UYWwXpp0cfCljBmMFpco/edit#slide=id.ga456d5535c_2_53](https://docs.google.com/presentation/d/1q8q1xE-c35tvCsRXZay98s2UYWwXpp0cfCljBmMFpco/edit#slide=id.ga456d5535c_2_53) + + +--- + +## Kollaboratives Arbeiten und Versionskontrolle mit Git + +Robert Haase + +Published 2024-04-15 + +Licensed CC-BY-4.0 + + + +Gemeinsames Arbeiten im Internet stellt uns vor neue Herausforderungen: Wer hat eine Datei wann hochgeladen? Wer hat zum Inhalt beigetragen? Wie kann man Inhalte zusammenfuehren, wenn mehrere Mitarbeiter gleichzeitig Aenderungen gemacht haben? Das Versionskontrollwerkzeug git stellt eine umfassende Loesung fuer solche Fragen bereit. Die Onlineplatform github.com stellt nicht nur Softwareentwicklern weltweit eine git-getriebene Platform zur Verfuegung und erlaubt ihnen effektiv zusammen zu arbeiten. In diesem Workshop lernen wir: + +Infuerung in FAIR-Prinzipien im Softwarecontext +Arbeiten mit git: Pull-requests +Aufloesen von Merge-Konflikten +Automatisiertes Archivieren von Inhalten nach Zenodo.org +Eigene Webseiten auf github.io publizieren + + +Tags: Research Data Management, FAIR-Principles, Git, Zenodo + +Content type: Slides + +[https://zenodo.org/records/10972692](https://zenodo.org/records/10972692) + +[https://doi.org/10.5281/zenodo.10972692](https://doi.org/10.5281/zenodo.10972692) + + +--- + +## Lecture Applied Bioimage Analysis 2020 + +Robert Haase + + + +Slides, scripts, data and other exercise materials of the BioImage Analysis lecture at CMCB TU Dresden 2020 + +Tags: Imagej, Bioimage Analysis + +Content type: Slides + +[https://git.mpi-cbg.de/rhaase/lecture_applied_bioimage_analysis](https://git.mpi-cbg.de/rhaase/lecture_applied_bioimage_analysis) + + +--- + +## Lecture-materials of the DeepLife course + +Carl Herrmann, annavonbachmann, David Hoksza, Martin Schätz, Dario Malchiodi, jnguyenvan, Britta Velten, Elodie Laine, JanaBraunger, barwil + +Published 2023-12-06 + +Licensed UNKNOWN + + + +Tags: Bioinformatics + +Content type: Github Repository, Slides, Notebook + +[https://github.com/deeplife4eu/Lecture-materials/](https://github.com/deeplife4eu/Lecture-materials/) + + +--- + +## Machine Learning - Deep Learning. Applications to Bioimage Analysis + +Estibaliz Gómez-de-Mariscal + +Licensed UNKNOWN + + + +Tags: Artificial Intelligence, Bioimage Analysis + +Content type: Slides + +[https://raw.githubusercontent.com/esgomezm/esgomezm.github.io/master/assets/pdf/SPAOM2018/MachineLearning_SPAOMworkshop_public.pdf](https://raw.githubusercontent.com/esgomezm/esgomezm.github.io/master/assets/pdf/SPAOM2018/MachineLearning_SPAOMworkshop_public.pdf) + + +--- + +## Machine and Deep Learning on the cloud: Segmentation + +Ignacio Arganda-Carreras + +Licensed UNKNOWN + + + +Tags: Neubias, Artificial Intelligence, Bioimage Analysis + +Content type: Slides + +[https://docs.google.com/presentation/d/1oJoy9gHmUuSmUwCkPs_InJf_WZAzmLlUNvK1FUEB4PA/edit#slide=id.ge3a24e733b_0_54](https://docs.google.com/presentation/d/1oJoy9gHmUuSmUwCkPs_InJf_WZAzmLlUNvK1FUEB4PA/edit#slide=id.ge3a24e733b_0_54) + + +--- + +## Methods in bioimage analysis + +Christian Tischer + +Licensed CC-BY-4.0 + + + +Tags: Bioimage Analysis + +Content type: Online Tutorial, Video, Slides + +[https://www.ebi.ac.uk/training/events/methods-bioimage-analysis/](https://www.ebi.ac.uk/training/events/methods-bioimage-analysis/) + +[https://doi.org/10.6019/TOL.BioImageAnalysis22-w.2022.00001.1](https://doi.org/10.6019/TOL.BioImageAnalysis22-w.2022.00001.1) + +[https://drive.google.com/file/d/1MhuqfKhZcYu3bchWMqogIybKjamU5Msg/view](https://drive.google.com/file/d/1MhuqfKhZcYu3bchWMqogIybKjamU5Msg/view) + + +--- + +## MicroSam-Talks + +Constantin Pape + +Published 2024-05-23 + +Licensed CC-BY-4.0 + + + +Talks about Segment Anything for Microscopy: https://github.com/computational-cell-analytics/micro-sam. +Currently contains slides for two talks: + +Overview of Segment Anythign for Microscopy given at the SWISSBIAS online meeting in April 2024 +Talk about vision foundation models and Segment Anything for Microscopy given at Human Technopole as part of the EMBO Deep Learning Course in May 2024 + + +Tags: Bioimage Analysis, Artificial Intelligence + +Content type: Slides + +[https://zenodo.org/records/11265038](https://zenodo.org/records/11265038) + +[https://doi.org/10.5281/zenodo.11265038](https://doi.org/10.5281/zenodo.11265038) + + +--- + +## Microscopy data analysis: machine learning and the BioImage Archive + +Andrii Iudin, Anna Foix-Romero, Anna Kreshuk, Awais Athar, Beth Cimini, Dominik Kutra, Estibalis Gomez de Mariscal, Frances Wong, Guillaume Jacquemet, Kedar Narayan, Martin Weigert, Nodar Gogoberidze, Osman Salih, Petr Walczysko, Ryan Conrad, Simone Weyend, Sriram Sundar Somasundharam, Suganya Sivagurunathan, Ugis Sarkans + +Licensed CC-BY-4.0 + + + +The Microscopy data analysis: machine learning and the BioImage Archive course, which focused on introducing programmatic approaches used in the analysis of bioimage data via the BioImage Archive, ran in May 2023. + +Tags: Bioimage Analysis, Python, Artificial Intelligence + +Content type: Video, Slides + +[https://www.ebi.ac.uk/training/materials/microscopy-data-analysis-machine-learning-and-the-bioimage-archive-materials/](https://www.ebi.ac.uk/training/materials/microscopy-data-analysis-machine-learning-and-the-bioimage-archive-materials/) + + +--- + +## Multi-view fusion + +Robert Haase + +Licensed BSD-3-CLAUSE + + + +Lecture slides of a session on Multiview Fusion in Fiji + +Tags: Neubias, Imagej, Bioimage Analysis + +Content type: Slides + +[https://git.mpi-cbg.de/rhaase/lecture_multiview_registration](https://git.mpi-cbg.de/rhaase/lecture_multiview_registration) + + +--- + +## Multiplexed tissue imaging - tools and approaches + +Agustín Andrés Corbat, OmFrederic, Jonas Windhager, Kristína Lidayová + +Licensed CC-BY-4.0 + + + +Material for the I2K 2024 "Multiplexed tissue imaging - tools and approaches" workshop + +Tags: Bioimage Analysis + +Content type: Github Repository, Slides, Workshop + +[https://github.com/BIIFSweden/I2K2024-MTIWorkshop](https://github.com/BIIFSweden/I2K2024-MTIWorkshop) + +[https://docs.google.com/presentation/d/1R9-4lXAmTYuyFZpTMDR85SjnLsPZhVZ8/edit#slide=id.p1](https://docs.google.com/presentation/d/1R9-4lXAmTYuyFZpTMDR85SjnLsPZhVZ8/edit#slide=id.p1) + + +--- + +## My Journey Through Bioimage Analysis Teaching Methods From Classroom to Cloud + +Elnaz Fazeli + +Published 2024-02-19 + +Licensed CC-BY-4.0 + + + +In these slides I introducemy journey through teaching bioimage analysis courses in different formats, from in person courses to online material. I have an overview of different training formats and comparing these for different audiences.  + +Tags: Teaching + +Content type: Slides + +[https://zenodo.org/records/10679054](https://zenodo.org/records/10679054) + +[https://doi.org/10.5281/zenodo.10679054](https://doi.org/10.5281/zenodo.10679054) + + +--- + +## NEUBIAS Analyst School 2018 + +Assaf Zaritsky, Csaba Molnar, Vasja Urbancic, Richard Butler, Anna Kreshuk, Vannary Meas-Yedid + +Licensed UNKNOWN + + + +Tags: Neubias, Bioimage Analysis + +Content type: Slides, Code, Notebook + +[https://github.com/miura/NEUBIAS_AnalystSchool2018](https://github.com/miura/NEUBIAS_AnalystSchool2018) + + +--- + +## NEUBIAS Bioimage Analyst Course 2017 + +Curtis Rueden, Florian Levet, J.B. Sibarta, Alexandre Dafour, Daniel Sage, Sebastien Tosi, Michal Kozubek, Jean-Yves Tinevez, Kota Miura, et al. + +Licensed UNKNOWN + + + +Tags: Neubias, Bioimage Analysis + +Content type: Slides, Tutorial + +[https://github.com/miura/NEUBIAS_Bioimage_Analyst_Course2017](https://github.com/miura/NEUBIAS_Bioimage_Analyst_Course2017) + + +--- + +## NEUBIAS Bioimage Analyst School 2019 + +Kota Miura, Chong Zhang, Jean-Yves Tinevez, Robert Haase, Julius Hossein, Pejamn Rasti, David Rousseau, Ignacio Arganda-Carreras, Siân Culley, et al. + +Licensed UNKNOWN + + + +Tags: Neubias, Bioimage Analysis + +Content type: Slides, Code, Notebook + +[https://github.com/miura/NEUBIAS_AnalystSchool2019](https://github.com/miura/NEUBIAS_AnalystSchool2019) + + +--- + +## NEUBIAS Bioimage Analyst School 2020 + +Marion Louveaux, Stéphane Verger, Arianne Bercowsky Rama, Ignacio Arganda-Carreras, Estibaliz Gómez-de-Mariscal, Kota Miura, et al. + +Licensed UNKNOWN + + + +Tags: Neubias, Bioimage Analysis + +Content type: Slides, Code, Notebook + +[https://github.com/miura/NEUBIAS_AnalystSchool2020](https://github.com/miura/NEUBIAS_AnalystSchool2020) + + +--- + +## NFDI4BIOIMAGE + +Carsten Fortmann-Grote + +Licensed CC-BY-4.0 + + + +Presentation was given at the 2nd MPG-NFDI Workshop on April 18th about e NFDI4BIOIMAGE Consortium, FAIRification of Image (meta)data, Zarr, RFC, Training (TA5), contributing. + +Tags: Research Data Management, Bioimage Analysis, FAIR-Principles, Zarr, Nfdi4Bioimage + +Content type: Slides + +[https://zenodo.org/doi/10.5281/zenodo.11031746](https://zenodo.org/doi/10.5281/zenodo.11031746) + + +--- + +## NFDI4BIOIMAGE - An Initiative for a National Research Data Infrastructure for Microscopy Data + +Christian Schmidt, Elisa Ferrando-May + +Published 2021-04-29 + +Licensed CCY-BY-SA-4.0 + + + +Align existing and establish novel services & solutions for data management tasks throughout the bioimage data lifecycle. + +Tags: Nfdi4Bioimage, Research Data Management + +Content type: Conference Abstract, Slides + +[https://doi.org/10.11588/heidok.00029489](https://doi.org/10.11588/heidok.00029489) + + +--- + +## NFDI4BIOIMAGE - National Research Data Infrastructure for Microscopy and BioImage Analysis - Online Kick-Off 2023 + +Stefanie Weidtkamp-Peters + +Licensed CC-BY-4.0 + + + +NFDI4BIOIMAGE core mission, bioimage data challenge, task areas, FAIR bioimage workflows. + +Tags: Research Data Management, FAIR-Principles, Bioimage Analysis, Nfdi4Bioimage + +Content type: Slides + +[https://doi.org/10.5281/zenodo.8070038](https://doi.org/10.5281/zenodo.8070038) + + +--- + +## NFDI4BIOIMAGE - National Research Data Infrastructure for Microscopy and BioImage Analysis [conference talk: The Pelagic Imaging Consortium meets Helmholtz Imaging, 5.10.2023, Hamburg] + +Riccardo Massei + +Licensed CC-BY-4.0 + + + +NFDI4BIOIMAGE is a consortium within the framework of the National Research Data Infrastructure (NFDI) in Germany. In this talk, the consortium and the contribution to the work programme by the Helmholtz Centre for Environmental Research (UFZ) in Leipzig are outlined. + +Tags: Research Data Management, Bioimage Analysis, Nfdi4Bioimage Content type: Slides [https://zenodo.org/doi/10.5281/zenodo.8414318](https://zenodo.org/doi/10.5281/zenodo.8414318) +--- + +## Neubias Academy 2020: Introduction to Nuclei Segmentation with StarDist + +Martin Weigert, Olivier Burri, Siân Culley, Uwe Schmidt + +Licensed UNKNOWN + + + +Tags: Python, Neubias, Artificial Intelligence, Bioimage Analysis + +Content type: Slides, Notebook + +[https://github.com/maweigert/neubias_academy_stardist](https://github.com/maweigert/neubias_academy_stardist) + + +--- + +## Nextflow: Scalable and reproducible scientific workflows + +Floden Evan, Di Tommaso Paolo + +Published 2020-12-17 + +Licensed CC-BY-4.0 + + + +Nextflow is an open-source workflow management system that prioritizes portability and reproducibility. It enables users to develop and seamlessly scale genomics workflows locally, on HPC clusters, or in major cloud providers’ infrastructures. Developed since 2014 and backed by a fast-growing community, the Nextflow ecosystem is made up of users and developers across academia, government and industry. It counts over 1M downloads and over 10K users worldwide. + +Tags: Workflow Engine + +Content type: Slides + +[https://zenodo.org/records/4334697](https://zenodo.org/records/4334697) + +[https://doi.org/10.5281/zenodo.4334697](https://doi.org/10.5281/zenodo.4334697) + + --- ## Object Tracking and Track Analysis using TrackMate and CellTracksColab @@ -698,7 +1250,7 @@ Licensed GPL-3.0 I2K 2024 workshop materials for "Object Tracking and Track Analysis using TrackMate and CellTracksColab" -Tags: Bioimage Analysis, Training +Tags: Bioimage Analysis Content type: Github Repository, Tutorial, Workshop, Slides @@ -728,6 +1280,46 @@ Content type: Slides [https://doi.org/10.5281/zenodo.10990107](https://doi.org/10.5281/zenodo.10990107) +--- + +## Parallelization and heterogeneous computing: from pure CPU to GPU-accelerated image processing + +Robert Haase + +Licensed CC-BY-4.0 + + + +Content type: Slides + +[https://f1000research.com/slides/11-1171](https://f1000research.com/slides/11-1171) + +[https://doi.org/10.7490/f1000research.1119154.1](https://doi.org/10.7490/f1000research.1119154.1) + + +--- + +## QuPath: Open source software for analysing (awkward) images + +Peter Bankhead + +Published 2020-12-16 + +Licensed CC-BY-4.0 + + + +Slides from the CZI/EOSS online meeting in December 2020. + +Tags: Bioimage Analysis + +Content type: Slides + +[https://zenodo.org/records/4328911](https://zenodo.org/records/4328911) + +[https://doi.org/10.5281/zenodo.4328911](https://doi.org/10.5281/zenodo.4328911) + + --- ## RDF as a bridge to domain-platforms like OMERO, or There and back again. @@ -747,6 +1339,57 @@ Content type: Slides [https://zenodo.org/doi/10.5281/zenodo.10687658](https://zenodo.org/doi/10.5281/zenodo.10687658) +--- + +## Research Data Management Seminar - Slides + +Stefano Della Chiesa + +Published 2022-05-18 + +Licensed CC-BY-4.0 + + + +This Research Data Management (RDM) Slides introduce to the multidisciplinary knowledge and competencies required to address policy compliance and research data management best practices throughout a project lifecycle, and beyond it. + + + Module 1 - Introduces the RDM giving its context in the Research Data Governance + Module 2 - Illustrates the most important RDM policies and principles + Module 3 - Provides the most relevant RDM knowledge bricks + Module 4 - Discuss the Data Management Plans (DMPs), examples, templates and guidance + + +  + +Tags: Research Data Management + +Content type: Slides + +[https://zenodo.org/record/6602101](https://zenodo.org/record/6602101) + +[https://doi.org/10.5281/zenodo.6602101](https://doi.org/10.5281/zenodo.6602101) + + +--- + +## Sharing and licensing material + +Robert Haase + +Licensed CC-BY-4.0 + + + +Introduction to sharing resources online and licensing + +Tags: Sharing, Research Data Management + +Content type: Slides + +[https://f1000research.com/slides/10-519](https://f1000research.com/slides/10-519) + + --- ## So geschlossen wie nötig, so offen wie möglich - Datenschutz beim Umgang mit Forschungsdaten @@ -812,6 +1455,25 @@ Content type: Slides [https://zenodo.org/doi/10.5281/zenodo.8329305](https://zenodo.org/doi/10.5281/zenodo.8329305) +--- + +## Tracking Theory, TrackMate, and Mastodon + +Robert Haase + +Licensed BSD-3-CLAUSE + + + +Lecture slides of a session on Cell Tracking in Fiji + +Tags: Neubias, Imagej, Bioimage Analysis + +Content type: Slides + +[https://git.mpi-cbg.de/rhaase/lecture_tracking_trackmate](https://git.mpi-cbg.de/rhaase/lecture_tracking_trackmate) + + --- ## Welcome to BioImage Town @@ -831,6 +1493,57 @@ Content type: Slides [https://zenodo.org/doi/10.5281/zenodo.10008464](https://zenodo.org/doi/10.5281/zenodo.10008464) +--- + +## What is Bioimage Analysis? An Introduction + +Kota Miura + +Licensed UNKNOWN + + + +Tags: Neubias, Bioimage Analysis + +Content type: Slides + +[https://www.dropbox.com/s/5abw3cvxrhpobg4/20220923_DefragmentationTS.pdf?dl=0](https://www.dropbox.com/s/5abw3cvxrhpobg4/20220923_DefragmentationTS.pdf?dl=0) + + +--- + +## Working with objects in 2D and 3D + +Robert Haase + +Licensed BSD-3-CLAUSE + + + +Tags: Neubias, Imagej, Bioimage Analysis + +Content type: Slides + +[https://git.mpi-cbg.de/rhaase/lecture_working_with_objects_in_2d_and_3d](https://git.mpi-cbg.de/rhaase/lecture_working_with_objects_in_2d_and_3d) + + +--- + +## Working with pixels + +Robert Haase + +Licensed BSD-3-CLAUSE + + + +Tags: Neubias, Imagej, Bioimage Analysis + +Content type: Slides + +[https://git.mpi-cbg.de/rhaase/lecture_working_with_pixels](https://git.mpi-cbg.de/rhaase/lecture_working_with_pixels) + + --- ## YMIA - Python-Based Event Series Training Material @@ -845,13 +1558,30 @@ Licensed MIT This repository offer access to teaching material and useful resources for the YMIA - Python-Based Event Series. -Tags: Python, Large Language Models, Prompt Engineering, Biabob, Bioimage Analysis, Microscopy Image Analysis +Tags: Python, Artifical Intelligence, Bioimage Analysis Content type: Github Repository, Slides [https://github.com/rmassei/ymia_python_event_series_material](https://github.com/rmassei/ymia_python_event_series_material) +--- + +## ZIDAS 2020 Introduction to Deep Learning + +Estibaliz Gómez-de-Mariscal + +Licensed UNKNOWN + + + +Tags: Artificial Intelligence, Bioimage Analysis + +Content type: Slides + +[https://github.com/esgomezm/zidas2020_intro_DL](https://github.com/esgomezm/zidas2020_intro_DL) + + --- ## [N4BI AHM] Welcome to BioImage Town @@ -894,6 +1624,23 @@ Content type: Slides [https://zenodo.org/doi/10.5281/zenodo.10939519](https://zenodo.org/doi/10.5281/zenodo.10939519) +--- + +## ilastik: interactive machine learning for (bio)image analysis + +Anna Kreshuk, Dominik Kutra + +Licensed CC-BY-4.0 + + + +Tags: Artificial Intelligence, Bioimage Analysis + +Content type: Slides + +[https://zenodo.org/doi/10.5281/zenodo.4330625](https://zenodo.org/doi/10.5281/zenodo.4330625) + + --- ## rse-skills-workshop @@ -902,7 +1649,7 @@ Jack Atkinson Published 2023-12-22T17:39:48+00:00 -Licensed GNU GENERAL PUBLIC LICENSE V3.0 +Licensed GPL-3.0 diff --git a/_sources/content_types/tutorial.md b/_sources/content_types/tutorial.md index 0432e35b..e4cea7b9 100644 --- a/_sources/content_types/tutorial.md +++ b/_sources/content_types/tutorial.md @@ -73,9 +73,9 @@ Licensed UNKNOWN In this course you will learn how to use Docker, Compose and Kubernetes on your machine for better software building and testing. -Tags: Docker, Training +Tags: Docker -Content type: Videos, Tutorial, Online Course +Content type: Video, Tutorial, Online Course [https://www.udemy.com/course/docker-mastery/?srsltid=AfmBOornR5gRqOg-4v8Nsap1z24CaPPUPxg8JzyqEGZ6MvW_dh-sf4Af&couponCode=ST2MT110724BNEW](https://www.udemy.com/course/docker-mastery/?srsltid=AfmBOornR5gRqOg-4v8Nsap1z24CaPPUPxg8JzyqEGZ6MvW_dh-sf4Af&couponCode=ST2MT110724BNEW) @@ -183,7 +183,7 @@ Licensed BSD-3-CLAUSE Napari-ndev is a collection of widgets intended to serve any person seeking to process microscopy images from start to finish. The goal of this example pipeline is to get the user familiar with working with napari-ndev for batch processing and reproducibility (view Image Utilities and Workflow Widget). -Tags: Napari, Microscopy Image Analysis, Bioimage Analysis +Tags: Napari, Bioimage Analysis Content type: Documentation, Github Repository, Tutorial @@ -229,7 +229,7 @@ Sharing knowledge and data in the life sciences allows us to learn from each oth Tags: Open Science, Teaching, Sharing -Content type: Collection, Tutorial, Videos +Content type: Collection, Tutorial, Video [https://www.ebi.ac.uk/training/online/courses/finding-using-public-data/](https://www.ebi.ac.uk/training/online/courses/finding-using-public-data/) @@ -295,9 +295,9 @@ Licensed UNKNOWN Example Workflows / usage of the Glencoe Software. -Tags: OMERO, Training +Tags: OMERO -Content type: Videos, Tutorial, Collection +Content type: Video, Tutorial, Collection [https://www.glencoesoftware.com/media/webinars/](https://www.glencoesoftware.com/media/webinars/) @@ -314,7 +314,7 @@ Licensed BSD-3-CLAUSE Course and material for the clEsperanto workshop presented at I2K 2024 @ Human Technopol (Milan, Italy). The workshop is an hands-on demo of the clesperanto project, focussing on how to use the library for users who want use GPU-acceleration for their Image Processing pipeline. -Tags: Clesperanto, Training, Bioimage Analysis, Notebooks, Workflow +Tags: Bioimage Analysis Content type: Github Repository, Workshop, Tutorial, Notebook @@ -352,7 +352,7 @@ Licensed CC-BY-4.0 This lesson shows how to use Python and scikit-image to do basic image processing. -Tags: Segmentation, Bioimage Analysis, Training, Python, Scikit-Image, Image Segmentation +Tags: Bioimage Analysis, Python Content type: Tutorial, Workflow @@ -365,13 +365,13 @@ Content type: Tutorial, Workflow None -Licensed GPLV3 +Licensed GPL-3.0 The KNIME Image Processing Extension allows you to read in more than 140 different kinds of images and to apply well known methods on images, like preprocessing. segmentation, feature extraction, tracking and classification in KNIME. -Tags: Imagej, OMERO, Bioimage Data, Workflow +Tags: Imagej, OMERO, Workflow Content type: Tutorial, Online Tutorial, Documentation @@ -437,7 +437,7 @@ Licensed UNKNOWN Tags: Neubias, Bioimage Analysis -Content type: Slide, Tutorial +Content type: Slides, Tutorial [https://github.com/miura/NEUBIAS_Bioimage_Analyst_Course2017](https://github.com/miura/NEUBIAS_Bioimage_Analyst_Course2017) @@ -471,7 +471,7 @@ Licensed GPL-3.0 I2K 2024 workshop materials for "Object Tracking and Track Analysis using TrackMate and CellTracksColab" -Tags: Bioimage Analysis, Training +Tags: Bioimage Analysis Content type: Github Repository, Tutorial, Workshop, Slides @@ -490,7 +490,7 @@ Licensed CC-BY-4.0 -Tags: OMERO, Galaxy, Metadata +Tags: OMERO, Galaxy, Metadata, Nfdi4Bioimage Content type: Tutorial, Framework, Workflow @@ -543,9 +543,7 @@ Licensed CC-BY-4.0 Computational skills training at the UCL Sainsbury Wellcome Centre and Gatsby Computational Neuroscience Unit, delivered by members of the Neuroinformatics Unit. -Tags: Training - -Content type: Collection, Online Course, Videos, Tutorial +Content type: Collection, Online Course, Video, Tutorial [https://software-skills.neuroinformatics.dev/index.html](https://software-skills.neuroinformatics.dev/index.html) @@ -603,7 +601,7 @@ Licensed CC0-1.0 To submit, you’ll need to register an account, organise and upload your data, prepare a file list, and then submit using our web submission form. These steps are explained here. -Tags: Research Data Management, Image Data Management, Bioimage Data +Tags: Research Data Management Content type: Tutorial, Video @@ -673,7 +671,7 @@ Licensed BSD3-CLAUSE -Tags: Segmentation, Bioimage Analysis, Training +Tags: Bioimage Analysis Content type: Workshop, Github Repository, Tutorial diff --git a/_sources/content_types/video.md b/_sources/content_types/video.md index 20d4c271..9c792ee2 100644 --- a/_sources/content_types/video.md +++ b/_sources/content_types/video.md @@ -1,4 +1,46 @@ -# Video (24) +# Video (32) +## AI ML DL in Bioimage Analysis - Webinar + +Yannick KREMPP + +Published 2024-11-14 + +Licensed UNKNOWN + + + +A review of the tools, methods and concepts useful for biologists and life scientists as well as bioimage analysts. + +Tags: Artificial Intelligence, Bioimage Analysis + +Content type: Video, Slides, Webinar + +[https://www.youtube.com/watch?v=TJXNMIWtdac](https://www.youtube.com/watch?v=TJXNMIWtdac) + + +--- + +## Artificial Intelligence for Digital Pathology + +Jakob Nikolas Kather, Faisal Mahmood, Florian Jug + +Published 2024-11-08 + +Licensed UNKNOWN + + + +How can artificial intelligence be used for digital pathology? + +Tags: Artificial Intelligence + +Content type: Video + +[https://www.youtube.com/watch?v=Om9tl4Dh2yw](https://www.youtube.com/watch?v=Om9tl4Dh2yw) + + +--- + ## Bio Image Analysis Lecture 2020 @@ -59,6 +101,46 @@ Content type: Collection, Video [https://www.youtube.com/watch?v=A4po9z61TME](https://www.youtube.com/watch?v=A4po9z61TME) +--- + +## Docker Mastery - with Kubernetes + Swarm from a Docker Captain + +Bret Fisher + +Licensed UNKNOWN + + + +In this course you will learn how to use Docker, Compose and Kubernetes on your machine for better software building and testing. + +Tags: Docker + +Content type: Video, Tutorial, Online Course + +[https://www.udemy.com/course/docker-mastery/?srsltid=AfmBOornR5gRqOg-4v8Nsap1z24CaPPUPxg8JzyqEGZ6MvW_dh-sf4Af&couponCode=ST2MT110724BNEW](https://www.udemy.com/course/docker-mastery/?srsltid=AfmBOornR5gRqOg-4v8Nsap1z24CaPPUPxg8JzyqEGZ6MvW_dh-sf4Af&couponCode=ST2MT110724BNEW) + + +--- + +## Dr Guillaume Jacquemet on studying cancer cell metastasis in the era of deep learning for microscopy + +Guillaume Jacquemet + +Published 2024-10-24 + +Licensed UNKNOWN + + + +Leukocyte extravasation is a critical component of the innate immune response, while circulating tumour cell extravasation is a crucial step in metastasis formation. Despite their importance, these extravasation mechanisms remain incompletely understood. In this talk, Guillaume Jacquemet presents a novel imaging framework that integrates microfluidics with high-speed, label-free imaging to study the arrest of pancreatic cancer cells (PDAC) on human endothelial layers under physiological flow conditions. + +Tags: Artificial Intelligence, Bioimage Analysis + +Content type: Video, Slides + +[https://www.youtube.com/watch?v=KTdZBgSCYJQ](https://www.youtube.com/watch?v=KTdZBgSCYJQ) + + --- ## Erick Martins Ratamero - Expanding the OME ecosystem for imaging data management | SciPy 2024 @@ -71,7 +153,7 @@ Licensed YOUTUBE STANDARD LICENSE -Tags: Image Data Management, OMERO, Bioimage Analysis +Tags: OMERO, Bioimage Analysis Content type: Video, Presentation @@ -117,6 +199,46 @@ Content type: Collection, Video [https://www.youtube.com/playlist?list=PL5Edc1v41fyCLFZbBCLo41zFO-_cXBfAb](https://www.youtube.com/playlist?list=PL5Edc1v41fyCLFZbBCLo41zFO-_cXBfAb) +--- + +## Finding and using publicly available data + +Anna Swan + +Published 2024-01-01 + +Licensed CC-BY-4.0 + + + +Sharing knowledge and data in the life sciences allows us to learn from each other and built on what others have discovered. This collection of online courses brings together a variety of training, covering topics such as biocuration, open data, restricted access data and finding publicly available data, to help you discover and make the most of publicly available data in the life sciences. + +Tags: Open Science, Teaching, Sharing + +Content type: Collection, Tutorial, Video + +[https://www.ebi.ac.uk/training/online/courses/finding-using-public-data/](https://www.ebi.ac.uk/training/online/courses/finding-using-public-data/) + + +--- + +## Glencoe Software Webinars + +Chris Allan, Emil Rozbicki + +Licensed UNKNOWN + + + +Example Workflows / usage of the Glencoe Software. + +Tags: OMERO + +Content type: Video, Tutorial, Collection + +[https://www.glencoesoftware.com/media/webinars/](https://www.glencoesoftware.com/media/webinars/) + + --- ## Global BioImaging YouTube channel @@ -155,7 +277,7 @@ The open-source software OME Remote Objects (OMERO) is a data management softwar Tags: OMERO, Research Data Management, Nfdi4Bioimage, I3Dbio -Content type: Slide, Video +Content type: Slides, Video [https://zenodo.org/records/8323588](https://zenodo.org/records/8323588) @@ -221,7 +343,7 @@ Licensed CC-BY-4.0 Tags: Bioimage Analysis -Content type: Online Tutorial, Video, Slide +Content type: Online Tutorial, Video, Slides [https://www.ebi.ac.uk/training/events/methods-bioimage-analysis/](https://www.ebi.ac.uk/training/events/methods-bioimage-analysis/) @@ -242,7 +364,7 @@ Licensed CC-BY-4.0 The Microscopy data analysis: machine learning and the BioImage Archive course, which focused on introducing programmatic approaches used in the analysis of bioimage data via the BioImage Archive, ran in May 2023. -Tags: Microscopy Image Analysis, Python, Deep Learning +Tags: Bioimage Analysis, Python, Artificial Intelligence Content type: Video, Slides @@ -276,7 +398,7 @@ Licensed CC-BY-4.0 OME develops open-source software and data format standards for the storage and manipulation of biological microscopy data -Tags: Open Source Software, Microscopy Image Analysis, Bioimage Data +Tags: Open Source Software Content type: Video, Collection @@ -324,6 +446,42 @@ Content type: Collection, Video [https://www.youtube.com/watch?v=aRHNHk07t3Q&list=PLyCNTVs-UBvuJF7WausQ5q7v7pI1vEpI1](https://www.youtube.com/watch?v=aRHNHk07t3Q&list=PLyCNTVs-UBvuJF7WausQ5q7v7pI1vEpI1) +--- + +## SWC/GCNU Software Skills + +Licensed CC-BY-4.0 + + + +Computational skills training at the UCL Sainsbury Wellcome Centre and Gatsby Computational Neuroscience Unit, delivered by members of the Neuroinformatics Unit. + +Content type: Collection, Online Course, Video, Tutorial + +[https://software-skills.neuroinformatics.dev/index.html](https://software-skills.neuroinformatics.dev/index.html) + + +--- + +## Statistical Rethinking + +Richard McElreath + +Published 2023-01-02 + +Licensed UNKNOWN + + + +Video Lectures for Statistical Rethinking Course + +Tags: Statistics + +Content type: Video + +[https://www.youtube.com/playlist?list=PLDcUM9US4XdPz-KxHM4XHt7uUVGWWVSus](https://www.youtube.com/playlist?list=PLDcUM9US4XdPz-KxHM4XHt7uUVGWWVSus) + + --- ## Structuring of Data and Metadata in Bioimaging: Concepts and technical Solutions in the Context of Linked Data @@ -361,7 +519,7 @@ Licensed CC0-1.0 To submit, you’ll need to register an account, organise and upload your data, prepare a file list, and then submit using our web submission form. These steps are explained here. -Tags: Research Data Management, Image Data Management, Bioimage Data +Tags: Research Data Management Content type: Tutorial, Video diff --git a/_sources/content_types/website.md b/_sources/content_types/website.md index 12bbf8be..214d9b00 100644 --- a/_sources/content_types/website.md +++ b/_sources/content_types/website.md @@ -81,7 +81,7 @@ Licensed UNKNOWN The mission of Metrics Reloaded is to guide researchers in the selection of appropriate performance metrics for biomedical image analysis problems, as well as provide a comprehensive online resource for metric-related information and pitfalls -Tags: Bioimage Analysis, Image Segmentation, Machine Learning +Tags: Bioimage Analysis, Quality Control Content type: Website, Collection diff --git a/_sources/content_types/workshop.md b/_sources/content_types/workshop.md index 4cf3166a..0f254e2c 100644 --- a/_sources/content_types/workshop.md +++ b/_sources/content_types/workshop.md @@ -76,7 +76,7 @@ Licensed BSD-3-CLAUSE Course and material for the clEsperanto workshop presented at I2K 2024 @ Human Technopol (Milan, Italy). The workshop is an hands-on demo of the clesperanto project, focussing on how to use the library for users who want use GPU-acceleration for their Image Processing pipeline. -Tags: Clesperanto, Training, Bioimage Analysis, Notebooks, Workflow +Tags: Bioimage Analysis Content type: Github Repository, Workshop, Tutorial, Notebook @@ -95,7 +95,7 @@ Licensed APACHE-2.0 -Tags: Training +Tags: Bioimage Analysis Content type: Workshop, Notebook, Github Repository @@ -116,7 +116,7 @@ Licensed CC-BY-4.0 Material for the I2K 2024 "Multiplexed tissue imaging - tools and approaches" workshop -Tags: Bioimage Analysis, Microscopy Image Analysis +Tags: Bioimage Analysis Content type: Github Repository, Slides, Workshop @@ -139,7 +139,7 @@ Licensed GPL-3.0 I2K 2024 workshop materials for "Object Tracking and Track Analysis using TrackMate and CellTracksColab" -Tags: Bioimage Analysis, Training +Tags: Bioimage Analysis Content type: Github Repository, Tutorial, Workshop, Slides @@ -156,7 +156,7 @@ Licensed BSD3-CLAUSE -Tags: Segmentation, Bioimage Analysis, Training +Tags: Bioimage Analysis Content type: Workshop, Github Repository, Tutorial @@ -175,7 +175,7 @@ Licensed UNKNOWN -Tags: Bioimage Analysis, Microscopy Image Analysis +Tags: Bioimage Analysis Content type: Collection, Event, Forum Post, Workshop diff --git a/_sources/domain/biapol.github.io.md b/_sources/domain/biapol.github.io.md index 52f70f83..7946c355 100644 --- a/_sources/domain/biapol.github.io.md +++ b/_sources/domain/biapol.github.io.md @@ -9,7 +9,7 @@ Licensed CC-BY-4.0 Tags: OMERO, Python -Content type: Blog +Content type: Blog Post [https://biapol.github.io/blog/robert_haase/browsing_idr/readme.html](https://biapol.github.io/blog/robert_haase/browsing_idr/readme.html) @@ -26,7 +26,7 @@ Licensed CC-BY-4.0 Tags: Python, Conda, Mamba -Content type: Blog +Content type: Blog Post [https://biapol.github.io/blog/mara_lampert/getting_started_with_mambaforge_and_python/readme.html](https://biapol.github.io/blog/mara_lampert/getting_started_with_mambaforge_and_python/readme.html) @@ -88,7 +88,7 @@ Licensed CC-BY-4.0 Tags: Python, Artificial Intelligence, Bioimage Analysis -Content type: Blog +Content type: Blog Post [https://biapol.github.io/blog/marcelo_zoccoler/omero_scripts/readme.html](https://biapol.github.io/blog/marcelo_zoccoler/omero_scripts/readme.html) diff --git a/_sources/domain/docs.google.com.md b/_sources/domain/docs.google.com.md index e84bbb9d..801b1f94 100644 --- a/_sources/domain/docs.google.com.md +++ b/_sources/domain/docs.google.com.md @@ -7,7 +7,7 @@ Licensed UNKNOWN -Content type: Slide +Content type: Slides [https://docs.google.com/presentation/d/1henPIDTpHT3bc1Y26AltItAHJ2C5xCOl/edit#slide=id.p1](https://docs.google.com/presentation/d/1henPIDTpHT3bc1Y26AltItAHJ2C5xCOl/edit#slide=id.p1) @@ -24,7 +24,7 @@ Licensed UNKNOWN Tags: Bioimage Analysis -Content type: Slide +Content type: Slides [https://docs.google.com/presentation/d/1WG_4307XmKsGfWT3taxMvX2rZiG1k0SM1E7SAENJQkI/edit#slide=id.p](https://docs.google.com/presentation/d/1WG_4307XmKsGfWT3taxMvX2rZiG1k0SM1E7SAENJQkI/edit#slide=id.p) @@ -41,7 +41,7 @@ Licensed UNKNOWN Tags: Neubias, Bioimage Analysis -Content type: Slide +Content type: Slides [https://docs.google.com/presentation/d/1q8q1xE-c35tvCsRXZay98s2UYWwXpp0cfCljBmMFpco/edit#slide=id.ga456d5535c_2_53](https://docs.google.com/presentation/d/1q8q1xE-c35tvCsRXZay98s2UYWwXpp0cfCljBmMFpco/edit#slide=id.ga456d5535c_2_53) @@ -58,7 +58,7 @@ Licensed UNKNOWN Tags: Neubias, Artificial Intelligence, Bioimage Analysis -Content type: Slide +Content type: Slides [https://docs.google.com/presentation/d/1oJoy9gHmUuSmUwCkPs_InJf_WZAzmLlUNvK1FUEB4PA/edit#slide=id.ge3a24e733b_0_54](https://docs.google.com/presentation/d/1oJoy9gHmUuSmUwCkPs_InJf_WZAzmLlUNvK1FUEB4PA/edit#slide=id.ge3a24e733b_0_54) @@ -75,7 +75,7 @@ Licensed CC-BY-4.0 Material for the I2K 2024 "Multiplexed tissue imaging - tools and approaches" workshop -Tags: Bioimage Analysis, Microscopy Image Analysis +Tags: Bioimage Analysis Content type: Github Repository, Slides, Workshop diff --git a/_sources/domain/doi.org.md b/_sources/domain/doi.org.md index 9bafbb13..c55b7e34 100644 --- a/_sources/domain/doi.org.md +++ b/_sources/domain/doi.org.md @@ -172,6 +172,8 @@ Research data management is essential in nowadays research, and one of the big o In this presentation, Stefanie Weidtkamp-Peters will introduce the challenges for bioimaging data management, and the necessary steps to achieve data FAIRification. German BioImaging - GMB e.V., together with other institutions, contributes to this endeavor. Janina Hanne will present how the network of imaging core facilities, research groups and industry partners is key to the German bioimaging community’s aligned collaboration toward FAIR bioimaging data. These activities have paved the way for two data management initiatives in Germany: I3D:bio (Information Infrastructure for BioImage Data) and NFDI4BIOIMAGE, a consortium of the National Research Data Infrastructure. Christian Schmidt will introduce the goals and measures of these initiatives to the benefit of imaging scientist’s work and everyday practice.   +Tags: Nfdi4Bioimage, Research Data Management + [https://zenodo.org/records/7890311](https://zenodo.org/records/7890311) [https://doi.org/10.5281/zenodo.7890311](https://doi.org/10.5281/zenodo.7890311) @@ -249,7 +251,7 @@ Hoku West-Foyle Published 2025-01-16 -Licensed CC-ZERO +Licensed CC0-1.0 @@ -262,7 +264,7 @@ Licensed CC-ZERO ## Angebote der NFDI für die Forschung im Bereich Zoologie -Birgitta König-Ries, Robert Haase, Daniel Nüst, Konrad Förstner, Engel, Judith Sophie +Birgitta König-Ries, Robert Haase, Daniel Nüst, Konrad Förstner, Judith Sophie Engel Published 2024-12-04 @@ -272,6 +274,8 @@ Licensed CC-BY-4.0 In diesem Slidedeck geben wir einen Einblick in Angebote und Dienste der Nationalen Forschungsdaten Infrastruktur (NFDI), die Relevant für die Zoologie und angrenzende Disziplinen relevant sein könnten. +Tags: Nfdi4Bioimage, Research Data Management + [https://zenodo.org/records/14278058](https://zenodo.org/records/14278058) [https://doi.org/10.5281/zenodo.14278058](https://doi.org/10.5281/zenodo.14278058) @@ -347,7 +351,7 @@ Licensed CC-BY-4.0 The authors introduce BIOMERO (bioimage analysis in OMERO), a bridge connecting OMERO, a renowned bioimaging data management platform, FAIR workflows, and high-performance computing (HPC) environments. -Tags: OMERO, Workflow, Bioimage Analysis, Image Data Management +Tags: OMERO, Workflow, Bioimage Analysis Content type: Publication @@ -406,9 +410,9 @@ Licensed CC-BY-4.0 This presentation gives a short outline of the complexity of data and metadata in the bioimaging universe. It introduces NFDI4BIOIMAGE as a newly formed consortium as part of the German 'Nationale Forschungsdateninfrastruktur' (NFDI) and its goals and tools for data management including its current members on TU Dresden campus.   -Tags: Research Data Management, Tu Dresden, Bioimage Data, Nfdi4Bioimage +Tags: Research Data Management, Nfdi4Bioimage -Content type: Slide +Content type: Slides [https://zenodo.org/records/10083555](https://zenodo.org/records/10083555) @@ -472,9 +476,9 @@ Licensed CC-BY-4.0 Large Language Models (LLMs) change the way how we use computers. This also has impact on the bio-image analysis community. We can generate code that analyzes biomedical image data if we ask the right prompts. This talk outlines introduces basic principles, explains prompt engineering and how to apply it to bio-image analysis. We also compare how different LLM vendors perform on code generation tasks and which challenges are ahead for the bio-image analysis community. -Tags: Large Language Models, Python +Tags: Artificial Intelligence, Python -Content type: Slide +Content type: Slides [https://zenodo.org/records/10815329](https://zenodo.org/records/10815329) @@ -748,6 +752,8 @@ Licensed CC-BY-4.0 This slide deck introduces the version control tool git, related terminology and the Github Desktop app for managing files in Git[hub] repositories. We furthermore dive into:* Working with repositories* Collaborative with others* Github-Zenodo integration* Github pages* Artificial Intelligence answering Github Issues +Tags: Nfdi4Bioimage, Globias, Research Data Management, Research Software Management + [https://zenodo.org/records/14626054](https://zenodo.org/records/14626054) [https://doi.org/10.5281/zenodo.14626054](https://doi.org/10.5281/zenodo.14626054) @@ -812,7 +818,7 @@ Licensed CC-BY-4.0 In this interactive session, Carpentries team members will guide attendees through three stages of the backward design process to create a lesson development plan for the open source tool of their choosing. Attendees will leave having identified what practical skills they aim to teach (learning objectives), an approach for designing challenge questions (formative assessment), and mechanisms to give and receive feedback. -Content type: Slide +Content type: Slides [https://zenodo.org/records/4317149](https://zenodo.org/records/4317149) @@ -1183,6 +1189,8 @@ Published 2022-05-30 This thesis deals with concepts and solutions in the field of data management in everyday scientific life for image data from microscopy. The focus of the formulated requirements has so far been on published data, which represent only a small subset of the data generated in the scientific process. More and more, everyday research data are moving into the focus of the principles for the management of research data that were formulated early on (FAIR-principles). The adequate management of this mostly multimodal data is a real challenge in terms of its heterogeneity and scope. There is a lack of standardised and established workflows and also the software solutions available so far do not adequately reflect the special requirements of this area. However, the success of any data management process depends heavily on the degree of integration into the daily work routine. Data management must, as far as possible, fit seamlessly into this process. Microscopy data in the scientific process is embedded in pre-processing, which consists of preparatory laboratory work and the analytical evaluation of the microscopy data. In terms of volume, the image data often form the largest part of data generated within this entire research process. In this paper, we focus on concepts and techniques related to the handling and description of this image data and address the necessary basics. The aim is to improve the embedding of the existing data management solution for image data (OMERO) into the everyday scientific work. For this purpose, two independent software extensions for OMERO were implemented within the framework of this thesis: OpenLink and MDEmic. OpenLink simplifies the access to the data stored in the integrated repository in order to feed them into established workflows for further evaluations and enables not only the internal but also the external exchange of data without weakening the advantages of the data repository. The focus of the second implemented software solution, MDEmic, is on the capturing of relevant metadata for microscopy. Through the extended metadata collection, a corresponding linking of the multimodal data by means of a unique description and the corresponding semantic background is aimed at. The configurability of MDEmic is designed to address the currently very dynamic development of underlying concepts and formats. The main goal of MDEmic is to minimise the workload and to automate processes. This provides the scientist with a tool to handle this complex and extensive task of metadata acquisition for microscopic data in a simple way. With the help of the software, semantic and syntactic standardisation can take place without the scientist having to deal with the technical concepts. The generated metadata descriptions are automatically integrated into the image repository and, at the same time, can be transferred by the scientists into formats that are needed when publishing the data. +Tags: Nfdi4Bioimage, Research Data Managementv + [https://zenodo.org/records/6905931](https://zenodo.org/records/6905931) [https://doi.org/10.5281/zenodo.6905931](https://doi.org/10.5281/zenodo.6905931) @@ -2079,7 +2087,7 @@ Overview about decision making and how to influence decisions in the bio-image a Tags: Bioimage Analysis -Content type: Slide, Presentation +Content type: Slides, Presentation [https://f1000research.com/slides/11-746](https://f1000research.com/slides/11-746) @@ -2121,7 +2129,7 @@ The open-source software OME Remote Objects (OMERO) is a data management softwar Tags: OMERO, Research Data Management, Nfdi4Bioimage, I3Dbio -Content type: Slide, Video +Content type: Slides, Video [https://zenodo.org/records/8323588](https://zenodo.org/records/8323588) @@ -2323,7 +2331,7 @@ Cavanagh Published 2024-09-03 -Licensed CC-ZERO +Licensed CC0-1.0 @@ -2394,7 +2402,7 @@ Beyond the University of Konstanz, the Team is involved in a range of national a ## Interactive Image Data Flow Graphs -Martin Schätz, Martin Schätz +Martin Schätz Published 2022-10-17 @@ -2503,6 +2511,8 @@ See cross reference: 10.5281/zenodo.12547566 Key-Value pair template for annotation of datasets in OMERO for light- and electron microscopy data within the research group of Prof. Mueller-Reichert 10.5281/zenodo.12546808 Key-Value pair template for annotation of datasets in OMERO (PERIKLES study) +Tags: Nfdi4Bioimage, Research Data Management + [https://zenodo.org/records/12578084](https://zenodo.org/records/12578084) [https://doi.org/10.5281/zenodo.12578084](https://doi.org/10.5281/zenodo.12578084) @@ -2538,6 +2548,8 @@ See cross reference: 10.5281/zenodo.12547566 Key-Value pair template for annotation of datasets in OMERO (light- and electron microscopy data within the research group of Prof. Mueller-Reichert) 10.5281/zenodo.12578084 Key-Value pair template for annotation in OMERO for light microscopy data acquired with AxioScan7 - Core Facility Cellular Imaging (CFCI) +Tags: Nfdi4Bioimage, Research Data Management + [https://zenodo.org/records/12546808](https://zenodo.org/records/12546808) [https://doi.org/10.5281/zenodo.12546808](https://doi.org/10.5281/zenodo.12546808) @@ -2680,6 +2692,8 @@ Licensed CC-BY-4.0 This slide deck introduces Large Language Models to an audience of life-scientists. We first dive into terminology: Different kinds of Language Models and what they can be used for. The remaining slides are optional slides to allow us to dive deeper into topics such as tools for using LLMs in Science, Quality Assurance, Techniques such as Retrieval Augmented Generation and Prompt Engineering. +Tags: Globias, Artificial Intelligence + [https://zenodo.org/records/14418209](https://zenodo.org/records/14418209) [https://doi.org/10.5281/zenodo.14418209](https://doi.org/10.5281/zenodo.14418209) @@ -2899,7 +2913,7 @@ Licensed CC-BY-4.0 Tags: Bioimage Analysis -Content type: Online Tutorial, Video, Slide +Content type: Online Tutorial, Video, Slides [https://www.ebi.ac.uk/training/events/methods-bioimage-analysis/](https://www.ebi.ac.uk/training/events/methods-bioimage-analysis/) @@ -2942,7 +2956,7 @@ Overview of Segment Anythign for Microscopy given at the SWISSBIAS online meetin Talk about vision foundation models and Segment Anything for Microscopy given at Human Technopole as part of the EMBO Deep Learning Course in May 2024 -Tags: Image Segmentation, Bioimage Analysis, Deep Learning +Tags: Bioimage Analysis, Artificial Intelligence Content type: Slides @@ -2955,7 +2969,7 @@ Content type: Slides ## Modular training resources for bioimage analysis -Christian Tischer, Antonio Politi, Tim-Oliver Buchholz, Elnaz Fazeli, Nicola Gritti, Aliaksandr Halavatyi, Gonzalez Tirado, Sebastian, Julian Hennies, Toby Hodges, Arif Khan, Dominik Kutra, Stefania Marcotti, Bugra Oezdemir, Felix Schneider, Martin Schorb, Anniek Stokkermans, Yi Sun, Nima Vakili +Christian Tischer, Antonio Politi, Tim-Oliver Buchholz, Elnaz Fazeli, Nicola Gritti, Aliaksandr Halavatyi, Sebastian Gonzalez Tirado, Julian Hennies, Toby Hodges, Arif Khan, Dominik Kutra, Stefania Marcotti, Bugra Oezdemir, Felix Schneider, Martin Schorb, Anniek Stokkermans, Yi Sun, Nima Vakili Published 2024-12-03 @@ -2965,6 +2979,8 @@ Licensed CC-BY-4.0 Resources for teaching/preparing to teach bioimage analysis +Tags: Neubias, Bioimage Analysis + [https://zenodo.org/records/14264885](https://zenodo.org/records/14264885) [https://doi.org/10.5281/zenodo.14264885](https://doi.org/10.5281/zenodo.14264885) @@ -3108,9 +3124,9 @@ Licensed CCY-BY-SA-4.0 Align existing and establish novel services & solutions for data management tasks throughout the bioimage data lifecycle. -Tags: Nfdi4Bioimage, Image Data Management, Bioimage Data, Research Data Management +Tags: Nfdi4Bioimage, Research Data Management -Content type: Conference Abstract, Slide +Content type: Conference Abstract, Slides [https://doi.org/10.11588/heidok.00029489](https://doi.org/10.11588/heidok.00029489) @@ -3169,6 +3185,8 @@ Licensed CC-BY-4.0 These illustrations were contracted by the Heinrich Heine University Düsseldorf in the frame of the consortium NFDI4BIOIMAGE from Henning Falk for the purpose of education and public outreach. The illustrations are free to use under a CC-BY 4.0 license.AttributionPlease include an attribution similar to: "Data annoation matters", NFDI4BIOIMAGE Consortium (2024): NFDI4BIOIMAGE data management illustrations by Henning Falk, Zenodo, https://doi.org/10.5281/zenodo.14186100, is used under a CC-BY 4.0 license. Modifications to this illustration include cropping.   +Tags: Nfdi4Bioimage, Research Data Management + [https://zenodo.org/records/14186101](https://zenodo.org/records/14186101) [https://doi.org/10.5281/zenodo.14186101](https://doi.org/10.5281/zenodo.14186101) @@ -3207,6 +3225,8 @@ Licensed CC-BY-4.0 Raw microscopy image from the NFDI4Bioimage calendar October 2024 +Tags: Nfdi4Bioimage, Research Data Management + [https://zenodo.org/records/13837146](https://zenodo.org/records/13837146) [https://doi.org/10.5281/zenodo.13837146](https://doi.org/10.5281/zenodo.13837146) @@ -3248,7 +3268,7 @@ Nextflow is an open-source workflow management system that prioritizes portabili Tags: Workflow Engine -Content type: Slide +Content type: Slides [https://zenodo.org/records/4334697](https://zenodo.org/records/4334697) @@ -3271,6 +3291,8 @@ Presented at the 2024 FoundingGIDE event in Okazaki, Japan: https://founding-gid Note: much of the presentation was a demonstration of the OME2024-NGFF-Challenge -- https://ome.github.io/ome2024-ngff-challenge/ especially of querying an extraction of the metadata (https://github.com/ome/ome2024-ngff-challenge-metadata)   +Tags: Nfdi4Bioimage, Research Data Management + [https://zenodo.org/records/14234608](https://zenodo.org/records/14234608) [https://doi.org/10.5281/zenodo.14234608](https://doi.org/10.5281/zenodo.14234608) @@ -3366,7 +3388,7 @@ Licensed CC-BY-4.0 -Content type: Slide +Content type: Slides [https://f1000research.com/slides/11-1171](https://f1000research.com/slides/11-1171) @@ -3442,7 +3464,7 @@ Slides from the CZI/EOSS online meeting in December 2020. Tags: Bioimage Analysis -Content type: Slide +Content type: Slides [https://zenodo.org/records/4328911](https://zenodo.org/records/4328911) @@ -3463,7 +3485,7 @@ Licensed UNKNOWN Bioimaging data have significant potential for reuse, but unlocking this potential requires systematic archiving of data and metadata in public databases. The authors propose draft metadata guidelines to begin addressing the needs of diverse communities within light and electron microscopy. -Tags: Metadata, Bioimage Data, Image Data Management, Research Data Management +Tags: Metadata, Research Data Management Content type: Publication @@ -3537,7 +3559,7 @@ This Research Data Management (RDM) Slides introduce to the multidisciplinary kn Tags: Research Data Management -Content type: Slide +Content type: Slides [https://zenodo.org/record/6602101](https://zenodo.org/record/6602101) @@ -3741,6 +3763,8 @@ Content: ... +Tags: Nfdi4Bioimage, Research Data Management + [https://zenodo.org/records/7018750](https://zenodo.org/records/7018750) [https://doi.org/10.5281/zenodo.7018750](https://doi.org/10.5281/zenodo.7018750) @@ -3773,7 +3797,7 @@ Content type: Slides ## Terminology service for research data management and knowledge discovery in low-temperature plasma physics -Becker, Markus M., Chaerony Siffa, Ihda, Roman Baum +Markus M. Becker, Ihda Chaerony Siffa, Roman Baum Published 2024-12-11 @@ -3907,7 +3931,7 @@ Licensed UNKNOWN The BioImage Archive is a new archival data resource at the European Bioinformatics Institute (EMBL-EBI). -Tags: Image Data Management, Research Data Management, Bioimage Data +Tags: Research Data Management Content type: Publication @@ -3953,6 +3977,8 @@ Licensed CC-BY-4.0 Research data management (RDM) in microscopy and image analysis is a challenging task. Large files in proprietary formats, complex N-dimensional array structures, and various metadata models and formats can make image data handling inconvenient and difficult. For data organization, annotation, and sharing, researchers need solutions that fit everyday practice and comply with the FAIR (Findable, Accessible, Interoperable, Reusable) principles. International community-based efforts have begun creating open data models (OME), an open file format and translation library (OME-TIFF, Bio-Formats), data management software platforms, and microscopy metadata recommendations and annotation tools. Bringing these developments into practice requires support and training. Iterative feedback and tool improvement is needed to foster practical adoption by the scientific community. The Information Infrastructure for BioImage Data (I3D:bio) project works on guidelines, training resources, and practical assistance for FAIR microscopy RDM adoption with a focus on the management platform OMERO and metadata annotations. +Tags: Nfdi4Bioimage, Research Data Management + [https://zenodo.org/records/10805204](https://zenodo.org/records/10805204) [https://doi.org/10.5281/zenodo.10805204](https://doi.org/10.5281/zenodo.10805204) @@ -3972,7 +3998,7 @@ Licensed CC-BY-4.0 The Open Microscopy Environment (OME) defines a data model and a software implementation to serve as an informatics framework for imaging in biological microscopy experiments, including representation of acquisition parameters, annotations and image analysis results. -Tags: Microscopy Image Analysis, Bioimage Analysis +Tags: Bioimage Analysis Content type: Publication @@ -3995,6 +4021,8 @@ Licensed CC-BY-4.0 Germany’s National Research Data Infrastructure (NFDI) aims to establish a sustained, cross-disciplinary research data management (RDM) infrastructure that enables researchers to handle FAIR (findable, accessible, interoperable, reusable) data. While FAIR principles have been adopted by funders, policymakers, and publishers, their practical implementation remains an ongoing effort. In the field of bio-imaging, harmonization of data formats, metadata ontologies, and open data repositories is necessary to achieve FAIR data. The NFDI4BIOIMAGE was established to address these issues and develop tools and best practices to facilitate FAIR microscopy and image analysis data in alignment with international community activities. The consortium operates through its Data Stewards team to provide expertise and direct support to help overcome RDM challenges. The three Helmholtz Centers in NFDI4BIOIMAGE aim to collaborate closely with other centers and initiatives, such as HMC, Helmholtz AI, and HIP. Here we present NFDI4BIOIMAGE’s work and its significance for research in Helmholtz and beyond +Tags: Nfdi4Bioimage, Research Data Management + [https://zenodo.org/records/11501662](https://zenodo.org/records/11501662) [https://doi.org/10.5281/zenodo.11501662](https://doi.org/10.5281/zenodo.11501662) @@ -4016,7 +4044,7 @@ Presentation given at PoL BioImage Analysis Symposium Dresden 2023 Tags: Research Data Management, Nfdi4Bioimage -Content type: Slide +Content type: Slides [https://zenodo.org/records/8329306](https://zenodo.org/records/8329306) @@ -4037,6 +4065,8 @@ Licensed CC-BY-4.0 This talk will present the initiatives of the NFDI4BioImage consortium aimed at the long-term preservation of life science data. We will discuss our efforts to establish metadata standards, which are crucial for ensuring data reusability and integrity. The development of sustainable infrastructure is another key focus, enabling seamless data integration and analysis in the cloud. We will take a look at how we manage training materials and communicate with our community. Through these actions, NFDI4BioImage seeks to enable FAIR bioimage data management for German researchers, across disciplines and embedded in the international framework. +Tags: Nfdi4Bioimage, Research Data Management + [https://zenodo.org/records/13640979](https://zenodo.org/records/13640979) [https://doi.org/10.5281/zenodo.13640979](https://doi.org/10.5281/zenodo.13640979) @@ -4109,7 +4139,7 @@ The Data Steward Team of the NFDI4BIOIMAGE consortium presents themselves and th ## Working Group Charter. RDM Helpdesk Network -Judith Engel, Patrick Helling, Robert Herrenbrück, Marina Lemaire, Hela Mehrtens, Marcus Schmidt, Martha Stellmacher, Lukas Weimer, Cord Wiljes, Wolf Zinke +Judith Engel, Patrick Helling, Robert Herrenbrück, MarinaLemaire, Hela Mehrtens, Marcus Schmidt, Martha Stellmacher, Lukas Weimer, Cord Wiljes, Wolf Zinke Published 2024-11-04 @@ -4261,6 +4291,8 @@ Licensed CC-BY-4.0 Talk given at Georg-August-Universität Göttingen Campus Institute Data Science23rd January 2025 https://www.uni-goettingen.de/en/653203.html +Tags: Nfdi4Bioimage + [https://zenodo.org/records/14716546](https://zenodo.org/records/14716546) [https://doi.org/10.5281/zenodo.14716546](https://doi.org/10.5281/zenodo.14716546) @@ -4282,6 +4314,8 @@ CMCB LIFE SCIENCES SEMINARSTechnische Universität Dresden16th January 2025 https://tu-dresden.de/cmcb/crtd/news-termine/termine/cmcb-life-sciences-seminar-josh-moore-german-bioimaging-e-v-society-for-microscopy-and-image-analysis-constance   +Tags: Nfdi4Bioimage + [https://zenodo.org/records/14650434](https://zenodo.org/records/14650434) [https://doi.org/10.5281/zenodo.14650434](https://doi.org/10.5281/zenodo.14650434) @@ -4302,6 +4336,8 @@ Licensed CC-BY-4.0 Overview of Activities of the Team Image Data Analysis and Management of German BioImaging e.V.   +Tags: Nfdi4Bioimage, Research Data Management + [https://zenodo.org/records/10796364](https://zenodo.org/records/10796364) [https://doi.org/10.5281/zenodo.10796364](https://doi.org/10.5281/zenodo.10796364) @@ -4341,6 +4377,8 @@ Licensed CC-BY-4.0 Poster presented at the European Light Microscopy Initiative meeting in Liverpool (https://www.elmi2024.org/) +Tags: Nfdi4Bioimage, Research Data Management + [https://zenodo.org/records/11235513](https://zenodo.org/records/11235513) [https://doi.org/10.5281/zenodo.11235513](https://doi.org/10.5281/zenodo.11235513) @@ -4491,6 +4529,8 @@ Learn from each other, leverage different expertise Learn how to train users, establish sustainability strategies, and foster FAIR RDM for bioimaging at your institution +Tags: Nfdi4Bioimage, Research Data Management + [https://zenodo.org/records/14178789](https://zenodo.org/records/14178789) [https://doi.org/10.5281/zenodo.14178789](https://doi.org/10.5281/zenodo.14178789) @@ -4522,6 +4562,8 @@ Dr. Riccardo Massei (Helmholtz-Center for Environmental Research, UFZ, Leipzig) Dr. Michele Bortolomeazzi (DKFZ, Single cell Open Lab, bioimage data specialist, bioinformatician, staff scientist in the NFDI4BIOIMAGE project) Dr. Christian Schmidt (Science Manager for Research Data Management in Bioimaging, German Cancer Research Center, Heidelberg, Project Coordinator of the NFDI4BIOIMAGE project) +Tags: Nfdi4Bioimage, Research Data Management + [https://zenodo.org/records/11350689](https://zenodo.org/records/11350689) [https://doi.org/10.5281/zenodo.11350689](https://doi.org/10.5281/zenodo.11350689) @@ -4605,6 +4647,8 @@ Publishing datasets in public archives for bioimage dataKsenia Krooß /Hein Date & Venue:Thursday, Sept. 26, 5.30 p.m.Haus 22 / Paul Ehrlich Lecture Hall (H22-1)University Hospital Frankfurt +Tags: Nfdi4Bioimage, Research Data Management + [https://zenodo.org/records/13861026](https://zenodo.org/records/13861026) [https://doi.org/10.5281/zenodo.13861026](https://doi.org/10.5281/zenodo.13861026) diff --git a/_sources/domain/f1000research.com.md b/_sources/domain/f1000research.com.md index c93b98ad..e0428600 100644 --- a/_sources/domain/f1000research.com.md +++ b/_sources/domain/f1000research.com.md @@ -9,7 +9,7 @@ Licensed CC-BY-4.0 Tags: Research Data Management, Bioimage Analysis, Nfdi4Bioimage -Content type: Slide +Content type: Slides [https://f1000research.com/slides/12-1054](https://f1000research.com/slides/12-1054) @@ -62,7 +62,7 @@ Licensed CC-BY-4.0 Introduction to FAIR deep learning. Furthermore, tools to deploy trained DL models (deepImageJ), easily train and evaluate them (ZeroCostDL4Mic and DeepBacs) ensure reproducibility (DL4MicEverywhere), and share this technology in an open-source and reproducible manner (BioImage Model Zoo) are introduced. -Tags: Deep Learning, FAIR-Principles, Microscopy Image Analysis +Tags: Artificial Intelligence, FAIR-Principles, Bioimage Analysis Content type: Slides @@ -81,7 +81,7 @@ Licensed CC-BY-4.0 Tags: Python, Bioimage Analysis, Artificial Intelligence -Content type: Slide +Content type: Slides [https://f1000research.com/slides/12-971](https://f1000research.com/slides/12-971) @@ -121,7 +121,7 @@ Overview about decision making and how to influence decisions in the bio-image a Tags: Bioimage Analysis -Content type: Slide, Presentation +Content type: Slides, Presentation [https://f1000research.com/slides/11-746](https://f1000research.com/slides/11-746) @@ -138,7 +138,7 @@ Licensed CC-BY-4.0 -Content type: Slide +Content type: Slides [https://f1000research.com/slides/11-1171](https://f1000research.com/slides/11-1171) @@ -159,7 +159,7 @@ Licensed CC-BY-4.0 As an initiative within Germany's National Research Data Infrastructure, the authors conducted this community survey in summer 2021 to assess the state of the art of bioimaging RDM and the community needs. -Tags: Research Data Management, Image Data Management, Bioimage Data +Tags: Research Data Management Content type: Publication @@ -197,7 +197,7 @@ Introduction to sharing resources online and licensing Tags: Sharing, Research Data Management -Content type: Slide +Content type: Slides [https://f1000research.com/slides/10-519](https://f1000research.com/slides/10-519) diff --git a/_sources/domain/focalplane.biologists.com.md b/_sources/domain/focalplane.biologists.com.md index 13524d20..43b925fb 100644 --- a/_sources/domain/focalplane.biologists.com.md +++ b/_sources/domain/focalplane.biologists.com.md @@ -7,7 +7,7 @@ Mara Lampert Tags: Python, Napari, Bioimage Analysis -Content type: Blog +Content type: Blog Post [https://focalplane.biologists.com/2023/03/30/annotating-3d-images-in-napari/](https://focalplane.biologists.com/2023/03/30/annotating-3d-images-in-napari/) @@ -26,7 +26,7 @@ Introduction to version control using git for collaborative, reproducible script Tags: Sharing, Research Data Management -Content type: Blog +Content type: Blog Post [https://focalplane.biologists.com/2021/09/04/collaborative-bio-image-analysis-script-editing-with-git/](https://focalplane.biologists.com/2021/09/04/collaborative-bio-image-analysis-script-editing-with-git/) @@ -45,9 +45,9 @@ Licensed CC-BY-4.0 In this blog post the author demonstrates how chatGPT can be used to combine a fictive project description with a DMP specification to produce a project-specific DMP. -Tags: Research Data Management, Large Language Models, Artificial Intelligence +Tags: Research Data Management, Artificial Intelligence -Content type: Blog +Content type: Blog Post [https://focalplane.biologists.com/2023/11/06/creating-a-research-data-management-plan-using-chatgpt/](https://focalplane.biologists.com/2023/11/06/creating-a-research-data-management-plan-using-chatgpt/) @@ -64,7 +64,7 @@ Licensed UNKNOWN -Tags: Deep Learning, Microscopy, Microsycopy Image Analysis, Bio Image Analysis, Artifical Intelligence +Tags: Bio Image Analysis, Artifical Intelligence Content type: Blog Post @@ -81,7 +81,7 @@ Mara Lampert Tags: Python, Napari, Bioimage Analysis -Content type: Blog +Content type: Blog Post [https://focalplane.biologists.com/2023/05/03/feature-extraction-in-napari/](https://focalplane.biologists.com/2023/05/03/feature-extraction-in-napari/) @@ -96,7 +96,7 @@ Mara Lampert Tags: Github, Python, Science Communication -Content type: Blog +Content type: Blog Post [https://focalplane.biologists.com/2024/04/03/how-to-write-a-bug-report/](https://focalplane.biologists.com/2024/04/03/how-to-write-a-bug-report/) @@ -115,7 +115,7 @@ Blog post about why we should license our work and what is important when choosi Tags: Licensing, Research Data Management -Content type: Blog +Content type: Blog Post [https://focalplane.biologists.com/2023/05/06/if-you-license-it-itll-be-harder-to-steal-it-why-we-should-license-our-work/](https://focalplane.biologists.com/2023/05/06/if-you-license-it-itll-be-harder-to-steal-it-why-we-should-license-our-work/) @@ -132,7 +132,7 @@ Licensed CC-BY-4.0 Tags: Python, Conda, Mamba -Content type: Blog +Content type: Blog Post [https://focalplane.biologists.com/2022/12/08/managing-scientific-python-environments-using-conda-mamba-and-friends/](https://focalplane.biologists.com/2022/12/08/managing-scientific-python-environments-using-conda-mamba-and-friends/) @@ -147,7 +147,7 @@ Mara Lampert Tags: Python, Jupyter, Bioimage Analysis, Prompt Engineering, Biabob -Content type: Blog +Content type: Blog Post [https://focalplane.biologists.com/2024/07/18/prompt-engineering-in-bio-image-analysis/](https://focalplane.biologists.com/2024/07/18/prompt-engineering-in-bio-image-analysis/) @@ -162,7 +162,7 @@ Mara Lampert Tags: Python, Napari, Bioimage Analysis -Content type: Blog +Content type: Blog Post [https://focalplane.biologists.com/2023/04/13/quality-assurance-of-segmentation-results/](https://focalplane.biologists.com/2023/04/13/quality-assurance-of-segmentation-results/) @@ -177,7 +177,7 @@ Mara Lampert Tags: Python, Napari, Bioimage Analysis -Content type: Blog +Content type: Blog Post [https://focalplane.biologists.com/2023/03/02/rescaling-images-and-pixel-anisotropy/](https://focalplane.biologists.com/2023/03/02/rescaling-images-and-pixel-anisotropy/) @@ -192,7 +192,7 @@ Elisabeth Kugler Tags: Sharing, Research Data Management -Content type: Blog +Content type: Blog Post [https://focalplane.biologists.com/2023/07/26/sharing-your-poster-on-figshare/](https://focalplane.biologists.com/2023/07/26/sharing-your-poster-on-figshare/) @@ -211,7 +211,7 @@ Blog post about how to share data using zenodo.org Tags: Sharing, Research Data Management -Content type: Blog +Content type: Blog Post [https://focalplane.biologists.com/2023/02/15/sharing-research-data-with-zenodo/](https://focalplane.biologists.com/2023/02/15/sharing-research-data-with-zenodo/) @@ -226,7 +226,7 @@ Mara Lampert Tags: Python, Napari, Bioimage Analysis -Content type: Blog +Content type: Blog Post [https://focalplane.biologists.com/2023/06/01/tracking-in-napari/](https://focalplane.biologists.com/2023/06/01/tracking-in-napari/) diff --git a/_sources/domain/git.mpi-cbg.de.md b/_sources/domain/git.mpi-cbg.de.md index 278d2f40..483133c8 100644 --- a/_sources/domain/git.mpi-cbg.de.md +++ b/_sources/domain/git.mpi-cbg.de.md @@ -9,7 +9,7 @@ Licensed BSD-3-CLAUSE Tags: Neubias, Imagej, Bioimage Analysis -Content type: Slide +Content type: Slides [https://git.mpi-cbg.de/rhaase/lecture_imagej2_dev](https://git.mpi-cbg.de/rhaase/lecture_imagej2_dev) @@ -26,7 +26,7 @@ Licensed UNKNOWN Tags: Imagej, Bioimage Analysis -Content type: Slide +Content type: Slides [https://git.mpi-cbg.de/scicomp/bioimage_team/coursematerialimageanalysis/tree/master/ImageJMacro_24h_2017-01](https://git.mpi-cbg.de/scicomp/bioimage_team/coursematerialimageanalysis/tree/master/ImageJMacro_24h_2017-01) @@ -43,7 +43,7 @@ Slides, scripts, data and other exercise materials of the BioImage Analysis lect Tags: Imagej, Bioimage Analysis -Content type: Slide +Content type: Slides [https://git.mpi-cbg.de/rhaase/lecture_applied_bioimage_analysis](https://git.mpi-cbg.de/rhaase/lecture_applied_bioimage_analysis) @@ -62,7 +62,7 @@ Lecture slides of a session on Multiview Fusion in Fiji Tags: Neubias, Imagej, Bioimage Analysis -Content type: Slide +Content type: Slides [https://git.mpi-cbg.de/rhaase/lecture_multiview_registration](https://git.mpi-cbg.de/rhaase/lecture_multiview_registration) @@ -81,7 +81,7 @@ Lecture slides of a session on Cell Tracking in Fiji Tags: Neubias, Imagej, Bioimage Analysis -Content type: Slide +Content type: Slides [https://git.mpi-cbg.de/rhaase/lecture_tracking_trackmate](https://git.mpi-cbg.de/rhaase/lecture_tracking_trackmate) @@ -98,7 +98,7 @@ Licensed BSD-3-CLAUSE Tags: Neubias, Imagej, Bioimage Analysis -Content type: Slide +Content type: Slides [https://git.mpi-cbg.de/rhaase/lecture_working_with_objects_in_2d_and_3d](https://git.mpi-cbg.de/rhaase/lecture_working_with_objects_in_2d_and_3d) @@ -115,7 +115,7 @@ Licensed BSD-3-CLAUSE Tags: Neubias, Imagej, Bioimage Analysis -Content type: Slide +Content type: Slides [https://git.mpi-cbg.de/rhaase/lecture_working_with_pixels](https://git.mpi-cbg.de/rhaase/lecture_working_with_pixels) diff --git a/_sources/domain/github.com.md b/_sources/domain/github.com.md index f27b8f8d..1dd0772a 100644 --- a/_sources/domain/github.com.md +++ b/_sources/domain/github.com.md @@ -47,7 +47,7 @@ Licensed BSD-2-CLAUSE Tags: Neubias, Bioimage Analysis -Content type: Slide +Content type: Slides [https://github.com/RoccoDAnt/Defragmentation_TrainingSchool_EOSC-Life_2022/blob/main/Slides/Adding_a_workflow_to_BIAFLOWS.pdf](https://github.com/RoccoDAnt/Defragmentation_TrainingSchool_EOSC-Life_2022/blob/main/Slides/Adding_a_workflow_to_BIAFLOWS.pdf) @@ -81,7 +81,7 @@ Licensed CC-BY-4.0 Training resources for Students at Uni Leipzig who want to dive into bio-image data science with Python. The material developed here between April and July 2024. -Tags: Bioimage Analysis, Deep Learning, Microscopy Image Analysis, Python +Tags: Bioimage Analysis, Artificial Intelligence, Python Content type: Github Repository @@ -115,7 +115,7 @@ Licensed UNKNOWN -Content type: Slide +Content type: Slides [https://github.com/tischi/presentation-image-analysis](https://github.com/tischi/presentation-image-analysis) @@ -171,7 +171,7 @@ Licensed CC-BY-4.0 This repository contains training resources for Students at Uni Leipzig who want to dive into bio-image data science with Python. -Tags: Image Data Management, Deep Learning, Microscopy Image Analysis, Python +Tags: Research Data Management, Artificial Intelligence, Bioimage Analysis, Python Content type: Notebook @@ -249,7 +249,7 @@ Licensed UNKNOWN Tags: Artificial Intelligence, Bioimage Analysis -Content type: Slide +Content type: Slides [https://github.com/esgomezm/NEUBIAS_chapter_DL_2020](https://github.com/esgomezm/NEUBIAS_chapter_DL_2020) @@ -300,7 +300,7 @@ Licensed UNKNOWN Tags: Neubias, Cellprofiler, Bioimage Analysis -Content type: Slide +Content type: Slides [https://github.com/ahklemm/CellProfiler_Introduction](https://github.com/ahklemm/CellProfiler_Introduction) @@ -453,7 +453,7 @@ Licensed UNKNOWN Tags: Artificial Intelligence, Bioimage Analysis -Content type: Slide, Notebook +Content type: Slides, Notebook [https://github.com/JLrumberger/DL-MBL-2021](https://github.com/JLrumberger/DL-MBL-2021) @@ -646,7 +646,7 @@ Licensed BSD-3-CLAUSE Napari-ndev is a collection of widgets intended to serve any person seeking to process microscopy images from start to finish. The goal of this example pipeline is to get the user familiar with working with napari-ndev for batch processing and reproducibility (view Image Utilities and Workflow Widget). -Tags: Napari, Microscopy Image Analysis, Bioimage Analysis +Tags: Napari, Bioimage Analysis Content type: Documentation, Github Repository, Tutorial @@ -735,7 +735,7 @@ Licensed BSD-3-CLAUSE Course and material for the clEsperanto workshop presented at I2K 2024 @ Human Technopol (Milan, Italy). The workshop is an hands-on demo of the clesperanto project, focussing on how to use the library for users who want use GPU-acceleration for their Image Processing pipeline. -Tags: Clesperanto, Training, Bioimage Analysis, Notebooks, Workflow +Tags: Bioimage Analysis Content type: Github Repository, Workshop, Tutorial, Notebook @@ -754,7 +754,7 @@ Licensed APACHE-2.0 -Tags: Training +Tags: Bioimage Analysis Content type: Workshop, Notebook, Github Repository @@ -930,7 +930,7 @@ Licensed UNKNOWN Tags: Neubias, Imagej Macro, Bioimage Analysis -Content type: Slide, Code +Content type: Slides, Code [https://github.com/ahklemm/ImageJMacro_Introduction](https://github.com/ahklemm/ImageJMacro_Introduction) @@ -947,7 +947,7 @@ Licensed MIT This course consists of lectures and exercises that teach the background of deep learning for image analysis and show applications to classification and segmentation analysis problems. -Tags: Deep Learning, Pytorch, Segmentation, Python +Tags: Artificial Intelligence, Python Content type: Notebook @@ -1081,7 +1081,7 @@ Licensed CC-BY-4.0 Material for the I2K 2024 "Multiplexed tissue imaging - tools and approaches" workshop -Tags: Bioimage Analysis, Microscopy Image Analysis +Tags: Bioimage Analysis Content type: Github Repository, Slides, Workshop @@ -1117,7 +1117,7 @@ Licensed UNKNOWN Tags: Neubias, Bioimage Analysis -Content type: Slide, Code, Notebook +Content type: Slides, Code, Notebook [https://github.com/miura/NEUBIAS_AnalystSchool2018](https://github.com/miura/NEUBIAS_AnalystSchool2018) @@ -1134,7 +1134,7 @@ Licensed UNKNOWN Tags: Neubias, Bioimage Analysis -Content type: Slide, Tutorial +Content type: Slides, Tutorial [https://github.com/miura/NEUBIAS_Bioimage_Analyst_Course2017](https://github.com/miura/NEUBIAS_Bioimage_Analyst_Course2017) @@ -1151,7 +1151,7 @@ Licensed UNKNOWN Tags: Neubias, Bioimage Analysis -Content type: Slide, Code, Notebook +Content type: Slides, Code, Notebook [https://github.com/miura/NEUBIAS_AnalystSchool2019](https://github.com/miura/NEUBIAS_AnalystSchool2019) @@ -1168,7 +1168,7 @@ Licensed UNKNOWN Tags: Neubias, Bioimage Analysis -Content type: Slide, Code, Notebook +Content type: Slides, Code, Notebook [https://github.com/miura/NEUBIAS_AnalystSchool2020](https://github.com/miura/NEUBIAS_AnalystSchool2020) @@ -1223,7 +1223,7 @@ Licensed UNKNOWN Tags: Python, Neubias, Artificial Intelligence, Bioimage Analysis -Content type: Slide, Notebook +Content type: Slides, Notebook [https://github.com/maweigert/neubias_academy_stardist](https://github.com/maweigert/neubias_academy_stardist) @@ -1240,7 +1240,7 @@ Licensed BSD-3-CLAUSE Tutorial for running CellPose advanced functions -Tags: Cellpose, Segmentation +Tags: Bioimage Analysis, Artificial Intelligence Content type: Github Repository @@ -1312,7 +1312,7 @@ Licensed GPL-3.0 I2K 2024 workshop materials for "Object Tracking and Track Analysis using TrackMate and CellTracksColab" -Tags: Bioimage Analysis, Training +Tags: Bioimage Analysis Content type: Github Repository, Tutorial, Workshop, Slides @@ -1547,7 +1547,7 @@ Licensed UNKNOWN This tool is intended to link different research data management platforms with each other. -Tags: Research Data Management, Image Data Management +Tags: Research Data Management Content type: Github Repository @@ -1630,7 +1630,7 @@ Content type: Tutorial Ziv Yaniv et al. -Licensed APACHE-2.0 LICENSE +Licensed APACHE-2.0 @@ -1664,7 +1664,7 @@ Richard McElreath Published 2024-03-01 -Licensed CC0-1.0 LICENSE +Licensed CC0-1.0 @@ -1736,7 +1736,7 @@ Licensed BSD3-CLAUSE -Tags: Segmentation, Bioimage Analysis, Training +Tags: Bioimage Analysis Content type: Workshop, Github Repository, Tutorial @@ -1812,7 +1812,7 @@ Licensed MIT This repository offer access to teaching material and useful resources for the YMIA - Python-Based Event Series. -Tags: Python, Large Language Models, Prompt Engineering, Biabob, Bioimage Analysis, Microscopy Image Analysis +Tags: Python, Artifical Intelligence, Bioimage Analysis Content type: Github Repository, Slides @@ -1846,7 +1846,7 @@ Licensed UNKNOWN Tags: Artificial Intelligence, Bioimage Analysis -Content type: Slide +Content type: Slides [https://github.com/esgomezm/zidas2020_intro_DL](https://github.com/esgomezm/zidas2020_intro_DL) @@ -1884,7 +1884,7 @@ Licensed GPL-2.0 Java application to convert image file formats, including .mrxs, to an intermediate Zarr structure compatible with the OME-NGFF specification. -Tags: Open Source Software, Bioimage Data +Tags: Open Source Software Content type: Application, Github Repository @@ -2006,8 +2006,6 @@ Licensed BSD-2-CLAUSE Web page for validating OME-NGFF files. -Tags: Bioimage Data - Content type: Github Repository, Application [https://ome.github.io/ome-ngff-validator/](https://ome.github.io/ome-ngff-validator/) @@ -2019,7 +2017,7 @@ Content type: Github Repository, Application ## ome2024-ngff-challenge -Will Moore, Josh Moore, sherwoodf, jean-marie burel, Norman Rzepka, dependabot[bot], JensWendt, Joost de Folter, Torsten Stöter, AybukeKY, Eric Perlman, Tom Boissonnet +Will Moore, Josh Moore, sherwoodf, Jean-Marie Burel, Norman Rzepka, dependabot[bot], JensWendt, Joost de Folter, Torsten St\xF6ter, AybukeKY, Eric Perlman, Tom Boissonnet Published 2024-08-30T12:00:53+00:00 @@ -2029,7 +2027,7 @@ Licensed BSD-3-CLAUSE Project planning and material repository for the 2024 challenge to generate 1 PB of OME-Zarr data -Tags: Sharing +Tags: Sharing, Nfdi4Bioimage, Research Data Management Content type: Github Repository @@ -2044,7 +2042,7 @@ JanClusmann, Tim Lenz Published 2024-11-08T08:32:03+00:00 -Licensed GNU GENERAL PUBLIC LICENSE V3.0 +Licensed GPL-3.0 @@ -2094,7 +2092,7 @@ Licensed GPL-2.0 Java application to convert a directory of tiles to an OME-TIFF pyramid. This is the second half of iSyntax/.mrxs => OME-TIFF conversion. -Tags: Open Source Software, Bioimage Data +Tags: Open Source Software Content type: Application, Github Repository @@ -2109,7 +2107,7 @@ Jack Atkinson Published 2023-12-22T17:39:48+00:00 -Licensed GNU GENERAL PUBLIC LICENSE V3.0 +Licensed GPL-3.0 @@ -2126,11 +2124,11 @@ Content type: Github Repository, Slides ## scanpy-tutorials -Alex Wolf, pre-commit-ci[bot], Philipp A., Isaac Virshup, Ilan Gold, Giovanni Palla, Fidel Ramirez, Gökçen Eraslan, Sergei Rybakov, Abolfazl (Abe), Adam Gayoso, Dinesh Palli, Gregor Sturm, Jan Lause, Karin Hrovatin, Krzysztof Polanski, RaphaelBuzzi, Yimin Zheng, Yishen Miao, evanbiederstedt +Alex Wolf, pre-commit-ci[bot], Philipp A., Isaac Virshup, Ilan Gold, Giovanni Palla, Fidel Ramirez, G\xF6k\xE7en Eraslan, Sergei Rybakov, Abolfazl (Abe), Adam Gayoso, Dinesh Palli, Gregor Sturm, Jan Lause, Karin Hrovatin, Krzysztof Polanski, RaphaelBuzzi, Yimin Zheng, Yishen Miao, evanbiederstedt Published 2018-12-16T03:42:46+00:00 -Licensed BSD-3 +Licensed BSD-3-CLAUSE diff --git a/_sources/domain/www.biorxiv.org.md b/_sources/domain/www.biorxiv.org.md index 362e1e1b..f4ee66b6 100644 --- a/_sources/domain/www.biorxiv.org.md +++ b/_sources/domain/www.biorxiv.org.md @@ -79,7 +79,7 @@ Licensed CC-BY-4.0 Glittr.org is a platform that aggregates and indexes training materials on computational life sciences from public git repositories, making it easier for users to find, compare, and analyze these resources based on various metrics. By providing insights into the availability of materials, collaboration patterns, and licensing practices, Glittr.org supports adherence to the FAIR principles, benefiting the broader life sciences community. -Tags: Training, Bioimage Analysis, Research Data Management +Tags: Bioimage Analysis, Research Data Management Content type: Publication, Preprint diff --git a/_sources/domain/www.ebi.ac.uk.md b/_sources/domain/www.ebi.ac.uk.md index 571a7a6b..21bc50d4 100644 --- a/_sources/domain/www.ebi.ac.uk.md +++ b/_sources/domain/www.ebi.ac.uk.md @@ -5,7 +5,7 @@ Licensed CC0-1.0 -Tags: Bioimage Analysis, Deep Learning +Tags: Bioimage Analysis, Artificial Intelligence Content type: Collection, Data @@ -67,7 +67,7 @@ Licensed CC0 (MOSTLY, BUT CAN DIFFER DEPENDING ON RESOURCE) Online tutorial and webinar library, designed and delivered by EMBL-EBI experts -Tags: Bioinformatics, Training +Tags: Bioinformatics Content type: Collection @@ -90,7 +90,7 @@ Sharing knowledge and data in the life sciences allows us to learn from each oth Tags: Open Science, Teaching, Sharing -Content type: Collection, Tutorial, Videos +Content type: Collection, Tutorial, Video [https://www.ebi.ac.uk/training/online/courses/finding-using-public-data/](https://www.ebi.ac.uk/training/online/courses/finding-using-public-data/) @@ -107,7 +107,7 @@ Licensed CC-BY-4.0 Tags: Bioimage Analysis -Content type: Online Tutorial, Video, Slide +Content type: Online Tutorial, Video, Slides [https://www.ebi.ac.uk/training/events/methods-bioimage-analysis/](https://www.ebi.ac.uk/training/events/methods-bioimage-analysis/) @@ -128,7 +128,7 @@ Licensed CC-BY-4.0 The Microscopy data analysis: machine learning and the BioImage Archive course, which focused on introducing programmatic approaches used in the analysis of bioimage data via the BioImage Archive, ran in May 2023. -Tags: Microscopy Image Analysis, Python, Deep Learning +Tags: Bioimage Analysis, Python, Artificial Intelligence Content type: Video, Slides @@ -145,7 +145,7 @@ Licensed CC0-1.0 Recommended Metadata for Biological Images (REMBI) provides guidelines for metadata for biological images to enable the FAIR sharing of scientific data. -Tags: FAIR-Principles, Metadata, Image Data Management, Research Data Management, Bioimage Data +Tags: FAIR-Principles, Metadata, Research Data Management Content type: Collection @@ -162,7 +162,7 @@ Licensed CC0-1.0 To submit, you’ll need to register an account, organise and upload your data, prepare a file list, and then submit using our web submission form. These steps are explained here. -Tags: Research Data Management, Image Data Management, Bioimage Data +Tags: Research Data Management Content type: Tutorial, Video diff --git a/_sources/domain/www.nature.com.md b/_sources/domain/www.nature.com.md index e650422d..bc193258 100644 --- a/_sources/domain/www.nature.com.md +++ b/_sources/domain/www.nature.com.md @@ -60,7 +60,7 @@ Licensed CC-BY-4.0 The authors show the utility of Minimum Information for High Content Screening Microscopy Experiments (MIHCSME) for High Content Screening (HCS) data using multiple examples from the Leiden FAIR Cell Observatory, a Euro-Bioimaging flagship node for high content screening and the pilot node for implementing FAIR bioimaging data throughout the Netherlands Bioimaging network. -Tags: FAIR-Principles, Metadata, Research Data Management, Image Data Management, Bioimage Data +Tags: FAIR-Principles, Metadata, Research Data Management Content type: Publication @@ -160,13 +160,13 @@ Content type: Publication Shanghang Zhang, Gaole Dai, Tiejun Huang, Jianxu Chen -Licensed ['CC-BY-NC-SA'] +Licensed CC-BY-NC-SA Multimodal large language models have been recognized as a historical milestone in the field of artificial intelligence and have demonstrated revolutionary potentials not only in commercial applications, but also for many scientific fields. Here we give a brief overview of multimodal large language models through the lens of bioimage analysis and discuss how we could build these models as a community to facilitate biology research -Tags: Bioimage Analysis, Large Language Models, FAIR-Principles, Workflow +Tags: Bioimage Analysis, FAIR-Principles, Workflow Content type: Publication @@ -206,7 +206,7 @@ Licensed UNKNOWN Bioimaging data have significant potential for reuse, but unlocking this potential requires systematic archiving of data and metadata in public databases. The authors propose draft metadata guidelines to begin addressing the needs of diverse communities within light and electron microscopy. -Tags: Metadata, Bioimage Data, Image Data Management, Research Data Management +Tags: Metadata, Research Data Management Content type: Publication @@ -229,7 +229,7 @@ Licensed UNKNOWN This Focus issue features a series of papers offering guidelines and tools for improving the tracking and reporting of microscopy metadata with an emphasis on reproducibility and data re-use. -Tags: Reproducibility, Metadata, Bioimage Data +Tags: Reproducibility, Metadata Content type: Collection diff --git a/_sources/domain/www.youtube.com.md b/_sources/domain/www.youtube.com.md index 1b0f3b0b..7456ac21 100644 --- a/_sources/domain/www.youtube.com.md +++ b/_sources/domain/www.youtube.com.md @@ -11,9 +11,9 @@ Licensed UNKNOWN A review of the tools, methods and concepts useful for biologists and life scientists as well as bioimage analysts. -Tags: Deep Learning, Machine Learning, Artificial Intelligence, Bioimage Analysis, Large Language Models +Tags: Artificial Intelligence, Bioimage Analysis -Content type: Youtube Video, Slides, Webinar +Content type: Video, Slides, Webinar [https://www.youtube.com/watch?v=TJXNMIWtdac](https://www.youtube.com/watch?v=TJXNMIWtdac) @@ -34,7 +34,7 @@ How can artificial intelligence be used for digital pathology? Tags: Artificial Intelligence -Content type: Youtube Video +Content type: Video [https://www.youtube.com/watch?v=Om9tl4Dh2yw](https://www.youtube.com/watch?v=Om9tl4Dh2yw) @@ -115,9 +115,9 @@ Licensed UNKNOWN Leukocyte extravasation is a critical component of the innate immune response, while circulating tumour cell extravasation is a crucial step in metastasis formation. Despite their importance, these extravasation mechanisms remain incompletely understood. In this talk, Guillaume Jacquemet presents a novel imaging framework that integrates microfluidics with high-speed, label-free imaging to study the arrest of pancreatic cancer cells (PDAC) on human endothelial layers under physiological flow conditions. -Tags: Deep Learning, Microscopy Image Analysis +Tags: Artificial Intelligence, Bioimage Analysis -Content type: Youtube Video, Slides +Content type: Video, Slides [https://www.youtube.com/watch?v=KTdZBgSCYJQ](https://www.youtube.com/watch?v=KTdZBgSCYJQ) @@ -134,7 +134,7 @@ Licensed YOUTUBE STANDARD LICENSE -Tags: Image Data Management, OMERO, Bioimage Analysis +Tags: OMERO, Bioimage Analysis Content type: Video, Presentation @@ -218,7 +218,7 @@ The open-source software OME Remote Objects (OMERO) is a data management softwar Tags: OMERO, Research Data Management, Nfdi4Bioimage, I3Dbio -Content type: Slide, Video +Content type: Slides, Video [https://zenodo.org/records/8323588](https://zenodo.org/records/8323588) @@ -269,7 +269,7 @@ Licensed CC-BY-4.0 OME develops open-source software and data format standards for the storage and manipulation of biological microscopy data -Tags: Open Source Software, Microscopy Image Analysis, Bioimage Data +Tags: Open Source Software Content type: Video, Collection @@ -333,7 +333,7 @@ Video Lectures for Statistical Rethinking Course Tags: Statistics -Content type: Youtube Video +Content type: Video [https://www.youtube.com/playlist?list=PLDcUM9US4XdPz-KxHM4XHt7uUVGWWVSus](https://www.youtube.com/playlist?list=PLDcUM9US4XdPz-KxHM4XHt7uUVGWWVSus) diff --git a/_sources/domain/zenodo.org.md b/_sources/domain/zenodo.org.md index 4748f959..55777ec0 100644 --- a/_sources/domain/zenodo.org.md +++ b/_sources/domain/zenodo.org.md @@ -136,6 +136,8 @@ Research data management is essential in nowadays research, and one of the big o In this presentation, Stefanie Weidtkamp-Peters will introduce the challenges for bioimaging data management, and the necessary steps to achieve data FAIRification. German BioImaging - GMB e.V., together with other institutions, contributes to this endeavor. Janina Hanne will present how the network of imaging core facilities, research groups and industry partners is key to the German bioimaging community’s aligned collaboration toward FAIR bioimaging data. These activities have paved the way for two data management initiatives in Germany: I3D:bio (Information Infrastructure for BioImage Data) and NFDI4BIOIMAGE, a consortium of the National Research Data Infrastructure. Christian Schmidt will introduce the goals and measures of these initiatives to the benefit of imaging scientist’s work and everyday practice.   +Tags: Nfdi4Bioimage, Research Data Management + [https://zenodo.org/records/7890311](https://zenodo.org/records/7890311) [https://doi.org/10.5281/zenodo.7890311](https://doi.org/10.5281/zenodo.7890311) @@ -213,7 +215,7 @@ Hoku West-Foyle Published 2025-01-16 -Licensed CC-ZERO +Licensed CC0-1.0 @@ -226,7 +228,7 @@ Licensed CC-ZERO ## Angebote der NFDI für die Forschung im Bereich Zoologie -Birgitta König-Ries, Robert Haase, Daniel Nüst, Konrad Förstner, Engel, Judith Sophie +Birgitta König-Ries, Robert Haase, Daniel Nüst, Konrad Förstner, Judith Sophie Engel Published 2024-12-04 @@ -236,6 +238,8 @@ Licensed CC-BY-4.0 In diesem Slidedeck geben wir einen Einblick in Angebote und Dienste der Nationalen Forschungsdaten Infrastruktur (NFDI), die Relevant für die Zoologie und angrenzende Disziplinen relevant sein könnten. +Tags: Nfdi4Bioimage, Research Data Management + [https://zenodo.org/records/14278058](https://zenodo.org/records/14278058) [https://doi.org/10.5281/zenodo.14278058](https://doi.org/10.5281/zenodo.14278058) @@ -311,9 +315,9 @@ Licensed CC-BY-4.0 This presentation gives a short outline of the complexity of data and metadata in the bioimaging universe. It introduces NFDI4BIOIMAGE as a newly formed consortium as part of the German 'Nationale Forschungsdateninfrastruktur' (NFDI) and its goals and tools for data management including its current members on TU Dresden campus.   -Tags: Research Data Management, Tu Dresden, Bioimage Data, Nfdi4Bioimage +Tags: Research Data Management, Nfdi4Bioimage -Content type: Slide +Content type: Slides [https://zenodo.org/records/10083555](https://zenodo.org/records/10083555) @@ -377,9 +381,9 @@ Licensed CC-BY-4.0 Large Language Models (LLMs) change the way how we use computers. This also has impact on the bio-image analysis community. We can generate code that analyzes biomedical image data if we ask the right prompts. This talk outlines introduces basic principles, explains prompt engineering and how to apply it to bio-image analysis. We also compare how different LLM vendors perform on code generation tasks and which challenges are ahead for the bio-image analysis community. -Tags: Large Language Models, Python +Tags: Artificial Intelligence, Python -Content type: Slide +Content type: Slides [https://zenodo.org/records/10815329](https://zenodo.org/records/10815329) @@ -398,7 +402,7 @@ Licensed CC-BY-4.0 These are the PPTx training resources for Students at Uni Leipzig who want to dive into bio-image data science with Python. The material developed here between April and July 2024. -Tags: Bioimage Analysis, Deep Learning, Microscopy Image Analysis, Python +Tags: Bioimage Analysis, Artificial Intelligence, Python Content type: Slides @@ -662,6 +666,8 @@ Licensed CC-BY-4.0 This slide deck introduces the version control tool git, related terminology and the Github Desktop app for managing files in Git[hub] repositories. We furthermore dive into:* Working with repositories* Collaborative with others* Github-Zenodo integration* Github pages* Artificial Intelligence answering Github Issues +Tags: Nfdi4Bioimage, Globias, Research Data Management, Research Software Management + [https://zenodo.org/records/14626054](https://zenodo.org/records/14626054) [https://doi.org/10.5281/zenodo.14626054](https://doi.org/10.5281/zenodo.14626054) @@ -745,7 +751,7 @@ Licensed CC-BY-4.0 In this interactive session, Carpentries team members will guide attendees through three stages of the backward design process to create a lesson development plan for the open source tool of their choosing. Attendees will leave having identified what practical skills they aim to teach (learning objectives), an approach for designing challenge questions (formative assessment), and mechanisms to give and receive feedback. -Content type: Slide +Content type: Slides [https://zenodo.org/records/4317149](https://zenodo.org/records/4317149) @@ -1118,6 +1124,8 @@ Published 2022-05-30 This thesis deals with concepts and solutions in the field of data management in everyday scientific life for image data from microscopy. The focus of the formulated requirements has so far been on published data, which represent only a small subset of the data generated in the scientific process. More and more, everyday research data are moving into the focus of the principles for the management of research data that were formulated early on (FAIR-principles). The adequate management of this mostly multimodal data is a real challenge in terms of its heterogeneity and scope. There is a lack of standardised and established workflows and also the software solutions available so far do not adequately reflect the special requirements of this area. However, the success of any data management process depends heavily on the degree of integration into the daily work routine. Data management must, as far as possible, fit seamlessly into this process. Microscopy data in the scientific process is embedded in pre-processing, which consists of preparatory laboratory work and the analytical evaluation of the microscopy data. In terms of volume, the image data often form the largest part of data generated within this entire research process. In this paper, we focus on concepts and techniques related to the handling and description of this image data and address the necessary basics. The aim is to improve the embedding of the existing data management solution for image data (OMERO) into the everyday scientific work. For this purpose, two independent software extensions for OMERO were implemented within the framework of this thesis: OpenLink and MDEmic. OpenLink simplifies the access to the data stored in the integrated repository in order to feed them into established workflows for further evaluations and enables not only the internal but also the external exchange of data without weakening the advantages of the data repository. The focus of the second implemented software solution, MDEmic, is on the capturing of relevant metadata for microscopy. Through the extended metadata collection, a corresponding linking of the multimodal data by means of a unique description and the corresponding semantic background is aimed at. The configurability of MDEmic is designed to address the currently very dynamic development of underlying concepts and formats. The main goal of MDEmic is to minimise the workload and to automate processes. This provides the scientist with a tool to handle this complex and extensive task of metadata acquisition for microscopic data in a simple way. With the help of the software, semantic and syntactic standardisation can take place without the scientist having to deal with the technical concepts. The generated metadata descriptions are automatically integrated into the image repository and, at the same time, can be transferred by the scientists into formats that are needed when publishing the data. +Tags: Nfdi4Bioimage, Research Data Managementv + [https://zenodo.org/records/6905931](https://zenodo.org/records/6905931) [https://doi.org/10.5281/zenodo.6905931](https://doi.org/10.5281/zenodo.6905931) @@ -2008,7 +2016,7 @@ The open-source software OME Remote Objects (OMERO) is a data management softwar Tags: OMERO, Research Data Management, Nfdi4Bioimage, I3Dbio -Content type: Slide, Video +Content type: Slides, Video [https://zenodo.org/records/8323588](https://zenodo.org/records/8323588) @@ -2210,7 +2218,7 @@ Cavanagh Published 2024-09-03 -Licensed CC-ZERO +Licensed CC0-1.0 @@ -2300,7 +2308,7 @@ Beyond the University of Konstanz, the Team is involved in a range of national a ## Interactive Image Data Flow Graphs -Martin Schätz, Martin Schätz +Martin Schätz Published 2022-10-17 @@ -2430,6 +2438,8 @@ See cross reference: 10.5281/zenodo.12547566 Key-Value pair template for annotation of datasets in OMERO for light- and electron microscopy data within the research group of Prof. Mueller-Reichert 10.5281/zenodo.12546808 Key-Value pair template for annotation of datasets in OMERO (PERIKLES study) +Tags: Nfdi4Bioimage, Research Data Management + [https://zenodo.org/records/12578084](https://zenodo.org/records/12578084) [https://doi.org/10.5281/zenodo.12578084](https://doi.org/10.5281/zenodo.12578084) @@ -2465,6 +2475,8 @@ See cross reference: 10.5281/zenodo.12547566 Key-Value pair template for annotation of datasets in OMERO (light- and electron microscopy data within the research group of Prof. Mueller-Reichert) 10.5281/zenodo.12578084 Key-Value pair template for annotation in OMERO for light microscopy data acquired with AxioScan7 - Core Facility Cellular Imaging (CFCI) +Tags: Nfdi4Bioimage, Research Data Management + [https://zenodo.org/records/12546808](https://zenodo.org/records/12546808) [https://doi.org/10.5281/zenodo.12546808](https://doi.org/10.5281/zenodo.12546808) @@ -2607,6 +2619,8 @@ Licensed CC-BY-4.0 This slide deck introduces Large Language Models to an audience of life-scientists. We first dive into terminology: Different kinds of Language Models and what they can be used for. The remaining slides are optional slides to allow us to dive deeper into topics such as tools for using LLMs in Science, Quality Assurance, Techniques such as Retrieval Augmented Generation and Prompt Engineering. +Tags: Globias, Artificial Intelligence + [https://zenodo.org/records/14418209](https://zenodo.org/records/14418209) [https://doi.org/10.5281/zenodo.14418209](https://doi.org/10.5281/zenodo.14418209) @@ -2833,7 +2847,7 @@ Overview of Segment Anythign for Microscopy given at the SWISSBIAS online meetin Talk about vision foundation models and Segment Anything for Microscopy given at Human Technopole as part of the EMBO Deep Learning Course in May 2024 -Tags: Image Segmentation, Bioimage Analysis, Deep Learning +Tags: Bioimage Analysis, Artificial Intelligence Content type: Slides @@ -2846,7 +2860,7 @@ Content type: Slides ## Modular training resources for bioimage analysis -Christian Tischer, Antonio Politi, Tim-Oliver Buchholz, Elnaz Fazeli, Nicola Gritti, Aliaksandr Halavatyi, Gonzalez Tirado, Sebastian, Julian Hennies, Toby Hodges, Arif Khan, Dominik Kutra, Stefania Marcotti, Bugra Oezdemir, Felix Schneider, Martin Schorb, Anniek Stokkermans, Yi Sun, Nima Vakili +Christian Tischer, Antonio Politi, Tim-Oliver Buchholz, Elnaz Fazeli, Nicola Gritti, Aliaksandr Halavatyi, Sebastian Gonzalez Tirado, Julian Hennies, Toby Hodges, Arif Khan, Dominik Kutra, Stefania Marcotti, Bugra Oezdemir, Felix Schneider, Martin Schorb, Anniek Stokkermans, Yi Sun, Nima Vakili Published 2024-12-03 @@ -2856,6 +2870,8 @@ Licensed CC-BY-4.0 Resources for teaching/preparing to teach bioimage analysis +Tags: Neubias, Bioimage Analysis + [https://zenodo.org/records/14264885](https://zenodo.org/records/14264885) [https://doi.org/10.5281/zenodo.14264885](https://doi.org/10.5281/zenodo.14264885) @@ -3020,6 +3036,8 @@ Licensed CC-BY-4.0 These illustrations were contracted by the Heinrich Heine University Düsseldorf in the frame of the consortium NFDI4BIOIMAGE from Henning Falk for the purpose of education and public outreach. The illustrations are free to use under a CC-BY 4.0 license.AttributionPlease include an attribution similar to: "Data annoation matters", NFDI4BIOIMAGE Consortium (2024): NFDI4BIOIMAGE data management illustrations by Henning Falk, Zenodo, https://doi.org/10.5281/zenodo.14186100, is used under a CC-BY 4.0 license. Modifications to this illustration include cropping.   +Tags: Nfdi4Bioimage, Research Data Management + [https://zenodo.org/records/14186101](https://zenodo.org/records/14186101) [https://doi.org/10.5281/zenodo.14186101](https://doi.org/10.5281/zenodo.14186101) @@ -3058,6 +3076,8 @@ Licensed CC-BY-4.0 Raw microscopy image from the NFDI4Bioimage calendar October 2024 +Tags: Nfdi4Bioimage, Research Data Management + [https://zenodo.org/records/13837146](https://zenodo.org/records/13837146) [https://doi.org/10.5281/zenodo.13837146](https://doi.org/10.5281/zenodo.13837146) @@ -3099,7 +3119,7 @@ Nextflow is an open-source workflow management system that prioritizes portabili Tags: Workflow Engine -Content type: Slide +Content type: Slides [https://zenodo.org/records/4334697](https://zenodo.org/records/4334697) @@ -3122,6 +3142,8 @@ Presented at the 2024 FoundingGIDE event in Okazaki, Japan: https://founding-gid Note: much of the presentation was a demonstration of the OME2024-NGFF-Challenge -- https://ome.github.io/ome2024-ngff-challenge/ especially of querying an extraction of the metadata (https://github.com/ome/ome2024-ngff-challenge-metadata)   +Tags: Nfdi4Bioimage, Research Data Management + [https://zenodo.org/records/14234608](https://zenodo.org/records/14234608) [https://doi.org/10.5281/zenodo.14234608](https://doi.org/10.5281/zenodo.14234608) @@ -3223,7 +3245,7 @@ Slides from the CZI/EOSS online meeting in December 2020. Tags: Bioimage Analysis -Content type: Slide +Content type: Slides [https://zenodo.org/records/4328911](https://zenodo.org/records/4328911) @@ -3312,7 +3334,7 @@ This Research Data Management (RDM) Slides introduce to the multidisciplinary kn Tags: Research Data Management -Content type: Slide +Content type: Slides [https://zenodo.org/record/6602101](https://zenodo.org/record/6602101) @@ -3499,6 +3521,8 @@ Content: ... +Tags: Nfdi4Bioimage, Research Data Management + [https://zenodo.org/records/7018750](https://zenodo.org/records/7018750) [https://doi.org/10.5281/zenodo.7018750](https://doi.org/10.5281/zenodo.7018750) @@ -3531,7 +3555,7 @@ Content type: Slides ## Terminology service for research data management and knowledge discovery in low-temperature plasma physics -Becker, Markus M., Chaerony Siffa, Ihda, Roman Baum +Markus M. Becker, Ihda Chaerony Siffa, Roman Baum Published 2024-12-11 @@ -3665,6 +3689,8 @@ Licensed CC-BY-4.0 Research data management (RDM) in microscopy and image analysis is a challenging task. Large files in proprietary formats, complex N-dimensional array structures, and various metadata models and formats can make image data handling inconvenient and difficult. For data organization, annotation, and sharing, researchers need solutions that fit everyday practice and comply with the FAIR (Findable, Accessible, Interoperable, Reusable) principles. International community-based efforts have begun creating open data models (OME), an open file format and translation library (OME-TIFF, Bio-Formats), data management software platforms, and microscopy metadata recommendations and annotation tools. Bringing these developments into practice requires support and training. Iterative feedback and tool improvement is needed to foster practical adoption by the scientific community. The Information Infrastructure for BioImage Data (I3D:bio) project works on guidelines, training resources, and practical assistance for FAIR microscopy RDM adoption with a focus on the management platform OMERO and metadata annotations. +Tags: Nfdi4Bioimage, Research Data Management + [https://zenodo.org/records/10805204](https://zenodo.org/records/10805204) [https://doi.org/10.5281/zenodo.10805204](https://doi.org/10.5281/zenodo.10805204) @@ -3684,6 +3710,8 @@ Licensed CC-BY-4.0 Germany’s National Research Data Infrastructure (NFDI) aims to establish a sustained, cross-disciplinary research data management (RDM) infrastructure that enables researchers to handle FAIR (findable, accessible, interoperable, reusable) data. While FAIR principles have been adopted by funders, policymakers, and publishers, their practical implementation remains an ongoing effort. In the field of bio-imaging, harmonization of data formats, metadata ontologies, and open data repositories is necessary to achieve FAIR data. The NFDI4BIOIMAGE was established to address these issues and develop tools and best practices to facilitate FAIR microscopy and image analysis data in alignment with international community activities. The consortium operates through its Data Stewards team to provide expertise and direct support to help overcome RDM challenges. The three Helmholtz Centers in NFDI4BIOIMAGE aim to collaborate closely with other centers and initiatives, such as HMC, Helmholtz AI, and HIP. Here we present NFDI4BIOIMAGE’s work and its significance for research in Helmholtz and beyond +Tags: Nfdi4Bioimage, Research Data Management + [https://zenodo.org/records/11501662](https://zenodo.org/records/11501662) [https://doi.org/10.5281/zenodo.11501662](https://doi.org/10.5281/zenodo.11501662) @@ -3722,6 +3750,8 @@ Licensed CC-BY-4.0 This talk will present the initiatives of the NFDI4BioImage consortium aimed at the long-term preservation of life science data. We will discuss our efforts to establish metadata standards, which are crucial for ensuring data reusability and integrity. The development of sustainable infrastructure is another key focus, enabling seamless data integration and analysis in the cloud. We will take a look at how we manage training materials and communicate with our community. Through these actions, NFDI4BioImage seeks to enable FAIR bioimage data management for German researchers, across disciplines and embedded in the international framework. +Tags: Nfdi4Bioimage, Research Data Management + [https://zenodo.org/records/13640979](https://zenodo.org/records/13640979) [https://doi.org/10.5281/zenodo.13640979](https://doi.org/10.5281/zenodo.13640979) @@ -3813,7 +3843,7 @@ The Data Steward Team of the NFDI4BIOIMAGE consortium presents themselves and th ## Working Group Charter. RDM Helpdesk Network -Judith Engel, Patrick Helling, Robert Herrenbrück, Marina Lemaire, Hela Mehrtens, Marcus Schmidt, Martha Stellmacher, Lukas Weimer, Cord Wiljes, Wolf Zinke +Judith Engel, Patrick Helling, Robert Herrenbrück, MarinaLemaire, Hela Mehrtens, Marcus Schmidt, Martha Stellmacher, Lukas Weimer, Cord Wiljes, Wolf Zinke Published 2024-11-04 @@ -3944,6 +3974,8 @@ Licensed CC-BY-4.0 Talk given at Georg-August-Universität Göttingen Campus Institute Data Science23rd January 2025 https://www.uni-goettingen.de/en/653203.html +Tags: Nfdi4Bioimage + [https://zenodo.org/records/14716546](https://zenodo.org/records/14716546) [https://doi.org/10.5281/zenodo.14716546](https://doi.org/10.5281/zenodo.14716546) @@ -3965,6 +3997,8 @@ CMCB LIFE SCIENCES SEMINARSTechnische Universität Dresden16th January 2025 https://tu-dresden.de/cmcb/crtd/news-termine/termine/cmcb-life-sciences-seminar-josh-moore-german-bioimaging-e-v-society-for-microscopy-and-image-analysis-constance   +Tags: Nfdi4Bioimage + [https://zenodo.org/records/14650434](https://zenodo.org/records/14650434) [https://doi.org/10.5281/zenodo.14650434](https://doi.org/10.5281/zenodo.14650434) @@ -4004,6 +4038,8 @@ Licensed CC-BY-4.0 Overview of Activities of the Team Image Data Analysis and Management of German BioImaging e.V.   +Tags: Nfdi4Bioimage, Research Data Management + [https://zenodo.org/records/10796364](https://zenodo.org/records/10796364) [https://doi.org/10.5281/zenodo.10796364](https://doi.org/10.5281/zenodo.10796364) @@ -4043,6 +4079,8 @@ Licensed CC-BY-4.0 Poster presented at the European Light Microscopy Initiative meeting in Liverpool (https://www.elmi2024.org/) +Tags: Nfdi4Bioimage, Research Data Management + [https://zenodo.org/records/11235513](https://zenodo.org/records/11235513) [https://doi.org/10.5281/zenodo.11235513](https://doi.org/10.5281/zenodo.11235513) @@ -4231,6 +4269,8 @@ Learn from each other, leverage different expertise Learn how to train users, establish sustainability strategies, and foster FAIR RDM for bioimaging at your institution +Tags: Nfdi4Bioimage, Research Data Management + [https://zenodo.org/records/14178789](https://zenodo.org/records/14178789) [https://doi.org/10.5281/zenodo.14178789](https://doi.org/10.5281/zenodo.14178789) @@ -4262,6 +4302,8 @@ Dr. Riccardo Massei (Helmholtz-Center for Environmental Research, UFZ, Leipzig) Dr. Michele Bortolomeazzi (DKFZ, Single cell Open Lab, bioimage data specialist, bioinformatician, staff scientist in the NFDI4BIOIMAGE project) Dr. Christian Schmidt (Science Manager for Research Data Management in Bioimaging, German Cancer Research Center, Heidelberg, Project Coordinator of the NFDI4BIOIMAGE project) +Tags: Nfdi4Bioimage, Research Data Management + [https://zenodo.org/records/11350689](https://zenodo.org/records/11350689) [https://doi.org/10.5281/zenodo.11350689](https://doi.org/10.5281/zenodo.11350689) @@ -4345,6 +4387,8 @@ Publishing datasets in public archives for bioimage dataKsenia Krooß /Hein Date & Venue:Thursday, Sept. 26, 5.30 p.m.Haus 22 / Paul Ehrlich Lecture Hall (H22-1)University Hospital Frankfurt +Tags: Nfdi4Bioimage, Research Data Management + [https://zenodo.org/records/13861026](https://zenodo.org/records/13861026) [https://doi.org/10.5281/zenodo.13861026](https://doi.org/10.5281/zenodo.13861026) @@ -4362,7 +4406,7 @@ Licensed CC-BY-4.0 Tags: Artificial Intelligence, Bioimage Analysis -Content type: Slide +Content type: Slides [https://zenodo.org/doi/10.5281/zenodo.4330625](https://zenodo.org/doi/10.5281/zenodo.4330625) diff --git a/_sources/licenses/bsd-2-clause.md b/_sources/licenses/bsd-2-clause.md index c5d37ef5..af1da960 100644 --- a/_sources/licenses/bsd-2-clause.md +++ b/_sources/licenses/bsd-2-clause.md @@ -9,7 +9,7 @@ Licensed BSD-2-CLAUSE Tags: Neubias, Bioimage Analysis -Content type: Slide +Content type: Slides [https://github.com/RoccoDAnt/Defragmentation_TrainingSchool_EOSC-Life_2022/blob/main/Slides/Adding_a_workflow_to_BIAFLOWS.pdf](https://github.com/RoccoDAnt/Defragmentation_TrainingSchool_EOSC-Life_2022/blob/main/Slides/Adding_a_workflow_to_BIAFLOWS.pdf) @@ -105,8 +105,6 @@ Licensed BSD-2-CLAUSE Web page for validating OME-NGFF files. -Tags: Bioimage Data - Content type: Github Repository, Application [https://ome.github.io/ome-ngff-validator/](https://ome.github.io/ome-ngff-validator/) diff --git a/_sources/licenses/bsd-3-clause.md b/_sources/licenses/bsd-3-clause.md index 26405dd0..c2ca3dd0 100644 --- a/_sources/licenses/bsd-3-clause.md +++ b/_sources/licenses/bsd-3-clause.md @@ -1,4 +1,4 @@ -# Bsd-3-clause (23) +# Bsd-3-clause (26) ## 2020 BioImage Analysis Survey: Community experiences and needs for the future Nasim Jamali, Ellen T. A. Dobson, Kevin W. Eliceiri, Anne E. Carpenter, Beth A. Cimini @@ -90,6 +90,44 @@ Content type: Notebook [https://github.com/CellProfiler/tutorials](https://github.com/CellProfiler/tutorials) +--- + +## Community-developed checklists for publishing images and image analyses + +Beth Cimini et al. + +Licensed BSD-3-CLAUSE + + + +This book is a companion to the Nature Methods publication Community-developed checklists for publishing images and image analyses. In this paper, members of QUAREP-LiMi have proposed 3 sets of standards for publishing image figures and image analysis - minimal requirements, recommended additions, and ideal comprehensive goals. By following this guidance, we hope to remove some of the stress non-experts may face in determining what they need to do, and we also believe that researchers will find their science more interpretable and more reproducible. + +Tags: Bioimage Analysis, Research Data Management + +Content type: Notebook, Collection + +[https://quarep-limi.github.io/WG12_checklists_for_image_publishing/intro.html](https://quarep-limi.github.io/WG12_checklists_for_image_publishing/intro.html) + + +--- + +## Elastix tutorial + +Marvin Albert + +Licensed BSD-3-CLAUSE + + + +Tutorial material for teaching the basics of (itk-)elastix for image registration in microscopy images. + +Tags: Image Registration, Itk, Elastix + +Content type: Notebook, Collection + +[https://m-albert.github.io/elastix_tutorial/intro.html](https://m-albert.github.io/elastix_tutorial/intro.html) + + --- ## Example Pipeline Tutorial @@ -104,7 +142,7 @@ Licensed BSD-3-CLAUSE Napari-ndev is a collection of widgets intended to serve any person seeking to process microscopy images from start to finish. The goal of this example pipeline is to get the user familiar with working with napari-ndev for batch processing and reproducibility (view Image Utilities and Workflow Widget). -Tags: Napari, Microscopy Image Analysis, Bioimage Analysis +Tags: Napari, Bioimage Analysis Content type: Documentation, Github Repository, Tutorial @@ -142,7 +180,7 @@ Licensed BSD-3-CLAUSE Course and material for the clEsperanto workshop presented at I2K 2024 @ Human Technopol (Milan, Italy). The workshop is an hands-on demo of the clesperanto project, focussing on how to use the library for users who want use GPU-acceleration for their Image Processing pipeline. -Tags: Clesperanto, Training, Bioimage Analysis, Notebooks, Workflow +Tags: Bioimage Analysis Content type: Github Repository, Workshop, Tutorial, Notebook @@ -214,7 +252,7 @@ Licensed BSD-3-CLAUSE Tags: Neubias, Imagej, Bioimage Analysis -Content type: Slide +Content type: Slides [https://git.mpi-cbg.de/rhaase/lecture_imagej2_dev](https://git.mpi-cbg.de/rhaase/lecture_imagej2_dev) @@ -233,7 +271,7 @@ Lecture slides of a session on Multiview Fusion in Fiji Tags: Neubias, Imagej, Bioimage Analysis -Content type: Slide +Content type: Slides [https://git.mpi-cbg.de/rhaase/lecture_multiview_registration](https://git.mpi-cbg.de/rhaase/lecture_multiview_registration) @@ -265,7 +303,7 @@ Licensed BSD-3-CLAUSE Tutorial for running CellPose advanced functions -Tags: Cellpose, Segmentation +Tags: Bioimage Analysis, Artificial Intelligence Content type: Github Repository @@ -305,7 +343,7 @@ Lecture slides of a session on Cell Tracking in Fiji Tags: Neubias, Imagej, Bioimage Analysis -Content type: Slide +Content type: Slides [https://git.mpi-cbg.de/rhaase/lecture_tracking_trackmate](https://git.mpi-cbg.de/rhaase/lecture_tracking_trackmate) @@ -360,7 +398,7 @@ Licensed BSD-3-CLAUSE Tags: Neubias, Imagej, Bioimage Analysis -Content type: Slide +Content type: Slides [https://git.mpi-cbg.de/rhaase/lecture_working_with_objects_in_2d_and_3d](https://git.mpi-cbg.de/rhaase/lecture_working_with_objects_in_2d_and_3d) @@ -377,7 +415,7 @@ Licensed BSD-3-CLAUSE Tags: Neubias, Imagej, Bioimage Analysis -Content type: Slide +Content type: Slides [https://git.mpi-cbg.de/rhaase/lecture_working_with_pixels](https://git.mpi-cbg.de/rhaase/lecture_working_with_pixels) @@ -403,7 +441,7 @@ Content type: Notebook ## ome2024-ngff-challenge -Will Moore, Josh Moore, sherwoodf, jean-marie burel, Norman Rzepka, dependabot[bot], JensWendt, Joost de Folter, Torsten Stöter, AybukeKY, Eric Perlman, Tom Boissonnet +Will Moore, Josh Moore, sherwoodf, Jean-Marie Burel, Norman Rzepka, dependabot[bot], JensWendt, Joost de Folter, Torsten St\xF6ter, AybukeKY, Eric Perlman, Tom Boissonnet Published 2024-08-30T12:00:53+00:00 @@ -413,7 +451,7 @@ Licensed BSD-3-CLAUSE Project planning and material repository for the 2024 challenge to generate 1 PB of OME-Zarr data -Tags: Sharing +Tags: Sharing, Nfdi4Bioimage, Research Data Management Content type: Github Repository @@ -422,3 +460,24 @@ Content type: Github Repository --- +## scanpy-tutorials + +Alex Wolf, pre-commit-ci[bot], Philipp A., Isaac Virshup, Ilan Gold, Giovanni Palla, Fidel Ramirez, G\xF6k\xE7en Eraslan, Sergei Rybakov, Abolfazl (Abe), Adam Gayoso, Dinesh Palli, Gregor Sturm, Jan Lause, Karin Hrovatin, Krzysztof Polanski, RaphaelBuzzi, Yimin Zheng, Yishen Miao, evanbiederstedt + +Published 2018-12-16T03:42:46+00:00 + +Licensed BSD-3-CLAUSE + + + +Scanpy Tutorials. + +Tags: Single-Cell Analysis, Bioimage Analysis + +Content type: Github Repository + +[https://github.com/scverse/scanpy-tutorials](https://github.com/scverse/scanpy-tutorials) + + +--- + diff --git a/_sources/licenses/cc-by-4.0.md b/_sources/licenses/cc-by-4.0.md index bf361b1d..51049c0c 100644 --- a/_sources/licenses/cc-by-4.0.md +++ b/_sources/licenses/cc-by-4.0.md @@ -172,6 +172,8 @@ Research data management is essential in nowadays research, and one of the big o In this presentation, Stefanie Weidtkamp-Peters will introduce the challenges for bioimaging data management, and the necessary steps to achieve data FAIRification. German BioImaging - GMB e.V., together with other institutions, contributes to this endeavor. Janina Hanne will present how the network of imaging core facilities, research groups and industry partners is key to the German bioimaging community’s aligned collaboration toward FAIR bioimaging data. These activities have paved the way for two data management initiatives in Germany: I3D:bio (Information Infrastructure for BioImage Data) and NFDI4BIOIMAGE, a consortium of the National Research Data Infrastructure. Christian Schmidt will introduce the goals and measures of these initiatives to the benefit of imaging scientist’s work and everyday practice.   +Tags: Nfdi4Bioimage, Research Data Management + [https://zenodo.org/records/7890311](https://zenodo.org/records/7890311) [https://doi.org/10.5281/zenodo.7890311](https://doi.org/10.5281/zenodo.7890311) @@ -262,7 +264,7 @@ Content type: Slides ## Angebote der NFDI für die Forschung im Bereich Zoologie -Birgitta König-Ries, Robert Haase, Daniel Nüst, Konrad Förstner, Engel, Judith Sophie +Birgitta König-Ries, Robert Haase, Daniel Nüst, Konrad Förstner, Judith Sophie Engel Published 2024-12-04 @@ -272,6 +274,8 @@ Licensed CC-BY-4.0 In diesem Slidedeck geben wir einen Einblick in Angebote und Dienste der Nationalen Forschungsdaten Infrastruktur (NFDI), die Relevant für die Zoologie und angrenzende Disziplinen relevant sein könnten. +Tags: Nfdi4Bioimage, Research Data Management + [https://zenodo.org/records/14278058](https://zenodo.org/records/14278058) [https://doi.org/10.5281/zenodo.14278058](https://doi.org/10.5281/zenodo.14278058) @@ -327,7 +331,7 @@ Licensed CC-BY-4.0 Training resources for Students at Uni Leipzig who want to dive into bio-image data science with Python. The material developed here between April and July 2024. -Tags: Bioimage Analysis, Deep Learning, Microscopy Image Analysis, Python +Tags: Bioimage Analysis, Artificial Intelligence, Python Content type: Github Repository @@ -348,7 +352,7 @@ Licensed CC-BY-4.0 The authors introduce BIOMERO (bioimage analysis in OMERO), a bridge connecting OMERO, a renowned bioimaging data management platform, FAIR workflows, and high-performance computing (HPC) environments. -Tags: OMERO, Workflow, Bioimage Analysis, Image Data Management +Tags: OMERO, Workflow, Bioimage Analysis Content type: Publication @@ -386,9 +390,9 @@ Licensed CC-BY-4.0 This presentation gives a short outline of the complexity of data and metadata in the bioimaging universe. It introduces NFDI4BIOIMAGE as a newly formed consortium as part of the German 'Nationale Forschungsdateninfrastruktur' (NFDI) and its goals and tools for data management including its current members on TU Dresden campus.   -Tags: Research Data Management, Tu Dresden, Bioimage Data, Nfdi4Bioimage +Tags: Research Data Management, Nfdi4Bioimage -Content type: Slide +Content type: Slides [https://zenodo.org/records/10083555](https://zenodo.org/records/10083555) @@ -452,9 +456,9 @@ Licensed CC-BY-4.0 Large Language Models (LLMs) change the way how we use computers. This also has impact on the bio-image analysis community. We can generate code that analyzes biomedical image data if we ask the right prompts. This talk outlines introduces basic principles, explains prompt engineering and how to apply it to bio-image analysis. We also compare how different LLM vendors perform on code generation tasks and which challenges are ahead for the bio-image analysis community. -Tags: Large Language Models, Python +Tags: Artificial Intelligence, Python -Content type: Slide +Content type: Slides [https://zenodo.org/records/10815329](https://zenodo.org/records/10815329) @@ -473,7 +477,7 @@ Licensed CC-BY-4.0 This repository contains training resources for Students at Uni Leipzig who want to dive into bio-image data science with Python. -Tags: Image Data Management, Deep Learning, Microscopy Image Analysis, Python +Tags: Research Data Management, Artificial Intelligence, Bioimage Analysis, Python Content type: Notebook @@ -492,7 +496,7 @@ Licensed CC-BY-4.0 These are the PPTx training resources for Students at Uni Leipzig who want to dive into bio-image data science with Python. The material developed here between April and July 2024. -Tags: Bioimage Analysis, Deep Learning, Microscopy Image Analysis, Python +Tags: Bioimage Analysis, Artificial Intelligence, Python Content type: Slides @@ -543,8 +547,6 @@ Licensed CC-BY-4.0 Bio-Formats is a standalone Java library for reading and writing life sciences image file formats. There are several scripts for using Bio-Formats on the command line, which are listed here. -Tags: Bioimage Data - Content type: Documentation [https://bio-formats.readthedocs.io/en/v8.0.0/users/comlinetools/index.html](https://bio-formats.readthedocs.io/en/v8.0.0/users/comlinetools/index.html) @@ -579,7 +581,7 @@ Licensed CC-BY-4.0 Tags: OMERO, Python -Content type: Blog +Content type: Blog Post [https://biapol.github.io/blog/robert_haase/browsing_idr/readme.html](https://biapol.github.io/blog/robert_haase/browsing_idr/readme.html) @@ -809,7 +811,7 @@ Licensed CC-BY-4.0 Tags: Research Data Management, Bioimage Analysis, Nfdi4Bioimage -Content type: Slide +Content type: Slides [https://f1000research.com/slides/12-1054](https://f1000research.com/slides/12-1054) @@ -866,6 +868,8 @@ Licensed CC-BY-4.0 This slide deck introduces the version control tool git, related terminology and the Github Desktop app for managing files in Git[hub] repositories. We furthermore dive into:* Working with repositories* Collaborative with others* Github-Zenodo integration* Github pages* Artificial Intelligence answering Github Issues +Tags: Nfdi4Bioimage, Globias, Research Data Management, Research Software Management + [https://zenodo.org/records/14626054](https://zenodo.org/records/14626054) [https://doi.org/10.5281/zenodo.14626054](https://doi.org/10.5281/zenodo.14626054) @@ -885,7 +889,7 @@ Introduction to version control using git for collaborative, reproducible script Tags: Sharing, Research Data Management -Content type: Blog +Content type: Blog Post [https://focalplane.biologists.com/2021/09/04/collaborative-bio-image-analysis-script-editing-with-git/](https://focalplane.biologists.com/2021/09/04/collaborative-bio-image-analysis-script-editing-with-git/) @@ -983,9 +987,9 @@ Licensed CC-BY-4.0 In this blog post the author demonstrates how chatGPT can be used to combine a fictive project description with a DMP specification to produce a project-specific DMP. -Tags: Research Data Management, Large Language Models, Artificial Intelligence +Tags: Research Data Management, Artificial Intelligence -Content type: Blog +Content type: Blog Post [https://focalplane.biologists.com/2023/11/06/creating-a-research-data-management-plan-using-chatgpt/](https://focalplane.biologists.com/2023/11/06/creating-a-research-data-management-plan-using-chatgpt/) @@ -1004,7 +1008,7 @@ Licensed CC-BY-4.0 In this interactive session, Carpentries team members will guide attendees through three stages of the backward design process to create a lesson development plan for the open source tool of their choosing. Attendees will leave having identified what practical skills they aim to teach (learning objectives), an approach for designing challenge questions (formative assessment), and mechanisms to give and receive feedback. -Content type: Slide +Content type: Slides [https://zenodo.org/records/4317149](https://zenodo.org/records/4317149) @@ -1139,7 +1143,7 @@ Licensed CC-BY-4.0 Explore fundamental topics on research data management (RDM), how DataPLANT implements these aspects to support plant researchers with RDM tools and services, read guides and manuals or search for some teaching materials. -Tags: Research Data Management, Training, Dataplant +Tags: Research Data Management, Dataplant Content type: Collection @@ -2012,7 +2016,7 @@ Licensed CC-BY-4.0 The authors show the utility of Minimum Information for High Content Screening Microscopy Experiments (MIHCSME) for High Content Screening (HCS) data using multiple examples from the Leiden FAIR Cell Observatory, a Euro-Bioimaging flagship node for high content screening and the pilot node for implementing FAIR bioimaging data throughout the Netherlands Bioimaging network. -Tags: FAIR-Principles, Metadata, Research Data Management, Image Data Management, Bioimage Data +Tags: FAIR-Principles, Metadata, Research Data Management Content type: Publication @@ -2048,7 +2052,7 @@ Licensed CC-BY-4.0 Introduction to FAIR deep learning. Furthermore, tools to deploy trained DL models (deepImageJ), easily train and evaluate them (ZeroCostDL4Mic and DeepBacs) ensure reproducibility (DL4MicEverywhere), and share this technology in an open-source and reproducible manner (BioImage Model Zoo) are introduced. -Tags: Deep Learning, FAIR-Principles, Microscopy Image Analysis +Tags: Artificial Intelligence, FAIR-Principles, Bioimage Analysis Content type: Slides @@ -2071,7 +2075,7 @@ Sharing knowledge and data in the life sciences allows us to learn from each oth Tags: Open Science, Teaching, Sharing -Content type: Collection, Tutorial, Videos +Content type: Collection, Tutorial, Video [https://www.ebi.ac.uk/training/online/courses/finding-using-public-data/](https://www.ebi.ac.uk/training/online/courses/finding-using-public-data/) @@ -2190,7 +2194,7 @@ Licensed CC-BY-4.0 Tags: Python, Bioimage Analysis, Artificial Intelligence -Content type: Slide +Content type: Slides [https://f1000research.com/slides/12-971](https://f1000research.com/slides/12-971) @@ -2259,7 +2263,7 @@ Licensed CC-BY-4.0 Tags: Python, Conda, Mamba -Content type: Blog +Content type: Blog Post [https://biapol.github.io/blog/mara_lampert/getting_started_with_mambaforge_and_python/readme.html](https://biapol.github.io/blog/mara_lampert/getting_started_with_mambaforge_and_python/readme.html) @@ -2555,7 +2559,7 @@ Overview about decision making and how to influence decisions in the bio-image a Tags: Bioimage Analysis -Content type: Slide, Presentation +Content type: Slides, Presentation [https://f1000research.com/slides/11-746](https://f1000research.com/slides/11-746) @@ -2597,7 +2601,7 @@ The open-source software OME Remote Objects (OMERO) is a data management softwar Tags: OMERO, Research Data Management, Nfdi4Bioimage, I3Dbio -Content type: Slide, Video +Content type: Slides, Video [https://zenodo.org/records/8323588](https://zenodo.org/records/8323588) @@ -2667,7 +2671,7 @@ Blog post about why we should license our work and what is important when choosi Tags: Licensing, Research Data Management -Content type: Blog +Content type: Blog Post [https://focalplane.biologists.com/2023/05/06/if-you-license-it-itll-be-harder-to-steal-it-why-we-should-license-our-work/](https://focalplane.biologists.com/2023/05/06/if-you-license-it-itll-be-harder-to-steal-it-why-we-should-license-our-work/) @@ -2699,7 +2703,7 @@ Licensed CC-BY-4.0 This lesson shows how to use Python and scikit-image to do basic image processing. -Tags: Segmentation, Bioimage Analysis, Training, Python, Scikit-Image, Image Segmentation +Tags: Bioimage Analysis, Python Content type: Tutorial, Workflow @@ -2839,7 +2843,7 @@ Beyond the University of Konstanz, the Team is involved in a range of national a ## Interactive Image Data Flow Graphs -Martin Schätz, Martin Schätz +Martin Schätz Published 2022-10-17 @@ -2947,6 +2951,8 @@ See cross reference: 10.5281/zenodo.12547566 Key-Value pair template for annotation of datasets in OMERO for light- and electron microscopy data within the research group of Prof. Mueller-Reichert 10.5281/zenodo.12546808 Key-Value pair template for annotation of datasets in OMERO (PERIKLES study) +Tags: Nfdi4Bioimage, Research Data Management + [https://zenodo.org/records/12578084](https://zenodo.org/records/12578084) [https://doi.org/10.5281/zenodo.12578084](https://doi.org/10.5281/zenodo.12578084) @@ -2982,6 +2988,8 @@ See cross reference: 10.5281/zenodo.12547566 Key-Value pair template for annotation of datasets in OMERO (light- and electron microscopy data within the research group of Prof. Mueller-Reichert) 10.5281/zenodo.12578084 Key-Value pair template for annotation in OMERO for light microscopy data acquired with AxioScan7 - Core Facility Cellular Imaging (CFCI) +Tags: Nfdi4Bioimage, Research Data Management + [https://zenodo.org/records/12546808](https://zenodo.org/records/12546808) [https://doi.org/10.5281/zenodo.12546808](https://doi.org/10.5281/zenodo.12546808) @@ -3124,6 +3132,8 @@ Licensed CC-BY-4.0 This slide deck introduces Large Language Models to an audience of life-scientists. We first dive into terminology: Different kinds of Language Models and what they can be used for. The remaining slides are optional slides to allow us to dive deeper into topics such as tools for using LLMs in Science, Quality Assurance, Techniques such as Retrieval Augmented Generation and Prompt Engineering. +Tags: Globias, Artificial Intelligence + [https://zenodo.org/records/14418209](https://zenodo.org/records/14418209) [https://doi.org/10.5281/zenodo.14418209](https://doi.org/10.5281/zenodo.14418209) @@ -3301,7 +3311,7 @@ Licensed CC-BY-4.0 -Tags: Bioimage Analysis, Open Science, Microscopy Image Analysis, Microscopy +Tags: Bioimage Analysis, Open Science, Microscopy Content type: Publication @@ -3337,7 +3347,7 @@ Licensed CC-BY-4.0 Tags: Python, Conda, Mamba -Content type: Blog +Content type: Blog Post [https://focalplane.biologists.com/2022/12/08/managing-scientific-python-environments-using-conda-mamba-and-friends/](https://focalplane.biologists.com/2022/12/08/managing-scientific-python-environments-using-conda-mamba-and-friends/) @@ -3409,7 +3419,7 @@ Licensed CC-BY-4.0 Tags: Bioimage Analysis -Content type: Online Tutorial, Video, Slide +Content type: Online Tutorial, Video, Slides [https://www.ebi.ac.uk/training/events/methods-bioimage-analysis/](https://www.ebi.ac.uk/training/events/methods-bioimage-analysis/) @@ -3437,7 +3447,7 @@ Overview of Segment Anythign for Microscopy given at the SWISSBIAS online meetin Talk about vision foundation models and Segment Anything for Microscopy given at Human Technopole as part of the EMBO Deep Learning Course in May 2024 -Tags: Image Segmentation, Bioimage Analysis, Deep Learning +Tags: Bioimage Analysis, Artificial Intelligence Content type: Slides @@ -3458,7 +3468,7 @@ Licensed CC-BY-4.0 The Microscopy data analysis: machine learning and the BioImage Archive course, which focused on introducing programmatic approaches used in the analysis of bioimage data via the BioImage Archive, ran in May 2023. -Tags: Microscopy Image Analysis, Python, Deep Learning +Tags: Bioimage Analysis, Python, Artificial Intelligence Content type: Video, Slides @@ -3479,7 +3489,7 @@ Licensed CC-BY-4.0 The Brain Imaging Data Structure (BIDS) is a specification for organizing, sharing, and archiving neuroimaging data and metadata in a reusable way. -Tags: Research Data Management, Image Data Management, Bioimage Data +Tags: Research Data Management Content type: Publication @@ -3513,7 +3523,7 @@ Content type: Publication ## Modular training resources for bioimage analysis -Christian Tischer, Antonio Politi, Tim-Oliver Buchholz, Elnaz Fazeli, Nicola Gritti, Aliaksandr Halavatyi, Gonzalez Tirado, Sebastian, Julian Hennies, Toby Hodges, Arif Khan, Dominik Kutra, Stefania Marcotti, Bugra Oezdemir, Felix Schneider, Martin Schorb, Anniek Stokkermans, Yi Sun, Nima Vakili +Christian Tischer, Antonio Politi, Tim-Oliver Buchholz, Elnaz Fazeli, Nicola Gritti, Aliaksandr Halavatyi, Sebastian Gonzalez Tirado, Julian Hennies, Toby Hodges, Arif Khan, Dominik Kutra, Stefania Marcotti, Bugra Oezdemir, Felix Schneider, Martin Schorb, Anniek Stokkermans, Yi Sun, Nima Vakili Published 2024-12-03 @@ -3523,6 +3533,8 @@ Licensed CC-BY-4.0 Resources for teaching/preparing to teach bioimage analysis +Tags: Neubias, Bioimage Analysis + [https://zenodo.org/records/14264885](https://zenodo.org/records/14264885) [https://doi.org/10.5281/zenodo.14264885](https://doi.org/10.5281/zenodo.14264885) @@ -3603,7 +3615,7 @@ Licensed CC-BY-4.0 Material for the I2K 2024 "Multiplexed tissue imaging - tools and approaches" workshop -Tags: Bioimage Analysis, Microscopy Image Analysis +Tags: Bioimage Analysis Content type: Github Repository, Slides, Workshop @@ -3727,6 +3739,8 @@ Licensed CC-BY-4.0 These illustrations were contracted by the Heinrich Heine University Düsseldorf in the frame of the consortium NFDI4BIOIMAGE from Henning Falk for the purpose of education and public outreach. The illustrations are free to use under a CC-BY 4.0 license.AttributionPlease include an attribution similar to: "Data annoation matters", NFDI4BIOIMAGE Consortium (2024): NFDI4BIOIMAGE data management illustrations by Henning Falk, Zenodo, https://doi.org/10.5281/zenodo.14186100, is used under a CC-BY 4.0 license. Modifications to this illustration include cropping.   +Tags: Nfdi4Bioimage, Research Data Management + [https://zenodo.org/records/14186101](https://zenodo.org/records/14186101) [https://doi.org/10.5281/zenodo.14186101](https://doi.org/10.5281/zenodo.14186101) @@ -3801,6 +3815,8 @@ Licensed CC-BY-4.0 Raw microscopy image from the NFDI4Bioimage calendar October 2024 +Tags: Nfdi4Bioimage, Research Data Management + [https://zenodo.org/records/13837146](https://zenodo.org/records/13837146) [https://doi.org/10.5281/zenodo.13837146](https://doi.org/10.5281/zenodo.13837146) @@ -3842,7 +3858,7 @@ Nextflow is an open-source workflow management system that prioritizes portabili Tags: Workflow Engine -Content type: Slide +Content type: Slides [https://zenodo.org/records/4334697](https://zenodo.org/records/4334697) @@ -3897,6 +3913,8 @@ Presented at the 2024 FoundingGIDE event in Okazaki, Japan: https://founding-gid Note: much of the presentation was a demonstration of the OME2024-NGFF-Challenge -- https://ome.github.io/ome2024-ngff-challenge/ especially of querying an extraction of the metadata (https://github.com/ome/ome2024-ngff-challenge-metadata)   +Tags: Nfdi4Bioimage, Research Data Management + [https://zenodo.org/records/14234608](https://zenodo.org/records/14234608) [https://doi.org/10.5281/zenodo.14234608](https://doi.org/10.5281/zenodo.14234608) @@ -3950,7 +3968,7 @@ Licensed CC-BY-4.0 OME develops open-source software and data format standards for the storage and manipulation of biological microscopy data -Tags: Open Source Software, Microscopy Image Analysis, Bioimage Data +Tags: Open Source Software Content type: Video, Collection @@ -4030,7 +4048,7 @@ Licensed CC-BY-4.0 -Tags: OMERO, Galaxy, Metadata +Tags: OMERO, Galaxy, Metadata, Nfdi4Bioimage Content type: Tutorial, Framework, Workflow @@ -4047,7 +4065,7 @@ Licensed CC-BY-4.0 -Content type: Slide +Content type: Slides [https://f1000research.com/slides/11-1171](https://f1000research.com/slides/11-1171) @@ -4138,7 +4156,7 @@ Licensed CC-BY-4.0 This book contains the quantitative analysis labs for the QI CSHL course, 2024 -Tags: Segmentation, Python +Tags: Python Content type: Notebook @@ -4197,7 +4215,7 @@ Slides from the CZI/EOSS online meeting in December 2020. Tags: Bioimage Analysis -Content type: Slide +Content type: Slides [https://zenodo.org/records/4328911](https://zenodo.org/records/4328911) @@ -4356,7 +4374,7 @@ This Research Data Management (RDM) Slides introduce to the multidisciplinary kn Tags: Research Data Management -Content type: Slide +Content type: Slides [https://zenodo.org/record/6602101](https://zenodo.org/record/6602101) @@ -4417,7 +4435,7 @@ Licensed CC-BY-4.0 As an initiative within Germany's National Research Data Infrastructure, the authors conducted this community survey in summer 2021 to assess the state of the art of bioimaging RDM and the community needs. -Tags: Research Data Management, Image Data Management, Bioimage Data +Tags: Research Data Management Content type: Publication @@ -4492,7 +4510,7 @@ Licensed CC-BY-4.0 Tags: Python, Artificial Intelligence, Bioimage Analysis -Content type: Blog +Content type: Blog Post [https://biapol.github.io/blog/marcelo_zoccoler/omero_scripts/readme.html](https://biapol.github.io/blog/marcelo_zoccoler/omero_scripts/readme.html) @@ -4507,9 +4525,7 @@ Licensed CC-BY-4.0 Computational skills training at the UCL Sainsbury Wellcome Centre and Gatsby Computational Neuroscience Unit, delivered by members of the Neuroinformatics Unit. -Tags: Training - -Content type: Collection, Online Course, Videos, Tutorial +Content type: Collection, Online Course, Video, Tutorial [https://software-skills.neuroinformatics.dev/index.html](https://software-skills.neuroinformatics.dev/index.html) @@ -4528,7 +4544,7 @@ Introduction to sharing resources online and licensing Tags: Sharing, Research Data Management -Content type: Slide +Content type: Slides [https://f1000research.com/slides/10-519](https://f1000research.com/slides/10-519) @@ -4547,7 +4563,7 @@ Blog post about how to share data using zenodo.org Tags: Sharing, Research Data Management -Content type: Blog +Content type: Blog Post [https://focalplane.biologists.com/2023/02/15/sharing-research-data-with-zenodo/](https://focalplane.biologists.com/2023/02/15/sharing-research-data-with-zenodo/) @@ -4670,6 +4686,8 @@ Content: ... +Tags: Nfdi4Bioimage, Research Data Management + [https://zenodo.org/records/7018750](https://zenodo.org/records/7018750) [https://doi.org/10.5281/zenodo.7018750](https://doi.org/10.5281/zenodo.7018750) @@ -4712,7 +4730,7 @@ Licensed CC-BY-4.0 The authors offer trainers some simple rules, to help make their training materials FAIR, enabling others to find, (re)use, and adapt them. -Tags: Metadata, Bioinformatics, FAIR-Principles, Training +Tags: Metadata, Bioinformatics, FAIR-Principles Content type: Publication @@ -4723,7 +4741,7 @@ Content type: Publication ## Terminology service for research data management and knowledge discovery in low-temperature plasma physics -Becker, Markus M., Chaerony Siffa, Ihda, Roman Baum +Markus M. Becker, Ihda Chaerony Siffa, Roman Baum Published 2024-12-11 @@ -4901,6 +4919,8 @@ Licensed CC-BY-4.0 Research data management (RDM) in microscopy and image analysis is a challenging task. Large files in proprietary formats, complex N-dimensional array structures, and various metadata models and formats can make image data handling inconvenient and difficult. For data organization, annotation, and sharing, researchers need solutions that fit everyday practice and comply with the FAIR (Findable, Accessible, Interoperable, Reusable) principles. International community-based efforts have begun creating open data models (OME), an open file format and translation library (OME-TIFF, Bio-Formats), data management software platforms, and microscopy metadata recommendations and annotation tools. Bringing these developments into practice requires support and training. Iterative feedback and tool improvement is needed to foster practical adoption by the scientific community. The Information Infrastructure for BioImage Data (I3D:bio) project works on guidelines, training resources, and practical assistance for FAIR microscopy RDM adoption with a focus on the management platform OMERO and metadata annotations. +Tags: Nfdi4Bioimage, Research Data Management + [https://zenodo.org/records/10805204](https://zenodo.org/records/10805204) [https://doi.org/10.5281/zenodo.10805204](https://doi.org/10.5281/zenodo.10805204) @@ -4920,7 +4940,7 @@ Licensed CC-BY-4.0 The Open Microscopy Environment (OME) defines a data model and a software implementation to serve as an informatics framework for imaging in biological microscopy experiments, including representation of acquisition parameters, annotations and image analysis results. -Tags: Microscopy Image Analysis, Bioimage Analysis +Tags: Bioimage Analysis Content type: Publication @@ -4958,6 +4978,8 @@ Licensed CC-BY-4.0 Germany’s National Research Data Infrastructure (NFDI) aims to establish a sustained, cross-disciplinary research data management (RDM) infrastructure that enables researchers to handle FAIR (findable, accessible, interoperable, reusable) data. While FAIR principles have been adopted by funders, policymakers, and publishers, their practical implementation remains an ongoing effort. In the field of bio-imaging, harmonization of data formats, metadata ontologies, and open data repositories is necessary to achieve FAIR data. The NFDI4BIOIMAGE was established to address these issues and develop tools and best practices to facilitate FAIR microscopy and image analysis data in alignment with international community activities. The consortium operates through its Data Stewards team to provide expertise and direct support to help overcome RDM challenges. The three Helmholtz Centers in NFDI4BIOIMAGE aim to collaborate closely with other centers and initiatives, such as HMC, Helmholtz AI, and HIP. Here we present NFDI4BIOIMAGE’s work and its significance for research in Helmholtz and beyond +Tags: Nfdi4Bioimage, Research Data Management + [https://zenodo.org/records/11501662](https://zenodo.org/records/11501662) [https://doi.org/10.5281/zenodo.11501662](https://doi.org/10.5281/zenodo.11501662) @@ -4996,6 +5018,8 @@ Licensed CC-BY-4.0 This talk will present the initiatives of the NFDI4BioImage consortium aimed at the long-term preservation of life science data. We will discuss our efforts to establish metadata standards, which are crucial for ensuring data reusability and integrity. The development of sustainable infrastructure is another key focus, enabling seamless data integration and analysis in the cloud. We will take a look at how we manage training materials and communicate with our community. Through these actions, NFDI4BioImage seeks to enable FAIR bioimage data management for German researchers, across disciplines and embedded in the international framework. +Tags: Nfdi4Bioimage, Research Data Management + [https://zenodo.org/records/13640979](https://zenodo.org/records/13640979) [https://doi.org/10.5281/zenodo.13640979](https://doi.org/10.5281/zenodo.13640979) @@ -5076,7 +5100,7 @@ Licensed CC-BY-4.0 Glittr.org is a platform that aggregates and indexes training materials on computational life sciences from public git repositories, making it easier for users to find, compare, and analyze these resources based on various metrics. By providing insights into the availability of materials, collaboration patterns, and licensing practices, Glittr.org supports adherence to the FAIR principles, benefiting the broader life sciences community. -Tags: Training, Bioimage Analysis, Research Data Management +Tags: Bioimage Analysis, Research Data Management Content type: Publication, Preprint @@ -5144,7 +5168,7 @@ The Data Steward Team of the NFDI4BIOIMAGE consortium presents themselves and th ## Working Group Charter. RDM Helpdesk Network -Judith Engel, Patrick Helling, Robert Herrenbrück, Marina Lemaire, Hela Mehrtens, Marcus Schmidt, Martha Stellmacher, Lukas Weimer, Cord Wiljes, Wolf Zinke +Judith Engel, Patrick Helling, Robert Herrenbrück, MarinaLemaire, Hela Mehrtens, Marcus Schmidt, Martha Stellmacher, Lukas Weimer, Cord Wiljes, Wolf Zinke Published 2024-11-04 @@ -5275,6 +5299,8 @@ Licensed CC-BY-4.0 Talk given at Georg-August-Universität Göttingen Campus Institute Data Science23rd January 2025 https://www.uni-goettingen.de/en/653203.html +Tags: Nfdi4Bioimage + [https://zenodo.org/records/14716546](https://zenodo.org/records/14716546) [https://doi.org/10.5281/zenodo.14716546](https://doi.org/10.5281/zenodo.14716546) @@ -5296,6 +5322,8 @@ CMCB LIFE SCIENCES SEMINARSTechnische Universität Dresden16th January 2025 https://tu-dresden.de/cmcb/crtd/news-termine/termine/cmcb-life-sciences-seminar-josh-moore-german-bioimaging-e-v-society-for-microscopy-and-image-analysis-constance   +Tags: Nfdi4Bioimage + [https://zenodo.org/records/14650434](https://zenodo.org/records/14650434) [https://doi.org/10.5281/zenodo.14650434](https://doi.org/10.5281/zenodo.14650434) @@ -5335,6 +5363,8 @@ Licensed CC-BY-4.0 Overview of Activities of the Team Image Data Analysis and Management of German BioImaging e.V.   +Tags: Nfdi4Bioimage, Research Data Management + [https://zenodo.org/records/10796364](https://zenodo.org/records/10796364) [https://doi.org/10.5281/zenodo.10796364](https://doi.org/10.5281/zenodo.10796364) @@ -5374,6 +5404,8 @@ Licensed CC-BY-4.0 Poster presented at the European Light Microscopy Initiative meeting in Liverpool (https://www.elmi2024.org/) +Tags: Nfdi4Bioimage, Research Data Management + [https://zenodo.org/records/11235513](https://zenodo.org/records/11235513) [https://doi.org/10.5281/zenodo.11235513](https://doi.org/10.5281/zenodo.11235513) @@ -5562,6 +5594,8 @@ Learn from each other, leverage different expertise Learn how to train users, establish sustainability strategies, and foster FAIR RDM for bioimaging at your institution +Tags: Nfdi4Bioimage, Research Data Management + [https://zenodo.org/records/14178789](https://zenodo.org/records/14178789) [https://doi.org/10.5281/zenodo.14178789](https://doi.org/10.5281/zenodo.14178789) @@ -5593,6 +5627,8 @@ Dr. Riccardo Massei (Helmholtz-Center for Environmental Research, UFZ, Leipzig) Dr. Michele Bortolomeazzi (DKFZ, Single cell Open Lab, bioimage data specialist, bioinformatician, staff scientist in the NFDI4BIOIMAGE project) Dr. Christian Schmidt (Science Manager for Research Data Management in Bioimaging, German Cancer Research Center, Heidelberg, Project Coordinator of the NFDI4BIOIMAGE project) +Tags: Nfdi4Bioimage, Research Data Management + [https://zenodo.org/records/11350689](https://zenodo.org/records/11350689) [https://doi.org/10.5281/zenodo.11350689](https://doi.org/10.5281/zenodo.11350689) @@ -5676,6 +5712,8 @@ Publishing datasets in public archives for bioimage dataKsenia Krooß /Hein Date & Venue:Thursday, Sept. 26, 5.30 p.m.Haus 22 / Paul Ehrlich Lecture Hall (H22-1)University Hospital Frankfurt +Tags: Nfdi4Bioimage, Research Data Management + [https://zenodo.org/records/13861026](https://zenodo.org/records/13861026) [https://doi.org/10.5281/zenodo.13861026](https://doi.org/10.5281/zenodo.13861026) @@ -5693,7 +5731,7 @@ Licensed CC-BY-4.0 Tags: Artificial Intelligence, Bioimage Analysis -Content type: Slide +Content type: Slides [https://zenodo.org/doi/10.5281/zenodo.4330625](https://zenodo.org/doi/10.5281/zenodo.4330625) diff --git a/_sources/licenses/cc-by-sa-4.0.md b/_sources/licenses/cc-by-sa-4.0.md index 60f93838..03c5eeae 100644 --- a/_sources/licenses/cc-by-sa-4.0.md +++ b/_sources/licenses/cc-by-sa-4.0.md @@ -30,7 +30,7 @@ Licensed CC-BY-SA-4.0 -Tags: Research Data Management, Bioimage Analysis, Image Data Management, Open Science +Tags: Research Data Management, Bioimage Analysis, Open Science Content type: Slides, Presentation diff --git a/_sources/licenses/cc0-1.0.md b/_sources/licenses/cc0-1.0.md index 04650824..e90d5a1c 100644 --- a/_sources/licenses/cc0-1.0.md +++ b/_sources/licenses/cc0-1.0.md @@ -1,4 +1,4 @@ -# Cc0-1.0 (10) +# Cc0-1.0 (13) ## A Fiji Scripting Tutorial Albert Cardona @@ -14,6 +14,23 @@ Content type: Notebook [https://syn.mrc-lmb.cam.ac.uk/acardona/fiji-tutorial/](https://syn.mrc-lmb.cam.ac.uk/acardona/fiji-tutorial/) +--- + +## Andor Dragonfly confocal image of BPAE cells stained for actin, IMS file format + +Hoku West-Foyle + +Published 2025-01-16 + +Licensed CC0-1.0 + + + +[https://zenodo.org/records/14675120](https://zenodo.org/records/14675120) + +[https://doi.org/10.5281/zenodo.14675120](https://doi.org/10.5281/zenodo.14675120) + + --- ## BioImage Archive AI Gallery @@ -22,7 +39,7 @@ Licensed CC0-1.0 -Tags: Bioimage Analysis, Deep Learning +Tags: Bioimage Analysis, Artificial Intelligence Content type: Collection, Data @@ -73,7 +90,7 @@ Licensed CC0-1.0 In this paper we introduce two sets of checklists. One is concerned with the publication of images. The other one gives instructions for the publication of image analysis. -Tags: Bioimage Data, Microscopy Image Analysis +Tags: Bioimage Analysis Content type: Forum Post @@ -116,6 +133,25 @@ Content type: Book [https://cytomining.github.io/profiling-handbook/](https://cytomining.github.io/profiling-handbook/) +--- + +## Ink in a dish + +Cavanagh + +Published 2024-09-03 + +Licensed CC0-1.0 + + + +A test data set for troublshooting. no scientific meaning. + +[https://zenodo.org/records/13642395](https://zenodo.org/records/13642395) + +[https://doi.org/10.5281/zenodo.13642395](https://doi.org/10.5281/zenodo.13642395) + + --- ## Online_R_learning @@ -147,13 +183,34 @@ Licensed CC0-1.0 Recommended Metadata for Biological Images (REMBI) provides guidelines for metadata for biological images to enable the FAIR sharing of scientific data. -Tags: FAIR-Principles, Metadata, Image Data Management, Research Data Management, Bioimage Data +Tags: FAIR-Principles, Metadata, Research Data Management Content type: Collection [https://www.ebi.ac.uk/bioimage-archive/rembi-help-overview/](https://www.ebi.ac.uk/bioimage-archive/rembi-help-overview/) +--- + +## Statistical Rethinking + +Richard McElreath + +Published 2024-03-01 + +Licensed CC0-1.0 + + + +This course teaches data analysis, but it focuses on scientific models. The unfortunate truth about data is that nothing much can be done with it, until we say what caused it. We will prioritize conceptual, causal models and precise questions about those models. We will use Bayesian data analysis to connect scientific models to evidence. And we will learn powerful computational tools for coping with high-dimension, imperfect data of the kind that biologists and social scientists face. + +Tags: Statistics + +Content type: Github Repository + +[https://github.com/rmcelreath/stat_rethinking_2024](https://github.com/rmcelreath/stat_rethinking_2024) + + --- ## Submitting data to the BioImage Archive @@ -164,7 +221,7 @@ Licensed CC0-1.0 To submit, you’ll need to register an account, organise and upload your data, prepare a file list, and then submit using our web submission form. These steps are explained here. -Tags: Research Data Management, Image Data Management, Bioimage Data +Tags: Research Data Management Content type: Tutorial, Video diff --git a/_sources/licenses/gpl-2.0.md b/_sources/licenses/gpl-2.0.md index 351b760b..a408fdbc 100644 --- a/_sources/licenses/gpl-2.0.md +++ b/_sources/licenses/gpl-2.0.md @@ -7,7 +7,7 @@ Licensed GPL-2.0 An easy to use and open source converter for bioimaging data. NGFF-Converter is a GUI application for conversion of bioimage formats into OME-NGFF (Next-Generation File Format) or OME-TIFF. -Tags: Bioimage Data, Open Source Software +Tags: Open Source Software Content type: Application @@ -102,7 +102,7 @@ Licensed GPL-2.0 Java application to convert image file formats, including .mrxs, to an intermediate Zarr structure compatible with the OME-NGFF specification. -Tags: Open Source Software, Bioimage Data +Tags: Open Source Software Content type: Application, Github Repository @@ -121,7 +121,7 @@ Licensed GPL-2.0 Java application to convert a directory of tiles to an OME-TIFF pyramid. This is the second half of iSyntax/.mrxs => OME-TIFF conversion. -Tags: Open Source Software, Bioimage Data +Tags: Open Source Software Content type: Application, Github Repository diff --git a/_sources/licenses/gpl-3.0.md b/_sources/licenses/gpl-3.0.md new file mode 100644 index 00000000..6c64590a --- /dev/null +++ b/_sources/licenses/gpl-3.0.md @@ -0,0 +1,140 @@ +# Gpl-3.0 (7) +## IAFIG-RMS Python for Bioimage Analysis Course + +Aurelien Barbotin, Chas Nelson, Dominic Waithe, Ola (Alexandra) Tarkowska, Mikolaj Kundegorski, Stephen Cross, Todd Fallesen + +Licensed GPL-3.0 + + + +Tags: Bioimage Analysis + +Content type: Notebook + +[https://github.com/RMS-DAIM/Python-for-Bioimage-Analysis](https://github.com/RMS-DAIM/Python-for-Bioimage-Analysis) + + +--- + +## KNIME Image Processing + +None + +Licensed GPL-3.0 + + + +The KNIME Image Processing Extension allows you to read in more than 140 different kinds of images and to apply well known methods on images, like preprocessing. segmentation, feature extraction, tracking and classification in KNIME. + +Tags: Imagej, OMERO, Workflow + +Content type: Tutorial, Online Tutorial, Documentation + +[https://www.knime.com/community/image-processing](https://www.knime.com/community/image-processing) + + +--- + +## Object Tracking and Track Analysis using TrackMate and CellTracksColab + +Joanna Pylvänäinen + +Published None + +Licensed GPL-3.0 + + + +I2K 2024 workshop materials for "Object Tracking and Track Analysis using TrackMate and CellTracksColab" + +Tags: Bioimage Analysis + +Content type: Github Repository, Tutorial, Workshop, Slides + +[https://github.com/CellMigrationLab/I2K_2024](https://github.com/CellMigrationLab/I2K_2024) + + +--- + +## ZEN & Python workshop + +Licensed GPL-3.0 + + + +Tags: Python, Napari, Bioimage Analysis + +Content type: Collection, Notebook + +[https://github.com/zeissmicroscopy/ZEN_Python_OAD_workshop](https://github.com/zeissmicroscopy/ZEN_Python_OAD_workshop) + + +--- + +## patho_prompt_injection + +JanClusmann, Tim Lenz + +Published 2024-11-08T08:32:03+00:00 + +Licensed GPL-3.0 + + + + + +Tags: Histopathology, Bioimage Analysis + +Content type: Github Repository, Notebook + +[https://github.com/KatherLab/patho_prompt_injection](https://github.com/KatherLab/patho_prompt_injection) + + +--- + +## quantixed/TheDigitalCell: First complete code set + +Stephen Royle + +Published 2019-04-17 + +Licensed GPL-3.0 + + + +First complete code set for The Digital Cell book. + +Tags: Bioimage Analysis + +Content type: Code + +[https://github.com/quantixed/TheDigitalCell](https://github.com/quantixed/TheDigitalCell) + +[https://zenodo.org/records/2643411](https://zenodo.org/records/2643411) + +[https://doi.org/10.5281/zenodo.2643411](https://doi.org/10.5281/zenodo.2643411) + + +--- + +## rse-skills-workshop + +Jack Atkinson + +Published 2023-12-22T17:39:48+00:00 + +Licensed GPL-3.0 + + + +Teaching materials for improving research software writing abilities. + +Tags: Research Software Engineering + +Content type: Github Repository, Slides + +[https://github.com/jatkinson1000/rse-skills-workshop](https://github.com/jatkinson1000/rse-skills-workshop) + + +--- + diff --git a/_sources/licenses/mit.md b/_sources/licenses/mit.md index 26a1b771..61ff447f 100644 --- a/_sources/licenses/mit.md +++ b/_sources/licenses/mit.md @@ -9,7 +9,7 @@ Licensed MIT BioEngine, a Python package designed for flexible deployment and execution of bioimage analysis models and workflows using AI, accessible via HTTP API and RPC. -Tags: Workflow Engine, Deep Learning, Python +Tags: Workflow Engine, Artificial Intelligence, Python Content type: Documentation @@ -177,7 +177,7 @@ Licensed MIT This course consists of lectures and exercises that teach the background of deep learning for image analysis and show applications to classification and segmentation analysis problems. -Tags: Deep Learning, Pytorch, Segmentation, Python +Tags: Artificial Intelligence, Python Content type: Notebook @@ -376,7 +376,7 @@ Licensed MIT This repository offer access to teaching material and useful resources for the YMIA - Python-Based Event Series. -Tags: Python, Large Language Models, Prompt Engineering, Biabob, Bioimage Analysis, Microscopy Image Analysis +Tags: Python, Artifical Intelligence, Bioimage Analysis Content type: Github Repository, Slides diff --git a/_sources/licenses/unknown.md b/_sources/licenses/unknown.md index 0894b32c..fb6c4770 100644 --- a/_sources/licenses/unknown.md +++ b/_sources/licenses/unknown.md @@ -28,9 +28,9 @@ Licensed UNKNOWN A review of the tools, methods and concepts useful for biologists and life scientists as well as bioimage analysts. -Tags: Deep Learning, Machine Learning, Artificial Intelligence, Bioimage Analysis, Large Language Models +Tags: Artificial Intelligence, Bioimage Analysis -Content type: Youtube Video, Slides, Webinar +Content type: Video, Slides, Webinar [https://www.youtube.com/watch?v=TJXNMIWtdac](https://www.youtube.com/watch?v=TJXNMIWtdac) @@ -85,7 +85,7 @@ How can artificial intelligence be used for digital pathology? Tags: Artificial Intelligence -Content type: Youtube Video +Content type: Video [https://www.youtube.com/watch?v=Om9tl4Dh2yw](https://www.youtube.com/watch?v=Om9tl4Dh2yw) @@ -121,7 +121,7 @@ Licensed UNKNOWN -Content type: Slide +Content type: Slides [https://github.com/tischi/presentation-image-analysis](https://github.com/tischi/presentation-image-analysis) @@ -193,7 +193,7 @@ Licensed UNKNOWN Tags: Artificial Intelligence, Bioimage Analysis -Content type: Slide +Content type: Slides [https://github.com/esgomezm/NEUBIAS_chapter_DL_2020](https://github.com/esgomezm/NEUBIAS_chapter_DL_2020) @@ -274,7 +274,7 @@ Licensed UNKNOWN Tags: Neubias, Cellprofiler, Bioimage Analysis -Content type: Slide +Content type: Slides [https://github.com/ahklemm/CellProfiler_Introduction](https://github.com/ahklemm/CellProfiler_Introduction) @@ -359,7 +359,7 @@ Licensed UNKNOWN -Tags: Deep Learning, Microscopy, Microsycopy Image Analysis, Bio Image Analysis, Artifical Intelligence +Tags: Bio Image Analysis, Artifical Intelligence Content type: Blog Post @@ -378,7 +378,7 @@ Licensed UNKNOWN Tags: Artificial Intelligence, Bioimage Analysis -Content type: Slide, Notebook +Content type: Slides, Notebook [https://github.com/JLrumberger/DL-MBL-2021](https://github.com/JLrumberger/DL-MBL-2021) @@ -488,9 +488,9 @@ Licensed UNKNOWN In this course you will learn how to use Docker, Compose and Kubernetes on your machine for better software building and testing. -Tags: Docker, Training +Tags: Docker -Content type: Videos, Tutorial, Online Course +Content type: Video, Tutorial, Online Course [https://www.udemy.com/course/docker-mastery/?srsltid=AfmBOornR5gRqOg-4v8Nsap1z24CaPPUPxg8JzyqEGZ6MvW_dh-sf4Af&couponCode=ST2MT110724BNEW](https://www.udemy.com/course/docker-mastery/?srsltid=AfmBOornR5gRqOg-4v8Nsap1z24CaPPUPxg8JzyqEGZ6MvW_dh-sf4Af&couponCode=ST2MT110724BNEW) @@ -509,9 +509,9 @@ Licensed UNKNOWN Leukocyte extravasation is a critical component of the innate immune response, while circulating tumour cell extravasation is a crucial step in metastasis formation. Despite their importance, these extravasation mechanisms remain incompletely understood. In this talk, Guillaume Jacquemet presents a novel imaging framework that integrates microfluidics with high-speed, label-free imaging to study the arrest of pancreatic cancer cells (PDAC) on human endothelial layers under physiological flow conditions. -Tags: Deep Learning, Microscopy Image Analysis +Tags: Artificial Intelligence, Bioimage Analysis -Content type: Youtube Video, Slides +Content type: Video, Slides [https://www.youtube.com/watch?v=KTdZBgSCYJQ](https://www.youtube.com/watch?v=KTdZBgSCYJQ) @@ -621,7 +621,7 @@ Sharing your data can benefit your career in some interesting ways. In this post Tags: Research Data Management, Sharing -Content type: Blog +Content type: Blog Post [https://web.library.uq.edu.au/blog/2022/03/five-great-reasons-share-your-research-data](https://web.library.uq.edu.au/blog/2022/03/five-great-reasons-share-your-research-data) @@ -689,9 +689,9 @@ Licensed UNKNOWN Example Workflows / usage of the Glencoe Software. -Tags: OMERO, Training +Tags: OMERO -Content type: Videos, Tutorial, Collection +Content type: Video, Tutorial, Collection [https://www.glencoesoftware.com/media/webinars/](https://www.glencoesoftware.com/media/webinars/) @@ -708,7 +708,7 @@ Licensed UNKNOWN A Microscopy Research Data Management Resource. -Tags: Metadata, I3Dbio, Research Data Management, Bioimage Data +Tags: Metadata, I3Dbio, Research Data Management Content type: Collection @@ -742,7 +742,7 @@ Licensed UNKNOWN -Content type: Slide +Content type: Slides [https://docs.google.com/presentation/d/1henPIDTpHT3bc1Y26AltItAHJ2C5xCOl/edit#slide=id.p1](https://docs.google.com/presentation/d/1henPIDTpHT3bc1Y26AltItAHJ2C5xCOl/edit#slide=id.p1) @@ -759,7 +759,7 @@ Licensed UNKNOWN Tags: Bioimage Analysis -Content type: Slide +Content type: Slides [https://docs.google.com/presentation/d/1WG_4307XmKsGfWT3taxMvX2rZiG1k0SM1E7SAENJQkI/edit#slide=id.p](https://docs.google.com/presentation/d/1WG_4307XmKsGfWT3taxMvX2rZiG1k0SM1E7SAENJQkI/edit#slide=id.p) @@ -776,7 +776,7 @@ Licensed UNKNOWN Tags: Neubias, Imagej Macro, Bioimage Analysis -Content type: Slide, Code +Content type: Slides, Code [https://github.com/ahklemm/ImageJMacro_Introduction](https://github.com/ahklemm/ImageJMacro_Introduction) @@ -793,7 +793,7 @@ Licensed UNKNOWN Tags: Imagej, Bioimage Analysis -Content type: Slide +Content type: Slides [https://git.mpi-cbg.de/scicomp/bioimage_team/coursematerialimageanalysis/tree/master/ImageJMacro_24h_2017-01](https://git.mpi-cbg.de/scicomp/bioimage_team/coursematerialimageanalysis/tree/master/ImageJMacro_24h_2017-01) @@ -810,7 +810,7 @@ Licensed UNKNOWN Tags: Neubias, Bioimage Analysis -Content type: Slide +Content type: Slides [https://docs.google.com/presentation/d/1q8q1xE-c35tvCsRXZay98s2UYWwXpp0cfCljBmMFpco/edit#slide=id.ga456d5535c_2_53](https://docs.google.com/presentation/d/1q8q1xE-c35tvCsRXZay98s2UYWwXpp0cfCljBmMFpco/edit#slide=id.ga456d5535c_2_53) @@ -880,7 +880,7 @@ Licensed UNKNOWN Tags: Artificial Intelligence, Bioimage Analysis -Content type: Slide +Content type: Slides [https://raw.githubusercontent.com/esgomezm/esgomezm.github.io/master/assets/pdf/SPAOM2018/MachineLearning_SPAOMworkshop_public.pdf](https://raw.githubusercontent.com/esgomezm/esgomezm.github.io/master/assets/pdf/SPAOM2018/MachineLearning_SPAOMworkshop_public.pdf) @@ -912,7 +912,7 @@ Licensed UNKNOWN Tags: Neubias, Artificial Intelligence, Bioimage Analysis -Content type: Slide +Content type: Slides [https://docs.google.com/presentation/d/1oJoy9gHmUuSmUwCkPs_InJf_WZAzmLlUNvK1FUEB4PA/edit#slide=id.ge3a24e733b_0_54](https://docs.google.com/presentation/d/1oJoy9gHmUuSmUwCkPs_InJf_WZAzmLlUNvK1FUEB4PA/edit#slide=id.ge3a24e733b_0_54) @@ -963,7 +963,7 @@ Licensed UNKNOWN The mission of Metrics Reloaded is to guide researchers in the selection of appropriate performance metrics for biomedical image analysis problems, as well as provide a comprehensive online resource for metric-related information and pitfalls -Tags: Bioimage Analysis, Image Segmentation, Machine Learning +Tags: Bioimage Analysis, Quality Control Content type: Website, Collection @@ -995,7 +995,7 @@ Licensed UNKNOWN Tags: Neubias, Bioimage Analysis -Content type: Slide, Code, Notebook +Content type: Slides, Code, Notebook [https://github.com/miura/NEUBIAS_AnalystSchool2018](https://github.com/miura/NEUBIAS_AnalystSchool2018) @@ -1012,7 +1012,7 @@ Licensed UNKNOWN Tags: Neubias, Bioimage Analysis -Content type: Slide, Tutorial +Content type: Slides, Tutorial [https://github.com/miura/NEUBIAS_Bioimage_Analyst_Course2017](https://github.com/miura/NEUBIAS_Bioimage_Analyst_Course2017) @@ -1029,7 +1029,7 @@ Licensed UNKNOWN Tags: Neubias, Bioimage Analysis -Content type: Slide, Code, Notebook +Content type: Slides, Code, Notebook [https://github.com/miura/NEUBIAS_AnalystSchool2019](https://github.com/miura/NEUBIAS_AnalystSchool2019) @@ -1046,7 +1046,7 @@ Licensed UNKNOWN Tags: Neubias, Bioimage Analysis -Content type: Slide, Code, Notebook +Content type: Slides, Code, Notebook [https://github.com/miura/NEUBIAS_AnalystSchool2020](https://github.com/miura/NEUBIAS_AnalystSchool2020) @@ -1080,7 +1080,7 @@ Licensed UNKNOWN Tags: Python, Neubias, Artificial Intelligence, Bioimage Analysis -Content type: Slide, Notebook +Content type: Slides, Notebook [https://github.com/maweigert/neubias_academy_stardist](https://github.com/maweigert/neubias_academy_stardist) @@ -1229,7 +1229,7 @@ Licensed UNKNOWN This tool is intended to link different research data management platforms with each other. -Tags: Research Data Management, Image Data Management +Tags: Research Data Management Content type: Github Repository @@ -1250,7 +1250,7 @@ Licensed UNKNOWN Bioimaging data have significant potential for reuse, but unlocking this potential requires systematic archiving of data and metadata in public databases. The authors propose draft metadata guidelines to begin addressing the needs of diverse communities within light and electron microscopy. -Tags: Metadata, Bioimage Data, Image Data Management, Research Data Management +Tags: Metadata, Research Data Management Content type: Publication @@ -1294,7 +1294,7 @@ Licensed UNKNOWN This Focus issue features a series of papers offering guidelines and tools for improving the tracking and reporting of microscopy metadata with an emphasis on reproducibility and data re-use. -Tags: Reproducibility, Metadata, Bioimage Data +Tags: Reproducibility, Metadata Content type: Collection @@ -1396,7 +1396,7 @@ Video Lectures for Statistical Rethinking Course Tags: Statistics -Content type: Youtube Video +Content type: Video [https://www.youtube.com/playlist?list=PLDcUM9US4XdPz-KxHM4XHt7uUVGWWVSus](https://www.youtube.com/playlist?list=PLDcUM9US4XdPz-KxHM4XHt7uUVGWWVSus) @@ -1445,7 +1445,7 @@ Licensed UNKNOWN The BioImage Archive is a new archival data resource at the European Bioinformatics Institute (EMBL-EBI). -Tags: Image Data Management, Research Data Management, Bioimage Data +Tags: Research Data Management Content type: Publication @@ -1468,7 +1468,7 @@ Licensed UNKNOWN Rigorous record-keeping and quality control are required to ensure the quality, reproducibility and value of imaging data. The 4DN Initiative and BINA here propose light Microscopy Metadata specifications that extend the OME data model, scale with experimental intent and complexity, and make it possible for scientists to create comprehensive records of imaging experiments. -Tags: Reproducibility, Microscopy Image Analysis, Metadata, Image Data Management, Bioimage Data +Tags: Reproducibility, Bioimage Analysis, Metadata Content type: Publication @@ -1500,7 +1500,7 @@ Licensed UNKNOWN -Tags: Bioimage Analysis, Microscopy Image Analysis +Tags: Bioimage Analysis Content type: Collection, Event, Forum Post, Workshop @@ -1519,7 +1519,7 @@ Licensed UNKNOWN Tags: Neubias, Bioimage Analysis -Content type: Slide +Content type: Slides [https://www.dropbox.com/s/5abw3cvxrhpobg4/20220923_DefragmentationTS.pdf?dl=0](https://www.dropbox.com/s/5abw3cvxrhpobg4/20220923_DefragmentationTS.pdf?dl=0) @@ -1536,7 +1536,7 @@ Licensed UNKNOWN Tags: Artificial Intelligence, Bioimage Analysis -Content type: Slide +Content type: Slides [https://github.com/esgomezm/zidas2020_intro_DL](https://github.com/esgomezm/zidas2020_intro_DL) diff --git a/_sources/tags/artificial_intelligence.md b/_sources/tags/artificial_intelligence.md index 1a0e0d89..06e48529 100644 --- a/_sources/tags/artificial_intelligence.md +++ b/_sources/tags/artificial_intelligence.md @@ -1,4 +1,4 @@ -# Artificial intelligence (32) +# Artificial intelligence (44) ## AI ML DL in Bioimage Analysis - Webinar Yannick KREMPP @@ -11,9 +11,9 @@ Licensed UNKNOWN A review of the tools, methods and concepts useful for biologists and life scientists as well as bioimage analysts. -Tags: Deep Learning, Machine Learning, Artificial Intelligence, Bioimage Analysis, Large Language Models +Tags: Artificial Intelligence, Bioimage Analysis -Content type: Youtube Video, Slides, Webinar +Content type: Video, Slides, Webinar [https://www.youtube.com/watch?v=TJXNMIWtdac](https://www.youtube.com/watch?v=TJXNMIWtdac) @@ -51,11 +51,30 @@ How can artificial intelligence be used for digital pathology? Tags: Artificial Intelligence -Content type: Youtube Video +Content type: Video [https://www.youtube.com/watch?v=Om9tl4Dh2yw](https://www.youtube.com/watch?v=Om9tl4Dh2yw) +--- + +## BIDS-lecture-2024 + +Robert Haase + +Licensed CC-BY-4.0 + + + +Training resources for Students at Uni Leipzig who want to dive into bio-image data science with Python. The material developed here between April and July 2024. + +Tags: Bioimage Analysis, Artificial Intelligence, Python + +Content type: Github Repository + +[https://github.com/ScaDS/BIDS-lecture-2024/](https://github.com/ScaDS/BIDS-lecture-2024/) + + --- ## Bio-image Analysis Code Generation using bia-bob @@ -77,6 +96,67 @@ Tags: Artificial Intelligence, Bioimage Analysis [https://doi.org/10.5281/zenodo.13908108](https://doi.org/10.5281/zenodo.13908108) +--- + +## Bio-image Analysis with the Help of Large Language Models + +Robert Haase + +Published 2024-03-13 + +Licensed CC-BY-4.0 + + + +Large Language Models (LLMs) change the way how we use computers. This also has impact on the bio-image analysis community. We can generate code that analyzes biomedical image data if we ask the right prompts. This talk outlines introduces basic principles, explains prompt engineering and how to apply it to bio-image analysis. We also compare how different LLM vendors perform on code generation tasks and which challenges are ahead for the bio-image analysis community. + +Tags: Artificial Intelligence, Python + +Content type: Slides + +[https://zenodo.org/records/10815329](https://zenodo.org/records/10815329) + +[https://doi.org/10.5281/zenodo.10815329](https://doi.org/10.5281/zenodo.10815329) + + +--- + +## Bio-image Data Science + +Robert Haase + +Licensed CC-BY-4.0 + + + +This repository contains training resources for Students at Uni Leipzig who want to dive into bio-image data science with Python. + +Tags: Research Data Management, Artificial Intelligence, Bioimage Analysis, Python + +Content type: Notebook + +[https://github.com/ScaDS/BIDS-lecture-2024](https://github.com/ScaDS/BIDS-lecture-2024) + + +--- + +## Bio-image Data Science Lectures @ Uni Leipzig / ScaDS.AI + +Robert Haase + +Licensed CC-BY-4.0 + + + +These are the PPTx training resources for Students at Uni Leipzig who want to dive into bio-image data science with Python. The material developed here between April and July 2024. + +Tags: Bioimage Analysis, Artificial Intelligence, Python + +Content type: Slides + +[https://zenodo.org/records/12623730](https://zenodo.org/records/12623730) + + --- ## BioEngine @@ -96,6 +176,40 @@ Content type: Publication [https://ai4life.eurobioimaging.eu/announcing-bioengine/](https://ai4life.eurobioimaging.eu/announcing-bioengine/) +--- + +## BioEngine Documentation + +Wei Ouyang, Nanguage, Jeremy Metz, Craig Russell + +Licensed MIT + + + +BioEngine, a Python package designed for flexible deployment and execution of bioimage analysis models and workflows using AI, accessible via HTTP API and RPC. + +Tags: Workflow Engine, Artificial Intelligence, Python + +Content type: Documentation + +[https://bioimage-io.github.io/bioengine/#/](https://bioimage-io.github.io/bioengine/#/) + + +--- + +## BioImage Archive AI Gallery + +Licensed CC0-1.0 + + + +Tags: Bioimage Analysis, Artificial Intelligence + +Content type: Collection, Data + +[https://www.ebi.ac.uk/bioimage-archive/galleries/AI.html](https://www.ebi.ac.uk/bioimage-archive/galleries/AI.html) + + --- ## Bioimage Model Zoo @@ -123,7 +237,7 @@ Licensed UNKNOWN Tags: Artificial Intelligence, Bioimage Analysis -Content type: Slide +Content type: Slides [https://github.com/esgomezm/NEUBIAS_chapter_DL_2020](https://github.com/esgomezm/NEUBIAS_chapter_DL_2020) @@ -208,9 +322,9 @@ Licensed CC-BY-4.0 In this blog post the author demonstrates how chatGPT can be used to combine a fictive project description with a DMP specification to produce a project-specific DMP. -Tags: Research Data Management, Large Language Models, Artificial Intelligence +Tags: Research Data Management, Artificial Intelligence -Content type: Blog +Content type: Blog Post [https://focalplane.biologists.com/2023/11/06/creating-a-research-data-management-plan-using-chatgpt/](https://focalplane.biologists.com/2023/11/06/creating-a-research-data-management-plan-using-chatgpt/) @@ -244,7 +358,7 @@ Licensed UNKNOWN Tags: Artificial Intelligence, Bioimage Analysis -Content type: Slide, Notebook +Content type: Slides, Notebook [https://github.com/JLrumberger/DL-MBL-2021](https://github.com/JLrumberger/DL-MBL-2021) @@ -317,6 +431,27 @@ Content type: Book [https://cytomining.github.io/DeepProfiler-handbook/docs/00-welcome.html](https://cytomining.github.io/DeepProfiler-handbook/docs/00-welcome.html) +--- + +## Dr Guillaume Jacquemet on studying cancer cell metastasis in the era of deep learning for microscopy + +Guillaume Jacquemet + +Published 2024-10-24 + +Licensed UNKNOWN + + + +Leukocyte extravasation is a critical component of the innate immune response, while circulating tumour cell extravasation is a crucial step in metastasis formation. Despite their importance, these extravasation mechanisms remain incompletely understood. In this talk, Guillaume Jacquemet presents a novel imaging framework that integrates microfluidics with high-speed, label-free imaging to study the arrest of pancreatic cancer cells (PDAC) on human endothelial layers under physiological flow conditions. + +Tags: Artificial Intelligence, Bioimage Analysis + +Content type: Video, Slides + +[https://www.youtube.com/watch?v=KTdZBgSCYJQ](https://www.youtube.com/watch?v=KTdZBgSCYJQ) + + --- ## EMBL Deep Learning course 2019 exercises and materials @@ -368,6 +503,25 @@ Content type: Notebook [https://github.com/kreshuklab/teaching-dl-course-2023](https://github.com/kreshuklab/teaching-dl-course-2023) +--- + +## FAIRy deep-learning for bioImage analysis + +Estibaliz Gómez de Mariscal + +Licensed CC-BY-4.0 + + + +Introduction to FAIR deep learning. Furthermore, tools to deploy trained DL models (deepImageJ), easily train and evaluate them (ZeroCostDL4Mic and DeepBacs) ensure reproducibility (DL4MicEverywhere), and share this technology in an open-source and reproducible manner (BioImage Model Zoo) are introduced. + +Tags: Artificial Intelligence, FAIR-Principles, Bioimage Analysis + +Content type: Slides + +[https://f1000research.com/slides/13-147](https://f1000research.com/slides/13-147) + + --- ## Generative artificial intelligence for bio-image analysis @@ -380,11 +534,30 @@ Licensed CC-BY-4.0 Tags: Python, Bioimage Analysis, Artificial Intelligence -Content type: Slide +Content type: Slides [https://f1000research.com/slides/12-971](https://f1000research.com/slides/12-971) +--- + +## Introduction to Deep Learning for Microscopy + +Costantin Pape + +Licensed MIT + + + +This course consists of lectures and exercises that teach the background of deep learning for image analysis and show applications to classification and segmentation analysis problems. + +Tags: Artificial Intelligence, Python + +Content type: Notebook + +[https://github.com/computational-cell-analytics/dl-for-micro](https://github.com/computational-cell-analytics/dl-for-micro) + + --- ## Kreshuk Lab's EMBL EIPP predoc course teaching material @@ -408,19 +581,19 @@ Content type: Tutorial Robert Haase -Published 2024-08-27 +Published 2024-12-12 Licensed CC-BY-4.0 -Large Language Models (LLMs) are changing the way how humans interact with computers. This has impact on all scientific fields by enabling new ways to achieve for example data analysis goals. In this talk we will go through an introduction to LLMs with respect to applications in the life sciences, focusing on bio-image analysis. We will see how to generate text and images using LLMs and how LLMs can extract information from reproducibly images through code-generation. We will go through selected prompt engineering techniques enabling scientists to tune the output of LLMs towards their scientific goal and how to do quality assurance in this context. +This slide deck introduces Large Language Models to an audience of life-scientists. We first dive into terminology: Different kinds of Language Models and what they can be used for. The remaining slides are optional slides to allow us to dive deeper into topics such as tools for using LLMs in Science, Quality Assurance, Techniques such as Retrieval Augmented Generation and Prompt Engineering. -Tags: Artificial Intelligence +Tags: Globias, Artificial Intelligence -[https://zenodo.org/records/13379394](https://zenodo.org/records/13379394) +[https://zenodo.org/records/14418209](https://zenodo.org/records/14418209) -[https://doi.org/10.5281/zenodo.13379394](https://doi.org/10.5281/zenodo.13379394) +[https://doi.org/10.5281/zenodo.14418209](https://doi.org/10.5281/zenodo.14418209) --- @@ -435,7 +608,7 @@ Licensed UNKNOWN Tags: Artificial Intelligence, Bioimage Analysis -Content type: Slide +Content type: Slides [https://raw.githubusercontent.com/esgomezm/esgomezm.github.io/master/assets/pdf/SPAOM2018/MachineLearning_SPAOMworkshop_public.pdf](https://raw.githubusercontent.com/esgomezm/esgomezm.github.io/master/assets/pdf/SPAOM2018/MachineLearning_SPAOMworkshop_public.pdf) @@ -452,11 +625,58 @@ Licensed UNKNOWN Tags: Neubias, Artificial Intelligence, Bioimage Analysis -Content type: Slide +Content type: Slides [https://docs.google.com/presentation/d/1oJoy9gHmUuSmUwCkPs_InJf_WZAzmLlUNvK1FUEB4PA/edit#slide=id.ge3a24e733b_0_54](https://docs.google.com/presentation/d/1oJoy9gHmUuSmUwCkPs_InJf_WZAzmLlUNvK1FUEB4PA/edit#slide=id.ge3a24e733b_0_54) +--- + +## MicroSam-Talks + +Constantin Pape + +Published 2024-05-23 + +Licensed CC-BY-4.0 + + + +Talks about Segment Anything for Microscopy: https://github.com/computational-cell-analytics/micro-sam. +Currently contains slides for two talks: + +Overview of Segment Anythign for Microscopy given at the SWISSBIAS online meeting in April 2024 +Talk about vision foundation models and Segment Anything for Microscopy given at Human Technopole as part of the EMBO Deep Learning Course in May 2024 + + +Tags: Bioimage Analysis, Artificial Intelligence + +Content type: Slides + +[https://zenodo.org/records/11265038](https://zenodo.org/records/11265038) + +[https://doi.org/10.5281/zenodo.11265038](https://doi.org/10.5281/zenodo.11265038) + + +--- + +## Microscopy data analysis: machine learning and the BioImage Archive + +Andrii Iudin, Anna Foix-Romero, Anna Kreshuk, Awais Athar, Beth Cimini, Dominik Kutra, Estibalis Gomez de Mariscal, Frances Wong, Guillaume Jacquemet, Kedar Narayan, Martin Weigert, Nodar Gogoberidze, Osman Salih, Petr Walczysko, Ryan Conrad, Simone Weyend, Sriram Sundar Somasundharam, Suganya Sivagurunathan, Ugis Sarkans + +Licensed CC-BY-4.0 + + + +The Microscopy data analysis: machine learning and the BioImage Archive course, which focused on introducing programmatic approaches used in the analysis of bioimage data via the BioImage Archive, ran in May 2023. + +Tags: Bioimage Analysis, Python, Artificial Intelligence + +Content type: Video, Slides + +[https://www.ebi.ac.uk/training/materials/microscopy-data-analysis-machine-learning-and-the-bioimage-archive-materials/](https://www.ebi.ac.uk/training/materials/microscopy-data-analysis-machine-learning-and-the-bioimage-archive-materials/) + + --- ## Neubias Academy 2020: Introduction to Nuclei Segmentation with StarDist @@ -469,11 +689,30 @@ Licensed UNKNOWN Tags: Python, Neubias, Artificial Intelligence, Bioimage Analysis -Content type: Slide, Notebook +Content type: Slides, Notebook [https://github.com/maweigert/neubias_academy_stardist](https://github.com/maweigert/neubias_academy_stardist) +--- + +## NeubiasPasteur2023_AdvancedCellPose + +Gaelle Letort + +Licensed BSD-3-CLAUSE + + + +Tutorial for running CellPose advanced functions + +Tags: Bioimage Analysis, Artificial Intelligence + +Content type: Github Repository + +[https://github.com/gletort/NeubiasPasteur2023_AdvancedCellPose](https://github.com/gletort/NeubiasPasteur2023_AdvancedCellPose) + + --- ## Running Deep-Learning Scripts in the BiA-PoL Omero Server @@ -486,7 +725,7 @@ Licensed CC-BY-4.0 Tags: Python, Artificial Intelligence, Bioimage Analysis -Content type: Blog +Content type: Blog Post [https://biapol.github.io/blog/marcelo_zoccoler/omero_scripts/readme.html](https://biapol.github.io/blog/marcelo_zoccoler/omero_scripts/readme.html) @@ -518,7 +757,7 @@ Licensed UNKNOWN Tags: Artificial Intelligence, Bioimage Analysis -Content type: Slide +Content type: Slides [https://github.com/esgomezm/zidas2020_intro_DL](https://github.com/esgomezm/zidas2020_intro_DL) @@ -535,7 +774,7 @@ Licensed CC-BY-4.0 Tags: Artificial Intelligence, Bioimage Analysis -Content type: Slide +Content type: Slides [https://zenodo.org/doi/10.5281/zenodo.4330625](https://zenodo.org/doi/10.5281/zenodo.4330625) diff --git a/_sources/tags/bioimage_analysis.md b/_sources/tags/bioimage_analysis.md index f73a40b0..df9dfca3 100644 --- a/_sources/tags/bioimage_analysis.md +++ b/_sources/tags/bioimage_analysis.md @@ -1,4 +1,4 @@ -# Bioimage analysis (174) +# Bioimage analysis (183) ## 2020 BioImage Analysis Survey: Community experiences and needs for the future Nasim Jamali, Ellen T. A. Dobson, Kevin W. Eliceiri, Anne E. Carpenter, Beth A. Cimini @@ -104,9 +104,9 @@ Licensed UNKNOWN A review of the tools, methods and concepts useful for biologists and life scientists as well as bioimage analysts. -Tags: Deep Learning, Machine Learning, Artificial Intelligence, Bioimage Analysis, Large Language Models +Tags: Artificial Intelligence, Bioimage Analysis -Content type: Youtube Video, Slides, Webinar +Content type: Video, Slides, Webinar [https://www.youtube.com/watch?v=TJXNMIWtdac](https://www.youtube.com/watch?v=TJXNMIWtdac) @@ -140,7 +140,7 @@ Licensed BSD-2-CLAUSE Tags: Neubias, Bioimage Analysis -Content type: Slide +Content type: Slides [https://github.com/RoccoDAnt/Defragmentation_TrainingSchool_EOSC-Life_2022/blob/main/Slides/Adding_a_workflow_to_BIAFLOWS.pdf](https://github.com/RoccoDAnt/Defragmentation_TrainingSchool_EOSC-Life_2022/blob/main/Slides/Adding_a_workflow_to_BIAFLOWS.pdf) @@ -172,7 +172,7 @@ Mara Lampert Tags: Python, Napari, Bioimage Analysis -Content type: Blog +Content type: Blog Post [https://focalplane.biologists.com/2023/03/30/annotating-3d-images-in-napari/](https://focalplane.biologists.com/2023/03/30/annotating-3d-images-in-napari/) @@ -189,7 +189,7 @@ Licensed CC-BY-4.0 Training resources for Students at Uni Leipzig who want to dive into bio-image data science with Python. The material developed here between April and July 2024. -Tags: Bioimage Analysis, Deep Learning, Microscopy Image Analysis, Python +Tags: Bioimage Analysis, Artificial Intelligence, Python Content type: Github Repository @@ -210,7 +210,7 @@ Licensed CC-BY-4.0 The authors introduce BIOMERO (bioimage analysis in OMERO), a bridge connecting OMERO, a renowned bioimaging data management platform, FAIR workflows, and high-performance computing (HPC) environments. -Tags: OMERO, Workflow, Bioimage Analysis, Image Data Management +Tags: OMERO, Workflow, Bioimage Analysis Content type: Publication @@ -324,6 +324,25 @@ Content type: Workshop, Collection [https://github.com/Koushouu/Bioimage-Analysis-Workshop-Taipei/](https://github.com/Koushouu/Bioimage-Analysis-Workshop-Taipei/) +--- + +## Bio-image Data Science + +Robert Haase + +Licensed CC-BY-4.0 + + + +This repository contains training resources for Students at Uni Leipzig who want to dive into bio-image data science with Python. + +Tags: Research Data Management, Artificial Intelligence, Bioimage Analysis, Python + +Content type: Notebook + +[https://github.com/ScaDS/BIDS-lecture-2024](https://github.com/ScaDS/BIDS-lecture-2024) + + --- ## Bio-image Data Science Lectures @ Uni Leipzig / ScaDS.AI @@ -336,7 +355,7 @@ Licensed CC-BY-4.0 These are the PPTx training resources for Students at Uni Leipzig who want to dive into bio-image data science with Python. The material developed here between April and July 2024. -Tags: Bioimage Analysis, Deep Learning, Microscopy Image Analysis, Python +Tags: Bioimage Analysis, Artificial Intelligence, Python Content type: Slides @@ -385,7 +404,7 @@ Licensed CC0-1.0 -Tags: Bioimage Analysis, Deep Learning +Tags: Bioimage Analysis, Artificial Intelligence Content type: Collection, Data @@ -532,7 +551,7 @@ Licensed UNKNOWN Tags: Artificial Intelligence, Bioimage Analysis -Content type: Slide +Content type: Slides [https://github.com/esgomezm/NEUBIAS_chapter_DL_2020](https://github.com/esgomezm/NEUBIAS_chapter_DL_2020) @@ -647,7 +666,7 @@ Licensed UNKNOWN Tags: Neubias, Cellprofiler, Bioimage Analysis -Content type: Slide +Content type: Slides [https://github.com/ahklemm/CellProfiler_Introduction](https://github.com/ahklemm/CellProfiler_Introduction) @@ -698,7 +717,7 @@ Licensed CC-BY-4.0 Tags: Research Data Management, Bioimage Analysis, Nfdi4Bioimage -Content type: Slide +Content type: Slides [https://f1000research.com/slides/12-1054](https://f1000research.com/slides/12-1054) @@ -722,6 +741,27 @@ Content type: Publication [https://onlinelibrary.wiley.com/doi/full/10.1111/jmi.13192](https://onlinelibrary.wiley.com/doi/full/10.1111/jmi.13192) +--- + +## Checklists for publishing images and image analysis + +Christopher Schmied + +Published 2023-09-14 + +Licensed CC0-1.0 + + + +In this paper we introduce two sets of checklists. One is concerned with the publication of images. The other one gives instructions for the publication of image analysis. + +Tags: Bioimage Analysis + +Content type: Forum Post + +[https://forum.image.sc/t/checklists-for-publishing-images-and-image-analysis/86304](https://forum.image.sc/t/checklists-for-publishing-images-and-image-analysis/86304) + + --- ## Chris Halvin YouTube channel @@ -760,7 +800,7 @@ Tags: Artificial Intelligence, Bioimage Analysis Beth Cimini et al. -Licensed BSD LICENSE +Licensed BSD-3-CLAUSE @@ -859,7 +899,7 @@ Licensed UNKNOWN Tags: Artificial Intelligence, Bioimage Analysis -Content type: Slide, Notebook +Content type: Slides, Notebook [https://github.com/JLrumberger/DL-MBL-2021](https://github.com/JLrumberger/DL-MBL-2021) @@ -891,7 +931,7 @@ Licensed CC-BY-SA-4.0 -Tags: Research Data Management, Bioimage Analysis, Image Data Management, Open Science +Tags: Research Data Management, Bioimage Analysis, Open Science Content type: Slides, Presentation @@ -972,6 +1012,27 @@ Content type: Presentation [https://drive.google.com/file/d/1pPVUUMi5w2Ojw_SaBzSQVaXUuIKtQ7Ma/view](https://drive.google.com/file/d/1pPVUUMi5w2Ojw_SaBzSQVaXUuIKtQ7Ma/view) +--- + +## Dr Guillaume Jacquemet on studying cancer cell metastasis in the era of deep learning for microscopy + +Guillaume Jacquemet + +Published 2024-10-24 + +Licensed UNKNOWN + + + +Leukocyte extravasation is a critical component of the innate immune response, while circulating tumour cell extravasation is a crucial step in metastasis formation. Despite their importance, these extravasation mechanisms remain incompletely understood. In this talk, Guillaume Jacquemet presents a novel imaging framework that integrates microfluidics with high-speed, label-free imaging to study the arrest of pancreatic cancer cells (PDAC) on human endothelial layers under physiological flow conditions. + +Tags: Artificial Intelligence, Bioimage Analysis + +Content type: Video, Slides + +[https://www.youtube.com/watch?v=KTdZBgSCYJQ](https://www.youtube.com/watch?v=KTdZBgSCYJQ) + + --- ## EDAM-bioimaging - The ontology of bioimage informatics operations, topics, data, and formats @@ -1050,7 +1111,7 @@ Licensed YOUTUBE STANDARD LICENSE -Tags: Image Data Management, OMERO, Bioimage Analysis +Tags: OMERO, Bioimage Analysis Content type: Video, Presentation @@ -1137,7 +1198,7 @@ Licensed BSD-3-CLAUSE Napari-ndev is a collection of widgets intended to serve any person seeking to process microscopy images from start to finish. The goal of this example pipeline is to get the user familiar with working with napari-ndev for batch processing and reproducibility (view Image Utilities and Workflow Widget). -Tags: Napari, Microscopy Image Analysis, Bioimage Analysis +Tags: Napari, Bioimage Analysis Content type: Documentation, Github Repository, Tutorial @@ -1161,6 +1222,25 @@ Content type: Collection, Video [https://www.youtube.com/watch?v=8zd4KTy-oYI&list=PLW-oxncaXRqU4XqduJzwFHvWLF06PvdVm](https://www.youtube.com/watch?v=8zd4KTy-oYI&list=PLW-oxncaXRqU4XqduJzwFHvWLF06PvdVm) +--- + +## FAIRy deep-learning for bioImage analysis + +Estibaliz Gómez de Mariscal + +Licensed CC-BY-4.0 + + + +Introduction to FAIR deep learning. Furthermore, tools to deploy trained DL models (deepImageJ), easily train and evaluate them (ZeroCostDL4Mic and DeepBacs) ensure reproducibility (DL4MicEverywhere), and share this technology in an open-source and reproducible manner (BioImage Model Zoo) are introduced. + +Tags: Artificial Intelligence, FAIR-Principles, Bioimage Analysis + +Content type: Slides + +[https://f1000research.com/slides/13-147](https://f1000research.com/slides/13-147) + + --- ## Feature extraction in napari @@ -1171,7 +1251,7 @@ Mara Lampert Tags: Python, Napari, Bioimage Analysis -Content type: Blog +Content type: Blog Post [https://focalplane.biologists.com/2023/05/03/feature-extraction-in-napari/](https://focalplane.biologists.com/2023/05/03/feature-extraction-in-napari/) @@ -1269,7 +1349,7 @@ Licensed CC-BY-4.0 Tags: Python, Bioimage Analysis, Artificial Intelligence -Content type: Slide +Content type: Slides [https://f1000research.com/slides/12-971](https://f1000research.com/slides/12-971) @@ -1341,7 +1421,7 @@ Overview about decision making and how to influence decisions in the bio-image a Tags: Bioimage Analysis -Content type: Slide, Presentation +Content type: Slides, Presentation [https://f1000research.com/slides/11-746](https://f1000research.com/slides/11-746) @@ -1360,13 +1440,34 @@ Licensed BSD-3-CLAUSE Course and material for the clEsperanto workshop presented at I2K 2024 @ Human Technopol (Milan, Italy). The workshop is an hands-on demo of the clesperanto project, focussing on how to use the library for users who want use GPU-acceleration for their Image Processing pipeline. -Tags: Clesperanto, Training, Bioimage Analysis, Notebooks, Workflow +Tags: Bioimage Analysis Content type: Github Repository, Workshop, Tutorial, Notebook [https://github.com/StRigaud/clesperanto_workshop_I2K24?tab=readme-ov-file](https://github.com/StRigaud/clesperanto_workshop_I2K24?tab=readme-ov-file) +--- + +## I2K2024 workshop material - Lazy Parallel Processing and Visualization of Large Data with ImgLib2, BigDataViewer, the N5-API, and Spark + +Stephan Saalfeld, Tobias Pietzsch + +Published None + +Licensed APACHE-2.0 + + + +Tags: Bioimage Analysis + +Content type: Workshop, Notebook, Github Repository + +[https://saalfeldlab.github.io/i2k2024-lazy-workshop/](https://saalfeldlab.github.io/i2k2024-lazy-workshop/) + +[https://github.com/saalfeldlab/i2k2024-lazy-workshop](https://github.com/saalfeldlab/i2k2024-lazy-workshop) + + --- ## I2K2024(virtual) - Bio-Image Analysis Code Generation @@ -1458,7 +1559,7 @@ Licensed CC-BY-4.0 This lesson shows how to use Python and scikit-image to do basic image processing. -Tags: Segmentation, Bioimage Analysis, Training, Python, Scikit-Image, Image Segmentation +Tags: Bioimage Analysis, Python Content type: Tutorial, Workflow @@ -1494,7 +1595,7 @@ Licensed UNKNOWN Tags: Bioimage Analysis -Content type: Slide +Content type: Slides [https://docs.google.com/presentation/d/1WG_4307XmKsGfWT3taxMvX2rZiG1k0SM1E7SAENJQkI/edit#slide=id.p](https://docs.google.com/presentation/d/1WG_4307XmKsGfWT3taxMvX2rZiG1k0SM1E7SAENJQkI/edit#slide=id.p) @@ -1547,7 +1648,7 @@ Licensed UNKNOWN Tags: Neubias, Imagej Macro, Bioimage Analysis -Content type: Slide, Code +Content type: Slides, Code [https://github.com/ahklemm/ImageJMacro_Introduction](https://github.com/ahklemm/ImageJMacro_Introduction) @@ -1564,7 +1665,7 @@ Licensed BSD-3-CLAUSE Tags: Neubias, Imagej, Bioimage Analysis -Content type: Slide +Content type: Slides [https://git.mpi-cbg.de/rhaase/lecture_imagej2_dev](https://git.mpi-cbg.de/rhaase/lecture_imagej2_dev) @@ -1628,7 +1729,7 @@ Licensed UNKNOWN Tags: Imagej, Bioimage Analysis -Content type: Slide +Content type: Slides [https://git.mpi-cbg.de/scicomp/bioimage_team/coursematerialimageanalysis/tree/master/ImageJMacro_24h_2017-01](https://git.mpi-cbg.de/scicomp/bioimage_team/coursematerialimageanalysis/tree/master/ImageJMacro_24h_2017-01) @@ -1645,7 +1746,7 @@ Licensed UNKNOWN Tags: Neubias, Bioimage Analysis -Content type: Slide +Content type: Slides [https://docs.google.com/presentation/d/1q8q1xE-c35tvCsRXZay98s2UYWwXpp0cfCljBmMFpco/edit#slide=id.ga456d5535c_2_53](https://docs.google.com/presentation/d/1q8q1xE-c35tvCsRXZay98s2UYWwXpp0cfCljBmMFpco/edit#slide=id.ga456d5535c_2_53) @@ -1662,7 +1763,7 @@ Slides, scripts, data and other exercise materials of the BioImage Analysis lect Tags: Imagej, Bioimage Analysis -Content type: Slide +Content type: Slides [https://git.mpi-cbg.de/rhaase/lecture_applied_bioimage_analysis](https://git.mpi-cbg.de/rhaase/lecture_applied_bioimage_analysis) @@ -1679,7 +1780,7 @@ Licensed UNKNOWN Tags: Artificial Intelligence, Bioimage Analysis -Content type: Slide +Content type: Slides [https://raw.githubusercontent.com/esgomezm/esgomezm.github.io/master/assets/pdf/SPAOM2018/MachineLearning_SPAOMworkshop_public.pdf](https://raw.githubusercontent.com/esgomezm/esgomezm.github.io/master/assets/pdf/SPAOM2018/MachineLearning_SPAOMworkshop_public.pdf) @@ -1711,7 +1812,7 @@ Licensed UNKNOWN Tags: Neubias, Artificial Intelligence, Bioimage Analysis -Content type: Slide +Content type: Slides [https://docs.google.com/presentation/d/1oJoy9gHmUuSmUwCkPs_InJf_WZAzmLlUNvK1FUEB4PA/edit#slide=id.ge3a24e733b_0_54](https://docs.google.com/presentation/d/1oJoy9gHmUuSmUwCkPs_InJf_WZAzmLlUNvK1FUEB4PA/edit#slide=id.ge3a24e733b_0_54) @@ -1728,7 +1829,7 @@ Licensed CC-BY-4.0 -Tags: Bioimage Analysis, Open Science, Microscopy Image Analysis, Microscopy +Tags: Bioimage Analysis, Open Science, Microscopy Content type: Publication @@ -1779,7 +1880,7 @@ Licensed CC-BY-4.0 Tags: Bioimage Analysis -Content type: Online Tutorial, Video, Slide +Content type: Online Tutorial, Video, Slides [https://www.ebi.ac.uk/training/events/methods-bioimage-analysis/](https://www.ebi.ac.uk/training/events/methods-bioimage-analysis/) @@ -1798,7 +1899,7 @@ Licensed UNKNOWN The mission of Metrics Reloaded is to guide researchers in the selection of appropriate performance metrics for biomedical image analysis problems, as well as provide a comprehensive online resource for metric-related information and pitfalls -Tags: Bioimage Analysis, Image Segmentation, Machine Learning +Tags: Bioimage Analysis, Quality Control Content type: Website, Collection @@ -1824,7 +1925,7 @@ Overview of Segment Anythign for Microscopy given at the SWISSBIAS online meetin Talk about vision foundation models and Segment Anything for Microscopy given at Human Technopole as part of the EMBO Deep Learning Course in May 2024 -Tags: Image Segmentation, Bioimage Analysis, Deep Learning +Tags: Bioimage Analysis, Artificial Intelligence Content type: Slides @@ -1833,6 +1934,46 @@ Content type: Slides [https://doi.org/10.5281/zenodo.11265038](https://doi.org/10.5281/zenodo.11265038) +--- + +## Microscopy data analysis: machine learning and the BioImage Archive + +Andrii Iudin, Anna Foix-Romero, Anna Kreshuk, Awais Athar, Beth Cimini, Dominik Kutra, Estibalis Gomez de Mariscal, Frances Wong, Guillaume Jacquemet, Kedar Narayan, Martin Weigert, Nodar Gogoberidze, Osman Salih, Petr Walczysko, Ryan Conrad, Simone Weyend, Sriram Sundar Somasundharam, Suganya Sivagurunathan, Ugis Sarkans + +Licensed CC-BY-4.0 + + + +The Microscopy data analysis: machine learning and the BioImage Archive course, which focused on introducing programmatic approaches used in the analysis of bioimage data via the BioImage Archive, ran in May 2023. + +Tags: Bioimage Analysis, Python, Artificial Intelligence + +Content type: Video, Slides + +[https://www.ebi.ac.uk/training/materials/microscopy-data-analysis-machine-learning-and-the-bioimage-archive-materials/](https://www.ebi.ac.uk/training/materials/microscopy-data-analysis-machine-learning-and-the-bioimage-archive-materials/) + + +--- + +## Modular training resources for bioimage analysis + +Christian Tischer, Antonio Politi, Tim-Oliver Buchholz, Elnaz Fazeli, Nicola Gritti, Aliaksandr Halavatyi, Sebastian Gonzalez Tirado, Julian Hennies, Toby Hodges, Arif Khan, Dominik Kutra, Stefania Marcotti, Bugra Oezdemir, Felix Schneider, Martin Schorb, Anniek Stokkermans, Yi Sun, Nima Vakili + +Published 2024-12-03 + +Licensed CC-BY-4.0 + + + +Resources for teaching/preparing to teach bioimage analysis + +Tags: Neubias, Bioimage Analysis + +[https://zenodo.org/records/14264885](https://zenodo.org/records/14264885) + +[https://doi.org/10.5281/zenodo.14264885](https://doi.org/10.5281/zenodo.14264885) + + --- ## MorphoLibJ documentation @@ -1860,7 +2001,7 @@ Lecture slides of a session on Multiview Fusion in Fiji Tags: Neubias, Imagej, Bioimage Analysis -Content type: Slide +Content type: Slides [https://git.mpi-cbg.de/rhaase/lecture_multiview_registration](https://git.mpi-cbg.de/rhaase/lecture_multiview_registration) @@ -1871,13 +2012,13 @@ Content type: Slide Shanghang Zhang, Gaole Dai, Tiejun Huang, Jianxu Chen -Licensed ['CC-BY-NC-SA'] +Licensed CC-BY-NC-SA Multimodal large language models have been recognized as a historical milestone in the field of artificial intelligence and have demonstrated revolutionary potentials not only in commercial applications, but also for many scientific fields. Here we give a brief overview of multimodal large language models through the lens of bioimage analysis and discuss how we could build these models as a community to facilitate biology research -Tags: Bioimage Analysis, Large Language Models, FAIR-Principles, Workflow +Tags: Bioimage Analysis, FAIR-Principles, Workflow Content type: Publication @@ -1898,7 +2039,7 @@ Licensed CC-BY-4.0 Material for the I2K 2024 "Multiplexed tissue imaging - tools and approaches" workshop -Tags: Bioimage Analysis, Microscopy Image Analysis +Tags: Bioimage Analysis Content type: Github Repository, Slides, Workshop @@ -1934,7 +2075,7 @@ Licensed UNKNOWN Tags: Neubias, Bioimage Analysis -Content type: Slide, Code, Notebook +Content type: Slides, Code, Notebook [https://github.com/miura/NEUBIAS_AnalystSchool2018](https://github.com/miura/NEUBIAS_AnalystSchool2018) @@ -1951,7 +2092,7 @@ Licensed UNKNOWN Tags: Neubias, Bioimage Analysis -Content type: Slide, Tutorial +Content type: Slides, Tutorial [https://github.com/miura/NEUBIAS_Bioimage_Analyst_Course2017](https://github.com/miura/NEUBIAS_Bioimage_Analyst_Course2017) @@ -1968,7 +2109,7 @@ Licensed UNKNOWN Tags: Neubias, Bioimage Analysis -Content type: Slide, Code, Notebook +Content type: Slides, Code, Notebook [https://github.com/miura/NEUBIAS_AnalystSchool2019](https://github.com/miura/NEUBIAS_AnalystSchool2019) @@ -1985,7 +2126,7 @@ Licensed UNKNOWN Tags: Neubias, Bioimage Analysis -Content type: Slide, Code, Notebook +Content type: Slides, Code, Notebook [https://github.com/miura/NEUBIAS_AnalystSchool2020](https://github.com/miura/NEUBIAS_AnalystSchool2020) @@ -2078,11 +2219,30 @@ Licensed UNKNOWN Tags: Python, Neubias, Artificial Intelligence, Bioimage Analysis -Content type: Slide, Notebook +Content type: Slides, Notebook [https://github.com/maweigert/neubias_academy_stardist](https://github.com/maweigert/neubias_academy_stardist) +--- + +## NeubiasPasteur2023_AdvancedCellPose + +Gaelle Letort + +Licensed BSD-3-CLAUSE + + + +Tutorial for running CellPose advanced functions + +Tags: Bioimage Analysis, Artificial Intelligence + +Content type: Github Repository + +[https://github.com/gletort/NeubiasPasteur2023_AdvancedCellPose](https://github.com/gletort/NeubiasPasteur2023_AdvancedCellPose) + + --- ## OMERO - HCS analysis pipeline using Jupyter Notebooks @@ -2108,7 +2268,7 @@ Content type: Github Repository Rémy Jean Daniel Dornier -Licensed ['CC-BY-NC-SA-4.0'] +Licensed CC-BY-NC-SA-4.0 @@ -2154,7 +2314,7 @@ Licensed GPL-3.0 I2K 2024 workshop materials for "Object Tracking and Track Analysis using TrackMate and CellTracksColab" -Tags: Bioimage Analysis, Training +Tags: Bioimage Analysis Content type: Github Repository, Tutorial, Workshop, Slides @@ -2233,7 +2393,7 @@ Mara Lampert Tags: Python, Jupyter, Bioimage Analysis, Prompt Engineering, Biabob -Content type: Blog +Content type: Blog Post [https://focalplane.biologists.com/2024/07/18/prompt-engineering-in-bio-image-analysis/](https://focalplane.biologists.com/2024/07/18/prompt-engineering-in-bio-image-analysis/) @@ -2307,7 +2467,7 @@ Slides from the CZI/EOSS online meeting in December 2020. Tags: Bioimage Analysis -Content type: Slide +Content type: Slides [https://zenodo.org/records/4328911](https://zenodo.org/records/4328911) @@ -2324,7 +2484,7 @@ Mara Lampert Tags: Python, Napari, Bioimage Analysis -Content type: Blog +Content type: Blog Post [https://focalplane.biologists.com/2023/04/13/quality-assurance-of-segmentation-results/](https://focalplane.biologists.com/2023/04/13/quality-assurance-of-segmentation-results/) @@ -2358,7 +2518,7 @@ Mara Lampert Tags: Python, Napari, Bioimage Analysis -Content type: Blog +Content type: Blog Post [https://focalplane.biologists.com/2023/03/02/rescaling-images-and-pixel-anisotropy/](https://focalplane.biologists.com/2023/03/02/rescaling-images-and-pixel-anisotropy/) @@ -2375,7 +2535,7 @@ Licensed CC-BY-4.0 Tags: Python, Artificial Intelligence, Bioimage Analysis -Content type: Blog +Content type: Blog Post [https://biapol.github.io/blog/marcelo_zoccoler/omero_scripts/readme.html](https://biapol.github.io/blog/marcelo_zoccoler/omero_scripts/readme.html) @@ -2422,7 +2582,7 @@ Content type: Code Ziv Yaniv et al. -Licensed APACHE-2.0 LICENSE +Licensed APACHE-2.0 @@ -2498,7 +2658,7 @@ Licensed CC-BY-4.0 The Open Microscopy Environment (OME) defines a data model and a software implementation to serve as an informatics framework for imaging in biological microscopy experiments, including representation of acquisition parameters, annotations and image analysis results. -Tags: Microscopy Image Analysis, Bioimage Analysis +Tags: Bioimage Analysis Content type: Publication @@ -2507,6 +2667,27 @@ Content type: Publication [https://doi.org/10.1186/gb-2005-6-5-r47](https://doi.org/10.1186/gb-2005-6-5-r47) +--- + +## Towards community-driven metadata standards for light microscopy - tiered specifications extending the OME model + +Mathias Hammer, Maximiliaan Huisman, Alessandro Rigano, Ulrike Boehm, James J. Chambers, et al. + +Published 2022-07-10 + +Licensed UNKNOWN + + + +Rigorous record-keeping and quality control are required to ensure the quality, reproducibility and value of imaging data. The 4DN Initiative and BINA here propose light Microscopy Metadata specifications that extend the OME data model, scale with experimental intent and complexity, and make it possible for scientists to create comprehensive records of imaging experiments. + +Tags: Reproducibility, Bioimage Analysis, Metadata + +Content type: Publication + +[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9271325/](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9271325/) + + --- ## Tracking Theory, TrackMate, and Mastodon @@ -2521,7 +2702,7 @@ Lecture slides of a session on Cell Tracking in Fiji Tags: Neubias, Imagej, Bioimage Analysis -Content type: Slide +Content type: Slides [https://git.mpi-cbg.de/rhaase/lecture_tracking_trackmate](https://git.mpi-cbg.de/rhaase/lecture_tracking_trackmate) @@ -2536,7 +2717,7 @@ Mara Lampert Tags: Python, Napari, Bioimage Analysis -Content type: Blog +Content type: Blog Post [https://focalplane.biologists.com/2023/06/01/tracking-in-napari/](https://focalplane.biologists.com/2023/06/01/tracking-in-napari/) @@ -2585,7 +2766,7 @@ Licensed BSD3-CLAUSE -Tags: Segmentation, Bioimage Analysis, Training +Tags: Bioimage Analysis Content type: Workshop, Github Repository, Tutorial @@ -2623,7 +2804,7 @@ Licensed UNKNOWN -Tags: Bioimage Analysis, Microscopy Image Analysis +Tags: Bioimage Analysis Content type: Collection, Event, Forum Post, Workshop @@ -2642,7 +2823,7 @@ Licensed CC-BY-4.0 Glittr.org is a platform that aggregates and indexes training materials on computational life sciences from public git repositories, making it easier for users to find, compare, and analyze these resources based on various metrics. By providing insights into the availability of materials, collaboration patterns, and licensing practices, Glittr.org supports adherence to the FAIR principles, benefiting the broader life sciences community. -Tags: Training, Bioimage Analysis, Research Data Management +Tags: Bioimage Analysis, Research Data Management Content type: Publication, Preprint @@ -2699,7 +2880,7 @@ Licensed UNKNOWN Tags: Neubias, Bioimage Analysis -Content type: Slide +Content type: Slides [https://www.dropbox.com/s/5abw3cvxrhpobg4/20220923_DefragmentationTS.pdf?dl=0](https://www.dropbox.com/s/5abw3cvxrhpobg4/20220923_DefragmentationTS.pdf?dl=0) @@ -2735,7 +2916,7 @@ Licensed BSD-3-CLAUSE Tags: Neubias, Imagej, Bioimage Analysis -Content type: Slide +Content type: Slides [https://git.mpi-cbg.de/rhaase/lecture_working_with_objects_in_2d_and_3d](https://git.mpi-cbg.de/rhaase/lecture_working_with_objects_in_2d_and_3d) @@ -2752,7 +2933,7 @@ Licensed BSD-3-CLAUSE Tags: Neubias, Imagej, Bioimage Analysis -Content type: Slide +Content type: Slides [https://git.mpi-cbg.de/rhaase/lecture_working_with_pixels](https://git.mpi-cbg.de/rhaase/lecture_working_with_pixels) @@ -2786,7 +2967,7 @@ Licensed MIT This repository offer access to teaching material and useful resources for the YMIA - Python-Based Event Series. -Tags: Python, Large Language Models, Prompt Engineering, Biabob, Bioimage Analysis, Microscopy Image Analysis +Tags: Python, Artifical Intelligence, Bioimage Analysis Content type: Github Repository, Slides @@ -2820,7 +3001,7 @@ Licensed UNKNOWN Tags: Artificial Intelligence, Bioimage Analysis -Content type: Slide +Content type: Slides [https://github.com/esgomezm/zidas2020_intro_DL](https://github.com/esgomezm/zidas2020_intro_DL) @@ -2930,7 +3111,7 @@ Licensed CC-BY-4.0 Tags: Artificial Intelligence, Bioimage Analysis -Content type: Slide +Content type: Slides [https://zenodo.org/doi/10.5281/zenodo.4330625](https://zenodo.org/doi/10.5281/zenodo.4330625) @@ -2964,7 +3145,7 @@ JanClusmann, Tim Lenz Published 2024-11-08T08:32:03+00:00 -Licensed GNU GENERAL PUBLIC LICENSE V3.0 +Licensed GPL-3.0 @@ -3006,11 +3187,11 @@ Content type: Code ## scanpy-tutorials -Alex Wolf, pre-commit-ci[bot], Philipp A., Isaac Virshup, Ilan Gold, Giovanni Palla, Fidel Ramirez, Gökçen Eraslan, Sergei Rybakov, Abolfazl (Abe), Adam Gayoso, Dinesh Palli, Gregor Sturm, Jan Lause, Karin Hrovatin, Krzysztof Polanski, RaphaelBuzzi, Yimin Zheng, Yishen Miao, evanbiederstedt +Alex Wolf, pre-commit-ci[bot], Philipp A., Isaac Virshup, Ilan Gold, Giovanni Palla, Fidel Ramirez, G\xF6k\xE7en Eraslan, Sergei Rybakov, Abolfazl (Abe), Adam Gayoso, Dinesh Palli, Gregor Sturm, Jan Lause, Karin Hrovatin, Krzysztof Polanski, RaphaelBuzzi, Yimin Zheng, Yishen Miao, evanbiederstedt Published 2018-12-16T03:42:46+00:00 -Licensed BSD-3 +Licensed BSD-3-CLAUSE diff --git a/_sources/tags/bioimage_data.md b/_sources/tags/bioimage_data.md deleted file mode 100644 index 1e69e2cb..00000000 --- a/_sources/tags/bioimage_data.md +++ /dev/null @@ -1,405 +0,0 @@ -# Bioimage data (20) -## Bio-Image Data Strudel for Workshop on Research Data Management in TU Dresden Core Facilities - -Cornelia Wetzker - -Published 2023-11-08 - -Licensed CC-BY-4.0 - - - -This presentation gives a short outline of the complexity of data and metadata in the bioimaging universe. It introduces NFDI4BIOIMAGE as a newly formed consortium as part of the German 'Nationale Forschungsdateninfrastruktur' (NFDI) and its goals and tools for data management including its current members on TU Dresden campus.   - -Tags: Research Data Management, Tu Dresden, Bioimage Data, Nfdi4Bioimage - -Content type: Slide - -[https://zenodo.org/records/10083555](https://zenodo.org/records/10083555) - -[https://doi.org/10.5281/zenodo.10083555](https://doi.org/10.5281/zenodo.10083555) - - ---- - -## BioFormats Command line (CLI) tools - -Published 2024-10-24 - -Licensed CC-BY-4.0 - - - -Bio-Formats is a standalone Java library for reading and writing life sciences image file formats. There are several scripts for using Bio-Formats on the command line, which are listed here. - -Tags: Bioimage Data - -Content type: Documentation - -[https://bio-formats.readthedocs.io/en/v8.0.0/users/comlinetools/index.html](https://bio-formats.readthedocs.io/en/v8.0.0/users/comlinetools/index.html) - - ---- - -## Checklists for publishing images and image analysis - -Christopher Schmied - -Published 2023-09-14 - -Licensed CC0-1.0 - - - -In this paper we introduce two sets of checklists. One is concerned with the publication of images. The other one gives instructions for the publication of image analysis. - -Tags: Bioimage Data, Microscopy Image Analysis - -Content type: Forum Post - -[https://forum.image.sc/t/checklists-for-publishing-images-and-image-analysis/86304](https://forum.image.sc/t/checklists-for-publishing-images-and-image-analysis/86304) - - ---- - -## FAIR High Content Screening in Bioimaging - -Rohola Hosseini, Matthijs Vlasveld, Joost Willemse, Bob van de Water, Sylvia E. Le Dévédec, Katherine J. Wolstencroft - -Published 2023-07-17 - -Licensed CC-BY-4.0 - - - -The authors show the utility of Minimum Information for High Content Screening Microscopy Experiments (MIHCSME) for High Content Screening (HCS) data using multiple examples from the Leiden FAIR Cell Observatory, a Euro-Bioimaging flagship node for high content screening and the pilot node for implementing FAIR bioimaging data throughout the Netherlands Bioimaging network. - -Tags: FAIR-Principles, Metadata, Research Data Management, Image Data Management, Bioimage Data - -Content type: Publication - -[https://www.nature.com/articles/s41597-023-02367-w](https://www.nature.com/articles/s41597-023-02367-w) - - ---- - -## I3D bio – Information Infrastructure for BioImage Data - Bioimage Metadata - -Christian Schmidt - -Licensed UNKNOWN - - - -A Microscopy Research Data Management Resource. - -Tags: Metadata, I3Dbio, Research Data Management, Bioimage Data - -Content type: Collection - -[https://gerbi-gmb.de/i3dbio/i3dbio-rdm/i3dbio-bioimage-metadata/](https://gerbi-gmb.de/i3dbio/i3dbio-rdm/i3dbio-bioimage-metadata/) - - ---- - -## KNIME Image Processing - -None - -Licensed GPLV3 - - - -The KNIME Image Processing Extension allows you to read in more than 140 different kinds of images and to apply well known methods on images, like preprocessing. segmentation, feature extraction, tracking and classification in KNIME. - -Tags: Imagej, OMERO, Bioimage Data, Workflow - -Content type: Tutorial, Online Tutorial, Documentation - -[https://www.knime.com/community/image-processing](https://www.knime.com/community/image-processing) - - ---- - -## Microscopy-BIDS - An Extension to the Brain Imaging Data Structure for Microscopy Data - -Marie-Hélène Bourget, Lee Kamentsky, Satrajit S. Ghosh, Giacomo Mazzamuto, Alberto Lazari, et al. - -Published 2022-04-19 - -Licensed CC-BY-4.0 - - - -The Brain Imaging Data Structure (BIDS) is a specification for organizing, sharing, and archiving neuroimaging data and metadata in a reusable way. - -Tags: Research Data Management, Image Data Management, Bioimage Data - -Content type: Publication - -[https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2022.871228/full](https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2022.871228/full) - - ---- - -## NFDI4BIOIMAGE - An Initiative for a National Research Data Infrastructure for Microscopy Data - -Christian Schmidt, Elisa Ferrando-May - -Published 2021-04-29 - -Licensed CCY-BY-SA-4.0 - - - -Align existing and establish novel services & solutions for data management tasks throughout the bioimage data lifecycle. - -Tags: Nfdi4Bioimage, Image Data Management, Bioimage Data, Research Data Management - -Content type: Conference Abstract, Slide - -[https://doi.org/10.11588/heidok.00029489](https://doi.org/10.11588/heidok.00029489) - - ---- - -## NGFF Converter - -Licensed GPL-2.0 - - - -An easy to use and open source converter for bioimaging data. NGFF-Converter is a GUI application for conversion of bioimage formats into OME-NGFF (Next-Generation File Format) or OME-TIFF. - -Tags: Bioimage Data, Open Source Software - -Content type: Application - -[https://www.glencoesoftware.com/products/ngff-converter/](https://www.glencoesoftware.com/products/ngff-converter/) - - ---- - -## Open Micoscropy Environment (OME) Youtube Channel - -Published None - -Licensed CC-BY-4.0 - - - -OME develops open-source software and data format standards for the storage and manipulation of biological microscopy data - -Tags: Open Source Software, Microscopy Image Analysis, Bioimage Data - -Content type: Video, Collection - -[https://www.youtube.com/@OpenMicroscopyEnvironment](https://www.youtube.com/@OpenMicroscopyEnvironment) - - ---- - -## REMBI - Recommended Metadata for Biological Images—enabling reuse of microscopy data in biology - -Ugis Sarkans, Wah Chiu, Lucy Collinson, Michele C. Darrow, Jan Ellenberg, David Grunwald, et al. - -Published 2021-05-21 - -Licensed UNKNOWN - - - -Bioimaging data have significant potential for reuse, but unlocking this potential requires systematic archiving of data and metadata in public databases. The authors propose draft metadata guidelines to begin addressing the needs of diverse communities within light and electron microscopy. - -Tags: Metadata, Bioimage Data, Image Data Management, Research Data Management - -Content type: Publication - -[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8606015/](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8606015/) - -[https://www.nature.com/articles/s41592-021-01166-8](https://www.nature.com/articles/s41592-021-01166-8) - -[https://doi.org/10.1038/s41592-021-01166-8](https://doi.org/10.1038/s41592-021-01166-8) - - ---- - -## REMBI Overview - -Licensed CC0-1.0 - - - -Recommended Metadata for Biological Images (REMBI) provides guidelines for metadata for biological images to enable the FAIR sharing of scientific data. - -Tags: FAIR-Principles, Metadata, Image Data Management, Research Data Management, Bioimage Data - -Content type: Collection - -[https://www.ebi.ac.uk/bioimage-archive/rembi-help-overview/](https://www.ebi.ac.uk/bioimage-archive/rembi-help-overview/) - - ---- - -## Reporting and reproducibility in microscopy - -Published 2021-12-03 - -Licensed UNKNOWN - - - -This Focus issue features a series of papers offering guidelines and tools for improving the tracking and reporting of microscopy metadata with an emphasis on reproducibility and data re-use. - -Tags: Reproducibility, Metadata, Bioimage Data - -Content type: Collection - -[https://www.nature.com/collections/djiciihhjh](https://www.nature.com/collections/djiciihhjh) - - ---- - -## Research data management for bioimaging - the 2021 NFDI4BIOIMAGE community survey - -Christian Schmidt, Janina Hanne, Josh Moore, Christian Meesters, Elisa Ferrando-May, et al. - -Published 2022-09-20 - -Licensed CC-BY-4.0 - - - -As an initiative within Germany's National Research Data Infrastructure, the authors conducted this community survey in summer 2021 to assess the state of the art of bioimaging RDM and the community needs. - -Tags: Research Data Management, Image Data Management, Bioimage Data - -Content type: Publication - -[https://f1000research.com/articles/11-638/v2](https://f1000research.com/articles/11-638/v2) - - ---- - -## Submitting data to the BioImage Archive - -Licensed CC0-1.0 - - - -To submit, you’ll need to register an account, organise and upload your data, prepare a file list, and then submit using our web submission form. These steps are explained here. - -Tags: Research Data Management, Image Data Management, Bioimage Data - -Content type: Tutorial, Video - -[https://www.ebi.ac.uk/bioimage-archive/submit/](https://www.ebi.ac.uk/bioimage-archive/submit/) - - ---- - -## The BioImage Archive – Building a Home for Life-Sciences Microscopy Data - -Matthew Hartley, Gerard J. Kleywegt, Ardan Patwardhan, Ugis Sarkans, Jason R. Swedlow, Alvis Brazma - -Published 2022-06-22 - -Licensed UNKNOWN - - - -The BioImage Archive is a new archival data resource at the European Bioinformatics Institute (EMBL-EBI). - -Tags: Image Data Management, Research Data Management, Bioimage Data - -Content type: Publication - -[https://www.sciencedirect.com/science/article/pii/S0022283622000791?via%3Dihub](https://www.sciencedirect.com/science/article/pii/S0022283622000791?via%3Dihub) - -[https://doi.org/10.1016/j.jmb.2022.167505](https://doi.org/10.1016/j.jmb.2022.167505) - - ---- - -## Towards community-driven metadata standards for light microscopy - tiered specifications extending the OME model - -Mathias Hammer, Maximiliaan Huisman, Alessandro Rigano, Ulrike Boehm, James J. Chambers, et al. - -Published 2022-07-10 - -Licensed UNKNOWN - - - -Rigorous record-keeping and quality control are required to ensure the quality, reproducibility and value of imaging data. The 4DN Initiative and BINA here propose light Microscopy Metadata specifications that extend the OME data model, scale with experimental intent and complexity, and make it possible for scientists to create comprehensive records of imaging experiments. - -Tags: Reproducibility, Microscopy Image Analysis, Metadata, Image Data Management, Bioimage Data - -Content type: Publication - -[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9271325/](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9271325/) - - ---- - -## bioformats2raw Converter - -Melissa Linkert, Chris Allan, Josh Moore, Sébastien Besson, David Gault, et al. - -Licensed GPL-2.0 - - - -Java application to convert image file formats, including .mrxs, to an intermediate Zarr structure compatible with the OME-NGFF specification. - -Tags: Open Source Software, Bioimage Data - -Content type: Application, Github Repository - -[https://github.com/glencoesoftware/bioformats2raw](https://github.com/glencoesoftware/bioformats2raw) - - ---- - -## ome-ngff-validator - -Will Moore, Josh Moore, Yaroslav Halchenko, Sébastien Besson - -Published 2022-09-29 - -Licensed BSD-2-CLAUSE - - - -Web page for validating OME-NGFF files. - -Tags: Bioimage Data - -Content type: Github Repository, Application - -[https://ome.github.io/ome-ngff-validator/](https://ome.github.io/ome-ngff-validator/) - -[https://github.com/ome/ome-ngff-validator](https://github.com/ome/ome-ngff-validator) - - ---- - -## raw2ometiff Converter - -Melissa Linkert, Chris Allan, Sébastien Besson, Josh Moore - -Licensed GPL-2.0 - - - -Java application to convert a directory of tiles to an OME-TIFF pyramid. This is the second half of iSyntax/.mrxs => OME-TIFF conversion. - -Tags: Open Source Software, Bioimage Data - -Content type: Application, Github Repository - -[https://github.com/glencoesoftware/raw2ometiff](https://github.com/glencoesoftware/raw2ometiff) - - ---- - diff --git a/_sources/tags/bioinformatics.md b/_sources/tags/bioinformatics.md index 4cbf44c4..1b753e72 100644 --- a/_sources/tags/bioinformatics.md +++ b/_sources/tags/bioinformatics.md @@ -64,7 +64,7 @@ Licensed CC0 (MOSTLY, BUT CAN DIFFER DEPENDING ON RESOURCE) Online tutorial and webinar library, designed and delivered by EMBL-EBI experts -Tags: Bioinformatics, Training +Tags: Bioinformatics Content type: Collection @@ -132,7 +132,7 @@ Licensed CC-BY-4.0 The authors offer trainers some simple rules, to help make their training materials FAIR, enabling others to find, (re)use, and adapt them. -Tags: Metadata, Bioinformatics, FAIR-Principles, Training +Tags: Metadata, Bioinformatics, FAIR-Principles Content type: Publication diff --git a/_sources/tags/deep_learning.md b/_sources/tags/deep_learning.md deleted file mode 100644 index fcae6696..00000000 --- a/_sources/tags/deep_learning.md +++ /dev/null @@ -1,238 +0,0 @@ -# Deep learning (12) -## AI ML DL in Bioimage Analysis - Webinar - -Yannick KREMPP - -Published 2024-11-14 - -Licensed UNKNOWN - - - -A review of the tools, methods and concepts useful for biologists and life scientists as well as bioimage analysts. - -Tags: Deep Learning, Machine Learning, Artificial Intelligence, Bioimage Analysis, Large Language Models - -Content type: Youtube Video, Slides, Webinar - -[https://www.youtube.com/watch?v=TJXNMIWtdac](https://www.youtube.com/watch?v=TJXNMIWtdac) - - ---- - -## BIDS-lecture-2024 - -Robert Haase - -Licensed CC-BY-4.0 - - - -Training resources for Students at Uni Leipzig who want to dive into bio-image data science with Python. The material developed here between April and July 2024. - -Tags: Bioimage Analysis, Deep Learning, Microscopy Image Analysis, Python - -Content type: Github Repository - -[https://github.com/ScaDS/BIDS-lecture-2024/](https://github.com/ScaDS/BIDS-lecture-2024/) - - ---- - -## Bio-image Data Science - -Robert Haase - -Licensed CC-BY-4.0 - - - -This repository contains training resources for Students at Uni Leipzig who want to dive into bio-image data science with Python. - -Tags: Image Data Management, Deep Learning, Microscopy Image Analysis, Python - -Content type: Notebook - -[https://github.com/ScaDS/BIDS-lecture-2024](https://github.com/ScaDS/BIDS-lecture-2024) - - ---- - -## Bio-image Data Science Lectures @ Uni Leipzig / ScaDS.AI - -Robert Haase - -Licensed CC-BY-4.0 - - - -These are the PPTx training resources for Students at Uni Leipzig who want to dive into bio-image data science with Python. The material developed here between April and July 2024. - -Tags: Bioimage Analysis, Deep Learning, Microscopy Image Analysis, Python - -Content type: Slides - -[https://zenodo.org/records/12623730](https://zenodo.org/records/12623730) - - ---- - -## BioEngine Documentation - -Wei Ouyang, Nanguage, Jeremy Metz, Craig Russell - -Licensed MIT - - - -BioEngine, a Python package designed for flexible deployment and execution of bioimage analysis models and workflows using AI, accessible via HTTP API and RPC. - -Tags: Workflow Engine, Deep Learning, Python - -Content type: Documentation - -[https://bioimage-io.github.io/bioengine/#/](https://bioimage-io.github.io/bioengine/#/) - - ---- - -## BioImage Archive AI Gallery - -Licensed CC0-1.0 - - - -Tags: Bioimage Analysis, Deep Learning - -Content type: Collection, Data - -[https://www.ebi.ac.uk/bioimage-archive/galleries/AI.html](https://www.ebi.ac.uk/bioimage-archive/galleries/AI.html) - - ---- - -## DL4MicEverywhere – Overcoming reproducibility challenges in deep learning microscopy imaging - -Iván Hidalgo-Cenalmor - -Published 2024-07-29 - -Licensed UNKNOWN - - - -Tags: Deep Learning, Microscopy, Microsycopy Image Analysis, Bio Image Analysis, Artifical Intelligence - -Content type: Blog Post - -[https://focalplane.biologists.com/2024/07/29/dl4miceverywhere-overcoming-reproducibility-challenges-in-deep-learning-microscopy-imaging/](https://focalplane.biologists.com/2024/07/29/dl4miceverywhere-overcoming-reproducibility-challenges-in-deep-learning-microscopy-imaging/) - - ---- - -## Dr Guillaume Jacquemet on studying cancer cell metastasis in the era of deep learning for microscopy - -Guillaume Jacquemet - -Published 2024-10-24 - -Licensed UNKNOWN - - - -Leukocyte extravasation is a critical component of the innate immune response, while circulating tumour cell extravasation is a crucial step in metastasis formation. Despite their importance, these extravasation mechanisms remain incompletely understood. In this talk, Guillaume Jacquemet presents a novel imaging framework that integrates microfluidics with high-speed, label-free imaging to study the arrest of pancreatic cancer cells (PDAC) on human endothelial layers under physiological flow conditions. - -Tags: Deep Learning, Microscopy Image Analysis - -Content type: Youtube Video, Slides - -[https://www.youtube.com/watch?v=KTdZBgSCYJQ](https://www.youtube.com/watch?v=KTdZBgSCYJQ) - - ---- - -## FAIRy deep-learning for bioImage analysis - -Estibaliz Gómez de Mariscal - -Licensed CC-BY-4.0 - - - -Introduction to FAIR deep learning. Furthermore, tools to deploy trained DL models (deepImageJ), easily train and evaluate them (ZeroCostDL4Mic and DeepBacs) ensure reproducibility (DL4MicEverywhere), and share this technology in an open-source and reproducible manner (BioImage Model Zoo) are introduced. - -Tags: Deep Learning, FAIR-Principles, Microscopy Image Analysis - -Content type: Slides - -[https://f1000research.com/slides/13-147](https://f1000research.com/slides/13-147) - - ---- - -## Introduction to Deep Learning for Microscopy - -Costantin Pape - -Licensed MIT - - - -This course consists of lectures and exercises that teach the background of deep learning for image analysis and show applications to classification and segmentation analysis problems. - -Tags: Deep Learning, Pytorch, Segmentation, Python - -Content type: Notebook - -[https://github.com/computational-cell-analytics/dl-for-micro](https://github.com/computational-cell-analytics/dl-for-micro) - - ---- - -## MicroSam-Talks - -Constantin Pape - -Published 2024-05-23 - -Licensed CC-BY-4.0 - - - -Talks about Segment Anything for Microscopy: https://github.com/computational-cell-analytics/micro-sam. -Currently contains slides for two talks: - -Overview of Segment Anythign for Microscopy given at the SWISSBIAS online meeting in April 2024 -Talk about vision foundation models and Segment Anything for Microscopy given at Human Technopole as part of the EMBO Deep Learning Course in May 2024 - - -Tags: Image Segmentation, Bioimage Analysis, Deep Learning - -Content type: Slides - -[https://zenodo.org/records/11265038](https://zenodo.org/records/11265038) - -[https://doi.org/10.5281/zenodo.11265038](https://doi.org/10.5281/zenodo.11265038) - - ---- - -## Microscopy data analysis: machine learning and the BioImage Archive - -Andrii Iudin, Anna Foix-Romero, Anna Kreshuk, Awais Athar, Beth Cimini, Dominik Kutra, Estibalis Gomez de Mariscal, Frances Wong, Guillaume Jacquemet, Kedar Narayan, Martin Weigert, Nodar Gogoberidze, Osman Salih, Petr Walczysko, Ryan Conrad, Simone Weyend, Sriram Sundar Somasundharam, Suganya Sivagurunathan, Ugis Sarkans - -Licensed CC-BY-4.0 - - - -The Microscopy data analysis: machine learning and the BioImage Archive course, which focused on introducing programmatic approaches used in the analysis of bioimage data via the BioImage Archive, ran in May 2023. - -Tags: Microscopy Image Analysis, Python, Deep Learning - -Content type: Video, Slides - -[https://www.ebi.ac.uk/training/materials/microscopy-data-analysis-machine-learning-and-the-bioimage-archive-materials/](https://www.ebi.ac.uk/training/materials/microscopy-data-analysis-machine-learning-and-the-bioimage-archive-materials/) - - ---- - diff --git a/_sources/tags/fair-principles.md b/_sources/tags/fair-principles.md index 381ddd14..0b3e4ec3 100644 --- a/_sources/tags/fair-principles.md +++ b/_sources/tags/fair-principles.md @@ -142,7 +142,7 @@ Licensed CC-BY-4.0 The authors show the utility of Minimum Information for High Content Screening Microscopy Experiments (MIHCSME) for High Content Screening (HCS) data using multiple examples from the Leiden FAIR Cell Observatory, a Euro-Bioimaging flagship node for high content screening and the pilot node for implementing FAIR bioimaging data throughout the Netherlands Bioimaging network. -Tags: FAIR-Principles, Metadata, Research Data Management, Image Data Management, Bioimage Data +Tags: FAIR-Principles, Metadata, Research Data Management Content type: Publication @@ -178,7 +178,7 @@ Licensed CC-BY-4.0 Introduction to FAIR deep learning. Furthermore, tools to deploy trained DL models (deepImageJ), easily train and evaluate them (ZeroCostDL4Mic and DeepBacs) ensure reproducibility (DL4MicEverywhere), and share this technology in an open-source and reproducible manner (BioImage Model Zoo) are introduced. -Tags: Deep Learning, FAIR-Principles, Microscopy Image Analysis +Tags: Artificial Intelligence, FAIR-Principles, Bioimage Analysis Content type: Slides @@ -265,13 +265,13 @@ Content type: Publication Shanghang Zhang, Gaole Dai, Tiejun Huang, Jianxu Chen -Licensed ['CC-BY-NC-SA'] +Licensed CC-BY-NC-SA Multimodal large language models have been recognized as a historical milestone in the field of artificial intelligence and have demonstrated revolutionary potentials not only in commercial applications, but also for many scientific fields. Here we give a brief overview of multimodal large language models through the lens of bioimage analysis and discuss how we could build these models as a community to facilitate biology research -Tags: Bioimage Analysis, Large Language Models, FAIR-Principles, Workflow +Tags: Bioimage Analysis, FAIR-Principles, Workflow Content type: Publication @@ -408,7 +408,7 @@ Licensed CC0-1.0 Recommended Metadata for Biological Images (REMBI) provides guidelines for metadata for biological images to enable the FAIR sharing of scientific data. -Tags: FAIR-Principles, Metadata, Image Data Management, Research Data Management, Bioimage Data +Tags: FAIR-Principles, Metadata, Research Data Management Content type: Collection @@ -469,7 +469,7 @@ Licensed CC-BY-4.0 The authors offer trainers some simple rules, to help make their training materials FAIR, enabling others to find, (re)use, and adapt them. -Tags: Metadata, Bioinformatics, FAIR-Principles, Training +Tags: Metadata, Bioinformatics, FAIR-Principles Content type: Publication diff --git a/_sources/tags/image_data_management.md b/_sources/tags/image_data_management.md deleted file mode 100644 index 15bdfb06..00000000 --- a/_sources/tags/image_data_management.md +++ /dev/null @@ -1,283 +0,0 @@ -# Image data management (14) -## BIOMERO - A scalable and extensible image analysis framework - -Torec T. Luik, Rodrigo Rosas-Bertolini, Eric A.J. Reits, Ron A. Hoebe, Przemek M. Krawczyk - -Published None - -Licensed CC-BY-4.0 - - - -The authors introduce BIOMERO (bioimage analysis in OMERO), a bridge connecting OMERO, a renowned bioimaging data management platform, FAIR workflows, and high-performance computing (HPC) environments. - -Tags: OMERO, Workflow, Bioimage Analysis, Image Data Management - -Content type: Publication - -[https://doi.org/10.1016/j.patter.2024.101024](https://doi.org/10.1016/j.patter.2024.101024) - - ---- - -## Bio-image Data Science - -Robert Haase - -Licensed CC-BY-4.0 - - - -This repository contains training resources for Students at Uni Leipzig who want to dive into bio-image data science with Python. - -Tags: Image Data Management, Deep Learning, Microscopy Image Analysis, Python - -Content type: Notebook - -[https://github.com/ScaDS/BIDS-lecture-2024](https://github.com/ScaDS/BIDS-lecture-2024) - - ---- - -## Data management at France BioImaging - -Published 2023-07-05 - -Licensed CC-BY-SA-4.0 - - - -Tags: Research Data Management, Bioimage Analysis, Image Data Management, Open Science - -Content type: Slides, Presentation - -[https://omero-fbi.fr/slides/elmi23_cfd/main.html#/title-slide](https://omero-fbi.fr/slides/elmi23_cfd/main.html#/title-slide) - - ---- - -## Erick Martins Ratamero - Expanding the OME ecosystem for imaging data management | SciPy 2024 - -SciPy, Erick Martins Ratamero - -Published 2024-08-19 - -Licensed YOUTUBE STANDARD LICENSE - - - -Tags: Image Data Management, OMERO, Bioimage Analysis - -Content type: Video, Presentation - -[https://www.youtube.com/watch?v=GmhyDNm1RsM](https://www.youtube.com/watch?v=GmhyDNm1RsM) - - ---- - -## FAIR High Content Screening in Bioimaging - -Rohola Hosseini, Matthijs Vlasveld, Joost Willemse, Bob van de Water, Sylvia E. Le Dévédec, Katherine J. Wolstencroft - -Published 2023-07-17 - -Licensed CC-BY-4.0 - - - -The authors show the utility of Minimum Information for High Content Screening Microscopy Experiments (MIHCSME) for High Content Screening (HCS) data using multiple examples from the Leiden FAIR Cell Observatory, a Euro-Bioimaging flagship node for high content screening and the pilot node for implementing FAIR bioimaging data throughout the Netherlands Bioimaging network. - -Tags: FAIR-Principles, Metadata, Research Data Management, Image Data Management, Bioimage Data - -Content type: Publication - -[https://www.nature.com/articles/s41597-023-02367-w](https://www.nature.com/articles/s41597-023-02367-w) - - ---- - -## Microscopy-BIDS - An Extension to the Brain Imaging Data Structure for Microscopy Data - -Marie-Hélène Bourget, Lee Kamentsky, Satrajit S. Ghosh, Giacomo Mazzamuto, Alberto Lazari, et al. - -Published 2022-04-19 - -Licensed CC-BY-4.0 - - - -The Brain Imaging Data Structure (BIDS) is a specification for organizing, sharing, and archiving neuroimaging data and metadata in a reusable way. - -Tags: Research Data Management, Image Data Management, Bioimage Data - -Content type: Publication - -[https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2022.871228/full](https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2022.871228/full) - - ---- - -## NFDI4BIOIMAGE - An Initiative for a National Research Data Infrastructure for Microscopy Data - -Christian Schmidt, Elisa Ferrando-May - -Published 2021-04-29 - -Licensed CCY-BY-SA-4.0 - - - -Align existing and establish novel services & solutions for data management tasks throughout the bioimage data lifecycle. - -Tags: Nfdi4Bioimage, Image Data Management, Bioimage Data, Research Data Management - -Content type: Conference Abstract, Slide - -[https://doi.org/10.11588/heidok.00029489](https://doi.org/10.11588/heidok.00029489) - - ---- - -## RDM_system_connector - -SaibotMagd - -Licensed UNKNOWN - - - -This tool is intended to link different research data management platforms with each other. - -Tags: Research Data Management, Image Data Management - -Content type: Github Repository - -[https://github.com/SaibotMagd/RDM_system_connector](https://github.com/SaibotMagd/RDM_system_connector) - - ---- - -## REMBI - Recommended Metadata for Biological Images—enabling reuse of microscopy data in biology - -Ugis Sarkans, Wah Chiu, Lucy Collinson, Michele C. Darrow, Jan Ellenberg, David Grunwald, et al. - -Published 2021-05-21 - -Licensed UNKNOWN - - - -Bioimaging data have significant potential for reuse, but unlocking this potential requires systematic archiving of data and metadata in public databases. The authors propose draft metadata guidelines to begin addressing the needs of diverse communities within light and electron microscopy. - -Tags: Metadata, Bioimage Data, Image Data Management, Research Data Management - -Content type: Publication - -[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8606015/](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8606015/) - -[https://www.nature.com/articles/s41592-021-01166-8](https://www.nature.com/articles/s41592-021-01166-8) - -[https://doi.org/10.1038/s41592-021-01166-8](https://doi.org/10.1038/s41592-021-01166-8) - - ---- - -## REMBI Overview - -Licensed CC0-1.0 - - - -Recommended Metadata for Biological Images (REMBI) provides guidelines for metadata for biological images to enable the FAIR sharing of scientific data. - -Tags: FAIR-Principles, Metadata, Image Data Management, Research Data Management, Bioimage Data - -Content type: Collection - -[https://www.ebi.ac.uk/bioimage-archive/rembi-help-overview/](https://www.ebi.ac.uk/bioimage-archive/rembi-help-overview/) - - ---- - -## Research data management for bioimaging - the 2021 NFDI4BIOIMAGE community survey - -Christian Schmidt, Janina Hanne, Josh Moore, Christian Meesters, Elisa Ferrando-May, et al. - -Published 2022-09-20 - -Licensed CC-BY-4.0 - - - -As an initiative within Germany's National Research Data Infrastructure, the authors conducted this community survey in summer 2021 to assess the state of the art of bioimaging RDM and the community needs. - -Tags: Research Data Management, Image Data Management, Bioimage Data - -Content type: Publication - -[https://f1000research.com/articles/11-638/v2](https://f1000research.com/articles/11-638/v2) - - ---- - -## Submitting data to the BioImage Archive - -Licensed CC0-1.0 - - - -To submit, you’ll need to register an account, organise and upload your data, prepare a file list, and then submit using our web submission form. These steps are explained here. - -Tags: Research Data Management, Image Data Management, Bioimage Data - -Content type: Tutorial, Video - -[https://www.ebi.ac.uk/bioimage-archive/submit/](https://www.ebi.ac.uk/bioimage-archive/submit/) - - ---- - -## The BioImage Archive – Building a Home for Life-Sciences Microscopy Data - -Matthew Hartley, Gerard J. Kleywegt, Ardan Patwardhan, Ugis Sarkans, Jason R. Swedlow, Alvis Brazma - -Published 2022-06-22 - -Licensed UNKNOWN - - - -The BioImage Archive is a new archival data resource at the European Bioinformatics Institute (EMBL-EBI). - -Tags: Image Data Management, Research Data Management, Bioimage Data - -Content type: Publication - -[https://www.sciencedirect.com/science/article/pii/S0022283622000791?via%3Dihub](https://www.sciencedirect.com/science/article/pii/S0022283622000791?via%3Dihub) - -[https://doi.org/10.1016/j.jmb.2022.167505](https://doi.org/10.1016/j.jmb.2022.167505) - - ---- - -## Towards community-driven metadata standards for light microscopy - tiered specifications extending the OME model - -Mathias Hammer, Maximiliaan Huisman, Alessandro Rigano, Ulrike Boehm, James J. Chambers, et al. - -Published 2022-07-10 - -Licensed UNKNOWN - - - -Rigorous record-keeping and quality control are required to ensure the quality, reproducibility and value of imaging data. The 4DN Initiative and BINA here propose light Microscopy Metadata specifications that extend the OME data model, scale with experimental intent and complexity, and make it possible for scientists to create comprehensive records of imaging experiments. - -Tags: Reproducibility, Microscopy Image Analysis, Metadata, Image Data Management, Bioimage Data - -Content type: Publication - -[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9271325/](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9271325/) - - ---- - diff --git a/_sources/tags/imagej.md b/_sources/tags/imagej.md index 67ab6bf0..08163150 100644 --- a/_sources/tags/imagej.md +++ b/_sources/tags/imagej.md @@ -79,7 +79,7 @@ Licensed BSD-3-CLAUSE Tags: Neubias, Imagej, Bioimage Analysis -Content type: Slide +Content type: Slides [https://git.mpi-cbg.de/rhaase/lecture_imagej2_dev](https://git.mpi-cbg.de/rhaase/lecture_imagej2_dev) @@ -128,7 +128,7 @@ Licensed UNKNOWN Tags: Imagej, Bioimage Analysis -Content type: Slide +Content type: Slides [https://git.mpi-cbg.de/scicomp/bioimage_team/coursematerialimageanalysis/tree/master/ImageJMacro_24h_2017-01](https://git.mpi-cbg.de/scicomp/bioimage_team/coursematerialimageanalysis/tree/master/ImageJMacro_24h_2017-01) @@ -158,13 +158,13 @@ Content type: Publication, Documentation None -Licensed GPLV3 +Licensed GPL-3.0 The KNIME Image Processing Extension allows you to read in more than 140 different kinds of images and to apply well known methods on images, like preprocessing. segmentation, feature extraction, tracking and classification in KNIME. -Tags: Imagej, OMERO, Bioimage Data, Workflow +Tags: Imagej, OMERO, Workflow Content type: Tutorial, Online Tutorial, Documentation @@ -183,7 +183,7 @@ Slides, scripts, data and other exercise materials of the BioImage Analysis lect Tags: Imagej, Bioimage Analysis -Content type: Slide +Content type: Slides [https://git.mpi-cbg.de/rhaase/lecture_applied_bioimage_analysis](https://git.mpi-cbg.de/rhaase/lecture_applied_bioimage_analysis) @@ -240,7 +240,7 @@ Lecture slides of a session on Multiview Fusion in Fiji Tags: Neubias, Imagej, Bioimage Analysis -Content type: Slide +Content type: Slides [https://git.mpi-cbg.de/rhaase/lecture_multiview_registration](https://git.mpi-cbg.de/rhaase/lecture_multiview_registration) @@ -295,7 +295,7 @@ Lecture slides of a session on Cell Tracking in Fiji Tags: Neubias, Imagej, Bioimage Analysis -Content type: Slide +Content type: Slides [https://git.mpi-cbg.de/rhaase/lecture_tracking_trackmate](https://git.mpi-cbg.de/rhaase/lecture_tracking_trackmate) @@ -312,7 +312,7 @@ Licensed BSD-3-CLAUSE Tags: Neubias, Imagej, Bioimage Analysis -Content type: Slide +Content type: Slides [https://git.mpi-cbg.de/rhaase/lecture_working_with_objects_in_2d_and_3d](https://git.mpi-cbg.de/rhaase/lecture_working_with_objects_in_2d_and_3d) @@ -329,7 +329,7 @@ Licensed BSD-3-CLAUSE Tags: Neubias, Imagej, Bioimage Analysis -Content type: Slide +Content type: Slides [https://git.mpi-cbg.de/rhaase/lecture_working_with_pixels](https://git.mpi-cbg.de/rhaase/lecture_working_with_pixels) diff --git a/_sources/tags/large_language_models.md b/_sources/tags/large_language_models.md deleted file mode 100644 index 6d459f4d..00000000 --- a/_sources/tags/large_language_models.md +++ /dev/null @@ -1,108 +0,0 @@ -# Large language models (5) -## AI ML DL in Bioimage Analysis - Webinar - -Yannick KREMPP - -Published 2024-11-14 - -Licensed UNKNOWN - - - -A review of the tools, methods and concepts useful for biologists and life scientists as well as bioimage analysts. - -Tags: Deep Learning, Machine Learning, Artificial Intelligence, Bioimage Analysis, Large Language Models - -Content type: Youtube Video, Slides, Webinar - -[https://www.youtube.com/watch?v=TJXNMIWtdac](https://www.youtube.com/watch?v=TJXNMIWtdac) - - ---- - -## Bio-image Analysis with the Help of Large Language Models - -Robert Haase - -Published 2024-03-13 - -Licensed CC-BY-4.0 - - - -Large Language Models (LLMs) change the way how we use computers. This also has impact on the bio-image analysis community. We can generate code that analyzes biomedical image data if we ask the right prompts. This talk outlines introduces basic principles, explains prompt engineering and how to apply it to bio-image analysis. We also compare how different LLM vendors perform on code generation tasks and which challenges are ahead for the bio-image analysis community. - -Tags: Large Language Models, Python - -Content type: Slide - -[https://zenodo.org/records/10815329](https://zenodo.org/records/10815329) - -[https://doi.org/10.5281/zenodo.10815329](https://doi.org/10.5281/zenodo.10815329) - - ---- - -## Creating a Research Data Management Plan using chatGPT - -Robert Haase - -Published 2023-11-06 - -Licensed CC-BY-4.0 - - - -In this blog post the author demonstrates how chatGPT can be used to combine a fictive project description with a DMP specification to produce a project-specific DMP. - -Tags: Research Data Management, Large Language Models, Artificial Intelligence - -Content type: Blog - -[https://focalplane.biologists.com/2023/11/06/creating-a-research-data-management-plan-using-chatgpt/](https://focalplane.biologists.com/2023/11/06/creating-a-research-data-management-plan-using-chatgpt/) - - ---- - -## Multimodal large language models for bioimage analysis - -Shanghang Zhang, Gaole Dai, Tiejun Huang, Jianxu Chen - -Licensed ['CC-BY-NC-SA'] - - - -Multimodal large language models have been recognized as a historical milestone in the field of artificial intelligence and have demonstrated revolutionary potentials not only in commercial applications, but also for many scientific fields. Here we give a brief overview of multimodal large language models through the lens of bioimage analysis and discuss how we could build these models as a community to facilitate biology research - -Tags: Bioimage Analysis, Large Language Models, FAIR-Principles, Workflow - -Content type: Publication - -[https://www.nature.com/articles/s41592-024-02334-2](https://www.nature.com/articles/s41592-024-02334-2) - -[https://arxiv.org/abs/2407.19778](https://arxiv.org/abs/2407.19778) - - ---- - -## YMIA - Python-Based Event Series Training Material - -Riccardo Massei, Robert Haase, ENicolay - -Published None - -Licensed MIT - - - -This repository offer access to teaching material and useful resources for the YMIA - Python-Based Event Series. - -Tags: Python, Large Language Models, Prompt Engineering, Biabob, Bioimage Analysis, Microscopy Image Analysis - -Content type: Github Repository, Slides - -[https://github.com/rmassei/ymia_python_event_series_material](https://github.com/rmassei/ymia_python_event_series_material) - - ---- - diff --git a/_sources/tags/licensing.md b/_sources/tags/licensing.md index a98f1838..1f2b58d2 100644 --- a/_sources/tags/licensing.md +++ b/_sources/tags/licensing.md @@ -84,7 +84,7 @@ Blog post about why we should license our work and what is important when choosi Tags: Licensing, Research Data Management -Content type: Blog +Content type: Blog Post [https://focalplane.biologists.com/2023/05/06/if-you-license-it-itll-be-harder-to-steal-it-why-we-should-license-our-work/](https://focalplane.biologists.com/2023/05/06/if-you-license-it-itll-be-harder-to-steal-it-why-we-should-license-our-work/) diff --git a/_sources/tags/metadata.md b/_sources/tags/metadata.md index 69d16312..a8a34306 100644 --- a/_sources/tags/metadata.md +++ b/_sources/tags/metadata.md @@ -28,7 +28,7 @@ Licensed CC-BY-4.0 The authors show the utility of Minimum Information for High Content Screening Microscopy Experiments (MIHCSME) for High Content Screening (HCS) data using multiple examples from the Leiden FAIR Cell Observatory, a Euro-Bioimaging flagship node for high content screening and the pilot node for implementing FAIR bioimaging data throughout the Netherlands Bioimaging network. -Tags: FAIR-Principles, Metadata, Research Data Management, Image Data Management, Bioimage Data +Tags: FAIR-Principles, Metadata, Research Data Management Content type: Publication @@ -47,7 +47,7 @@ Licensed UNKNOWN A Microscopy Research Data Management Resource. -Tags: Metadata, I3Dbio, Research Data Management, Bioimage Data +Tags: Metadata, I3Dbio, Research Data Management Content type: Collection @@ -153,7 +153,7 @@ Licensed CC-BY-4.0 -Tags: OMERO, Galaxy, Metadata +Tags: OMERO, Galaxy, Metadata, Nfdi4Bioimage Content type: Tutorial, Framework, Workflow @@ -174,7 +174,7 @@ Licensed UNKNOWN Bioimaging data have significant potential for reuse, but unlocking this potential requires systematic archiving of data and metadata in public databases. The authors propose draft metadata guidelines to begin addressing the needs of diverse communities within light and electron microscopy. -Tags: Metadata, Bioimage Data, Image Data Management, Research Data Management +Tags: Metadata, Research Data Management Content type: Publication @@ -195,7 +195,7 @@ Licensed CC0-1.0 Recommended Metadata for Biological Images (REMBI) provides guidelines for metadata for biological images to enable the FAIR sharing of scientific data. -Tags: FAIR-Principles, Metadata, Image Data Management, Research Data Management, Bioimage Data +Tags: FAIR-Principles, Metadata, Research Data Management Content type: Collection @@ -214,7 +214,7 @@ Licensed UNKNOWN This Focus issue features a series of papers offering guidelines and tools for improving the tracking and reporting of microscopy metadata with an emphasis on reproducibility and data re-use. -Tags: Reproducibility, Metadata, Bioimage Data +Tags: Reproducibility, Metadata Content type: Collection @@ -235,7 +235,7 @@ Licensed CC-BY-4.0 The authors offer trainers some simple rules, to help make their training materials FAIR, enabling others to find, (re)use, and adapt them. -Tags: Metadata, Bioinformatics, FAIR-Principles, Training +Tags: Metadata, Bioinformatics, FAIR-Principles Content type: Publication @@ -256,7 +256,7 @@ Licensed UNKNOWN Rigorous record-keeping and quality control are required to ensure the quality, reproducibility and value of imaging data. The 4DN Initiative and BINA here propose light Microscopy Metadata specifications that extend the OME data model, scale with experimental intent and complexity, and make it possible for scientists to create comprehensive records of imaging experiments. -Tags: Reproducibility, Microscopy Image Analysis, Metadata, Image Data Management, Bioimage Data +Tags: Reproducibility, Bioimage Analysis, Metadata Content type: Publication diff --git a/_sources/tags/microscopy_image_analysis.md b/_sources/tags/microscopy_image_analysis.md deleted file mode 100644 index 7b82b9fd..00000000 --- a/_sources/tags/microscopy_image_analysis.md +++ /dev/null @@ -1,302 +0,0 @@ -# Microscopy image analysis (15) -## BIDS-lecture-2024 - -Robert Haase - -Licensed CC-BY-4.0 - - - -Training resources for Students at Uni Leipzig who want to dive into bio-image data science with Python. The material developed here between April and July 2024. - -Tags: Bioimage Analysis, Deep Learning, Microscopy Image Analysis, Python - -Content type: Github Repository - -[https://github.com/ScaDS/BIDS-lecture-2024/](https://github.com/ScaDS/BIDS-lecture-2024/) - - ---- - -## Bio-image Data Science - -Robert Haase - -Licensed CC-BY-4.0 - - - -This repository contains training resources for Students at Uni Leipzig who want to dive into bio-image data science with Python. - -Tags: Image Data Management, Deep Learning, Microscopy Image Analysis, Python - -Content type: Notebook - -[https://github.com/ScaDS/BIDS-lecture-2024](https://github.com/ScaDS/BIDS-lecture-2024) - - ---- - -## Bio-image Data Science Lectures @ Uni Leipzig / ScaDS.AI - -Robert Haase - -Licensed CC-BY-4.0 - - - -These are the PPTx training resources for Students at Uni Leipzig who want to dive into bio-image data science with Python. The material developed here between April and July 2024. - -Tags: Bioimage Analysis, Deep Learning, Microscopy Image Analysis, Python - -Content type: Slides - -[https://zenodo.org/records/12623730](https://zenodo.org/records/12623730) - - ---- - -## Checklists for publishing images and image analysis - -Christopher Schmied - -Published 2023-09-14 - -Licensed CC0-1.0 - - - -In this paper we introduce two sets of checklists. One is concerned with the publication of images. The other one gives instructions for the publication of image analysis. - -Tags: Bioimage Data, Microscopy Image Analysis - -Content type: Forum Post - -[https://forum.image.sc/t/checklists-for-publishing-images-and-image-analysis/86304](https://forum.image.sc/t/checklists-for-publishing-images-and-image-analysis/86304) - - ---- - -## Dr Guillaume Jacquemet on studying cancer cell metastasis in the era of deep learning for microscopy - -Guillaume Jacquemet - -Published 2024-10-24 - -Licensed UNKNOWN - - - -Leukocyte extravasation is a critical component of the innate immune response, while circulating tumour cell extravasation is a crucial step in metastasis formation. Despite their importance, these extravasation mechanisms remain incompletely understood. In this talk, Guillaume Jacquemet presents a novel imaging framework that integrates microfluidics with high-speed, label-free imaging to study the arrest of pancreatic cancer cells (PDAC) on human endothelial layers under physiological flow conditions. - -Tags: Deep Learning, Microscopy Image Analysis - -Content type: Youtube Video, Slides - -[https://www.youtube.com/watch?v=KTdZBgSCYJQ](https://www.youtube.com/watch?v=KTdZBgSCYJQ) - - ---- - -## Example Pipeline Tutorial - -Tim Monko - -Published 2024-10-28 - -Licensed BSD-3-CLAUSE - - - -Napari-ndev is a collection of widgets intended to serve any person seeking to process microscopy images from start to finish. The goal of this example pipeline is to get the user familiar with working with napari-ndev for batch processing and reproducibility (view Image Utilities and Workflow Widget). - -Tags: Napari, Microscopy Image Analysis, Bioimage Analysis - -Content type: Documentation, Github Repository, Tutorial - -[https://timmonko.github.io/napari-ndev/tutorial/01_example_pipeline/](https://timmonko.github.io/napari-ndev/tutorial/01_example_pipeline/) - -[https://github.com/timmonko/napari-ndev](https://github.com/timmonko/napari-ndev) - - ---- - -## FAIRy deep-learning for bioImage analysis - -Estibaliz Gómez de Mariscal - -Licensed CC-BY-4.0 - - - -Introduction to FAIR deep learning. Furthermore, tools to deploy trained DL models (deepImageJ), easily train and evaluate them (ZeroCostDL4Mic and DeepBacs) ensure reproducibility (DL4MicEverywhere), and share this technology in an open-source and reproducible manner (BioImage Model Zoo) are introduced. - -Tags: Deep Learning, FAIR-Principles, Microscopy Image Analysis - -Content type: Slides - -[https://f1000research.com/slides/13-147](https://f1000research.com/slides/13-147) - - ---- - -## Making the most of bioimaging data through interdisciplinary interactions - -Virginie Uhlmann, Matthew Hartley, Josh Moore, Erin Weisbart, Assaf Zaritsky - -Published 2024-10-23 - -Licensed CC-BY-4.0 - - - -Tags: Bioimage Analysis, Open Science, Microscopy Image Analysis, Microscopy - -Content type: Publication - -[https://journals.biologists.com/jcs/article/137/20/jcs262139/362478/Making-the-most-of-bioimaging-data-through](https://journals.biologists.com/jcs/article/137/20/jcs262139/362478/Making-the-most-of-bioimaging-data-through) - - ---- - -## Microscopy data analysis: machine learning and the BioImage Archive - -Andrii Iudin, Anna Foix-Romero, Anna Kreshuk, Awais Athar, Beth Cimini, Dominik Kutra, Estibalis Gomez de Mariscal, Frances Wong, Guillaume Jacquemet, Kedar Narayan, Martin Weigert, Nodar Gogoberidze, Osman Salih, Petr Walczysko, Ryan Conrad, Simone Weyend, Sriram Sundar Somasundharam, Suganya Sivagurunathan, Ugis Sarkans - -Licensed CC-BY-4.0 - - - -The Microscopy data analysis: machine learning and the BioImage Archive course, which focused on introducing programmatic approaches used in the analysis of bioimage data via the BioImage Archive, ran in May 2023. - -Tags: Microscopy Image Analysis, Python, Deep Learning - -Content type: Video, Slides - -[https://www.ebi.ac.uk/training/materials/microscopy-data-analysis-machine-learning-and-the-bioimage-archive-materials/](https://www.ebi.ac.uk/training/materials/microscopy-data-analysis-machine-learning-and-the-bioimage-archive-materials/) - - ---- - -## Multiplexed tissue imaging - tools and approaches - -Agustín Andrés Corbat, OmFrederic, Jonas Windhager, Kristína Lidayová - -Licensed CC-BY-4.0 - - - -Material for the I2K 2024 "Multiplexed tissue imaging - tools and approaches" workshop - -Tags: Bioimage Analysis, Microscopy Image Analysis - -Content type: Github Repository, Slides, Workshop - -[https://github.com/BIIFSweden/I2K2024-MTIWorkshop](https://github.com/BIIFSweden/I2K2024-MTIWorkshop) - -[https://docs.google.com/presentation/d/1R9-4lXAmTYuyFZpTMDR85SjnLsPZhVZ8/edit#slide=id.p1](https://docs.google.com/presentation/d/1R9-4lXAmTYuyFZpTMDR85SjnLsPZhVZ8/edit#slide=id.p1) - - ---- - -## Open Micoscropy Environment (OME) Youtube Channel - -Published None - -Licensed CC-BY-4.0 - - - -OME develops open-source software and data format standards for the storage and manipulation of biological microscopy data - -Tags: Open Source Software, Microscopy Image Analysis, Bioimage Data - -Content type: Video, Collection - -[https://www.youtube.com/@OpenMicroscopyEnvironment](https://www.youtube.com/@OpenMicroscopyEnvironment) - - ---- - -## The Open Microscopy Environment (OME) Data Model and XML file - open tools for informatics and quantitative analysis in biological imaging - -Ilya G. Goldberg, Chris Allan, Jean-Marie Burel, Doug Creager, Andrea Falconi, et. al - -Published 2005-05-03 - -Licensed CC-BY-4.0 - - - -The Open Microscopy Environment (OME) defines a data model and a software implementation to serve as an informatics framework for imaging in biological microscopy experiments, including representation of acquisition parameters, annotations and image analysis results. - -Tags: Microscopy Image Analysis, Bioimage Analysis - -Content type: Publication - -[https://genomebiology.biomedcentral.com/articles/10.1186/gb-2005-6-5-r47](https://genomebiology.biomedcentral.com/articles/10.1186/gb-2005-6-5-r47) - -[https://doi.org/10.1186/gb-2005-6-5-r47](https://doi.org/10.1186/gb-2005-6-5-r47) - - ---- - -## Towards community-driven metadata standards for light microscopy - tiered specifications extending the OME model - -Mathias Hammer, Maximiliaan Huisman, Alessandro Rigano, Ulrike Boehm, James J. Chambers, et al. - -Published 2022-07-10 - -Licensed UNKNOWN - - - -Rigorous record-keeping and quality control are required to ensure the quality, reproducibility and value of imaging data. The 4DN Initiative and BINA here propose light Microscopy Metadata specifications that extend the OME data model, scale with experimental intent and complexity, and make it possible for scientists to create comprehensive records of imaging experiments. - -Tags: Reproducibility, Microscopy Image Analysis, Metadata, Image Data Management, Bioimage Data - -Content type: Publication - -[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9271325/](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9271325/) - - ---- - -## Upcoming Image Analysis Events - -Curtis Rueden, Albane de la Villegeorges, Simon F. Nørrelykke, Romain Guiet, Olivier Burri, et al. - -Licensed UNKNOWN - - - -Tags: Bioimage Analysis, Microscopy Image Analysis - -Content type: Collection, Event, Forum Post, Workshop - -[https://forum.image.sc/t/upcoming-image-analysis-events/60018/67](https://forum.image.sc/t/upcoming-image-analysis-events/60018/67) - - ---- - -## YMIA - Python-Based Event Series Training Material - -Riccardo Massei, Robert Haase, ENicolay - -Published None - -Licensed MIT - - - -This repository offer access to teaching material and useful resources for the YMIA - Python-Based Event Series. - -Tags: Python, Large Language Models, Prompt Engineering, Biabob, Bioimage Analysis, Microscopy Image Analysis - -Content type: Github Repository, Slides - -[https://github.com/rmassei/ymia_python_event_series_material](https://github.com/rmassei/ymia_python_event_series_material) - - ---- - diff --git a/_sources/tags/napari.md b/_sources/tags/napari.md index 6a96fd50..c3ddd99f 100644 --- a/_sources/tags/napari.md +++ b/_sources/tags/napari.md @@ -7,7 +7,7 @@ Mara Lampert Tags: Python, Napari, Bioimage Analysis -Content type: Blog +Content type: Blog Post [https://focalplane.biologists.com/2023/03/30/annotating-3d-images-in-napari/](https://focalplane.biologists.com/2023/03/30/annotating-3d-images-in-napari/) @@ -77,7 +77,7 @@ Licensed BSD-3-CLAUSE Napari-ndev is a collection of widgets intended to serve any person seeking to process microscopy images from start to finish. The goal of this example pipeline is to get the user familiar with working with napari-ndev for batch processing and reproducibility (view Image Utilities and Workflow Widget). -Tags: Napari, Microscopy Image Analysis, Bioimage Analysis +Tags: Napari, Bioimage Analysis Content type: Documentation, Github Repository, Tutorial @@ -96,7 +96,7 @@ Mara Lampert Tags: Python, Napari, Bioimage Analysis -Content type: Blog +Content type: Blog Post [https://focalplane.biologists.com/2023/05/03/feature-extraction-in-napari/](https://focalplane.biologists.com/2023/05/03/feature-extraction-in-napari/) @@ -166,7 +166,7 @@ Mara Lampert Tags: Python, Napari, Bioimage Analysis -Content type: Blog +Content type: Blog Post [https://focalplane.biologists.com/2023/04/13/quality-assurance-of-segmentation-results/](https://focalplane.biologists.com/2023/04/13/quality-assurance-of-segmentation-results/) @@ -181,7 +181,7 @@ Mara Lampert Tags: Python, Napari, Bioimage Analysis -Content type: Blog +Content type: Blog Post [https://focalplane.biologists.com/2023/03/02/rescaling-images-and-pixel-anisotropy/](https://focalplane.biologists.com/2023/03/02/rescaling-images-and-pixel-anisotropy/) @@ -196,7 +196,7 @@ Mara Lampert Tags: Python, Napari, Bioimage Analysis -Content type: Blog +Content type: Blog Post [https://focalplane.biologists.com/2023/06/01/tracking-in-napari/](https://focalplane.biologists.com/2023/06/01/tracking-in-napari/) diff --git a/_sources/tags/neubias.md b/_sources/tags/neubias.md index 3ba34e82..e760490f 100644 --- a/_sources/tags/neubias.md +++ b/_sources/tags/neubias.md @@ -1,4 +1,4 @@ -# Neubias (26) +# Neubias (27) ## Adding a Workflow to BIAFLOWS Sébastien Tosi, Volker Baecker, Benjamin Pavie @@ -9,7 +9,7 @@ Licensed BSD-2-CLAUSE Tags: Neubias, Bioimage Analysis -Content type: Slide +Content type: Slides [https://github.com/RoccoDAnt/Defragmentation_TrainingSchool_EOSC-Life_2022/blob/main/Slides/Adding_a_workflow_to_BIAFLOWS.pdf](https://github.com/RoccoDAnt/Defragmentation_TrainingSchool_EOSC-Life_2022/blob/main/Slides/Adding_a_workflow_to_BIAFLOWS.pdf) @@ -43,7 +43,7 @@ Licensed UNKNOWN Tags: Neubias, Cellprofiler, Bioimage Analysis -Content type: Slide +Content type: Slides [https://github.com/ahklemm/CellProfiler_Introduction](https://github.com/ahklemm/CellProfiler_Introduction) @@ -149,7 +149,7 @@ Licensed UNKNOWN Tags: Neubias, Imagej Macro, Bioimage Analysis -Content type: Slide, Code +Content type: Slides, Code [https://github.com/ahklemm/ImageJMacro_Introduction](https://github.com/ahklemm/ImageJMacro_Introduction) @@ -166,7 +166,7 @@ Licensed BSD-3-CLAUSE Tags: Neubias, Imagej, Bioimage Analysis -Content type: Slide +Content type: Slides [https://git.mpi-cbg.de/rhaase/lecture_imagej2_dev](https://git.mpi-cbg.de/rhaase/lecture_imagej2_dev) @@ -183,7 +183,7 @@ Licensed UNKNOWN Tags: Neubias, Bioimage Analysis -Content type: Slide +Content type: Slides [https://docs.google.com/presentation/d/1q8q1xE-c35tvCsRXZay98s2UYWwXpp0cfCljBmMFpco/edit#slide=id.ga456d5535c_2_53](https://docs.google.com/presentation/d/1q8q1xE-c35tvCsRXZay98s2UYWwXpp0cfCljBmMFpco/edit#slide=id.ga456d5535c_2_53) @@ -200,11 +200,32 @@ Licensed UNKNOWN Tags: Neubias, Artificial Intelligence, Bioimage Analysis -Content type: Slide +Content type: Slides [https://docs.google.com/presentation/d/1oJoy9gHmUuSmUwCkPs_InJf_WZAzmLlUNvK1FUEB4PA/edit#slide=id.ge3a24e733b_0_54](https://docs.google.com/presentation/d/1oJoy9gHmUuSmUwCkPs_InJf_WZAzmLlUNvK1FUEB4PA/edit#slide=id.ge3a24e733b_0_54) +--- + +## Modular training resources for bioimage analysis + +Christian Tischer, Antonio Politi, Tim-Oliver Buchholz, Elnaz Fazeli, Nicola Gritti, Aliaksandr Halavatyi, Sebastian Gonzalez Tirado, Julian Hennies, Toby Hodges, Arif Khan, Dominik Kutra, Stefania Marcotti, Bugra Oezdemir, Felix Schneider, Martin Schorb, Anniek Stokkermans, Yi Sun, Nima Vakili + +Published 2024-12-03 + +Licensed CC-BY-4.0 + + + +Resources for teaching/preparing to teach bioimage analysis + +Tags: Neubias, Bioimage Analysis + +[https://zenodo.org/records/14264885](https://zenodo.org/records/14264885) + +[https://doi.org/10.5281/zenodo.14264885](https://doi.org/10.5281/zenodo.14264885) + + --- ## Multi-view fusion @@ -219,7 +240,7 @@ Lecture slides of a session on Multiview Fusion in Fiji Tags: Neubias, Imagej, Bioimage Analysis -Content type: Slide +Content type: Slides [https://git.mpi-cbg.de/rhaase/lecture_multiview_registration](https://git.mpi-cbg.de/rhaase/lecture_multiview_registration) @@ -251,7 +272,7 @@ Licensed UNKNOWN Tags: Neubias, Bioimage Analysis -Content type: Slide, Code, Notebook +Content type: Slides, Code, Notebook [https://github.com/miura/NEUBIAS_AnalystSchool2018](https://github.com/miura/NEUBIAS_AnalystSchool2018) @@ -268,7 +289,7 @@ Licensed UNKNOWN Tags: Neubias, Bioimage Analysis -Content type: Slide, Tutorial +Content type: Slides, Tutorial [https://github.com/miura/NEUBIAS_Bioimage_Analyst_Course2017](https://github.com/miura/NEUBIAS_Bioimage_Analyst_Course2017) @@ -285,7 +306,7 @@ Licensed UNKNOWN Tags: Neubias, Bioimage Analysis -Content type: Slide, Code, Notebook +Content type: Slides, Code, Notebook [https://github.com/miura/NEUBIAS_AnalystSchool2019](https://github.com/miura/NEUBIAS_AnalystSchool2019) @@ -302,7 +323,7 @@ Licensed UNKNOWN Tags: Neubias, Bioimage Analysis -Content type: Slide, Code, Notebook +Content type: Slides, Code, Notebook [https://github.com/miura/NEUBIAS_AnalystSchool2020](https://github.com/miura/NEUBIAS_AnalystSchool2020) @@ -334,7 +355,7 @@ Licensed UNKNOWN Tags: Python, Neubias, Artificial Intelligence, Bioimage Analysis -Content type: Slide, Notebook +Content type: Slides, Notebook [https://github.com/maweigert/neubias_academy_stardist](https://github.com/maweigert/neubias_academy_stardist) @@ -387,7 +408,7 @@ Lecture slides of a session on Cell Tracking in Fiji Tags: Neubias, Imagej, Bioimage Analysis -Content type: Slide +Content type: Slides [https://git.mpi-cbg.de/rhaase/lecture_tracking_trackmate](https://git.mpi-cbg.de/rhaase/lecture_tracking_trackmate) @@ -404,7 +425,7 @@ Licensed UNKNOWN Tags: Neubias, Bioimage Analysis -Content type: Slide +Content type: Slides [https://www.dropbox.com/s/5abw3cvxrhpobg4/20220923_DefragmentationTS.pdf?dl=0](https://www.dropbox.com/s/5abw3cvxrhpobg4/20220923_DefragmentationTS.pdf?dl=0) @@ -421,7 +442,7 @@ Licensed BSD-3-CLAUSE Tags: Neubias, Imagej, Bioimage Analysis -Content type: Slide +Content type: Slides [https://git.mpi-cbg.de/rhaase/lecture_working_with_objects_in_2d_and_3d](https://git.mpi-cbg.de/rhaase/lecture_working_with_objects_in_2d_and_3d) @@ -438,7 +459,7 @@ Licensed BSD-3-CLAUSE Tags: Neubias, Imagej, Bioimage Analysis -Content type: Slide +Content type: Slides [https://git.mpi-cbg.de/rhaase/lecture_working_with_pixels](https://git.mpi-cbg.de/rhaase/lecture_working_with_pixels) diff --git a/_sources/tags/nfdi4bioimage.md b/_sources/tags/nfdi4bioimage.md index cd8cbb8c..5cec85a8 100644 --- a/_sources/tags/nfdi4bioimage.md +++ b/_sources/tags/nfdi4bioimage.md @@ -1,4 +1,4 @@ -# Nfdi4bioimage (23) +# Nfdi4bioimage (44) ## A Cloud-Optimized Storage for Interactive Access of Large Arrays Josh Moore, Susanne Kunis @@ -14,6 +14,54 @@ Content type: Publication, Conference Abstract [https://doi.org/10.52825/cordi.v1i.285](https://doi.org/10.52825/cordi.v1i.285) +--- + +## A journey to FAIR microscopy data + +Stefanie Weidtkamp-Peters, Janina Hanne, Christian Schmidt + +Published 2023-05-03 + +Licensed CC-BY-4.0 + + + +Oral presentation, 32nd MoMAN "From Molecules to Man" Seminar, Ulm, online. Monday February 6th, 2023 + +Abstract: + +Research data management is essential in nowadays research, and one of the big opportunities to accelerate collaborative and innovative scientific projects. To achieve this goal, all our data needs to be FAIR (findable, accessible, interoperable, reproducible). For data acquired on microscopes, however, a common ground for FAIR data sharing is still to be established. Plenty of work on file formats, data bases, and training needs to be performed to highlight the value of data sharing and exploit its potential for bioimaging data. + +In this presentation, Stefanie Weidtkamp-Peters will introduce the challenges for bioimaging data management, and the necessary steps to achieve data FAIRification. German BioImaging - GMB e.V., together with other institutions, contributes to this endeavor. Janina Hanne will present how the network of imaging core facilities, research groups and industry partners is key to the German bioimaging community’s aligned collaboration toward FAIR bioimaging data. These activities have paved the way for two data management initiatives in Germany: I3D:bio (Information Infrastructure for BioImage Data) and NFDI4BIOIMAGE, a consortium of the National Research Data Infrastructure. Christian Schmidt will introduce the goals and measures of these initiatives to the benefit of imaging scientist’s work and everyday practice.   + +Tags: Nfdi4Bioimage, Research Data Management + +[https://zenodo.org/records/7890311](https://zenodo.org/records/7890311) + +[https://doi.org/10.5281/zenodo.7890311](https://doi.org/10.5281/zenodo.7890311) + + +--- + +## Angebote der NFDI für die Forschung im Bereich Zoologie + +Birgitta König-Ries, Robert Haase, Daniel Nüst, Konrad Förstner, Judith Sophie Engel + +Published 2024-12-04 + +Licensed CC-BY-4.0 + + + +In diesem Slidedeck geben wir einen Einblick in Angebote und Dienste der Nationalen Forschungsdaten Infrastruktur (NFDI), die Relevant für die Zoologie und angrenzende Disziplinen relevant sein könnten. + +Tags: Nfdi4Bioimage, Research Data Management + +[https://zenodo.org/records/14278058](https://zenodo.org/records/14278058) + +[https://doi.org/10.5281/zenodo.14278058](https://doi.org/10.5281/zenodo.14278058) + + --- ## Bio-Image Data Strudel for Workshop on Research Data Management in TU Dresden Core Facilities @@ -28,9 +76,9 @@ Licensed CC-BY-4.0 This presentation gives a short outline of the complexity of data and metadata in the bioimaging universe. It introduces NFDI4BIOIMAGE as a newly formed consortium as part of the German 'Nationale Forschungsdateninfrastruktur' (NFDI) and its goals and tools for data management including its current members on TU Dresden campus.   -Tags: Research Data Management, Tu Dresden, Bioimage Data, Nfdi4Bioimage +Tags: Research Data Management, Nfdi4Bioimage -Content type: Slide +Content type: Slides [https://zenodo.org/records/10083555](https://zenodo.org/records/10083555) @@ -49,11 +97,51 @@ Licensed CC-BY-4.0 Tags: Research Data Management, Bioimage Analysis, Nfdi4Bioimage -Content type: Slide +Content type: Slides [https://f1000research.com/slides/12-1054](https://f1000research.com/slides/12-1054) +--- + +## Collaborative Working and Version Control with git[hub] + +Robert Haase + +Published 2024-01-10 + +Licensed CC-BY-4.0 + + + +This slide deck introduces the version control tool git, related terminology and the Github Desktop app for managing files in Git[hub] repositories. We furthermore dive into:* Working with repositories* Collaborative with others* Github-Zenodo integration* Github pages* Artificial Intelligence answering Github Issues + +Tags: Nfdi4Bioimage, Globias, Research Data Management, Research Software Management + +[https://zenodo.org/records/14626054](https://zenodo.org/records/14626054) + +[https://doi.org/10.5281/zenodo.14626054](https://doi.org/10.5281/zenodo.14626054) + + +--- + +## Engineering a Software Environment for Research Data Management of Microscopy Image Data in a Core Facility + +Kunis + +Published 2022-05-30 + + + +This thesis deals with concepts and solutions in the field of data management in everyday scientific life for image data from microscopy. The focus of the formulated requirements has so far been on published data, which represent only a small subset of the data generated in the scientific process. More and more, everyday research data are moving into the focus of the principles for the management of research data that were formulated early on (FAIR-principles). The adequate management of this mostly multimodal data is a real challenge in terms of its heterogeneity and scope. There is a lack of standardised and established workflows and also the software solutions available so far do not adequately reflect the special requirements of this area. However, the success of any data management process depends heavily on the degree of integration into the daily work routine. Data management must, as far as possible, fit seamlessly into this process. Microscopy data in the scientific process is embedded in pre-processing, which consists of preparatory laboratory work and the analytical evaluation of the microscopy data. In terms of volume, the image data often form the largest part of data generated within this entire research process. In this paper, we focus on concepts and techniques related to the handling and description of this image data and address the necessary basics. The aim is to improve the embedding of the existing data management solution for image data (OMERO) into the everyday scientific work. For this purpose, two independent software extensions for OMERO were implemented within the framework of this thesis: OpenLink and MDEmic. OpenLink simplifies the access to the data stored in the integrated repository in order to feed them into established workflows for further evaluations and enables not only the internal but also the external exchange of data without weakening the advantages of the data repository. The focus of the second implemented software solution, MDEmic, is on the capturing of relevant metadata for microscopy. Through the extended metadata collection, a corresponding linking of the multimodal data by means of a unique description and the corresponding semantic background is aimed at. The configurability of MDEmic is designed to address the currently very dynamic development of underlying concepts and formats. The main goal of MDEmic is to minimise the workload and to automate processes. This provides the scientist with a tool to handle this complex and extensive task of metadata acquisition for microscopic data in a simple way. With the help of the software, semantic and syntactic standardisation can take place without the scientist having to deal with the technical concepts. The generated metadata descriptions are automatically integrated into the image repository and, at the same time, can be transferred by the scientists into formats that are needed when publishing the data. + +Tags: Nfdi4Bioimage, Research Data Managementv + +[https://zenodo.org/records/6905931](https://zenodo.org/records/6905931) + +[https://doi.org/10.5281/zenodo.6905931](https://doi.org/10.5281/zenodo.6905931) + + --- ## I3D:bio's OMERO training material: Re-usable, adjustable, multi-purpose slides for local user training @@ -70,7 +158,7 @@ The open-source software OME Remote Objects (OMERO) is a data management softwar Tags: OMERO, Research Data Management, Nfdi4Bioimage, I3Dbio -Content type: Slide, Video +Content type: Slides, Video [https://zenodo.org/records/8323588](https://zenodo.org/records/8323588) @@ -79,6 +167,81 @@ Content type: Slide, Video [https://doi.org/10.5281/zenodo.8323588](https://doi.org/10.5281/zenodo.8323588) +--- + +## Key-Value pair template for annotation in OMERO for light microscopy data acquired with AxioScan7 - Core Facility Cellular Imaging (CFCI) + +Silke Tulok, Anja Nobst, Anett Jannasch, Tom Boissonnet, Gunar Fabig + +Published 2024-06-28 + +Licensed CC-BY-4.0 + + + +This Key-Value pair template is used for the data documentation during imaging experiments and the later data annotation in OMERO. It is tailored for the usage and image acquisition at the slide scanning system Zeiss AxioScan 7 in the Core Facility Cellular Imaging (CFCI). It contains important metadata of the imaging experiment, which are not saved in the corresponding imaging files. All users of the Core Facility Cellular Imaging are trained to use that file to document their imaging parameters directly during the data acquisition with the possibility for a later upload to OMERO. Furthermore, there is a corresponding public example image used in the publication "Setting up an institutional OMERO environment for bioimage data: perspectives from both facility staff and users" and is available here: +https://omero.med.tu-dresden.de/webclient/?show=image-33248 +This template was developed by the CFCI staff during the setup and usage of the AxioScan 7 and is based on the REMBI recommendations (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8606015). +With this template it is possible to create a csv-file, that can be used to annotate an image or dataset in OMERO using the annotation script (https://github.com/ome/omero-scripts/blob/develop/omero/annotation_scripts/). +How to use: + +fill the template sheet  with your metadata +select and copy the data range containing the Keys and Values +open a new excel sheet and paste transpose in cell A1  +Important: cell A1 contains always the name 'dataset' and cell A2 contains the exact name of the image/dataset, which should be annotated in OMERO +save the new excel sheet in csv-file (comma separated values) format + +An example can be seen in sheet 3 'csv_AxioScan'. +Important note: The code has to be 8-Bit UCS transformation format (UTF-8) otherwise several characters (for example µ, %,°) might be not able to decode by the annotation script. We encountered this issue with old Microsoft-Office versions (MS Office 2016).  +Note: By filling the values in the excel sheet, avoid the usage of comma as decimal delimiter. +See cross reference: +10.5281/zenodo.12547566 Key-Value pair template for annotation of datasets in OMERO for light- and electron microscopy data within the research group of Prof. Mueller-Reichert +10.5281/zenodo.12546808 Key-Value pair template for annotation of datasets in OMERO (PERIKLES study) + +Tags: Nfdi4Bioimage, Research Data Management + +[https://zenodo.org/records/12578084](https://zenodo.org/records/12578084) + +[https://doi.org/10.5281/zenodo.12578084](https://doi.org/10.5281/zenodo.12578084) + + +--- + +## Key-Value pair template for annotation of datasets in OMERO (PERIKLES study) + +Anett Jannasch, Silke Tulok, Vanessa Aphaia Fiona Fuchs, Tom Boissonnet, Christian Schmidt, Michele Bortolomeazzi, Gunar Fabig, Chukwuebuka Okafornta + +Published 2024-06-26 + +Licensed CC-BY-4.0 + + + +This is a Key-Value pair template used for the annotation of datasets in OMERO. It is tailored for a research study (PERIKLES project) on the biocompatibility of newly designed biomaterials out of pericardial tissue for cardiovascular substitutes (https://doi.org/10.1063/5.0182672) conducted in the research department of Cardiac Surgery at the Faculty of Medicine Carl Gustav Carus at the Technische Universität Dresden . A corresponding public example dataset is used in the publication "Setting up an institutional OMERO environment for bioimage data: perspectives from both facility staff and users" and is available here +(https://omero.med.tu-dresden.de/webclient/?show=dataset-1557). +The template is based on the REMBI recommendations (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8606015) and it was developed during the PoL-Bio-Image Analysis Symposium in Dresden Aug 28th- Sept 1th 2023.  +With this template it is possible to create a csv-file, that can be used to annotate a dataset in OMERO using the annotation script (https://github.com/ome/omero-scripts/blob/develop/omero/annotation_scripts/). +How to use: +select and copy the data range containing Keys and Values +open a new excel sheet and paste transpose in column B1 +type in A1 'dataset' +insert in A2 the exact name of the dataset, which should be annotated in OMERO +save the new excel sheet in csv- (comma seperated values) file format + +Example can be seen in sheet 1 'csv import'. Important note; the code has to be 8-Bit UCS transformation format (UTF-8) otherwise several characters (for example µ, %,°) might not be able to decode by the annotation script. We encountered this issue with old Microsoft Office versions (e.g. MS Office 2016).  +Note: By filling the values in the excel sheet, avoid the usage of decimal delimiter. +  +See cross reference: +10.5281/zenodo.12547566 Key-Value pair template for annotation of datasets in OMERO (light- and electron microscopy data within the research group of Prof. Mueller-Reichert) +10.5281/zenodo.12578084 Key-Value pair template for annotation in OMERO for light microscopy data acquired with AxioScan7 - Core Facility Cellular Imaging (CFCI) + +Tags: Nfdi4Bioimage, Research Data Management + +[https://zenodo.org/records/12546808](https://zenodo.org/records/12546808) + +[https://doi.org/10.5281/zenodo.12546808](https://doi.org/10.5281/zenodo.12546808) + + --- ## NFDI - Daten als gemeinsames Gut für exzellente Forschung, organisiert durch die Wissenschaft in Deutschland. @@ -129,9 +292,9 @@ Licensed CCY-BY-SA-4.0 Align existing and establish novel services & solutions for data management tasks throughout the bioimage data lifecycle. -Tags: Nfdi4Bioimage, Image Data Management, Bioimage Data, Research Data Management +Tags: Nfdi4Bioimage, Research Data Management -Content type: Conference Abstract, Slide +Content type: Conference Abstract, Slides [https://doi.org/10.11588/heidok.00029489](https://doi.org/10.11588/heidok.00029489) @@ -174,6 +337,28 @@ Content type: Slides [https://zenodo.org/doi/10.5281/zenodo.8414318](https://zenodo.org/doi/10.5281/zenodo.8414318) +--- + +## NFDI4BIOIMAGE data management illustrations by Henning Falk + +NFDI4BIOIMAGE Consortium + +Published 2024-11-29 + +Licensed CC-BY-4.0 + + + +These illustrations were contracted by the Heinrich Heine University Düsseldorf in the frame of the consortium NFDI4BIOIMAGE from Henning Falk for the purpose of education and public outreach. The illustrations are free to use under a CC-BY 4.0 license.AttributionPlease include an attribution similar to: "Data annoation matters", NFDI4BIOIMAGE Consortium (2024): NFDI4BIOIMAGE data management illustrations by Henning Falk, Zenodo, https://doi.org/10.5281/zenodo.14186100, is used under a CC-BY 4.0 license. Modifications to this illustration include cropping. +  + +Tags: Nfdi4Bioimage, Research Data Management + +[https://zenodo.org/records/14186101](https://zenodo.org/records/14186101) + +[https://doi.org/10.5281/zenodo.14186101](https://doi.org/10.5281/zenodo.14186101) + + --- ## NFDI4BIOIMAGE: Perspective for a national bioimaging standard @@ -229,6 +414,27 @@ Content type: Github Repository [https://zenodo.org/doi/10.5281/zenodo.10609770](https://zenodo.org/doi/10.5281/zenodo.10609770) +--- + +## NFDI4Bioimage Calendar 2024 October; original image + +Christian Jüngst, Peter Zentis + +Published 2024-09-25 + +Licensed CC-BY-4.0 + + + +Raw microscopy image from the NFDI4Bioimage calendar October 2024 + +Tags: Nfdi4Bioimage, Research Data Management + +[https://zenodo.org/records/13837146](https://zenodo.org/records/13837146) + +[https://doi.org/10.5281/zenodo.13837146](https://doi.org/10.5281/zenodo.13837146) + + --- ## OME-NGFF: a next-generation file format for expanding bioimaging data-access strategies @@ -246,6 +452,29 @@ Content type: Publication [https://www.nature.com/articles/s41592-021-01326-w](https://www.nature.com/articles/s41592-021-01326-w) +--- + +## OME2024 NGFF Challenge Results + +Josh Moore + +Published 2024-11-01 + +Licensed CC-BY-4.0 + + + +Presented at the 2024 FoundingGIDE event in Okazaki, Japan: https://founding-gide.eurobioimaging.eu/event/foundinggide-community-event-2024/ +Note: much of the presentation was a demonstration of the OME2024-NGFF-Challenge -- https://ome.github.io/ome2024-ngff-challenge/ especially of querying an extraction of the metadata (https://github.com/ome/ome2024-ngff-challenge-metadata) +  + +Tags: Nfdi4Bioimage, Research Data Management + +[https://zenodo.org/records/14234608](https://zenodo.org/records/14234608) + +[https://doi.org/10.5281/zenodo.14234608](https://doi.org/10.5281/zenodo.14234608) + + --- ## OMERO for microscopy research data management @@ -265,6 +494,25 @@ Content type: Publication [https://analyticalscience.wiley.com/do/10.1002/was.0004000267/](https://analyticalscience.wiley.com/do/10.1002/was.0004000267/) +--- + +## Overview of the Galaxy OMERO-suite - Upload images and metadata in OMERO using Galaxy + +Riccardo Massei, Björn Grüning + +Published 2024-12-02 + +Licensed CC-BY-4.0 + + + +Tags: OMERO, Galaxy, Metadata, Nfdi4Bioimage + +Content type: Tutorial, Framework, Workflow + +[https://training.galaxyproject.org/training-material/topics/imaging/tutorials/omero-suite/tutorial.html](https://training.galaxyproject.org/training-material/topics/imaging/tutorials/omero-suite/tutorial.html) + + --- ## Research data management for bioimaging: the 2021 NFDI4BIOIMAGE community survey @@ -303,27 +551,82 @@ Content type: Publication ## Structuring of Data and Metadata in Bioimaging: Concepts and technical Solutions in the Context of Linked Data -Susanne Kunis +Sarah Weischer, Jens Wendt, Thomas Zobel -Published 2022-08-24 +Published 2022-07-12 Licensed CC-BY-4.0 -guided walkthrough of poster at https://doi.org/10.5281/zenodo.6821815 +Provides an overview of contexts, frameworks, and models from the world of bioimage data as well as metadata. Visualizes the techniques for structuring this data as Linked Data. (Walkthrough Video: https://doi.org/10.5281/zenodo.7018928 ) + +Content: -which provides an overview of contexts, frameworks, and models from the world of bioimage data as well as metadata and the techniques for structuring this data as Linked Data. -You can also watch the video in the browser on the I3D:bio website. + Types of metadata + Data formats + Data Models Microscopy Data + Tools to edit/gather metadata + ISA Framework + FDO Framework + Ontology + RDF + JSON-LD + SPARQL + Knowledge Graph + Linked Data + Smart Data + ... + Tags: Nfdi4Bioimage, Research Data Management -Content type: Video +[https://zenodo.org/records/7018750](https://zenodo.org/records/7018750) + +[https://doi.org/10.5281/zenodo.7018750](https://doi.org/10.5281/zenodo.7018750) + + +--- + +## The Information Infrastructure for BioImage Data (I3D:bio) project to advance FAIR microscopy data management for the community + +Christian Schmidt, Michele Bortolomeazzi, Tom Boissonnet, Julia Dohle, Tobias Wernet, Janina Hanne, Roland Nitschke, Susanne Kunis, Karen Bernhardt, Stefanie Weidtkamp-Peters, Elisa Ferrando-May + +Published 2024-03-04 + +Licensed CC-BY-4.0 -[https://zenodo.org/record/7018929](https://zenodo.org/record/7018929) -[https://doi.org/10.5281/zenodo.7018929](https://doi.org/10.5281/zenodo.7018929) + +Research data management (RDM) in microscopy and image analysis is a challenging task. Large files in proprietary formats, complex N-dimensional array structures, and various metadata models and formats can make image data handling inconvenient and difficult. For data organization, annotation, and sharing, researchers need solutions that fit everyday practice and comply with the FAIR (Findable, Accessible, Interoperable, Reusable) principles. International community-based efforts have begun creating open data models (OME), an open file format and translation library (OME-TIFF, Bio-Formats), data management software platforms, and microscopy metadata recommendations and annotation tools. Bringing these developments into practice requires support and training. Iterative feedback and tool improvement is needed to foster practical adoption by the scientific community. The Information Infrastructure for BioImage Data (I3D:bio) project works on guidelines, training resources, and practical assistance for FAIR microscopy RDM adoption with a focus on the management platform OMERO and metadata annotations. + +Tags: Nfdi4Bioimage, Research Data Management + +[https://zenodo.org/records/10805204](https://zenodo.org/records/10805204) + +[https://doi.org/10.5281/zenodo.10805204](https://doi.org/10.5281/zenodo.10805204) + + +--- + +## The role of Helmholtz Centers in NFDI4BIOIMAGE - A national consortium enhancing FAIR data management for microscopy and bioimage analysis + +Riccardo Massei, Christian Schmidt, Michele Bortolomeazzi, Julia Thoennissen, Jan Bumberger, Timo Dickscheid, Jan-Philipp Mallm, Elisa Ferrando-May + +Published 2024-06-06 + +Licensed CC-BY-4.0 + + + +Germany’s National Research Data Infrastructure (NFDI) aims to establish a sustained, cross-disciplinary research data management (RDM) infrastructure that enables researchers to handle FAIR (findable, accessible, interoperable, reusable) data. While FAIR principles have been adopted by funders, policymakers, and publishers, their practical implementation remains an ongoing effort. In the field of bio-imaging, harmonization of data formats, metadata ontologies, and open data repositories is necessary to achieve FAIR data. The NFDI4BIOIMAGE was established to address these issues and develop tools and best practices to facilitate FAIR microscopy and image analysis data in alignment with international community activities. The consortium operates through its Data Stewards team to provide expertise and direct support to help overcome RDM challenges. The three Helmholtz Centers in NFDI4BIOIMAGE aim to collaborate closely with other centers and initiatives, such as HMC, Helmholtz AI, and HIP. Here we present NFDI4BIOIMAGE’s work and its significance for research in Helmholtz and beyond + +Tags: Nfdi4Bioimage, Research Data Management + +[https://zenodo.org/records/11501662](https://zenodo.org/records/11501662) + +[https://doi.org/10.5281/zenodo.11501662](https://doi.org/10.5281/zenodo.11501662) --- @@ -345,6 +648,27 @@ Content type: Slides [https://zenodo.org/doi/10.5281/zenodo.8329305](https://zenodo.org/doi/10.5281/zenodo.8329305) +--- + +## Towards Preservation of Life Science Data with NFDI4BIOIMAGE + +Robert Haase + +Published 2024-09-03 + +Licensed CC-BY-4.0 + + + +This talk will present the initiatives of the NFDI4BioImage consortium aimed at the long-term preservation of life science data. We will discuss our efforts to establish metadata standards, which are crucial for ensuring data reusability and integrity. The development of sustainable infrastructure is another key focus, enabling seamless data integration and analysis in the cloud. We will take a look at how we manage training materials and communicate with our community. Through these actions, NFDI4BioImage seeks to enable FAIR bioimage data management for German researchers, across disciplines and embedded in the international framework. + +Tags: Nfdi4Bioimage, Research Data Management + +[https://zenodo.org/records/13640979](https://zenodo.org/records/13640979) + +[https://doi.org/10.5281/zenodo.13640979](https://doi.org/10.5281/zenodo.13640979) + + --- ## Welcome to BioImage Town @@ -383,6 +707,95 @@ Content type: Poster [https://zenodo.org/doi/10.5281/zenodo.10730423](https://zenodo.org/doi/10.5281/zenodo.10730423) +--- + +## [CIDAS] Scalable strategies for a next-generation of FAIR bioimaging + +Josh Moore + +Published 2025-01-23 + +Licensed CC-BY-4.0 + + + +Talk given at Georg-August-Universität Göttingen Campus Institute Data Science23rd January 2025 +https://www.uni-goettingen.de/en/653203.html + +Tags: Nfdi4Bioimage + +[https://zenodo.org/records/14716546](https://zenodo.org/records/14716546) + +[https://doi.org/10.5281/zenodo.14716546](https://doi.org/10.5281/zenodo.14716546) + + +--- + +## [CMCB] Scalable strategies for a next-generation of FAIR bioimaging + +Josh Moore + +Published 2025-01-16 + +Licensed CC-BY-4.0 + + + +CMCB LIFE SCIENCES SEMINARSTechnische Universität Dresden16th January 2025 +https://tu-dresden.de/cmcb/crtd/news-termine/termine/cmcb-life-sciences-seminar-josh-moore-german-bioimaging-e-v-society-for-microscopy-and-image-analysis-constance +  + +Tags: Nfdi4Bioimage + +[https://zenodo.org/records/14650434](https://zenodo.org/records/14650434) + +[https://doi.org/10.5281/zenodo.14650434](https://doi.org/10.5281/zenodo.14650434) + + +--- + +## [Community Meeting 2024] Overview Team Image Data Analysis and Management + +Susanne Kunis, Thomas Zobel + +Published 2024-03-08 + +Licensed CC-BY-4.0 + + + +Overview of Activities of the Team Image Data Analysis and Management of German BioImaging e.V. +  + +Tags: Nfdi4Bioimage, Research Data Management + +[https://zenodo.org/records/10796364](https://zenodo.org/records/10796364) + +[https://doi.org/10.5281/zenodo.10796364](https://doi.org/10.5281/zenodo.10796364) + + +--- + +## [ELMI 2024] AI's Dirty Little Secret: Without +FAIR Data, It's Just Fancy Math + +Josh Moore, Susanne Kunis + +Published 2024-05-21 + +Licensed CC-BY-4.0 + + + +Poster presented at the European Light Microscopy Initiative meeting in Liverpool (https://www.elmi2024.org/) + +Tags: Nfdi4Bioimage, Research Data Management + +[https://zenodo.org/records/11235513](https://zenodo.org/records/11235513) + +[https://doi.org/10.5281/zenodo.11235513](https://doi.org/10.5281/zenodo.11235513) + + --- ## [ELMI 2024] AI's Dirty Little Secret: Without FAIR Data, It's Just Fancy Math @@ -442,3 +855,135 @@ Content type: Slides --- +## [Workshop Material] Fit for OMERO - How imaging facilities and IT departments work together to enable RDM for bioimaging, October 16-17, 2024, Heidelberg + +Tom Boissonnet, Bettina Hagen, Susanne Kunis, Christian Schmidt, Stefanie Weidtkamp-Peters + +Published 2024-11-18 + +Licensed CC-BY-4.0 + + + +Fit for OMERO: How imaging facilities and IT departments work together to enable RDM for bioimaging +Description: +Research data management (RDM) in bioimaging is challenging because of large file sizes, heterogeneous file formats and the variability of imaging methods. The image data management system OMERO (OME Remote Objects) allows for centralized and secure storage, organization, annotation, and interrogation of microscopy data by researchers. It is an internationally well-supported open-source software tool that has become one of the best-known image data management tools among bioimaging scientists. Nevertheless, the de novo setup of OMERO at an institute is a multi-stakeholder process that demands time, funds, organization and iterative implementation. In this workshop, participants learn how to begin setting up OMERO-based image data management at their institution. The topics include: + +Stakeholder identification at the university / research institute +Process management, time line expectations, and resources planning +Learning about each other‘s perspectives on chances and challenges for RDM +Funding opportunities and strategies for IT and imaging core facilities +Hands-on: Setting up an OMERO server in a virtual machine environment + +Target audience: +This workshop was directed at universities and research institutions who consider or plan to implement OMERO, or are in an early phase of implementation. This workshop was intended for teams from IT departments and imaging facilities to participate together with one person from the IT department, and one person from the imaging core facility at the same institution. +The trainers: + +Prof. Dr. Stefanie Weidtkamp-Peters (Imaging Core Facility Head, Center for Advanced Imaging, Heinrich Heine University of Düsseldorf) +Dr. Susanne Kunis (Software architect, OMERO administrator, metadata specialist, University of Osnabrück) +Dr. Tom Boissonnet (OMERO admin and image metadata specialist, Center for Advanced Imaging, Heinrich Heine University of Düsseldorf) +Dr. Bettina Hagen (IT Administration and service specialist, Max Planck Institute for the Biology of Ageing, Cologne)  +Dr. Christian Schmidt (Science Manager for Research Data Management in Bioimaging, German Cancer Research Center (DKFZ), Heidelberg) + +Time and place +The format was a two-day, in-person workshop (October 16-17, 2024). Location: Heidelberg, Germany +Workshop learning goals + +Learn the steps to establish a local RDM environment fit for bioimaging data +Create a network of IT experts and bioimaging specialists for bioimage RDM across institutions +Establish a stakeholder process management for installing OMERO-based RDM +Learn from each other, leverage different expertise +Learn how to train users, establish sustainability strategies, and foster FAIR RDM for bioimaging at your institution + + +Tags: Nfdi4Bioimage, Research Data Management + +[https://zenodo.org/records/14178789](https://zenodo.org/records/14178789) + +[https://doi.org/10.5281/zenodo.14178789](https://doi.org/10.5281/zenodo.14178789) + + +--- + +## [Workshop] Bioimage data management and analysis with OMERO + +Riccardo Massei, Michele Bortolomeazzi, Christian Schmidt + +Published 2024-05-13 + +Licensed CC-BY-4.0 + + + +Here we share the material used in a workshop held on May 13th, 2024, at the German Cancer Research Center in Heidelberg (on-premise) +Description:Microscopy experiments generate information-rich, multi-dimensional data, allowing us to investigate biological processes at high spatial and temporal resolution. Image processing and analysis is a standard procedure to retrieve quantitative information from biological imaging. Due to the complex nature of bioimaging files that often come in proprietary formats, it can be challenging to organize, structure, and annotate bioimaging data throughout a project. Data often needs to be moved between collaboration partners, transformed into open formats, processed with a variety of software tools, and exported to smaller-sized images for presentation. The path from image acquisition to final publication figures with quantitative results must be documented and reproducible. +In this workshop, participants learn how to use OMERO to organize their data and enrich the bioimage data with structured metadata annotations.We also focus on image analysis workflows in combination with OMERO based on the Fiji/ImageJ software and using Jupyter Notebooks. In the last part, we explore how OMERO can be used to create publication figures and prepare bioimage data for publication in a suitable repository such as the Bioimage Archive. +Module 1 (9 am - 10.15 am): Basics of OMERO, data structuring and annotation +Module 2 (10.45 am - 12.45 pm): OMERO and Fiji +Module 3 (1.45 pm - 3.45 pm): OMERO and Jupyter Notebooks +Module 4 (4.15 pm - 6. pm): Publication-ready figures and data with OMERO +The target group for this workshopThis workshop is directed at researchers at all career levels who plan to or have started to use OMERO for their microscopy research data management. We encourage the workshop participants to bring example data from their research to discuss suitable metadata annotation for their everyday practice. +Prerequisites:Users should bring their laptops and have access to the internet through one of the following options:- eduroam- institutional WiFi- VPN connection to their institutional networks to access OMERO +Who are the trainers? +Dr. Riccardo Massei (Helmholtz-Center for Environmental Research, UFZ, Leipzig) - Data Steward for Bioimaging Data in NFDI4BIOIMAGE +Dr. Michele Bortolomeazzi (DKFZ, Single cell Open Lab, bioimage data specialist, bioinformatician, staff scientist in the NFDI4BIOIMAGE project) +Dr. Christian Schmidt (Science Manager for Research Data Management in Bioimaging, German Cancer Research Center, Heidelberg, Project Coordinator of the NFDI4BIOIMAGE project) + +Tags: Nfdi4Bioimage, Research Data Management + +[https://zenodo.org/records/11350689](https://zenodo.org/records/11350689) + +[https://doi.org/10.5281/zenodo.11350689](https://doi.org/10.5281/zenodo.11350689) + + +--- + +## [Workshop] Research Data Management for Microscopy and BioImage Analysis + +Christian Schmidt, Tom Boissonnet, Michele Bortolomeazzi, Ksenia Krooß + +Published 2024-09-30 + +Licensed CC-BY-4.0 + + + +Research Data Management for Microscopy and BioImage Analysis + +Introduction to BioImaging Research Data Management, NFDI4BIOIMAGE and I3D:bioChristian Schmidt /DKFZ Heidelberg +OMERO as a tool for bioimaging data managementTom Boissonnet /Heinrich-Heine Universität Düsseldorf +Reproducible image analysis workflows with OMERO software APIsMichele Bortolomeazzi /DKFZ Heidelberg +Publishing datasets in public archives for bioimage dataKsenia Krooß /Heinrich-Heine Universität Düsseldorf + +Date & Venue:Thursday, Sept. 26, 5.30 p.m.Haus 22 / Paul Ehrlich Lecture Hall (H22-1)University Hospital Frankfurt + +Tags: Nfdi4Bioimage, Research Data Management + +[https://zenodo.org/records/13861026](https://zenodo.org/records/13861026) + +[https://doi.org/10.5281/zenodo.13861026](https://doi.org/10.5281/zenodo.13861026) + + +--- + +## ome2024-ngff-challenge + +Will Moore, Josh Moore, sherwoodf, Jean-Marie Burel, Norman Rzepka, dependabot[bot], JensWendt, Joost de Folter, Torsten St\xF6ter, AybukeKY, Eric Perlman, Tom Boissonnet + +Published 2024-08-30T12:00:53+00:00 + +Licensed BSD-3-CLAUSE + + + +Project planning and material repository for the 2024 challenge to generate 1 PB of OME-Zarr data + +Tags: Sharing, Nfdi4Bioimage, Research Data Management + +Content type: Github Repository + +[https://github.com/ome/ome2024-ngff-challenge](https://github.com/ome/ome2024-ngff-challenge) + + +--- + diff --git a/_sources/tags/omero.md b/_sources/tags/omero.md index 9b14fc0d..8ef8e751 100644 --- a/_sources/tags/omero.md +++ b/_sources/tags/omero.md @@ -11,7 +11,7 @@ Licensed CC-BY-4.0 The authors introduce BIOMERO (bioimage analysis in OMERO), a bridge connecting OMERO, a renowned bioimaging data management platform, FAIR workflows, and high-performance computing (HPC) environments. -Tags: OMERO, Workflow, Bioimage Analysis, Image Data Management +Tags: OMERO, Workflow, Bioimage Analysis Content type: Publication @@ -51,7 +51,7 @@ Licensed CC-BY-4.0 Tags: OMERO, Python -Content type: Blog +Content type: Blog Post [https://biapol.github.io/blog/robert_haase/browsing_idr/readme.html](https://biapol.github.io/blog/robert_haase/browsing_idr/readme.html) @@ -68,7 +68,7 @@ Licensed YOUTUBE STANDARD LICENSE -Tags: Image Data Management, OMERO, Bioimage Analysis +Tags: OMERO, Bioimage Analysis Content type: Video, Presentation @@ -119,9 +119,9 @@ Licensed UNKNOWN Example Workflows / usage of the Glencoe Software. -Tags: OMERO, Training +Tags: OMERO -Content type: Videos, Tutorial, Collection +Content type: Video, Tutorial, Collection [https://www.glencoesoftware.com/media/webinars/](https://www.glencoesoftware.com/media/webinars/) @@ -161,7 +161,7 @@ The open-source software OME Remote Objects (OMERO) is a data management softwar Tags: OMERO, Research Data Management, Nfdi4Bioimage, I3Dbio -Content type: Slide, Video +Content type: Slides, Video [https://zenodo.org/records/8323588](https://zenodo.org/records/8323588) @@ -176,13 +176,13 @@ Content type: Slide, Video None -Licensed GPLV3 +Licensed GPL-3.0 The KNIME Image Processing Extension allows you to read in more than 140 different kinds of images and to apply well known methods on images, like preprocessing. segmentation, feature extraction, tracking and classification in KNIME. -Tags: Imagej, OMERO, Bioimage Data, Workflow +Tags: Imagej, OMERO, Workflow Content type: Tutorial, Online Tutorial, Documentation @@ -278,7 +278,7 @@ Content type: Github Repository Rémy Jean Daniel Dornier -Licensed ['CC-BY-NC-SA-4.0'] +Licensed CC-BY-NC-SA-4.0 @@ -431,7 +431,7 @@ Licensed CC-BY-4.0 -Tags: OMERO, Galaxy, Metadata +Tags: OMERO, Galaxy, Metadata, Nfdi4Bioimage Content type: Tutorial, Framework, Workflow diff --git a/_sources/tags/open_science.md b/_sources/tags/open_science.md index c2b5e349..3183bb1f 100644 --- a/_sources/tags/open_science.md +++ b/_sources/tags/open_science.md @@ -51,7 +51,7 @@ Licensed CC-BY-SA-4.0 -Tags: Research Data Management, Bioimage Analysis, Image Data Management, Open Science +Tags: Research Data Management, Bioimage Analysis, Open Science Content type: Slides, Presentation @@ -74,7 +74,7 @@ Sharing knowledge and data in the life sciences allows us to learn from each oth Tags: Open Science, Teaching, Sharing -Content type: Collection, Tutorial, Videos +Content type: Collection, Tutorial, Video [https://www.ebi.ac.uk/training/online/courses/finding-using-public-data/](https://www.ebi.ac.uk/training/online/courses/finding-using-public-data/) @@ -112,7 +112,7 @@ Licensed CC-BY-4.0 -Tags: Bioimage Analysis, Open Science, Microscopy Image Analysis, Microscopy +Tags: Bioimage Analysis, Open Science, Microscopy Content type: Publication diff --git a/_sources/tags/open_source_software.md b/_sources/tags/open_source_software.md index 734c2b8c..73241bcc 100644 --- a/_sources/tags/open_source_software.md +++ b/_sources/tags/open_source_software.md @@ -26,7 +26,7 @@ Licensed GPL-2.0 An easy to use and open source converter for bioimaging data. NGFF-Converter is a GUI application for conversion of bioimage formats into OME-NGFF (Next-Generation File Format) or OME-TIFF. -Tags: Bioimage Data, Open Source Software +Tags: Open Source Software Content type: Application @@ -45,7 +45,7 @@ Licensed CC-BY-4.0 OME develops open-source software and data format standards for the storage and manipulation of biological microscopy data -Tags: Open Source Software, Microscopy Image Analysis, Bioimage Data +Tags: Open Source Software Content type: Video, Collection @@ -102,7 +102,7 @@ Licensed GPL-2.0 Java application to convert image file formats, including .mrxs, to an intermediate Zarr structure compatible with the OME-NGFF specification. -Tags: Open Source Software, Bioimage Data +Tags: Open Source Software Content type: Application, Github Repository @@ -121,7 +121,7 @@ Licensed GPL-2.0 Java application to convert a directory of tiles to an OME-TIFF pyramid. This is the second half of iSyntax/.mrxs => OME-TIFF conversion. -Tags: Open Source Software, Bioimage Data +Tags: Open Source Software Content type: Application, Github Repository diff --git a/_sources/tags/python.md b/_sources/tags/python.md index abb9e21f..5be5c84e 100644 --- a/_sources/tags/python.md +++ b/_sources/tags/python.md @@ -24,7 +24,7 @@ Mara Lampert Tags: Python, Napari, Bioimage Analysis -Content type: Blog +Content type: Blog Post [https://focalplane.biologists.com/2023/03/30/annotating-3d-images-in-napari/](https://focalplane.biologists.com/2023/03/30/annotating-3d-images-in-napari/) @@ -41,7 +41,7 @@ Licensed CC-BY-4.0 Training resources for Students at Uni Leipzig who want to dive into bio-image data science with Python. The material developed here between April and July 2024. -Tags: Bioimage Analysis, Deep Learning, Microscopy Image Analysis, Python +Tags: Bioimage Analysis, Artificial Intelligence, Python Content type: Github Repository @@ -79,9 +79,9 @@ Licensed CC-BY-4.0 Large Language Models (LLMs) change the way how we use computers. This also has impact on the bio-image analysis community. We can generate code that analyzes biomedical image data if we ask the right prompts. This talk outlines introduces basic principles, explains prompt engineering and how to apply it to bio-image analysis. We also compare how different LLM vendors perform on code generation tasks and which challenges are ahead for the bio-image analysis community. -Tags: Large Language Models, Python +Tags: Artificial Intelligence, Python -Content type: Slide +Content type: Slides [https://zenodo.org/records/10815329](https://zenodo.org/records/10815329) @@ -100,7 +100,7 @@ Licensed CC-BY-4.0 This repository contains training resources for Students at Uni Leipzig who want to dive into bio-image data science with Python. -Tags: Image Data Management, Deep Learning, Microscopy Image Analysis, Python +Tags: Research Data Management, Artificial Intelligence, Bioimage Analysis, Python Content type: Notebook @@ -119,7 +119,7 @@ Licensed CC-BY-4.0 These are the PPTx training resources for Students at Uni Leipzig who want to dive into bio-image data science with Python. The material developed here between April and July 2024. -Tags: Bioimage Analysis, Deep Learning, Microscopy Image Analysis, Python +Tags: Bioimage Analysis, Artificial Intelligence, Python Content type: Slides @@ -155,7 +155,7 @@ Licensed MIT BioEngine, a Python package designed for flexible deployment and execution of bioimage analysis models and workflows using AI, accessible via HTTP API and RPC. -Tags: Workflow Engine, Deep Learning, Python +Tags: Workflow Engine, Artificial Intelligence, Python Content type: Documentation @@ -208,7 +208,7 @@ Licensed CC-BY-4.0 Tags: OMERO, Python -Content type: Blog +Content type: Blog Post [https://biapol.github.io/blog/robert_haase/browsing_idr/readme.html](https://biapol.github.io/blog/robert_haase/browsing_idr/readme.html) @@ -410,7 +410,7 @@ Mara Lampert Tags: Python, Napari, Bioimage Analysis -Content type: Blog +Content type: Blog Post [https://focalplane.biologists.com/2023/05/03/feature-extraction-in-napari/](https://focalplane.biologists.com/2023/05/03/feature-extraction-in-napari/) @@ -459,7 +459,7 @@ Licensed CC-BY-4.0 Tags: Python, Bioimage Analysis, Artificial Intelligence -Content type: Slide +Content type: Slides [https://f1000research.com/slides/12-971](https://f1000research.com/slides/12-971) @@ -476,7 +476,7 @@ Licensed CC-BY-4.0 Tags: Python, Conda, Mamba -Content type: Blog +Content type: Blog Post [https://biapol.github.io/blog/mara_lampert/getting_started_with_mambaforge_and_python/readme.html](https://biapol.github.io/blog/mara_lampert/getting_started_with_mambaforge_and_python/readme.html) @@ -491,7 +491,7 @@ Mara Lampert Tags: Github, Python, Science Communication -Content type: Blog +Content type: Blog Post [https://focalplane.biologists.com/2024/04/03/how-to-write-a-bug-report/](https://focalplane.biologists.com/2024/04/03/how-to-write-a-bug-report/) @@ -508,7 +508,7 @@ Licensed CC-BY-4.0 This lesson shows how to use Python and scikit-image to do basic image processing. -Tags: Segmentation, Bioimage Analysis, Training, Python, Scikit-Image, Image Segmentation +Tags: Bioimage Analysis, Python Content type: Tutorial, Workflow @@ -597,7 +597,7 @@ Licensed MIT This course consists of lectures and exercises that teach the background of deep learning for image analysis and show applications to classification and segmentation analysis problems. -Tags: Deep Learning, Pytorch, Segmentation, Python +Tags: Artificial Intelligence, Python Content type: Notebook @@ -633,7 +633,7 @@ Licensed CC-BY-4.0 Tags: Python, Conda, Mamba -Content type: Blog +Content type: Blog Post [https://focalplane.biologists.com/2022/12/08/managing-scientific-python-environments-using-conda-mamba-and-friends/](https://focalplane.biologists.com/2022/12/08/managing-scientific-python-environments-using-conda-mamba-and-friends/) @@ -686,7 +686,7 @@ Licensed CC-BY-4.0 The Microscopy data analysis: machine learning and the BioImage Archive course, which focused on introducing programmatic approaches used in the analysis of bioimage data via the BioImage Archive, ran in May 2023. -Tags: Microscopy Image Analysis, Python, Deep Learning +Tags: Bioimage Analysis, Python, Artificial Intelligence Content type: Video, Slides @@ -739,7 +739,7 @@ Licensed UNKNOWN Tags: Python, Neubias, Artificial Intelligence, Bioimage Analysis -Content type: Slide, Notebook +Content type: Slides, Notebook [https://github.com/maweigert/neubias_academy_stardist](https://github.com/maweigert/neubias_academy_stardist) @@ -869,7 +869,7 @@ Mara Lampert Tags: Python, Jupyter, Bioimage Analysis, Prompt Engineering, Biabob -Content type: Blog +Content type: Blog Post [https://focalplane.biologists.com/2024/07/18/prompt-engineering-in-bio-image-analysis/](https://focalplane.biologists.com/2024/07/18/prompt-engineering-in-bio-image-analysis/) @@ -935,7 +935,7 @@ Licensed CC-BY-4.0 This book contains the quantitative analysis labs for the QI CSHL course, 2024 -Tags: Segmentation, Python +Tags: Python Content type: Notebook @@ -992,7 +992,7 @@ Mara Lampert Tags: Python, Napari, Bioimage Analysis -Content type: Blog +Content type: Blog Post [https://focalplane.biologists.com/2023/04/13/quality-assurance-of-segmentation-results/](https://focalplane.biologists.com/2023/04/13/quality-assurance-of-segmentation-results/) @@ -1007,7 +1007,7 @@ Mara Lampert Tags: Python, Napari, Bioimage Analysis -Content type: Blog +Content type: Blog Post [https://focalplane.biologists.com/2023/03/02/rescaling-images-and-pixel-anisotropy/](https://focalplane.biologists.com/2023/03/02/rescaling-images-and-pixel-anisotropy/) @@ -1024,7 +1024,7 @@ Licensed CC-BY-4.0 Tags: Python, Artificial Intelligence, Bioimage Analysis -Content type: Blog +Content type: Blog Post [https://biapol.github.io/blog/marcelo_zoccoler/omero_scripts/readme.html](https://biapol.github.io/blog/marcelo_zoccoler/omero_scripts/readme.html) @@ -1109,7 +1109,7 @@ Mara Lampert Tags: Python, Napari, Bioimage Analysis -Content type: Blog +Content type: Blog Post [https://focalplane.biologists.com/2023/06/01/tracking-in-napari/](https://focalplane.biologists.com/2023/06/01/tracking-in-napari/) @@ -1147,7 +1147,7 @@ Licensed MIT This repository offer access to teaching material and useful resources for the YMIA - Python-Based Event Series. -Tags: Python, Large Language Models, Prompt Engineering, Biabob, Bioimage Analysis, Microscopy Image Analysis +Tags: Python, Artifical Intelligence, Bioimage Analysis Content type: Github Repository, Slides diff --git a/_sources/tags/research_data_management.md b/_sources/tags/research_data_management.md index 308bca8c..070eaf9d 100644 --- a/_sources/tags/research_data_management.md +++ b/_sources/tags/research_data_management.md @@ -1,4 +1,4 @@ -# Research data management (110) +# Research data management (128) ## "ZENODO und Co." Was bringt und wer braucht ein Repositorium? Elfi Hesse, Jan-Christoph Deinert, Christian Löschen @@ -71,6 +71,33 @@ Content type: Publication [https://www.nature.com/articles/s41592-018-0195-8](https://www.nature.com/articles/s41592-018-0195-8) +--- + +## A journey to FAIR microscopy data + +Stefanie Weidtkamp-Peters, Janina Hanne, Christian Schmidt + +Published 2023-05-03 + +Licensed CC-BY-4.0 + + + +Oral presentation, 32nd MoMAN "From Molecules to Man" Seminar, Ulm, online. Monday February 6th, 2023 + +Abstract: + +Research data management is essential in nowadays research, and one of the big opportunities to accelerate collaborative and innovative scientific projects. To achieve this goal, all our data needs to be FAIR (findable, accessible, interoperable, reproducible). For data acquired on microscopes, however, a common ground for FAIR data sharing is still to be established. Plenty of work on file formats, data bases, and training needs to be performed to highlight the value of data sharing and exploit its potential for bioimaging data. + +In this presentation, Stefanie Weidtkamp-Peters will introduce the challenges for bioimaging data management, and the necessary steps to achieve data FAIRification. German BioImaging - GMB e.V., together with other institutions, contributes to this endeavor. Janina Hanne will present how the network of imaging core facilities, research groups and industry partners is key to the German bioimaging community’s aligned collaboration toward FAIR bioimaging data. These activities have paved the way for two data management initiatives in Germany: I3D:bio (Information Infrastructure for BioImage Data) and NFDI4BIOIMAGE, a consortium of the National Research Data Infrastructure. Christian Schmidt will introduce the goals and measures of these initiatives to the benefit of imaging scientist’s work and everyday practice.   + +Tags: Nfdi4Bioimage, Research Data Management + +[https://zenodo.org/records/7890311](https://zenodo.org/records/7890311) + +[https://doi.org/10.5281/zenodo.7890311](https://doi.org/10.5281/zenodo.7890311) + + --- ## A practical guide to bioimaging research data management in core facilities @@ -128,6 +155,27 @@ Content type: Slides [https://doi.org/10.5281/zenodo.11472148](https://doi.org/10.5281/zenodo.11472148) +--- + +## Angebote der NFDI für die Forschung im Bereich Zoologie + +Birgitta König-Ries, Robert Haase, Daniel Nüst, Konrad Förstner, Judith Sophie Engel + +Published 2024-12-04 + +Licensed CC-BY-4.0 + + + +In diesem Slidedeck geben wir einen Einblick in Angebote und Dienste der Nationalen Forschungsdaten Infrastruktur (NFDI), die Relevant für die Zoologie und angrenzende Disziplinen relevant sein könnten. + +Tags: Nfdi4Bioimage, Research Data Management + +[https://zenodo.org/records/14278058](https://zenodo.org/records/14278058) + +[https://doi.org/10.5281/zenodo.14278058](https://doi.org/10.5281/zenodo.14278058) + + --- ## Best practice data life cycle approaches for the life sciences @@ -180,15 +228,34 @@ Licensed CC-BY-4.0 This presentation gives a short outline of the complexity of data and metadata in the bioimaging universe. It introduces NFDI4BIOIMAGE as a newly formed consortium as part of the German 'Nationale Forschungsdateninfrastruktur' (NFDI) and its goals and tools for data management including its current members on TU Dresden campus.   -Tags: Research Data Management, Tu Dresden, Bioimage Data, Nfdi4Bioimage +Tags: Research Data Management, Nfdi4Bioimage -Content type: Slide +Content type: Slides [https://zenodo.org/records/10083555](https://zenodo.org/records/10083555) [https://doi.org/10.5281/zenodo.10083555](https://doi.org/10.5281/zenodo.10083555) +--- + +## Bio-image Data Science + +Robert Haase + +Licensed CC-BY-4.0 + + + +This repository contains training resources for Students at Uni Leipzig who want to dive into bio-image data science with Python. + +Tags: Research Data Management, Artificial Intelligence, Bioimage Analysis, Python + +Content type: Notebook + +[https://github.com/ScaDS/BIDS-lecture-2024](https://github.com/ScaDS/BIDS-lecture-2024) + + --- ## Bring your own data workshops @@ -229,7 +296,7 @@ Licensed CC-BY-4.0 Tags: Research Data Management, Bioimage Analysis, Nfdi4Bioimage -Content type: Slide +Content type: Slides [https://f1000research.com/slides/12-1054](https://f1000research.com/slides/12-1054) @@ -253,6 +320,27 @@ Content type: Publication [https://onlinelibrary.wiley.com/doi/full/10.1111/jmi.13192](https://onlinelibrary.wiley.com/doi/full/10.1111/jmi.13192) +--- + +## Collaborative Working and Version Control with git[hub] + +Robert Haase + +Published 2024-01-10 + +Licensed CC-BY-4.0 + + + +This slide deck introduces the version control tool git, related terminology and the Github Desktop app for managing files in Git[hub] repositories. We furthermore dive into:* Working with repositories* Collaborative with others* Github-Zenodo integration* Github pages* Artificial Intelligence answering Github Issues + +Tags: Nfdi4Bioimage, Globias, Research Data Management, Research Software Management + +[https://zenodo.org/records/14626054](https://zenodo.org/records/14626054) + +[https://doi.org/10.5281/zenodo.14626054](https://doi.org/10.5281/zenodo.14626054) + + --- ## Collaborative bio-image analysis script editing with git @@ -267,7 +355,7 @@ Introduction to version control using git for collaborative, reproducible script Tags: Sharing, Research Data Management -Content type: Blog +Content type: Blog Post [https://focalplane.biologists.com/2021/09/04/collaborative-bio-image-analysis-script-editing-with-git/](https://focalplane.biologists.com/2021/09/04/collaborative-bio-image-analysis-script-editing-with-git/) @@ -297,7 +385,7 @@ Content type: Poster Beth Cimini et al. -Licensed BSD LICENSE +Licensed BSD-3-CLAUSE @@ -349,9 +437,9 @@ Licensed CC-BY-4.0 In this blog post the author demonstrates how chatGPT can be used to combine a fictive project description with a DMP specification to produce a project-specific DMP. -Tags: Research Data Management, Large Language Models, Artificial Intelligence +Tags: Research Data Management, Artificial Intelligence -Content type: Blog +Content type: Blog Post [https://focalplane.biologists.com/2023/11/06/creating-a-research-data-management-plan-using-chatgpt/](https://focalplane.biologists.com/2023/11/06/creating-a-research-data-management-plan-using-chatgpt/) @@ -431,7 +519,7 @@ Job Fermie Tags: Research Data Management -Content type: Blog +Content type: Blog Post [https://blog.delmic.com/data-handling-in-large-scale-electron-microscopy](https://blog.delmic.com/data-handling-in-large-scale-electron-microscopy) @@ -465,7 +553,7 @@ Licensed CC-BY-SA-4.0 -Tags: Research Data Management, Bioimage Analysis, Image Data Management, Open Science +Tags: Research Data Management, Bioimage Analysis, Open Science Content type: Slides, Presentation @@ -484,7 +572,7 @@ Licensed CC-BY-4.0 Explore fundamental topics on research data management (RDM), how DataPLANT implements these aspects to support plant researchers with RDM tools and services, read guides and manuals or search for some teaching materials. -Tags: Research Data Management, Training, Dataplant +Tags: Research Data Management, Dataplant Content type: Collection @@ -767,7 +855,7 @@ Licensed CC-BY-4.0 The authors show the utility of Minimum Information for High Content Screening Microscopy Experiments (MIHCSME) for High Content Screening (HCS) data using multiple examples from the Leiden FAIR Cell Observatory, a Euro-Bioimaging flagship node for high content screening and the pilot node for implementing FAIR bioimaging data throughout the Netherlands Bioimaging network. -Tags: FAIR-Principles, Metadata, Research Data Management, Image Data Management, Bioimage Data +Tags: FAIR-Principles, Metadata, Research Data Management Content type: Publication @@ -841,7 +929,7 @@ Sharing your data can benefit your career in some interesting ways. In this post Tags: Research Data Management, Sharing -Content type: Blog +Content type: Blog Post [https://web.library.uq.edu.au/blog/2022/03/five-great-reasons-share-your-research-data](https://web.library.uq.edu.au/blog/2022/03/five-great-reasons-share-your-research-data) @@ -979,7 +1067,7 @@ Licensed UNKNOWN A Microscopy Research Data Management Resource. -Tags: Metadata, I3Dbio, Research Data Management, Bioimage Data +Tags: Metadata, I3Dbio, Research Data Management Content type: Collection @@ -1019,7 +1107,7 @@ The open-source software OME Remote Objects (OMERO) is a data management softwar Tags: OMERO, Research Data Management, Nfdi4Bioimage, I3Dbio -Content type: Slide, Video +Content type: Slides, Video [https://zenodo.org/records/8323588](https://zenodo.org/records/8323588) @@ -1042,7 +1130,7 @@ Blog post about why we should license our work and what is important when choosi Tags: Licensing, Research Data Management -Content type: Blog +Content type: Blog Post [https://focalplane.biologists.com/2023/05/06/if-you-license-it-itll-be-harder-to-steal-it-why-we-should-license-our-work/](https://focalplane.biologists.com/2023/05/06/if-you-license-it-itll-be-harder-to-steal-it-why-we-should-license-our-work/) @@ -1068,6 +1156,81 @@ Content type: Slides [https://zenodo.org/records/4778265](https://zenodo.org/records/4778265) +--- + +## Key-Value pair template for annotation in OMERO for light microscopy data acquired with AxioScan7 - Core Facility Cellular Imaging (CFCI) + +Silke Tulok, Anja Nobst, Anett Jannasch, Tom Boissonnet, Gunar Fabig + +Published 2024-06-28 + +Licensed CC-BY-4.0 + + + +This Key-Value pair template is used for the data documentation during imaging experiments and the later data annotation in OMERO. It is tailored for the usage and image acquisition at the slide scanning system Zeiss AxioScan 7 in the Core Facility Cellular Imaging (CFCI). It contains important metadata of the imaging experiment, which are not saved in the corresponding imaging files. All users of the Core Facility Cellular Imaging are trained to use that file to document their imaging parameters directly during the data acquisition with the possibility for a later upload to OMERO. Furthermore, there is a corresponding public example image used in the publication "Setting up an institutional OMERO environment for bioimage data: perspectives from both facility staff and users" and is available here: +https://omero.med.tu-dresden.de/webclient/?show=image-33248 +This template was developed by the CFCI staff during the setup and usage of the AxioScan 7 and is based on the REMBI recommendations (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8606015). +With this template it is possible to create a csv-file, that can be used to annotate an image or dataset in OMERO using the annotation script (https://github.com/ome/omero-scripts/blob/develop/omero/annotation_scripts/). +How to use: + +fill the template sheet  with your metadata +select and copy the data range containing the Keys and Values +open a new excel sheet and paste transpose in cell A1  +Important: cell A1 contains always the name 'dataset' and cell A2 contains the exact name of the image/dataset, which should be annotated in OMERO +save the new excel sheet in csv-file (comma separated values) format + +An example can be seen in sheet 3 'csv_AxioScan'. +Important note: The code has to be 8-Bit UCS transformation format (UTF-8) otherwise several characters (for example µ, %,°) might be not able to decode by the annotation script. We encountered this issue with old Microsoft-Office versions (MS Office 2016).  +Note: By filling the values in the excel sheet, avoid the usage of comma as decimal delimiter. +See cross reference: +10.5281/zenodo.12547566 Key-Value pair template for annotation of datasets in OMERO for light- and electron microscopy data within the research group of Prof. Mueller-Reichert +10.5281/zenodo.12546808 Key-Value pair template for annotation of datasets in OMERO (PERIKLES study) + +Tags: Nfdi4Bioimage, Research Data Management + +[https://zenodo.org/records/12578084](https://zenodo.org/records/12578084) + +[https://doi.org/10.5281/zenodo.12578084](https://doi.org/10.5281/zenodo.12578084) + + +--- + +## Key-Value pair template for annotation of datasets in OMERO (PERIKLES study) + +Anett Jannasch, Silke Tulok, Vanessa Aphaia Fiona Fuchs, Tom Boissonnet, Christian Schmidt, Michele Bortolomeazzi, Gunar Fabig, Chukwuebuka Okafornta + +Published 2024-06-26 + +Licensed CC-BY-4.0 + + + +This is a Key-Value pair template used for the annotation of datasets in OMERO. It is tailored for a research study (PERIKLES project) on the biocompatibility of newly designed biomaterials out of pericardial tissue for cardiovascular substitutes (https://doi.org/10.1063/5.0182672) conducted in the research department of Cardiac Surgery at the Faculty of Medicine Carl Gustav Carus at the Technische Universität Dresden . A corresponding public example dataset is used in the publication "Setting up an institutional OMERO environment for bioimage data: perspectives from both facility staff and users" and is available here +(https://omero.med.tu-dresden.de/webclient/?show=dataset-1557). +The template is based on the REMBI recommendations (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8606015) and it was developed during the PoL-Bio-Image Analysis Symposium in Dresden Aug 28th- Sept 1th 2023.  +With this template it is possible to create a csv-file, that can be used to annotate a dataset in OMERO using the annotation script (https://github.com/ome/omero-scripts/blob/develop/omero/annotation_scripts/). +How to use: +select and copy the data range containing Keys and Values +open a new excel sheet and paste transpose in column B1 +type in A1 'dataset' +insert in A2 the exact name of the dataset, which should be annotated in OMERO +save the new excel sheet in csv- (comma seperated values) file format + +Example can be seen in sheet 1 'csv import'. Important note; the code has to be 8-Bit UCS transformation format (UTF-8) otherwise several characters (for example µ, %,°) might not be able to decode by the annotation script. We encountered this issue with old Microsoft Office versions (e.g. MS Office 2016).  +Note: By filling the values in the excel sheet, avoid the usage of decimal delimiter. +  +See cross reference: +10.5281/zenodo.12547566 Key-Value pair template for annotation of datasets in OMERO (light- and electron microscopy data within the research group of Prof. Mueller-Reichert) +10.5281/zenodo.12578084 Key-Value pair template for annotation in OMERO for light microscopy data acquired with AxioScan7 - Core Facility Cellular Imaging (CFCI) + +Tags: Nfdi4Bioimage, Research Data Management + +[https://zenodo.org/records/12546808](https://zenodo.org/records/12546808) + +[https://doi.org/10.5281/zenodo.12546808](https://doi.org/10.5281/zenodo.12546808) + + --- ## Kollaboratives Arbeiten und Versionskontrolle mit Git @@ -1178,7 +1341,7 @@ Licensed CC-BY-4.0 The Brain Imaging Data Structure (BIDS) is a specification for organizing, sharing, and archiving neuroimaging data and metadata in a reusable way. -Tags: Research Data Management, Image Data Management, Bioimage Data +Tags: Research Data Management Content type: Publication @@ -1235,9 +1398,9 @@ Licensed CCY-BY-SA-4.0 Align existing and establish novel services & solutions for data management tasks throughout the bioimage data lifecycle. -Tags: Nfdi4Bioimage, Image Data Management, Bioimage Data, Research Data Management +Tags: Nfdi4Bioimage, Research Data Management -Content type: Conference Abstract, Slide +Content type: Conference Abstract, Slides [https://doi.org/10.11588/heidok.00029489](https://doi.org/10.11588/heidok.00029489) @@ -1280,6 +1443,28 @@ Content type: Slides [https://zenodo.org/doi/10.5281/zenodo.8414318](https://zenodo.org/doi/10.5281/zenodo.8414318) +--- + +## NFDI4BIOIMAGE data management illustrations by Henning Falk + +NFDI4BIOIMAGE Consortium + +Published 2024-11-29 + +Licensed CC-BY-4.0 + + + +These illustrations were contracted by the Heinrich Heine University Düsseldorf in the frame of the consortium NFDI4BIOIMAGE from Henning Falk for the purpose of education and public outreach. The illustrations are free to use under a CC-BY 4.0 license.AttributionPlease include an attribution similar to: "Data annoation matters", NFDI4BIOIMAGE Consortium (2024): NFDI4BIOIMAGE data management illustrations by Henning Falk, Zenodo, https://doi.org/10.5281/zenodo.14186100, is used under a CC-BY 4.0 license. Modifications to this illustration include cropping. +  + +Tags: Nfdi4Bioimage, Research Data Management + +[https://zenodo.org/records/14186101](https://zenodo.org/records/14186101) + +[https://doi.org/10.5281/zenodo.14186101](https://doi.org/10.5281/zenodo.14186101) + + --- ## NFDI4Bioimage - TA3-Hackathon - UoC-2023 (Cologne Hackathon) @@ -1318,6 +1503,27 @@ Content type: Github Repository [https://zenodo.org/doi/10.5281/zenodo.10609770](https://zenodo.org/doi/10.5281/zenodo.10609770) +--- + +## NFDI4Bioimage Calendar 2024 October; original image + +Christian Jüngst, Peter Zentis + +Published 2024-09-25 + +Licensed CC-BY-4.0 + + + +Raw microscopy image from the NFDI4Bioimage calendar October 2024 + +Tags: Nfdi4Bioimage, Research Data Management + +[https://zenodo.org/records/13837146](https://zenodo.org/records/13837146) + +[https://doi.org/10.5281/zenodo.13837146](https://doi.org/10.5281/zenodo.13837146) + + --- ## OME Event Database @@ -1348,6 +1554,29 @@ Content type: Publication [https://www.nature.com/articles/s41592-021-01326-w](https://www.nature.com/articles/s41592-021-01326-w) +--- + +## OME2024 NGFF Challenge Results + +Josh Moore + +Published 2024-11-01 + +Licensed CC-BY-4.0 + + + +Presented at the 2024 FoundingGIDE event in Okazaki, Japan: https://founding-gide.eurobioimaging.eu/event/foundinggide-community-event-2024/ +Note: much of the presentation was a demonstration of the OME2024-NGFF-Challenge -- https://ome.github.io/ome2024-ngff-challenge/ especially of querying an extraction of the metadata (https://github.com/ome/ome2024-ngff-challenge-metadata) +  + +Tags: Nfdi4Bioimage, Research Data Management + +[https://zenodo.org/records/14234608](https://zenodo.org/records/14234608) + +[https://doi.org/10.5281/zenodo.14234608](https://doi.org/10.5281/zenodo.14234608) + + --- ## OMERO for microscopy research data management @@ -1436,7 +1665,7 @@ Jennifer Waters Tags: Research Data Management -Content type: Blog +Content type: Blog Post [https://datamanagement.hms.harvard.edu/news/promoting-data-management-nikon-imaging-center-and-cell-biology-microscopy-facility](https://datamanagement.hms.harvard.edu/news/promoting-data-management-nikon-imaging-center-and-cell-biology-microscopy-facility) @@ -1553,7 +1782,7 @@ Licensed UNKNOWN This tool is intended to link different research data management platforms with each other. -Tags: Research Data Management, Image Data Management +Tags: Research Data Management Content type: Github Repository @@ -1574,7 +1803,7 @@ Licensed UNKNOWN Bioimaging data have significant potential for reuse, but unlocking this potential requires systematic archiving of data and metadata in public databases. The authors propose draft metadata guidelines to begin addressing the needs of diverse communities within light and electron microscopy. -Tags: Metadata, Bioimage Data, Image Data Management, Research Data Management +Tags: Metadata, Research Data Management Content type: Publication @@ -1595,7 +1824,7 @@ Licensed CC0-1.0 Recommended Metadata for Biological Images (REMBI) provides guidelines for metadata for biological images to enable the FAIR sharing of scientific data. -Tags: FAIR-Principles, Metadata, Image Data Management, Research Data Management, Bioimage Data +Tags: FAIR-Principles, Metadata, Research Data Management Content type: Collection @@ -1627,7 +1856,7 @@ This Research Data Management (RDM) Slides introduce to the multidisciplinary kn Tags: Research Data Management -Content type: Slide +Content type: Slides [https://zenodo.org/record/6602101](https://zenodo.org/record/6602101) @@ -1686,7 +1915,7 @@ Licensed CC-BY-4.0 As an initiative within Germany's National Research Data Infrastructure, the authors conducted this community survey in summer 2021 to assess the state of the art of bioimaging RDM and the community needs. -Tags: Research Data Management, Image Data Management, Bioimage Data +Tags: Research Data Management Content type: Publication @@ -1737,7 +1966,7 @@ Elisabeth Kugler Tags: Sharing, Research Data Management -Content type: Blog +Content type: Blog Post [https://focalplane.biologists.com/2023/07/26/sharing-your-poster-on-figshare/](https://focalplane.biologists.com/2023/07/26/sharing-your-poster-on-figshare/) @@ -1756,7 +1985,7 @@ Introduction to sharing resources online and licensing Tags: Sharing, Research Data Management -Content type: Slide +Content type: Slides [https://f1000research.com/slides/10-519](https://f1000research.com/slides/10-519) @@ -1775,7 +2004,7 @@ Blog post about how to share data using zenodo.org Tags: Sharing, Research Data Management -Content type: Blog +Content type: Blog Post [https://focalplane.biologists.com/2023/02/15/sharing-research-data-with-zenodo/](https://focalplane.biologists.com/2023/02/15/sharing-research-data-with-zenodo/) @@ -1822,27 +2051,40 @@ Content type: Tutorial ## Structuring of Data and Metadata in Bioimaging: Concepts and technical Solutions in the Context of Linked Data -Susanne Kunis +Sarah Weischer, Jens Wendt, Thomas Zobel -Published 2022-08-24 +Published 2022-07-12 Licensed CC-BY-4.0 -guided walkthrough of poster at https://doi.org/10.5281/zenodo.6821815 +Provides an overview of contexts, frameworks, and models from the world of bioimage data as well as metadata. Visualizes the techniques for structuring this data as Linked Data. (Walkthrough Video: https://doi.org/10.5281/zenodo.7018928 ) -which provides an overview of contexts, frameworks, and models from the world of bioimage data as well as metadata and the techniques for structuring this data as Linked Data. +Content: -You can also watch the video in the browser on the I3D:bio website. -Tags: Nfdi4Bioimage, Research Data Management + Types of metadata + Data formats + Data Models Microscopy Data + Tools to edit/gather metadata + ISA Framework + FDO Framework + Ontology + RDF + JSON-LD + SPARQL + Knowledge Graph + Linked Data + Smart Data + ... -Content type: Video -[https://zenodo.org/record/7018929](https://zenodo.org/record/7018929) +Tags: Nfdi4Bioimage, Research Data Management -[https://doi.org/10.5281/zenodo.7018929](https://doi.org/10.5281/zenodo.7018929) +[https://zenodo.org/records/7018750](https://zenodo.org/records/7018750) + +[https://doi.org/10.5281/zenodo.7018750](https://doi.org/10.5281/zenodo.7018750) --- @@ -1855,7 +2097,7 @@ Licensed CC0-1.0 To submit, you’ll need to register an account, organise and upload your data, prepare a file list, and then submit using our web submission form. These steps are explained here. -Tags: Research Data Management, Image Data Management, Bioimage Data +Tags: Research Data Management Content type: Tutorial, Video @@ -1914,7 +2156,7 @@ Licensed UNKNOWN The BioImage Archive is a new archival data resource at the European Bioinformatics Institute (EMBL-EBI). -Tags: Image Data Management, Research Data Management, Bioimage Data +Tags: Research Data Management Content type: Publication @@ -1946,6 +2188,48 @@ Content type: Publication [https://doi.org/10.1038/sdata.2016.18](https://doi.org/10.1038/sdata.2016.18) +--- + +## The Information Infrastructure for BioImage Data (I3D:bio) project to advance FAIR microscopy data management for the community + +Christian Schmidt, Michele Bortolomeazzi, Tom Boissonnet, Julia Dohle, Tobias Wernet, Janina Hanne, Roland Nitschke, Susanne Kunis, Karen Bernhardt, Stefanie Weidtkamp-Peters, Elisa Ferrando-May + +Published 2024-03-04 + +Licensed CC-BY-4.0 + + + +Research data management (RDM) in microscopy and image analysis is a challenging task. Large files in proprietary formats, complex N-dimensional array structures, and various metadata models and formats can make image data handling inconvenient and difficult. For data organization, annotation, and sharing, researchers need solutions that fit everyday practice and comply with the FAIR (Findable, Accessible, Interoperable, Reusable) principles. International community-based efforts have begun creating open data models (OME), an open file format and translation library (OME-TIFF, Bio-Formats), data management software platforms, and microscopy metadata recommendations and annotation tools. Bringing these developments into practice requires support and training. Iterative feedback and tool improvement is needed to foster practical adoption by the scientific community. The Information Infrastructure for BioImage Data (I3D:bio) project works on guidelines, training resources, and practical assistance for FAIR microscopy RDM adoption with a focus on the management platform OMERO and metadata annotations. + +Tags: Nfdi4Bioimage, Research Data Management + +[https://zenodo.org/records/10805204](https://zenodo.org/records/10805204) + +[https://doi.org/10.5281/zenodo.10805204](https://doi.org/10.5281/zenodo.10805204) + + +--- + +## The role of Helmholtz Centers in NFDI4BIOIMAGE - A national consortium enhancing FAIR data management for microscopy and bioimage analysis + +Riccardo Massei, Christian Schmidt, Michele Bortolomeazzi, Julia Thoennissen, Jan Bumberger, Timo Dickscheid, Jan-Philipp Mallm, Elisa Ferrando-May + +Published 2024-06-06 + +Licensed CC-BY-4.0 + + + +Germany’s National Research Data Infrastructure (NFDI) aims to establish a sustained, cross-disciplinary research data management (RDM) infrastructure that enables researchers to handle FAIR (findable, accessible, interoperable, reusable) data. While FAIR principles have been adopted by funders, policymakers, and publishers, their practical implementation remains an ongoing effort. In the field of bio-imaging, harmonization of data formats, metadata ontologies, and open data repositories is necessary to achieve FAIR data. The NFDI4BIOIMAGE was established to address these issues and develop tools and best practices to facilitate FAIR microscopy and image analysis data in alignment with international community activities. The consortium operates through its Data Stewards team to provide expertise and direct support to help overcome RDM challenges. The three Helmholtz Centers in NFDI4BIOIMAGE aim to collaborate closely with other centers and initiatives, such as HMC, Helmholtz AI, and HIP. Here we present NFDI4BIOIMAGE’s work and its significance for research in Helmholtz and beyond + +Tags: Nfdi4Bioimage, Research Data Management + +[https://zenodo.org/records/11501662](https://zenodo.org/records/11501662) + +[https://doi.org/10.5281/zenodo.11501662](https://doi.org/10.5281/zenodo.11501662) + + --- ## Thinking data management on different scales @@ -1965,6 +2249,27 @@ Content type: Slides [https://zenodo.org/doi/10.5281/zenodo.8329305](https://zenodo.org/doi/10.5281/zenodo.8329305) +--- + +## Towards Preservation of Life Science Data with NFDI4BIOIMAGE + +Robert Haase + +Published 2024-09-03 + +Licensed CC-BY-4.0 + + + +This talk will present the initiatives of the NFDI4BioImage consortium aimed at the long-term preservation of life science data. We will discuss our efforts to establish metadata standards, which are crucial for ensuring data reusability and integrity. The development of sustainable infrastructure is another key focus, enabling seamless data integration and analysis in the cloud. We will take a look at how we manage training materials and communicate with our community. Through these actions, NFDI4BioImage seeks to enable FAIR bioimage data management for German researchers, across disciplines and embedded in the international framework. + +Tags: Nfdi4Bioimage, Research Data Management + +[https://zenodo.org/records/13640979](https://zenodo.org/records/13640979) + +[https://doi.org/10.5281/zenodo.13640979](https://doi.org/10.5281/zenodo.13640979) + + --- ## Train-the-Trainer Concept on Research Data Management @@ -2002,7 +2307,7 @@ Licensed CC-BY-4.0 Glittr.org is a platform that aggregates and indexes training materials on computational life sciences from public git repositories, making it easier for users to find, compare, and analyze these resources based on various metrics. By providing insights into the availability of materials, collaboration patterns, and licensing practices, Glittr.org supports adherence to the FAIR principles, benefiting the broader life sciences community. -Tags: Training, Bioimage Analysis, Research Data Management +Tags: Bioimage Analysis, Research Data Management Content type: Publication, Preprint @@ -2047,6 +2352,50 @@ Content type: Poster [https://zenodo.org/doi/10.5281/zenodo.8340247](https://zenodo.org/doi/10.5281/zenodo.8340247) +--- + +## [Community Meeting 2024] Overview Team Image Data Analysis and Management + +Susanne Kunis, Thomas Zobel + +Published 2024-03-08 + +Licensed CC-BY-4.0 + + + +Overview of Activities of the Team Image Data Analysis and Management of German BioImaging e.V. +  + +Tags: Nfdi4Bioimage, Research Data Management + +[https://zenodo.org/records/10796364](https://zenodo.org/records/10796364) + +[https://doi.org/10.5281/zenodo.10796364](https://doi.org/10.5281/zenodo.10796364) + + +--- + +## [ELMI 2024] AI's Dirty Little Secret: Without +FAIR Data, It's Just Fancy Math + +Josh Moore, Susanne Kunis + +Published 2024-05-21 + +Licensed CC-BY-4.0 + + + +Poster presented at the European Light Microscopy Initiative meeting in Liverpool (https://www.elmi2024.org/) + +Tags: Nfdi4Bioimage, Research Data Management + +[https://zenodo.org/records/11235513](https://zenodo.org/records/11235513) + +[https://doi.org/10.5281/zenodo.11235513](https://doi.org/10.5281/zenodo.11235513) + + --- ## [ELMI 2024] AI's Dirty Little Secret: Without FAIR Data, It's Just Fancy Math @@ -2127,6 +2476,117 @@ Content type: Slides [https://zenodo.org/doi/10.5281/zenodo.10939519](https://zenodo.org/doi/10.5281/zenodo.10939519) +--- + +## [Workshop Material] Fit for OMERO - How imaging facilities and IT departments work together to enable RDM for bioimaging, October 16-17, 2024, Heidelberg + +Tom Boissonnet, Bettina Hagen, Susanne Kunis, Christian Schmidt, Stefanie Weidtkamp-Peters + +Published 2024-11-18 + +Licensed CC-BY-4.0 + + + +Fit for OMERO: How imaging facilities and IT departments work together to enable RDM for bioimaging +Description: +Research data management (RDM) in bioimaging is challenging because of large file sizes, heterogeneous file formats and the variability of imaging methods. The image data management system OMERO (OME Remote Objects) allows for centralized and secure storage, organization, annotation, and interrogation of microscopy data by researchers. It is an internationally well-supported open-source software tool that has become one of the best-known image data management tools among bioimaging scientists. Nevertheless, the de novo setup of OMERO at an institute is a multi-stakeholder process that demands time, funds, organization and iterative implementation. In this workshop, participants learn how to begin setting up OMERO-based image data management at their institution. The topics include: + +Stakeholder identification at the university / research institute +Process management, time line expectations, and resources planning +Learning about each other‘s perspectives on chances and challenges for RDM +Funding opportunities and strategies for IT and imaging core facilities +Hands-on: Setting up an OMERO server in a virtual machine environment + +Target audience: +This workshop was directed at universities and research institutions who consider or plan to implement OMERO, or are in an early phase of implementation. This workshop was intended for teams from IT departments and imaging facilities to participate together with one person from the IT department, and one person from the imaging core facility at the same institution. +The trainers: + +Prof. Dr. Stefanie Weidtkamp-Peters (Imaging Core Facility Head, Center for Advanced Imaging, Heinrich Heine University of Düsseldorf) +Dr. Susanne Kunis (Software architect, OMERO administrator, metadata specialist, University of Osnabrück) +Dr. Tom Boissonnet (OMERO admin and image metadata specialist, Center for Advanced Imaging, Heinrich Heine University of Düsseldorf) +Dr. Bettina Hagen (IT Administration and service specialist, Max Planck Institute for the Biology of Ageing, Cologne)  +Dr. Christian Schmidt (Science Manager for Research Data Management in Bioimaging, German Cancer Research Center (DKFZ), Heidelberg) + +Time and place +The format was a two-day, in-person workshop (October 16-17, 2024). Location: Heidelberg, Germany +Workshop learning goals + +Learn the steps to establish a local RDM environment fit for bioimaging data +Create a network of IT experts and bioimaging specialists for bioimage RDM across institutions +Establish a stakeholder process management for installing OMERO-based RDM +Learn from each other, leverage different expertise +Learn how to train users, establish sustainability strategies, and foster FAIR RDM for bioimaging at your institution + + +Tags: Nfdi4Bioimage, Research Data Management + +[https://zenodo.org/records/14178789](https://zenodo.org/records/14178789) + +[https://doi.org/10.5281/zenodo.14178789](https://doi.org/10.5281/zenodo.14178789) + + +--- + +## [Workshop] Bioimage data management and analysis with OMERO + +Riccardo Massei, Michele Bortolomeazzi, Christian Schmidt + +Published 2024-05-13 + +Licensed CC-BY-4.0 + + + +Here we share the material used in a workshop held on May 13th, 2024, at the German Cancer Research Center in Heidelberg (on-premise) +Description:Microscopy experiments generate information-rich, multi-dimensional data, allowing us to investigate biological processes at high spatial and temporal resolution. Image processing and analysis is a standard procedure to retrieve quantitative information from biological imaging. Due to the complex nature of bioimaging files that often come in proprietary formats, it can be challenging to organize, structure, and annotate bioimaging data throughout a project. Data often needs to be moved between collaboration partners, transformed into open formats, processed with a variety of software tools, and exported to smaller-sized images for presentation. The path from image acquisition to final publication figures with quantitative results must be documented and reproducible. +In this workshop, participants learn how to use OMERO to organize their data and enrich the bioimage data with structured metadata annotations.We also focus on image analysis workflows in combination with OMERO based on the Fiji/ImageJ software and using Jupyter Notebooks. In the last part, we explore how OMERO can be used to create publication figures and prepare bioimage data for publication in a suitable repository such as the Bioimage Archive. +Module 1 (9 am - 10.15 am): Basics of OMERO, data structuring and annotation +Module 2 (10.45 am - 12.45 pm): OMERO and Fiji +Module 3 (1.45 pm - 3.45 pm): OMERO and Jupyter Notebooks +Module 4 (4.15 pm - 6. pm): Publication-ready figures and data with OMERO +The target group for this workshopThis workshop is directed at researchers at all career levels who plan to or have started to use OMERO for their microscopy research data management. We encourage the workshop participants to bring example data from their research to discuss suitable metadata annotation for their everyday practice. +Prerequisites:Users should bring their laptops and have access to the internet through one of the following options:- eduroam- institutional WiFi- VPN connection to their institutional networks to access OMERO +Who are the trainers? +Dr. Riccardo Massei (Helmholtz-Center for Environmental Research, UFZ, Leipzig) - Data Steward for Bioimaging Data in NFDI4BIOIMAGE +Dr. Michele Bortolomeazzi (DKFZ, Single cell Open Lab, bioimage data specialist, bioinformatician, staff scientist in the NFDI4BIOIMAGE project) +Dr. Christian Schmidt (Science Manager for Research Data Management in Bioimaging, German Cancer Research Center, Heidelberg, Project Coordinator of the NFDI4BIOIMAGE project) + +Tags: Nfdi4Bioimage, Research Data Management + +[https://zenodo.org/records/11350689](https://zenodo.org/records/11350689) + +[https://doi.org/10.5281/zenodo.11350689](https://doi.org/10.5281/zenodo.11350689) + + +--- + +## [Workshop] Research Data Management for Microscopy and BioImage Analysis + +Christian Schmidt, Tom Boissonnet, Michele Bortolomeazzi, Ksenia Krooß + +Published 2024-09-30 + +Licensed CC-BY-4.0 + + + +Research Data Management for Microscopy and BioImage Analysis + +Introduction to BioImaging Research Data Management, NFDI4BIOIMAGE and I3D:bioChristian Schmidt /DKFZ Heidelberg +OMERO as a tool for bioimaging data managementTom Boissonnet /Heinrich-Heine Universität Düsseldorf +Reproducible image analysis workflows with OMERO software APIsMichele Bortolomeazzi /DKFZ Heidelberg +Publishing datasets in public archives for bioimage dataKsenia Krooß /Heinrich-Heine Universität Düsseldorf + +Date & Venue:Thursday, Sept. 26, 5.30 p.m.Haus 22 / Paul Ehrlich Lecture Hall (H22-1)University Hospital Frankfurt + +Tags: Nfdi4Bioimage, Research Data Management + +[https://zenodo.org/records/13861026](https://zenodo.org/records/13861026) + +[https://doi.org/10.5281/zenodo.13861026](https://doi.org/10.5281/zenodo.13861026) + + --- ## cba-support-template @@ -2146,6 +2606,27 @@ Content type: Tutorial [https://git.embl.de/grp-cba/cba-support-template](https://git.embl.de/grp-cba/cba-support-template) +--- + +## ome2024-ngff-challenge + +Will Moore, Josh Moore, sherwoodf, Jean-Marie Burel, Norman Rzepka, dependabot[bot], JensWendt, Joost de Folter, Torsten St\xF6ter, AybukeKY, Eric Perlman, Tom Boissonnet + +Published 2024-08-30T12:00:53+00:00 + +Licensed BSD-3-CLAUSE + + + +Project planning and material repository for the 2024 challenge to generate 1 PB of OME-Zarr data + +Tags: Sharing, Nfdi4Bioimage, Research Data Management + +Content type: Github Repository + +[https://github.com/ome/ome2024-ngff-challenge](https://github.com/ome/ome2024-ngff-challenge) + + --- ## re3data.org - registry of Research Data Repositories diff --git a/_sources/tags/segmentation.md b/_sources/tags/segmentation.md deleted file mode 100644 index 81f0eeb5..00000000 --- a/_sources/tags/segmentation.md +++ /dev/null @@ -1,96 +0,0 @@ -# Segmentation (5) -## Image Processing with Python - -Mark Meysenburg, Toby Hodges, Dominik Kutra, Erin Becker, David Palmquist, et al. - -Licensed CC-BY-4.0 - - - -This lesson shows how to use Python and scikit-image to do basic image processing. - -Tags: Segmentation, Bioimage Analysis, Training, Python, Scikit-Image, Image Segmentation - -Content type: Tutorial, Workflow - -[https://datacarpentry.org/image-processing/key-points.html](https://datacarpentry.org/image-processing/key-points.html) - - ---- - -## Introduction to Deep Learning for Microscopy - -Costantin Pape - -Licensed MIT - - - -This course consists of lectures and exercises that teach the background of deep learning for image analysis and show applications to classification and segmentation analysis problems. - -Tags: Deep Learning, Pytorch, Segmentation, Python - -Content type: Notebook - -[https://github.com/computational-cell-analytics/dl-for-micro](https://github.com/computational-cell-analytics/dl-for-micro) - - ---- - -## NeubiasPasteur2023_AdvancedCellPose - -Gaelle Letort - -Licensed BSD-3-CLAUSE - - - -Tutorial for running CellPose advanced functions - -Tags: Cellpose, Segmentation - -Content type: Github Repository - -[https://github.com/gletort/NeubiasPasteur2023_AdvancedCellPose](https://github.com/gletort/NeubiasPasteur2023_AdvancedCellPose) - - ---- - -## QI 2024 Analysis Lab Manual - -Beth Cimini, Florian Jug, QI 2024 - -Licensed CC-BY-4.0 - - - -This book contains the quantitative analysis labs for the QI CSHL course, 2024 - -Tags: Segmentation, Python - -Content type: Notebook - -[https://bethac07.github.io/qi_2024_analysis_lab_manual/intro.html](https://bethac07.github.io/qi_2024_analysis_lab_manual/intro.html) - - ---- - -## Ultrack I2K 2024 Workshop Materials - -Jordão Bragantini, Teun Huijben - -Licensed BSD3-CLAUSE - - - -Tags: Segmentation, Bioimage Analysis, Training - -Content type: Workshop, Github Repository, Tutorial - -[https://github.com/royerlab/ultrack-i2k2024](https://github.com/royerlab/ultrack-i2k2024) - -[https://royerlab.github.io/ultrack-i2k2024/](https://royerlab.github.io/ultrack-i2k2024/) - - ---- - diff --git a/_sources/tags/sharing.md b/_sources/tags/sharing.md index 1eccc921..773f5e8a 100644 --- a/_sources/tags/sharing.md +++ b/_sources/tags/sharing.md @@ -32,7 +32,7 @@ Introduction to version control using git for collaborative, reproducible script Tags: Sharing, Research Data Management -Content type: Blog +Content type: Blog Post [https://focalplane.biologists.com/2021/09/04/collaborative-bio-image-analysis-script-editing-with-git/](https://focalplane.biologists.com/2021/09/04/collaborative-bio-image-analysis-script-editing-with-git/) @@ -53,7 +53,7 @@ Sharing knowledge and data in the life sciences allows us to learn from each oth Tags: Open Science, Teaching, Sharing -Content type: Collection, Tutorial, Videos +Content type: Collection, Tutorial, Video [https://www.ebi.ac.uk/training/online/courses/finding-using-public-data/](https://www.ebi.ac.uk/training/online/courses/finding-using-public-data/) @@ -72,7 +72,7 @@ Sharing your data can benefit your career in some interesting ways. In this post Tags: Research Data Management, Sharing -Content type: Blog +Content type: Blog Post [https://web.library.uq.edu.au/blog/2022/03/five-great-reasons-share-your-research-data](https://web.library.uq.edu.au/blog/2022/03/five-great-reasons-share-your-research-data) @@ -102,7 +102,7 @@ Elisabeth Kugler Tags: Sharing, Research Data Management -Content type: Blog +Content type: Blog Post [https://focalplane.biologists.com/2023/07/26/sharing-your-poster-on-figshare/](https://focalplane.biologists.com/2023/07/26/sharing-your-poster-on-figshare/) @@ -121,7 +121,7 @@ Introduction to sharing resources online and licensing Tags: Sharing, Research Data Management -Content type: Slide +Content type: Slides [https://f1000research.com/slides/10-519](https://f1000research.com/slides/10-519) @@ -140,7 +140,7 @@ Blog post about how to share data using zenodo.org Tags: Sharing, Research Data Management -Content type: Blog +Content type: Blog Post [https://focalplane.biologists.com/2023/02/15/sharing-research-data-with-zenodo/](https://focalplane.biologists.com/2023/02/15/sharing-research-data-with-zenodo/) @@ -200,7 +200,7 @@ Content type: Publication ## ome2024-ngff-challenge -Will Moore, Josh Moore, sherwoodf, jean-marie burel, Norman Rzepka, dependabot[bot], JensWendt, Joost de Folter, Torsten Stöter, AybukeKY, Eric Perlman, Tom Boissonnet +Will Moore, Josh Moore, sherwoodf, Jean-Marie Burel, Norman Rzepka, dependabot[bot], JensWendt, Joost de Folter, Torsten St\xF6ter, AybukeKY, Eric Perlman, Tom Boissonnet Published 2024-08-30T12:00:53+00:00 @@ -210,7 +210,7 @@ Licensed BSD-3-CLAUSE Project planning and material repository for the 2024 challenge to generate 1 PB of OME-Zarr data -Tags: Sharing +Tags: Sharing, Nfdi4Bioimage, Research Data Management Content type: Github Repository diff --git a/_sources/tags/training.md b/_sources/tags/training.md deleted file mode 100644 index 60858a0d..00000000 --- a/_sources/tags/training.md +++ /dev/null @@ -1,233 +0,0 @@ -# Training (12) -## DataPLANT knowledge base - -Published 2022-12-14 - -Licensed CC-BY-4.0 - - - -Explore fundamental topics on research data management (RDM), how DataPLANT implements these aspects to support plant researchers with RDM tools and services, read guides and manuals or search for some teaching materials. - -Tags: Research Data Management, Training, Dataplant - -Content type: Collection - -[https://nfdi4plants.org/nfdi4plants.knowledgebase/index.html](https://nfdi4plants.org/nfdi4plants.knowledgebase/index.html) - - ---- - -## Docker Mastery - with Kubernetes + Swarm from a Docker Captain - -Bret Fisher - -Licensed UNKNOWN - - - -In this course you will learn how to use Docker, Compose and Kubernetes on your machine for better software building and testing. - -Tags: Docker, Training - -Content type: Videos, Tutorial, Online Course - -[https://www.udemy.com/course/docker-mastery/?srsltid=AfmBOornR5gRqOg-4v8Nsap1z24CaPPUPxg8JzyqEGZ6MvW_dh-sf4Af&couponCode=ST2MT110724BNEW](https://www.udemy.com/course/docker-mastery/?srsltid=AfmBOornR5gRqOg-4v8Nsap1z24CaPPUPxg8JzyqEGZ6MvW_dh-sf4Af&couponCode=ST2MT110724BNEW) - - ---- - -## EMBL-EBI material collection - -EMBL-EBI - -Licensed CC0 (MOSTLY, BUT CAN DIFFER DEPENDING ON RESOURCE) - - - -Online tutorial and webinar library, designed and delivered by EMBL-EBI experts - -Tags: Bioinformatics, Training - -Content type: Collection - -[https://www.ebi.ac.uk/training/on-demand?facets=type:Course%20materials&query=](https://www.ebi.ac.uk/training/on-demand?facets=type:Course%20materials&query=) - - ---- - -## Glencoe Software Webinars - -Chris Allan, Emil Rozbicki - -Licensed UNKNOWN - - - -Example Workflows / usage of the Glencoe Software. - -Tags: OMERO, Training - -Content type: Videos, Tutorial, Collection - -[https://www.glencoesoftware.com/media/webinars/](https://www.glencoesoftware.com/media/webinars/) - - ---- - -## I2K 2024: clEsperanto - GPU-Accelerated Image Processing Library - -Stephane Rigaud, Robert Haase - -Licensed BSD-3-CLAUSE - - - -Course and material for the clEsperanto workshop presented at I2K 2024 @ Human Technopol (Milan, Italy). The workshop is an hands-on demo of the clesperanto project, focussing on how to use the library for users who want use GPU-acceleration for their Image Processing pipeline. - -Tags: Clesperanto, Training, Bioimage Analysis, Notebooks, Workflow - -Content type: Github Repository, Workshop, Tutorial, Notebook - -[https://github.com/StRigaud/clesperanto_workshop_I2K24?tab=readme-ov-file](https://github.com/StRigaud/clesperanto_workshop_I2K24?tab=readme-ov-file) - - ---- - -## I2K2024 workshop material - Lazy Parallel Processing and Visualization of Large Data with ImgLib2, BigDataViewer, the N5-API, and Spark - -Stephan Saalfeld, Tobias Pietzsch - -Published None - -Licensed APACHE-2.0 - - - -Tags: Training - -Content type: Workshop, Notebook, Github Repository - -[https://saalfeldlab.github.io/i2k2024-lazy-workshop/](https://saalfeldlab.github.io/i2k2024-lazy-workshop/) - -[https://github.com/saalfeldlab/i2k2024-lazy-workshop](https://github.com/saalfeldlab/i2k2024-lazy-workshop) - - ---- - -## Image Processing with Python - -Mark Meysenburg, Toby Hodges, Dominik Kutra, Erin Becker, David Palmquist, et al. - -Licensed CC-BY-4.0 - - - -This lesson shows how to use Python and scikit-image to do basic image processing. - -Tags: Segmentation, Bioimage Analysis, Training, Python, Scikit-Image, Image Segmentation - -Content type: Tutorial, Workflow - -[https://datacarpentry.org/image-processing/key-points.html](https://datacarpentry.org/image-processing/key-points.html) - - ---- - -## Object Tracking and Track Analysis using TrackMate and CellTracksColab - -Joanna Pylvänäinen - -Published None - -Licensed GPL-3.0 - - - -I2K 2024 workshop materials for "Object Tracking and Track Analysis using TrackMate and CellTracksColab" - -Tags: Bioimage Analysis, Training - -Content type: Github Repository, Tutorial, Workshop, Slides - -[https://github.com/CellMigrationLab/I2K_2024](https://github.com/CellMigrationLab/I2K_2024) - - ---- - -## SWC/GCNU Software Skills - -Licensed CC-BY-4.0 - - - -Computational skills training at the UCL Sainsbury Wellcome Centre and Gatsby Computational Neuroscience Unit, delivered by members of the Neuroinformatics Unit. - -Tags: Training - -Content type: Collection, Online Course, Videos, Tutorial - -[https://software-skills.neuroinformatics.dev/index.html](https://software-skills.neuroinformatics.dev/index.html) - - ---- - -## Ten simple rules for making training materials FAIR - -Leyla Garcia, Bérénice Batut, Melissa L. Burke, Mateusz Kuzak, Fotis Psomopoulos, et al. - -Published 2020-05-21 - -Licensed CC-BY-4.0 - - - -The authors offer trainers some simple rules, to help make their training materials FAIR, enabling others to find, (re)use, and adapt them. - -Tags: Metadata, Bioinformatics, FAIR-Principles, Training - -Content type: Publication - -[https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1007854](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1007854) - - ---- - -## Ultrack I2K 2024 Workshop Materials - -Jordão Bragantini, Teun Huijben - -Licensed BSD3-CLAUSE - - - -Tags: Segmentation, Bioimage Analysis, Training - -Content type: Workshop, Github Repository, Tutorial - -[https://github.com/royerlab/ultrack-i2k2024](https://github.com/royerlab/ultrack-i2k2024) - -[https://royerlab.github.io/ultrack-i2k2024/](https://royerlab.github.io/ultrack-i2k2024/) - - ---- - -## Using Glittr.org to find, compare and re-use online training materials - -Geert van Geest, Yann Haefliger, Monique Zahn-Zabal, Patricia M. Palagi - -Licensed CC-BY-4.0 - - - -Glittr.org is a platform that aggregates and indexes training materials on computational life sciences from public git repositories, making it easier for users to find, compare, and analyze these resources based on various metrics. By providing insights into the availability of materials, collaboration patterns, and licensing practices, Glittr.org supports adherence to the FAIR principles, benefiting the broader life sciences community. - -Tags: Training, Bioimage Analysis, Research Data Management - -Content type: Publication, Preprint - -[https://www.biorxiv.org/content/10.1101/2024.08.20.608021v1](https://www.biorxiv.org/content/10.1101/2024.08.20.608021v1) - - ---- - diff --git a/_sources/tags/workflow.md b/_sources/tags/workflow.md index 0ff79ae9..df40f45c 100644 --- a/_sources/tags/workflow.md +++ b/_sources/tags/workflow.md @@ -1,4 +1,4 @@ -# Workflow (10) +# Workflow (9) ## BIOMERO - A scalable and extensible image analysis framework Torec T. Luik, Rodrigo Rosas-Bertolini, Eric A.J. Reits, Ron A. Hoebe, Przemek M. Krawczyk @@ -11,7 +11,7 @@ Licensed CC-BY-4.0 The authors introduce BIOMERO (bioimage analysis in OMERO), a bridge connecting OMERO, a renowned bioimaging data management platform, FAIR workflows, and high-performance computing (HPC) environments. -Tags: OMERO, Workflow, Bioimage Analysis, Image Data Management +Tags: OMERO, Workflow, Bioimage Analysis Content type: Publication @@ -50,38 +50,19 @@ Content type: Online Tutorial, Tutorial [https://galaxy-au-training.github.io/tutorials/modules/workflows/](https://galaxy-au-training.github.io/tutorials/modules/workflows/) ---- - -## I2K 2024: clEsperanto - GPU-Accelerated Image Processing Library - -Stephane Rigaud, Robert Haase - -Licensed BSD-3-CLAUSE - - - -Course and material for the clEsperanto workshop presented at I2K 2024 @ Human Technopol (Milan, Italy). The workshop is an hands-on demo of the clesperanto project, focussing on how to use the library for users who want use GPU-acceleration for their Image Processing pipeline. - -Tags: Clesperanto, Training, Bioimage Analysis, Notebooks, Workflow - -Content type: Github Repository, Workshop, Tutorial, Notebook - -[https://github.com/StRigaud/clesperanto_workshop_I2K24?tab=readme-ov-file](https://github.com/StRigaud/clesperanto_workshop_I2K24?tab=readme-ov-file) - - --- ## KNIME Image Processing None -Licensed GPLV3 +Licensed GPL-3.0 The KNIME Image Processing Extension allows you to read in more than 140 different kinds of images and to apply well known methods on images, like preprocessing. segmentation, feature extraction, tracking and classification in KNIME. -Tags: Imagej, OMERO, Bioimage Data, Workflow +Tags: Imagej, OMERO, Workflow Content type: Tutorial, Online Tutorial, Documentation @@ -94,13 +75,13 @@ Content type: Tutorial, Online Tutorial, Documentation Shanghang Zhang, Gaole Dai, Tiejun Huang, Jianxu Chen -Licensed ['CC-BY-NC-SA'] +Licensed CC-BY-NC-SA Multimodal large language models have been recognized as a historical milestone in the field of artificial intelligence and have demonstrated revolutionary potentials not only in commercial applications, but also for many scientific fields. Here we give a brief overview of multimodal large language models through the lens of bioimage analysis and discuss how we could build these models as a community to facilitate biology research -Tags: Bioimage Analysis, Large Language Models, FAIR-Principles, Workflow +Tags: Bioimage Analysis, FAIR-Principles, Workflow Content type: Publication diff --git a/_sources/tags/workflow_engine.md b/_sources/tags/workflow_engine.md index f2a04f0d..c2f625a8 100644 --- a/_sources/tags/workflow_engine.md +++ b/_sources/tags/workflow_engine.md @@ -62,7 +62,7 @@ Licensed MIT BioEngine, a Python package designed for flexible deployment and execution of bioimage analysis models and workflows using AI, accessible via HTTP API and RPC. -Tags: Workflow Engine, Deep Learning, Python +Tags: Workflow Engine, Artificial Intelligence, Python Content type: Documentation @@ -187,7 +187,7 @@ Nextflow is an open-source workflow management system that prioritizes portabili Tags: Workflow Engine -Content type: Slide +Content type: Slides [https://zenodo.org/records/4334697](https://zenodo.org/records/4334697) diff --git a/_sources/whats_new.md b/_sources/whats_new.md index 29b7f40d..28a7af17 100644 --- a/_sources/whats_new.md +++ b/_sources/whats_new.md @@ -5,7 +5,7 @@ Hoku West-Foyle Published 2025-01-16 -Licensed CC-ZERO +Licensed CC0-1.0 @@ -28,6 +28,8 @@ Licensed CC-BY-4.0 This slide deck introduces the version control tool git, related terminology and the Github Desktop app for managing files in Git[hub] repositories. We furthermore dive into:* Working with repositories* Collaborative with others* Github-Zenodo integration* Github pages* Artificial Intelligence answering Github Issues +Tags: Nfdi4Bioimage, Globias, Research Data Management, Research Software Management + [https://zenodo.org/records/14626054](https://zenodo.org/records/14626054) [https://doi.org/10.5281/zenodo.14626054](https://doi.org/10.5281/zenodo.14626054) @@ -73,7 +75,7 @@ Licensed CC-BY-4.0 ## Modular training resources for bioimage analysis -Christian Tischer, Antonio Politi, Tim-Oliver Buchholz, Elnaz Fazeli, Nicola Gritti, Aliaksandr Halavatyi, Gonzalez Tirado, Sebastian, Julian Hennies, Toby Hodges, Arif Khan, Dominik Kutra, Stefania Marcotti, Bugra Oezdemir, Felix Schneider, Martin Schorb, Anniek Stokkermans, Yi Sun, Nima Vakili +Christian Tischer, Antonio Politi, Tim-Oliver Buchholz, Elnaz Fazeli, Nicola Gritti, Aliaksandr Halavatyi, Sebastian Gonzalez Tirado, Julian Hennies, Toby Hodges, Arif Khan, Dominik Kutra, Stefania Marcotti, Bugra Oezdemir, Felix Schneider, Martin Schorb, Anniek Stokkermans, Yi Sun, Nima Vakili Published 2024-12-03 @@ -83,6 +85,8 @@ Licensed CC-BY-4.0 Resources for teaching/preparing to teach bioimage analysis +Tags: Neubias, Bioimage Analysis + [https://zenodo.org/records/14264885](https://zenodo.org/records/14264885) [https://doi.org/10.5281/zenodo.14264885](https://doi.org/10.5281/zenodo.14264885) @@ -141,6 +145,8 @@ Licensed CC-BY-4.0 Talk given at Georg-August-Universität Göttingen Campus Institute Data Science23rd January 2025 https://www.uni-goettingen.de/en/653203.html +Tags: Nfdi4Bioimage + [https://zenodo.org/records/14716546](https://zenodo.org/records/14716546) [https://doi.org/10.5281/zenodo.14716546](https://doi.org/10.5281/zenodo.14716546) @@ -162,6 +168,8 @@ CMCB LIFE SCIENCES SEMINARSTechnische Universität Dresden16th January 2025 https://tu-dresden.de/cmcb/crtd/news-termine/termine/cmcb-life-sciences-seminar-josh-moore-german-bioimaging-e-v-society-for-microscopy-and-image-analysis-constance   +Tags: Nfdi4Bioimage + [https://zenodo.org/records/14650434](https://zenodo.org/records/14650434) [https://doi.org/10.5281/zenodo.14650434](https://doi.org/10.5281/zenodo.14650434) @@ -188,6 +196,8 @@ Publishing datasets in public archives for bioimage dataKsenia Krooß /Hein Date & Venue:Thursday, Sept. 26, 5.30 p.m.Haus 22 / Paul Ehrlich Lecture Hall (H22-1)University Hospital Frankfurt +Tags: Nfdi4Bioimage, Research Data Management + [https://zenodo.org/records/13861026](https://zenodo.org/records/13861026) [https://doi.org/10.5281/zenodo.13861026](https://doi.org/10.5281/zenodo.13861026) diff --git a/authors/astrid_schauss.html b/authors/astrid_schauss.html index 2f119ed6..7b6a9b90 100644 --- a/authors/astrid_schauss.html +++ b/authors/astrid_schauss.html @@ -182,36 +182,29 @@

By tag

By content type

@@ -236,11 +228,12 @@ diff --git a/authors/beatriz_serrano-solano.html b/authors/beatriz_serrano-solano.html index 8027ba25..c0da3a58 100644 --- a/authors/beatriz_serrano-solano.html +++ b/authors/beatriz_serrano-solano.html @@ -182,36 +182,29 @@

By tag

By content type

@@ -236,11 +228,12 @@ @@ -525,7 +518,7 @@

Image analysis in Galaxyhttps://docs.google.com/presentation/d/1WG_4307XmKsGfWT3taxMvX2rZiG1k0SM1E7SAENJQkI/edit#slide=id.p

diff --git a/authors/chris_allan.html b/authors/chris_allan.html index d1b8b6d2..6cd94c7f 100644 --- a/authors/chris_allan.html +++ b/authors/chris_allan.html @@ -182,36 +182,29 @@

By tag

By content type

@@ -236,11 +228,12 @@ @@ -480,8 +473,8 @@

Glencoe Software Webinarshttps://www.glencoesoftware.com/media/webinars/

@@ -500,7 +493,7 @@

The Open Microscopy Environment (OME) Data Model and XML file - open tools f

Published 2005-05-03

Licensed CC-BY-4.0

The Open Microscopy Environment (OME) defines a data model and a software implementation to serve as an informatics framework for imaging in biological microscopy experiments, including representation of acquisition parameters, annotations and image analysis results.

-

Tags: Microscopy Image Analysis, Bioimage Analysis

+

Tags: Bioimage Analysis

Content type: Publication

https://genomebiology.biomedcentral.com/articles/10.1186/gb-2005-6-5-r47

https://doi.org/10.1186/gb-2005-6-5-r47

@@ -511,7 +504,7 @@

bioformats2raw Converterglencoesoftware/bioformats2raw

@@ -521,7 +514,7 @@

raw2ometiff ConverterMelissa Linkert, Chris Allan, Sébastien Besson, Josh Moore

Licensed GPL-2.0

Java application to convert a directory of tiles to an OME-TIFF pyramid. This is the second half of iSyntax/.mrxs => OME-TIFF conversion.

-

Tags: Open Source Software, Bioimage Data

+

Tags: Open Source Software

Content type: Application, Github Repository

glencoesoftware/raw2ometiff

diff --git a/authors/christian_schmidt.html b/authors/christian_schmidt.html index 08e93f69..92c2cd86 100644 --- a/authors/christian_schmidt.html +++ b/authors/christian_schmidt.html @@ -182,36 +182,29 @@

By tag

By content type

@@ -236,11 +228,12 @@ @@ -498,6 +491,7 @@

A journey to FAIR microscopy datahttps://zenodo.org/records/7890311

https://doi.org/10.5281/zenodo.7890311

@@ -526,7 +520,7 @@

I3D bio – Information Infrastructure for BioImage Data - Bioimage Metadata

Christian Schmidt

Licensed UNKNOWN

A Microscopy Research Data Management Resource.

-

Tags: Metadata, I3Dbio, Research Data Management, Bioimage Data

+

Tags: Metadata, I3Dbio, Research Data Management

Content type: Collection

https://gerbi-gmb.de/i3dbio/i3dbio-rdm/i3dbio-bioimage-metadata/

@@ -538,7 +532,7 @@

I3D:bio’s OMERO training material: Re-usable, adjustable, multi-purpose sl

Licensed CC-BY-4.0

The open-source software OME Remote Objects (OMERO) is a data management software that allows storing, organizing, and annotating bioimaging/microscopy data. OMERO has become one of the best-known systems for bioimage data management in the bioimaging community. The Information Infrastructure for BioImage Data (I3D:bio) project facilitates the uptake of OMERO into research data management (RDM) practices at universities and research institutions in Germany. Since the adoption of OMERO into researchers’ daily routines requires intensive training, a broad portfolio of training resources for OMERO is an asset. On top of using the OMERO guides curated by the Open Microscopy Environment Consortium (OME) team, imaging core facility staff at institutions where OMERO is used often prepare additional material tailored to be applicable for their own OMERO instances. Based on experience gathered in the Research Data Management for Microscopy group (RDM4mic) in Germany, and in the use cases in the I3D:bio project, we created a set of reusable, adjustable, openly available slide decks to serve as the basis for tailored training lectures, video tutorials, and self-guided instruction manuals directed at beginners in using OMERO. The material is published as an open educational resource complementing the existing resources for OMERO contributed by the community.

Tags: OMERO, Research Data Management, Nfdi4Bioimage, I3Dbio

-

Content type: Slide, Video

+

Content type: Slides, Video

https://zenodo.org/records/8323588

https://www.youtube.com/playlist?list=PL2k-L-zWPoR7SHjG1HhDIwLZj0MB_stlU

https://doi.org/10.5281/zenodo.8323588

@@ -565,6 +559,7 @@

Key-Value pair template for annotation of datasets in OMERO (PERIKLES study) See cross reference: 10.5281/zenodo.12547566 Key-Value pair template for annotation of datasets in OMERO (light- and electron microscopy data within the research group of Prof. Mueller-Reichert) 10.5281/zenodo.12578084 Key-Value pair template for annotation in OMERO for light microscopy data acquired with AxioScan7 - Core Facility Cellular Imaging (CFCI)

+

Tags: Nfdi4Bioimage, Research Data Management

https://zenodo.org/records/12546808

https://doi.org/10.5281/zenodo.12546808

@@ -611,8 +606,8 @@

NFDI4BIOIMAGE - An Initiative for a National Research Data Infrastructure fo

Published 2021-04-29

Licensed CCY-BY-SA-4.0

Align existing and establish novel services & solutions for data management tasks throughout the bioimage data lifecycle.

-

Tags: Nfdi4Bioimage, Image Data Management, Bioimage Data, Research Data Management

-

Content type: Conference Abstract, Slide

+

Tags: Nfdi4Bioimage, Research Data Management

+

Content type: Conference Abstract, Slides

https://doi.org/10.11588/heidok.00029489

@@ -622,7 +617,7 @@

Research data management for bioimaging - the 2021 NFDI4BIOIMAGE community s

Published 2022-09-20

Licensed CC-BY-4.0

As an initiative within Germany’s National Research Data Infrastructure, the authors conducted this community survey in summer 2021 to assess the state of the art of bioimaging RDM and the community needs.

-

Tags: Research Data Management, Image Data Management, Bioimage Data

+

Tags: Research Data Management

Content type: Publication

https://f1000research.com/articles/11-638/v2

@@ -642,6 +637,7 @@

The Information Infrastructure for BioImage Data (I3D:bio) project to advanc

Published 2024-03-04

Licensed CC-BY-4.0

Research data management (RDM) in microscopy and image analysis is a challenging task. Large files in proprietary formats, complex N-dimensional array structures, and various metadata models and formats can make image data handling inconvenient and difficult. For data organization, annotation, and sharing, researchers need solutions that fit everyday practice and comply with the FAIR (Findable, Accessible, Interoperable, Reusable) principles. International community-based efforts have begun creating open data models (OME), an open file format and translation library (OME-TIFF, Bio-Formats), data management software platforms, and microscopy metadata recommendations and annotation tools. Bringing these developments into practice requires support and training. Iterative feedback and tool improvement is needed to foster practical adoption by the scientific community. The Information Infrastructure for BioImage Data (I3D:bio) project works on guidelines, training resources, and practical assistance for FAIR microscopy RDM adoption with a focus on the management platform OMERO and metadata annotations.

+

Tags: Nfdi4Bioimage, Research Data Management

https://zenodo.org/records/10805204

https://doi.org/10.5281/zenodo.10805204

@@ -652,6 +648,7 @@

The role of Helmholtz Centers in NFDI4BIOIMAGE - A national consortium enhan

Published 2024-06-06

Licensed CC-BY-4.0

Germany’s National Research Data Infrastructure (NFDI) aims to establish a sustained, cross-disciplinary research data management (RDM) infrastructure that enables researchers to handle FAIR (findable, accessible, interoperable, reusable) data. While FAIR principles have been adopted by funders, policymakers, and publishers, their practical implementation remains an ongoing effort. In the field of bio-imaging, harmonization of data formats, metadata ontologies, and open data repositories is necessary to achieve FAIR data. The NFDI4BIOIMAGE was established to address these issues and develop tools and best practices to facilitate FAIR microscopy and image analysis data in alignment with international community activities. The consortium operates through its Data Stewards team to provide expertise and direct support to help overcome RDM challenges. The three Helmholtz Centers in NFDI4BIOIMAGE aim to collaborate closely with other centers and initiatives, such as HMC, Helmholtz AI, and HIP. Here we present NFDI4BIOIMAGE’s work and its significance for research in Helmholtz and beyond

+

Tags: Nfdi4Bioimage, Research Data Management

https://zenodo.org/records/11501662

https://doi.org/10.5281/zenodo.11501662

@@ -705,6 +702,7 @@

[Workshop Material] Fit for OMERO - How imaging facilities and IT department Establish a stakeholder process management for installing OMERO-based RDM Learn from each other, leverage different expertise Learn how to train users, establish sustainability strategies, and foster FAIR RDM for bioimaging at your institution

+

Tags: Nfdi4Bioimage, Research Data Management

https://zenodo.org/records/14178789

https://doi.org/10.5281/zenodo.14178789

@@ -727,6 +725,7 @@

[Workshop] Bioimage data management and analysis with OMEROhttps://zenodo.org/records/11350689

https://doi.org/10.5281/zenodo.11350689

@@ -786,6 +785,7 @@

[Workshop] Research Data Management for Microscopy and BioImage Analysis

Date & Venue:Thursday, Sept. 26, 5.30 p.m.Haus 22 / Paul Ehrlich Lecture Hall (H22-1)University Hospital Frankfurt

+

Tags: Nfdi4Bioimage, Research Data Management

https://zenodo.org/records/13861026

https://doi.org/10.5281/zenodo.13861026

diff --git a/authors/christian_tischer.html b/authors/christian_tischer.html index a85bd4ef..b5a76cf2 100644 --- a/authors/christian_tischer.html +++ b/authors/christian_tischer.html @@ -182,36 +182,29 @@

By tag

By content type

@@ -236,11 +228,12 @@ @@ -516,7 +509,7 @@

BigDataProcessor2: A free and open-source Fiji plugin for inspection and pro

Bio Image Analysis#

Christian Tischer

Licensed UNKNOWN

-

Content type: Slide

+

Content type: Slides

tischi/presentation-image-analysis

@@ -551,7 +544,7 @@

Methods in bioimage analysishttps://www.ebi.ac.uk/training/events/methods-bioimage-analysis/

https://doi.org/10.6019/TOL.BioImageAnalysis22-w.2022.00001.1

https://drive.google.com/file/d/1MhuqfKhZcYu3bchWMqogIybKjamU5Msg/view

@@ -559,10 +552,11 @@

Methods in bioimage analysis

Modular training resources for bioimage analysis#

-

Christian Tischer, Antonio Politi, Tim-Oliver Buchholz, Elnaz Fazeli, Nicola Gritti, Aliaksandr Halavatyi, Gonzalez Tirado, Sebastian, Julian Hennies, Toby Hodges, Arif Khan, Dominik Kutra, Stefania Marcotti, Bugra Oezdemir, Felix Schneider, Martin Schorb, Anniek Stokkermans, Yi Sun, Nima Vakili

+

Christian Tischer, Antonio Politi, Tim-Oliver Buchholz, Elnaz Fazeli, Nicola Gritti, Aliaksandr Halavatyi, Sebastian Gonzalez Tirado, Julian Hennies, Toby Hodges, Arif Khan, Dominik Kutra, Stefania Marcotti, Bugra Oezdemir, Felix Schneider, Martin Schorb, Anniek Stokkermans, Yi Sun, Nima Vakili

Published 2024-12-03

Licensed CC-BY-4.0

Resources for teaching/preparing to teach bioimage analysis

+

Tags: Neubias, Bioimage Analysis

https://zenodo.org/records/14264885

https://doi.org/10.5281/zenodo.14264885

diff --git a/authors/constantin_pape.html b/authors/constantin_pape.html index 838fa3e4..cf370a34 100644 --- a/authors/constantin_pape.html +++ b/authors/constantin_pape.html @@ -182,36 +182,29 @@

By tag

By content type

@@ -236,11 +228,12 @@ @@ -491,7 +484,7 @@

DL@MBL 2021 ExercisesJan Funke, Constantin Pape, Morgan Schwartz, Xiaoyan

Licensed UNKNOWN

Tags: Artificial Intelligence, Bioimage Analysis

-

Content type: Slide, Notebook

+

Content type: Slides, Notebook

JLrumberger/DL-MBL-2021

@@ -513,7 +506,7 @@

MicroSam-Talkshttps://zenodo.org/records/11265038

https://doi.org/10.5281/zenodo.11265038

diff --git a/authors/cornelia_wetzker.html b/authors/cornelia_wetzker.html index 6869c00d..d40e37ed 100644 --- a/authors/cornelia_wetzker.html +++ b/authors/cornelia_wetzker.html @@ -182,36 +182,29 @@

By tag

By content type

@@ -236,11 +228,12 @@ @@ -492,8 +485,8 @@

Bio-Image Data Strudel for Workshop on Research Data Management in TU Dresde

Published 2023-11-08

Licensed CC-BY-4.0

This presentation gives a short outline of the complexity of data and metadata in the bioimaging universe. It introduces NFDI4BIOIMAGE as a newly formed consortium as part of the German ‘Nationale Forschungsdateninfrastruktur’ (NFDI) and its goals and tools for data management including its current members on TU Dresden campus.  

-

Tags: Research Data Management, Tu Dresden, Bioimage Data, Nfdi4Bioimage

-

Content type: Slide

+

Tags: Research Data Management, Nfdi4Bioimage

+

Content type: Slides

https://zenodo.org/records/10083555

https://doi.org/10.5281/zenodo.10083555

diff --git a/authors/dominik_kutra.html b/authors/dominik_kutra.html index 2ee5c2e6..bd150337 100644 --- a/authors/dominik_kutra.html +++ b/authors/dominik_kutra.html @@ -182,36 +182,29 @@

By tag

By content type

@@ -236,11 +228,12 @@ @@ -481,7 +474,7 @@

Image Processing with Pythonhttps://datacarpentry.org/image-processing/key-points.html

@@ -491,17 +484,18 @@

Microscopy data analysis: machine learning and the BioImage ArchiveAndrii Iudin, Anna Foix-Romero, Anna Kreshuk, Awais Athar, Beth Cimini, Dominik Kutra, Estibalis Gomez de Mariscal, Frances Wong, Guillaume Jacquemet, Kedar Narayan, Martin Weigert, Nodar Gogoberidze, Osman Salih, Petr Walczysko, Ryan Conrad, Simone Weyend, Sriram Sundar Somasundharam, Suganya Sivagurunathan, Ugis Sarkans

Licensed CC-BY-4.0

The Microscopy data analysis: machine learning and the BioImage Archive course, which focused on introducing programmatic approaches used in the analysis of bioimage data via the BioImage Archive, ran in May 2023.

-

Tags: Microscopy Image Analysis, Python, Deep Learning

+

Tags: Bioimage Analysis, Python, Artificial Intelligence

Content type: Video, Slides

https://www.ebi.ac.uk/training/materials/microscopy-data-analysis-machine-learning-and-the-bioimage-archive-materials/

Modular training resources for bioimage analysis#

-

Christian Tischer, Antonio Politi, Tim-Oliver Buchholz, Elnaz Fazeli, Nicola Gritti, Aliaksandr Halavatyi, Gonzalez Tirado, Sebastian, Julian Hennies, Toby Hodges, Arif Khan, Dominik Kutra, Stefania Marcotti, Bugra Oezdemir, Felix Schneider, Martin Schorb, Anniek Stokkermans, Yi Sun, Nima Vakili

+

Christian Tischer, Antonio Politi, Tim-Oliver Buchholz, Elnaz Fazeli, Nicola Gritti, Aliaksandr Halavatyi, Sebastian Gonzalez Tirado, Julian Hennies, Toby Hodges, Arif Khan, Dominik Kutra, Stefania Marcotti, Bugra Oezdemir, Felix Schneider, Martin Schorb, Anniek Stokkermans, Yi Sun, Nima Vakili

Published 2024-12-03

Licensed CC-BY-4.0

Resources for teaching/preparing to teach bioimage analysis

+

Tags: Neubias, Bioimage Analysis

https://zenodo.org/records/14264885

https://doi.org/10.5281/zenodo.14264885

@@ -521,7 +515,7 @@

ilastik: interactive machine learning for (bio)image analysishttps://zenodo.org/doi/10.5281/zenodo.4330625

diff --git a/authors/elisa_ferrando-may.html b/authors/elisa_ferrando-may.html index 924a192e..91420ecc 100644 --- a/authors/elisa_ferrando-may.html +++ b/authors/elisa_ferrando-may.html @@ -182,36 +182,29 @@

By tag

By content type

@@ -236,11 +228,12 @@ @@ -492,7 +485,7 @@

I3D:bio’s OMERO training material: Re-usable, adjustable, multi-purpose sl

Licensed CC-BY-4.0

The open-source software OME Remote Objects (OMERO) is a data management software that allows storing, organizing, and annotating bioimaging/microscopy data. OMERO has become one of the best-known systems for bioimage data management in the bioimaging community. The Information Infrastructure for BioImage Data (I3D:bio) project facilitates the uptake of OMERO into research data management (RDM) practices at universities and research institutions in Germany. Since the adoption of OMERO into researchers’ daily routines requires intensive training, a broad portfolio of training resources for OMERO is an asset. On top of using the OMERO guides curated by the Open Microscopy Environment Consortium (OME) team, imaging core facility staff at institutions where OMERO is used often prepare additional material tailored to be applicable for their own OMERO instances. Based on experience gathered in the Research Data Management for Microscopy group (RDM4mic) in Germany, and in the use cases in the I3D:bio project, we created a set of reusable, adjustable, openly available slide decks to serve as the basis for tailored training lectures, video tutorials, and self-guided instruction manuals directed at beginners in using OMERO. The material is published as an open educational resource complementing the existing resources for OMERO contributed by the community.

Tags: OMERO, Research Data Management, Nfdi4Bioimage, I3Dbio

-

Content type: Slide, Video

+

Content type: Slides, Video

https://zenodo.org/records/8323588

https://www.youtube.com/playlist?list=PL2k-L-zWPoR7SHjG1HhDIwLZj0MB_stlU

https://doi.org/10.5281/zenodo.8323588

@@ -504,8 +497,8 @@

NFDI4BIOIMAGE - An Initiative for a National Research Data Infrastructure fo

Published 2021-04-29

Licensed CCY-BY-SA-4.0

Align existing and establish novel services & solutions for data management tasks throughout the bioimage data lifecycle.

-

Tags: Nfdi4Bioimage, Image Data Management, Bioimage Data, Research Data Management

-

Content type: Conference Abstract, Slide

+

Tags: Nfdi4Bioimage, Research Data Management

+

Content type: Conference Abstract, Slides

https://doi.org/10.11588/heidok.00029489

@@ -515,7 +508,7 @@

Research data management for bioimaging - the 2021 NFDI4BIOIMAGE community s

Published 2022-09-20

Licensed CC-BY-4.0

As an initiative within Germany’s National Research Data Infrastructure, the authors conducted this community survey in summer 2021 to assess the state of the art of bioimaging RDM and the community needs.

-

Tags: Research Data Management, Image Data Management, Bioimage Data

+

Tags: Research Data Management

Content type: Publication

https://f1000research.com/articles/11-638/v2

@@ -535,6 +528,7 @@

The Information Infrastructure for BioImage Data (I3D:bio) project to advanc

Published 2024-03-04

Licensed CC-BY-4.0

Research data management (RDM) in microscopy and image analysis is a challenging task. Large files in proprietary formats, complex N-dimensional array structures, and various metadata models and formats can make image data handling inconvenient and difficult. For data organization, annotation, and sharing, researchers need solutions that fit everyday practice and comply with the FAIR (Findable, Accessible, Interoperable, Reusable) principles. International community-based efforts have begun creating open data models (OME), an open file format and translation library (OME-TIFF, Bio-Formats), data management software platforms, and microscopy metadata recommendations and annotation tools. Bringing these developments into practice requires support and training. Iterative feedback and tool improvement is needed to foster practical adoption by the scientific community. The Information Infrastructure for BioImage Data (I3D:bio) project works on guidelines, training resources, and practical assistance for FAIR microscopy RDM adoption with a focus on the management platform OMERO and metadata annotations.

+

Tags: Nfdi4Bioimage, Research Data Management

https://zenodo.org/records/10805204

https://doi.org/10.5281/zenodo.10805204

@@ -545,6 +539,7 @@

The role of Helmholtz Centers in NFDI4BIOIMAGE - A national consortium enhan

Published 2024-06-06

Licensed CC-BY-4.0

Germany’s National Research Data Infrastructure (NFDI) aims to establish a sustained, cross-disciplinary research data management (RDM) infrastructure that enables researchers to handle FAIR (findable, accessible, interoperable, reusable) data. While FAIR principles have been adopted by funders, policymakers, and publishers, their practical implementation remains an ongoing effort. In the field of bio-imaging, harmonization of data formats, metadata ontologies, and open data repositories is necessary to achieve FAIR data. The NFDI4BIOIMAGE was established to address these issues and develop tools and best practices to facilitate FAIR microscopy and image analysis data in alignment with international community activities. The consortium operates through its Data Stewards team to provide expertise and direct support to help overcome RDM challenges. The three Helmholtz Centers in NFDI4BIOIMAGE aim to collaborate closely with other centers and initiatives, such as HMC, Helmholtz AI, and HIP. Here we present NFDI4BIOIMAGE’s work and its significance for research in Helmholtz and beyond

+

Tags: Nfdi4Bioimage, Research Data Management

https://zenodo.org/records/11501662

https://doi.org/10.5281/zenodo.11501662

diff --git a/authors/elnaz_fazeli.html b/authors/elnaz_fazeli.html index 8e943c0f..da46c7ce 100644 --- a/authors/elnaz_fazeli.html +++ b/authors/elnaz_fazeli.html @@ -182,36 +182,29 @@

By tag

By content type

@@ -236,11 +228,12 @@ @@ -497,10 +490,11 @@

From Cells to Pixels: Bridging Biologists and Image Analysts Through a Comm

Modular training resources for bioimage analysis#

-

Christian Tischer, Antonio Politi, Tim-Oliver Buchholz, Elnaz Fazeli, Nicola Gritti, Aliaksandr Halavatyi, Gonzalez Tirado, Sebastian, Julian Hennies, Toby Hodges, Arif Khan, Dominik Kutra, Stefania Marcotti, Bugra Oezdemir, Felix Schneider, Martin Schorb, Anniek Stokkermans, Yi Sun, Nima Vakili

+

Christian Tischer, Antonio Politi, Tim-Oliver Buchholz, Elnaz Fazeli, Nicola Gritti, Aliaksandr Halavatyi, Sebastian Gonzalez Tirado, Julian Hennies, Toby Hodges, Arif Khan, Dominik Kutra, Stefania Marcotti, Bugra Oezdemir, Felix Schneider, Martin Schorb, Anniek Stokkermans, Yi Sun, Nima Vakili

Published 2024-12-03

Licensed CC-BY-4.0

Resources for teaching/preparing to teach bioimage analysis

+

Tags: Neubias, Bioimage Analysis

https://zenodo.org/records/14264885

https://doi.org/10.5281/zenodo.14264885

diff --git "a/authors/estibaliz_g\303\263mez-de-mariscal.html" "b/authors/estibaliz_g\303\263mez-de-mariscal.html" index b37b7f76..e4bdd049 100644 --- "a/authors/estibaliz_g\303\263mez-de-mariscal.html" +++ "b/authors/estibaliz_g\303\263mez-de-mariscal.html" @@ -182,36 +182,29 @@

By tag

By content type

@@ -236,11 +228,12 @@ @@ -480,7 +473,7 @@

Building a Bioimage Analysis Workflow using Deep Learningesgomezm/NEUBIAS_chapter_DL_2020

@@ -507,7 +500,7 @@

Machine Learning - Deep Learning. Applications to Bioimage AnalysisEstibaliz Gómez-de-Mariscal

Licensed UNKNOWN

Tags: Artificial Intelligence, Bioimage Analysis

-

Content type: Slide

+

Content type: Slides

https://raw.githubusercontent.com/esgomezm/esgomezm.github.io/master/assets/pdf/SPAOM2018/MachineLearning_SPAOMworkshop_public.pdf

@@ -516,7 +509,7 @@

NEUBIAS Bioimage Analyst School 2020miura/NEUBIAS_AnalystSchool2020

@@ -525,7 +518,7 @@

ZIDAS 2020 Introduction to Deep Learningesgomezm/zidas2020_intro_DL

diff --git a/authors/et_al..html b/authors/et_al..html index cb0f5ecb..dc4c7c18 100644 --- a/authors/et_al..html +++ b/authors/et_al..html @@ -182,36 +182,29 @@

By tag

By content type

@@ -236,11 +228,12 @@ @@ -540,7 +533,7 @@

Image Processing with Pythonhttps://datacarpentry.org/image-processing/key-points.html

@@ -578,7 +571,7 @@

Microscopy-BIDS - An Extension to the Brain Imaging Data Structure for Micro

Published 2022-04-19

Licensed CC-BY-4.0

The Brain Imaging Data Structure (BIDS) is a specification for organizing, sharing, and archiving neuroimaging data and metadata in a reusable way.

-

Tags: Research Data Management, Image Data Management, Bioimage Data

+

Tags: Research Data Management

Content type: Publication

https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2022.871228/full

@@ -595,7 +588,7 @@

NEUBIAS Bioimage Analyst Course 2017miura/NEUBIAS_Bioimage_Analyst_Course2017

@@ -604,7 +597,7 @@

NEUBIAS Bioimage Analyst School 2019miura/NEUBIAS_AnalystSchool2019

@@ -613,7 +606,7 @@

NEUBIAS Bioimage Analyst School 2020miura/NEUBIAS_AnalystSchool2020

@@ -643,7 +636,7 @@

REMBI - Recommended Metadata for Biological Images—enabling reuse of micro

Published 2021-05-21

Licensed UNKNOWN

Bioimaging data have significant potential for reuse, but unlocking this potential requires systematic archiving of data and metadata in public databases. The authors propose draft metadata guidelines to begin addressing the needs of diverse communities within light and electron microscopy.

-

Tags: Metadata, Bioimage Data, Image Data Management, Research Data Management

+

Tags: Metadata, Research Data Management

Content type: Publication

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8606015/

https://www.nature.com/articles/s41592-021-01166-8

@@ -656,7 +649,7 @@

Research data management for bioimaging - the 2021 NFDI4BIOIMAGE community s

Published 2022-09-20

Licensed CC-BY-4.0

As an initiative within Germany’s National Research Data Infrastructure, the authors conducted this community survey in summer 2021 to assess the state of the art of bioimaging RDM and the community needs.

-

Tags: Research Data Management, Image Data Management, Bioimage Data

+

Tags: Research Data Management

Content type: Publication

https://f1000research.com/articles/11-638/v2

@@ -667,7 +660,7 @@

Ten simple rules for making training materials FAIRPublished 2020-05-21

Licensed CC-BY-4.0

The authors offer trainers some simple rules, to help make their training materials FAIR, enabling others to find, (re)use, and adapt them.

-

Tags: Metadata, Bioinformatics, FAIR-Principles, Training

+

Tags: Metadata, Bioinformatics, FAIR-Principles

Content type: Publication

https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1007854

@@ -678,7 +671,7 @@

Towards community-driven metadata standards for light microscopy - tiered sp

Published 2022-07-10

Licensed UNKNOWN

Rigorous record-keeping and quality control are required to ensure the quality, reproducibility and value of imaging data. The 4DN Initiative and BINA here propose light Microscopy Metadata specifications that extend the OME data model, scale with experimental intent and complexity, and make it possible for scientists to create comprehensive records of imaging experiments.

-

Tags: Reproducibility, Microscopy Image Analysis, Metadata, Image Data Management, Bioimage Data

+

Tags: Reproducibility, Bioimage Analysis, Metadata

Content type: Publication

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9271325/

@@ -697,7 +690,7 @@

Understanding metric-related pitfalls in image analysis validation#

Curtis Rueden, Albane de la Villegeorges, Simon F. Nørrelykke, Romain Guiet, Olivier Burri, et al.

Licensed UNKNOWN

-

Tags: Bioimage Analysis, Microscopy Image Analysis

+

Tags: Bioimage Analysis

Content type: Collection, Event, Forum Post, Workshop

https://forum.image.sc/t/upcoming-image-analysis-events/60018/67

@@ -727,7 +720,7 @@

bioformats2raw Converterglencoesoftware/bioformats2raw

diff --git a/authors/florian_jug.html b/authors/florian_jug.html index eeed19f6..cf6c8971 100644 --- a/authors/florian_jug.html +++ b/authors/florian_jug.html @@ -182,36 +182,29 @@

By tag

By content type

@@ -236,11 +228,12 @@ @@ -491,7 +484,7 @@

Artificial Intelligence for Digital Pathologyhttps://www.youtube.com/watch?v=Om9tl4Dh2yw

@@ -519,7 +512,7 @@

QI 2024 Analysis Lab Manualhttps://bethac07.github.io/qi_2024_analysis_lab_manual/intro.html

diff --git a/authors/guillaume_witz.html b/authors/guillaume_witz.html index 972cc15f..8ef1f9f1 100644 --- a/authors/guillaume_witz.html +++ b/authors/guillaume_witz.html @@ -182,36 +182,29 @@

By tag

By content type

@@ -236,11 +228,12 @@ @@ -538,7 +531,7 @@

Jupyter for interactive cloud computinghttps://docs.google.com/presentation/d/1q8q1xE-c35tvCsRXZay98s2UYWwXpp0cfCljBmMFpco/edit#slide=id.ga456d5535c_2_53

diff --git a/authors/isabel_kemmer.html b/authors/isabel_kemmer.html index 4d78d7f1..b34fa9bb 100644 --- a/authors/isabel_kemmer.html +++ b/authors/isabel_kemmer.html @@ -182,36 +182,29 @@

By tag

By content type

@@ -236,11 +228,12 @@ diff --git a/authors/jean-marie_burel.html b/authors/jean-marie_burel.html index 54762d3e..901dac80 100644 --- a/authors/jean-marie_burel.html +++ b/authors/jean-marie_burel.html @@ -182,36 +182,29 @@

By tag

By content type

@@ -236,11 +228,12 @@ @@ -522,7 +515,7 @@

The Open Microscopy Environment (OME) Data Model and XML file - open tools f

Published 2005-05-03

Licensed CC-BY-4.0

The Open Microscopy Environment (OME) defines a data model and a software implementation to serve as an informatics framework for imaging in biological microscopy experiments, including representation of acquisition parameters, annotations and image analysis results.

-

Tags: Microscopy Image Analysis, Bioimage Analysis

+

Tags: Bioimage Analysis

Content type: Publication

https://genomebiology.biomedcentral.com/articles/10.1186/gb-2005-6-5-r47

https://doi.org/10.1186/gb-2005-6-5-r47

@@ -530,11 +523,11 @@

The Open Microscopy Environment (OME) Data Model and XML file - open tools f

ome2024-ngff-challenge#

-

Will Moore, Josh Moore, sherwoodf, jean-marie burel, Norman Rzepka, dependabot[bot], JensWendt, Joost de Folter, Torsten Stöter, AybukeKY, Eric Perlman, Tom Boissonnet

+

Will Moore, Josh Moore, sherwoodf, Jean-Marie Burel, Norman Rzepka, dependabot[bot], JensWendt, Joost de Folter, Torsten St\xF6ter, AybukeKY, Eric Perlman, Tom Boissonnet

Published 2024-08-30T12:00:53+00:00

Licensed BSD-3-CLAUSE

Project planning and material repository for the 2024 challenge to generate 1 PB of OME-Zarr data

-

Tags: Sharing

+

Tags: Sharing, Nfdi4Bioimage, Research Data Management

Content type: Github Repository

ome/ome2024-ngff-challenge

diff --git a/authors/jens_wendt.html b/authors/jens_wendt.html index b9a13526..9f405fd6 100644 --- a/authors/jens_wendt.html +++ b/authors/jens_wendt.html @@ -182,36 +182,29 @@

By tag

By content type

@@ -236,11 +228,12 @@ @@ -543,6 +536,7 @@

Structuring of Data and Metadata in Bioimaging: Concepts and technical Solut ... +

Tags: Nfdi4Bioimage, Research Data Management

https://zenodo.org/records/7018750

https://doi.org/10.5281/zenodo.7018750

diff --git a/authors/josh_moore.html b/authors/josh_moore.html index e83194a2..ea3abe0d 100644 --- a/authors/josh_moore.html +++ b/authors/josh_moore.html @@ -182,36 +182,29 @@

By tag

By content type

@@ -236,11 +228,12 @@ @@ -520,7 +513,7 @@

Making the most of bioimaging data through interdisciplinary interactionsVirginie Uhlmann, Matthew Hartley, Josh Moore, Erin Weisbart, Assaf Zaritsky

Published 2024-10-23

Licensed CC-BY-4.0

-

Tags: Bioimage Analysis, Open Science, Microscopy Image Analysis, Microscopy

+

Tags: Bioimage Analysis, Open Science, Microscopy

Content type: Publication

https://journals.biologists.com/jcs/article/137/20/jcs262139/362478/Making-the-most-of-bioimaging-data-through

@@ -551,6 +544,7 @@

OME2024 NGFF Challenge Resultshttps://founding-gide.eurobioimaging.eu/event/foundinggide-community-event-2024/ Note: much of the presentation was a demonstration of the OME2024-NGFF-Challenge – https://ome.github.io/ome2024-ngff-challenge/ especially of querying an extraction of the metadata (ome/ome2024-ngff-challenge-metadata)  

+

Tags: Nfdi4Bioimage, Research Data Management

https://zenodo.org/records/14234608

https://doi.org/10.5281/zenodo.14234608

@@ -571,7 +565,7 @@

Research data management for bioimaging - the 2021 NFDI4BIOIMAGE community s

Published 2022-09-20

Licensed CC-BY-4.0

As an initiative within Germany’s National Research Data Infrastructure, the authors conducted this community survey in summer 2021 to assess the state of the art of bioimaging RDM and the community needs.

-

Tags: Research Data Management, Image Data Management, Bioimage Data

+

Tags: Research Data Management

Content type: Publication

https://f1000research.com/articles/11-638/v2

@@ -621,6 +615,7 @@

[CIDAS] Scalable strategies for a next-generation of FAIR bioimagingLicensed CC-BY-4.0

Talk given at Georg-August-Universität Göttingen Campus Institute Data Science23rd January 2025 https://www.uni-goettingen.de/en/653203.html

+

Tags: Nfdi4Bioimage

https://zenodo.org/records/14716546

https://doi.org/10.5281/zenodo.14716546

@@ -633,6 +628,7 @@

[CMCB] Scalable strategies for a next-generation of FAIR bioimagingCMCB LIFE SCIENCES SEMINARSTechnische Universität Dresden16th January 2025 https://tu-dresden.de/cmcb/crtd/news-termine/termine/cmcb-life-sciences-seminar-josh-moore-german-bioimaging-e-v-society-for-microscopy-and-image-analysis-constance  

+

Tags: Nfdi4Bioimage

https://zenodo.org/records/14650434

https://doi.org/10.5281/zenodo.14650434

@@ -664,6 +660,7 @@

[ELMI 2024] AI’s Dirty Little Secret: Withouthttps://www.elmi2024.org/)

+

Tags: Nfdi4Bioimage, Research Data Management

https://zenodo.org/records/11235513

https://doi.org/10.5281/zenodo.11235513

@@ -738,7 +735,7 @@

bioformats2raw Converterglencoesoftware/bioformats2raw

@@ -749,7 +746,6 @@

ome-ngff-validatorhttps://ome.github.io/ome-ngff-validator/

ome/ome-ngff-validator

@@ -757,11 +753,11 @@

ome-ngff-validator

ome2024-ngff-challenge#

-

Will Moore, Josh Moore, sherwoodf, jean-marie burel, Norman Rzepka, dependabot[bot], JensWendt, Joost de Folter, Torsten Stöter, AybukeKY, Eric Perlman, Tom Boissonnet

+

Will Moore, Josh Moore, sherwoodf, Jean-Marie Burel, Norman Rzepka, dependabot[bot], JensWendt, Joost de Folter, Torsten St\xF6ter, AybukeKY, Eric Perlman, Tom Boissonnet

Published 2024-08-30T12:00:53+00:00

Licensed BSD-3-CLAUSE

Project planning and material repository for the 2024 challenge to generate 1 PB of OME-Zarr data

-

Tags: Sharing

+

Tags: Sharing, Nfdi4Bioimage, Research Data Management

Content type: Github Repository

ome/ome2024-ngff-challenge

@@ -771,7 +767,7 @@

raw2ometiff ConverterMelissa Linkert, Chris Allan, Sébastien Besson, Josh Moore

Licensed GPL-2.0

Java application to convert a directory of tiles to an OME-TIFF pyramid. This is the second half of iSyntax/.mrxs => OME-TIFF conversion.

-

Tags: Open Source Software, Bioimage Data

+

Tags: Open Source Software

Content type: Application, Github Repository

glencoesoftware/raw2ometiff

diff --git a/authors/kota_miura.html b/authors/kota_miura.html index 248746f8..07624e33 100644 --- a/authors/kota_miura.html +++ b/authors/kota_miura.html @@ -182,36 +182,29 @@

By tag

By content type

@@ -236,11 +228,12 @@ @@ -551,7 +544,7 @@

NEUBIAS Bioimage Analyst Course 2017miura/NEUBIAS_Bioimage_Analyst_Course2017

@@ -560,7 +553,7 @@

NEUBIAS Bioimage Analyst School 2019miura/NEUBIAS_AnalystSchool2019

@@ -569,7 +562,7 @@

NEUBIAS Bioimage Analyst School 2020miura/NEUBIAS_AnalystSchool2020

@@ -578,7 +571,7 @@

What is Bioimage Analysis? An Introductionhttps://www.dropbox.com/s/5abw3cvxrhpobg4/20220923_DefragmentationTS.pdf?dl=0

diff --git a/authors/mara_lampert.html b/authors/mara_lampert.html index e24e5cbe..4522a04f 100644 --- a/authors/mara_lampert.html +++ b/authors/mara_lampert.html @@ -182,36 +182,29 @@

By tag

By content type

@@ -236,11 +228,12 @@ @@ -482,7 +475,7 @@

Mara lampert (8)#

Mara Lampert

Tags: Python, Napari, Bioimage Analysis

-

Content type: Blog

+

Content type: Blog Post

https://focalplane.biologists.com/2023/03/30/annotating-3d-images-in-napari/

@@ -490,7 +483,7 @@

Annotating 3D images in napari#

Mara Lampert

Tags: Python, Napari, Bioimage Analysis

-

Content type: Blog

+

Content type: Blog Post

https://focalplane.biologists.com/2023/05/03/feature-extraction-in-napari/

@@ -499,7 +492,7 @@

Getting started with Mambaforge and Pythonhttps://biapol.github.io/blog/mara_lampert/getting_started_with_mambaforge_and_python/readme.html

@@ -507,7 +500,7 @@

Getting started with Mambaforge and Python#

Mara Lampert

Tags: Github, Python, Science Communication

-

Content type: Blog

+

Content type: Blog Post

https://focalplane.biologists.com/2024/04/03/how-to-write-a-bug-report/

@@ -515,7 +508,7 @@

How to write a bug report#

Mara Lampert

Tags: Python, Jupyter, Bioimage Analysis, Prompt Engineering, Biabob

-

Content type: Blog

+

Content type: Blog Post

https://focalplane.biologists.com/2024/07/18/prompt-engineering-in-bio-image-analysis/

@@ -523,7 +516,7 @@

Prompt Engineering in Bio-image Analysis#

Mara Lampert

Tags: Python, Napari, Bioimage Analysis

-

Content type: Blog

+

Content type: Blog Post

https://focalplane.biologists.com/2023/04/13/quality-assurance-of-segmentation-results/

@@ -531,7 +524,7 @@

Quality assurance of segmentation results#

Mara Lampert

Tags: Python, Napari, Bioimage Analysis

-

Content type: Blog

+

Content type: Blog Post

https://focalplane.biologists.com/2023/03/02/rescaling-images-and-pixel-anisotropy/

@@ -539,7 +532,7 @@

Rescaling images and pixel (an)isotropy#

Mara Lampert

Tags: Python, Napari, Bioimage Analysis

-

Content type: Blog

+

Content type: Blog Post

https://focalplane.biologists.com/2023/06/01/tracking-in-napari/

diff --git "a/authors/martin_sch\303\244tz.html" "b/authors/martin_sch\303\244tz.html" index 83ef17c4..39de25a1 100644 --- "a/authors/martin_sch\303\244tz.html" +++ "b/authors/martin_sch\303\244tz.html" @@ -182,36 +182,29 @@

By tag

By content type

@@ -236,11 +228,12 @@ @@ -498,7 +491,7 @@

ImageJ tool for percentage estimation of pneumonia in lungs

Interactive Image Data Flow Graphs#

-

Martin Schätz, Martin Schätz

+

Martin Schätz

Published 2022-10-17

Licensed CC-BY-4.0

The slides were presented during the Macro programming with ImageJ workshop (https://www.16mcm.cz/programme/#workshops) which was part of the 16th Multinational Congress on Microscopy. It is a collection and “reshuffle” of slides originally made by Robert Haase on topics from Image Analysis in general up to User-friendly GPU-accelerated bio-image analysis and CLIJ2.

diff --git a/authors/martin_weigert.html b/authors/martin_weigert.html index 9d22e035..54c32a1a 100644 --- a/authors/martin_weigert.html +++ b/authors/martin_weigert.html @@ -182,36 +182,29 @@

By tag

By content type

@@ -236,11 +228,12 @@ @@ -537,7 +530,7 @@

Microscopy data analysis: machine learning and the BioImage ArchiveAndrii Iudin, Anna Foix-Romero, Anna Kreshuk, Awais Athar, Beth Cimini, Dominik Kutra, Estibalis Gomez de Mariscal, Frances Wong, Guillaume Jacquemet, Kedar Narayan, Martin Weigert, Nodar Gogoberidze, Osman Salih, Petr Walczysko, Ryan Conrad, Simone Weyend, Sriram Sundar Somasundharam, Suganya Sivagurunathan, Ugis Sarkans

Licensed CC-BY-4.0

The Microscopy data analysis: machine learning and the BioImage Archive course, which focused on introducing programmatic approaches used in the analysis of bioimage data via the BioImage Archive, ran in May 2023.

-

Tags: Microscopy Image Analysis, Python, Deep Learning

+

Tags: Bioimage Analysis, Python, Artificial Intelligence

Content type: Video, Slides

https://www.ebi.ac.uk/training/materials/microscopy-data-analysis-machine-learning-and-the-bioimage-archive-materials/

@@ -547,7 +540,7 @@

Neubias Academy 2020: Introduction to Nuclei Segmentation with StarDistMartin Weigert, Olivier Burri, Siân Culley, Uwe Schmidt

Licensed UNKNOWN

Tags: Python, Neubias, Artificial Intelligence, Bioimage Analysis

-

Content type: Slide, Notebook

+

Content type: Slides, Notebook

maweigert/neubias_academy_stardist

diff --git a/authors/michael_gerlach.html b/authors/michael_gerlach.html index 471aaac3..1e5aa50a 100644 --- a/authors/michael_gerlach.html +++ b/authors/michael_gerlach.html @@ -182,36 +182,29 @@

By tag

By content type

@@ -236,11 +228,12 @@ diff --git a/authors/michele_bortolomeazzi.html b/authors/michele_bortolomeazzi.html index 11930df2..c3f8b437 100644 --- a/authors/michele_bortolomeazzi.html +++ b/authors/michele_bortolomeazzi.html @@ -182,36 +182,29 @@

By tag

By content type

@@ -236,11 +228,12 @@ @@ -486,7 +479,7 @@

I3D:bio’s OMERO training material: Re-usable, adjustable, multi-purpose sl

Licensed CC-BY-4.0

The open-source software OME Remote Objects (OMERO) is a data management software that allows storing, organizing, and annotating bioimaging/microscopy data. OMERO has become one of the best-known systems for bioimage data management in the bioimaging community. The Information Infrastructure for BioImage Data (I3D:bio) project facilitates the uptake of OMERO into research data management (RDM) practices at universities and research institutions in Germany. Since the adoption of OMERO into researchers’ daily routines requires intensive training, a broad portfolio of training resources for OMERO is an asset. On top of using the OMERO guides curated by the Open Microscopy Environment Consortium (OME) team, imaging core facility staff at institutions where OMERO is used often prepare additional material tailored to be applicable for their own OMERO instances. Based on experience gathered in the Research Data Management for Microscopy group (RDM4mic) in Germany, and in the use cases in the I3D:bio project, we created a set of reusable, adjustable, openly available slide decks to serve as the basis for tailored training lectures, video tutorials, and self-guided instruction manuals directed at beginners in using OMERO. The material is published as an open educational resource complementing the existing resources for OMERO contributed by the community.

Tags: OMERO, Research Data Management, Nfdi4Bioimage, I3Dbio

-

Content type: Slide, Video

+

Content type: Slides, Video

https://zenodo.org/records/8323588

https://www.youtube.com/playlist?list=PL2k-L-zWPoR7SHjG1HhDIwLZj0MB_stlU

https://doi.org/10.5281/zenodo.8323588

@@ -513,6 +506,7 @@

Key-Value pair template for annotation of datasets in OMERO (PERIKLES study) See cross reference: 10.5281/zenodo.12547566 Key-Value pair template for annotation of datasets in OMERO (light- and electron microscopy data within the research group of Prof. Mueller-Reichert) 10.5281/zenodo.12578084 Key-Value pair template for annotation in OMERO for light microscopy data acquired with AxioScan7 - Core Facility Cellular Imaging (CFCI)

+

Tags: Nfdi4Bioimage, Research Data Management

https://zenodo.org/records/12546808

https://doi.org/10.5281/zenodo.12546808

@@ -570,6 +564,7 @@

The Information Infrastructure for BioImage Data (I3D:bio) project to advanc

Published 2024-03-04

Licensed CC-BY-4.0

Research data management (RDM) in microscopy and image analysis is a challenging task. Large files in proprietary formats, complex N-dimensional array structures, and various metadata models and formats can make image data handling inconvenient and difficult. For data organization, annotation, and sharing, researchers need solutions that fit everyday practice and comply with the FAIR (Findable, Accessible, Interoperable, Reusable) principles. International community-based efforts have begun creating open data models (OME), an open file format and translation library (OME-TIFF, Bio-Formats), data management software platforms, and microscopy metadata recommendations and annotation tools. Bringing these developments into practice requires support and training. Iterative feedback and tool improvement is needed to foster practical adoption by the scientific community. The Information Infrastructure for BioImage Data (I3D:bio) project works on guidelines, training resources, and practical assistance for FAIR microscopy RDM adoption with a focus on the management platform OMERO and metadata annotations.

+

Tags: Nfdi4Bioimage, Research Data Management

https://zenodo.org/records/10805204

https://doi.org/10.5281/zenodo.10805204

@@ -580,6 +575,7 @@

The role of Helmholtz Centers in NFDI4BIOIMAGE - A national consortium enhan

Published 2024-06-06

Licensed CC-BY-4.0

Germany’s National Research Data Infrastructure (NFDI) aims to establish a sustained, cross-disciplinary research data management (RDM) infrastructure that enables researchers to handle FAIR (findable, accessible, interoperable, reusable) data. While FAIR principles have been adopted by funders, policymakers, and publishers, their practical implementation remains an ongoing effort. In the field of bio-imaging, harmonization of data formats, metadata ontologies, and open data repositories is necessary to achieve FAIR data. The NFDI4BIOIMAGE was established to address these issues and develop tools and best practices to facilitate FAIR microscopy and image analysis data in alignment with international community activities. The consortium operates through its Data Stewards team to provide expertise and direct support to help overcome RDM challenges. The three Helmholtz Centers in NFDI4BIOIMAGE aim to collaborate closely with other centers and initiatives, such as HMC, Helmholtz AI, and HIP. Here we present NFDI4BIOIMAGE’s work and its significance for research in Helmholtz and beyond

+

Tags: Nfdi4Bioimage, Research Data Management

https://zenodo.org/records/11501662

https://doi.org/10.5281/zenodo.11501662

@@ -602,6 +598,7 @@

[Workshop] Bioimage data management and analysis with OMEROhttps://zenodo.org/records/11350689

https://doi.org/10.5281/zenodo.11350689

@@ -617,6 +614,7 @@

[Workshop] Research Data Management for Microscopy and BioImage Analysis

Date & Venue:Thursday, Sept. 26, 5.30 p.m.Haus 22 / Paul Ehrlich Lecture Hall (H22-1)University Hospital Frankfurt

+

Tags: Nfdi4Bioimage, Research Data Management

https://zenodo.org/records/13861026

https://doi.org/10.5281/zenodo.13861026

diff --git a/authors/nicolas_chiaruttini.html b/authors/nicolas_chiaruttini.html index 998ce92c..c8d1ee32 100644 --- a/authors/nicolas_chiaruttini.html +++ b/authors/nicolas_chiaruttini.html @@ -182,36 +182,29 @@

By tag

By content type

@@ -236,11 +228,12 @@ diff --git a/authors/olivier_burri.html b/authors/olivier_burri.html index f41f34b1..5cea9091 100644 --- a/authors/olivier_burri.html +++ b/authors/olivier_burri.html @@ -182,36 +182,29 @@

By tag

By content type

@@ -236,11 +228,12 @@ @@ -561,7 +554,7 @@

Neubias Academy 2020: Introduction to Nuclei Segmentation with StarDistMartin Weigert, Olivier Burri, Siân Culley, Uwe Schmidt

Licensed UNKNOWN

Tags: Python, Neubias, Artificial Intelligence, Bioimage Analysis

-

Content type: Slide, Notebook

+

Content type: Slides, Notebook

maweigert/neubias_academy_stardist

@@ -569,7 +562,7 @@

Neubias Academy 2020: Introduction to Nuclei Segmentation with StarDistUpcoming Image Analysis Events#

Curtis Rueden, Albane de la Villegeorges, Simon F. Nørrelykke, Romain Guiet, Olivier Burri, et al.

Licensed UNKNOWN

-

Tags: Bioimage Analysis, Microscopy Image Analysis

+

Tags: Bioimage Analysis

Content type: Collection, Event, Forum Post, Workshop

https://forum.image.sc/t/upcoming-image-analysis-events/60018/67

diff --git a/authors/pia_voigt.html b/authors/pia_voigt.html index 41917a28..e034864a 100644 --- a/authors/pia_voigt.html +++ b/authors/pia_voigt.html @@ -182,36 +182,29 @@

By tag

By content type

@@ -236,11 +228,12 @@ diff --git a/authors/readme.html b/authors/readme.html index 0a980e27..0b00e8dd 100644 --- a/authors/readme.html +++ b/authors/readme.html @@ -182,36 +182,29 @@

By tag

By content type

@@ -236,11 +228,12 @@ diff --git a/authors/riccardo_massei.html b/authors/riccardo_massei.html index 641ab8b5..79682a37 100644 --- a/authors/riccardo_massei.html +++ b/authors/riccardo_massei.html @@ -182,36 +182,29 @@

By tag

By content type

@@ -236,11 +228,12 @@ @@ -570,7 +563,7 @@

Overview of the Galaxy OMERO-suite - Upload images and metadata in OMERO usi

Riccardo Massei, Björn Grüning

Published 2024-12-02

Licensed CC-BY-4.0

-

Tags: OMERO, Galaxy, Metadata

+

Tags: OMERO, Galaxy, Metadata, Nfdi4Bioimage

Content type: Tutorial, Framework, Workflow

https://training.galaxyproject.org/training-material/topics/imaging/tutorials/omero-suite/tutorial.html

@@ -581,6 +574,7 @@

The role of Helmholtz Centers in NFDI4BIOIMAGE - A national consortium enhan

Published 2024-06-06

Licensed CC-BY-4.0

Germany’s National Research Data Infrastructure (NFDI) aims to establish a sustained, cross-disciplinary research data management (RDM) infrastructure that enables researchers to handle FAIR (findable, accessible, interoperable, reusable) data. While FAIR principles have been adopted by funders, policymakers, and publishers, their practical implementation remains an ongoing effort. In the field of bio-imaging, harmonization of data formats, metadata ontologies, and open data repositories is necessary to achieve FAIR data. The NFDI4BIOIMAGE was established to address these issues and develop tools and best practices to facilitate FAIR microscopy and image analysis data in alignment with international community activities. The consortium operates through its Data Stewards team to provide expertise and direct support to help overcome RDM challenges. The three Helmholtz Centers in NFDI4BIOIMAGE aim to collaborate closely with other centers and initiatives, such as HMC, Helmholtz AI, and HIP. Here we present NFDI4BIOIMAGE’s work and its significance for research in Helmholtz and beyond

+

Tags: Nfdi4Bioimage, Research Data Management

https://zenodo.org/records/11501662

https://doi.org/10.5281/zenodo.11501662

@@ -601,7 +595,7 @@

YMIA - Python-Based Event Series Training MaterialPublished None

Licensed MIT

This repository offer access to teaching material and useful resources for the YMIA - Python-Based Event Series.

-

Tags: Python, Large Language Models, Prompt Engineering, Biabob, Bioimage Analysis, Microscopy Image Analysis

+

Tags: Python, Artifical Intelligence, Bioimage Analysis

Content type: Github Repository, Slides

rmassei/ymia_python_event_series_material

@@ -624,6 +618,7 @@

[Workshop] Bioimage data management and analysis with OMEROhttps://zenodo.org/records/11350689

https://doi.org/10.5281/zenodo.11350689

diff --git a/authors/robert_haase.html b/authors/robert_haase.html index b91363e1..58f4db9c 100644 --- a/authors/robert_haase.html +++ b/authors/robert_haase.html @@ -182,36 +182,29 @@

By tag

By content type

@@ -236,11 +228,12 @@ @@ -546,10 +539,11 @@

A study on long-term reproducibility of image analysis results on ImageJ and

Angebote der NFDI für die Forschung im Bereich Zoologie#

-

Birgitta König-Ries, Robert Haase, Daniel Nüst, Konrad Förstner, Engel, Judith Sophie

+

Birgitta König-Ries, Robert Haase, Daniel Nüst, Konrad Förstner, Judith Sophie Engel

Published 2024-12-04

Licensed CC-BY-4.0

In diesem Slidedeck geben wir einen Einblick in Angebote und Dienste der Nationalen Forschungsdaten Infrastruktur (NFDI), die Relevant für die Zoologie und angrenzende Disziplinen relevant sein könnten.

+

Tags: Nfdi4Bioimage, Research Data Management

https://zenodo.org/records/14278058

https://doi.org/10.5281/zenodo.14278058

@@ -559,7 +553,7 @@

BIDS-lecture-2024ScaDS/BIDS-lecture-2024

@@ -594,8 +588,8 @@

Bio-image Analysis with the Help of Large Language Modelshttps://zenodo.org/records/10815329

https://doi.org/10.5281/zenodo.10815329

@@ -605,7 +599,7 @@

Bio-image Data ScienceRobert Haase

Licensed CC-BY-4.0

This repository contains training resources for Students at Uni Leipzig who want to dive into bio-image data science with Python.

-

Tags: Image Data Management, Deep Learning, Microscopy Image Analysis, Python

+

Tags: Research Data Management, Artificial Intelligence, Bioimage Analysis, Python

Content type: Notebook

ScaDS/BIDS-lecture-2024

@@ -615,7 +609,7 @@

Bio-image Data Science Lectures @ Uni Leipzig / https://zenodo.org/records/12623730

@@ -634,7 +628,7 @@

Browsing the Open Microscopy Image Data Resource with Pythonhttps://biapol.github.io/blog/robert_haase/browsing_idr/readme.html

@@ -654,7 +648,7 @@

Challenges and opportunities for bio-image analysis core-facilitiesRobert Haase

Licensed CC-BY-4.0

Tags: Research Data Management, Bioimage Analysis, Nfdi4Bioimage

-

Content type: Slide

+

Content type: Slides

https://f1000research.com/slides/12-1054

@@ -684,6 +678,7 @@

Collaborative Working and Version Control with git[hub]Published 2024-01-10

Licensed CC-BY-4.0

This slide deck introduces the version control tool git, related terminology and the Github Desktop app for managing files in Git[hub] repositories. We furthermore dive into:* Working with repositories* Collaborative with others* Github-Zenodo integration* Github pages* Artificial Intelligence answering Github Issues

+

Tags: Nfdi4Bioimage, Globias, Research Data Management, Research Software Management

https://zenodo.org/records/14626054

https://doi.org/10.5281/zenodo.14626054

@@ -694,7 +689,7 @@

Collaborative bio-image analysis script editing with githttps://focalplane.biologists.com/2021/09/04/collaborative-bio-image-analysis-script-editing-with-git/

@@ -704,8 +699,8 @@

Creating a Research Data Management Plan using chatGPTPublished 2023-11-06

Licensed CC-BY-4.0

In this blog post the author demonstrates how chatGPT can be used to combine a fictive project description with a DMP specification to produce a project-specific DMP.

-

Tags: Research Data Management, Large Language Models, Artificial Intelligence

-

Content type: Blog

+

Tags: Research Data Management, Artificial Intelligence

+

Content type: Blog Post

https://focalplane.biologists.com/2023/11/06/creating-a-research-data-management-plan-using-chatgpt/

@@ -769,7 +764,7 @@

Generative artificial intelligence for bio-image analysishttps://f1000research.com/slides/12-971

@@ -864,7 +859,7 @@

Hitchhiking through a diverse Bio-image Analysis Software Universehttps://f1000research.com/slides/11-746

https://doi.org/10.7490/f1000research.1119026.1

@@ -874,7 +869,7 @@

I2K 2024: clEsperanto - GPU-Accelerated Image Processing LibraryStRigaud/clesperanto_workshop_I2K24

@@ -895,7 +890,7 @@

If you license it, it’ll be harder to steal it. Why we should license our

Licensed CC-BY-4.0

Blog post about why we should license our work and what is important when choosing a license.

Tags: Licensing, Research Data Management

-

Content type: Blog

+

Content type: Blog Post

https://focalplane.biologists.com/2023/05/06/if-you-license-it-itll-be-harder-to-steal-it-why-we-should-license-our-work/

@@ -904,7 +899,7 @@

ImageJ2 API-beatinghttps://git.mpi-cbg.de/rhaase/lecture_imagej2_dev

@@ -921,7 +916,7 @@

Introduction to ImageJ macro programming, Scientific Computing Facility, MPI

Robert Haase, Benoit Lombardot

Licensed UNKNOWN

Tags: Imagej, Bioimage Analysis

-

Content type: Slide

+

Content type: Slides

https://git.mpi-cbg.de/scicomp/bioimage_team/coursematerialimageanalysis/tree/master/ImageJMacro_24h_2017-01

@@ -948,6 +943,7 @@

Large Language Models: An Introduction for Life Scientistshttps://zenodo.org/records/14418209

https://doi.org/10.5281/zenodo.14418209

@@ -957,7 +953,7 @@

Lecture Applied Bioimage Analysis 2020https://git.mpi-cbg.de/rhaase/lecture_applied_bioimage_analysis

@@ -966,7 +962,7 @@

Managing Scientific Python environments using Conda, Mamba and friendsRobert Haase

Licensed CC-BY-4.0

Tags: Python, Conda, Mamba

-

Content type: Blog

+

Content type: Blog Post

https://focalplane.biologists.com/2022/12/08/managing-scientific-python-environments-using-conda-mamba-and-friends/

@@ -985,7 +981,7 @@

Multi-view fusionhttps://git.mpi-cbg.de/rhaase/lecture_multiview_registration

@@ -994,7 +990,7 @@

NEUBIAS Bioimage Analyst School 2019miura/NEUBIAS_AnalystSchool2019

@@ -1034,7 +1030,7 @@

Optimisation and Validation of a Swarm Intelligence based Segmentation Algor

Parallelization and heterogeneous computing: from pure CPU to GPU-accelerated image processing#

Robert Haase

Licensed CC-BY-4.0

-

Content type: Slide

+

Content type: Slides

https://f1000research.com/slides/11-1171

https://doi.org/10.7490/f1000research.1119154.1

@@ -1055,7 +1051,7 @@

Sharing and licensing materialhttps://f1000research.com/slides/10-519

@@ -1065,7 +1061,7 @@

Sharing research data with Zenodozenodo.org

Tags: Sharing, Research Data Management

-

Content type: Blog

+

Content type: Blog Post

https://focalplane.biologists.com/2023/02/15/sharing-research-data-with-zenodo/

@@ -1096,6 +1092,7 @@

Towards Preservation of Life Science Data with NFDI4BIOIMAGEhttps://zenodo.org/records/13640979

https://doi.org/10.5281/zenodo.13640979

@@ -1116,7 +1113,7 @@

Tracking Theory, TrackMate, and Mastodonhttps://git.mpi-cbg.de/rhaase/lecture_tracking_trackmate

@@ -1125,7 +1122,7 @@

Working with objects in 2D and 3Dhttps://git.mpi-cbg.de/rhaase/lecture_working_with_objects_in_2d_and_3d

@@ -1134,7 +1131,7 @@

Working with pixelshttps://git.mpi-cbg.de/rhaase/lecture_working_with_pixels

@@ -1144,7 +1141,7 @@

YMIA - Python-Based Event Series Training MaterialPublished None

Licensed MIT

This repository offer access to teaching material and useful resources for the YMIA - Python-Based Event Series.

-

Tags: Python, Large Language Models, Prompt Engineering, Biabob, Bioimage Analysis, Microscopy Image Analysis

+

Tags: Python, Artifical Intelligence, Bioimage Analysis

Content type: Github Repository, Slides

rmassei/ymia_python_event_series_material

diff --git a/authors/roland_nitschke.html b/authors/roland_nitschke.html index 5b18bd37..eb8028af 100644 --- a/authors/roland_nitschke.html +++ b/authors/roland_nitschke.html @@ -182,36 +182,29 @@

By tag

By content type

@@ -236,11 +228,12 @@ @@ -724,6 +717,7 @@

The Information Infrastructure for BioImage Data (I3D:bio) project to advanc

Published 2024-03-04

Licensed CC-BY-4.0

Research data management (RDM) in microscopy and image analysis is a challenging task. Large files in proprietary formats, complex N-dimensional array structures, and various metadata models and formats can make image data handling inconvenient and difficult. For data organization, annotation, and sharing, researchers need solutions that fit everyday practice and comply with the FAIR (Findable, Accessible, Interoperable, Reusable) principles. International community-based efforts have begun creating open data models (OME), an open file format and translation library (OME-TIFF, Bio-Formats), data management software platforms, and microscopy metadata recommendations and annotation tools. Bringing these developments into practice requires support and training. Iterative feedback and tool improvement is needed to foster practical adoption by the scientific community. The Information Infrastructure for BioImage Data (I3D:bio) project works on guidelines, training resources, and practical assistance for FAIR microscopy RDM adoption with a focus on the management platform OMERO and metadata annotations.

+

Tags: Nfdi4Bioimage, Research Data Management

https://zenodo.org/records/10805204

https://doi.org/10.5281/zenodo.10805204

diff --git a/authors/romain_guiet.html b/authors/romain_guiet.html index ce74f864..6c945554 100644 --- a/authors/romain_guiet.html +++ b/authors/romain_guiet.html @@ -182,36 +182,29 @@

By tag

By content type

@@ -236,11 +228,12 @@ @@ -659,7 +652,7 @@

Test Dataset for Whole Slide Image Registration#

Curtis Rueden, Albane de la Villegeorges, Simon F. Nørrelykke, Romain Guiet, Olivier Burri, et al.

Licensed UNKNOWN

-

Tags: Bioimage Analysis, Microscopy Image Analysis

+

Tags: Bioimage Analysis

Content type: Collection, Event, Forum Post, Workshop

https://forum.image.sc/t/upcoming-image-analysis-events/60018/67

diff --git a/authors/silke_tulok.html b/authors/silke_tulok.html index 1e4e6c68..a89966fb 100644 --- a/authors/silke_tulok.html +++ b/authors/silke_tulok.html @@ -182,36 +182,29 @@

By tag

By content type

@@ -236,11 +228,12 @@ @@ -527,6 +520,7 @@

Key-Value pair template for annotation in OMERO for light microscopy data ac See cross reference: 10.5281/zenodo.12547566 Key-Value pair template for annotation of datasets in OMERO for light- and electron microscopy data within the research group of Prof. Mueller-Reichert 10.5281/zenodo.12546808 Key-Value pair template for annotation of datasets in OMERO (PERIKLES study)

+

Tags: Nfdi4Bioimage, Research Data Management

https://zenodo.org/records/12578084

https://doi.org/10.5281/zenodo.12578084

@@ -552,6 +546,7 @@

Key-Value pair template for annotation of datasets in OMERO (PERIKLES study) See cross reference: 10.5281/zenodo.12547566 Key-Value pair template for annotation of datasets in OMERO (light- and electron microscopy data within the research group of Prof. Mueller-Reichert) 10.5281/zenodo.12578084 Key-Value pair template for annotation in OMERO for light microscopy data acquired with AxioScan7 - Core Facility Cellular Imaging (CFCI)

+

Tags: Nfdi4Bioimage, Research Data Management

https://zenodo.org/records/12546808

https://doi.org/10.5281/zenodo.12546808

diff --git a/authors/stefanie_weidtkamp-peters.html b/authors/stefanie_weidtkamp-peters.html index 5012188b..08f62ad8 100644 --- a/authors/stefanie_weidtkamp-peters.html +++ b/authors/stefanie_weidtkamp-peters.html @@ -182,36 +182,29 @@

By tag

By content type

@@ -236,11 +228,12 @@ @@ -490,6 +483,7 @@

A journey to FAIR microscopy datahttps://zenodo.org/records/7890311

https://doi.org/10.5281/zenodo.7890311

@@ -745,7 +739,7 @@

I3D:bio’s OMERO training material: Re-usable, adjustable, multi-purpose sl

Licensed CC-BY-4.0

The open-source software OME Remote Objects (OMERO) is a data management software that allows storing, organizing, and annotating bioimaging/microscopy data. OMERO has become one of the best-known systems for bioimage data management in the bioimaging community. The Information Infrastructure for BioImage Data (I3D:bio) project facilitates the uptake of OMERO into research data management (RDM) practices at universities and research institutions in Germany. Since the adoption of OMERO into researchers’ daily routines requires intensive training, a broad portfolio of training resources for OMERO is an asset. On top of using the OMERO guides curated by the Open Microscopy Environment Consortium (OME) team, imaging core facility staff at institutions where OMERO is used often prepare additional material tailored to be applicable for their own OMERO instances. Based on experience gathered in the Research Data Management for Microscopy group (RDM4mic) in Germany, and in the use cases in the I3D:bio project, we created a set of reusable, adjustable, openly available slide decks to serve as the basis for tailored training lectures, video tutorials, and self-guided instruction manuals directed at beginners in using OMERO. The material is published as an open educational resource complementing the existing resources for OMERO contributed by the community.

Tags: OMERO, Research Data Management, Nfdi4Bioimage, I3Dbio

-

Content type: Slide, Video

+

Content type: Slides, Video

https://zenodo.org/records/8323588

https://www.youtube.com/playlist?list=PL2k-L-zWPoR7SHjG1HhDIwLZj0MB_stlU

https://doi.org/10.5281/zenodo.8323588

@@ -785,6 +779,7 @@

The Information Infrastructure for BioImage Data (I3D:bio) project to advanc

Published 2024-03-04

Licensed CC-BY-4.0

Research data management (RDM) in microscopy and image analysis is a challenging task. Large files in proprietary formats, complex N-dimensional array structures, and various metadata models and formats can make image data handling inconvenient and difficult. For data organization, annotation, and sharing, researchers need solutions that fit everyday practice and comply with the FAIR (Findable, Accessible, Interoperable, Reusable) principles. International community-based efforts have begun creating open data models (OME), an open file format and translation library (OME-TIFF, Bio-Formats), data management software platforms, and microscopy metadata recommendations and annotation tools. Bringing these developments into practice requires support and training. Iterative feedback and tool improvement is needed to foster practical adoption by the scientific community. The Information Infrastructure for BioImage Data (I3D:bio) project works on guidelines, training resources, and practical assistance for FAIR microscopy RDM adoption with a focus on the management platform OMERO and metadata annotations.

+

Tags: Nfdi4Bioimage, Research Data Management

https://zenodo.org/records/10805204

https://doi.org/10.5281/zenodo.10805204

@@ -828,6 +823,7 @@

[Workshop Material] Fit for OMERO - How imaging facilities and IT department Establish a stakeholder process management for installing OMERO-based RDM Learn from each other, leverage different expertise Learn how to train users, establish sustainability strategies, and foster FAIR RDM for bioimaging at your institution

+

Tags: Nfdi4Bioimage, Research Data Management

https://zenodo.org/records/14178789

https://doi.org/10.5281/zenodo.14178789

diff --git a/authors/susanne_kunis.html b/authors/susanne_kunis.html index 2e05bf99..da74805f 100644 --- a/authors/susanne_kunis.html +++ b/authors/susanne_kunis.html @@ -182,36 +182,29 @@

By tag

By content type

@@ -236,11 +228,12 @@ @@ -510,7 +503,7 @@

I3D:bio’s OMERO training material: Re-usable, adjustable, multi-purpose sl

Licensed CC-BY-4.0

The open-source software OME Remote Objects (OMERO) is a data management software that allows storing, organizing, and annotating bioimaging/microscopy data. OMERO has become one of the best-known systems for bioimage data management in the bioimaging community. The Information Infrastructure for BioImage Data (I3D:bio) project facilitates the uptake of OMERO into research data management (RDM) practices at universities and research institutions in Germany. Since the adoption of OMERO into researchers’ daily routines requires intensive training, a broad portfolio of training resources for OMERO is an asset. On top of using the OMERO guides curated by the Open Microscopy Environment Consortium (OME) team, imaging core facility staff at institutions where OMERO is used often prepare additional material tailored to be applicable for their own OMERO instances. Based on experience gathered in the Research Data Management for Microscopy group (RDM4mic) in Germany, and in the use cases in the I3D:bio project, we created a set of reusable, adjustable, openly available slide decks to serve as the basis for tailored training lectures, video tutorials, and self-guided instruction manuals directed at beginners in using OMERO. The material is published as an open educational resource complementing the existing resources for OMERO contributed by the community.

Tags: OMERO, Research Data Management, Nfdi4Bioimage, I3Dbio

-

Content type: Slide, Video

+

Content type: Slides, Video

https://zenodo.org/records/8323588

https://www.youtube.com/playlist?list=PL2k-L-zWPoR7SHjG1HhDIwLZj0MB_stlU

https://doi.org/10.5281/zenodo.8323588

@@ -573,6 +566,7 @@

The Information Infrastructure for BioImage Data (I3D:bio) project to advanc

Published 2024-03-04

Licensed CC-BY-4.0

Research data management (RDM) in microscopy and image analysis is a challenging task. Large files in proprietary formats, complex N-dimensional array structures, and various metadata models and formats can make image data handling inconvenient and difficult. For data organization, annotation, and sharing, researchers need solutions that fit everyday practice and comply with the FAIR (Findable, Accessible, Interoperable, Reusable) principles. International community-based efforts have begun creating open data models (OME), an open file format and translation library (OME-TIFF, Bio-Formats), data management software platforms, and microscopy metadata recommendations and annotation tools. Bringing these developments into practice requires support and training. Iterative feedback and tool improvement is needed to foster practical adoption by the scientific community. The Information Infrastructure for BioImage Data (I3D:bio) project works on guidelines, training resources, and practical assistance for FAIR microscopy RDM adoption with a focus on the management platform OMERO and metadata annotations.

+

Tags: Nfdi4Bioimage, Research Data Management

https://zenodo.org/records/10805204

https://doi.org/10.5281/zenodo.10805204

@@ -594,6 +588,7 @@

[Community Meeting 2024] Overview Team Image Data Analysis and ManagementLicensed CC-BY-4.0

Overview of Activities of the Team Image Data Analysis and Management of German BioImaging e.V.  

+

Tags: Nfdi4Bioimage, Research Data Management

https://zenodo.org/records/10796364

https://doi.org/10.5281/zenodo.10796364

@@ -615,6 +610,7 @@

[ELMI 2024] AI’s Dirty Little Secret: Withouthttps://www.elmi2024.org/)

+

Tags: Nfdi4Bioimage, Research Data Management

https://zenodo.org/records/11235513

https://doi.org/10.5281/zenodo.11235513

@@ -668,6 +664,7 @@

[Workshop Material] Fit for OMERO - How imaging facilities and IT department Establish a stakeholder process management for installing OMERO-based RDM Learn from each other, leverage different expertise Learn how to train users, establish sustainability strategies, and foster FAIR RDM for bioimaging at your institution

+

Tags: Nfdi4Bioimage, Research Data Management

https://zenodo.org/records/14178789

https://doi.org/10.5281/zenodo.14178789

diff --git a/authors/thomas_zobel.html b/authors/thomas_zobel.html index c7a738bc..9c698436 100644 --- a/authors/thomas_zobel.html +++ b/authors/thomas_zobel.html @@ -182,36 +182,29 @@

By tag

By content type

@@ -236,11 +228,12 @@ @@ -507,7 +500,7 @@

I3D:bio’s OMERO training material: Re-usable, adjustable, multi-purpose sl

Licensed CC-BY-4.0

The open-source software OME Remote Objects (OMERO) is a data management software that allows storing, organizing, and annotating bioimaging/microscopy data. OMERO has become one of the best-known systems for bioimage data management in the bioimaging community. The Information Infrastructure for BioImage Data (I3D:bio) project facilitates the uptake of OMERO into research data management (RDM) practices at universities and research institutions in Germany. Since the adoption of OMERO into researchers’ daily routines requires intensive training, a broad portfolio of training resources for OMERO is an asset. On top of using the OMERO guides curated by the Open Microscopy Environment Consortium (OME) team, imaging core facility staff at institutions where OMERO is used often prepare additional material tailored to be applicable for their own OMERO instances. Based on experience gathered in the Research Data Management for Microscopy group (RDM4mic) in Germany, and in the use cases in the I3D:bio project, we created a set of reusable, adjustable, openly available slide decks to serve as the basis for tailored training lectures, video tutorials, and self-guided instruction manuals directed at beginners in using OMERO. The material is published as an open educational resource complementing the existing resources for OMERO contributed by the community.

Tags: OMERO, Research Data Management, Nfdi4Bioimage, I3Dbio

-

Content type: Slide, Video

+

Content type: Slides, Video

https://zenodo.org/records/8323588

https://www.youtube.com/playlist?list=PL2k-L-zWPoR7SHjG1HhDIwLZj0MB_stlU

https://doi.org/10.5281/zenodo.8323588

@@ -546,6 +539,7 @@

Structuring of Data and Metadata in Bioimaging: Concepts and technical Solut ... +

Tags: Nfdi4Bioimage, Research Data Management

https://zenodo.org/records/7018750

https://doi.org/10.5281/zenodo.7018750

@@ -557,6 +551,7 @@

[Community Meeting 2024] Overview Team Image Data Analysis and ManagementLicensed CC-BY-4.0

Overview of Activities of the Team Image Data Analysis and Management of German BioImaging e.V.  

+

Tags: Nfdi4Bioimage, Research Data Management

https://zenodo.org/records/10796364

https://doi.org/10.5281/zenodo.10796364

diff --git a/authors/toby_hodges.html b/authors/toby_hodges.html index d768891a..7fe68bae 100644 --- a/authors/toby_hodges.html +++ b/authors/toby_hodges.html @@ -182,36 +182,29 @@

By tag

By content type

@@ -236,11 +228,12 @@ @@ -480,7 +473,7 @@

Creating open computational curriculahttps://zenodo.org/records/4317149

https://doi.org/10.5281/zenodo.4317149

@@ -490,17 +483,18 @@

Image Processing with Pythonhttps://datacarpentry.org/image-processing/key-points.html

Modular training resources for bioimage analysis#

-

Christian Tischer, Antonio Politi, Tim-Oliver Buchholz, Elnaz Fazeli, Nicola Gritti, Aliaksandr Halavatyi, Gonzalez Tirado, Sebastian, Julian Hennies, Toby Hodges, Arif Khan, Dominik Kutra, Stefania Marcotti, Bugra Oezdemir, Felix Schneider, Martin Schorb, Anniek Stokkermans, Yi Sun, Nima Vakili

+

Christian Tischer, Antonio Politi, Tim-Oliver Buchholz, Elnaz Fazeli, Nicola Gritti, Aliaksandr Halavatyi, Sebastian Gonzalez Tirado, Julian Hennies, Toby Hodges, Arif Khan, Dominik Kutra, Stefania Marcotti, Bugra Oezdemir, Felix Schneider, Martin Schorb, Anniek Stokkermans, Yi Sun, Nima Vakili

Published 2024-12-03

Licensed CC-BY-4.0

Resources for teaching/preparing to teach bioimage analysis

+

Tags: Neubias, Bioimage Analysis

https://zenodo.org/records/14264885

https://doi.org/10.5281/zenodo.14264885

diff --git a/authors/tom_boissonnet.html b/authors/tom_boissonnet.html index f070c55b..77eab4f2 100644 --- a/authors/tom_boissonnet.html +++ b/authors/tom_boissonnet.html @@ -182,36 +182,29 @@

By tag

By content type

@@ -236,11 +228,12 @@ @@ -515,7 +508,7 @@

I3D:bio’s OMERO training material: Re-usable, adjustable, multi-purpose sl

Licensed CC-BY-4.0

The open-source software OME Remote Objects (OMERO) is a data management software that allows storing, organizing, and annotating bioimaging/microscopy data. OMERO has become one of the best-known systems for bioimage data management in the bioimaging community. The Information Infrastructure for BioImage Data (I3D:bio) project facilitates the uptake of OMERO into research data management (RDM) practices at universities and research institutions in Germany. Since the adoption of OMERO into researchers’ daily routines requires intensive training, a broad portfolio of training resources for OMERO is an asset. On top of using the OMERO guides curated by the Open Microscopy Environment Consortium (OME) team, imaging core facility staff at institutions where OMERO is used often prepare additional material tailored to be applicable for their own OMERO instances. Based on experience gathered in the Research Data Management for Microscopy group (RDM4mic) in Germany, and in the use cases in the I3D:bio project, we created a set of reusable, adjustable, openly available slide decks to serve as the basis for tailored training lectures, video tutorials, and self-guided instruction manuals directed at beginners in using OMERO. The material is published as an open educational resource complementing the existing resources for OMERO contributed by the community.

Tags: OMERO, Research Data Management, Nfdi4Bioimage, I3Dbio

-

Content type: Slide, Video

+

Content type: Slides, Video

https://zenodo.org/records/8323588

https://www.youtube.com/playlist?list=PL2k-L-zWPoR7SHjG1HhDIwLZj0MB_stlU

https://doi.org/10.5281/zenodo.8323588

@@ -542,6 +535,7 @@

Key-Value pair template for annotation in OMERO for light microscopy data ac See cross reference: 10.5281/zenodo.12547566 Key-Value pair template for annotation of datasets in OMERO for light- and electron microscopy data within the research group of Prof. Mueller-Reichert 10.5281/zenodo.12546808 Key-Value pair template for annotation of datasets in OMERO (PERIKLES study)

+

Tags: Nfdi4Bioimage, Research Data Management

https://zenodo.org/records/12578084

https://doi.org/10.5281/zenodo.12578084

@@ -567,6 +561,7 @@

Key-Value pair template for annotation of datasets in OMERO (PERIKLES study) See cross reference: 10.5281/zenodo.12547566 Key-Value pair template for annotation of datasets in OMERO (light- and electron microscopy data within the research group of Prof. Mueller-Reichert) 10.5281/zenodo.12578084 Key-Value pair template for annotation in OMERO for light microscopy data acquired with AxioScan7 - Core Facility Cellular Imaging (CFCI)

+

Tags: Nfdi4Bioimage, Research Data Management

https://zenodo.org/records/12546808

https://doi.org/10.5281/zenodo.12546808

@@ -624,6 +619,7 @@

The Information Infrastructure for BioImage Data (I3D:bio) project to advanc

Published 2024-03-04

Licensed CC-BY-4.0

Research data management (RDM) in microscopy and image analysis is a challenging task. Large files in proprietary formats, complex N-dimensional array structures, and various metadata models and formats can make image data handling inconvenient and difficult. For data organization, annotation, and sharing, researchers need solutions that fit everyday practice and comply with the FAIR (Findable, Accessible, Interoperable, Reusable) principles. International community-based efforts have begun creating open data models (OME), an open file format and translation library (OME-TIFF, Bio-Formats), data management software platforms, and microscopy metadata recommendations and annotation tools. Bringing these developments into practice requires support and training. Iterative feedback and tool improvement is needed to foster practical adoption by the scientific community. The Information Infrastructure for BioImage Data (I3D:bio) project works on guidelines, training resources, and practical assistance for FAIR microscopy RDM adoption with a focus on the management platform OMERO and metadata annotations.

+

Tags: Nfdi4Bioimage, Research Data Management

https://zenodo.org/records/10805204

https://doi.org/10.5281/zenodo.10805204

@@ -657,6 +653,7 @@

[Workshop Material] Fit for OMERO - How imaging facilities and IT department Establish a stakeholder process management for installing OMERO-based RDM Learn from each other, leverage different expertise Learn how to train users, establish sustainability strategies, and foster FAIR RDM for bioimaging at your institution

+

Tags: Nfdi4Bioimage, Research Data Management

https://zenodo.org/records/14178789

https://doi.org/10.5281/zenodo.14178789

@@ -716,17 +713,18 @@

[Workshop] Research Data Management for Microscopy and BioImage Analysis

Date & Venue:Thursday, Sept. 26, 5.30 p.m.Haus 22 / Paul Ehrlich Lecture Hall (H22-1)University Hospital Frankfurt

+

Tags: Nfdi4Bioimage, Research Data Management

https://zenodo.org/records/13861026

https://doi.org/10.5281/zenodo.13861026

ome2024-ngff-challenge#

-

Will Moore, Josh Moore, sherwoodf, jean-marie burel, Norman Rzepka, dependabot[bot], JensWendt, Joost de Folter, Torsten Stöter, AybukeKY, Eric Perlman, Tom Boissonnet

+

Will Moore, Josh Moore, sherwoodf, Jean-Marie Burel, Norman Rzepka, dependabot[bot], JensWendt, Joost de Folter, Torsten St\xF6ter, AybukeKY, Eric Perlman, Tom Boissonnet

Published 2024-08-30T12:00:53+00:00

Licensed BSD-3-CLAUSE

Project planning and material repository for the 2024 challenge to generate 1 PB of OME-Zarr data

-

Tags: Sharing

+

Tags: Sharing, Nfdi4Bioimage, Research Data Management

Content type: Github Repository

ome/ome2024-ngff-challenge

diff --git "a/authors/torsten_st\303\266ter.html" "b/authors/torsten_st\303\266ter.html" deleted file mode 100644 index ca3cbdf1..00000000 --- "a/authors/torsten_st\303\266ter.html" +++ /dev/null @@ -1,640 +0,0 @@ - - - - - - - - - - - Torsten stöter (5) — NFDI4BioImage Training Materials - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
- - - - - - -
- - - Ctrl+K -
- - -
- -
- - -
-
- - -
-
- - - - - - -
- - - -
- - - - -
- -
-
- - - - - -
-
- -
-
-
- - -
-
-
-
-
- - -
- -
- - - -
- -
-
- -
-
- -
- -
- -
- - -
- -
- -
- - - - - -
- - -
- - - - - - -
- - -
- - - - - - - - - - - - - -
- -
- -
-
- - - -
-

Torsten stöter (5)

- -
- -
-
- - - - -
- -
-

Torsten stöter (5)#

-
-

Combining the BIDS and ARC Directory Structures for Multimodal Research Data Organization#

-

Torsten Stöter, Tobias Gottschall, Andrea Schrader, Peter Zentis, Monica Valencia-Schneider, Niraj Kandpal, Werner Zuschratter, Astrid Schauss, Timo Dickscheid, Timo Mühlhaus, Dirk von Suchodoletz

-

Licensed CC-BY-4.0

-

Interdisciplinary collaboration and integrating large, diverse datasets are crucial for answering complex research questions, requiring multimodal data analysis and adherence to FAIR principles. To address challenges in capturing the full research cycle and contextualizing data, DataPLANT developed the Annotated Research Context (ARC), while the neuroimaging community extended the Brain Imaging Data Structure (BIDS) for microscopic image data, both providing standardized, file system-based storage structures for organizing and sharing data with metadata.

-

Tags: Research Data Management, FAIR-Principles

-

Content type: Poster

-

https://zenodo.org/doi/10.5281/zenodo.8349562

-
-
-
-

Data stewardship and research data management tools for multimodal linking of imaging data in plasma medicine#

-

Mohsen Ahmadi, Robert Wagner, Philipp Mattern, Nick Plathe, Sander Bekeschus, Markus M. Becker, Torsten Stöter, Stefanie Weidtkamp-Peters

-

Published 2023-11-03

-

Licensed CC-BY-4.0

-

A more detailed understanding of the effect of plasmas on biological systems can be fostered by combining data from different imaging modalities, such as optical imaging, fluorescence imaging, and mass spectrometry imaging. This, however, requires the implementation and use of sophisticated research data management (RDM) solutions to incorporate the influence of plasma parameters and treatment procedures as well as the effects of plasma on the treated targets. In order to address this, RDM activities on different levels and from different perspectives are started and brought together within the framework of the NFDI consortium NFDI4BIOIMAGE.

-

https://zenodo.org/records/10069368

-

https://doi.org/10.5281/zenodo.10069368

-
-
-
-

NFDI4Bioimage - TA3-Hackathon - UoC-2023 (Cologne Hackathon)#

-

Mohamed M. Abdrabbou, Mehrnaz Babaki, Tom Boissonnet, Michele Bortolomeazzi, Eik Dahms, Vanessa A. F. Fuchs, Moritz Hoevels, Niraj Kandpal, Christoph Möhl, Joshua A. Moore, Astrid Schauss, Andrea Schrader, Torsten Stöter, Julia Thönnißen, Monica Valencia-S., H. Lukas Weil, Jens Wendt and Peter Zentis

-

Licensed CC-BY-4.0

-

Tags: Arc, Dataplant, Hackathon, Nfdi4Bioimage, OMERO, Python, Research Data Management

-

Content type: Event, Publication, Documentation

-

NFDI4BIOIMAGE/Cologne-Hackathon-2023

-

https://doi.org/10.5281/zenodo.10609770

-
-
-
-

NFDI4Bioimage - TA3-Hackathon - UoC-2023 (Cologne-Hackathon-2023, GitHub repository)#

-

Mohamed Abdrabbou, Mehrnaz Babaki, Tom Boissonnet, Michele Bortolomeazzi, Eik Dahms, Vanessa Fuchs, A. F. Moritz Hoevels, Niraj Kandpal, Christoph Möhl, Joshua A. Moore, Astrid Schauss, Andrea Schrader, Torsten Stöter, Julia Thönnißen, Monica Valencia-S., H. Lukas Weil, Jens Wendt, Peter Zentis

-

Licensed CC-BY-4.0

-

This repository documents the first NFDI4Bioimage - TA3-Hackathon - UoC-2023 (Cologne Hackathon), where topics like ‘Interoperability’, ‘REMBI / Mapping’, and ‘Neuroglancer (OMERO / zarr)’ were explored through collaborative discussions and workflow sessions, culminating in reports that bridge NFDI4Bioimage to DataPLANT. Funded by various DFG initiatives, this event emphasized documentation and use cases, contributing preparatory work for future interoperability projects at the 2nd de.NBI BioHackathon in Bielefeld.

-

Tags: Research Data Management, FAIR-Principles, Bioimage Analysis, Nfdi4Bioimage

-

Content type: Github Repository

-

https://zenodo.org/doi/10.5281/zenodo.10609770

-
-
-
-

ome2024-ngff-challenge#

-

Will Moore, Josh Moore, sherwoodf, jean-marie burel, Norman Rzepka, dependabot[bot], JensWendt, Joost de Folter, Torsten Stöter, AybukeKY, Eric Perlman, Tom Boissonnet

-

Published 2024-08-30T12:00:53+00:00

-

Licensed BSD-3-CLAUSE

-

Project planning and material repository for the 2024 challenge to generate 1 PB of OME-Zarr data

-

Tags: Sharing

-

Content type: Github Repository

-

ome/ome2024-ngff-challenge

-
-
-
- - - - -
- - - - - - -
- -
-
-
- -
- - - - - - - -
-
- -
- -
- -

-By Robert Haase, Clément Caporal,... and the NFDI4BioImage Initiative -

- -
- -
- - -
- -
-

- Last updated on 2025-02-01. -
-

-
- -
- -
-

-Copyright: Licensed CC-BY 4.0 unless mentioned otherwise. -Contributions and feedback are welcome. -

- -
-
- -
-
- - -
-
-
- - - - - -
-
- - \ No newline at end of file diff --git a/authors/ugis_sarkans.html b/authors/ugis_sarkans.html index 780c1c6c..ccdc6708 100644 --- a/authors/ugis_sarkans.html +++ b/authors/ugis_sarkans.html @@ -182,36 +182,29 @@

By tag

By content type

@@ -236,11 +228,12 @@ @@ -488,7 +481,7 @@

Microscopy data analysis: machine learning and the BioImage ArchiveAndrii Iudin, Anna Foix-Romero, Anna Kreshuk, Awais Athar, Beth Cimini, Dominik Kutra, Estibalis Gomez de Mariscal, Frances Wong, Guillaume Jacquemet, Kedar Narayan, Martin Weigert, Nodar Gogoberidze, Osman Salih, Petr Walczysko, Ryan Conrad, Simone Weyend, Sriram Sundar Somasundharam, Suganya Sivagurunathan, Ugis Sarkans

Licensed CC-BY-4.0

The Microscopy data analysis: machine learning and the BioImage Archive course, which focused on introducing programmatic approaches used in the analysis of bioimage data via the BioImage Archive, ran in May 2023.

-

Tags: Microscopy Image Analysis, Python, Deep Learning

+

Tags: Bioimage Analysis, Python, Artificial Intelligence

Content type: Video, Slides

https://www.ebi.ac.uk/training/materials/microscopy-data-analysis-machine-learning-and-the-bioimage-archive-materials/

@@ -509,7 +502,7 @@

REMBI - Recommended Metadata for Biological Images—enabling reuse of micro

Published 2021-05-21

Licensed UNKNOWN

Bioimaging data have significant potential for reuse, but unlocking this potential requires systematic archiving of data and metadata in public databases. The authors propose draft metadata guidelines to begin addressing the needs of diverse communities within light and electron microscopy.

-

Tags: Metadata, Bioimage Data, Image Data Management, Research Data Management

+

Tags: Metadata, Research Data Management

Content type: Publication

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8606015/

https://www.nature.com/articles/s41592-021-01166-8

@@ -522,7 +515,7 @@

The BioImage Archive – Building a Home for Life-Sciences Microscopy DataPublished 2022-06-22

Licensed UNKNOWN

The BioImage Archive is a new archival data resource at the European Bioinformatics Institute (EMBL-EBI).

-

Tags: Image Data Management, Research Data Management, Bioimage Data

+

Tags: Research Data Management

Content type: Publication

https://www.sciencedirect.com/science/article/pii/S0022283622000791?via%3Dihub

https://doi.org/10.1016/j.jmb.2022.167505

diff --git a/content_types/blog.html b/content_types/blog post.html similarity index 86% rename from content_types/blog.html rename to content_types/blog post.html index 45e649c9..7c1d5082 100644 --- a/content_types/blog.html +++ b/content_types/blog post.html @@ -8,7 +8,7 @@ - Blog (19) — NFDI4BioImage Training Materials + Blog post (23) — NFDI4BioImage Training Materials @@ -60,7 +60,7 @@ - + @@ -182,36 +182,29 @@

By tag

By content type

@@ -236,11 +228,12 @@ @@ -341,7 +334,7 @@ -
  • -

    Blog (19)

    +

    Blog post (23)

    @@ -458,9 +451,12 @@

    Contents

    next

    -

    Slide (41)

    +

    Slides (82)

     diff --git a/content_types/readme.html b/content_types/readme.html index 460f9233..2ffaa168 100644 --- a/content_types/readme.html +++ b/content_types/readme.html @@ -182,36 +182,29 @@

    By tag

    By content type

    @@ -236,11 +228,12 @@ diff --git a/content_types/slide.html b/content_types/slide.html deleted file mode 100644 index 26f4ff2c..00000000 --- a/content_types/slide.html +++ /dev/null @@ -1,1087 +0,0 @@ - - - - - - - - - - - Slide (41) — NFDI4BioImage Training Materials - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    - - - - - - -
    - - - Ctrl+K -
    -     - -
    - -
    - - -
    -
    - - -
    -
    - - - - -
    - - - -
    - - - - -
    - -
    -
    - - - - - -
    -
    - -
    -
    -
    - - -
    -
    -
    -
    -
    - - -
    - -
    - - - -
    - -
    -
    - -
    -
    - -
    - -
    - -
    - - -
    - -
    - -
    - - - - - -
    - - -
    - - - - - - -
    - - -
    - - - - - - - - - - - - - -
    - -
    - -
    -
    - - - -
    -

    Slide (41)

    - -
    -
    - -
    -

    Contents

    -
    - -
    -
    -
    - - - - -
    - -
    -

    Slide (41)#

    -
    -

    Adding a Workflow to BIAFLOWS#

    -

    Sébastien Tosi, Volker Baecker, Benjamin Pavie

    -

    Licensed BSD-2-CLAUSE

    -

    Tags: Neubias, Bioimage Analysis

    -

    Content type: Slide

    -

    RoccoDAnt/Defragmentation_TrainingSchool_EOSC-Life_2022

    -
    -
    -
    -

    Bio Image Analysis#

    -

    Christian Tischer

    -

    Licensed UNKNOWN

    -

    Content type: Slide

    -

    tischi/presentation-image-analysis

    -
    -
    -
    -

    Bio-Image Data Strudel for Workshop on Research Data Management in TU Dresden Core Facilities#

    -

    Cornelia Wetzker

    -

    Published 2023-11-08

    -

    Licensed CC-BY-4.0

    -

    This presentation gives a short outline of the complexity of data and metadata in the bioimaging universe. It introduces NFDI4BIOIMAGE as a newly formed consortium as part of the German ‘Nationale Forschungsdateninfrastruktur’ (NFDI) and its goals and tools for data management including its current members on TU Dresden campus.  

    -

    Tags: Research Data Management, Tu Dresden, Bioimage Data, Nfdi4Bioimage

    -

    Content type: Slide

    -

    https://zenodo.org/records/10083555

    -

    https://doi.org/10.5281/zenodo.10083555

    -
    -
    -
    -

    Bio-image Analysis with the Help of Large Language Models#

    -

    Robert Haase

    -

    Published 2024-03-13

    -

    Licensed CC-BY-4.0

    -

    Large Language Models (LLMs) change the way how we use computers. This also has impact on the bio-image analysis community. We can generate code that analyzes biomedical image data if we ask the right prompts. This talk outlines introduces basic principles, explains prompt engineering and how to apply it to bio-image analysis. We also compare how different LLM vendors perform on code generation tasks and which challenges are ahead for the bio-image analysis community.

    -

    Tags: Large Language Models, Python

    -

    Content type: Slide

    -

    https://zenodo.org/records/10815329

    -

    https://doi.org/10.5281/zenodo.10815329

    -
    -
    -
    -

    Building a Bioimage Analysis Workflow using Deep Learning#

    -

    Estibaliz Gómez-de-Mariscal

    -

    Licensed UNKNOWN

    -

    Tags: Artificial Intelligence, Bioimage Analysis

    -

    Content type: Slide

    -

    esgomezm/NEUBIAS_chapter_DL_2020

    -
    -
    -
    -

    CellProfiler Introduction#

    -

    Anna Klemm

    -

    Licensed UNKNOWN

    -

    Tags: Neubias, Cellprofiler, Bioimage Analysis

    -

    Content type: Slide

    -

    ahklemm/CellProfiler_Introduction

    -
    -
    -
    -

    Challenges and opportunities for bio-image analysis core-facilities#

    -

    Robert Haase

    -

    Licensed CC-BY-4.0

    -

    Tags: Research Data Management, Bioimage Analysis, Nfdi4Bioimage

    -

    Content type: Slide

    -

    https://f1000research.com/slides/12-1054

    -
    -
    -
    -

    Creating open computational curricula#

    -

    Kari Jordan, Zhian Kamvar, Toby Hodges

    -

    Published 2020-12-11

    -

    Licensed CC-BY-4.0

    -

    In this interactive session, Carpentries team members will guide attendees through three stages of the backward design process to create a lesson development plan for the open source tool of their choosing. Attendees will leave having identified what practical skills they aim to teach (learning objectives), an approach for designing challenge questions (formative assessment), and mechanisms to give and receive feedback.

    -

    Content type: Slide

    -

    https://zenodo.org/records/4317149

    -

    https://doi.org/10.5281/zenodo.4317149

    -
    -
    -
    -

    DL@MBL 2021 Exercises#

    -

    Jan Funke, Constantin Pape, Morgan Schwartz, Xiaoyan

    -

    Licensed UNKNOWN

    -

    Tags: Artificial Intelligence, Bioimage Analysis

    -

    Content type: Slide, Notebook

    -

    JLrumberger/DL-MBL-2021

    -
    -
    -
    -

    Generative artificial intelligence for bio-image analysis#

    -

    Robert Haase

    -

    Licensed CC-BY-4.0

    -

    Tags: Python, Bioimage Analysis, Artificial Intelligence

    -

    Content type: Slide

    -

    https://f1000research.com/slides/12-971

    -
    -
    -
    -

    Hitchhiking through a diverse Bio-image Analysis Software Universe#

    -

    Robert Haase

    -

    Published 2022-07-22

    -

    Licensed CC-BY-4.0

    -

    Overview about decision making and how to influence decisions in the bio-image analysis software context.

    -

    Tags: Bioimage Analysis

    -

    Content type: Slide, Presentation

    -

    https://f1000research.com/slides/11-746

    -

    https://doi.org/10.7490/f1000research.1119026.1

    -
    -
    -
    -

    I3D:bio’s OMERO training material: Re-usable, adjustable, multi-purpose slides for local user training#

    -

    Christian Schmidt, Michele Bortolomeazzi, Tom Boissonnet, Carsten Fortmann-Grote, Julia Dohle, Peter Zentis, Niraj Kandpal, Susanne Kunis, Thomas Zobel, Stefanie Weidtkamp-Peters, Elisa Ferrando-May

    -

    Published 2023-11-13

    -

    Licensed CC-BY-4.0

    -

    The open-source software OME Remote Objects (OMERO) is a data management software that allows storing, organizing, and annotating bioimaging/microscopy data. OMERO has become one of the best-known systems for bioimage data management in the bioimaging community. The Information Infrastructure for BioImage Data (I3D:bio) project facilitates the uptake of OMERO into research data management (RDM) practices at universities and research institutions in Germany. Since the adoption of OMERO into researchers’ daily routines requires intensive training, a broad portfolio of training resources for OMERO is an asset. On top of using the OMERO guides curated by the Open Microscopy Environment Consortium (OME) team, imaging core facility staff at institutions where OMERO is used often prepare additional material tailored to be applicable for their own OMERO instances. Based on experience gathered in the Research Data Management for Microscopy group (RDM4mic) in Germany, and in the use cases in the I3D:bio project, we created a set of reusable, adjustable, openly available slide decks to serve as the basis for tailored training lectures, video tutorials, and self-guided instruction manuals directed at beginners in using OMERO. The material is published as an open educational resource complementing the existing resources for OMERO contributed by the community.

    -

    Tags: OMERO, Research Data Management, Nfdi4Bioimage, I3Dbio

    -

    Content type: Slide, Video

    -

    https://zenodo.org/records/8323588

    -

    https://www.youtube.com/playlist?list=PL2k-L-zWPoR7SHjG1HhDIwLZj0MB_stlU

    -

    https://doi.org/10.5281/zenodo.8323588

    -
    -
    -
    -

    Image Data Services at Euro-BioImaging: Community efforts towards FAIR Image Data and Analysis Services#

    -

    Aastha Mathur

    -

    Licensed UNKNOWN

    -

    Content type: Slide

    -

    https://docs.google.com/presentation/d/1henPIDTpHT3bc1Y26AltItAHJ2C5xCOl/edit#slide=id.p1

    -
    -
    -
    -

    Image analysis in Galaxy#

    -

    Beatriz Serrano-Solano, Björn Grüning

    -

    Licensed UNKNOWN

    -

    Tags: Bioimage Analysis

    -

    Content type: Slide

    -

    https://docs.google.com/presentation/d/1WG_4307XmKsGfWT3taxMvX2rZiG1k0SM1E7SAENJQkI/edit#slide=id.p

    -
    -
    -
    -

    ImageJ Macro Introduction#

    -

    Anna Klemm

    -

    Licensed UNKNOWN

    -

    Tags: Neubias, Imagej Macro, Bioimage Analysis

    -

    Content type: Slide, Code

    -

    ahklemm/ImageJMacro_Introduction

    -
    -
    -
    -

    ImageJ2 API-beating#

    -

    Robert Haase

    -

    Licensed BSD-3-CLAUSE

    -

    Tags: Neubias, Imagej, Bioimage Analysis

    -

    Content type: Slide

    -

    https://git.mpi-cbg.de/rhaase/lecture_imagej2_dev

    -
    -
    -
    -

    Introduction to ImageJ macro programming, Scientific Computing Facility, MPI CBG Dresden#

    -

    Robert Haase, Benoit Lombardot

    -

    Licensed UNKNOWN

    -

    Tags: Imagej, Bioimage Analysis

    -

    Content type: Slide

    -

    https://git.mpi-cbg.de/scicomp/bioimage_team/coursematerialimageanalysis/tree/master/ImageJMacro_24h_2017-01

    -
    -
    -
    -

    Jupyter for interactive cloud computing#

    -

    Guillaume Witz

    -

    Licensed UNKNOWN

    -

    Tags: Neubias, Bioimage Analysis

    -

    Content type: Slide

    -

    https://docs.google.com/presentation/d/1q8q1xE-c35tvCsRXZay98s2UYWwXpp0cfCljBmMFpco/edit#slide=id.ga456d5535c_2_53

    -
    -
    -
    -

    Lecture Applied Bioimage Analysis 2020#

    -

    Robert Haase

    -

    Slides, scripts, data and other exercise materials of the BioImage Analysis lecture at CMCB TU Dresden 2020

    -

    Tags: Imagej, Bioimage Analysis

    -

    Content type: Slide

    -

    https://git.mpi-cbg.de/rhaase/lecture_applied_bioimage_analysis

    -
    -
    -
    -

    Machine Learning - Deep Learning. Applications to Bioimage Analysis#

    -

    Estibaliz Gómez-de-Mariscal

    -

    Licensed UNKNOWN

    -

    Tags: Artificial Intelligence, Bioimage Analysis

    -

    Content type: Slide

    -

    https://raw.githubusercontent.com/esgomezm/esgomezm.github.io/master/assets/pdf/SPAOM2018/MachineLearning_SPAOMworkshop_public.pdf

    -
    -
    -
    -

    Machine and Deep Learning on the cloud: Segmentation#

    -

    Ignacio Arganda-Carreras

    -

    Licensed UNKNOWN

    -

    Tags: Neubias, Artificial Intelligence, Bioimage Analysis

    -

    Content type: Slide

    -

    https://docs.google.com/presentation/d/1oJoy9gHmUuSmUwCkPs_InJf_WZAzmLlUNvK1FUEB4PA/edit#slide=id.ge3a24e733b_0_54

    -
    -
    -
    -

    Methods in bioimage analysis#

    -

    Christian Tischer

    -

    Licensed CC-BY-4.0

    -

    Tags: Bioimage Analysis

    -

    Content type: Online Tutorial, Video, Slide

    -

    https://www.ebi.ac.uk/training/events/methods-bioimage-analysis/

    -

    https://doi.org/10.6019/TOL.BioImageAnalysis22-w.2022.00001.1

    -

    https://drive.google.com/file/d/1MhuqfKhZcYu3bchWMqogIybKjamU5Msg/view

    -
    -
    -
    -

    Multi-view fusion#

    -

    Robert Haase

    -

    Licensed BSD-3-CLAUSE

    -

    Lecture slides of a session on Multiview Fusion in Fiji

    -

    Tags: Neubias, Imagej, Bioimage Analysis

    -

    Content type: Slide

    -

    https://git.mpi-cbg.de/rhaase/lecture_multiview_registration

    -
    -
    -
    -

    NEUBIAS Analyst School 2018#

    -

    Assaf Zaritsky, Csaba Molnar, Vasja Urbancic, Richard Butler, Anna Kreshuk, Vannary Meas-Yedid

    -

    Licensed UNKNOWN

    -

    Tags: Neubias, Bioimage Analysis

    -

    Content type: Slide, Code, Notebook

    -

    miura/NEUBIAS_AnalystSchool2018

    -
    -
    -
    -

    NEUBIAS Bioimage Analyst Course 2017#

    -

    Curtis Rueden, Florian Levet, J.B. Sibarta, Alexandre Dafour, Daniel Sage, Sebastien Tosi, Michal Kozubek, Jean-Yves Tinevez, Kota Miura, et al.

    -

    Licensed UNKNOWN

    -

    Tags: Neubias, Bioimage Analysis

    -

    Content type: Slide, Tutorial

    -

    miura/NEUBIAS_Bioimage_Analyst_Course2017

    -
    -
    -
    -

    NEUBIAS Bioimage Analyst School 2019#

    -

    Kota Miura, Chong Zhang, Jean-Yves Tinevez, Robert Haase, Julius Hossein, Pejamn Rasti, David Rousseau, Ignacio Arganda-Carreras, Siân Culley, et al.

    -

    Licensed UNKNOWN

    -

    Tags: Neubias, Bioimage Analysis

    -

    Content type: Slide, Code, Notebook

    -

    miura/NEUBIAS_AnalystSchool2019

    -
    -
    -
    -

    NEUBIAS Bioimage Analyst School 2020#

    -

    Marion Louveaux, Stéphane Verger, Arianne Bercowsky Rama, Ignacio Arganda-Carreras, Estibaliz Gómez-de-Mariscal, Kota Miura, et al.

    -

    Licensed UNKNOWN

    -

    Tags: Neubias, Bioimage Analysis

    -

    Content type: Slide, Code, Notebook

    -

    miura/NEUBIAS_AnalystSchool2020

    -
    -
    -
    -

    NFDI4BIOIMAGE - An Initiative for a National Research Data Infrastructure for Microscopy Data#

    -

    Christian Schmidt, Elisa Ferrando-May

    -

    Published 2021-04-29

    -

    Licensed CCY-BY-SA-4.0

    -

    Align existing and establish novel services & solutions for data management tasks throughout the bioimage data lifecycle.

    -

    Tags: Nfdi4Bioimage, Image Data Management, Bioimage Data, Research Data Management

    -

    Content type: Conference Abstract, Slide

    -

    https://doi.org/10.11588/heidok.00029489

    -
    -
    -
    -

    Neubias Academy 2020: Introduction to Nuclei Segmentation with StarDist#

    -

    Martin Weigert, Olivier Burri, Siân Culley, Uwe Schmidt

    -

    Licensed UNKNOWN

    -

    Tags: Python, Neubias, Artificial Intelligence, Bioimage Analysis

    -

    Content type: Slide, Notebook

    -

    maweigert/neubias_academy_stardist

    -
    -
    -
    -

    Nextflow: Scalable and reproducible scientific workflows#

    -

    Floden Evan, Di Tommaso Paolo

    -

    Published 2020-12-17

    -

    Licensed CC-BY-4.0

    -

    Nextflow is an open-source workflow management system that prioritizes portability and reproducibility. It enables users to develop and seamlessly scale genomics workflows locally, on HPC clusters, or in major cloud providers’ infrastructures. Developed since 2014 and backed by a fast-growing community, the Nextflow ecosystem is made up of users and developers across academia, government and industry. It counts over 1M downloads and over 10K users worldwide.

    -

    Tags: Workflow Engine

    -

    Content type: Slide

    -

    https://zenodo.org/records/4334697

    -

    https://doi.org/10.5281/zenodo.4334697

    -
    -
    -
    -

    Parallelization and heterogeneous computing: from pure CPU to GPU-accelerated image processing#

    -

    Robert Haase

    -

    Licensed CC-BY-4.0

    -

    Content type: Slide

    -

    https://f1000research.com/slides/11-1171

    -

    https://doi.org/10.7490/f1000research.1119154.1

    -
    -
    -
    -

    QuPath: Open source software for analysing (awkward) images#

    -

    Peter Bankhead

    -

    Published 2020-12-16

    -

    Licensed CC-BY-4.0

    -

    Slides from the CZI/EOSS online meeting in December 2020.

    -

    Tags: Bioimage Analysis

    -

    Content type: Slide

    -

    https://zenodo.org/records/4328911

    -

    https://doi.org/10.5281/zenodo.4328911

    -
    -
    -
    -

    Research Data Management Seminar - Slides#

    -

    Stefano Della Chiesa

    -

    Published 2022-05-18

    -

    Licensed CC-BY-4.0

    -

    This Research Data Management (RDM) Slides introduce to the multidisciplinary knowledge and competencies required to address policy compliance and research data management best practices throughout a project lifecycle, and beyond it.

    -
    Module 1 - Introduces the RDM giving its context in the Research Data Governance
-Module 2 - Illustrates the most important RDM policies and principles
-Module 3 - Provides the most relevant RDM knowledge bricks
-Module 4 - Discuss the Data Management Plans (DMPs), examples, templates and guidance
-
    -
    -

     

    -

    Tags: Research Data Management

    -

    Content type: Slide

    -

    https://zenodo.org/record/6602101

    -

    https://doi.org/10.5281/zenodo.6602101

    -
    -
    -
    -

    Sharing and licensing material#

    -

    Robert Haase

    -

    Licensed CC-BY-4.0

    -

    Introduction to sharing resources online and licensing

    -

    Tags: Sharing, Research Data Management

    -

    Content type: Slide

    -

    https://f1000research.com/slides/10-519

    -
    -
    -
    -

    Thinking data management on different scales#

    -

    Susanne Kunis

    -

    Published 2023-08-31

    -

    Licensed CC-BY-4.0

    -

    Presentation given at PoL BioImage Analysis Symposium Dresden 2023

    -

    Tags: Research Data Management, Nfdi4Bioimage

    -

    Content type: Slide

    -

    https://zenodo.org/records/8329306

    -

    https://doi.org/10.5281/zenodo.8329306

    -
    -
    -
    -

    Tracking Theory, TrackMate, and Mastodon#

    -

    Robert Haase

    -

    Licensed BSD-3-CLAUSE

    -

    Lecture slides of a session on Cell Tracking in Fiji

    -

    Tags: Neubias, Imagej, Bioimage Analysis

    -

    Content type: Slide

    -

    https://git.mpi-cbg.de/rhaase/lecture_tracking_trackmate

    -
    -
    -
    -

    What is Bioimage Analysis? An Introduction#

    -

    Kota Miura

    -

    Licensed UNKNOWN

    -

    Tags: Neubias, Bioimage Analysis

    -

    Content type: Slide

    -

    https://www.dropbox.com/s/5abw3cvxrhpobg4/20220923_DefragmentationTS.pdf?dl=0

    -
    -
    -
    -

    Working with objects in 2D and 3D#

    -

    Robert Haase

    -

    Licensed BSD-3-CLAUSE

    -

    Tags: Neubias, Imagej, Bioimage Analysis

    -

    Content type: Slide

    -

    https://git.mpi-cbg.de/rhaase/lecture_working_with_objects_in_2d_and_3d

    -
    -
    -
    -

    Working with pixels#

    -

    Robert Haase

    -

    Licensed BSD-3-CLAUSE

    -

    Tags: Neubias, Imagej, Bioimage Analysis

    -

    Content type: Slide

    -

    https://git.mpi-cbg.de/rhaase/lecture_working_with_pixels

    -
    -
    -
    -

    ZIDAS 2020 Introduction to Deep Learning#

    -

    Estibaliz Gómez-de-Mariscal

    -

    Licensed UNKNOWN

    -

    Tags: Artificial Intelligence, Bioimage Analysis

    -

    Content type: Slide

    -

    esgomezm/zidas2020_intro_DL

    -
    -
    -
    -

    ilastik: interactive machine learning for (bio)image analysis#

    -

    Anna Kreshuk, Dominik Kutra

    -

    Licensed CC-BY-4.0

    -

    Tags: Artificial Intelligence, Bioimage Analysis

    -

    Content type: Slide

    -

    https://zenodo.org/doi/10.5281/zenodo.4330625

    -
    -
    -
    - - - - -
    - - - - - - - - -
    - - - - -
    - - -
    -
    - Contents -
    -
    - -
    - - -
    -
    - -
    - -
    - -

    -By Robert Haase, Clément Caporal,... and the NFDI4BioImage Initiative -

    - -
    - -
    - - -
    - -
    -

    - Last updated on 2025-02-01. -
    -

    -
    - -
    - -
    -

    -Copyright: Licensed CC-BY 4.0 unless mentioned otherwise. -Contributions and feedback are welcome. -

    - -
    -
    - -
    -
    - - -
    -
    -
    - - - - - -
    -
    - - \ No newline at end of file diff --git a/content_types/slides.html b/content_types/slides.html index 4ea3eb83..6259c0ab 100644 --- a/content_types/slides.html +++ b/content_types/slides.html @@ -8,7 +8,7 @@ - Slides (42) — NFDI4BioImage Training Materials + Slides (82) — NFDI4BioImage Training Materials @@ -64,7 +64,7 @@ - + @@ -182,36 +182,29 @@

    By tag

    By content type

    @@ -236,11 +228,12 @@ @@ -447,7 +440,7 @@
    -

    Slides (42)

    +

    Slides (82)

    @@ -460,11 +453,20 @@

    Contents

  • “ZENODO und Co.” Was bringt und wer braucht ein Repositorium?
  • A Glimpse of the Open-Source FLIM Analysis Software Tools FLIMfit, FLUTE and napari-flim-phasor-plotter
  • AI ML DL in Bioimage Analysis - Webinar
    • +
  • Adding a Workflow to BIAFLOWS
  • Alles meins – oder!? Urheberrechte klären für Forschungsdaten
    • +
  • Bio Image Analysis
    • +
  • Bio-Image Data Strudel for Workshop on Research Data Management in TU Dresden Core Facilities
    • +
  • Bio-image Analysis with the Help of Large Language Models
  • Bio-image Data Science Lectures @ Uni Leipzig / ScaDS.AI
    • +
  • Building a Bioimage Analysis Workflow using Deep Learning
    • +
  • CellProfiler Introduction
    • +
  • Challenges and opportunities for bio-image analysis core-facilities
  • Crashkurs Forschungsdatenmanagement
    • +
  • Creating open computational curricula
  • Cultivating Open Training
  • Cultivating Open Training to advance Bio-image Analysis
    • +
  • DL@MBL 2021 Exercises
  • Data management at France BioImaging
  • Datenmanagement
  • Datenmanagement im Fokus: Organisation, Speicherstrategien und Datenschutz
    • @@ -476,28 +478,59 @@

    Contents

  • FAIRy deep-learning for bioImage analysis
  • From Paper to Pixels: Navigation through your Research Data - presentations of speakers
  • Galaxy Training Material
    • +
  • Generative artificial intelligence for bio-image analysis
  • Hackaton Results - Conversion of KNIME image analysis workflows to Galaxy
    • +
  • Hitchhiking through a diverse Bio-image Analysis Software Universe
    • +
  • I3D:bio’s OMERO training material: Re-usable, adjustable, multi-purpose slides for local user training
    • +
  • Image Data Services at Euro-BioImaging: Community efforts towards FAIR Image Data and Analysis Services
    • +
  • Image analysis in Galaxy
    • +
  • ImageJ Macro Introduction
    • +
  • ImageJ2 API-beating
  • Intro napari slides
    • +
  • Introduction to ImageJ macro programming, Scientific Computing Facility, MPI CBG Dresden
  • Introduction to Research Data Management and Open Research
    • +
  • Jupyter for interactive cloud computing
  • Kollaboratives Arbeiten und Versionskontrolle mit Git
    • +
  • Lecture Applied Bioimage Analysis 2020
  • Lecture-materials of the DeepLife course
    • +
  • Machine Learning - Deep Learning. Applications to Bioimage Analysis
    • +
  • Machine and Deep Learning on the cloud: Segmentation
    • +
  • Methods in bioimage analysis
  • MicroSam-Talks
  • Microscopy data analysis: machine learning and the BioImage Archive
    • +
  • Multi-view fusion
  • Multiplexed tissue imaging - tools and approaches
  • My Journey Through Bioimage Analysis Teaching Methods From Classroom to Cloud
    • +
  • NEUBIAS Analyst School 2018
    • +
  • NEUBIAS Bioimage Analyst Course 2017
    • +
  • NEUBIAS Bioimage Analyst School 2019
    • +
  • NEUBIAS Bioimage Analyst School 2020
  • NFDI4BIOIMAGE
    • +
  • NFDI4BIOIMAGE - An Initiative for a National Research Data Infrastructure for Microscopy Data
  • NFDI4BIOIMAGE - National Research Data Infrastructure for Microscopy and BioImage Analysis - Online Kick-Off 2023
  • NFDI4BIOIMAGE - National Research Data Infrastructure for Microscopy and BioImage Analysis [conference talk: The Pelagic Imaging Consortium meets Helmholtz Imaging, 5.10.2023, Hamburg]
    • +
  • Neubias Academy 2020: Introduction to Nuclei Segmentation with StarDist
    • +
  • Nextflow: Scalable and reproducible scientific workflows
  • Object Tracking and Track Analysis using TrackMate and CellTracksColab
  • Open Science, Sharing & Licensing
    • +
  • Parallelization and heterogeneous computing: from pure CPU to GPU-accelerated image processing
    • +
  • QuPath: Open source software for analysing (awkward) images
  • RDF as a bridge to domain-platforms like OMERO, or There and back again.
    • +
  • Research Data Management Seminar - Slides
    • +
  • Sharing and licensing material
  • So geschlossen wie nötig, so offen wie möglich - Datenschutz beim Umgang mit Forschungsdaten
  • Sustainable Data Stewardship
  • Thinking data management on different scales
    • +
  • Tracking Theory, TrackMate, and Mastodon
  • Welcome to BioImage Town
    • +
  • What is Bioimage Analysis? An Introduction
    • +
  • Working with objects in 2D and 3D
    • +
  • Working with pixels
  • YMIA - Python-Based Event Series Training Material
    • +
  • ZIDAS 2020 Introduction to Deep Learning
  • [N4BI AHM] Welcome to BioImage Town
  • [Short Talk] NFDI4BIOIMAGE - A consortium in the National Research Data Infrastructure
    • +
  • ilastik: interactive machine learning for (bio)image analysis
  • rse-skills-workshop
    • @@ -510,8 +543,8 @@

    Contents

    -
    -

    Slides (42)#

    +
    +

    Slides (82)#

    “ZENODO und Co.” Was bringt und wer braucht ein Repositorium?#

    Elfi Hesse, Jan-Christoph Deinert, Christian Löschen

    @@ -540,11 +573,20 @@

    AI ML DL in Bioimage Analysis - Webinarhttps://www.youtube.com/watch?v=TJXNMIWtdac

    +
    +

    Adding a Workflow to BIAFLOWS#

    +

    Sébastien Tosi, Volker Baecker, Benjamin Pavie

    +

    Licensed BSD-2-CLAUSE

    +

    Tags: Neubias, Bioimage Analysis

    +

    Content type: Slides

    +

    RoccoDAnt/Defragmentation_TrainingSchool_EOSC-Life_2022

    +
    +

    Alles meins – oder!? Urheberrechte klären für Forschungsdaten#

    Stephan Wünsche

    @@ -559,16 +601,75 @@

    Alles meins – oder!? Urheberrechte klären für Forschungsdatenhttps://doi.org/10.5281/zenodo.11472148

    +
    +

    Bio Image Analysis#

    +

    Christian Tischer

    +

    Licensed UNKNOWN

    +

    Content type: Slides

    +

    tischi/presentation-image-analysis

    +
    +
    +
    +

    Bio-Image Data Strudel for Workshop on Research Data Management in TU Dresden Core Facilities#

    +

    Cornelia Wetzker

    +

    Published 2023-11-08

    +

    Licensed CC-BY-4.0

    +

    This presentation gives a short outline of the complexity of data and metadata in the bioimaging universe. It introduces NFDI4BIOIMAGE as a newly formed consortium as part of the German ‘Nationale Forschungsdateninfrastruktur’ (NFDI) and its goals and tools for data management including its current members on TU Dresden campus.  

    +

    Tags: Research Data Management, Nfdi4Bioimage

    +

    Content type: Slides

    +

    https://zenodo.org/records/10083555

    +

    https://doi.org/10.5281/zenodo.10083555

    +
    +
    +
    +

    Bio-image Analysis with the Help of Large Language Models#

    +

    Robert Haase

    +

    Published 2024-03-13

    +

    Licensed CC-BY-4.0

    +

    Large Language Models (LLMs) change the way how we use computers. This also has impact on the bio-image analysis community. We can generate code that analyzes biomedical image data if we ask the right prompts. This talk outlines introduces basic principles, explains prompt engineering and how to apply it to bio-image analysis. We also compare how different LLM vendors perform on code generation tasks and which challenges are ahead for the bio-image analysis community.

    +

    Tags: Artificial Intelligence, Python

    +

    Content type: Slides

    +

    https://zenodo.org/records/10815329

    +

    https://doi.org/10.5281/zenodo.10815329

    +
    +

    Bio-image Data Science Lectures @ Uni Leipzig / ScaDS.AI#

    Robert Haase

    Licensed CC-BY-4.0

    These are the PPTx training resources for Students at Uni Leipzig who want to dive into bio-image data science with Python. The material developed here between April and July 2024.

    -

    Tags: Bioimage Analysis, Deep Learning, Microscopy Image Analysis, Python

    +

    Tags: Bioimage Analysis, Artificial Intelligence, Python

    Content type: Slides

    https://zenodo.org/records/12623730

    +
    +

    Building a Bioimage Analysis Workflow using Deep Learning#

    +

    Estibaliz Gómez-de-Mariscal

    +

    Licensed UNKNOWN

    +

    Tags: Artificial Intelligence, Bioimage Analysis

    +

    Content type: Slides

    +

    esgomezm/NEUBIAS_chapter_DL_2020

    +
    +
    +
    +

    CellProfiler Introduction#

    +

    Anna Klemm

    +

    Licensed UNKNOWN

    +

    Tags: Neubias, Cellprofiler, Bioimage Analysis

    +

    Content type: Slides

    +

    ahklemm/CellProfiler_Introduction

    +
    +
    +
    +

    Challenges and opportunities for bio-image analysis core-facilities#

    +

    Robert Haase

    +

    Licensed CC-BY-4.0

    +

    Tags: Research Data Management, Bioimage Analysis, Nfdi4Bioimage

    +

    Content type: Slides

    +

    https://f1000research.com/slides/12-1054

    +
    +

    Crashkurs Forschungsdatenmanagement#

    Barbara Weiner, Stephan Wünsche, Stefan Kühne, Pia Voigt, Sebastian Frericks, Clemens Hoffmann, Romy Elze, Ronny Gey

    @@ -582,6 +683,17 @@

    Crashkurs Forschungsdatenmanagementhttps://doi.org/10.5281/zenodo.3778431

    +
    +

    Creating open computational curricula#

    +

    Kari Jordan, Zhian Kamvar, Toby Hodges

    +

    Published 2020-12-11

    +

    Licensed CC-BY-4.0

    +

    In this interactive session, Carpentries team members will guide attendees through three stages of the backward design process to create a lesson development plan for the open source tool of their choosing. Attendees will leave having identified what practical skills they aim to teach (learning objectives), an approach for designing challenge questions (formative assessment), and mechanisms to give and receive feedback.

    +

    Content type: Slides

    +

    https://zenodo.org/records/4317149

    +

    https://doi.org/10.5281/zenodo.4317149

    +
    +

    Cultivating Open Training#

    Robert Haase

    @@ -606,11 +718,20 @@

    Cultivating Open Training to advance Bio-image Analysishttps://doi.org/10.5281/zenodo.11066250

    +
    +

    DL@MBL 2021 Exercises#

    +

    Jan Funke, Constantin Pape, Morgan Schwartz, Xiaoyan

    +

    Licensed UNKNOWN

    +

    Tags: Artificial Intelligence, Bioimage Analysis

    +

    Content type: Slides, Notebook

    +

    JLrumberger/DL-MBL-2021

    +
    +

    Data management at France BioImaging#

    Published 2023-07-05

    Licensed CC-BY-SA-4.0

    -

    Tags: Research Data Management, Bioimage Analysis, Image Data Management, Open Science

    +

    Tags: Research Data Management, Bioimage Analysis, Open Science

    Content type: Slides, Presentation

    https://omero-fbi.fr/slides/elmi23_cfd/main.html#/title-slide

    @@ -676,8 +797,8 @@

    Dr Guillaume Jacquemet on studying cancer cell metastasis in the era of deep

    Published 2024-10-24

    Licensed UNKNOWN

    Leukocyte extravasation is a critical component of the innate immune response, while circulating tumour cell extravasation is a crucial step in metastasis formation. Despite their importance, these extravasation mechanisms remain incompletely understood. In this talk, Guillaume Jacquemet presents a novel imaging framework that integrates microfluidics with high-speed, label-free imaging to study the arrest of pancreatic cancer cells (PDAC) on human endothelial layers under physiological flow conditions.

    -

    Tags: Deep Learning, Microscopy Image Analysis

    -

    Content type: Youtube Video, Slides

    +

    Tags: Artificial Intelligence, Bioimage Analysis

    +

    Content type: Video, Slides

    https://www.youtube.com/watch?v=KTdZBgSCYJQ

    @@ -711,7 +832,7 @@

    FAIRy deep-learning for bioImage analysishttps://f1000research.com/slides/13-147

    @@ -740,6 +861,15 @@

    Galaxy Training Materialgalaxyproject/training-material

    +
    +

    Generative artificial intelligence for bio-image analysis#

    +

    Robert Haase

    +

    Licensed CC-BY-4.0

    +

    Tags: Python, Bioimage Analysis, Artificial Intelligence

    +

    Content type: Slides

    +

    https://f1000research.com/slides/12-971

    +
    +

    Hackaton Results - Conversion of KNIME image analysis workflows to Galaxy#

    Riccardo Massei

    @@ -750,6 +880,66 @@

    Hackaton Results - Conversion of KNIME image analysis workflows to Galaxyhttps://zenodo.org/doi/10.5281/zenodo.10793699

    +
    +

    Hitchhiking through a diverse Bio-image Analysis Software Universe#

    +

    Robert Haase

    +

    Published 2022-07-22

    +

    Licensed CC-BY-4.0

    +

    Overview about decision making and how to influence decisions in the bio-image analysis software context.

    +

    Tags: Bioimage Analysis

    +

    Content type: Slides, Presentation

    +

    https://f1000research.com/slides/11-746

    +

    https://doi.org/10.7490/f1000research.1119026.1

    +
    +
    +
    +

    I3D:bio’s OMERO training material: Re-usable, adjustable, multi-purpose slides for local user training#

    +

    Christian Schmidt, Michele Bortolomeazzi, Tom Boissonnet, Carsten Fortmann-Grote, Julia Dohle, Peter Zentis, Niraj Kandpal, Susanne Kunis, Thomas Zobel, Stefanie Weidtkamp-Peters, Elisa Ferrando-May

    +

    Published 2023-11-13

    +

    Licensed CC-BY-4.0

    +

    The open-source software OME Remote Objects (OMERO) is a data management software that allows storing, organizing, and annotating bioimaging/microscopy data. OMERO has become one of the best-known systems for bioimage data management in the bioimaging community. The Information Infrastructure for BioImage Data (I3D:bio) project facilitates the uptake of OMERO into research data management (RDM) practices at universities and research institutions in Germany. Since the adoption of OMERO into researchers’ daily routines requires intensive training, a broad portfolio of training resources for OMERO is an asset. On top of using the OMERO guides curated by the Open Microscopy Environment Consortium (OME) team, imaging core facility staff at institutions where OMERO is used often prepare additional material tailored to be applicable for their own OMERO instances. Based on experience gathered in the Research Data Management for Microscopy group (RDM4mic) in Germany, and in the use cases in the I3D:bio project, we created a set of reusable, adjustable, openly available slide decks to serve as the basis for tailored training lectures, video tutorials, and self-guided instruction manuals directed at beginners in using OMERO. The material is published as an open educational resource complementing the existing resources for OMERO contributed by the community.

    +

    Tags: OMERO, Research Data Management, Nfdi4Bioimage, I3Dbio

    +

    Content type: Slides, Video

    +

    https://zenodo.org/records/8323588

    +

    https://www.youtube.com/playlist?list=PL2k-L-zWPoR7SHjG1HhDIwLZj0MB_stlU

    +

    https://doi.org/10.5281/zenodo.8323588

    +
    +
    +
    +

    Image Data Services at Euro-BioImaging: Community efforts towards FAIR Image Data and Analysis Services#

    +

    Aastha Mathur

    +

    Licensed UNKNOWN

    +

    Content type: Slides

    +

    https://docs.google.com/presentation/d/1henPIDTpHT3bc1Y26AltItAHJ2C5xCOl/edit#slide=id.p1

    +
    +
    +
    +

    Image analysis in Galaxy#

    +

    Beatriz Serrano-Solano, Björn Grüning

    +

    Licensed UNKNOWN

    +

    Tags: Bioimage Analysis

    +

    Content type: Slides

    +

    https://docs.google.com/presentation/d/1WG_4307XmKsGfWT3taxMvX2rZiG1k0SM1E7SAENJQkI/edit#slide=id.p

    +
    +
    +
    +

    ImageJ Macro Introduction#

    +

    Anna Klemm

    +

    Licensed UNKNOWN

    +

    Tags: Neubias, Imagej Macro, Bioimage Analysis

    +

    Content type: Slides, Code

    +

    ahklemm/ImageJMacro_Introduction

    +
    +
    +
    +

    ImageJ2 API-beating#

    +

    Robert Haase

    +

    Licensed BSD-3-CLAUSE

    +

    Tags: Neubias, Imagej, Bioimage Analysis

    +

    Content type: Slides

    +

    https://git.mpi-cbg.de/rhaase/lecture_imagej2_dev

    +
    +

    Intro napari slides#

    Peter Sobolewski

    @@ -760,6 +950,15 @@

    Intro napari slideshttps://thejacksonlaboratory.github.io/intro-napari-slides/#/section

    +
    +

    Introduction to ImageJ macro programming, Scientific Computing Facility, MPI CBG Dresden#

    +

    Robert Haase, Benoit Lombardot

    +

    Licensed UNKNOWN

    +

    Tags: Imagej, Bioimage Analysis

    +

    Content type: Slides

    +

    https://git.mpi-cbg.de/scicomp/bioimage_team/coursematerialimageanalysis/tree/master/ImageJMacro_24h_2017-01

    +
    +

    Introduction to Research Data Management and Open Research#

    Shanmugasundaram

    @@ -771,6 +970,15 @@

    Introduction to Research Data Management and Open Researchhttps://zenodo.org/records/4778265

    +
    +

    Jupyter for interactive cloud computing#

    +

    Guillaume Witz

    +

    Licensed UNKNOWN

    +

    Tags: Neubias, Bioimage Analysis

    +

    Content type: Slides

    +

    https://docs.google.com/presentation/d/1q8q1xE-c35tvCsRXZay98s2UYWwXpp0cfCljBmMFpco/edit#slide=id.ga456d5535c_2_53

    +
    +

    Kollaboratives Arbeiten und Versionskontrolle mit Git#

    Robert Haase

    @@ -788,6 +996,15 @@

    Kollaboratives Arbeiten und Versionskontrolle mit Githttps://doi.org/10.5281/zenodo.10972692

    +
    +

    Lecture Applied Bioimage Analysis 2020#

    +

    Robert Haase

    +

    Slides, scripts, data and other exercise materials of the BioImage Analysis lecture at CMCB TU Dresden 2020

    +

    Tags: Imagej, Bioimage Analysis

    +

    Content type: Slides

    +

    https://git.mpi-cbg.de/rhaase/lecture_applied_bioimage_analysis

    +
    +

    Lecture-materials of the DeepLife course#

    Carl Herrmann, annavonbachmann, David Hoksza, Martin Schätz, Dario Malchiodi, jnguyenvan, Britta Velten, Elodie Laine, JanaBraunger, barwil

    @@ -798,6 +1015,35 @@

    Lecture-materials of the DeepLife coursedeeplife4eu/Lecture-materials

    +
    +

    Machine Learning - Deep Learning. Applications to Bioimage Analysis#

    +

    Estibaliz Gómez-de-Mariscal

    +

    Licensed UNKNOWN

    +

    Tags: Artificial Intelligence, Bioimage Analysis

    +

    Content type: Slides

    +

    https://raw.githubusercontent.com/esgomezm/esgomezm.github.io/master/assets/pdf/SPAOM2018/MachineLearning_SPAOMworkshop_public.pdf

    +
    +
    +
    +

    Machine and Deep Learning on the cloud: Segmentation#

    +

    Ignacio Arganda-Carreras

    +

    Licensed UNKNOWN

    +

    Tags: Neubias, Artificial Intelligence, Bioimage Analysis

    +

    Content type: Slides

    +

    https://docs.google.com/presentation/d/1oJoy9gHmUuSmUwCkPs_InJf_WZAzmLlUNvK1FUEB4PA/edit#slide=id.ge3a24e733b_0_54

    +
    +
    +
    +

    Methods in bioimage analysis#

    +

    Christian Tischer

    +

    Licensed CC-BY-4.0

    +

    Tags: Bioimage Analysis

    +

    Content type: Online Tutorial, Video, Slides

    +

    https://www.ebi.ac.uk/training/events/methods-bioimage-analysis/

    +

    https://doi.org/10.6019/TOL.BioImageAnalysis22-w.2022.00001.1

    +

    https://drive.google.com/file/d/1MhuqfKhZcYu3bchWMqogIybKjamU5Msg/view

    +
    +

    MicroSam-Talks#

    Constantin Pape

    @@ -807,7 +1053,7 @@

    MicroSam-Talkshttps://zenodo.org/records/11265038

    https://doi.org/10.5281/zenodo.11265038

    @@ -818,17 +1064,27 @@

    Microscopy data analysis: machine learning and the BioImage ArchiveAndrii Iudin, Anna Foix-Romero, Anna Kreshuk, Awais Athar, Beth Cimini, Dominik Kutra, Estibalis Gomez de Mariscal, Frances Wong, Guillaume Jacquemet, Kedar Narayan, Martin Weigert, Nodar Gogoberidze, Osman Salih, Petr Walczysko, Ryan Conrad, Simone Weyend, Sriram Sundar Somasundharam, Suganya Sivagurunathan, Ugis Sarkans

    Licensed CC-BY-4.0

    The Microscopy data analysis: machine learning and the BioImage Archive course, which focused on introducing programmatic approaches used in the analysis of bioimage data via the BioImage Archive, ran in May 2023.

    -

    Tags: Microscopy Image Analysis, Python, Deep Learning

    +

    Tags: Bioimage Analysis, Python, Artificial Intelligence

    Content type: Video, Slides

    https://www.ebi.ac.uk/training/materials/microscopy-data-analysis-machine-learning-and-the-bioimage-archive-materials/

    +
    +

    Multi-view fusion#

    +

    Robert Haase

    +

    Licensed BSD-3-CLAUSE

    +

    Lecture slides of a session on Multiview Fusion in Fiji

    +

    Tags: Neubias, Imagej, Bioimage Analysis

    +

    Content type: Slides

    +

    https://git.mpi-cbg.de/rhaase/lecture_multiview_registration

    +
    +

    Multiplexed tissue imaging - tools and approaches#

    Agustín Andrés Corbat, OmFrederic, Jonas Windhager, Kristína Lidayová

    Licensed CC-BY-4.0

    Material for the I2K 2024 “Multiplexed tissue imaging - tools and approaches” workshop

    -

    Tags: Bioimage Analysis, Microscopy Image Analysis

    +

    Tags: Bioimage Analysis

    Content type: Github Repository, Slides, Workshop

    BIIFSweden/I2K2024-MTIWorkshop

    https://docs.google.com/presentation/d/1R9-4lXAmTYuyFZpTMDR85SjnLsPZhVZ8/edit#slide=id.p1

    @@ -846,6 +1102,42 @@

    My Journey Through Bioimage Analysis Teaching Methods From Classroom to Clou

    https://doi.org/10.5281/zenodo.10679054

    +
    +

    NEUBIAS Analyst School 2018#

    +

    Assaf Zaritsky, Csaba Molnar, Vasja Urbancic, Richard Butler, Anna Kreshuk, Vannary Meas-Yedid

    +

    Licensed UNKNOWN

    +

    Tags: Neubias, Bioimage Analysis

    +

    Content type: Slides, Code, Notebook

    +

    miura/NEUBIAS_AnalystSchool2018

    +
    +
    +
    +

    NEUBIAS Bioimage Analyst Course 2017#

    +

    Curtis Rueden, Florian Levet, J.B. Sibarta, Alexandre Dafour, Daniel Sage, Sebastien Tosi, Michal Kozubek, Jean-Yves Tinevez, Kota Miura, et al.

    +

    Licensed UNKNOWN

    +

    Tags: Neubias, Bioimage Analysis

    +

    Content type: Slides, Tutorial

    +

    miura/NEUBIAS_Bioimage_Analyst_Course2017

    +
    +
    +
    +

    NEUBIAS Bioimage Analyst School 2019#

    +

    Kota Miura, Chong Zhang, Jean-Yves Tinevez, Robert Haase, Julius Hossein, Pejamn Rasti, David Rousseau, Ignacio Arganda-Carreras, Siân Culley, et al.

    +

    Licensed UNKNOWN

    +

    Tags: Neubias, Bioimage Analysis

    +

    Content type: Slides, Code, Notebook

    +

    miura/NEUBIAS_AnalystSchool2019

    +
    +
    +
    +

    NEUBIAS Bioimage Analyst School 2020#

    +

    Marion Louveaux, Stéphane Verger, Arianne Bercowsky Rama, Ignacio Arganda-Carreras, Estibaliz Gómez-de-Mariscal, Kota Miura, et al.

    +

    Licensed UNKNOWN

    +

    Tags: Neubias, Bioimage Analysis

    +

    Content type: Slides, Code, Notebook

    +

    miura/NEUBIAS_AnalystSchool2020

    +
    +

    NFDI4BIOIMAGE#

    Carsten Fortmann-Grote

    @@ -856,6 +1148,17 @@

    NFDI4BIOIMAGEhttps://zenodo.org/doi/10.5281/zenodo.11031746

    +
    +

    NFDI4BIOIMAGE - An Initiative for a National Research Data Infrastructure for Microscopy Data#

    +

    Christian Schmidt, Elisa Ferrando-May

    +

    Published 2021-04-29

    +

    Licensed CCY-BY-SA-4.0

    +

    Align existing and establish novel services & solutions for data management tasks throughout the bioimage data lifecycle.

    +

    Tags: Nfdi4Bioimage, Research Data Management

    +

    Content type: Conference Abstract, Slides

    +

    https://doi.org/10.11588/heidok.00029489

    +
    +

    NFDI4BIOIMAGE - National Research Data Infrastructure for Microscopy and BioImage Analysis - Online Kick-Off 2023#

    Stefanie Weidtkamp-Peters

    @@ -876,13 +1179,34 @@

    NFDI4BIOIMAGE - National Research Data Infrastructure for Microscopy and Bio

    https://zenodo.org/doi/10.5281/zenodo.8414318

    +
    +

    Neubias Academy 2020: Introduction to Nuclei Segmentation with StarDist#

    +

    Martin Weigert, Olivier Burri, Siân Culley, Uwe Schmidt

    +

    Licensed UNKNOWN

    +

    Tags: Python, Neubias, Artificial Intelligence, Bioimage Analysis

    +

    Content type: Slides, Notebook

    +

    maweigert/neubias_academy_stardist

    +
    +
    +
    +

    Nextflow: Scalable and reproducible scientific workflows#

    +

    Floden Evan, Di Tommaso Paolo

    +

    Published 2020-12-17

    +

    Licensed CC-BY-4.0

    +

    Nextflow is an open-source workflow management system that prioritizes portability and reproducibility. It enables users to develop and seamlessly scale genomics workflows locally, on HPC clusters, or in major cloud providers’ infrastructures. Developed since 2014 and backed by a fast-growing community, the Nextflow ecosystem is made up of users and developers across academia, government and industry. It counts over 1M downloads and over 10K users worldwide.

    +

    Tags: Workflow Engine

    +

    Content type: Slides

    +

    https://zenodo.org/records/4334697

    +

    https://doi.org/10.5281/zenodo.4334697

    +
    +

    Object Tracking and Track Analysis using TrackMate and CellTracksColab#

    Joanna Pylvänäinen

    Published None

    Licensed GPL-3.0

    I2K 2024 workshop materials for “Object Tracking and Track Analysis using TrackMate and CellTracksColab”

    -

    Tags: Bioimage Analysis, Training

    +

    Tags: Bioimage Analysis

    Content type: Github Repository, Tutorial, Workshop, Slides

    CellMigrationLab/I2K_2024

    @@ -899,6 +1223,27 @@

    Open Science, Sharing & Licensinghttps://doi.org/10.5281/zenodo.10990107

    +
    +

    Parallelization and heterogeneous computing: from pure CPU to GPU-accelerated image processing#

    +

    Robert Haase

    +

    Licensed CC-BY-4.0

    +

    Content type: Slides

    +

    https://f1000research.com/slides/11-1171

    +

    https://doi.org/10.7490/f1000research.1119154.1

    +
    +
    +
    +

    QuPath: Open source software for analysing (awkward) images#

    +

    Peter Bankhead

    +

    Published 2020-12-16

    +

    Licensed CC-BY-4.0

    +

    Slides from the CZI/EOSS online meeting in December 2020.

    +

    Tags: Bioimage Analysis

    +

    Content type: Slides

    +

    https://zenodo.org/records/4328911

    +

    https://doi.org/10.5281/zenodo.4328911

    +
    +

    RDF as a bridge to domain-platforms like OMERO, or There and back again.#

    Josh Moore, Andra Waagmeester, Kristina Hettne, Katherine Wolstencroft, Susanne Kunis

    @@ -909,6 +1254,35 @@

    RDF as a bridge to domain-platforms like OMERO, or There and back again.https://zenodo.org/doi/10.5281/zenodo.10687658

    +
    +

    Research Data Management Seminar - Slides#

    +

    Stefano Della Chiesa

    +

    Published 2022-05-18

    +

    Licensed CC-BY-4.0

    +

    This Research Data Management (RDM) Slides introduce to the multidisciplinary knowledge and competencies required to address policy compliance and research data management best practices throughout a project lifecycle, and beyond it.

    +
    Module 1 - Introduces the RDM giving its context in the Research Data Governance
+Module 2 - Illustrates the most important RDM policies and principles
+Module 3 - Provides the most relevant RDM knowledge bricks
+Module 4 - Discuss the Data Management Plans (DMPs), examples, templates and guidance
+
    +
    +

     

    +

    Tags: Research Data Management

    +

    Content type: Slides

    +

    https://zenodo.org/record/6602101

    +

    https://doi.org/10.5281/zenodo.6602101

    +
    +
    +
    +

    Sharing and licensing material#

    +

    Robert Haase

    +

    Licensed CC-BY-4.0

    +

    Introduction to sharing resources online and licensing

    +

    Tags: Sharing, Research Data Management

    +

    Content type: Slides

    +

    https://f1000research.com/slides/10-519

    +
    +

    So geschlossen wie nötig, so offen wie möglich - Datenschutz beim Umgang mit Forschungsdaten#

    Pia Voigt

    @@ -943,6 +1317,16 @@

    Thinking data management on different scaleshttps://zenodo.org/doi/10.5281/zenodo.8329305

    +
    +

    Tracking Theory, TrackMate, and Mastodon#

    +

    Robert Haase

    +

    Licensed BSD-3-CLAUSE

    +

    Lecture slides of a session on Cell Tracking in Fiji

    +

    Tags: Neubias, Imagej, Bioimage Analysis

    +

    Content type: Slides

    +

    https://git.mpi-cbg.de/rhaase/lecture_tracking_trackmate

    +
    +

    Welcome to BioImage Town#

    Josh Moore

    @@ -953,17 +1337,53 @@

    Welcome to BioImage Townhttps://zenodo.org/doi/10.5281/zenodo.10008464

    +
    +

    What is Bioimage Analysis? An Introduction#

    +

    Kota Miura

    +

    Licensed UNKNOWN

    +

    Tags: Neubias, Bioimage Analysis

    +

    Content type: Slides

    +

    https://www.dropbox.com/s/5abw3cvxrhpobg4/20220923_DefragmentationTS.pdf?dl=0

    +
    +
    +
    +

    Working with objects in 2D and 3D#

    +

    Robert Haase

    +

    Licensed BSD-3-CLAUSE

    +

    Tags: Neubias, Imagej, Bioimage Analysis

    +

    Content type: Slides

    +

    https://git.mpi-cbg.de/rhaase/lecture_working_with_objects_in_2d_and_3d

    +
    +
    +
    +

    Working with pixels#

    +

    Robert Haase

    +

    Licensed BSD-3-CLAUSE

    +

    Tags: Neubias, Imagej, Bioimage Analysis

    +

    Content type: Slides

    +

    https://git.mpi-cbg.de/rhaase/lecture_working_with_pixels

    +
    +

    YMIA - Python-Based Event Series Training Material#

    Riccardo Massei, Robert Haase, ENicolay

    Published None

    Licensed MIT

    This repository offer access to teaching material and useful resources for the YMIA - Python-Based Event Series.

    -

    Tags: Python, Large Language Models, Prompt Engineering, Biabob, Bioimage Analysis, Microscopy Image Analysis

    +

    Tags: Python, Artifical Intelligence, Bioimage Analysis

    Content type: Github Repository, Slides

    rmassei/ymia_python_event_series_material

    +
    +

    ZIDAS 2020 Introduction to Deep Learning#

    +

    Estibaliz Gómez-de-Mariscal

    +

    Licensed UNKNOWN

    +

    Tags: Artificial Intelligence, Bioimage Analysis

    +

    Content type: Slides

    +

    esgomezm/zidas2020_intro_DL

    +
    +

    [N4BI AHM] Welcome to BioImage Town#

    Josh Moore

    @@ -986,11 +1406,20 @@

    [Short Talk] NFDI4BIOIMAGE - A consortium in the National Research Data Infr

    https://zenodo.org/doi/10.5281/zenodo.10939519

    +
    +

    ilastik: interactive machine learning for (bio)image analysis#

    +

    Anna Kreshuk, Dominik Kutra

    +

    Licensed CC-BY-4.0

    +

    Tags: Artificial Intelligence, Bioimage Analysis

    +

    Content type: Slides

    +

    https://zenodo.org/doi/10.5281/zenodo.4330625

    +
    +

    rse-skills-workshop#

    Jack Atkinson

    Published 2023-12-22T17:39:48+00:00

    -

    Licensed GNU GENERAL PUBLIC LICENSE V3.0

    +

    Licensed GPL-3.0

    Teaching materials for improving research software writing abilities.

    Tags: Research Software Engineering

    Content type: Github Repository, Slides

    @@ -1030,12 +1459,12 @@

    rse-skills-workshop

    previous

    -

    Slide (41)

    +

    Publication (61)

     rse-skills-workshop“ZENODO und Co.” Was bringt und wer braucht ein Repositorium?
  • A Glimpse of the Open-Source FLIM Analysis Software Tools FLIMfit, FLUTE and napari-flim-phasor-plotter
  • AI ML DL in Bioimage Analysis - Webinar
    • +
  • Adding a Workflow to BIAFLOWS
  • Alles meins – oder!? Urheberrechte klären für Forschungsdaten
    • +
  • Bio Image Analysis
    • +
  • Bio-Image Data Strudel for Workshop on Research Data Management in TU Dresden Core Facilities
    • +
  • Bio-image Analysis with the Help of Large Language Models
  • Bio-image Data Science Lectures @ Uni Leipzig / ScaDS.AI
    • +
  • Building a Bioimage Analysis Workflow using Deep Learning
    • +
  • CellProfiler Introduction
    • +
  • Challenges and opportunities for bio-image analysis core-facilities
  • Crashkurs Forschungsdatenmanagement
    • +
  • Creating open computational curricula
  • Cultivating Open Training
  • Cultivating Open Training to advance Bio-image Analysis
    • +
  • DL@MBL 2021 Exercises
  • Data management at France BioImaging
  • Datenmanagement
  • Datenmanagement im Fokus: Organisation, Speicherstrategien und Datenschutz
    • @@ -1083,28 +1521,59 @@

    rse-skills-workshopFAIRy deep-learning for bioImage analysis
  • From Paper to Pixels: Navigation through your Research Data - presentations of speakers
  • Galaxy Training Material
    • +
  • Generative artificial intelligence for bio-image analysis
  • Hackaton Results - Conversion of KNIME image analysis workflows to Galaxy
    • +
  • Hitchhiking through a diverse Bio-image Analysis Software Universe
    • +
  • I3D:bio’s OMERO training material: Re-usable, adjustable, multi-purpose slides for local user training
    • +
  • Image Data Services at Euro-BioImaging: Community efforts towards FAIR Image Data and Analysis Services
    • +
  • Image analysis in Galaxy
    • +
  • ImageJ Macro Introduction
    • +
  • ImageJ2 API-beating
  • Intro napari slides
    • +
  • Introduction to ImageJ macro programming, Scientific Computing Facility, MPI CBG Dresden
  • Introduction to Research Data Management and Open Research
    • +
  • Jupyter for interactive cloud computing
  • Kollaboratives Arbeiten und Versionskontrolle mit Git
    • +
  • Lecture Applied Bioimage Analysis 2020
  • Lecture-materials of the DeepLife course
    • +
  • Machine Learning - Deep Learning. Applications to Bioimage Analysis
    • +
  • Machine and Deep Learning on the cloud: Segmentation
    • +
  • Methods in bioimage analysis
  • MicroSam-Talks
  • Microscopy data analysis: machine learning and the BioImage Archive
    • +
  • Multi-view fusion
  • Multiplexed tissue imaging - tools and approaches
  • My Journey Through Bioimage Analysis Teaching Methods From Classroom to Cloud
    • +
  • NEUBIAS Analyst School 2018
    • +
  • NEUBIAS Bioimage Analyst Course 2017
    • +
  • NEUBIAS Bioimage Analyst School 2019
    • +
  • NEUBIAS Bioimage Analyst School 2020
  • NFDI4BIOIMAGE
    • +
  • NFDI4BIOIMAGE - An Initiative for a National Research Data Infrastructure for Microscopy Data
  • NFDI4BIOIMAGE - National Research Data Infrastructure for Microscopy and BioImage Analysis - Online Kick-Off 2023
  • NFDI4BIOIMAGE - National Research Data Infrastructure for Microscopy and BioImage Analysis [conference talk: The Pelagic Imaging Consortium meets Helmholtz Imaging, 5.10.2023, Hamburg]
    • +
  • Neubias Academy 2020: Introduction to Nuclei Segmentation with StarDist
    • +
  • Nextflow: Scalable and reproducible scientific workflows
  • Object Tracking and Track Analysis using TrackMate and CellTracksColab
  • Open Science, Sharing & Licensing
    • +
  • Parallelization and heterogeneous computing: from pure CPU to GPU-accelerated image processing
    • +
  • QuPath: Open source software for analysing (awkward) images
  • RDF as a bridge to domain-platforms like OMERO, or There and back again.
    • +
  • Research Data Management Seminar - Slides
    • +
  • Sharing and licensing material
  • So geschlossen wie nötig, so offen wie möglich - Datenschutz beim Umgang mit Forschungsdaten
  • Sustainable Data Stewardship
  • Thinking data management on different scales
    • +
  • Tracking Theory, TrackMate, and Mastodon
  • Welcome to BioImage Town
    • +
  • What is Bioimage Analysis? An Introduction
    • +
  • Working with objects in 2D and 3D
    • +
  • Working with pixels
  • YMIA - Python-Based Event Series Training Material
    • +
  • ZIDAS 2020 Introduction to Deep Learning
  • [N4BI AHM] Welcome to BioImage Town
  • [Short Talk] NFDI4BIOIMAGE - A consortium in the National Research Data Infrastructure
    • +
  • ilastik: interactive machine learning for (bio)image analysis
  • rse-skills-workshop
    • diff --git a/content_types/tutorial.html b/content_types/tutorial.html index bc50b03f..1e943175 100644 --- a/content_types/tutorial.html +++ b/content_types/tutorial.html @@ -63,8 +63,8 @@ - - + + @@ -182,36 +182,29 @@

    By tag

    By content type

    @@ -236,11 +228,12 @@ @@ -551,8 +544,8 @@

    Docker Mastery - with Kubernetes + Swarm from a Docker Captainhttps://www.udemy.com/course/docker-mastery/?srsltid=AfmBOornR5gRqOg-4v8Nsap1z24CaPPUPxg8JzyqEGZ6MvW_dh-sf4Af&couponCode=ST2MT110724BNEW

    @@ -609,7 +602,7 @@

    Example Pipeline Tutorialhttps://timmonko.github.io/napari-ndev/tutorial/01_example_pipeline/

    timmonko/napari-ndev

    @@ -633,7 +626,7 @@

    Finding and using publicly available datahttps://www.ebi.ac.uk/training/online/courses/finding-using-public-data/

    @@ -668,8 +661,8 @@

    Glencoe Software Webinarshttps://www.glencoesoftware.com/media/webinars/

    @@ -678,7 +671,7 @@

    I2K 2024: clEsperanto - GPU-Accelerated Image Processing LibraryStRigaud/clesperanto_workshop_I2K24

    @@ -698,7 +691,7 @@

    Image Processing with Pythonhttps://datacarpentry.org/image-processing/key-points.html

    @@ -706,9 +699,9 @@

    Image Processing with Python

    KNIME Image Processing#

    None

    -

    Licensed GPLV3

    +

    Licensed GPL-3.0

    The KNIME Image Processing Extension allows you to read in more than 140 different kinds of images and to apply well known methods on images, like preprocessing. segmentation, feature extraction, tracking and classification in KNIME.

    -

    Tags: Imagej, OMERO, Bioimage Data, Workflow

    +

    Tags: Imagej, OMERO, Workflow

    Content type: Tutorial, Online Tutorial, Documentation

    https://www.knime.com/community/image-processing

    @@ -743,7 +736,7 @@

    NEUBIAS Bioimage Analyst Course 2017miura/NEUBIAS_Bioimage_Analyst_Course2017

    @@ -761,7 +754,7 @@

    Object Tracking and Track Analysis using TrackMate and CellTracksColabPublished None

    Licensed GPL-3.0

    I2K 2024 workshop materials for “Object Tracking and Track Analysis using TrackMate and CellTracksColab”

    -

    Tags: Bioimage Analysis, Training

    +

    Tags: Bioimage Analysis

    Content type: Github Repository, Tutorial, Workshop, Slides

    CellMigrationLab/I2K_2024

    @@ -771,7 +764,7 @@

    Overview of the Galaxy OMERO-suite - Upload images and metadata in OMERO usi

    Riccardo Massei, Björn Grüning

    Published 2024-12-02

    Licensed CC-BY-4.0

    -

    Tags: OMERO, Galaxy, Metadata

    +

    Tags: OMERO, Galaxy, Metadata, Nfdi4Bioimage

    Content type: Tutorial, Framework, Workflow

    https://training.galaxyproject.org/training-material/topics/imaging/tutorials/omero-suite/tutorial.html

    @@ -798,8 +791,7 @@

    Research data - what are the key issues to consider when publishing this kin

    SWC/GCNU Software Skills#

    Licensed CC-BY-4.0

    Computational skills training at the UCL Sainsbury Wellcome Centre and Gatsby Computational Neuroscience Unit, delivered by members of the Neuroinformatics Unit.

    -

    Tags: Training

    -

    Content type: Collection, Online Course, Videos, Tutorial

    +

    Content type: Collection, Online Course, Video, Tutorial

    https://software-skills.neuroinformatics.dev/index.html

    @@ -830,7 +822,7 @@

    Source Control Using Git and GitHub#

    Licensed CC0-1.0

    To submit, you’ll need to register an account, organise and upload your data, prepare a file list, and then submit using our web submission form. These steps are explained here.

    -

    Tags: Research Data Management, Image Data Management, Bioimage Data

    +

    Tags: Research Data Management

    Content type: Tutorial, Video

    https://www.ebi.ac.uk/bioimage-archive/submit/

    @@ -867,7 +859,7 @@

    Tracking of mitochondria and capturing mitoflashesUltrack I2K 2024 Workshop Materials#

    Jordão Bragantini, Teun Huijben

    Licensed BSD3-CLAUSE

    -

    Tags: Segmentation, Bioimage Analysis, Training

    +

    Tags: Bioimage Analysis

    Content type: Workshop, Github Repository, Tutorial

    royerlab/ultrack-i2k2024

    https://royerlab.github.io/ultrack-i2k2024/

    @@ -955,7 +947,7 @@

    nextflow-workshop

    previous

    -

    Slides (42)

    +

    Slides (82)

     nextflow-workshop

    next

    -

    Video (24)

    +

    Video (32)

     diff --git a/content_types/video.html b/content_types/video.html index 8b076cc5..1eb0fb6e 100644 --- a/content_types/video.html +++ b/content_types/video.html @@ -8,7 +8,7 @@ - Video (24) — NFDI4BioImage Training Materials + Video (32) — NFDI4BioImage Training Materials @@ -182,36 +182,29 @@

    By tag

    By content type

    @@ -236,11 +228,12 @@ @@ -447,7 +440,7 @@
    -

    Video (24)

    +

    Video (32)

    @@ -457,14 +450,20 @@

    Contents

    +

    Tags: Nfdi4Bioimage, Research Data Management

    https://zenodo.org/records/7018750

    https://doi.org/10.5281/zenodo.7018750

    @@ -2826,7 +2831,7 @@

    Sustainable Data Stewardship

    Terminology service for research data management and knowledge discovery in low-temperature plasma physics#

    -

    Becker, Markus M., Chaerony Siffa, Ihda, Roman Baum

    +

    Markus M. Becker, Ihda Chaerony Siffa, Roman Baum

    Published 2024-12-11

    Licensed CC-BY-4.0

    Abstract: @@ -2879,7 +2884,7 @@

    The BioImage Archive – Building a Home for Life-Sciences Microscopy DataPublished 2022-06-22

    Licensed UNKNOWN

    The BioImage Archive is a new archival data resource at the European Bioinformatics Institute (EMBL-EBI).

    -

    Tags: Image Data Management, Research Data Management, Bioimage Data

    +

    Tags: Research Data Management

    Content type: Publication

    https://www.sciencedirect.com/science/article/pii/S0022283622000791?via%3Dihub

    https://doi.org/10.1016/j.jmb.2022.167505

    @@ -2903,6 +2908,7 @@

    The Information Infrastructure for BioImage Data (I3D:bio) project to advanc

    Published 2024-03-04

    Licensed CC-BY-4.0

    Research data management (RDM) in microscopy and image analysis is a challenging task. Large files in proprietary formats, complex N-dimensional array structures, and various metadata models and formats can make image data handling inconvenient and difficult. For data organization, annotation, and sharing, researchers need solutions that fit everyday practice and comply with the FAIR (Findable, Accessible, Interoperable, Reusable) principles. International community-based efforts have begun creating open data models (OME), an open file format and translation library (OME-TIFF, Bio-Formats), data management software platforms, and microscopy metadata recommendations and annotation tools. Bringing these developments into practice requires support and training. Iterative feedback and tool improvement is needed to foster practical adoption by the scientific community. The Information Infrastructure for BioImage Data (I3D:bio) project works on guidelines, training resources, and practical assistance for FAIR microscopy RDM adoption with a focus on the management platform OMERO and metadata annotations.

    +

    Tags: Nfdi4Bioimage, Research Data Management

    https://zenodo.org/records/10805204

    https://doi.org/10.5281/zenodo.10805204

    @@ -2913,7 +2919,7 @@

    The Open Microscopy Environment (OME) Data Model and XML file - open tools f

    Published 2005-05-03

    Licensed CC-BY-4.0

    The Open Microscopy Environment (OME) defines a data model and a software implementation to serve as an informatics framework for imaging in biological microscopy experiments, including representation of acquisition parameters, annotations and image analysis results.

    -

    Tags: Microscopy Image Analysis, Bioimage Analysis

    +

    Tags: Bioimage Analysis

    Content type: Publication

    https://genomebiology.biomedcentral.com/articles/10.1186/gb-2005-6-5-r47

    https://doi.org/10.1186/gb-2005-6-5-r47

    @@ -2925,6 +2931,7 @@

    The role of Helmholtz Centers in NFDI4BIOIMAGE - A national consortium enhan

    Published 2024-06-06

    Licensed CC-BY-4.0

    Germany’s National Research Data Infrastructure (NFDI) aims to establish a sustained, cross-disciplinary research data management (RDM) infrastructure that enables researchers to handle FAIR (findable, accessible, interoperable, reusable) data. While FAIR principles have been adopted by funders, policymakers, and publishers, their practical implementation remains an ongoing effort. In the field of bio-imaging, harmonization of data formats, metadata ontologies, and open data repositories is necessary to achieve FAIR data. The NFDI4BIOIMAGE was established to address these issues and develop tools and best practices to facilitate FAIR microscopy and image analysis data in alignment with international community activities. The consortium operates through its Data Stewards team to provide expertise and direct support to help overcome RDM challenges. The three Helmholtz Centers in NFDI4BIOIMAGE aim to collaborate closely with other centers and initiatives, such as HMC, Helmholtz AI, and HIP. Here we present NFDI4BIOIMAGE’s work and its significance for research in Helmholtz and beyond

    +

    Tags: Nfdi4Bioimage, Research Data Management

    https://zenodo.org/records/11501662

    https://doi.org/10.5281/zenodo.11501662

    @@ -2936,7 +2943,7 @@

    Thinking data management on different scaleshttps://zenodo.org/records/8329306

    https://doi.org/10.5281/zenodo.8329306

    @@ -2947,6 +2954,7 @@

    Towards Preservation of Life Science Data with NFDI4BIOIMAGEhttps://zenodo.org/records/13640979

    https://doi.org/10.5281/zenodo.13640979

    @@ -2986,7 +2994,7 @@

    Who you gonna call? - Data Stewards to the rescue

    Working Group Charter. RDM Helpdesk Network#

    -

    Judith Engel, Patrick Helling, Robert Herrenbrück, Marina Lemaire, Hela Mehrtens, Marcus Schmidt, Martha Stellmacher, Lukas Weimer, Cord Wiljes, Wolf Zinke

    +

    Judith Engel, Patrick Helling, Robert Herrenbrück, MarinaLemaire, Hela Mehrtens, Marcus Schmidt, Martha Stellmacher, Lukas Weimer, Cord Wiljes, Wolf Zinke

    Published 2024-11-04

    Licensed CC-BY-4.0

    Support is an essential component of an efficient infrastructure for research data management (RDM). Helpdesks guide researchers through this complex landscape and provide reliable support about all questions regarding research data management, including support for technical services, best practices, requirements of funding organizations and legal topics. In NFDI, most consortia have already established or are planning to establish helpdesks to support their specific communities. On a local level, many institutions have set up RDM helpdesks that provide support for the researchers of their own institution. Additional RDM support services are offered by RDM federal state initiatives, by research data centers, by specialist libraries, by the EOSC, and by providers of RDM-relevant tools. Helpdesks cover a wide range of institutions, disciplines, topics, methodologies and target audiences. However, the individual helpdesks are not yet interconnected and therefore cannot complement one another in an efficient way: Given the wide and constantly increasing complexity of RDM, no single helpdesk can provide the expertise for all potential support requests. Therefore, we see great potential in combining the efforts and resources of the existing RDM helpdesks into an efficient and comprehensive national RDM support network in order to provide optimal and tailored RDM support to all researchers and research-related institutions in Germany and in an international context.

    @@ -3067,6 +3075,7 @@

    [CIDAS] Scalable strategies for a next-generation of FAIR bioimagingLicensed CC-BY-4.0

    Talk given at Georg-August-Universität Göttingen Campus Institute Data Science23rd January 2025 https://www.uni-goettingen.de/en/653203.html

    +

    Tags: Nfdi4Bioimage

    https://zenodo.org/records/14716546

    https://doi.org/10.5281/zenodo.14716546

    @@ -3079,6 +3088,7 @@

    [CMCB] Scalable strategies for a next-generation of FAIR bioimagingCMCB LIFE SCIENCES SEMINARSTechnische Universität Dresden16th January 2025 https://tu-dresden.de/cmcb/crtd/news-termine/termine/cmcb-life-sciences-seminar-josh-moore-german-bioimaging-e-v-society-for-microscopy-and-image-analysis-constance  

    +

    Tags: Nfdi4Bioimage

    https://zenodo.org/records/14650434

    https://doi.org/10.5281/zenodo.14650434

    @@ -3090,6 +3100,7 @@

    [Community Meeting 2024] Overview Team Image Data Analysis and ManagementLicensed CC-BY-4.0

    Overview of Activities of the Team Image Data Analysis and Management of German BioImaging e.V.  

    +

    Tags: Nfdi4Bioimage, Research Data Management

    https://zenodo.org/records/10796364

    https://doi.org/10.5281/zenodo.10796364

    @@ -3111,6 +3122,7 @@

    [ELMI 2024] AI’s Dirty Little Secret: Withouthttps://www.elmi2024.org/)

    +

    Tags: Nfdi4Bioimage, Research Data Management

    https://zenodo.org/records/11235513

    https://doi.org/10.5281/zenodo.11235513

    @@ -3199,6 +3211,7 @@

    [Workshop Material] Fit for OMERO - How imaging facilities and IT department Establish a stakeholder process management for installing OMERO-based RDM Learn from each other, leverage different expertise Learn how to train users, establish sustainability strategies, and foster FAIR RDM for bioimaging at your institution

    +

    Tags: Nfdi4Bioimage, Research Data Management

    https://zenodo.org/records/14178789

    https://doi.org/10.5281/zenodo.14178789

    @@ -3221,6 +3234,7 @@

    [Workshop] Bioimage data management and analysis with OMEROhttps://zenodo.org/records/11350689

    https://doi.org/10.5281/zenodo.11350689

    @@ -3280,6 +3294,7 @@

    [Workshop] Research Data Management for Microscopy and BioImage Analysis

    Date & Venue:Thursday, Sept. 26, 5.30 p.m.Haus 22 / Paul Ehrlich Lecture Hall (H22-1)University Hospital Frankfurt

    +

    Tags: Nfdi4Bioimage, Research Data Management

    https://zenodo.org/records/13861026

    https://doi.org/10.5281/zenodo.13861026

    diff --git a/domain/f1000research.com.html b/domain/f1000research.com.html index 8c967941..69f19afd 100644 --- a/domain/f1000research.com.html +++ b/domain/f1000research.com.html @@ -182,36 +182,29 @@

    By tag

    By content type

    @@ -236,11 +228,12 @@ @@ -486,7 +479,7 @@

    Challenges and opportunities for bio-image analysis core-facilitiesRobert Haase

    Licensed CC-BY-4.0

    Tags: Research Data Management, Bioimage Analysis, Nfdi4Bioimage

    -

    Content type: Slide

    +

    Content type: Slides

    https://f1000research.com/slides/12-1054

    @@ -514,7 +507,7 @@

    FAIRy deep-learning for bioImage analysishttps://f1000research.com/slides/13-147

    @@ -524,7 +517,7 @@

    Generative artificial intelligence for bio-image analysishttps://f1000research.com/slides/12-971

    @@ -545,7 +538,7 @@

    Hitchhiking through a diverse Bio-image Analysis Software Universehttps://f1000research.com/slides/11-746

    https://doi.org/10.7490/f1000research.1119026.1

    @@ -554,7 +547,7 @@

    Hitchhiking through a diverse Bio-image Analysis Software Universe#

    Robert Haase

    Licensed CC-BY-4.0

    -

    Content type: Slide

    +

    Content type: Slides

    https://f1000research.com/slides/11-1171

    https://doi.org/10.7490/f1000research.1119154.1

    @@ -565,7 +558,7 @@

    Research data management for bioimaging - the 2021 NFDI4BIOIMAGE community s

    Published 2022-09-20

    Licensed CC-BY-4.0

    As an initiative within Germany’s National Research Data Infrastructure, the authors conducted this community survey in summer 2021 to assess the state of the art of bioimaging RDM and the community needs.

    -

    Tags: Research Data Management, Image Data Management, Bioimage Data

    +

    Tags: Research Data Management

    Content type: Publication

    https://f1000research.com/articles/11-638/v2

    @@ -585,7 +578,7 @@

    Sharing and licensing materialhttps://f1000research.com/slides/10-519

    diff --git a/domain/focalplane.biologists.com.html b/domain/focalplane.biologists.com.html index 171dabf2..b1f2fcdd 100644 --- a/domain/focalplane.biologists.com.html +++ b/domain/focalplane.biologists.com.html @@ -182,36 +182,29 @@

    By tag

    By content type

    @@ -236,11 +228,12 @@ @@ -488,7 +481,7 @@

    Focalp

    Annotating 3D images in napari#

    Mara Lampert

    Tags: Python, Napari, Bioimage Analysis

    -

    Content type: Blog

    +

    Content type: Blog Post

    https://focalplane.biologists.com/2023/03/30/annotating-3d-images-in-napari/

    @@ -498,7 +491,7 @@

    Collaborative bio-image analysis script editing with githttps://focalplane.biologists.com/2021/09/04/collaborative-bio-image-analysis-script-editing-with-git/

    @@ -508,8 +501,8 @@

    Creating a Research Data Management Plan using chatGPTPublished 2023-11-06

    Licensed CC-BY-4.0

    In this blog post the author demonstrates how chatGPT can be used to combine a fictive project description with a DMP specification to produce a project-specific DMP.

    -

    Tags: Research Data Management, Large Language Models, Artificial Intelligence

    -

    Content type: Blog

    +

    Tags: Research Data Management, Artificial Intelligence

    +

    Content type: Blog Post

    https://focalplane.biologists.com/2023/11/06/creating-a-research-data-management-plan-using-chatgpt/

    @@ -518,7 +511,7 @@

    DL4MicEverywhere – Overcoming reproducibility challenges in deep learning

    Iván Hidalgo-Cenalmor

    Published 2024-07-29

    Licensed UNKNOWN

    -

    Tags: Deep Learning, Microscopy, Microsycopy Image Analysis, Bio Image Analysis, Artifical Intelligence

    +

    Tags: Bio Image Analysis, Artifical Intelligence

    Content type: Blog Post

    https://focalplane.biologists.com/2024/07/29/dl4miceverywhere-overcoming-reproducibility-challenges-in-deep-learning-microscopy-imaging/

    @@ -527,7 +520,7 @@

    DL4MicEverywhere – Overcoming reproducibility challenges in deep learning

    Feature extraction in napari#

    Mara Lampert

    Tags: Python, Napari, Bioimage Analysis

    -

    Content type: Blog

    +

    Content type: Blog Post

    https://focalplane.biologists.com/2023/05/03/feature-extraction-in-napari/

    @@ -535,7 +528,7 @@

    Feature extraction in napari#

    Mara Lampert

    Tags: Github, Python, Science Communication

    -

    Content type: Blog

    +

    Content type: Blog Post

    https://focalplane.biologists.com/2024/04/03/how-to-write-a-bug-report/

    @@ -545,7 +538,7 @@

    If you license it, it’ll be harder to steal it. Why we should license our

    Licensed CC-BY-4.0

    Blog post about why we should license our work and what is important when choosing a license.

    Tags: Licensing, Research Data Management

    -

    Content type: Blog

    +

    Content type: Blog Post

    https://focalplane.biologists.com/2023/05/06/if-you-license-it-itll-be-harder-to-steal-it-why-we-should-license-our-work/

    @@ -554,7 +547,7 @@

    Managing Scientific Python environments using Conda, Mamba and friendsRobert Haase

    Licensed CC-BY-4.0

    Tags: Python, Conda, Mamba

    -

    Content type: Blog

    +

    Content type: Blog Post

    https://focalplane.biologists.com/2022/12/08/managing-scientific-python-environments-using-conda-mamba-and-friends/

    @@ -562,7 +555,7 @@

    Managing Scientific Python environments using Conda, Mamba and friendsPrompt Engineering in Bio-image Analysis#

    Mara Lampert

    Tags: Python, Jupyter, Bioimage Analysis, Prompt Engineering, Biabob

    -

    Content type: Blog

    +

    Content type: Blog Post

    https://focalplane.biologists.com/2024/07/18/prompt-engineering-in-bio-image-analysis/

    @@ -570,7 +563,7 @@

    Prompt Engineering in Bio-image Analysis#

    Mara Lampert

    Tags: Python, Napari, Bioimage Analysis

    -

    Content type: Blog

    +

    Content type: Blog Post

    https://focalplane.biologists.com/2023/04/13/quality-assurance-of-segmentation-results/

    @@ -578,7 +571,7 @@

    Quality assurance of segmentation results#

    Mara Lampert

    Tags: Python, Napari, Bioimage Analysis

    -

    Content type: Blog

    +

    Content type: Blog Post

    https://focalplane.biologists.com/2023/03/02/rescaling-images-and-pixel-anisotropy/

    @@ -586,7 +579,7 @@

    Rescaling images and pixel (an)isotropy#

    Elisabeth Kugler

    Tags: Sharing, Research Data Management

    -

    Content type: Blog

    +

    Content type: Blog Post

    https://focalplane.biologists.com/2023/07/26/sharing-your-poster-on-figshare/

    @@ -596,7 +589,7 @@

    Sharing research data with Zenodozenodo.org

    Tags: Sharing, Research Data Management

    -

    Content type: Blog

    +

    Content type: Blog Post

    https://focalplane.biologists.com/2023/02/15/sharing-research-data-with-zenodo/

    @@ -604,7 +597,7 @@

    Sharing research data with Zenodo#

    Mara Lampert

    Tags: Python, Napari, Bioimage Analysis

    -

    Content type: Blog

    +

    Content type: Blog Post

    https://focalplane.biologists.com/2023/06/01/tracking-in-napari/

    diff --git a/domain/git.mpi-cbg.de.html b/domain/git.mpi-cbg.de.html index abbd0f17..002916e1 100644 --- a/domain/git.mpi-cbg.de.html +++ b/domain/git.mpi-cbg.de.html @@ -182,36 +182,29 @@

    By tag

    By content type

    @@ -236,11 +228,12 @@ @@ -482,7 +475,7 @@

    ImageJ2 API-beatinghttps://git.mpi-cbg.de/rhaase/lecture_imagej2_dev

    @@ -491,7 +484,7 @@

    Introduction to ImageJ macro programming, Scientific Computing Facility, MPI

    Robert Haase, Benoit Lombardot

    Licensed UNKNOWN

    Tags: Imagej, Bioimage Analysis

    -

    Content type: Slide

    +

    Content type: Slides

    https://git.mpi-cbg.de/scicomp/bioimage_team/coursematerialimageanalysis/tree/master/ImageJMacro_24h_2017-01

    @@ -500,7 +493,7 @@

    Lecture Applied Bioimage Analysis 2020https://git.mpi-cbg.de/rhaase/lecture_applied_bioimage_analysis

    @@ -510,7 +503,7 @@

    Multi-view fusionhttps://git.mpi-cbg.de/rhaase/lecture_multiview_registration

    @@ -520,7 +513,7 @@

    Tracking Theory, TrackMate, and Mastodonhttps://git.mpi-cbg.de/rhaase/lecture_tracking_trackmate

    @@ -529,7 +522,7 @@

    Working with objects in 2D and 3Dhttps://git.mpi-cbg.de/rhaase/lecture_working_with_objects_in_2d_and_3d

    @@ -538,7 +531,7 @@

    Working with pixelshttps://git.mpi-cbg.de/rhaase/lecture_working_with_pixels

    diff --git a/domain/github.com.html b/domain/github.com.html index 263ac4ba..6e1e8784 100644 --- a/domain/github.com.html +++ b/domain/github.com.html @@ -182,36 +182,29 @@

    By tag

    By content type

    @@ -236,11 +228,12 @@ @@ -618,7 +611,7 @@

    Adding a Workflow to BIAFLOWSRoccoDAnt/Defragmentation_TrainingSchool_EOSC-Life_2022

    @@ -636,7 +629,7 @@

    BIDS-lecture-2024ScaDS/BIDS-lecture-2024

    @@ -654,7 +647,7 @@

    Basics of Image Processing and Analysis#

    Christian Tischer

    Licensed UNKNOWN

    -

    Content type: Slide

    +

    Content type: Slides

    tischi/presentation-image-analysis

    @@ -684,7 +677,7 @@

    Bio-image Data ScienceRobert Haase

    Licensed CC-BY-4.0

    This repository contains training resources for Students at Uni Leipzig who want to dive into bio-image data science with Python.

    -

    Tags: Image Data Management, Deep Learning, Microscopy Image Analysis, Python

    +

    Tags: Research Data Management, Artificial Intelligence, Bioimage Analysis, Python

    Content type: Notebook

    ScaDS/BIDS-lecture-2024

    @@ -725,7 +718,7 @@

    Building a Bioimage Analysis Workflow using Deep Learningesgomezm/NEUBIAS_chapter_DL_2020

    @@ -752,7 +745,7 @@

    CellProfiler Introductionahklemm/CellProfiler_Introduction

    @@ -833,7 +826,7 @@

    DL@MBL 2021 ExercisesJan Funke, Constantin Pape, Morgan Schwartz, Xiaoyan

    Licensed UNKNOWN

    Tags: Artificial Intelligence, Bioimage Analysis

    -

    Content type: Slide, Notebook

    +

    Content type: Slides, Notebook

    JLrumberger/DL-MBL-2021

    @@ -935,7 +928,7 @@

    Example Pipeline Tutorialhttps://timmonko.github.io/napari-ndev/tutorial/01_example_pipeline/

    timmonko/napari-ndev

    @@ -982,7 +975,7 @@

    I2K 2024: clEsperanto - GPU-Accelerated Image Processing LibraryStRigaud/clesperanto_workshop_I2K24

    @@ -992,7 +985,7 @@

    I2K2024 workshop material - Lazy Parallel Processing and Visualization of La

    Stephan Saalfeld, Tobias Pietzsch

    Published None

    Licensed APACHE-2.0

    -

    Tags: Training

    +

    Tags: Bioimage Analysis

    Content type: Workshop, Notebook, Github Repository

    https://saalfeldlab.github.io/i2k2024-lazy-workshop/

    saalfeldlab/i2k2024-lazy-workshop

    @@ -1085,7 +1078,7 @@

    ImageJ Macro Introductionahklemm/ImageJMacro_Introduction

    @@ -1094,7 +1087,7 @@

    Introduction to Deep Learning for Microscopycomputational-cell-analytics/dl-for-micro

    @@ -1165,7 +1158,7 @@

    Multiplexed tissue imaging - tools and approachesAgustín Andrés Corbat, OmFrederic, Jonas Windhager, Kristína Lidayová

    Licensed CC-BY-4.0

    Material for the I2K 2024 “Multiplexed tissue imaging - tools and approaches” workshop

    -

    Tags: Bioimage Analysis, Microscopy Image Analysis

    +

    Tags: Bioimage Analysis

    Content type: Github Repository, Slides, Workshop

    BIIFSweden/I2K2024-MTIWorkshop

    https://docs.google.com/presentation/d/1R9-4lXAmTYuyFZpTMDR85SjnLsPZhVZ8/edit#slide=id.p1

    @@ -1184,7 +1177,7 @@

    NEUBIAS Analyst School 2018miura/NEUBIAS_AnalystSchool2018

    @@ -1193,7 +1186,7 @@

    NEUBIAS Bioimage Analyst Course 2017miura/NEUBIAS_Bioimage_Analyst_Course2017

    @@ -1202,7 +1195,7 @@

    NEUBIAS Bioimage Analyst School 2019miura/NEUBIAS_AnalystSchool2019

    @@ -1211,7 +1204,7 @@

    NEUBIAS Bioimage Analyst School 2020miura/NEUBIAS_AnalystSchool2020

    @@ -1240,7 +1233,7 @@

    Neubias Academy 2020: Introduction to Nuclei Segmentation with StarDistMartin Weigert, Olivier Burri, Siân Culley, Uwe Schmidt

    Licensed UNKNOWN

    Tags: Python, Neubias, Artificial Intelligence, Bioimage Analysis

    -

    Content type: Slide, Notebook

    +

    Content type: Slides, Notebook

    maweigert/neubias_academy_stardist

    @@ -1249,7 +1242,7 @@

    NeubiasPasteur2023_AdvancedCellPosegletort/NeubiasPasteur2023_AdvancedCellPose

    @@ -1287,7 +1280,7 @@

    Object Tracking and Track Analysis using TrackMate and CellTracksColabPublished None

    Licensed GPL-3.0

    I2K 2024 workshop materials for “Object Tracking and Track Analysis using TrackMate and CellTracksColab”

    -

    Tags: Bioimage Analysis, Training

    +

    Tags: Bioimage Analysis

    Content type: Github Repository, Tutorial, Workshop, Slides

    CellMigrationLab/I2K_2024

    @@ -1410,7 +1403,7 @@

    RDM_system_connectorSaibotMagd

    Licensed UNKNOWN

    This tool is intended to link different research data management platforms with each other.

    -

    Tags: Research Data Management, Image Data Management

    +

    Tags: Research Data Management

    Content type: Github Repository

    SaibotMagd/RDM_system_connector

    @@ -1455,7 +1448,7 @@

    Setting up a remote desktop to use Napari in a browser

    SimpleITK-Notebooks#

    Ziv Yaniv et al.

    -

    Licensed APACHE-2.0 LICENSE

    +

    Licensed APACHE-2.0

    Jupyter notebooks for learning how to use SimpleITK

    Tags: Bioimage Analysis, Simpleitk

    Content type: Github Repository

    @@ -1473,7 +1466,7 @@

    Source Control Using Git and GitHub#

    Richard McElreath

    Published 2024-03-01

    -

    Licensed CC0-1.0 LICENSE

    +

    Licensed CC0-1.0

    This course teaches data analysis, but it focuses on scientific models. The unfortunate truth about data is that nothing much can be done with it, until we say what caused it. We will prioritize conceptual, causal models and precise questions about those models. We will use Bayesian data analysis to connect scientific models to evidence. And we will learn powerful computational tools for coping with high-dimension, imperfect data of the kind that biologists and social scientists face.

    Tags: Statistics

    Content type: Github Repository

    @@ -1510,7 +1503,7 @@

    Training Deep Learning Models for Vision - Compact Course#

    Jordão Bragantini, Teun Huijben

    Licensed BSD3-CLAUSE

    -

    Tags: Segmentation, Bioimage Analysis, Training

    +

    Tags: Bioimage Analysis

    Content type: Workshop, Github Repository, Tutorial

    royerlab/ultrack-i2k2024

    https://royerlab.github.io/ultrack-i2k2024/

    @@ -1550,7 +1543,7 @@

    YMIA - Python-Based Event Series Training MaterialPublished None

    Licensed MIT

    This repository offer access to teaching material and useful resources for the YMIA - Python-Based Event Series.

    -

    Tags: Python, Large Language Models, Prompt Engineering, Biabob, Bioimage Analysis, Microscopy Image Analysis

    +

    Tags: Python, Artifical Intelligence, Bioimage Analysis

    Content type: Github Repository, Slides

    rmassei/ymia_python_event_series_material

    @@ -1568,7 +1561,7 @@

    ZIDAS 2020 Introduction to Deep Learningesgomezm/zidas2020_intro_DL

    @@ -1588,7 +1581,7 @@

    bioformats2raw Converterglencoesoftware/bioformats2raw

    @@ -1651,7 +1644,6 @@

    ome-ngff-validatorhttps://ome.github.io/ome-ngff-validator/

    ome/ome-ngff-validator

    @@ -1659,11 +1651,11 @@

    ome-ngff-validator

    ome2024-ngff-challenge#

    -

    Will Moore, Josh Moore, sherwoodf, jean-marie burel, Norman Rzepka, dependabot[bot], JensWendt, Joost de Folter, Torsten Stöter, AybukeKY, Eric Perlman, Tom Boissonnet

    +

    Will Moore, Josh Moore, sherwoodf, Jean-Marie Burel, Norman Rzepka, dependabot[bot], JensWendt, Joost de Folter, Torsten St\xF6ter, AybukeKY, Eric Perlman, Tom Boissonnet

    Published 2024-08-30T12:00:53+00:00

    Licensed BSD-3-CLAUSE

    Project planning and material repository for the 2024 challenge to generate 1 PB of OME-Zarr data

    -

    Tags: Sharing

    +

    Tags: Sharing, Nfdi4Bioimage, Research Data Management

    Content type: Github Repository

    ome/ome2024-ngff-challenge

    @@ -1672,7 +1664,7 @@

    ome2024-ngff-challengepatho_prompt_injection#

    JanClusmann, Tim Lenz

    Published 2024-11-08T08:32:03+00:00

    -

    Licensed GNU GENERAL PUBLIC LICENSE V3.0

    +

    Licensed GPL-3.0

    Tags: Histopathology, Bioimage Analysis

    Content type: Github Repository, Notebook

    KatherLab/patho_prompt_injection

    @@ -1696,7 +1688,7 @@

    raw2ometiff ConverterMelissa Linkert, Chris Allan, Sébastien Besson, Josh Moore

    Licensed GPL-2.0

    Java application to convert a directory of tiles to an OME-TIFF pyramid. This is the second half of iSyntax/.mrxs => OME-TIFF conversion.

    -

    Tags: Open Source Software, Bioimage Data

    +

    Tags: Open Source Software

    Content type: Application, Github Repository

    glencoesoftware/raw2ometiff

    @@ -1705,7 +1697,7 @@

    raw2ometiff Converterrse-skills-workshop#

    Jack Atkinson

    Published 2023-12-22T17:39:48+00:00

    -

    Licensed GNU GENERAL PUBLIC LICENSE V3.0

    +

    Licensed GPL-3.0

    Teaching materials for improving research software writing abilities.

    Tags: Research Software Engineering

    Content type: Github Repository, Slides

    @@ -1714,9 +1706,9 @@

    rse-skills-workshop

    scanpy-tutorials#

    -

    Alex Wolf, pre-commit-ci[bot], Philipp A., Isaac Virshup, Ilan Gold, Giovanni Palla, Fidel Ramirez, Gökçen Eraslan, Sergei Rybakov, Abolfazl (Abe), Adam Gayoso, Dinesh Palli, Gregor Sturm, Jan Lause, Karin Hrovatin, Krzysztof Polanski, RaphaelBuzzi, Yimin Zheng, Yishen Miao, evanbiederstedt

    +

    Alex Wolf, pre-commit-ci[bot], Philipp A., Isaac Virshup, Ilan Gold, Giovanni Palla, Fidel Ramirez, G\xF6k\xE7en Eraslan, Sergei Rybakov, Abolfazl (Abe), Adam Gayoso, Dinesh Palli, Gregor Sturm, Jan Lause, Karin Hrovatin, Krzysztof Polanski, RaphaelBuzzi, Yimin Zheng, Yishen Miao, evanbiederstedt

    Published 2018-12-16T03:42:46+00:00

    -

    Licensed BSD-3

    +

    Licensed BSD-3-CLAUSE

    Scanpy Tutorials.

    Tags: Single-Cell Analysis, Bioimage Analysis

    Content type: Github Repository

    diff --git a/domain/www.biorxiv.org.html b/domain/www.biorxiv.org.html index bd8e6be4..176ba169 100644 --- a/domain/www.biorxiv.org.html +++ b/domain/www.biorxiv.org.html @@ -182,36 +182,29 @@

    By tag

    By content type

    @@ -236,11 +228,12 @@ @@ -517,7 +510,7 @@

    Using Glittr.org

    Geert van Geest, Yann Haefliger, Monique Zahn-Zabal, Patricia M. Palagi

    Licensed CC-BY-4.0

    Glittr.org is a platform that aggregates and indexes training materials on computational life sciences from public git repositories, making it easier for users to find, compare, and analyze these resources based on various metrics. By providing insights into the availability of materials, collaboration patterns, and licensing practices, Glittr.org supports adherence to the FAIR principles, benefiting the broader life sciences community.

    -

    Tags: Training, Bioimage Analysis, Research Data Management

    +

    Tags: Bioimage Analysis, Research Data Management

    Content type: Publication, Preprint

    https://www.biorxiv.org/content/10.1101/2024.08.20.608021v1

    diff --git a/domain/www.ebi.ac.uk.html b/domain/www.ebi.ac.uk.html index 92ae995a..78a6d3b5 100644 --- a/domain/www.ebi.ac.uk.html +++ b/domain/www.ebi.ac.uk.html @@ -182,36 +182,29 @@

    By tag

    By content type

    @@ -236,11 +228,12 @@ @@ -483,7 +476,7 @@

    Www.ebi.ac.uk @@ -516,7 +509,7 @@

    EMBL-EBI material collectionhttps://www.ebi.ac.uk/training/on-demand?facets=type:Course%20materials&query=

    @@ -528,7 +521,7 @@

    Finding and using publicly available datahttps://www.ebi.ac.uk/training/online/courses/finding-using-public-data/

    @@ -537,7 +530,7 @@

    Methods in bioimage analysishttps://www.ebi.ac.uk/training/events/methods-bioimage-analysis/

    https://doi.org/10.6019/TOL.BioImageAnalysis22-w.2022.00001.1

    https://drive.google.com/file/d/1MhuqfKhZcYu3bchWMqogIybKjamU5Msg/view

    @@ -548,7 +541,7 @@

    Microscopy data analysis: machine learning and the BioImage ArchiveAndrii Iudin, Anna Foix-Romero, Anna Kreshuk, Awais Athar, Beth Cimini, Dominik Kutra, Estibalis Gomez de Mariscal, Frances Wong, Guillaume Jacquemet, Kedar Narayan, Martin Weigert, Nodar Gogoberidze, Osman Salih, Petr Walczysko, Ryan Conrad, Simone Weyend, Sriram Sundar Somasundharam, Suganya Sivagurunathan, Ugis Sarkans

    Licensed CC-BY-4.0

    The Microscopy data analysis: machine learning and the BioImage Archive course, which focused on introducing programmatic approaches used in the analysis of bioimage data via the BioImage Archive, ran in May 2023.

    -

    Tags: Microscopy Image Analysis, Python, Deep Learning

    +

    Tags: Bioimage Analysis, Python, Artificial Intelligence

    Content type: Video, Slides

    https://www.ebi.ac.uk/training/materials/microscopy-data-analysis-machine-learning-and-the-bioimage-archive-materials/

    @@ -557,7 +550,7 @@

    Microscopy data analysis: machine learning and the BioImage ArchiveREMBI Overview#

    Licensed CC0-1.0

    Recommended Metadata for Biological Images (REMBI) provides guidelines for metadata for biological images to enable the FAIR sharing of scientific data.

    -

    Tags: FAIR-Principles, Metadata, Image Data Management, Research Data Management, Bioimage Data

    +

    Tags: FAIR-Principles, Metadata, Research Data Management

    Content type: Collection

    https://www.ebi.ac.uk/bioimage-archive/rembi-help-overview/

    @@ -566,7 +559,7 @@

    REMBI Overview#

    Licensed CC0-1.0

    To submit, you’ll need to register an account, organise and upload your data, prepare a file list, and then submit using our web submission form. These steps are explained here.

    -

    Tags: Research Data Management, Image Data Management, Bioimage Data

    +

    Tags: Research Data Management

    Content type: Tutorial, Video

    https://www.ebi.ac.uk/bioimage-archive/submit/

    diff --git a/domain/www.nature.com.html b/domain/www.nature.com.html index c73073ea..54bfb663 100644 --- a/domain/www.nature.com.html +++ b/domain/www.nature.com.html @@ -182,36 +182,29 @@

    By tag

    By content type

    @@ -236,11 +228,12 @@ @@ -518,7 +511,7 @@

    FAIR High Content Screening in Bioimaginghttps://www.nature.com/articles/s41597-023-02367-w

    @@ -572,9 +565,9 @@

    Modeling community standards for metadata as templates makes data FAIR

    Multimodal large language models for bioimage analysis#

    Shanghang Zhang, Gaole Dai, Tiejun Huang, Jianxu Chen

    -

    Licensed [‘CC-BY-NC-SA’]

    +

    Licensed CC-BY-NC-SA

    Multimodal large language models have been recognized as a historical milestone in the field of artificial intelligence and have demonstrated revolutionary potentials not only in commercial applications, but also for many scientific fields. Here we give a brief overview of multimodal large language models through the lens of bioimage analysis and discuss how we could build these models as a community to facilitate biology research

    -

    Tags: Bioimage Analysis, Large Language Models, FAIR-Principles, Workflow

    +

    Tags: Bioimage Analysis, FAIR-Principles, Workflow

    Content type: Publication

    https://www.nature.com/articles/s41592-024-02334-2

    https://arxiv.org/abs/2407.19778

    @@ -595,7 +588,7 @@

    REMBI - Recommended Metadata for Biological Images—enabling reuse of micro

    Published 2021-05-21

    Licensed UNKNOWN

    Bioimaging data have significant potential for reuse, but unlocking this potential requires systematic archiving of data and metadata in public databases. The authors propose draft metadata guidelines to begin addressing the needs of diverse communities within light and electron microscopy.

    -

    Tags: Metadata, Bioimage Data, Image Data Management, Research Data Management

    +

    Tags: Metadata, Research Data Management

    Content type: Publication

    https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8606015/

    https://www.nature.com/articles/s41592-021-01166-8

    @@ -607,7 +600,7 @@

    Reporting and reproducibility in microscopyhttps://www.nature.com/collections/djiciihhjh

    diff --git a/domain/www.youtube.com.html b/domain/www.youtube.com.html index 7e6400b3..ca996e2b 100644 --- a/domain/www.youtube.com.html +++ b/domain/www.youtube.com.html @@ -182,36 +182,29 @@

    By tag

    By content type

    @@ -236,11 +228,12 @@ @@ -498,8 +491,8 @@

    AI ML DL in Bioimage Analysis - Webinarhttps://www.youtube.com/watch?v=TJXNMIWtdac

    @@ -510,7 +503,7 @@

    Artificial Intelligence for Digital Pathologyhttps://www.youtube.com/watch?v=Om9tl4Dh2yw

    @@ -553,8 +546,8 @@

    Dr Guillaume Jacquemet on studying cancer cell metastasis in the era of deep

    Published 2024-10-24

    Licensed UNKNOWN

    Leukocyte extravasation is a critical component of the innate immune response, while circulating tumour cell extravasation is a crucial step in metastasis formation. Despite their importance, these extravasation mechanisms remain incompletely understood. In this talk, Guillaume Jacquemet presents a novel imaging framework that integrates microfluidics with high-speed, label-free imaging to study the arrest of pancreatic cancer cells (PDAC) on human endothelial layers under physiological flow conditions.

    -

    Tags: Deep Learning, Microscopy Image Analysis

    -

    Content type: Youtube Video, Slides

    +

    Tags: Artificial Intelligence, Bioimage Analysis

    +

    Content type: Video, Slides

    https://www.youtube.com/watch?v=KTdZBgSCYJQ

    @@ -563,7 +556,7 @@

    Erick Martins Ratamero - Expanding the OME ecosystem for imaging data manage

    SciPy, Erick Martins Ratamero

    Published 2024-08-19

    Licensed YOUTUBE STANDARD LICENSE

    -

    Tags: Image Data Management, OMERO, Bioimage Analysis

    +

    Tags: OMERO, Bioimage Analysis

    Content type: Video, Presentation

    https://www.youtube.com/watch?v=GmhyDNm1RsM

    @@ -608,7 +601,7 @@

    I3D:bio’s OMERO training material: Re-usable, adjustable, multi-purpose sl

    Licensed CC-BY-4.0

    The open-source software OME Remote Objects (OMERO) is a data management software that allows storing, organizing, and annotating bioimaging/microscopy data. OMERO has become one of the best-known systems for bioimage data management in the bioimaging community. The Information Infrastructure for BioImage Data (I3D:bio) project facilitates the uptake of OMERO into research data management (RDM) practices at universities and research institutions in Germany. Since the adoption of OMERO into researchers’ daily routines requires intensive training, a broad portfolio of training resources for OMERO is an asset. On top of using the OMERO guides curated by the Open Microscopy Environment Consortium (OME) team, imaging core facility staff at institutions where OMERO is used often prepare additional material tailored to be applicable for their own OMERO instances. Based on experience gathered in the Research Data Management for Microscopy group (RDM4mic) in Germany, and in the use cases in the I3D:bio project, we created a set of reusable, adjustable, openly available slide decks to serve as the basis for tailored training lectures, video tutorials, and self-guided instruction manuals directed at beginners in using OMERO. The material is published as an open educational resource complementing the existing resources for OMERO contributed by the community.

    Tags: OMERO, Research Data Management, Nfdi4Bioimage, I3Dbio

    -

    Content type: Slide, Video

    +

    Content type: Slides, Video

    https://zenodo.org/records/8323588

    https://www.youtube.com/playlist?list=PL2k-L-zWPoR7SHjG1HhDIwLZj0MB_stlU

    https://doi.org/10.5281/zenodo.8323588

    @@ -635,7 +628,7 @@

    Open Micoscropy Environment (OME) Youtube ChannelPublished None

    Licensed CC-BY-4.0

    OME develops open-source software and data format standards for the storage and manipulation of biological microscopy data

    -

    Tags: Open Source Software, Microscopy Image Analysis, Bioimage Data

    +

    Tags: Open Source Software

    Content type: Video, Collection

    https://www.youtube.com/@OpenMicroscopyEnvironment

    @@ -669,7 +662,7 @@

    Statistical RethinkingLicensed UNKNOWN

    Video Lectures for Statistical Rethinking Course

    Tags: Statistics

    -

    Content type: Youtube Video

    +

    Content type: Video

    https://www.youtube.com/playlist?list=PLDcUM9US4XdPz-KxHM4XHt7uUVGWWVSus

    diff --git a/domain/zenodo.org.html b/domain/zenodo.org.html index c65919d7..88197b1d 100644 --- a/domain/zenodo.org.html +++ b/domain/zenodo.org.html @@ -182,36 +182,29 @@

    By tag

    By content type

    @@ -236,11 +228,12 @@ @@ -714,6 +707,7 @@

    A journey to FAIR microscopy datahttps://zenodo.org/records/7890311

    https://doi.org/10.5281/zenodo.7890311

    @@ -757,17 +751,18 @@

    Alles meins – oder!? Urheberrechte klären für Forschungsdaten#

    Hoku West-Foyle

    Published 2025-01-16

    -

    Licensed CC-ZERO

    +

    Licensed CC0-1.0

    https://zenodo.org/records/14675120

    https://doi.org/10.5281/zenodo.14675120

    Angebote der NFDI für die Forschung im Bereich Zoologie#

    -

    Birgitta König-Ries, Robert Haase, Daniel Nüst, Konrad Förstner, Engel, Judith Sophie

    +

    Birgitta König-Ries, Robert Haase, Daniel Nüst, Konrad Förstner, Judith Sophie Engel

    Published 2024-12-04

    Licensed CC-BY-4.0

    In diesem Slidedeck geben wir einen Einblick in Angebote und Dienste der Nationalen Forschungsdaten Infrastruktur (NFDI), die Relevant für die Zoologie und angrenzende Disziplinen relevant sein könnten.

    +

    Tags: Nfdi4Bioimage, Research Data Management

    https://zenodo.org/records/14278058

    https://doi.org/10.5281/zenodo.14278058

    @@ -808,8 +803,8 @@

    Bio-Image Data Strudel for Workshop on Research Data Management in TU Dresde

    Published 2023-11-08

    Licensed CC-BY-4.0

    This presentation gives a short outline of the complexity of data and metadata in the bioimaging universe. It introduces NFDI4BIOIMAGE as a newly formed consortium as part of the German ‘Nationale Forschungsdateninfrastruktur’ (NFDI) and its goals and tools for data management including its current members on TU Dresden campus.  

    -

    Tags: Research Data Management, Tu Dresden, Bioimage Data, Nfdi4Bioimage

    -

    Content type: Slide

    +

    Tags: Research Data Management, Nfdi4Bioimage

    +

    Content type: Slides

    https://zenodo.org/records/10083555

    https://doi.org/10.5281/zenodo.10083555

    @@ -844,8 +839,8 @@

    Bio-image Analysis with the Help of Large Language Modelshttps://zenodo.org/records/10815329

    https://doi.org/10.5281/zenodo.10815329

    @@ -855,7 +850,7 @@

    Bio-image Data Science Lectures @ Uni Leipzig / https://zenodo.org/records/12623730

    @@ -999,6 +994,7 @@

    Collaborative Working and Version Control with git[hub]Published 2024-01-10

    Licensed CC-BY-4.0

    This slide deck introduces the version control tool git, related terminology and the Github Desktop app for managing files in Git[hub] repositories. We furthermore dive into:* Working with repositories* Collaborative with others* Github-Zenodo integration* Github pages* Artificial Intelligence answering Github Issues

    +

    Tags: Nfdi4Bioimage, Globias, Research Data Management, Research Software Management

    https://zenodo.org/records/14626054

    https://doi.org/10.5281/zenodo.14626054

    @@ -1043,7 +1039,7 @@

    Creating open computational curriculahttps://zenodo.org/records/4317149

    https://doi.org/10.5281/zenodo.4317149

    @@ -1244,6 +1240,7 @@

    Engineering a Software Environment for Research Data Management of Microscop

    Kunis

    Published 2022-05-30

    This thesis deals with concepts and solutions in the field of data management in everyday scientific life for image data from microscopy. The focus of the formulated requirements has so far been on published data, which represent only a small subset of the data generated in the scientific process. More and more, everyday research data are moving into the focus of the principles for the management of research data that were formulated early on (FAIR-principles). The adequate management of this mostly multimodal data is a real challenge in terms of its heterogeneity and scope. There is a lack of standardised and established workflows and also the software solutions available so far do not adequately reflect the special requirements of this area. However, the success of any data management process depends heavily on the degree of integration into the daily work routine. Data management must, as far as possible, fit seamlessly into this process. Microscopy data in the scientific process is embedded in pre-processing, which consists of preparatory laboratory work and the analytical evaluation of the microscopy data. In terms of volume, the image data often form the largest part of data generated within this entire research process. In this paper, we focus on concepts and techniques related to the handling and description of this image data and address the necessary basics. The aim is to improve the embedding of the existing data management solution for image data (OMERO) into the everyday scientific work. For this purpose, two independent software extensions for OMERO were implemented within the framework of this thesis: OpenLink and MDEmic. OpenLink simplifies the access to the data stored in the integrated repository in order to feed them into established workflows for further evaluations and enables not only the internal but also the external exchange of data without weakening the advantages of the data repository. The focus of the second implemented software solution, MDEmic, is on the capturing of relevant metadata for microscopy. Through the extended metadata collection, a corresponding linking of the multimodal data by means of a unique description and the corresponding semantic background is aimed at. The configurability of MDEmic is designed to address the currently very dynamic development of underlying concepts and formats. The main goal of MDEmic is to minimise the workload and to automate processes. This provides the scientist with a tool to handle this complex and extensive task of metadata acquisition for microscopic data in a simple way. With the help of the software, semantic and syntactic standardisation can take place without the scientist having to deal with the technical concepts. The generated metadata descriptions are automatically integrated into the image repository and, at the same time, can be transferred by the scientists into formats that are needed when publishing the data.

    +

    Tags: Nfdi4Bioimage, Research Data Managementv

    https://zenodo.org/records/6905931

    https://doi.org/10.5281/zenodo.6905931

    @@ -1819,7 +1816,7 @@

    I3D:bio’s OMERO training material: Re-usable, adjustable, multi-purpose sl

    Licensed CC-BY-4.0

    The open-source software OME Remote Objects (OMERO) is a data management software that allows storing, organizing, and annotating bioimaging/microscopy data. OMERO has become one of the best-known systems for bioimage data management in the bioimaging community. The Information Infrastructure for BioImage Data (I3D:bio) project facilitates the uptake of OMERO into research data management (RDM) practices at universities and research institutions in Germany. Since the adoption of OMERO into researchers’ daily routines requires intensive training, a broad portfolio of training resources for OMERO is an asset. On top of using the OMERO guides curated by the Open Microscopy Environment Consortium (OME) team, imaging core facility staff at institutions where OMERO is used often prepare additional material tailored to be applicable for their own OMERO instances. Based on experience gathered in the Research Data Management for Microscopy group (RDM4mic) in Germany, and in the use cases in the I3D:bio project, we created a set of reusable, adjustable, openly available slide decks to serve as the basis for tailored training lectures, video tutorials, and self-guided instruction manuals directed at beginners in using OMERO. The material is published as an open educational resource complementing the existing resources for OMERO contributed by the community.

    Tags: OMERO, Research Data Management, Nfdi4Bioimage, I3Dbio

    -

    Content type: Slide, Video

    +

    Content type: Slides, Video

    https://zenodo.org/records/8323588

    https://www.youtube.com/playlist?list=PL2k-L-zWPoR7SHjG1HhDIwLZj0MB_stlU

    https://doi.org/10.5281/zenodo.8323588

    @@ -1946,7 +1943,7 @@

    Implantation of abdominal imaging windows on the mouse liver - short version

    Ink in a dish#

    Cavanagh

    Published 2024-09-03

    -

    Licensed CC-ZERO

    +

    Licensed CC0-1.0

    A test data set for troublshooting. no scientific meaning.

    https://zenodo.org/records/13642395

    https://doi.org/10.5281/zenodo.13642395

    @@ -1995,7 +1992,7 @@

    Institutionalization and Collaboration as a Way of Addressing the Challenges

    Interactive Image Data Flow Graphs#

    -

    Martin Schätz, Martin Schätz

    +

    Martin Schätz

    Published 2022-10-17

    Licensed CC-BY-4.0

    The slides were presented during the Macro programming with ImageJ workshop (https://www.16mcm.cz/programme/#workshops) which was part of the 16th Multinational Congress on Microscopy. It is a collection and “reshuffle” of slides originally made by Robert Haase on topics from Image Analysis in general up to User-friendly GPU-accelerated bio-image analysis and CLIJ2.

    @@ -2074,6 +2071,7 @@

    Key-Value pair template for annotation in OMERO for light microscopy data ac See cross reference: 10.5281/zenodo.12547566 Key-Value pair template for annotation of datasets in OMERO for light- and electron microscopy data within the research group of Prof. Mueller-Reichert 10.5281/zenodo.12546808 Key-Value pair template for annotation of datasets in OMERO (PERIKLES study)

    +

    Tags: Nfdi4Bioimage, Research Data Management

    https://zenodo.org/records/12578084

    https://doi.org/10.5281/zenodo.12578084

    @@ -2099,6 +2097,7 @@

    Key-Value pair template for annotation of datasets in OMERO (PERIKLES study) See cross reference: 10.5281/zenodo.12547566 Key-Value pair template for annotation of datasets in OMERO (light- and electron microscopy data within the research group of Prof. Mueller-Reichert) 10.5281/zenodo.12578084 Key-Value pair template for annotation in OMERO for light microscopy data acquired with AxioScan7 - Core Facility Cellular Imaging (CFCI)

    +

    Tags: Nfdi4Bioimage, Research Data Management

    https://zenodo.org/records/12546808

    https://doi.org/10.5281/zenodo.12546808

    @@ -2182,6 +2181,7 @@

    Large Language Models: An Introduction for Life Scientistshttps://zenodo.org/records/14418209

    https://doi.org/10.5281/zenodo.14418209

    @@ -2305,7 +2305,7 @@

    MicroSam-Talkshttps://zenodo.org/records/11265038

    https://doi.org/10.5281/zenodo.11265038

    @@ -2313,10 +2313,11 @@

    MicroSam-Talks

    Modular training resources for bioimage analysis#

    -

    Christian Tischer, Antonio Politi, Tim-Oliver Buchholz, Elnaz Fazeli, Nicola Gritti, Aliaksandr Halavatyi, Gonzalez Tirado, Sebastian, Julian Hennies, Toby Hodges, Arif Khan, Dominik Kutra, Stefania Marcotti, Bugra Oezdemir, Felix Schneider, Martin Schorb, Anniek Stokkermans, Yi Sun, Nima Vakili

    +

    Christian Tischer, Antonio Politi, Tim-Oliver Buchholz, Elnaz Fazeli, Nicola Gritti, Aliaksandr Halavatyi, Sebastian Gonzalez Tirado, Julian Hennies, Toby Hodges, Arif Khan, Dominik Kutra, Stefania Marcotti, Bugra Oezdemir, Felix Schneider, Martin Schorb, Anniek Stokkermans, Yi Sun, Nima Vakili

    Published 2024-12-03

    Licensed CC-BY-4.0

    Resources for teaching/preparing to teach bioimage analysis

    +

    Tags: Neubias, Bioimage Analysis

    https://zenodo.org/records/14264885

    https://doi.org/10.5281/zenodo.14264885

    @@ -2404,6 +2405,7 @@

    NFDI4BIOIMAGE data management illustrations by Henning Falkhttps://doi.org/10.5281/zenodo.14186100, is used under a CC-BY 4.0 license. Modifications to this illustration include cropping.  

    +

    Tags: Nfdi4Bioimage, Research Data Management

    https://zenodo.org/records/14186101

    https://doi.org/10.5281/zenodo.14186101

    @@ -2424,6 +2426,7 @@

    NFDI4Bioimage Calendar 2024 October; original imagePublished 2024-09-25

    Licensed CC-BY-4.0

    Raw microscopy image from the NFDI4Bioimage calendar October 2024

    +

    Tags: Nfdi4Bioimage, Research Data Management

    https://zenodo.org/records/13837146

    https://doi.org/10.5281/zenodo.13837146

    @@ -2446,7 +2449,7 @@

    Nextflow: Scalable and reproducible scientific workflowshttps://zenodo.org/records/4334697

    https://doi.org/10.5281/zenodo.4334697

    @@ -2459,6 +2462,7 @@

    OME2024 NGFF Challenge Resultshttps://founding-gide.eurobioimaging.eu/event/foundinggide-community-event-2024/ Note: much of the presentation was a demonstration of the OME2024-NGFF-Challenge – https://ome.github.io/ome2024-ngff-challenge/ especially of querying an extraction of the metadata (ome/ome2024-ngff-challenge-metadata)  

    +

    Tags: Nfdi4Bioimage, Research Data Management

    https://zenodo.org/records/14234608

    https://doi.org/10.5281/zenodo.14234608

    @@ -2512,7 +2516,7 @@

    QuPath: Open source software for analysing (awkward) imageshttps://zenodo.org/records/4328911

    https://doi.org/10.5281/zenodo.4328911

    @@ -2561,7 +2565,7 @@

    Research Data Management Seminar - Slideshttps://zenodo.org/record/6602101

    https://doi.org/10.5281/zenodo.6602101

    @@ -2670,6 +2674,7 @@

    Structuring of Data and Metadata in Bioimaging: Concepts and technical Solut ... +

    Tags: Nfdi4Bioimage, Research Data Management

    https://zenodo.org/records/7018750

    https://doi.org/10.5281/zenodo.7018750

    @@ -2688,7 +2693,7 @@

    Sustainable Data Stewardship

    Terminology service for research data management and knowledge discovery in low-temperature plasma physics#

    -

    Becker, Markus M., Chaerony Siffa, Ihda, Roman Baum

    +

    Markus M. Becker, Ihda Chaerony Siffa, Roman Baum

    Published 2024-12-11

    Licensed CC-BY-4.0

    Abstract: @@ -2741,6 +2746,7 @@

    The Information Infrastructure for BioImage Data (I3D:bio) project to advanc

    Published 2024-03-04

    Licensed CC-BY-4.0

    Research data management (RDM) in microscopy and image analysis is a challenging task. Large files in proprietary formats, complex N-dimensional array structures, and various metadata models and formats can make image data handling inconvenient and difficult. For data organization, annotation, and sharing, researchers need solutions that fit everyday practice and comply with the FAIR (Findable, Accessible, Interoperable, Reusable) principles. International community-based efforts have begun creating open data models (OME), an open file format and translation library (OME-TIFF, Bio-Formats), data management software platforms, and microscopy metadata recommendations and annotation tools. Bringing these developments into practice requires support and training. Iterative feedback and tool improvement is needed to foster practical adoption by the scientific community. The Information Infrastructure for BioImage Data (I3D:bio) project works on guidelines, training resources, and practical assistance for FAIR microscopy RDM adoption with a focus on the management platform OMERO and metadata annotations.

    +

    Tags: Nfdi4Bioimage, Research Data Management

    https://zenodo.org/records/10805204

    https://doi.org/10.5281/zenodo.10805204

    @@ -2751,6 +2757,7 @@

    The role of Helmholtz Centers in NFDI4BIOIMAGE - A national consortium enhan

    Published 2024-06-06

    Licensed CC-BY-4.0

    Germany’s National Research Data Infrastructure (NFDI) aims to establish a sustained, cross-disciplinary research data management (RDM) infrastructure that enables researchers to handle FAIR (findable, accessible, interoperable, reusable) data. While FAIR principles have been adopted by funders, policymakers, and publishers, their practical implementation remains an ongoing effort. In the field of bio-imaging, harmonization of data formats, metadata ontologies, and open data repositories is necessary to achieve FAIR data. The NFDI4BIOIMAGE was established to address these issues and develop tools and best practices to facilitate FAIR microscopy and image analysis data in alignment with international community activities. The consortium operates through its Data Stewards team to provide expertise and direct support to help overcome RDM challenges. The three Helmholtz Centers in NFDI4BIOIMAGE aim to collaborate closely with other centers and initiatives, such as HMC, Helmholtz AI, and HIP. Here we present NFDI4BIOIMAGE’s work and its significance for research in Helmholtz and beyond

    +

    Tags: Nfdi4Bioimage, Research Data Management

    https://zenodo.org/records/11501662

    https://doi.org/10.5281/zenodo.11501662

    @@ -2771,6 +2778,7 @@

    Towards Preservation of Life Science Data with NFDI4BIOIMAGEhttps://zenodo.org/records/13640979

    https://doi.org/10.5281/zenodo.13640979

    @@ -2820,7 +2828,7 @@

    Who you gonna call? - Data Stewards to the rescue

    Working Group Charter. RDM Helpdesk Network#

    -

    Judith Engel, Patrick Helling, Robert Herrenbrück, Marina Lemaire, Hela Mehrtens, Marcus Schmidt, Martha Stellmacher, Lukas Weimer, Cord Wiljes, Wolf Zinke

    +

    Judith Engel, Patrick Helling, Robert Herrenbrück, MarinaLemaire, Hela Mehrtens, Marcus Schmidt, Martha Stellmacher, Lukas Weimer, Cord Wiljes, Wolf Zinke

    Published 2024-11-04

    Licensed CC-BY-4.0

    Support is an essential component of an efficient infrastructure for research data management (RDM). Helpdesks guide researchers through this complex landscape and provide reliable support about all questions regarding research data management, including support for technical services, best practices, requirements of funding organizations and legal topics. In NFDI, most consortia have already established or are planning to establish helpdesks to support their specific communities. On a local level, many institutions have set up RDM helpdesks that provide support for the researchers of their own institution. Additional RDM support services are offered by RDM federal state initiatives, by research data centers, by specialist libraries, by the EOSC, and by providers of RDM-relevant tools. Helpdesks cover a wide range of institutions, disciplines, topics, methodologies and target audiences. However, the individual helpdesks are not yet interconnected and therefore cannot complement one another in an efficient way: Given the wide and constantly increasing complexity of RDM, no single helpdesk can provide the expertise for all potential support requests. Therefore, we see great potential in combining the efforts and resources of the existing RDM helpdesks into an efficient and comprehensive national RDM support network in order to provide optimal and tailored RDM support to all researchers and research-related institutions in Germany and in an international context.

    @@ -2890,6 +2898,7 @@

    [CIDAS] Scalable strategies for a next-generation of FAIR bioimagingLicensed CC-BY-4.0

    Talk given at Georg-August-Universität Göttingen Campus Institute Data Science23rd January 2025 https://www.uni-goettingen.de/en/653203.html

    +

    Tags: Nfdi4Bioimage

    https://zenodo.org/records/14716546

    https://doi.org/10.5281/zenodo.14716546

    @@ -2902,6 +2911,7 @@

    [CMCB] Scalable strategies for a next-generation of FAIR bioimagingCMCB LIFE SCIENCES SEMINARSTechnische Universität Dresden16th January 2025 https://tu-dresden.de/cmcb/crtd/news-termine/termine/cmcb-life-sciences-seminar-josh-moore-german-bioimaging-e-v-society-for-microscopy-and-image-analysis-constance  

    +

    Tags: Nfdi4Bioimage

    https://zenodo.org/records/14650434

    https://doi.org/10.5281/zenodo.14650434

    @@ -2923,6 +2933,7 @@

    [Community Meeting 2024] Overview Team Image Data Analysis and ManagementLicensed CC-BY-4.0

    Overview of Activities of the Team Image Data Analysis and Management of German BioImaging e.V.  

    +

    Tags: Nfdi4Bioimage, Research Data Management

    https://zenodo.org/records/10796364

    https://doi.org/10.5281/zenodo.10796364

    @@ -2944,6 +2955,7 @@

    [ELMI 2024] AI’s Dirty Little Secret: Withouthttps://www.elmi2024.org/)

    +

    Tags: Nfdi4Bioimage, Research Data Management

    https://zenodo.org/records/11235513

    https://doi.org/10.5281/zenodo.11235513

    @@ -3052,6 +3064,7 @@

    [Workshop Material] Fit for OMERO - How imaging facilities and IT department Establish a stakeholder process management for installing OMERO-based RDM Learn from each other, leverage different expertise Learn how to train users, establish sustainability strategies, and foster FAIR RDM for bioimaging at your institution

    +

    Tags: Nfdi4Bioimage, Research Data Management

    https://zenodo.org/records/14178789

    https://doi.org/10.5281/zenodo.14178789

    @@ -3074,6 +3087,7 @@

    [Workshop] Bioimage data management and analysis with OMEROhttps://zenodo.org/records/11350689

    https://doi.org/10.5281/zenodo.11350689

    @@ -3133,6 +3147,7 @@

    [Workshop] Research Data Management for Microscopy and BioImage Analysis

    Date & Venue:Thursday, Sept. 26, 5.30 p.m.Haus 22 / Paul Ehrlich Lecture Hall (H22-1)University Hospital Frankfurt

    +

    Tags: Nfdi4Bioimage, Research Data Management

    https://zenodo.org/records/13861026

    https://doi.org/10.5281/zenodo.13861026

    @@ -3142,7 +3157,7 @@

    ilastik: interactive machine learning for (bio)image analysishttps://zenodo.org/doi/10.5281/zenodo.4330625

    diff --git a/export/readme.html b/export/readme.html index f1c8ac71..11764c55 100644 --- a/export/readme.html +++ b/export/readme.html @@ -182,36 +182,29 @@

    By tag

    By content type

    @@ -236,11 +228,12 @@ diff --git a/genindex.html b/genindex.html index 1609d91d..a789a9bb 100644 --- a/genindex.html +++ b/genindex.html @@ -181,36 +181,29 @@

    By tag

    By content type

    @@ -235,11 +227,12 @@ diff --git a/imprint.html b/imprint.html index 24181dd9..33d736b6 100644 --- a/imprint.html +++ b/imprint.html @@ -181,36 +181,29 @@

    By tag

    By content type

    @@ -235,11 +227,12 @@ diff --git a/licenses/all_rights_reserved.html b/licenses/all_rights_reserved.html index 4bad3c05..6464dc0c 100644 --- a/licenses/all_rights_reserved.html +++ b/licenses/all_rights_reserved.html @@ -182,36 +182,29 @@

    By tag

    By content type

    @@ -236,11 +228,12 @@ diff --git a/licenses/bsd-2-clause.html b/licenses/bsd-2-clause.html index fb805414..4b555a47 100644 --- a/licenses/bsd-2-clause.html +++ b/licenses/bsd-2-clause.html @@ -63,7 +63,7 @@ - + @@ -182,36 +182,29 @@

    By tag

    By content type

    @@ -236,11 +228,12 @@ @@ -482,7 +475,7 @@

    Adding a Workflow to BIAFLOWSRoccoDAnt/Defragmentation_TrainingSchool_EOSC-Life_2022

    @@ -533,7 +526,6 @@

    ome-ngff-validatorhttps://ome.github.io/ome-ngff-validator/

    ome/ome-ngff-validator

    @@ -585,7 +577,7 @@

    ome-ngff-validator

    next

    -

    Bsd-3-clause (23)

    +

    Bsd-3-clause (26)

     diff --git a/licenses/bsd-3-clause.html b/licenses/bsd-3-clause.html index 9b4e583f..189a335e 100644 --- a/licenses/bsd-3-clause.html +++ b/licenses/bsd-3-clause.html @@ -8,7 +8,7 @@ - Bsd-3-clause (23) — NFDI4BioImage Training Materials + Bsd-3-clause (26) — NFDI4BioImage Training Materials @@ -182,36 +182,29 @@

    By tag

    By content type

    @@ -236,11 +228,12 @@ @@ -447,7 +440,7 @@
    -

    Bsd-3-clause (23)

    +

    Bsd-3-clause (26)

    @@ -491,8 +487,8 @@

    Contents

    -
    -

    Bsd-3-clause (23)#

    +
    +

    Bsd-3-clause (26)#

    2020 BioImage Analysis Survey: Community experiences and needs for the future#

    Nasim Jamali, Ellen T. A. Dobson, Kevin W. Eliceiri, Anne E. Carpenter, Beth A. Cimini

    @@ -542,13 +538,33 @@

    CellProfiler tutorialsCellProfiler/tutorials

    +
    +

    Community-developed checklists for publishing images and image analyses#

    +

    Beth Cimini et al.

    +

    Licensed BSD-3-CLAUSE

    +

    This book is a companion to the Nature Methods publication Community-developed checklists for publishing images and image analyses. In this paper, members of QUAREP-LiMi have proposed 3 sets of standards for publishing image figures and image analysis - minimal requirements, recommended additions, and ideal comprehensive goals. By following this guidance, we hope to remove some of the stress non-experts may face in determining what they need to do, and we also believe that researchers will find their science more interpretable and more reproducible.

    +

    Tags: Bioimage Analysis, Research Data Management

    +

    Content type: Notebook, Collection

    +

    https://quarep-limi.github.io/WG12_checklists_for_image_publishing/intro.html

    +
    +
    +
    +

    Elastix tutorial#

    +

    Marvin Albert

    +

    Licensed BSD-3-CLAUSE

    +

    Tutorial material for teaching the basics of (itk-)elastix for image registration in microscopy images.

    +

    Tags: Image Registration, Itk, Elastix

    +

    Content type: Notebook, Collection

    +

    https://m-albert.github.io/elastix_tutorial/intro.html

    +
    +

    Example Pipeline Tutorial#

    Tim Monko

    Published 2024-10-28

    Licensed BSD-3-CLAUSE

    Napari-ndev is a collection of widgets intended to serve any person seeking to process microscopy images from start to finish. The goal of this example pipeline is to get the user familiar with working with napari-ndev for batch processing and reproducibility (view Image Utilities and Workflow Widget).

    -

    Tags: Napari, Microscopy Image Analysis, Bioimage Analysis

    +

    Tags: Napari, Bioimage Analysis

    Content type: Documentation, Github Repository, Tutorial

    https://timmonko.github.io/napari-ndev/tutorial/01_example_pipeline/

    timmonko/napari-ndev

    @@ -568,7 +584,7 @@

    I2K 2024: clEsperanto - GPU-Accelerated Image Processing LibraryStRigaud/clesperanto_workshop_I2K24

    @@ -606,7 +622,7 @@

    ImageJ2 API-beatinghttps://git.mpi-cbg.de/rhaase/lecture_imagej2_dev

    @@ -616,7 +632,7 @@

    Multi-view fusionhttps://git.mpi-cbg.de/rhaase/lecture_multiview_registration

    @@ -633,7 +649,7 @@

    NeubiasPasteur2023_AdvancedCellPosegletort/NeubiasPasteur2023_AdvancedCellPose

    @@ -654,7 +670,7 @@

    Tracking Theory, TrackMate, and Mastodonhttps://git.mpi-cbg.de/rhaase/lecture_tracking_trackmate

    @@ -683,7 +699,7 @@

    Working with objects in 2D and 3Dhttps://git.mpi-cbg.de/rhaase/lecture_working_with_objects_in_2d_and_3d

    @@ -692,7 +708,7 @@

    Working with pixelshttps://git.mpi-cbg.de/rhaase/lecture_working_with_pixels

    @@ -707,13 +723,24 @@

    numpy pandas course

    ome2024-ngff-challenge#

    -

    Will Moore, Josh Moore, sherwoodf, jean-marie burel, Norman Rzepka, dependabot[bot], JensWendt, Joost de Folter, Torsten Stöter, AybukeKY, Eric Perlman, Tom Boissonnet

    +

    Will Moore, Josh Moore, sherwoodf, Jean-Marie Burel, Norman Rzepka, dependabot[bot], JensWendt, Joost de Folter, Torsten St\xF6ter, AybukeKY, Eric Perlman, Tom Boissonnet

    Published 2024-08-30T12:00:53+00:00

    Licensed BSD-3-CLAUSE

    Project planning and material repository for the 2024 challenge to generate 1 PB of OME-Zarr data

    -

    Tags: Sharing

    +

    Tags: Sharing, Nfdi4Bioimage, Research Data Management

    Content type: Github Repository

    ome/ome2024-ngff-challenge

    +
    +
    +
    +

    scanpy-tutorials#

    +

    Alex Wolf, pre-commit-ci[bot], Philipp A., Isaac Virshup, Ilan Gold, Giovanni Palla, Fidel Ramirez, G\xF6k\xE7en Eraslan, Sergei Rybakov, Abolfazl (Abe), Adam Gayoso, Dinesh Palli, Gregor Sturm, Jan Lause, Karin Hrovatin, Krzysztof Polanski, RaphaelBuzzi, Yimin Zheng, Yishen Miao, evanbiederstedt

    +

    Published 2018-12-16T03:42:46+00:00

    +

    Licensed BSD-3-CLAUSE

    +

    Scanpy Tutorials.

    +

    Tags: Single-Cell Analysis, Bioimage Analysis

    +

    Content type: Github Repository

    +

    scverse/scanpy-tutorials

    @@ -788,6 +815,8 @@

    ome2024-ngff-challengeBioImage Analysis Notebooks
  • Bridging Imaging Users to Imaging Analysis - A community survey
  • CellProfiler tutorials
    • +
  • Community-developed checklists for publishing images and image analyses
    • +
  • Elastix tutorial
  • Example Pipeline Tutorial
  • Fundamentals of image analysis in Python with scikit-image, napari, and friends
  • I2K 2024: clEsperanto - GPU-Accelerated Image Processing Library
    • @@ -806,6 +835,7 @@

    ome2024-ngff-challengeWorking with pixels
  • numpy pandas course
  • ome2024-ngff-challenge
    • +
  • scanpy-tutorials

    • diff --git a/licenses/cc-by-4.0.html b/licenses/cc-by-4.0.html index 97c5a821..ef9366aa 100644 --- a/licenses/cc-by-4.0.html +++ b/licenses/cc-by-4.0.html @@ -64,7 +64,7 @@ - + @@ -182,36 +182,29 @@

    By tag

    By content type

    @@ -236,11 +228,12 @@ @@ -810,6 +803,7 @@

    A journey to FAIR microscopy datahttps://zenodo.org/records/7890311

    https://doi.org/10.5281/zenodo.7890311

    @@ -860,10 +854,11 @@

    Alles meins – oder!? Urheberrechte klären für Forschungsdaten

    Angebote der NFDI für die Forschung im Bereich Zoologie#

    -

    Birgitta König-Ries, Robert Haase, Daniel Nüst, Konrad Förstner, Engel, Judith Sophie

    +

    Birgitta König-Ries, Robert Haase, Daniel Nüst, Konrad Förstner, Judith Sophie Engel

    Published 2024-12-04

    Licensed CC-BY-4.0

    In diesem Slidedeck geben wir einen Einblick in Angebote und Dienste der Nationalen Forschungsdaten Infrastruktur (NFDI), die Relevant für die Zoologie und angrenzende Disziplinen relevant sein könnten.

    +

    Tags: Nfdi4Bioimage, Research Data Management

    https://zenodo.org/records/14278058

    https://doi.org/10.5281/zenodo.14278058

    @@ -893,7 +888,7 @@

    BIDS-lecture-2024ScaDS/BIDS-lecture-2024

    @@ -904,7 +899,7 @@

    BIOMERO - A scalable and extensible image analysis frameworkhttps://doi.org/10.1016/j.patter.2024.101024

    @@ -924,8 +919,8 @@

    Bio-Image Data Strudel for Workshop on Research Data Management in TU Dresde

    Published 2023-11-08

    Licensed CC-BY-4.0

    This presentation gives a short outline of the complexity of data and metadata in the bioimaging universe. It introduces NFDI4BIOIMAGE as a newly formed consortium as part of the German ‘Nationale Forschungsdateninfrastruktur’ (NFDI) and its goals and tools for data management including its current members on TU Dresden campus.  

    -

    Tags: Research Data Management, Tu Dresden, Bioimage Data, Nfdi4Bioimage

    -

    Content type: Slide

    +

    Tags: Research Data Management, Nfdi4Bioimage

    +

    Content type: Slides

    https://zenodo.org/records/10083555

    https://doi.org/10.5281/zenodo.10083555

    @@ -960,8 +955,8 @@

    Bio-image Analysis with the Help of Large Language Modelshttps://zenodo.org/records/10815329

    https://doi.org/10.5281/zenodo.10815329

    @@ -971,7 +966,7 @@

    Bio-image Data ScienceRobert Haase

    Licensed CC-BY-4.0

    This repository contains training resources for Students at Uni Leipzig who want to dive into bio-image data science with Python.

    -

    Tags: Image Data Management, Deep Learning, Microscopy Image Analysis, Python

    +

    Tags: Research Data Management, Artificial Intelligence, Bioimage Analysis, Python

    Content type: Notebook

    ScaDS/BIDS-lecture-2024

    @@ -981,7 +976,7 @@

    Bio-image Data Science Lectures @ Uni Leipzig / https://zenodo.org/records/12623730

    @@ -1008,7 +1003,6 @@

    BioFormats Command line (CLI) toolshttps://bio-formats.readthedocs.io/en/v8.0.0/users/comlinetools/index.html

    @@ -1027,7 +1021,7 @@

    Browsing the Open Microscopy Image Data Resource with Pythonhttps://biapol.github.io/blog/robert_haase/browsing_idr/readme.html

    @@ -1153,7 +1147,7 @@

    Challenges and opportunities for bio-image analysis core-facilitiesRobert Haase

    Licensed CC-BY-4.0

    Tags: Research Data Management, Bioimage Analysis, Nfdi4Bioimage

    -

    Content type: Slide

    +

    Content type: Slides

    https://f1000research.com/slides/12-1054

    @@ -1183,6 +1177,7 @@

    Collaborative Working and Version Control with git[hub]Published 2024-01-10

    Licensed CC-BY-4.0

    This slide deck introduces the version control tool git, related terminology and the Github Desktop app for managing files in Git[hub] repositories. We furthermore dive into:* Working with repositories* Collaborative with others* Github-Zenodo integration* Github pages* Artificial Intelligence answering Github Issues

    +

    Tags: Nfdi4Bioimage, Globias, Research Data Management, Research Software Management

    https://zenodo.org/records/14626054

    https://doi.org/10.5281/zenodo.14626054

    @@ -1193,7 +1188,7 @@

    Collaborative bio-image analysis script editing with githttps://focalplane.biologists.com/2021/09/04/collaborative-bio-image-analysis-script-editing-with-git/

    @@ -1245,8 +1240,8 @@

    Creating a Research Data Management Plan using chatGPTPublished 2023-11-06

    Licensed CC-BY-4.0

    In this blog post the author demonstrates how chatGPT can be used to combine a fictive project description with a DMP specification to produce a project-specific DMP.

    -

    Tags: Research Data Management, Large Language Models, Artificial Intelligence

    -

    Content type: Blog

    +

    Tags: Research Data Management, Artificial Intelligence

    +

    Content type: Blog Post

    https://focalplane.biologists.com/2023/11/06/creating-a-research-data-management-plan-using-chatgpt/

    @@ -1256,7 +1251,7 @@

    Creating open computational curriculahttps://zenodo.org/records/4317149

    https://doi.org/10.5281/zenodo.4317149

    @@ -1326,7 +1321,7 @@

    DataPLANT knowledge basehttps://nfdi4plants.org/nfdi4plants.knowledgebase/index.html

    @@ -1883,7 +1878,7 @@

    FAIR High Content Screening in Bioimaginghttps://www.nature.com/articles/s41597-023-02367-w

    @@ -1902,7 +1897,7 @@

    FAIRy deep-learning for bioImage analysishttps://f1000research.com/slides/13-147

    @@ -1914,7 +1909,7 @@

    Finding and using publicly available datahttps://www.ebi.ac.uk/training/online/courses/finding-using-public-data/

    @@ -1979,7 +1974,7 @@

    Generative artificial intelligence for bio-image analysishttps://f1000research.com/slides/12-971

    @@ -2018,7 +2013,7 @@

    Getting started with Mambaforge and Pythonhttps://biapol.github.io/blog/mara_lampert/getting_started_with_mambaforge_and_python/readme.html

    @@ -2182,7 +2177,7 @@

    Hitchhiking through a diverse Bio-image Analysis Software Universehttps://f1000research.com/slides/11-746

    https://doi.org/10.7490/f1000research.1119026.1

    @@ -2204,7 +2199,7 @@

    I3D:bio’s OMERO training material: Re-usable, adjustable, multi-purpose sl

    Licensed CC-BY-4.0

    The open-source software OME Remote Objects (OMERO) is a data management software that allows storing, organizing, and annotating bioimaging/microscopy data. OMERO has become one of the best-known systems for bioimage data management in the bioimaging community. The Information Infrastructure for BioImage Data (I3D:bio) project facilitates the uptake of OMERO into research data management (RDM) practices at universities and research institutions in Germany. Since the adoption of OMERO into researchers’ daily routines requires intensive training, a broad portfolio of training resources for OMERO is an asset. On top of using the OMERO guides curated by the Open Microscopy Environment Consortium (OME) team, imaging core facility staff at institutions where OMERO is used often prepare additional material tailored to be applicable for their own OMERO instances. Based on experience gathered in the Research Data Management for Microscopy group (RDM4mic) in Germany, and in the use cases in the I3D:bio project, we created a set of reusable, adjustable, openly available slide decks to serve as the basis for tailored training lectures, video tutorials, and self-guided instruction manuals directed at beginners in using OMERO. The material is published as an open educational resource complementing the existing resources for OMERO contributed by the community.

    Tags: OMERO, Research Data Management, Nfdi4Bioimage, I3Dbio

    -

    Content type: Slide, Video

    +

    Content type: Slides, Video

    https://zenodo.org/records/8323588

    https://www.youtube.com/playlist?list=PL2k-L-zWPoR7SHjG1HhDIwLZj0MB_stlU

    https://doi.org/10.5281/zenodo.8323588

    @@ -2253,7 +2248,7 @@

    If you license it, it’ll be harder to steal it. Why we should license our

    Licensed CC-BY-4.0

    Blog post about why we should license our work and what is important when choosing a license.

    Tags: Licensing, Research Data Management

    -

    Content type: Blog

    +

    Content type: Blog Post

    https://focalplane.biologists.com/2023/05/06/if-you-license-it-itll-be-harder-to-steal-it-why-we-should-license-our-work/

    @@ -2270,7 +2265,7 @@

    Image Processing with Pythonhttps://datacarpentry.org/image-processing/key-points.html

    @@ -2350,7 +2345,7 @@

    Institutionalization and Collaboration as a Way of Addressing the Challenges

    Interactive Image Data Flow Graphs#

    -

    Martin Schätz, Martin Schätz

    +

    Martin Schätz

    Published 2022-10-17

    Licensed CC-BY-4.0

    The slides were presented during the Macro programming with ImageJ workshop (https://www.16mcm.cz/programme/#workshops) which was part of the 16th Multinational Congress on Microscopy. It is a collection and “reshuffle” of slides originally made by Robert Haase on topics from Image Analysis in general up to User-friendly GPU-accelerated bio-image analysis and CLIJ2.

    @@ -2414,6 +2409,7 @@

    Key-Value pair template for annotation in OMERO for light microscopy data ac See cross reference: 10.5281/zenodo.12547566 Key-Value pair template for annotation of datasets in OMERO for light- and electron microscopy data within the research group of Prof. Mueller-Reichert 10.5281/zenodo.12546808 Key-Value pair template for annotation of datasets in OMERO (PERIKLES study)

    +

    Tags: Nfdi4Bioimage, Research Data Management

    https://zenodo.org/records/12578084

    https://doi.org/10.5281/zenodo.12578084

    @@ -2439,6 +2435,7 @@

    Key-Value pair template for annotation of datasets in OMERO (PERIKLES study) See cross reference: 10.5281/zenodo.12547566 Key-Value pair template for annotation of datasets in OMERO (light- and electron microscopy data within the research group of Prof. Mueller-Reichert) 10.5281/zenodo.12578084 Key-Value pair template for annotation in OMERO for light microscopy data acquired with AxioScan7 - Core Facility Cellular Imaging (CFCI)

    +

    Tags: Nfdi4Bioimage, Research Data Management

    https://zenodo.org/records/12546808

    https://doi.org/10.5281/zenodo.12546808

    @@ -2522,6 +2519,7 @@

    Large Language Models: An Introduction for Life Scientistshttps://zenodo.org/records/14418209

    https://doi.org/10.5281/zenodo.14418209

    @@ -2619,7 +2617,7 @@

    Making the most of bioimaging data through interdisciplinary interactionsVirginie Uhlmann, Matthew Hartley, Josh Moore, Erin Weisbart, Assaf Zaritsky

    Published 2024-10-23

    Licensed CC-BY-4.0

    -

    Tags: Bioimage Analysis, Open Science, Microscopy Image Analysis, Microscopy

    +

    Tags: Bioimage Analysis, Open Science, Microscopy

    Content type: Publication

    https://journals.biologists.com/jcs/article/137/20/jcs262139/362478/Making-the-most-of-bioimaging-data-through

    @@ -2638,7 +2636,7 @@

    Managing Scientific Python environments using Conda, Mamba and friendsRobert Haase

    Licensed CC-BY-4.0

    Tags: Python, Conda, Mamba

    -

    Content type: Blog

    +

    Content type: Blog Post

    https://focalplane.biologists.com/2022/12/08/managing-scientific-python-environments-using-conda-mamba-and-friends/

    @@ -2676,7 +2674,7 @@

    Methods in bioimage analysishttps://www.ebi.ac.uk/training/events/methods-bioimage-analysis/

    https://doi.org/10.6019/TOL.BioImageAnalysis22-w.2022.00001.1

    https://drive.google.com/file/d/1MhuqfKhZcYu3bchWMqogIybKjamU5Msg/view

    @@ -2691,7 +2689,7 @@

    MicroSam-Talkshttps://zenodo.org/records/11265038

    https://doi.org/10.5281/zenodo.11265038

    @@ -2702,7 +2700,7 @@

    Microscopy data analysis: machine learning and the BioImage ArchiveAndrii Iudin, Anna Foix-Romero, Anna Kreshuk, Awais Athar, Beth Cimini, Dominik Kutra, Estibalis Gomez de Mariscal, Frances Wong, Guillaume Jacquemet, Kedar Narayan, Martin Weigert, Nodar Gogoberidze, Osman Salih, Petr Walczysko, Ryan Conrad, Simone Weyend, Sriram Sundar Somasundharam, Suganya Sivagurunathan, Ugis Sarkans

    Licensed CC-BY-4.0

    The Microscopy data analysis: machine learning and the BioImage Archive course, which focused on introducing programmatic approaches used in the analysis of bioimage data via the BioImage Archive, ran in May 2023.

    -

    Tags: Microscopy Image Analysis, Python, Deep Learning

    +

    Tags: Bioimage Analysis, Python, Artificial Intelligence

    Content type: Video, Slides

    https://www.ebi.ac.uk/training/materials/microscopy-data-analysis-machine-learning-and-the-bioimage-archive-materials/

    @@ -2713,7 +2711,7 @@

    Microscopy-BIDS - An Extension to the Brain Imaging Data Structure for Micro

    Published 2022-04-19

    Licensed CC-BY-4.0

    The Brain Imaging Data Structure (BIDS) is a specification for organizing, sharing, and archiving neuroimaging data and metadata in a reusable way.

    -

    Tags: Research Data Management, Image Data Management, Bioimage Data

    +

    Tags: Research Data Management

    Content type: Publication

    https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2022.871228/full

    @@ -2732,10 +2730,11 @@

    Modeling community standards for metadata as templates makes data FAIR

    Modular training resources for bioimage analysis#

    -

    Christian Tischer, Antonio Politi, Tim-Oliver Buchholz, Elnaz Fazeli, Nicola Gritti, Aliaksandr Halavatyi, Gonzalez Tirado, Sebastian, Julian Hennies, Toby Hodges, Arif Khan, Dominik Kutra, Stefania Marcotti, Bugra Oezdemir, Felix Schneider, Martin Schorb, Anniek Stokkermans, Yi Sun, Nima Vakili

    +

    Christian Tischer, Antonio Politi, Tim-Oliver Buchholz, Elnaz Fazeli, Nicola Gritti, Aliaksandr Halavatyi, Sebastian Gonzalez Tirado, Julian Hennies, Toby Hodges, Arif Khan, Dominik Kutra, Stefania Marcotti, Bugra Oezdemir, Felix Schneider, Martin Schorb, Anniek Stokkermans, Yi Sun, Nima Vakili

    Published 2024-12-03

    Licensed CC-BY-4.0

    Resources for teaching/preparing to teach bioimage analysis

    +

    Tags: Neubias, Bioimage Analysis

    https://zenodo.org/records/14264885

    https://doi.org/10.5281/zenodo.14264885

    @@ -2778,7 +2777,7 @@

    Multiplexed tissue imaging - tools and approachesAgustín Andrés Corbat, OmFrederic, Jonas Windhager, Kristína Lidayová

    Licensed CC-BY-4.0

    Material for the I2K 2024 “Multiplexed tissue imaging - tools and approaches” workshop

    -

    Tags: Bioimage Analysis, Microscopy Image Analysis

    +

    Tags: Bioimage Analysis

    Content type: Github Repository, Slides, Workshop

    BIIFSweden/I2K2024-MTIWorkshop

    https://docs.google.com/presentation/d/1R9-4lXAmTYuyFZpTMDR85SjnLsPZhVZ8/edit#slide=id.p1

    @@ -2844,6 +2843,7 @@

    NFDI4BIOIMAGE data management illustrations by Henning Falkhttps://doi.org/10.5281/zenodo.14186100, is used under a CC-BY 4.0 license. Modifications to this illustration include cropping.  

    +

    Tags: Nfdi4Bioimage, Research Data Management

    https://zenodo.org/records/14186101

    https://doi.org/10.5281/zenodo.14186101

    @@ -2883,6 +2883,7 @@

    NFDI4Bioimage Calendar 2024 October; original imagePublished 2024-09-25

    Licensed CC-BY-4.0

    Raw microscopy image from the NFDI4Bioimage calendar October 2024

    +

    Tags: Nfdi4Bioimage, Research Data Management

    https://zenodo.org/records/13837146

    https://doi.org/10.5281/zenodo.13837146

    @@ -2905,7 +2906,7 @@

    Nextflow: Scalable and reproducible scientific workflowshttps://zenodo.org/records/4334697

    https://doi.org/10.5281/zenodo.4334697

    @@ -2935,6 +2936,7 @@

    OME2024 NGFF Challenge Resultshttps://founding-gide.eurobioimaging.eu/event/foundinggide-community-event-2024/ Note: much of the presentation was a demonstration of the OME2024-NGFF-Challenge – https://ome.github.io/ome2024-ngff-challenge/ especially of querying an extraction of the metadata (ome/ome2024-ngff-challenge-metadata)  

    +

    Tags: Nfdi4Bioimage, Research Data Management

    https://zenodo.org/records/14234608

    https://doi.org/10.5281/zenodo.14234608

    @@ -2963,7 +2965,7 @@

    Open Micoscropy Environment (OME) Youtube ChannelPublished None

    Licensed CC-BY-4.0

    OME develops open-source software and data format standards for the storage and manipulation of biological microscopy data

    -

    Tags: Open Source Software, Microscopy Image Analysis, Bioimage Data

    +

    Tags: Open Source Software

    Content type: Video, Collection

    https://www.youtube.com/@OpenMicroscopyEnvironment

    @@ -3005,7 +3007,7 @@

    Overview of the Galaxy OMERO-suite - Upload images and metadata in OMERO usi

    Riccardo Massei, Björn Grüning

    Published 2024-12-02

    Licensed CC-BY-4.0

    -

    Tags: OMERO, Galaxy, Metadata

    +

    Tags: OMERO, Galaxy, Metadata, Nfdi4Bioimage

    Content type: Tutorial, Framework, Workflow

    https://training.galaxyproject.org/training-material/topics/imaging/tutorials/omero-suite/tutorial.html

    @@ -3014,7 +3016,7 @@

    Overview of the Galaxy OMERO-suite - Upload images and metadata in OMERO usi

    Parallelization and heterogeneous computing: from pure CPU to GPU-accelerated image processing#

    Robert Haase

    Licensed CC-BY-4.0

    -

    Content type: Slide

    +

    Content type: Slides

    https://f1000research.com/slides/11-1171

    https://doi.org/10.7490/f1000research.1119154.1

    @@ -3062,7 +3064,7 @@

    QI 2024 Analysis Lab Manualhttps://bethac07.github.io/qi_2024_analysis_lab_manual/intro.html

    @@ -3093,7 +3095,7 @@

    QuPath: Open source software for analysing (awkward) imageshttps://zenodo.org/records/4328911

    https://doi.org/10.5281/zenodo.4328911

    @@ -3179,7 +3181,7 @@

    Research Data Management Seminar - Slideshttps://zenodo.org/record/6602101

    https://doi.org/10.5281/zenodo.6602101

    @@ -3211,7 +3213,7 @@

    Research data management for bioimaging - the 2021 NFDI4BIOIMAGE community s

    Published 2022-09-20

    Licensed CC-BY-4.0

    As an initiative within Germany’s National Research Data Infrastructure, the authors conducted this community survey in summer 2021 to assess the state of the art of bioimaging RDM and the community needs.

    -

    Tags: Research Data Management, Image Data Management, Bioimage Data

    +

    Tags: Research Data Management

    Content type: Publication

    https://f1000research.com/articles/11-638/v2

    @@ -3251,7 +3253,7 @@

    Running Deep-Learning Scripts in the BiA-PoL Omero Serverhttps://biapol.github.io/blog/marcelo_zoccoler/omero_scripts/readme.html

    @@ -3259,8 +3261,7 @@

    Running Deep-Learning Scripts in the BiA-PoL Omero Server#

    Licensed CC-BY-4.0

    Computational skills training at the UCL Sainsbury Wellcome Centre and Gatsby Computational Neuroscience Unit, delivered by members of the Neuroinformatics Unit.

    -

    Tags: Training

    -

    Content type: Collection, Online Course, Videos, Tutorial

    +

    Content type: Collection, Online Course, Video, Tutorial

    https://software-skills.neuroinformatics.dev/index.html

    @@ -3270,7 +3271,7 @@

    Sharing and licensing materialhttps://f1000research.com/slides/10-519

    @@ -3280,7 +3281,7 @@

    Sharing research data with Zenodozenodo.org

    Tags: Sharing, Research Data Management

    -

    Content type: Blog

    +

    Content type: Blog Post

    https://focalplane.biologists.com/2023/02/15/sharing-research-data-with-zenodo/

    @@ -3355,6 +3356,7 @@

    Structuring of Data and Metadata in Bioimaging: Concepts and technical Solut ...

    +

    Tags: Nfdi4Bioimage, Research Data Management

    https://zenodo.org/records/7018750

    https://doi.org/10.5281/zenodo.7018750

    @@ -3377,14 +3379,14 @@

    Ten simple rules for making training materials FAIRPublished 2020-05-21

    Licensed CC-BY-4.0

    The authors offer trainers some simple rules, to help make their training materials FAIR, enabling others to find, (re)use, and adapt them.

    -

    Tags: Metadata, Bioinformatics, FAIR-Principles, Training

    +

    Tags: Metadata, Bioinformatics, FAIR-Principles

    Content type: Publication

    https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1007854

    Terminology service for research data management and knowledge discovery in low-temperature plasma physics#

    -

    Becker, Markus M., Chaerony Siffa, Ihda, Roman Baum

    +

    Markus M. Becker, Ihda Chaerony Siffa, Roman Baum

    Published 2024-12-11

    Licensed CC-BY-4.0

    Abstract: @@ -3460,6 +3462,7 @@

    The Information Infrastructure for BioImage Data (I3D:bio) project to advanc

    Published 2024-03-04

    Licensed CC-BY-4.0

    Research data management (RDM) in microscopy and image analysis is a challenging task. Large files in proprietary formats, complex N-dimensional array structures, and various metadata models and formats can make image data handling inconvenient and difficult. For data organization, annotation, and sharing, researchers need solutions that fit everyday practice and comply with the FAIR (Findable, Accessible, Interoperable, Reusable) principles. International community-based efforts have begun creating open data models (OME), an open file format and translation library (OME-TIFF, Bio-Formats), data management software platforms, and microscopy metadata recommendations and annotation tools. Bringing these developments into practice requires support and training. Iterative feedback and tool improvement is needed to foster practical adoption by the scientific community. The Information Infrastructure for BioImage Data (I3D:bio) project works on guidelines, training resources, and practical assistance for FAIR microscopy RDM adoption with a focus on the management platform OMERO and metadata annotations.

    +

    Tags: Nfdi4Bioimage, Research Data Management

    https://zenodo.org/records/10805204

    https://doi.org/10.5281/zenodo.10805204

    @@ -3470,7 +3473,7 @@

    The Open Microscopy Environment (OME) Data Model and XML file - open tools f

    Published 2005-05-03

    Licensed CC-BY-4.0

    The Open Microscopy Environment (OME) defines a data model and a software implementation to serve as an informatics framework for imaging in biological microscopy experiments, including representation of acquisition parameters, annotations and image analysis results.

    -

    Tags: Microscopy Image Analysis, Bioimage Analysis

    +

    Tags: Bioimage Analysis

    Content type: Publication

    https://genomebiology.biomedcentral.com/articles/10.1186/gb-2005-6-5-r47

    https://doi.org/10.1186/gb-2005-6-5-r47

    @@ -3490,6 +3493,7 @@

    The role of Helmholtz Centers in NFDI4BIOIMAGE - A national consortium enhan

    Published 2024-06-06

    Licensed CC-BY-4.0

    Germany’s National Research Data Infrastructure (NFDI) aims to establish a sustained, cross-disciplinary research data management (RDM) infrastructure that enables researchers to handle FAIR (findable, accessible, interoperable, reusable) data. While FAIR principles have been adopted by funders, policymakers, and publishers, their practical implementation remains an ongoing effort. In the field of bio-imaging, harmonization of data formats, metadata ontologies, and open data repositories is necessary to achieve FAIR data. The NFDI4BIOIMAGE was established to address these issues and develop tools and best practices to facilitate FAIR microscopy and image analysis data in alignment with international community activities. The consortium operates through its Data Stewards team to provide expertise and direct support to help overcome RDM challenges. The three Helmholtz Centers in NFDI4BIOIMAGE aim to collaborate closely with other centers and initiatives, such as HMC, Helmholtz AI, and HIP. Here we present NFDI4BIOIMAGE’s work and its significance for research in Helmholtz and beyond

    +

    Tags: Nfdi4Bioimage, Research Data Management

    https://zenodo.org/records/11501662

    https://doi.org/10.5281/zenodo.11501662

    @@ -3510,6 +3514,7 @@

    Towards Preservation of Life Science Data with NFDI4BIOIMAGEhttps://zenodo.org/records/13640979

    https://doi.org/10.5281/zenodo.13640979

    @@ -3552,7 +3557,7 @@

    Using Glittr.org

    Geert van Geest, Yann Haefliger, Monique Zahn-Zabal, Patricia M. Palagi

    Licensed CC-BY-4.0

    Glittr.org is a platform that aggregates and indexes training materials on computational life sciences from public git repositories, making it easier for users to find, compare, and analyze these resources based on various metrics. By providing insights into the availability of materials, collaboration patterns, and licensing practices, Glittr.org supports adherence to the FAIR principles, benefiting the broader life sciences community.

    -

    Tags: Training, Bioimage Analysis, Research Data Management

    +

    Tags: Bioimage Analysis, Research Data Management

    Content type: Publication, Preprint

    https://www.biorxiv.org/content/10.1101/2024.08.20.608021v1

    @@ -3589,7 +3594,7 @@

    Who you gonna call? - Data Stewards to the rescue

    Working Group Charter. RDM Helpdesk Network#

    -

    Judith Engel, Patrick Helling, Robert Herrenbrück, Marina Lemaire, Hela Mehrtens, Marcus Schmidt, Martha Stellmacher, Lukas Weimer, Cord Wiljes, Wolf Zinke

    +

    Judith Engel, Patrick Helling, Robert Herrenbrück, MarinaLemaire, Hela Mehrtens, Marcus Schmidt, Martha Stellmacher, Lukas Weimer, Cord Wiljes, Wolf Zinke

    Published 2024-11-04

    Licensed CC-BY-4.0

    Support is an essential component of an efficient infrastructure for research data management (RDM). Helpdesks guide researchers through this complex landscape and provide reliable support about all questions regarding research data management, including support for technical services, best practices, requirements of funding organizations and legal topics. In NFDI, most consortia have already established or are planning to establish helpdesks to support their specific communities. On a local level, many institutions have set up RDM helpdesks that provide support for the researchers of their own institution. Additional RDM support services are offered by RDM federal state initiatives, by research data centers, by specialist libraries, by the EOSC, and by providers of RDM-relevant tools. Helpdesks cover a wide range of institutions, disciplines, topics, methodologies and target audiences. However, the individual helpdesks are not yet interconnected and therefore cannot complement one another in an efficient way: Given the wide and constantly increasing complexity of RDM, no single helpdesk can provide the expertise for all potential support requests. Therefore, we see great potential in combining the efforts and resources of the existing RDM helpdesks into an efficient and comprehensive national RDM support network in order to provide optimal and tailored RDM support to all researchers and research-related institutions in Germany and in an international context.

    @@ -3659,6 +3664,7 @@

    [CIDAS] Scalable strategies for a next-generation of FAIR bioimagingLicensed CC-BY-4.0

    Talk given at Georg-August-Universität Göttingen Campus Institute Data Science23rd January 2025 https://www.uni-goettingen.de/en/653203.html

    +

    Tags: Nfdi4Bioimage

    https://zenodo.org/records/14716546

    https://doi.org/10.5281/zenodo.14716546

    @@ -3671,6 +3677,7 @@

    [CMCB] Scalable strategies for a next-generation of FAIR bioimagingCMCB LIFE SCIENCES SEMINARSTechnische Universität Dresden16th January 2025 https://tu-dresden.de/cmcb/crtd/news-termine/termine/cmcb-life-sciences-seminar-josh-moore-german-bioimaging-e-v-society-for-microscopy-and-image-analysis-constance  

    +

    Tags: Nfdi4Bioimage

    https://zenodo.org/records/14650434

    https://doi.org/10.5281/zenodo.14650434

    @@ -3692,6 +3699,7 @@

    [Community Meeting 2024] Overview Team Image Data Analysis and ManagementLicensed CC-BY-4.0

    Overview of Activities of the Team Image Data Analysis and Management of German BioImaging e.V.  

    +

    Tags: Nfdi4Bioimage, Research Data Management

    https://zenodo.org/records/10796364

    https://doi.org/10.5281/zenodo.10796364

    @@ -3713,6 +3721,7 @@

    [ELMI 2024] AI’s Dirty Little Secret: Withouthttps://www.elmi2024.org/)

    +

    Tags: Nfdi4Bioimage, Research Data Management

    https://zenodo.org/records/11235513

    https://doi.org/10.5281/zenodo.11235513

    @@ -3821,6 +3830,7 @@

    [Workshop Material] Fit for OMERO - How imaging facilities and IT department Establish a stakeholder process management for installing OMERO-based RDM Learn from each other, leverage different expertise Learn how to train users, establish sustainability strategies, and foster FAIR RDM for bioimaging at your institution

    +

    Tags: Nfdi4Bioimage, Research Data Management

    https://zenodo.org/records/14178789

    https://doi.org/10.5281/zenodo.14178789

    @@ -3843,6 +3853,7 @@

    [Workshop] Bioimage data management and analysis with OMEROhttps://zenodo.org/records/11350689

    https://doi.org/10.5281/zenodo.11350689

    @@ -3902,6 +3913,7 @@

    [Workshop] Research Data Management for Microscopy and BioImage Analysis

    Date & Venue:Thursday, Sept. 26, 5.30 p.m.Haus 22 / Paul Ehrlich Lecture Hall (H22-1)University Hospital Frankfurt

    +

    Tags: Nfdi4Bioimage, Research Data Management

    https://zenodo.org/records/13861026

    https://doi.org/10.5281/zenodo.13861026

    @@ -3911,7 +3923,7 @@

    ilastik: interactive machine learning for (bio)image analysishttps://zenodo.org/doi/10.5281/zenodo.4330625

    @@ -4007,7 +4019,7 @@

    training-resources

    previous

    -

    Bsd-3-clause (23)

    +

    Bsd-3-clause (26)

     DOCUMENTATION_OPTIONS.pagename = 'licenses/cc-by-sa-4.0'; - + @@ -182,36 +182,29 @@

    By tag

    By content type

    @@ -236,11 +228,12 @@ @@ -493,7 +486,7 @@

    DALIA Interchange Format#

    Published 2023-07-05

    Licensed CC-BY-SA-4.0

    -

    Tags: Research Data Management, Bioimage Analysis, Image Data Management, Open Science

    +

    Tags: Research Data Management, Bioimage Analysis, Open Science

    Content type: Slides, Presentation

    https://omero-fbi.fr/slides/elmi23_cfd/main.html#/title-slide

    @@ -573,7 +566,7 @@

    introduction-to-image-analysis

    next

    -

    Cc0-1.0 (10)

    +

    Cc0-1.0 (13)

     diff --git a/licenses/cc0-1.0.html b/licenses/cc0-1.0.html index cafe7a34..780d9d15 100644 --- a/licenses/cc0-1.0.html +++ b/licenses/cc0-1.0.html @@ -8,7 +8,7 @@ - Cc0-1.0 (10) — NFDI4BioImage Training Materials + Cc0-1.0 (13) — NFDI4BioImage Training Materials @@ -182,36 +182,29 @@

    By tag

    By content type

    @@ -236,11 +228,12 @@ @@ -447,7 +440,7 @@
    -

    Cc0-1.0 (10)

    +

    Cc0-1.0 (13)

    @@ -458,14 +451,17 @@

    Contents

    @@ -478,8 +474,8 @@

    Contents

    -
    -

    Cc0-1.0 (10)#

    +
    +

    Cc0-1.0 (13)#

    A Fiji Scripting Tutorial#

    Albert Cardona

    @@ -489,10 +485,19 @@

    A Fiji Scripting Tutorialhttps://syn.mrc-lmb.cam.ac.uk/acardona/fiji-tutorial/

    +
    +

    Andor Dragonfly confocal image of BPAE cells stained for actin, IMS file format#

    +

    Hoku West-Foyle

    +

    Published 2025-01-16

    +

    Licensed CC0-1.0

    +

    https://zenodo.org/records/14675120

    +

    https://doi.org/10.5281/zenodo.14675120

    +
    +
    @@ -519,7 +524,7 @@

    Checklists for publishing images and image analysisPublished 2023-09-14

    Licensed CC0-1.0

    In this paper we introduce two sets of checklists. One is concerned with the publication of images. The other one gives instructions for the publication of image analysis.

    -

    Tags: Bioimage Data, Microscopy Image Analysis

    +

    Tags: Bioimage Analysis

    Content type: Forum Post

    https://forum.image.sc/t/checklists-for-publishing-images-and-image-analysis/86304

    @@ -543,6 +548,16 @@

    Image-based Profiling Handbookhttps://cytomining.github.io/profiling-handbook/

    +
    +

    Ink in a dish#

    +

    Cavanagh

    +

    Published 2024-09-03

    +

    Licensed CC0-1.0

    +

    A test data set for troublshooting. no scientific meaning.

    +

    https://zenodo.org/records/13642395

    +

    https://doi.org/10.5281/zenodo.13642395

    +
    +

    Online_R_learning#

    C. Li

    @@ -558,16 +573,27 @@

    Online_R_learning#

    Licensed CC0-1.0

    Recommended Metadata for Biological Images (REMBI) provides guidelines for metadata for biological images to enable the FAIR sharing of scientific data.

    -

    Tags: FAIR-Principles, Metadata, Image Data Management, Research Data Management, Bioimage Data

    +

    Tags: FAIR-Principles, Metadata, Research Data Management

    Content type: Collection

    https://www.ebi.ac.uk/bioimage-archive/rembi-help-overview/

    +
    +

    Statistical Rethinking#

    +

    Richard McElreath

    +

    Published 2024-03-01

    +

    Licensed CC0-1.0

    +

    This course teaches data analysis, but it focuses on scientific models. The unfortunate truth about data is that nothing much can be done with it, until we say what caused it. We will prioritize conceptual, causal models and precise questions about those models. We will use Bayesian data analysis to connect scientific models to evidence. And we will learn powerful computational tools for coping with high-dimension, imperfect data of the kind that biologists and social scientists face.

    +

    Tags: Statistics

    +

    Content type: Github Repository

    +

    rmcelreath/stat_rethinking_2024

    +
    +

    Submitting data to the BioImage Archive#

    Licensed CC0-1.0

    To submit, you’ll need to register an account, organise and upload your data, prepare a file list, and then submit using our web submission form. These steps are explained here.

    -

    Tags: Research Data Management, Image Data Management, Bioimage Data

    +

    Tags: Research Data Management

    Content type: Tutorial, Video

    https://www.ebi.ac.uk/bioimage-archive/submit/

    @@ -640,14 +666,17 @@

    Submitting data to the BioImage Archive

    diff --git a/licenses/gpl-2.0.html b/licenses/gpl-2.0.html index 03aa5221..eb0bd838 100644 --- a/licenses/gpl-2.0.html +++ b/licenses/gpl-2.0.html @@ -63,8 +63,8 @@ - - + + @@ -182,36 +182,29 @@

    By tag

    By content type

    @@ -236,11 +228,12 @@ @@ -481,7 +474,7 @@

    Gpl-2.0 (7)#

    Licensed GPL-2.0

    An easy to use and open source converter for bioimaging data. NGFF-Converter is a GUI application for conversion of bioimage formats into OME-NGFF (Next-Generation File Format) or OME-TIFF.

    -

    Tags: Bioimage Data, Open Source Software

    +

    Tags: Open Source Software

    Content type: Application

    https://www.glencoesoftware.com/products/ngff-converter/

    @@ -531,7 +524,7 @@

    bioformats2raw Converterglencoesoftware/bioformats2raw

    @@ -541,7 +534,7 @@

    raw2ometiff ConverterMelissa Linkert, Chris Allan, Sébastien Besson, Josh Moore

    Licensed GPL-2.0

    Java application to convert a directory of tiles to an OME-TIFF pyramid. This is the second half of iSyntax/.mrxs => OME-TIFF conversion.

    -

    Tags: Open Source Software, Bioimage Data

    +

    Tags: Open Source Software

    Content type: Application, Github Repository

    glencoesoftware/raw2ometiff

    @@ -584,15 +577,15 @@

    raw2ometiff Converter

    previous

    -

    Cc0-1.0 (10)

    +

    Cc0-1.0 (13)

    next

    -

    Mit (24)

    +

    Gpl-3.0 (7)

     diff --git a/tags/segmentation.html b/licenses/gpl-3.0.html similarity index 67% rename from tags/segmentation.html rename to licenses/gpl-3.0.html index c3910e1b..8746ad41 100644 --- a/tags/segmentation.html +++ b/licenses/gpl-3.0.html @@ -8,7 +8,7 @@ - Segmentation (5) — NFDI4BioImage Training Materials + Gpl-3.0 (7) — NFDI4BioImage Training Materials @@ -60,11 +60,11 @@ - + - - + + @@ -181,37 +181,30 @@
  • YML format

    • By tag

    -

    diff --git a/licenses/mit.html b/licenses/mit.html index 4973d138..2fa3e31e 100644 --- a/licenses/mit.html +++ b/licenses/mit.html @@ -64,7 +64,7 @@ - + @@ -182,36 +182,29 @@

    By tag

    By content type

    @@ -236,11 +228,12 @@ @@ -499,7 +492,7 @@

    BioEngine DocumentationWei Ouyang, Nanguage, Jeremy Metz, Craig Russell

    Licensed MIT

    BioEngine, a Python package designed for flexible deployment and execution of bioimage analysis models and workflows using AI, accessible via HTTP API and RPC.

    -

    Tags: Workflow Engine, Deep Learning, Python

    +

    Tags: Workflow Engine, Artificial Intelligence, Python

    Content type: Documentation

    https://bioimage-io.github.io/bioengine/#/

    @@ -588,7 +581,7 @@

    Introduction to Deep Learning for Microscopycomputational-cell-analytics/dl-for-micro

    @@ -692,7 +685,7 @@

    YMIA - Python-Based Event Series Training MaterialPublished None

    Licensed MIT

    This repository offer access to teaching material and useful resources for the YMIA - Python-Based Event Series.

    -

    Tags: Python, Large Language Models, Prompt Engineering, Biabob, Bioimage Analysis, Microscopy Image Analysis

    +

    Tags: Python, Artifical Intelligence, Bioimage Analysis

    Content type: Github Repository, Slides

    rmassei/ymia_python_event_series_material

    @@ -751,12 +744,12 @@

    cba-support-template

    previous

    -

    Gpl-2.0 (7)

    +

    Gpl-3.0 (7)

    By tag

    By content type

    @@ -236,11 +228,12 @@ diff --git a/licenses/unknown.html b/licenses/unknown.html index 8ad7b37d..661f51bf 100644 --- a/licenses/unknown.html +++ b/licenses/unknown.html @@ -182,36 +182,29 @@

    By tag

    By content type

    @@ -236,11 +228,12 @@ @@ -574,8 +567,8 @@

    AI ML DL in Bioimage Analysis - Webinarhttps://www.youtube.com/watch?v=TJXNMIWtdac

    @@ -604,7 +597,7 @@

    Artificial Intelligence for Digital Pathologyhttps://www.youtube.com/watch?v=Om9tl4Dh2yw

    @@ -623,7 +616,7 @@

    Best practice data life cycle approaches for the life sciences#

    Christian Tischer

    Licensed UNKNOWN

    -

    Content type: Slide

    +

    Content type: Slides

    tischi/presentation-image-analysis

    @@ -661,7 +654,7 @@

    Building a Bioimage Analysis Workflow using Deep Learningesgomezm/NEUBIAS_chapter_DL_2020

    @@ -704,7 +697,7 @@

    CellProfiler Introductionahklemm/CellProfiler_Introduction

    @@ -749,7 +742,7 @@

    DL4MicEverywhere – Overcoming reproducibility challenges in deep learning

    Iván Hidalgo-Cenalmor

    Published 2024-07-29

    Licensed UNKNOWN

    -

    Tags: Deep Learning, Microscopy, Microsycopy Image Analysis, Bio Image Analysis, Artifical Intelligence

    +

    Tags: Bio Image Analysis, Artifical Intelligence

    Content type: Blog Post

    https://focalplane.biologists.com/2024/07/29/dl4miceverywhere-overcoming-reproducibility-challenges-in-deep-learning-microscopy-imaging/

    @@ -759,7 +752,7 @@

    DL@MBL 2021 ExercisesJan Funke, Constantin Pape, Morgan Schwartz, Xiaoyan

    Licensed UNKNOWN

    Tags: Artificial Intelligence, Bioimage Analysis

    -

    Content type: Slide, Notebook

    +

    Content type: Slides, Notebook

    JLrumberger/DL-MBL-2021

    @@ -817,8 +810,8 @@

    Docker Mastery - with Kubernetes + Swarm from a Docker Captainhttps://www.udemy.com/course/docker-mastery/?srsltid=AfmBOornR5gRqOg-4v8Nsap1z24CaPPUPxg8JzyqEGZ6MvW_dh-sf4Af&couponCode=ST2MT110724BNEW

    @@ -828,8 +821,8 @@

    Dr Guillaume Jacquemet on studying cancer cell metastasis in the era of deep

    Published 2024-10-24

    Licensed UNKNOWN

    Leukocyte extravasation is a critical component of the innate immune response, while circulating tumour cell extravasation is a crucial step in metastasis formation. Despite their importance, these extravasation mechanisms remain incompletely understood. In this talk, Guillaume Jacquemet presents a novel imaging framework that integrates microfluidics with high-speed, label-free imaging to study the arrest of pancreatic cancer cells (PDAC) on human endothelial layers under physiological flow conditions.

    -

    Tags: Deep Learning, Microscopy Image Analysis

    -

    Content type: Youtube Video, Slides

    +

    Tags: Artificial Intelligence, Bioimage Analysis

    +

    Content type: Video, Slides

    https://www.youtube.com/watch?v=KTdZBgSCYJQ

    @@ -887,7 +880,7 @@

    Five great reasons to share your research datahttps://web.library.uq.edu.au/blog/2022/03/five-great-reasons-share-your-research-data

    @@ -923,8 +916,8 @@

    Glencoe Software Webinarshttps://www.glencoesoftware.com/media/webinars/

    @@ -933,7 +926,7 @@

    I3D bio – Information Infrastructure for BioImage Data - Bioimage Metadata

    Christian Schmidt

    Licensed UNKNOWN

    A Microscopy Research Data Management Resource.

    -

    Tags: Metadata, I3Dbio, Research Data Management, Bioimage Data

    +

    Tags: Metadata, I3Dbio, Research Data Management

    Content type: Collection

    https://gerbi-gmb.de/i3dbio/i3dbio-rdm/i3dbio-bioimage-metadata/

    @@ -951,7 +944,7 @@

    I3D:bio list of online training material#

    Aastha Mathur

    Licensed UNKNOWN

    -

    Content type: Slide

    +

    Content type: Slides

    https://docs.google.com/presentation/d/1henPIDTpHT3bc1Y26AltItAHJ2C5xCOl/edit#slide=id.p1

    @@ -960,7 +953,7 @@

    Image analysis in Galaxyhttps://docs.google.com/presentation/d/1WG_4307XmKsGfWT3taxMvX2rZiG1k0SM1E7SAENJQkI/edit#slide=id.p

    @@ -969,7 +962,7 @@

    ImageJ Macro Introductionahklemm/ImageJMacro_Introduction

    @@ -978,7 +971,7 @@

    Introduction to ImageJ macro programming, Scientific Computing Facility, MPI

    Robert Haase, Benoit Lombardot

    Licensed UNKNOWN

    Tags: Imagej, Bioimage Analysis

    -

    Content type: Slide

    +

    Content type: Slides

    https://git.mpi-cbg.de/scicomp/bioimage_team/coursematerialimageanalysis/tree/master/ImageJMacro_24h_2017-01

    @@ -987,7 +980,7 @@

    Jupyter for interactive cloud computinghttps://docs.google.com/presentation/d/1q8q1xE-c35tvCsRXZay98s2UYWwXpp0cfCljBmMFpco/edit#slide=id.ga456d5535c_2_53

    @@ -1024,7 +1017,7 @@

    Machine Learning - Deep Learning. Applications to Bioimage AnalysisEstibaliz Gómez-de-Mariscal

    Licensed UNKNOWN

    Tags: Artificial Intelligence, Bioimage Analysis

    -

    Content type: Slide

    +

    Content type: Slides

    https://raw.githubusercontent.com/esgomezm/esgomezm.github.io/master/assets/pdf/SPAOM2018/MachineLearning_SPAOMworkshop_public.pdf

    @@ -1041,7 +1034,7 @@

    Machine and Deep Learning on the cloud: SegmentationIgnacio Arganda-Carreras

    Licensed UNKNOWN

    Tags: Neubias, Artificial Intelligence, Bioimage Analysis

    -

    Content type: Slide

    +

    Content type: Slides

    https://docs.google.com/presentation/d/1oJoy9gHmUuSmUwCkPs_InJf_WZAzmLlUNvK1FUEB4PA/edit#slide=id.ge3a24e733b_0_54

    @@ -1068,7 +1061,7 @@

    Metabolic networks modelling with COBRApy#

    Licensed UNKNOWN

    The mission of Metrics Reloaded is to guide researchers in the selection of appropriate performance metrics for biomedical image analysis problems, as well as provide a comprehensive online resource for metric-related information and pitfalls

    -

    Tags: Bioimage Analysis, Image Segmentation, Machine Learning

    +

    Tags: Bioimage Analysis, Quality Control

    Content type: Website, Collection

    https://metrics-reloaded.dkfz.de/

    @@ -1085,7 +1078,7 @@

    NEUBIAS Analyst School 2018miura/NEUBIAS_AnalystSchool2018

    @@ -1094,7 +1087,7 @@

    NEUBIAS Bioimage Analyst Course 2017miura/NEUBIAS_Bioimage_Analyst_Course2017

    @@ -1103,7 +1096,7 @@

    NEUBIAS Bioimage Analyst School 2019miura/NEUBIAS_AnalystSchool2019

    @@ -1112,7 +1105,7 @@

    NEUBIAS Bioimage Analyst School 2020miura/NEUBIAS_AnalystSchool2020

    @@ -1130,7 +1123,7 @@

    Neubias Academy 2020: Introduction to Nuclei Segmentation with StarDistMartin Weigert, Olivier Burri, Siân Culley, Uwe Schmidt

    Licensed UNKNOWN

    Tags: Python, Neubias, Artificial Intelligence, Bioimage Analysis

    -

    Content type: Slide, Notebook

    +

    Content type: Slides, Notebook

    maweigert/neubias_academy_stardist

    @@ -1209,7 +1202,7 @@

    RDM_system_connectorSaibotMagd

    Licensed UNKNOWN

    This tool is intended to link different research data management platforms with each other.

    -

    Tags: Research Data Management, Image Data Management

    +

    Tags: Research Data Management

    Content type: Github Repository

    SaibotMagd/RDM_system_connector

    @@ -1220,7 +1213,7 @@

    REMBI - Recommended Metadata for Biological Images—enabling reuse of micro

    Published 2021-05-21

    Licensed UNKNOWN

    Bioimaging data have significant potential for reuse, but unlocking this potential requires systematic archiving of data and metadata in public databases. The authors propose draft metadata guidelines to begin addressing the needs of diverse communities within light and electron microscopy.

    -

    Tags: Metadata, Bioimage Data, Image Data Management, Research Data Management

    +

    Tags: Metadata, Research Data Management

    Content type: Publication

    https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8606015/

    https://www.nature.com/articles/s41592-021-01166-8

    @@ -1243,7 +1236,7 @@

    Reporting and reproducibility in microscopyhttps://www.nature.com/collections/djiciihhjh

    @@ -1297,7 +1290,7 @@

    Statistical RethinkingLicensed UNKNOWN

    Video Lectures for Statistical Rethinking Course

    Tags: Statistics

    -

    Content type: Youtube Video

    +

    Content type: Video

    https://www.youtube.com/playlist?list=PLDcUM9US4XdPz-KxHM4XHt7uUVGWWVSus

    @@ -1323,7 +1316,7 @@

    The BioImage Archive – Building a Home for Life-Sciences Microscopy DataPublished 2022-06-22

    Licensed UNKNOWN

    The BioImage Archive is a new archival data resource at the European Bioinformatics Institute (EMBL-EBI).

    -

    Tags: Image Data Management, Research Data Management, Bioimage Data

    +

    Tags: Research Data Management

    Content type: Publication

    https://www.sciencedirect.com/science/article/pii/S0022283622000791?via%3Dihub

    https://doi.org/10.1016/j.jmb.2022.167505

    @@ -1335,7 +1328,7 @@

    Towards community-driven metadata standards for light microscopy - tiered sp

    Published 2022-07-10

    Licensed UNKNOWN

    Rigorous record-keeping and quality control are required to ensure the quality, reproducibility and value of imaging data. The 4DN Initiative and BINA here propose light Microscopy Metadata specifications that extend the OME data model, scale with experimental intent and complexity, and make it possible for scientists to create comprehensive records of imaging experiments.

    -

    Tags: Reproducibility, Microscopy Image Analysis, Metadata, Image Data Management, Bioimage Data

    +

    Tags: Reproducibility, Bioimage Analysis, Metadata

    Content type: Publication

    https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9271325/

    @@ -1352,7 +1345,7 @@

    Training Deep Learning Models for Vision - Compact Course#

    Curtis Rueden, Albane de la Villegeorges, Simon F. Nørrelykke, Romain Guiet, Olivier Burri, et al.

    Licensed UNKNOWN

    -

    Tags: Bioimage Analysis, Microscopy Image Analysis

    +

    Tags: Bioimage Analysis

    Content type: Collection, Event, Forum Post, Workshop

    https://forum.image.sc/t/upcoming-image-analysis-events/60018/67

    @@ -1362,7 +1355,7 @@

    What is Bioimage Analysis? An Introductionhttps://www.dropbox.com/s/5abw3cvxrhpobg4/20220923_DefragmentationTS.pdf?dl=0

    @@ -1371,7 +1364,7 @@

    ZIDAS 2020 Introduction to Deep Learningesgomezm/zidas2020_intro_DL

    diff --git a/objects.inv b/objects.inv index ac0b1292e4525460e1c47c7aabdf297252265951..fe63867ed82422403583ff69c81f42144496e6ea 100644 GIT binary patch delta 2242 zcmV;z2tD_)6Ri@EgMV0ElH0fuexFm|#Z}CMA=PWFw4`;ZsuS*&LQ2=atwn`a z8-%lr^+~r3eXubURK=U$JOuiIV8CvrB2r>5R!T7VlQYgU-G2Zd9fl?@#oKY9>LjTu zF#{my9>LD?2NOV!A5Yj4HO1UKVoPhi=oXVAPZLH#TWsFvtThD!k@u}VX=)v`#bzH_ z3$8Tgn)0&49myJ}6mL5Ok<}GXv8UvOQCwb#zMU6lJK+RZ7ezrCZ&WQ>%IUd0_gdnd zXF_m>Ke`Zdnt#|0hEcdY+PtX6s>Row<(%bw|6t-MQ5WxGbB=H25#KswM@sDHlYtWt(^`(ub9tBTVTcJ4q} zc_wX}&&`D3>^|8L@|q$lpl<|oyL6;H^pa%vWXf@#e;%U9DdVU9M!167;Q3=@M1vJq z$g$%z{3g_?Y=w~7x*kz)0>6=C7u(3?EHo}#+UJXIM4iiD;5TyYVzU`t;6ZW8aN7}? zeWVz7PJf>vf`TI6K%kn#2u`zK|C`TXda%Dc;frQP35eZE8vwq>+$XFwqixyU``<8F3r{Ae4F((S5^d1^1*X zs}set*YJedF)}na!Y>X$u|CdA!RRw3(iDTtI)5hw=lNF`LQWI!TnD$vv!G3O(+1GF ze1W^gabpKbaNTBW#34@mJ7^p<)r|ap6w<1~vKD2Fl_J?knNXb7-+T~xU9!hg93Yp< z6UHh(rwI3UHiDd@coRRF>38aqQw=$PUa(6j!O;ZKpo^~rDOwD50H-d|dBYJ8ZYF}9 zqJP*tj$VqgGbwe?#5rdb!OrRLCV(72-a)0w5py_Sp%QC-hC^f35lv*a6I~?xF+7ZB zUde%`dWAVB&~0#aCeeDSJ{!BEe6DpbAobqb>Xh{B@V9*rX-ONy=nK(ziiQ(BMJ!Mp)Cd+z* zhE5lFwiKoe#z>{9;P+etCwL8-;%PH=tg*(+UIz88vf-HvY(fwfbW!}y0tZIuxPLw0 zWMrO*?>}BbOHG|Jv+U&g?i4NTTUE=PKW9VrsXrh|ATy{I^1AHisE|`5**Bdtk77Sd zSuQlWpJjpJFVny;-psrNXOx@sP@CSFEn7MwOgT{fr8?1P64De(+9v04f4e$v&IQ&) z(tBGJ7~U!H=Oy3u2S%$GawsBs!+%Isyqy)vyaUMLvpygtDMst`$j#yYCUD&1X*-?D z%bS_*;n!jF_A{?%7(^@^S=hP!(=v)zdXB`Rp>>?vpR|Hj*(}0 zr@*(pkzq=D=%JNtULS>Q@hp`luygtM=bw@Lomx;{lkbZT7Ig-H5*MV!2!E5*NA-u6 z!i#X%Et-uKf1@dgB7Gw0tAwt|{P>KaY+wr&GzdUe%4F$>u6w9$6yF&^lIG*(J->E;Le_VP8h>coXAXp4t%;6vdKDGBse%)v3c4J+T-T& z5zbRZu!I+({Cs8{|KVUJwSRqD5Qz%24!T*gK2Uhwfqp`(r2&?>exOAw`$^54z+$i4 zj;^Xhvrc<~EMd4gVv)H!N4D9!{Dem^zJUCcw&UtvE*CRp`JFy5?9hbaYj7fq_S*2t zfv$12?#~N;13tQq5_?HRWR7(&mohOWjCPU8Hq#OPDtg@??iogm(SI?P72EA#c_UoZ zVqx({&>8(_Bo~`^U_!ok@Uoq`WfH*O$sOUyv}F$s;o~{Q><1{2gsA^8W&Zm>+u7~v zL!RY+Jirx5xPr45GgeC4oU|%U<;Z7J$KrcD|K*-BYfo_RDTirg6zu`USD~toD(50qF zTEW;Q92A5x3g=9fINjsVp8m3^Q|0i6KWp0#2mA$NJZc{?^_8iMhr)Bv^Oy$ZPzZLBmna^&5 z`&MHjvLECv-LAs7nTrdD)810-YqAAhftUoF$_B(Ke;$a#^jo6w^b)XPsVbT+mKcmK_dqY+Cnlno6@foT4N0_QmXcyqi=aVXgth71;WV{#;pF@( z-i0EVA0ttCb~by=9Z#jLj~2?bA=B?hcZvQ}u}5tOgd!X0XP^A+)CrO%lgvKu4>(V< zua6cs?>m=A2JdrIT>!=xPfHuNC*URFjrm-%r1$v5#*1u@Urrc0DNd3(48gDhP=lIX Q5w2_7RKceFAFVO;0^Pn^2><{9 delta 2375 zcmV-N3ApyH60sAIgMV4gj@-BrzRy$eVn8``d%9=*7X$(9-E89QdSfI`g5)BwC61cY zlqist+da>dHwcgy@QbBL79~=mdM@kj`hG}?ELKTF2XmB4zognIUYEK$(m`vYP5s+i zR%EjyG|Si?O)JQI7b8Jcy!y>epdT0o?5WgDDcZ@6mID6C8Gq-Qet-`iBQuxc^)yg* zR9u%-0FZN!U~l<@4G_()q`TL*2i zyN|48rw#24@5-L`ENh%n&~^+Grz_sZo~mPl)AG#B(`jY4Gfr^zQ4*B#M%A+A9Y2+) z!6=&ZObFiaM}Hq8UK6{)Fq)PR?p)Mz)6z@Ma>??(f3R_qsEaqTc}Jhh2l@p1yzPVs zLx>Pm#K(%k<{hYj^WA*^Z_A!GHG|<`Pb(_2))W854|gv<+|4vNTwsxmYcjlnv%k#_ z|(c@TwtNJ z6twM`%6~2?#=X-gh>)O&HxQ`yGJ@Oe`+xE$Vq(}Fcz+*G#U~dhK^Z@Al$;rF)@K+o z62qWMX33$d!7&0r(CV0VOetvTgCyvR-99yC0*;YXMhe+7`t-!ttQW*-0Dws9QAG8o z;}z6NT~kU_IJ=Y7;Xgn{Uqd7MO7`!mTJwiOPNu;)n744P?zkn zk_R|T<&jdIpHqaRor@5!C|<=Yv;9t8cHF?3Kdsm$l;CKCWYonaLCKav9>A^3Ox|$9 zgMXWi5U(h9x09FV;>1cb2zkm`MX-1Jn+*`pk9SZT_CPzhUm+7~bAn4_(=)?Vwi8_? z;}{5&g;#Q+=|R)3W6*7MJU>iKKx-c&UQ_I%6~KStFqBX7kgp*z?#;fq2=R(Iy^K6u z%A>3smYr7wqY(rM^5fEGWDEBP*IL4yO@H1hW(2pEM54PVgEv#oOK7v8INudw*FJ zJ8sLfb#O+I6*Oa=xnhBg(6}St9_&1kUSD29OHI8p^Vr$ft5b9^cD#Ee(`GIB|E`8d#xJVf{S%aPlPE4 zD%R$iK64;pDYJyn;aH?SH|GM+M1S#XTND`R6!^;w?}vln)iY-(BB5cVDqb&=WZ6jM z@I^b3lCUSDAlxo+fO@A41F zEcW+vi!(bwI$U`&NbolQM0T-A^0QPYeU$wbBV?dc;JYr(G8I1z(9||BAAh83>Eblb zVDIv;PrnfVJGJ0l&AzQVIMf^bNuG(36U1y0$#o(G?07y1CGHZ>Rvh-ZoM;f^944 z9+sXn#(0}2GMnQgpKqmVvVTqv2@alC?B3pu+K1ikEnLZ(Q3bDL`S~n3_z7bnbsb|6 z$%={|`f9d4aCq^84#eoS0oJ%U(5jXDxQ8b2u(xf`H`SrprZ-BKFczO!WXtcwwtJVK z@C3#+45zf8p6>a4wo_K$_}7&knlUg3XR>IoEuS6umR8&0wBk47lYiSJadjplchn4O z?Ifmz$u1JPwmza?{rh7`bPhlVlQT#+^|M!Nsivo=yWceRAP(@j%;jX1Zl!`Y;qf47P z>MN^b@iiX)4M7Ov0e>+36J7p{l%^M({fZnh zydjX7JYA?yStZ27$1CG|gArODm3y!YFnllx;TuC6NL6L{1FvB05>lNB!QrN;5~piCOzROOiAiw>x0sVdJ>Yi9 zCGDuV==38@o_`T~5g#U_5bnnNx$FFecZts2oS@j>Mn;|Q_~6N8ohEp)iX<+v9NAiz z@KSW9s@6x|FWlyY=v_RP*`~|$8S6^{`|gg)!YxM83PNNQxK7wnA1GHFd!<*r7i_uA z8IJACOyvID;ONuCEnC=~hRwejtg}LW8Mh)ba9zOB z07I#Z=wkxvBb>ZU`e7WUNSf|G6H4t|7XbV$5!QtNsPg;190IOaUry>U0V&)_&+hY;;<4^t#SYW diff --git a/readme.html b/readme.html index bf5fea4e..9433ada6 100644 --- a/readme.html +++ b/readme.html @@ -183,36 +183,29 @@

    By tag

    By content type

    @@ -237,11 +229,12 @@ diff --git a/search.html b/search.html index 062b33cc..46ffb815 100644 --- a/search.html +++ b/search.html @@ -183,36 +183,29 @@

    By tag

    By content type

    @@ -237,11 +229,12 @@ diff --git a/searchindex.js b/searchindex.js index 08d7c34e..24cc006a 100644 --- a/searchindex.js +++ b/searchindex.js @@ -1 +1 @@ -Search.setIndex({"alltitles": {"10 frames of fluorescent particles": [[66, "frames-of-fluorescent-particles"], [75, "frames-of-fluorescent-particles"], [81, "frames-of-fluorescent-particles"]], "2020 BioImage Analysis Survey: Community experiences and needs for the future": [[53, "bioimage-analysis-survey-community-experiences-and-needs-for-the-future"], [66, "bioimage-analysis-survey-community-experiences-and-needs-for-the-future"], [70, "bioimage-analysis-survey-community-experiences-and-needs-for-the-future"], [80, "bioimage-analysis-survey-community-experiences-and-needs-for-the-future"], [91, "bioimage-analysis-survey-community-experiences-and-needs-for-the-future"]], "2022 MIC Workshop on Bioimage processing with Python": [[13, "mic-workshop-on-bioimage-processing-with-python"], [49, "mic-workshop-on-bioimage-processing-with-python"], [70, "mic-workshop-on-bioimage-processing-with-python"], [87, "mic-workshop-on-bioimage-processing-with-python"], [91, "mic-workshop-on-bioimage-processing-with-python"], [109, "mic-workshop-on-bioimage-processing-with-python"]], "3D Nuclei annotations and StarDist 3D model(s) (rat brain)": [[31, "d-nuclei-annotations-and-stardist-3d-model-s-rat-brain"], [66, "d-nuclei-annotations-and-stardist-3d-model-s-rat-brain"], [75, "d-nuclei-annotations-and-stardist-3d-model-s-rat-brain"], [81, "d-nuclei-annotations-and-stardist-3d-model-s-rat-brain"]], "6 Steps Towards Reproducible Research": [[41, "steps-towards-reproducible-research"], [75, "steps-towards-reproducible-research"], [81, "steps-towards-reproducible-research"], [111, "steps-towards-reproducible-research"]], "A Cloud-Optimized Storage for Interactive Access of Large Arrays": [[17, "a-cloud-optimized-storage-for-interactive-access-of-large-arrays"], [34, "a-cloud-optimized-storage-for-interactive-access-of-large-arrays"], [53, "a-cloud-optimized-storage-for-interactive-access-of-large-arrays"], [66, "a-cloud-optimized-storage-for-interactive-access-of-large-arrays"], [81, "a-cloud-optimized-storage-for-interactive-access-of-large-arrays"], [105, "a-cloud-optimized-storage-for-interactive-access-of-large-arrays"], [111, "a-cloud-optimized-storage-for-interactive-access-of-large-arrays"]], "A Fiji Scripting Tutorial": [[49, "a-fiji-scripting-tutorial"], [83, "a-fiji-scripting-tutorial"], [91, "a-fiji-scripting-tutorial"], [98, "a-fiji-scripting-tutorial"]], "A Glimpse of the Open-Source FLIM Analysis Software Tools FLIMfit, FLUTE and napari-flim-phasor-plotter": [[6, "a-glimpse-of-the-open-source-flim-analysis-software-tools-flimfit-flute-and-napari-flim-phasor-plotter"], [56, "a-glimpse-of-the-open-source-flim-analysis-software-tools-flimfit-flute-and-napari-flim-phasor-plotter"], [66, "a-glimpse-of-the-open-source-flim-analysis-software-tools-flimfit-flute-and-napari-flim-phasor-plotter"], [75, "a-glimpse-of-the-open-source-flim-analysis-software-tools-flimfit-flute-and-napari-flim-phasor-plotter"], [81, "a-glimpse-of-the-open-source-flim-analysis-software-tools-flimfit-flute-and-napari-flim-phasor-plotter"], [91, "a-glimpse-of-the-open-source-flim-analysis-software-tools-flimfit-flute-and-napari-flim-phasor-plotter"]], "A Hitchhiker\u2019s guide through the bio-image analysis software universe": [[4, "a-hitchhiker-s-guide-through-the-bio-image-analysis-software-universe"], [9, "a-hitchhiker-s-guide-through-the-bio-image-analysis-software-universe"], [29, "a-hitchhiker-s-guide-through-the-bio-image-analysis-software-universe"], [53, "a-hitchhiker-s-guide-through-the-bio-image-analysis-software-universe"], [81, "a-hitchhiker-s-guide-through-the-bio-image-analysis-software-universe"], [91, "a-hitchhiker-s-guide-through-the-bio-image-analysis-software-universe"]], "A biologist\u2019s guide to planning and performing quantitative bioimaging experiments": [[43, "a-biologists-guide-to-planning-and-performing-quantitative-bioimaging-experiments"], [53, "a-biologists-guide-to-planning-and-performing-quantitative-bioimaging-experiments"], [66, "a-biologists-guide-to-planning-and-performing-quantitative-bioimaging-experiments"], [80, "a-biologists-guide-to-planning-and-performing-quantitative-bioimaging-experiments"]], "A call for public archives for biological image data": [[39, "a-call-for-public-archives-for-biological-image-data"], [53, "a-call-for-public-archives-for-biological-image-data"], [73, "a-call-for-public-archives-for-biological-image-data"], [111, "a-call-for-public-archives-for-biological-image-data"]], "A journey to FAIR microscopy data": [[3, "a-journey-to-fair-microscopy-data"], [33, "a-journey-to-fair-microscopy-data"], [66, "a-journey-to-fair-microscopy-data"], [75, "a-journey-to-fair-microscopy-data"], [81, "a-journey-to-fair-microscopy-data"]], "A practical guide to bioimaging research data management in core facilities": [[3, "a-practical-guide-to-bioimaging-research-data-management-in-core-facilities"], [8, "a-practical-guide-to-bioimaging-research-data-management-in-core-facilities"], [30, "a-practical-guide-to-bioimaging-research-data-management-in-core-facilities"], [33, "a-practical-guide-to-bioimaging-research-data-management-in-core-facilities"], [34, "a-practical-guide-to-bioimaging-research-data-management-in-core-facilities"], [37, "a-practical-guide-to-bioimaging-research-data-management-in-core-facilities"], [53, "a-practical-guide-to-bioimaging-research-data-management-in-core-facilities"], [111, "a-practical-guide-to-bioimaging-research-data-management-in-core-facilities"]], "A study on long-term reproducibility of image analysis results on ImageJ and Fiji": [[12, "a-study-on-long-term-reproducibility-of-image-analysis-results-on-imagej-and-fiji"], [29, "a-study-on-long-term-reproducibility-of-image-analysis-results-on-imagej-and-fiji"], [51, "a-study-on-long-term-reproducibility-of-image-analysis-results-on-imagej-and-fiji"], [53, "a-study-on-long-term-reproducibility-of-image-analysis-results-on-imagej-and-fiji"], [98, "a-study-on-long-term-reproducibility-of-image-analysis-results-on-imagej-and-fiji"]], "AI ML DL in Bioimage Analysis - Webinar": [[56, "ai-ml-dl-in-bioimage-analysis-webinar"], [74, "ai-ml-dl-in-bioimage-analysis-webinar"], [87, "ai-ml-dl-in-bioimage-analysis-webinar"], [90, "ai-ml-dl-in-bioimage-analysis-webinar"], [91, "ai-ml-dl-in-bioimage-analysis-webinar"], [95, "ai-ml-dl-in-bioimage-analysis-webinar"], [99, "ai-ml-dl-in-bioimage-analysis-webinar"]], "AI4Life teams up with Galaxy Training Network (GTN) to enhance training resources": [[46, "ai4life-teams-up-with-galaxy-training-network-gtn-to-enhance-training-resources"], [87, "ai4life-teams-up-with-galaxy-training-network-gtn-to-enhance-training-resources"], [90, "ai4life-teams-up-with-galaxy-training-network-gtn-to-enhance-training-resources"], [91, "ai4life-teams-up-with-galaxy-training-network-gtn-to-enhance-training-resources"], [116, "ai4life-teams-up-with-galaxy-training-network-gtn-to-enhance-training-resources"]], "Abdominal Imaging Window (AIW) for Intravital Imaging": [[22, "abdominal-imaging-window-aiw-for-intravital-imaging"], [66, "abdominal-imaging-window-aiw-for-intravital-imaging"], [75, "abdominal-imaging-window-aiw-for-intravital-imaging"], [81, "abdominal-imaging-window-aiw-for-intravital-imaging"]], "Aberrated Bead Stack": [[66, "aberrated-bead-stack"], [75, "aberrated-bead-stack"], [81, "aberrated-bead-stack"]], "Abstract - NFDI Basic Service for Data Management Plans": [[45, "abstract-nfdi-basic-service-for-data-management-plans"], [81, "abstract-nfdi-basic-service-for-data-management-plans"], [111, "abstract-nfdi-basic-service-for-data-management-plans"]], "Acknowledgements": [[88, "acknowledgements"]], "Adding a Workflow to BIAFLOWS": [[55, "adding-a-workflow-to-biaflows"], [70, "adding-a-workflow-to-biaflows"], [79, "adding-a-workflow-to-biaflows"], [91, "adding-a-workflow-to-biaflows"], [104, "adding-a-workflow-to-biaflows"]], "All rights reserved (13)": [[78, null]], "Alles meins \u2013 oder!? Urheberrechte kl\u00e4ren f\u00fcr Forschungsdaten": [[56, "alles-meins-oder-urheberrechte-klaren-fur-forschungsdaten"], [66, "alles-meins-oder-urheberrechte-klaren-fur-forschungsdaten"], [75, "alles-meins-oder-urheberrechte-klaren-fur-forschungsdaten"], [81, "alles-meins-oder-urheberrechte-klaren-fur-forschungsdaten"], [100, "alles-meins-oder-urheberrechte-klaren-fur-forschungsdaten"], [111, "alles-meins-oder-urheberrechte-klaren-fur-forschungsdaten"]], "Analysis of High-Throughput Microscopy Image Data": [[4, "analysis-of-high-throughput-microscopy-image-data"], [18, "analysis-of-high-throughput-microscopy-image-data"], [70, "analysis-of-high-throughput-microscopy-image-data"], [87, "analysis-of-high-throughput-microscopy-image-data"], [91, "analysis-of-high-throughput-microscopy-image-data"]], "Andor Dragonfly confocal image of BPAE cells stained for actin, IMS file format": [[66, "andor-dragonfly-confocal-image-of-bpae-cells-stained-for-actin-ims-file-format"], [75, "andor-dragonfly-confocal-image-of-bpae-cells-stained-for-actin-ims-file-format"], [117, "andor-dragonfly-confocal-image-of-bpae-cells-stained-for-actin-ims-file-format"]], "Angaben gem\u00e4\u00df \u00a7 5 TMG": [[77, "angaben-gemasz-5-tmg"]], "Angebote der NFDI f\u00fcr die Forschung im Bereich Zoologie": [[29, "angebote-der-nfdi-fur-die-forschung-im-bereich-zoologie"], [66, "angebote-der-nfdi-fur-die-forschung-im-bereich-zoologie"], [75, "angebote-der-nfdi-fur-die-forschung-im-bereich-zoologie"], [81, "angebote-der-nfdi-fur-die-forschung-im-bereich-zoologie"]], "Annotated high-throughput microscopy image sets for validation": [[43, "annotated-high-throughput-microscopy-image-sets-for-validation"], [44, "annotated-high-throughput-microscopy-image-sets-for-validation"], [73, "annotated-high-throughput-microscopy-image-sets-for-validation"]], "Annotating 3D images in napari": [[19, "annotating-3d-images-in-napari"], [40, "annotating-3d-images-in-napari"], [68, "annotating-3d-images-in-napari"], [91, "annotating-3d-images-in-napari"], [103, "annotating-3d-images-in-napari"], [109, "annotating-3d-images-in-napari"]], "Artificial Blobs and Labels image": [[66, "artificial-blobs-and-labels-image"], [75, "artificial-blobs-and-labels-image"], [81, "artificial-blobs-and-labels-image"]], "Artificial Intelligence for Digital Pathology": [[12, "artificial-intelligence-for-digital-pathology"], [74, "artificial-intelligence-for-digital-pathology"], [87, "artificial-intelligence-for-digital-pathology"], [90, "artificial-intelligence-for-digital-pathology"]], "Artificial intelligence (32)": [[90, null]], "Astigmatic 4Pi bead stack": [[66, "astigmatic-4pi-bead-stack"], [75, "astigmatic-4pi-bead-stack"], [81, "astigmatic-4pi-bead-stack"]], "Astrid schauss (5)": [[0, null]], "Axioscan 7 fluorescent channels not displaying in QuPath": [[66, "axioscan-7-fluorescent-channels-not-displaying-in-qupath"], [75, "axioscan-7-fluorescent-channels-not-displaying-in-qupath"]], "BIDS-lecture-2024": [[29, "bids-lecture-2024"], [48, "bids-lecture-2024"], [70, "bids-lecture-2024"], [81, "bids-lecture-2024"], [91, "bids-lecture-2024"], [95, "bids-lecture-2024"], [102, "bids-lecture-2024"], [109, "bids-lecture-2024"]], "BIOMERO - A scalable and extensible image analysis framework": [[53, "biomero-a-scalable-and-extensible-image-analysis-framework"], [66, "biomero-a-scalable-and-extensible-image-analysis-framework"], [81, "biomero-a-scalable-and-extensible-image-analysis-framework"], [91, "biomero-a-scalable-and-extensible-image-analysis-framework"], [97, "biomero-a-scalable-and-extensible-image-analysis-framework"], [106, "biomero-a-scalable-and-extensible-image-analysis-framework"], [115, "biomero-a-scalable-and-extensible-image-analysis-framework"]], "Basics of Image Processing and Analysis": [[18, "basics-of-image-processing-and-analysis"], [41, "basics-of-image-processing-and-analysis"], [70, "basics-of-image-processing-and-analysis"], [78, "basics-of-image-processing-and-analysis"], [91, "basics-of-image-processing-and-analysis"]], "Beatriz serrano-solano (6)": [[1, null]], "Best practice data life cycle approaches for the life sciences": [[11, "best-practice-data-life-cycle-approaches-for-the-life-sciences"], [53, "best-practice-data-life-cycle-approaches-for-the-life-sciences"], [66, "best-practice-data-life-cycle-approaches-for-the-life-sciences"], [87, "best-practice-data-life-cycle-approaches-for-the-life-sciences"], [93, "best-practice-data-life-cycle-approaches-for-the-life-sciences"], [107, "best-practice-data-life-cycle-approaches-for-the-life-sciences"], [111, "best-practice-data-life-cycle-approaches-for-the-life-sciences"], [113, "best-practice-data-life-cycle-approaches-for-the-life-sciences"]], "BiaPy: Bioimage analysis pipelines in Python": [[11, "biapy-bioimage-analysis-pipelines-in-python"], [46, "biapy-bioimage-analysis-pipelines-in-python"], [109, "biapy-bioimage-analysis-pipelines-in-python"], [116, "biapy-bioimage-analysis-pipelines-in-python"]], "Biapol.github.io (6)": [[64, null]], "BigDataProcessor2: A free and open-source Fiji plugin for inspection and processing of TB sized image data": [[4, "bigdataprocessor2-a-free-and-open-source-fiji-plugin-for-inspection-and-processing-of-tb-sized-image-data"], [24, "bigdataprocessor2-a-free-and-open-source-fiji-plugin-for-inspection-and-processing-of-tb-sized-image-data"], [53, "bigdataprocessor2-a-free-and-open-source-fiji-plugin-for-inspection-and-processing-of-tb-sized-image-data"], [66, "bigdataprocessor2-a-free-and-open-source-fiji-plugin-for-inspection-and-processing-of-tb-sized-image-data"], [81, "bigdataprocessor2-a-free-and-open-source-fiji-plugin-for-inspection-and-processing-of-tb-sized-image-data"], [91, "bigdataprocessor2-a-free-and-open-source-fiji-plugin-for-inspection-and-processing-of-tb-sized-image-data"], [111, "bigdataprocessor2-a-free-and-open-source-fiji-plugin-for-inspection-and-processing-of-tb-sized-image-data"]], "Bio Image Analysis": [[4, "bio-image-analysis"], [55, "bio-image-analysis"], [70, "bio-image-analysis"], [87, "bio-image-analysis"]], "Bio Image Analysis Lecture 2020": [[43, "bio-image-analysis-lecture-2020"], [58, "bio-image-analysis-lecture-2020"], [74, "bio-image-analysis-lecture-2020"], [91, "bio-image-analysis-lecture-2020"]], "Bio-Image Data Strudel for Workshop on Research Data Management in TU Dresden Core Facilities": [[6, "bio-image-data-strudel-for-workshop-on-research-data-management-in-tu-dresden-core-facilities"], [55, "bio-image-data-strudel-for-workshop-on-research-data-management-in-tu-dresden-core-facilities"], [66, "bio-image-data-strudel-for-workshop-on-research-data-management-in-tu-dresden-core-facilities"], [75, "bio-image-data-strudel-for-workshop-on-research-data-management-in-tu-dresden-core-facilities"], [81, "bio-image-data-strudel-for-workshop-on-research-data-management-in-tu-dresden-core-facilities"], [92, "bio-image-data-strudel-for-workshop-on-research-data-management-in-tu-dresden-core-facilities"], [105, "bio-image-data-strudel-for-workshop-on-research-data-management-in-tu-dresden-core-facilities"], [111, "bio-image-data-strudel-for-workshop-on-research-data-management-in-tu-dresden-core-facilities"]], "Bio-image Analysis Code Generation": [[29, "bio-image-analysis-code-generation"], [66, "bio-image-analysis-code-generation"], [75, "bio-image-analysis-code-generation"], [81, "bio-image-analysis-code-generation"]], "Bio-image Analysis Code Generation using bia-bob": [[29, "bio-image-analysis-code-generation-using-bia-bob"], [66, "bio-image-analysis-code-generation-using-bia-bob"], [75, "bio-image-analysis-code-generation-using-bia-bob"], [81, "bio-image-analysis-code-generation-using-bia-bob"], [90, "bio-image-analysis-code-generation-using-bia-bob"], [91, "bio-image-analysis-code-generation-using-bia-bob"]], "Bio-image Analysis ICOB 2023": [[43, "bio-image-analysis-icob-2023"], [60, "bio-image-analysis-icob-2023"], [70, "bio-image-analysis-icob-2023"], [91, "bio-image-analysis-icob-2023"]], "Bio-image Analysis Workshop Kioto and Taipei 23/24": [[43, "bio-image-analysis-workshop-kioto-and-taipei-23-24"], [60, "bio-image-analysis-workshop-kioto-and-taipei-23-24"], [70, "bio-image-analysis-workshop-kioto-and-taipei-23-24"], [91, "bio-image-analysis-workshop-kioto-and-taipei-23-24"]], "Bio-image Analysis Workshop Taipei": [[43, "bio-image-analysis-workshop-taipei"], [60, "bio-image-analysis-workshop-taipei"], [70, "bio-image-analysis-workshop-taipei"], [91, "bio-image-analysis-workshop-taipei"]], "Bio-image Analysis with the Help of Large Language Models": [[29, "bio-image-analysis-with-the-help-of-large-language-models"], [55, "bio-image-analysis-with-the-help-of-large-language-models"], [66, "bio-image-analysis-with-the-help-of-large-language-models"], [75, "bio-image-analysis-with-the-help-of-large-language-models"], [81, "bio-image-analysis-with-the-help-of-large-language-models"], [99, "bio-image-analysis-with-the-help-of-large-language-models"], [109, "bio-image-analysis-with-the-help-of-large-language-models"]], "Bio-image Data Science": [[29, "bio-image-data-science"], [49, "bio-image-data-science"], [70, "bio-image-data-science"], [81, "bio-image-data-science"], [95, "bio-image-data-science"], [97, "bio-image-data-science"], [102, "bio-image-data-science"], [109, "bio-image-data-science"]], "Bio-image Data Science Lectures @ Uni Leipzig / ScaDS.AI": [[29, "bio-image-data-science-lectures-uni-leipzig-scads-ai"], [56, "bio-image-data-science-lectures-uni-leipzig-scads-ai"], [75, "bio-image-data-science-lectures-uni-leipzig-scads-ai"], [81, "bio-image-data-science-lectures-uni-leipzig-scads-ai"], [91, "bio-image-data-science-lectures-uni-leipzig-scads-ai"], [95, "bio-image-data-science-lectures-uni-leipzig-scads-ai"], [102, "bio-image-data-science-lectures-uni-leipzig-scads-ai"], [109, "bio-image-data-science-lectures-uni-leipzig-scads-ai"]], "Bio-image analysis, biostatistics, programming and machine learning for computational biology": [[29, "bio-image-analysis-biostatistics-programming-and-machine-learning-for-computational-biology"], [49, "bio-image-analysis-biostatistics-programming-and-machine-learning-for-computational-biology"], [70, "bio-image-analysis-biostatistics-programming-and-machine-learning-for-computational-biology"], [81, "bio-image-analysis-biostatistics-programming-and-machine-learning-for-computational-biology"], [91, "bio-image-analysis-biostatistics-programming-and-machine-learning-for-computational-biology"], [103, "bio-image-analysis-biostatistics-programming-and-machine-learning-for-computational-biology"], [109, "bio-image-analysis-biostatistics-programming-and-machine-learning-for-computational-biology"]], "Bio.tools database": [[43, "bio-tools-database"], [81, "bio-tools-database"], [93, "bio-tools-database"]], "BioEngine": [[1, "bioengine"], [53, "bioengine"], [87, "bioengine"], [90, "bioengine"], [116, "bioengine"]], "BioEngine Documentation": [[46, "bioengine-documentation"], [85, "bioengine-documentation"], [95, "bioengine-documentation"], [109, "bioengine-documentation"], [116, "bioengine-documentation"]], "BioFormats Command line (CLI) tools": [[46, "bioformats-command-line-cli-tools"], [81, "bioformats-command-line-cli-tools"], [92, "bioformats-command-line-cli-tools"]], "BioImage Analysis Notebooks": [[41, "bioimage-analysis-notebooks"], [49, "bioimage-analysis-notebooks"], [80, "bioimage-analysis-notebooks"], [81, "bioimage-analysis-notebooks"], [91, "bioimage-analysis-notebooks"], [109, "bioimage-analysis-notebooks"]], "BioImage Archive AI Gallery": [[43, "bioimage-archive-ai-gallery"], [44, "bioimage-archive-ai-gallery"], [72, "bioimage-archive-ai-gallery"], [83, "bioimage-archive-ai-gallery"], [91, "bioimage-archive-ai-gallery"], [95, "bioimage-archive-ai-gallery"]], "BioImage Archive Visual Gallery": [[43, "bioimage-archive-visual-gallery"], [44, "bioimage-archive-visual-gallery"], [72, "bioimage-archive-visual-gallery"], [83, "bioimage-archive-visual-gallery"], [91, "bioimage-archive-visual-gallery"]], "BioImage Archive Volume EM Gallery": [[43, "bioimage-archive-volume-em-gallery"], [44, "bioimage-archive-volume-em-gallery"], [72, "bioimage-archive-volume-em-gallery"], [83, "bioimage-archive-volume-em-gallery"], [91, "bioimage-archive-volume-em-gallery"]], "BioImage Data Analysis": [[18, "bioimage-data-analysis"], [41, "bioimage-data-analysis"], [78, "bioimage-data-analysis"], [91, "bioimage-data-analysis"], [104, "bioimage-data-analysis"]], "BioImage Informatics Index Training Materials": [[43, "bioimage-informatics-index-training-materials"], [91, "bioimage-informatics-index-training-materials"]], "Bioimage Archive": [[43, "bioimage-archive"], [44, "bioimage-archive"], [53, "bioimage-archive"], [72, "bioimage-archive"]], "Bioimage Model Zoo": [[43, "bioimage-model-zoo"], [87, "bioimage-model-zoo"], [90, "bioimage-model-zoo"], [91, "bioimage-model-zoo"]], "Bioimage analysis (174)": [[91, null]], "Bioimage analysis with Napari": [[43, "bioimage-analysis-with-napari"], [91, "bioimage-analysis-with-napari"], [103, "bioimage-analysis-with-napari"], [109, "bioimage-analysis-with-napari"]], "Bioimage data (20)": [[92, null]], "Bioinformatics (10)": [[93, null]], "Biologists, stop putting UMAP plots in your papers": [[87, "biologists-stop-putting-umap-plots-in-your-papers"]], "Biomero": [[48, "biomero"], [70, "biomero"], [106, "biomero"]], "Blog (19)": [[40, null]], "Book (19)": [[41, null]], "Bridging Imaging Users to Imaging Analysis - A community survey": [[52, "bridging-imaging-users-to-imaging-analysis-a-community-survey"], [53, "bridging-imaging-users-to-imaging-analysis-a-community-survey"], [70, "bridging-imaging-users-to-imaging-analysis-a-community-survey"], [71, "bridging-imaging-users-to-imaging-analysis-a-community-survey"], [80, "bridging-imaging-users-to-imaging-analysis-a-community-survey"], [91, "bridging-imaging-users-to-imaging-analysis-a-community-survey"]], "Bring your own data workshops": [[60, "bring-your-own-data-workshops"], [91, "bring-your-own-data-workshops"], [111, "bring-your-own-data-workshops"]], "Browsing the Open Microscopy Image Data Resource with Python": [[29, "browsing-the-open-microscopy-image-data-resource-with-python"], [40, "browsing-the-open-microscopy-image-data-resource-with-python"], [64, "browsing-the-open-microscopy-image-data-resource-with-python"], [81, "browsing-the-open-microscopy-image-data-resource-with-python"], [106, "browsing-the-open-microscopy-image-data-resource-with-python"], [109, "browsing-the-open-microscopy-image-data-resource-with-python"]], "Bsd-2-clause (7)": [[79, null]], "Bsd-3-clause (23)": [[80, null]], "Building FAIR image analysis pipelines for high-content-screening (HCS) data using Galaxy": [[1, "building-fair-image-analysis-pipelines-for-high-content-screening-hcs-data-using-galaxy"], [28, "building-fair-image-analysis-pipelines-for-high-content-screening-hcs-data-using-galaxy"], [66, "building-fair-image-analysis-pipelines-for-high-content-screening-hcs-data-using-galaxy"], [75, "building-fair-image-analysis-pipelines-for-high-content-screening-hcs-data-using-galaxy"], [81, "building-fair-image-analysis-pipelines-for-high-content-screening-hcs-data-using-galaxy"]], "Building a Bioimage Analysis Workflow using Deep Learning": [[10, "building-a-bioimage-analysis-workflow-using-deep-learning"], [55, "building-a-bioimage-analysis-workflow-using-deep-learning"], [70, "building-a-bioimage-analysis-workflow-using-deep-learning"], [87, "building-a-bioimage-analysis-workflow-using-deep-learning"], [90, "building-a-bioimage-analysis-workflow-using-deep-learning"], [91, "building-a-bioimage-analysis-workflow-using-deep-learning"]], "Building a FAIR image data ecosystem for microscopy communities": [[1, "building-a-fair-image-data-ecosystem-for-microscopy-communities"], [14, "building-a-fair-image-data-ecosystem-for-microscopy-communities"], [53, "building-a-fair-image-data-ecosystem-for-microscopy-communities"], [66, "building-a-fair-image-data-ecosystem-for-microscopy-communities"], [75, "building-a-fair-image-data-ecosystem-for-microscopy-communities"], [81, "building-a-fair-image-data-ecosystem-for-microscopy-communities"], [111, "building-a-fair-image-data-ecosystem-for-microscopy-communities"]], "CARE/Stardist tutorials for EMBO Practical Course \u2014 Computational optical biology 2022": [[21, "care-stardist-tutorials-for-embo-practical-course-computational-optical-biology-2022"], [49, "care-stardist-tutorials-for-embo-practical-course-computational-optical-biology-2022"], [70, "care-stardist-tutorials-for-embo-practical-course-computational-optical-biology-2022"], [87, "care-stardist-tutorials-for-embo-practical-course-computational-optical-biology-2022"], [90, "care-stardist-tutorials-for-embo-practical-course-computational-optical-biology-2022"], [91, "care-stardist-tutorials-for-embo-practical-course-computational-optical-biology-2022"], [109, "care-stardist-tutorials-for-embo-practical-course-computational-optical-biology-2022"]], "CLIJ: GPU-accelerated image processing for everyone": [[11, "clij-gpu-accelerated-image-processing-for-everyone"], [29, "clij-gpu-accelerated-image-processing-for-everyone"], [53, "clij-gpu-accelerated-image-processing-for-everyone"], [66, "clij-gpu-accelerated-image-processing-for-everyone"], [78, "clij-gpu-accelerated-image-processing-for-everyone"], [91, "clij-gpu-accelerated-image-processing-for-everyone"], [98, "clij-gpu-accelerated-image-processing-for-everyone"]], "COBA: Center for Open Bioimage Analysis YouTube Channel": [[43, "coba-center-for-open-bioimage-analysis-youtube-channel"], [58, "coba-center-for-open-bioimage-analysis-youtube-channel"], [74, "coba-center-for-open-bioimage-analysis-youtube-channel"], [91, "coba-center-for-open-bioimage-analysis-youtube-channel"]], "CSBDeep and StarDist @ I2K 2020": [[21, "csbdeep-and-stardist-i2k-2020"], [49, "csbdeep-and-stardist-i2k-2020"], [70, "csbdeep-and-stardist-i2k-2020"], [87, "csbdeep-and-stardist-i2k-2020"], [90, "csbdeep-and-stardist-i2k-2020"], [91, "csbdeep-and-stardist-i2k-2020"], [109, "csbdeep-and-stardist-i2k-2020"]], "CZI (Carl Zeiss Image) dataset with artificial test camera images with various dimension for testing libraries reading": [[66, "czi-carl-zeiss-image-dataset-with-artificial-test-camera-images-with-various-dimension-for-testing-libraries-reading"], [75, "czi-carl-zeiss-image-dataset-with-artificial-test-camera-images-with-various-dimension-for-testing-libraries-reading"], [81, "czi-carl-zeiss-image-dataset-with-artificial-test-camera-images-with-various-dimension-for-testing-libraries-reading"]], "CZI file examples": [[24, "czi-file-examples"], [66, "czi-file-examples"], [75, "czi-file-examples"], [81, "czi-file-examples"]], "CZI: Open Science Program Collection": [[43, "czi-open-science-program-collection"], [75, "czi-open-science-program-collection"]], "Cc-by-4.0 (249)": [[81, null]], "Cc-by-sa-4.0 (5)": [[82, null]], "Cc0-1.0 (10)": [[83, null]], "Cell Tracking Challenge - 2D Datasets": [[43, "cell-tracking-challenge-2d-datasets"], [44, "cell-tracking-challenge-2d-datasets"], [87, "cell-tracking-challenge-2d-datasets"], [91, "cell-tracking-challenge-2d-datasets"]], "Cell Tracking Challenge - 3D Datasets": [[43, "cell-tracking-challenge-3d-datasets"], [44, "cell-tracking-challenge-3d-datasets"], [87, "cell-tracking-challenge-3d-datasets"], [91, "cell-tracking-challenge-3d-datasets"]], "CellProfiler Introduction": [[55, "cellprofiler-introduction"], [70, "cellprofiler-introduction"], [87, "cellprofiler-introduction"], [91, "cellprofiler-introduction"], [104, "cellprofiler-introduction"]], "CellProfiler Practical NeuBIAS Lisbon 2017": [[4, "cellprofiler-practical-neubias-lisbon-2017"], [57, "cellprofiler-practical-neubias-lisbon-2017"], [70, "cellprofiler-practical-neubias-lisbon-2017"], [87, "cellprofiler-practical-neubias-lisbon-2017"], [91, "cellprofiler-practical-neubias-lisbon-2017"], [104, "cellprofiler-practical-neubias-lisbon-2017"]], "CellProfiler tutorials": [[49, "cellprofiler-tutorials"], [70, "cellprofiler-tutorials"], [80, "cellprofiler-tutorials"], [91, "cellprofiler-tutorials"]], "CellTrackColab": [[43, "celltrackcolab"], [49, "celltrackcolab"], [70, "celltrackcolab"], [71, "celltrackcolab"], [85, "celltrackcolab"]], "Cellpose model for Digital Phase Contrast images": [[25, "cellpose-model-for-digital-phase-contrast-images"], [31, "cellpose-model-for-digital-phase-contrast-images"], [66, "cellpose-model-for-digital-phase-contrast-images"], [75, "cellpose-model-for-digital-phase-contrast-images"], [81, "cellpose-model-for-digital-phase-contrast-images"]], "Cellpose models for Label Prediction from Brightfield and Digital Phase Contrast images": [[25, "cellpose-models-for-label-prediction-from-brightfield-and-digital-phase-contrast-images"], [31, "cellpose-models-for-label-prediction-from-brightfield-and-digital-phase-contrast-images"], [66, "cellpose-models-for-label-prediction-from-brightfield-and-digital-phase-contrast-images"], [75, "cellpose-models-for-label-prediction-from-brightfield-and-digital-phase-contrast-images"], [81, "cellpose-models-for-label-prediction-from-brightfield-and-digital-phase-contrast-images"]], "Center for Microscopy and Image Analysis How-to Guides": [[43, "center-for-microscopy-and-image-analysis-how-to-guides"]], "Challenges and opportunities for bio-image analysis core-facilities": [[29, "challenges-and-opportunities-for-bio-image-analysis-core-facilities"], [55, "challenges-and-opportunities-for-bio-image-analysis-core-facilities"], [67, "challenges-and-opportunities-for-bio-image-analysis-core-facilities"], [81, "challenges-and-opportunities-for-bio-image-analysis-core-facilities"], [91, "challenges-and-opportunities-for-bio-image-analysis-core-facilities"], [105, "challenges-and-opportunities-for-bio-image-analysis-core-facilities"], [111, "challenges-and-opportunities-for-bio-image-analysis-core-facilities"]], "Challenges and opportunities for bioimage analysis core-facilities": [[29, "challenges-and-opportunities-for-bioimage-analysis-core-facilities"], [53, "challenges-and-opportunities-for-bioimage-analysis-core-facilities"], [81, "challenges-and-opportunities-for-bioimage-analysis-core-facilities"], [91, "challenges-and-opportunities-for-bioimage-analysis-core-facilities"], [111, "challenges-and-opportunities-for-bioimage-analysis-core-facilities"]], "ChatGPT for Image Analysis": [[29, "chatgpt-for-image-analysis"], [66, "chatgpt-for-image-analysis"], [75, "chatgpt-for-image-analysis"], [81, "chatgpt-for-image-analysis"]], "Checklists for publishing images and image analysis": [[83, "checklists-for-publishing-images-and-image-analysis"], [92, "checklists-for-publishing-images-and-image-analysis"], [102, "checklists-for-publishing-images-and-image-analysis"]], "Chris Halvin YouTube channel": [[43, "chris-halvin-youtube-channel"], [58, "chris-halvin-youtube-channel"], [74, "chris-halvin-youtube-channel"], [87, "chris-halvin-youtube-channel"], [91, "chris-halvin-youtube-channel"], [103, "chris-halvin-youtube-channel"], [109, "chris-halvin-youtube-channel"]], "Chris allan (5)": [[2, null]], "Christian schmidt (19)": [[3, null]], "Christian tischer (14)": [[4, null]], "Code (10)": [[42, null]], "Collaborative Working and Version Control with git[hub]": [[29, "collaborative-working-and-version-control-with-git-hub"], [66, "collaborative-working-and-version-control-with-git-hub"], [75, "collaborative-working-and-version-control-with-git-hub"], [81, "collaborative-working-and-version-control-with-git-hub"], [117, "collaborative-working-and-version-control-with-git-hub"]], "Collaborative bio-image analysis script editing with git": [[29, "collaborative-bio-image-analysis-script-editing-with-git"], [40, "collaborative-bio-image-analysis-script-editing-with-git"], [68, "collaborative-bio-image-analysis-script-editing-with-git"], [81, "collaborative-bio-image-analysis-script-editing-with-git"], [111, "collaborative-bio-image-analysis-script-editing-with-git"], [113, "collaborative-bio-image-analysis-script-editing-with-git"]], "Collection (81)": [[43, null]], "Collection of teaching material for deep learning for (biomedical) image analysis": [[5, "collection-of-teaching-material-for-deep-learning-for-biomedical-image-analysis"], [70, "collection-of-teaching-material-for-deep-learning-for-biomedical-image-analysis"], [85, "collection-of-teaching-material-for-deep-learning-for-biomedical-image-analysis"], [90, "collection-of-teaching-material-for-deep-learning-for-biomedical-image-analysis"], [91, "collection-of-teaching-material-for-deep-learning-for-biomedical-image-analysis"]], "Combining the BIDS and ARC Directory Structures for Multimodal Research Data Organization": [[0, "combining-the-bids-and-arc-directory-structures-for-multimodal-research-data-organization"], [38, "combining-the-bids-and-arc-directory-structures-for-multimodal-research-data-organization"], [51, "combining-the-bids-and-arc-directory-structures-for-multimodal-research-data-organization"], [75, "combining-the-bids-and-arc-directory-structures-for-multimodal-research-data-organization"], [81, "combining-the-bids-and-arc-directory-structures-for-multimodal-research-data-organization"], [96, "combining-the-bids-and-arc-directory-structures-for-multimodal-research-data-organization"], [111, "combining-the-bids-and-arc-directory-structures-for-multimodal-research-data-organization"]], "Community-developed checklists for publishing images and image analyses": [[4, "community-developed-checklists-for-publishing-images-and-image-analyses"], [12, "community-developed-checklists-for-publishing-images-and-image-analyses"], [18, "community-developed-checklists-for-publishing-images-and-image-analyses"], [30, "community-developed-checklists-for-publishing-images-and-image-analyses"], [43, "community-developed-checklists-for-publishing-images-and-image-analyses"], [49, "community-developed-checklists-for-publishing-images-and-image-analyses"], [53, "community-developed-checklists-for-publishing-images-and-image-analyses"], [73, "community-developed-checklists-for-publishing-images-and-image-analyses"], [78, "community-developed-checklists-for-publishing-images-and-image-analyses"], [91, "community-developed-checklists-for-publishing-images-and-image-analyses"], [111, "community-developed-checklists-for-publishing-images-and-image-analyses"]], "Conference Slides - 4th Day of Intravital Microscopy": [[66, "conference-slides-4th-day-of-intravital-microscopy"], [75, "conference-slides-4th-day-of-intravital-microscopy"], [81, "conference-slides-4th-day-of-intravital-microscopy"]], "Constantin pape (8)": [[5, null]], "Contributing": [[88, "contributing"]], "Cornelia wetzker (6)": [[6, null]], "Course on Deep Learning for imaging using PyTorch": [[13, "course-on-deep-learning-for-imaging-using-pytorch"], [49, "course-on-deep-learning-for-imaging-using-pytorch"], [70, "course-on-deep-learning-for-imaging-using-pytorch"], [87, "course-on-deep-learning-for-imaging-using-pytorch"], [90, "course-on-deep-learning-for-imaging-using-pytorch"], [91, "course-on-deep-learning-for-imaging-using-pytorch"], [109, "course-on-deep-learning-for-imaging-using-pytorch"]], "Crashkurs Forschungsdatenmanagement": [[26, "crashkurs-forschungsdatenmanagement"], [56, "crashkurs-forschungsdatenmanagement"], [66, "crashkurs-forschungsdatenmanagement"], [75, "crashkurs-forschungsdatenmanagement"], [81, "crashkurs-forschungsdatenmanagement"], [111, "crashkurs-forschungsdatenmanagement"]], "Creating Workflows and Advanced Workflow Options": [[50, "creating-workflows-and-advanced-workflow-options"], [57, "creating-workflows-and-advanced-workflow-options"], [81, "creating-workflows-and-advanced-workflow-options"], [115, "creating-workflows-and-advanced-workflow-options"]], "Creating a Research Data Management Plan using chatGPT": [[29, "creating-a-research-data-management-plan-using-chatgpt"], [40, "creating-a-research-data-management-plan-using-chatgpt"], [68, "creating-a-research-data-management-plan-using-chatgpt"], [81, "creating-a-research-data-management-plan-using-chatgpt"], [90, "creating-a-research-data-management-plan-using-chatgpt"], [99, "creating-a-research-data-management-plan-using-chatgpt"], [111, "creating-a-research-data-management-plan-using-chatgpt"]], "Creating open computational curricula": [[36, "creating-open-computational-curricula"], [55, "creating-open-computational-curricula"], [66, "creating-open-computational-curricula"], [75, "creating-open-computational-curricula"], [81, "creating-open-computational-curricula"]], "Cultivating Open Training": [[29, "cultivating-open-training"], [56, "cultivating-open-training"], [66, "cultivating-open-training"], [75, "cultivating-open-training"], [81, "cultivating-open-training"], [91, "cultivating-open-training"], [96, "cultivating-open-training"], [100, "cultivating-open-training"], [107, "cultivating-open-training"], [111, "cultivating-open-training"]], "Cultivating Open Training to advance Bio-image Analysis": [[29, "cultivating-open-training-to-advance-bio-image-analysis"], [56, "cultivating-open-training-to-advance-bio-image-analysis"], [66, "cultivating-open-training-to-advance-bio-image-analysis"], [75, "cultivating-open-training-to-advance-bio-image-analysis"], [81, "cultivating-open-training-to-advance-bio-image-analysis"], [96, "cultivating-open-training-to-advance-bio-image-analysis"], [100, "cultivating-open-training-to-advance-bio-image-analysis"], [111, "cultivating-open-training-to-advance-bio-image-analysis"]], "DALIA Interchange Format": [[66, "dalia-interchange-format"], [75, "dalia-interchange-format"], [82, "dalia-interchange-format"]], "DEEP NAPARI : Napari as a tool for deep learning project management": [[49, "deep-napari-napari-as-a-tool-for-deep-learning-project-management"], [70, "deep-napari-napari-as-a-tool-for-deep-learning-project-management"], [87, "deep-napari-napari-as-a-tool-for-deep-learning-project-management"], [90, "deep-napari-napari-as-a-tool-for-deep-learning-project-management"], [91, "deep-napari-napari-as-a-tool-for-deep-learning-project-management"]], "DL4MicEverywhere": [[11, "dl4miceverywhere"], [43, "dl4miceverywhere"], [49, "dl4miceverywhere"], [70, "dl4miceverywhere"], [81, "dl4miceverywhere"], [91, "dl4miceverywhere"]], "DL4MicEverywhere \u2013 Overcoming reproducibility challenges in deep learning microscopy imaging": [[68, "dl4miceverywhere-overcoming-reproducibility-challenges-in-deep-learning-microscopy-imaging"], [87, "dl4miceverywhere-overcoming-reproducibility-challenges-in-deep-learning-microscopy-imaging"], [95, "dl4miceverywhere-overcoming-reproducibility-challenges-in-deep-learning-microscopy-imaging"]], "DL4Proteins-notebooks": [[43, "dl4proteins-notebooks"], [48, "dl4proteins-notebooks"], [70, "dl4proteins-notebooks"], [93, "dl4proteins-notebooks"], [117, "dl4proteins-notebooks"]], "DL@MBL 2021 Exercises": [[5, "dl-mbl-2021-exercises"], [49, "dl-mbl-2021-exercises"], [55, "dl-mbl-2021-exercises"], [70, "dl-mbl-2021-exercises"], [87, "dl-mbl-2021-exercises"], [90, "dl-mbl-2021-exercises"], [91, "dl-mbl-2021-exercises"]], "Dask Course": [[13, "dask-course"], [49, "dask-course"], [70, "dask-course"], [87, "dask-course"], [91, "dask-course"], [109, "dask-course"]], "Data (8)": [[44, null]], "Data Carpentry for Biologists": [[42, "data-carpentry-for-biologists"], [57, "data-carpentry-for-biologists"], [81, "data-carpentry-for-biologists"], [85, "data-carpentry-for-biologists"]], "Data Stewardship Wizard": [[50, "data-stewardship-wizard"], [59, "data-stewardship-wizard"], [94, "data-stewardship-wizard"], [96, "data-stewardship-wizard"], [111, "data-stewardship-wizard"]], "Data Visualization with Flying Colors": [[87, "data-visualization-with-flying-colors"]], "Data handling in large-scale electron microscopy": [[40, "data-handling-in-large-scale-electron-microscopy"], [111, "data-handling-in-large-scale-electron-microscopy"]], "Data life cycle": [[43, "data-life-cycle"], [50, "data-life-cycle"], [59, "data-life-cycle"], [81, "data-life-cycle"], [111, "data-life-cycle"]], "Data management at France BioImaging": [[56, "data-management-at-france-bioimaging"], [82, "data-management-at-france-bioimaging"], [91, "data-management-at-france-bioimaging"], [97, "data-management-at-france-bioimaging"], [107, "data-management-at-france-bioimaging"], [111, "data-management-at-france-bioimaging"]], "Data stewardship (6)": [[94, null]], "Data stewardship and research data management tools for multimodal linking of imaging data in plasma medicine": [[33, "data-stewardship-and-research-data-management-tools-for-multimodal-linking-of-imaging-data-in-plasma-medicine"], [38, "data-stewardship-and-research-data-management-tools-for-multimodal-linking-of-imaging-data-in-plasma-medicine"], [66, "data-stewardship-and-research-data-management-tools-for-multimodal-linking-of-imaging-data-in-plasma-medicine"], [75, "data-stewardship-and-research-data-management-tools-for-multimodal-linking-of-imaging-data-in-plasma-medicine"], [81, "data-stewardship-and-research-data-management-tools-for-multimodal-linking-of-imaging-data-in-plasma-medicine"]], "DataPLANT knowledge base": [[43, "dataplant-knowledge-base"], [81, "dataplant-knowledge-base"], [111, "dataplant-knowledge-base"], [114, "dataplant-knowledge-base"]], "DataViz protocols - An introduction to data visualization protocols for wet lab scientists": [[41, "dataviz-protocols-an-introduction-to-data-visualization-protocols-for-wet-lab-scientists"], [75, "dataviz-protocols-an-introduction-to-data-visualization-protocols-for-wet-lab-scientists"]], "Datenmanagement": [[29, "datenmanagement"], [56, "datenmanagement"], [66, "datenmanagement"], [75, "datenmanagement"], [81, "datenmanagement"], [111, "datenmanagement"]], "Datenmanagement im Fokus: Organisation, Speicherstrategien und Datenschutz": [[26, "datenmanagement-im-fokus-organisation-speicherstrategien-und-datenschutz"], [56, "datenmanagement-im-fokus-organisation-speicherstrategien-und-datenschutz"], [66, "datenmanagement-im-fokus-organisation-speicherstrategien-und-datenschutz"], [75, "datenmanagement-im-fokus-organisation-speicherstrategien-und-datenschutz"], [81, "datenmanagement-im-fokus-organisation-speicherstrategien-und-datenschutz"], [111, "datenmanagement-im-fokus-organisation-speicherstrategien-und-datenschutz"]], "Datenmanagementpl\u00e4ne erstellen - Teil 1": [[26, "datenmanagementplane-erstellen-teil-1"], [56, "datenmanagementplane-erstellen-teil-1"], [66, "datenmanagementplane-erstellen-teil-1"], [75, "datenmanagementplane-erstellen-teil-1"], [81, "datenmanagementplane-erstellen-teil-1"], [111, "datenmanagementplane-erstellen-teil-1"]], "Datenmanagementpl\u00e4ne erstellen - Teil 2": [[26, "datenmanagementplane-erstellen-teil-2"], [56, "datenmanagementplane-erstellen-teil-2"], [66, "datenmanagementplane-erstellen-teil-2"], [75, "datenmanagementplane-erstellen-teil-2"], [81, "datenmanagementplane-erstellen-teil-2"], [111, "datenmanagementplane-erstellen-teil-2"]], "Datenschutz": [[77, "datenschutz"]], "Deconvolution Test Dataset": [[31, "deconvolution-test-dataset"], [66, "deconvolution-test-dataset"], [75, "deconvolution-test-dataset"], [81, "deconvolution-test-dataset"]], "Deep Learning Based Segmentation For Biologists": [[49, "deep-learning-based-segmentation-for-biologists"], [70, "deep-learning-based-segmentation-for-biologists"], [90, "deep-learning-based-segmentation-for-biologists"], [109, "deep-learning-based-segmentation-for-biologists"]], "Deep Learning for image analysis - Exercises": [[21, "deep-learning-for-image-analysis-exercises"], [49, "deep-learning-for-image-analysis-exercises"], [70, "deep-learning-for-image-analysis-exercises"], [87, "deep-learning-for-image-analysis-exercises"], [90, "deep-learning-for-image-analysis-exercises"], [91, "deep-learning-for-image-analysis-exercises"]], "Deep Vision and Graphics": [[49, "deep-vision-and-graphics"], [70, "deep-vision-and-graphics"], [85, "deep-vision-and-graphics"], [90, "deep-vision-and-graphics"], [109, "deep-vision-and-graphics"]], "Deep learning (12)": [[95, null]], "DeepProfiler Handbook": [[41, "deepprofiler-handbook"], [70, "deepprofiler-handbook"], [87, "deepprofiler-handbook"], [90, "deepprofiler-handbook"], [91, "deepprofiler-handbook"]], "Developing (semi)automatic analysis pipelines and technological solutions for metadata annotation and management in high-content screening (HCS) bioimaging": [[28, "developing-semi-automatic-analysis-pipelines-and-technological-solutions-for-metadata-annotation-and-management-in-high-content-screening-hcs-bioimaging"], [51, "developing-semi-automatic-analysis-pipelines-and-technological-solutions-for-metadata-annotation-and-management-in-high-content-screening-hcs-bioimaging"], [66, "developing-semi-automatic-analysis-pipelines-and-technological-solutions-for-metadata-annotation-and-management-in-high-content-screening-hcs-bioimaging"], [81, "developing-semi-automatic-analysis-pipelines-and-technological-solutions-for-metadata-annotation-and-management-in-high-content-screening-hcs-bioimaging"], [91, "developing-semi-automatic-analysis-pipelines-and-technological-solutions-for-metadata-annotation-and-management-in-high-content-screening-hcs-bioimaging"]], "Developing open-source software for bioimage analysis: opportunities and challenges": [[29, "developing-open-source-software-for-bioimage-analysis-opportunities-and-challenges"], [53, "developing-open-source-software-for-bioimage-analysis-opportunities-and-challenges"], [67, "developing-open-source-software-for-bioimage-analysis-opportunities-and-challenges"], [81, "developing-open-source-software-for-bioimage-analysis-opportunities-and-challenges"], [104, "developing-open-source-software-for-bioimage-analysis-opportunities-and-challenges"]], "Development of a platform for advanced optics education, training and prototyping": [[66, "development-of-a-platform-for-advanced-optics-education-training-and-prototyping"], [75, "development-of-a-platform-for-advanced-optics-education-training-and-prototyping"], [81, "development-of-a-platform-for-advanced-optics-education-training-and-prototyping"]], "Diffusion Models for Image Restoration - An Introduction": [[87, "diffusion-models-for-image-restoration-an-introduction"], [91, "diffusion-models-for-image-restoration-an-introduction"]], "Digital Phase Contrast on Primary Dermal Human Fibroblasts cells": [[66, "digital-phase-contrast-on-primary-dermal-human-fibroblasts-cells"], [75, "digital-phase-contrast-on-primary-dermal-human-fibroblasts-cells"], [81, "digital-phase-contrast-on-primary-dermal-human-fibroblasts-cells"]], "DigitalSreeni YouTube Channel": [[43, "digitalsreeni-youtube-channel"], [58, "digitalsreeni-youtube-channel"], [74, "digitalsreeni-youtube-channel"], [109, "digitalsreeni-youtube-channel"]], "Di\u00e1taxis - A systematic approach to technical documentation authoring.": [[57, "diataxis-a-systematic-approach-to-technical-documentation-authoring"], [59, "diataxis-a-systematic-approach-to-technical-documentation-authoring"], [82, "diataxis-a-systematic-approach-to-technical-documentation-authoring"]], "Docker Mastery - with Kubernetes + Swarm from a Docker Captain": [[57, "docker-mastery-with-kubernetes-swarm-from-a-docker-captain"], [87, "docker-mastery-with-kubernetes-swarm-from-a-docker-captain"], [114, "docker-mastery-with-kubernetes-swarm-from-a-docker-captain"]], "Docs.google.com (5)": [[65, null]], "Document (7)": [[45, null]], "Documentation (19)": [[46, null]], "Doi.org (183)": [[66, null]], "Dokumentation und Anleitung zum elektronischen Laborbuch (eLabFTW)": [[45, "dokumentation-und-anleitung-zum-elektronischen-laborbuch-elabftw"], [46, "dokumentation-und-anleitung-zum-elektronischen-laborbuch-elabftw"], [57, "dokumentation-und-anleitung-zum-elektronischen-laborbuch-elabftw"], [111, "dokumentation-und-anleitung-zum-elektronischen-laborbuch-elabftw"]], "Dominik kutra (6)": [[7, null]], "Dr Guillaume Jacquemet on studying cancer cell metastasis in the era of deep learning for microscopy": [[56, "dr-guillaume-jacquemet-on-studying-cancer-cell-metastasis-in-the-era-of-deep-learning-for-microscopy"], [74, "dr-guillaume-jacquemet-on-studying-cancer-cell-metastasis-in-the-era-of-deep-learning-for-microscopy"], [87, "dr-guillaume-jacquemet-on-studying-cancer-cell-metastasis-in-the-era-of-deep-learning-for-microscopy"], [95, "dr-guillaume-jacquemet-on-studying-cancer-cell-metastasis-in-the-era-of-deep-learning-for-microscopy"], [102, "dr-guillaume-jacquemet-on-studying-cancer-cell-metastasis-in-the-era-of-deep-learning-for-microscopy"]], "EDAM-bioimaging - The ontology of bioimage informatics operations, topics, data, and formats": [[51, "edam-bioimaging-the-ontology-of-bioimage-informatics-operations-topics-data-and-formats"], [81, "edam-bioimaging-the-ontology-of-bioimage-informatics-operations-topics-data-and-formats"], [91, "edam-bioimaging-the-ontology-of-bioimage-informatics-operations-topics-data-and-formats"]], "EDAM-bioimaging: The ontology of bioimage informatics operations, topics, data, and formats (update 2020)": [[17, "edam-bioimaging-the-ontology-of-bioimage-informatics-operations-topics-data-and-formats-update-2020"], [18, "edam-bioimaging-the-ontology-of-bioimage-informatics-operations-topics-data-and-formats-update-2020"], [29, "edam-bioimaging-the-ontology-of-bioimage-informatics-operations-topics-data-and-formats-update-2020"], [51, "edam-bioimaging-the-ontology-of-bioimage-informatics-operations-topics-data-and-formats-update-2020"], [53, "edam-bioimaging-the-ontology-of-bioimage-informatics-operations-topics-data-and-formats-update-2020"], [67, "edam-bioimaging-the-ontology-of-bioimage-informatics-operations-topics-data-and-formats-update-2020"], [81, "edam-bioimaging-the-ontology-of-bioimage-informatics-operations-topics-data-and-formats-update-2020"], [101, "edam-bioimaging-the-ontology-of-bioimage-informatics-operations-topics-data-and-formats-update-2020"]], "EMBL Deep Learning course 2019 exercises and materials": [[21, "embl-deep-learning-course-2019-exercises-and-materials"], [49, "embl-deep-learning-course-2019-exercises-and-materials"], [70, "embl-deep-learning-course-2019-exercises-and-materials"], [87, "embl-deep-learning-course-2019-exercises-and-materials"], [90, "embl-deep-learning-course-2019-exercises-and-materials"], [109, "embl-deep-learning-course-2019-exercises-and-materials"]], "EMBL Deep Learning course 2021/22 exercises and materials": [[5, "embl-deep-learning-course-2021-22-exercises-and-materials"], [21, "embl-deep-learning-course-2021-22-exercises-and-materials"], [49, "embl-deep-learning-course-2021-22-exercises-and-materials"], [70, "embl-deep-learning-course-2021-22-exercises-and-materials"], [87, "embl-deep-learning-course-2021-22-exercises-and-materials"], [90, "embl-deep-learning-course-2021-22-exercises-and-materials"], [109, "embl-deep-learning-course-2021-22-exercises-and-materials"]], "EMBL Deep Learning course 2023 exercises and materials": [[21, "embl-deep-learning-course-2023-exercises-and-materials"], [49, "embl-deep-learning-course-2023-exercises-and-materials"], [70, "embl-deep-learning-course-2023-exercises-and-materials"], [87, "embl-deep-learning-course-2023-exercises-and-materials"], [90, "embl-deep-learning-course-2023-exercises-and-materials"], [109, "embl-deep-learning-course-2023-exercises-and-materials"]], "EMBL-EBI material collection": [[43, "embl-ebi-material-collection"], [72, "embl-ebi-material-collection"], [93, "embl-ebi-material-collection"], [114, "embl-ebi-material-collection"]], "EMBO Practical Course Advanced methods in bioimage analysis": [[47, "embo-practical-course-advanced-methods-in-bioimage-analysis"], [91, "embo-practical-course-advanced-methods-in-bioimage-analysis"]], "EPFLx: Image Processing and Analysis for Life Scientists": [[50, "epflx-image-processing-and-analysis-for-life-scientists"], [78, "epflx-image-processing-and-analysis-for-life-scientists"], [91, "epflx-image-processing-and-analysis-for-life-scientists"]], "Efficiently starting institutional research data management": [[45, "efficiently-starting-institutional-research-data-management"], [66, "efficiently-starting-institutional-research-data-management"], [75, "efficiently-starting-institutional-research-data-management"], [81, "efficiently-starting-institutional-research-data-management"], [111, "efficiently-starting-institutional-research-data-management"]], "Einblicke ins Forschungsdatenmanagement - Darf ich das ver\u00f6ffentlichen? Rechtsfragen im Umgang mit Forschungsdaten": [[26, "einblicke-ins-forschungsdatenmanagement-darf-ich-das-veroffentlichen-rechtsfragen-im-umgang-mit-forschungsdaten"], [56, "einblicke-ins-forschungsdatenmanagement-darf-ich-das-veroffentlichen-rechtsfragen-im-umgang-mit-forschungsdaten"], [66, "einblicke-ins-forschungsdatenmanagement-darf-ich-das-veroffentlichen-rechtsfragen-im-umgang-mit-forschungsdaten"], [75, "einblicke-ins-forschungsdatenmanagement-darf-ich-das-veroffentlichen-rechtsfragen-im-umgang-mit-forschungsdaten"], [81, "einblicke-ins-forschungsdatenmanagement-darf-ich-das-veroffentlichen-rechtsfragen-im-umgang-mit-forschungsdaten"], [111, "einblicke-ins-forschungsdatenmanagement-darf-ich-das-veroffentlichen-rechtsfragen-im-umgang-mit-forschungsdaten"]], "Elastix tutorial": [[43, "elastix-tutorial"], [49, "elastix-tutorial"]], "Elisa ferrando-may (7)": [[8, null]], "Elnaz fazeli (5)": [[9, null]], "EmbedSeg Repository": [[12, "embedseg-repository"], [48, "embedseg-repository"], [70, "embedseg-repository"], [91, "embedseg-repository"]], "Engineering a Software Environment for Research Data Management of Microscopy Image Data in a Core Facility": [[66, "engineering-a-software-environment-for-research-data-management-of-microscopy-image-data-in-a-core-facility"], [75, "engineering-a-software-environment-for-research-data-management-of-microscopy-image-data-in-a-core-facility"]], "Erick Martins Ratamero - Expanding the OME ecosystem for imaging data management | SciPy 2024": [[58, "erick-martins-ratamero-expanding-the-ome-ecosystem-for-imaging-data-management-scipy-2024"], [74, "erick-martins-ratamero-expanding-the-ome-ecosystem-for-imaging-data-management-scipy-2024"], [91, "erick-martins-ratamero-expanding-the-ome-ecosystem-for-imaging-data-management-scipy-2024"], [97, "erick-martins-ratamero-expanding-the-ome-ecosystem-for-imaging-data-management-scipy-2024"], [106, "erick-martins-ratamero-expanding-the-ome-ecosystem-for-imaging-data-management-scipy-2024"]], "Erstellung und Realisierung einer institutionellen Forschungsdaten-Policy": [[53, "erstellung-und-realisierung-einer-institutionellen-forschungsdaten-policy"], [66, "erstellung-und-realisierung-einer-institutionellen-forschungsdaten-policy"], [81, "erstellung-und-realisierung-einer-institutionellen-forschungsdaten-policy"], [111, "erstellung-und-realisierung-einer-institutionellen-forschungsdaten-policy"]], "Estibaliz g\u00f3mez-de-mariscal (5)": [[10, null]], "Et al. (25)": [[11, null]], "EuBIAS course 2013: Intensity Dynamics at the Periphery of Nucleus": [[18, "eubias-course-2013-intensity-dynamics-at-the-periphery-of-nucleus"], [41, "eubias-course-2013-intensity-dynamics-at-the-periphery-of-nucleus"], [57, "eubias-course-2013-intensity-dynamics-at-the-periphery-of-nucleus"], [70, "eubias-course-2013-intensity-dynamics-at-the-periphery-of-nucleus"], [87, "eubias-course-2013-intensity-dynamics-at-the-periphery-of-nucleus"], [91, "eubias-course-2013-intensity-dynamics-at-the-periphery-of-nucleus"], [104, "eubias-course-2013-intensity-dynamics-at-the-periphery-of-nucleus"]], "Euro-BioImaging Scientific Ambassadors Program": [[1, "euro-bioimaging-scientific-ambassadors-program"], [66, "euro-bioimaging-scientific-ambassadors-program"], [75, "euro-bioimaging-scientific-ambassadors-program"], [81, "euro-bioimaging-scientific-ambassadors-program"]], "Euro-BioImaging Communication YouTube Channel": [[43, "euro-bioimaging-communication-youtube-channel"], [58, "euro-bioimaging-communication-youtube-channel"], [74, "euro-bioimaging-communication-youtube-channel"]], "Euro-BioImaging ERIC Annual Report 2022": [[66, "euro-bioimaging-eric-annual-report-2022"], [75, "euro-bioimaging-eric-annual-report-2022"], [81, "euro-bioimaging-eric-annual-report-2022"]], "Euro-BioImaging/BatchConvert: v0.0.4": [[66, "euro-bioimaging-batchconvert-v0-0-4"], [75, "euro-bioimaging-batchconvert-v0-0-4"], [81, "euro-bioimaging-batchconvert-v0-0-4"]], "Euro-BioImaging\u2019s Guide to FAIR BioImage Data - Practical Tasks": [[14, "euro-bioimaging-s-guide-to-fair-bioimage-data-practical-tasks"], [56, "euro-bioimaging-s-guide-to-fair-bioimage-data-practical-tasks"], [57, "euro-bioimaging-s-guide-to-fair-bioimage-data-practical-tasks"], [66, "euro-bioimaging-s-guide-to-fair-bioimage-data-practical-tasks"], [75, "euro-bioimaging-s-guide-to-fair-bioimage-data-practical-tasks"], [81, "euro-bioimaging-s-guide-to-fair-bioimage-data-practical-tasks"], [91, "euro-bioimaging-s-guide-to-fair-bioimage-data-practical-tasks"], [96, "euro-bioimaging-s-guide-to-fair-bioimage-data-practical-tasks"], [111, "euro-bioimaging-s-guide-to-fair-bioimage-data-practical-tasks"]], "Euro-BioImaging\u2019s Template for Research Data Management Plans": [[14, "euro-bioimaging-s-template-for-research-data-management-plans"], [43, "euro-bioimaging-s-template-for-research-data-management-plans"], [57, "euro-bioimaging-s-template-for-research-data-management-plans"], [66, "euro-bioimaging-s-template-for-research-data-management-plans"], [75, "euro-bioimaging-s-template-for-research-data-management-plans"], [81, "euro-bioimaging-s-template-for-research-data-management-plans"], [91, "euro-bioimaging-s-template-for-research-data-management-plans"], [96, "euro-bioimaging-s-template-for-research-data-management-plans"], [111, "euro-bioimaging-s-template-for-research-data-management-plans"]], "Event (8)": [[47, null]], "Evident OIR sample files tiles + stitched image - FV 4000": [[24, "evident-oir-sample-files-tiles-stitched-image-fv-4000"], [66, "evident-oir-sample-files-tiles-stitched-image-fv-4000"], [75, "evident-oir-sample-files-tiles-stitched-image-fv-4000"], [81, "evident-oir-sample-files-tiles-stitched-image-fv-4000"]], "Evident OIR sample files with lambda scan - FV 4000": [[24, "evident-oir-sample-files-with-lambda-scan-fv-4000"], [66, "evident-oir-sample-files-with-lambda-scan-fv-4000"], [75, "evident-oir-sample-files-with-lambda-scan-fv-4000"], [81, "evident-oir-sample-files-with-lambda-scan-fv-4000"]], "Example Imaris ims datasets.": [[66, "example-imaris-ims-datasets"], [75, "example-imaris-ims-datasets"], [81, "example-imaris-ims-datasets"]], "Example Microscopy Metadata JSON files produced using Micro-Meta App to document example microscopy experiments performed at individual core facilities": [[30, "example-microscopy-metadata-json-files-produced-using-micro-meta-app-to-document-example-microscopy-experiments-performed-at-individual-core-facilities"], [33, "example-microscopy-metadata-json-files-produced-using-micro-meta-app-to-document-example-microscopy-experiments-performed-at-individual-core-facilities"], [66, "example-microscopy-metadata-json-files-produced-using-micro-meta-app-to-document-example-microscopy-experiments-performed-at-individual-core-facilities"], [75, "example-microscopy-metadata-json-files-produced-using-micro-meta-app-to-document-example-microscopy-experiments-performed-at-individual-core-facilities"], [81, "example-microscopy-metadata-json-files-produced-using-micro-meta-app-to-document-example-microscopy-experiments-performed-at-individual-core-facilities"]], "Example Operetta Dataset": [[24, "example-operetta-dataset"], [66, "example-operetta-dataset"], [75, "example-operetta-dataset"], [81, "example-operetta-dataset"]], "Example Pipeline Tutorial": [[46, "example-pipeline-tutorial"], [48, "example-pipeline-tutorial"], [57, "example-pipeline-tutorial"], [70, "example-pipeline-tutorial"], [80, "example-pipeline-tutorial"], [91, "example-pipeline-tutorial"], [102, "example-pipeline-tutorial"], [103, "example-pipeline-tutorial"]], "Excel template for adding Key-Value Pairs to images": [[16, "excel-template-for-adding-key-value-pairs-to-images"], [35, "excel-template-for-adding-key-value-pairs-to-images"], [66, "excel-template-for-adding-key-value-pairs-to-images"], [75, "excel-template-for-adding-key-value-pairs-to-images"], [81, "excel-template-for-adding-key-value-pairs-to-images"]], "Exclusion criteria": [[62, "exclusion-criteria"]], "F1000research.com (11)": [[67, null]], "FAIR BioImage Data": [[43, "fair-bioimage-data"], [58, "fair-bioimage-data"], [74, "fair-bioimage-data"], [81, "fair-bioimage-data"], [91, "fair-bioimage-data"], [111, "fair-bioimage-data"]], "FAIR High Content Screening in Bioimaging": [[53, "fair-high-content-screening-in-bioimaging"], [73, "fair-high-content-screening-in-bioimaging"], [81, "fair-high-content-screening-in-bioimaging"], [92, "fair-high-content-screening-in-bioimaging"], [96, "fair-high-content-screening-in-bioimaging"], [97, "fair-high-content-screening-in-bioimaging"], [101, "fair-high-content-screening-in-bioimaging"], [111, "fair-high-content-screening-in-bioimaging"]], "FAIR Priciples": [[43, "fair-priciples"], [81, "fair-priciples"], [94, "fair-priciples"], [96, "fair-priciples"], [111, "fair-priciples"]], "FAIRy deep-learning for bioImage analysis": [[56, "fairy-deep-learning-for-bioimage-analysis"], [67, "fairy-deep-learning-for-bioimage-analysis"], [81, "fairy-deep-learning-for-bioimage-analysis"], [95, "fairy-deep-learning-for-bioimage-analysis"], [96, "fairy-deep-learning-for-bioimage-analysis"], [102, "fairy-deep-learning-for-bioimage-analysis"]], "Fair-principles (27)": [[96, null]], "Feature extraction in napari": [[19, "feature-extraction-in-napari"], [40, "feature-extraction-in-napari"], [68, "feature-extraction-in-napari"], [91, "feature-extraction-in-napari"], [103, "feature-extraction-in-napari"], [109, "feature-extraction-in-napari"]], "Fiji": [[50, "fiji"], [79, "fiji"], [98, "fiji"], [106, "fiji"]], "Fiji Is Just ImageJ Tutorials": [[43, "fiji-is-just-imagej-tutorials"], [58, "fiji-is-just-imagej-tutorials"], [74, "fiji-is-just-imagej-tutorials"], [91, "fiji-is-just-imagej-tutorials"]], "Finding and Choosing a Data Repository": [[3, "finding-and-choosing-a-data-repository"], [57, "finding-and-choosing-a-data-repository"], [87, "finding-and-choosing-a-data-repository"], [111, "finding-and-choosing-a-data-repository"]], "Finding and using publicly available data": [[43, "finding-and-using-publicly-available-data"], [57, "finding-and-using-publicly-available-data"], [72, "finding-and-using-publicly-available-data"], [81, "finding-and-using-publicly-available-data"], [107, "finding-and-using-publicly-available-data"], [113, "finding-and-using-publicly-available-data"]], "Fit for OMERO: How imaging facilities and IT departments work together to enable RDM for bioimaging": [[60, "fit-for-omero-how-imaging-facilities-and-it-departments-work-together-to-enable-rdm-for-bioimaging"], [91, "fit-for-omero-how-imaging-facilities-and-it-departments-work-together-to-enable-rdm-for-bioimaging"], [106, "fit-for-omero-how-imaging-facilities-and-it-departments-work-together-to-enable-rdm-for-bioimaging"], [111, "fit-for-omero-how-imaging-facilities-and-it-departments-work-together-to-enable-rdm-for-bioimaging"]], "Five great reasons to share your research data": [[40, "five-great-reasons-to-share-your-research-data"], [87, "five-great-reasons-to-share-your-research-data"], [111, "five-great-reasons-to-share-your-research-data"], [113, "five-great-reasons-to-share-your-research-data"]], "Florian jug (6)": [[12, null]], "Focalplane.biologists.com (14)": [[68, null]], "Forschungsdaten.info": [[43, "forschungsdaten-info"], [111, "forschungsdaten-info"]], "Forschungsdaten.org": [[43, "forschungsdaten-org"], [81, "forschungsdaten-org"], [111, "forschungsdaten-org"]], "Forschungsdatenmanagement zukunftsfest gestalten \u2013 Impulse f\u00fcr die Strukturevaluation der Nationalen Forschungsdateninfrastruktur (NFDI)": [[66, "forschungsdatenmanagement-zukunftsfest-gestalten-impulse-fur-die-strukturevaluation-der-nationalen-forschungsdateninfrastruktur-nfdi"], [75, "forschungsdatenmanagement-zukunftsfest-gestalten-impulse-fur-die-strukturevaluation-der-nationalen-forschungsdateninfrastruktur-nfdi"], [81, "forschungsdatenmanagement-zukunftsfest-gestalten-impulse-fur-die-strukturevaluation-der-nationalen-forschungsdateninfrastruktur-nfdi"]], "Fractal Documentation": [[46, "fractal-documentation"], [109, "fractal-documentation"], [116, "fractal-documentation"]], "FriendsDontLetFriends": [[48, "friendsdontletfriends"], [70, "friendsdontletfriends"], [85, "friendsdontletfriends"]], "From Cells to Pixels: Bridging Biologists and Image Analysts Through a Common Language": [[9, "from-cells-to-pixels-bridging-biologists-and-image-analysts-through-a-common-language"], [66, "from-cells-to-pixels-bridging-biologists-and-image-analysts-through-a-common-language"], [75, "from-cells-to-pixels-bridging-biologists-and-image-analysts-through-a-common-language"], [81, "from-cells-to-pixels-bridging-biologists-and-image-analysts-through-a-common-language"]], "From Paper to Pixels: Navigation through your Research Data - presentations of speakers": [[6, "from-paper-to-pixels-navigation-through-your-research-data-presentations-of-speakers"], [28, "from-paper-to-pixels-navigation-through-your-research-data-presentations-of-speakers"], [37, "from-paper-to-pixels-navigation-through-your-research-data-presentations-of-speakers"], [56, "from-paper-to-pixels-navigation-through-your-research-data-presentations-of-speakers"], [66, "from-paper-to-pixels-navigation-through-your-research-data-presentations-of-speakers"], [75, "from-paper-to-pixels-navigation-through-your-research-data-presentations-of-speakers"], [81, "from-paper-to-pixels-navigation-through-your-research-data-presentations-of-speakers"], [111, "from-paper-to-pixels-navigation-through-your-research-data-presentations-of-speakers"]], "Fundamentals in digital image processing": [[13, "fundamentals-in-digital-image-processing"], [49, "fundamentals-in-digital-image-processing"], [70, "fundamentals-in-digital-image-processing"], [87, "fundamentals-in-digital-image-processing"], [91, "fundamentals-in-digital-image-processing"]], "Fundamentals of image analysis in Python with scikit-image, napari, and friends": [[49, "fundamentals-of-image-analysis-in-python-with-scikit-image-napari-and-friends"], [70, "fundamentals-of-image-analysis-in-python-with-scikit-image-napari-and-friends"], [80, "fundamentals-of-image-analysis-in-python-with-scikit-image-napari-and-friends"], [91, "fundamentals-of-image-analysis-in-python-with-scikit-image-napari-and-friends"], [109, "fundamentals-of-image-analysis-in-python-with-scikit-image-napari-and-friends"]], "Galaxy Documentation": [[46, "galaxy-documentation"], [116, "galaxy-documentation"]], "Galaxy Training": [[43, "galaxy-training"], [57, "galaxy-training"], [81, "galaxy-training"], [91, "galaxy-training"]], "Galaxy Training Material": [[56, "galaxy-training-material"], [57, "galaxy-training-material"], [70, "galaxy-training-material"], [85, "galaxy-training-material"]], "Galaxy workflows": [[50, "galaxy-workflows"], [57, "galaxy-workflows"], [83, "galaxy-workflows"], [115, "galaxy-workflows"]], "Generative artificial intelligence for bio-image analysis": [[29, "generative-artificial-intelligence-for-bio-image-analysis"], [55, "generative-artificial-intelligence-for-bio-image-analysis"], [67, "generative-artificial-intelligence-for-bio-image-analysis"], [81, "generative-artificial-intelligence-for-bio-image-analysis"], [90, "generative-artificial-intelligence-for-bio-image-analysis"], [91, "generative-artificial-intelligence-for-bio-image-analysis"], [109, "generative-artificial-intelligence-for-bio-image-analysis"]], "GerBI Teaching Resources Link List": [[43, "gerbi-teaching-resources-link-list"], [87, "gerbi-teaching-resources-link-list"]], "GerBI-Chat: Teil 1 - Vom Bedarf bis zum Gro\u00dfger\u00e4teantrag-Schreiben": [[0, "gerbi-chat-teil-1-vom-bedarf-bis-zum-groszgerateantrag-schreiben"], [32, "gerbi-chat-teil-1-vom-bedarf-bis-zum-groszgerateantrag-schreiben"], [66, "gerbi-chat-teil-1-vom-bedarf-bis-zum-groszgerateantrag-schreiben"], [75, "gerbi-chat-teil-1-vom-bedarf-bis-zum-groszgerateantrag-schreiben"], [81, "gerbi-chat-teil-1-vom-bedarf-bis-zum-groszgerateantrag-schreiben"]], "GerBI-Chat: Teil 2 - Wie schreibe ich am besten einen Gro\u00dfegr\u00e4teantrag": [[0, "gerbi-chat-teil-2-wie-schreibe-ich-am-besten-einen-groszegrateantrag"], [32, "gerbi-chat-teil-2-wie-schreibe-ich-am-besten-einen-groszegrateantrag"], [66, "gerbi-chat-teil-2-wie-schreibe-ich-am-besten-einen-groszegrateantrag"], [75, "gerbi-chat-teil-2-wie-schreibe-ich-am-besten-einen-groszegrateantrag"], [81, "gerbi-chat-teil-2-wie-schreibe-ich-am-besten-einen-groszegrateantrag"]], "Getting started with Mambaforge and Python": [[19, "getting-started-with-mambaforge-and-python"], [40, "getting-started-with-mambaforge-and-python"], [64, "getting-started-with-mambaforge-and-python"], [81, "getting-started-with-mambaforge-and-python"], [109, "getting-started-with-mambaforge-and-python"]], "Getting started with Python: intro and set-up a conda environment": [[28, "getting-started-with-python-intro-and-set-up-a-conda-environment"], [66, "getting-started-with-python-intro-and-set-up-a-conda-environment"], [75, "getting-started-with-python-intro-and-set-up-a-conda-environment"], [81, "getting-started-with-python-intro-and-set-up-a-conda-environment"]], "Ghent University Research Data Management (RDM) - policy and support": [[59, "ghent-university-research-data-management-rdm-policy-and-support"], [87, "ghent-university-research-data-management-rdm-policy-and-support"], [111, "ghent-university-research-data-management-rdm-policy-and-support"]], "Git.mpi-cbg.de (7)": [[69, null]], "Github repository (45)": [[48, null]], "Github.com (123)": [[70, null]], "Glencoe Software Webinars": [[2, "glencoe-software-webinars"], [43, "glencoe-software-webinars"], [57, "glencoe-software-webinars"], [87, "glencoe-software-webinars"], [106, "glencoe-software-webinars"], [114, "glencoe-software-webinars"]], "Global BioImaging Training Database": [[43, "global-bioimaging-training-database"], [47, "global-bioimaging-training-database"], [91, "global-bioimaging-training-database"]], "Global BioImaging YouTube channel": [[43, "global-bioimaging-youtube-channel"], [58, "global-bioimaging-youtube-channel"], [74, "global-bioimaging-youtube-channel"]], "Gpl-2.0 (7)": [[84, null]], "Guidance for Developing a Research Data Management (RDM) Policy": [[41, "guidance-for-developing-a-research-data-management-rdm-policy"], [66, "guidance-for-developing-a-research-data-management-rdm-policy"], [81, "guidance-for-developing-a-research-data-management-rdm-policy"], [111, "guidance-for-developing-a-research-data-management-rdm-policy"]], "Guillaume witz (8)": [[13, null]], "Gut Analysis Toolbox": [[10, "gut-analysis-toolbox"], [29, "gut-analysis-toolbox"], [66, "gut-analysis-toolbox"], [75, "gut-analysis-toolbox"], [81, "gut-analysis-toolbox"]], "Gut Analysis Toolbox: Training data and 2D models for segmenting enteric neurons, neuronal subtypes and ganglia": [[29, "gut-analysis-toolbox-training-data-and-2d-models-for-segmenting-enteric-neurons-neuronal-subtypes-and-ganglia"], [66, "gut-analysis-toolbox-training-data-and-2d-models-for-segmenting-enteric-neurons-neuronal-subtypes-and-ganglia"], [75, "gut-analysis-toolbox-training-data-and-2d-models-for-segmenting-enteric-neurons-neuronal-subtypes-and-ganglia"], [81, "gut-analysis-toolbox-training-data-and-2d-models-for-segmenting-enteric-neurons-neuronal-subtypes-and-ganglia"]], "Hackaton Results - Conversion of KNIME image analysis workflows to Galaxy": [[28, "hackaton-results-conversion-of-knime-image-analysis-workflows-to-galaxy"], [56, "hackaton-results-conversion-of-knime-image-analysis-workflows-to-galaxy"], [66, "hackaton-results-conversion-of-knime-image-analysis-workflows-to-galaxy"], [75, "hackaton-results-conversion-of-knime-image-analysis-workflows-to-galaxy"], [81, "hackaton-results-conversion-of-knime-image-analysis-workflows-to-galaxy"], [111, "hackaton-results-conversion-of-knime-image-analysis-workflows-to-galaxy"]], "Haftung f\u00fcr Inhalte": [[77, "haftung-fur-inhalte"]], "Haftung f\u00fcr Links": [[77, "haftung-fur-links"]], "Haftungsausschluss": [[77, "haftungsausschluss"]], "HeLa \u201cKyoto\u201d cells under the scope": [[31, "hela-kyoto-cells-under-the-scope"], [66, "hela-kyoto-cells-under-the-scope"], [75, "hela-kyoto-cells-under-the-scope"], [81, "hela-kyoto-cells-under-the-scope"]], "High throughput & automated data analysis and data management workflow with Cellprofiler and OMERO": [[16, "high-throughput-automated-data-analysis-and-data-management-workflow-with-cellprofiler-and-omero"], [35, "high-throughput-automated-data-analysis-and-data-management-workflow-with-cellprofiler-and-omero"], [43, "high-throughput-automated-data-analysis-and-data-management-workflow-with-cellprofiler-and-omero"], [75, "high-throughput-automated-data-analysis-and-data-management-workflow-with-cellprofiler-and-omero"], [81, "high-throughput-automated-data-analysis-and-data-management-workflow-with-cellprofiler-and-omero"], [91, "high-throughput-automated-data-analysis-and-data-management-workflow-with-cellprofiler-and-omero"], [106, "high-throughput-automated-data-analysis-and-data-management-workflow-with-cellprofiler-and-omero"]], "Highlights from the 2016-2020 NEUBIAS training schools for Bioimage Analysts: a success story and key asset for analysts and life scientists": [[18, "highlights-from-the-2016-2020-neubias-training-schools-for-bioimage-analysts-a-success-story-and-key-asset-for-analysts-and-life-scientists"], [29, "highlights-from-the-2016-2020-neubias-training-schools-for-bioimage-analysts-a-success-story-and-key-asset-for-analysts-and-life-scientists"], [31, "highlights-from-the-2016-2020-neubias-training-schools-for-bioimage-analysts-a-success-story-and-key-asset-for-analysts-and-life-scientists"], [53, "highlights-from-the-2016-2020-neubias-training-schools-for-bioimage-analysts-a-success-story-and-key-asset-for-analysts-and-life-scientists"], [67, "highlights-from-the-2016-2020-neubias-training-schools-for-bioimage-analysts-a-success-story-and-key-asset-for-analysts-and-life-scientists"], [81, "highlights-from-the-2016-2020-neubias-training-schools-for-bioimage-analysts-a-success-story-and-key-asset-for-analysts-and-life-scientists"], [91, "highlights-from-the-2016-2020-neubias-training-schools-for-bioimage-analysts-a-success-story-and-key-asset-for-analysts-and-life-scientists"], [104, "highlights-from-the-2016-2020-neubias-training-schools-for-bioimage-analysts-a-success-story-and-key-asset-for-analysts-and-life-scientists"]], "Hitchhiking through a diverse Bio-image Analysis Software Universe": [[29, "hitchhiking-through-a-diverse-bio-image-analysis-software-universe"], [55, "hitchhiking-through-a-diverse-bio-image-analysis-software-universe"], [66, "hitchhiking-through-a-diverse-bio-image-analysis-software-universe"], [67, "hitchhiking-through-a-diverse-bio-image-analysis-software-universe"], [81, "hitchhiking-through-a-diverse-bio-image-analysis-software-universe"], [91, "hitchhiking-through-a-diverse-bio-image-analysis-software-universe"]], "How to contribute": [[62, null]], "How to get started with Jupyter and Colab": [[58, "how-to-get-started-with-jupyter-and-colab"], [74, "how-to-get-started-with-jupyter-and-colab"]], "How to use this resource": [[88, "how-to-use-this-resource"]], "How to write a bug report": [[19, "how-to-write-a-bug-report"], [40, "how-to-write-a-bug-report"], [68, "how-to-write-a-bug-report"], [109, "how-to-write-a-bug-report"]], "Human DAB staining Axioscan BF 20x": [[66, "human-dab-staining-axioscan-bf-20x"], [75, "human-dab-staining-axioscan-bf-20x"], [81, "human-dab-staining-axioscan-bf-20x"]], "I2K 2024: clEsperanto - GPU-Accelerated Image Processing Library": [[29, "i2k-2024-clesperanto-gpu-accelerated-image-processing-library"], [48, "i2k-2024-clesperanto-gpu-accelerated-image-processing-library"], [49, "i2k-2024-clesperanto-gpu-accelerated-image-processing-library"], [57, "i2k-2024-clesperanto-gpu-accelerated-image-processing-library"], [60, "i2k-2024-clesperanto-gpu-accelerated-image-processing-library"], [70, "i2k-2024-clesperanto-gpu-accelerated-image-processing-library"], [80, "i2k-2024-clesperanto-gpu-accelerated-image-processing-library"], [91, "i2k-2024-clesperanto-gpu-accelerated-image-processing-library"], [114, "i2k-2024-clesperanto-gpu-accelerated-image-processing-library"], [115, "i2k-2024-clesperanto-gpu-accelerated-image-processing-library"]], "I2K2024 workshop material - Lazy Parallel Processing and Visualization of Large Data with ImgLib2, BigDataViewer, the N5-API, and Spark": [[48, "i2k2024-workshop-material-lazy-parallel-processing-and-visualization-of-large-data-with-imglib2-bigdataviewer-the-n5-api-and-spark"], [49, "i2k2024-workshop-material-lazy-parallel-processing-and-visualization-of-large-data-with-imglib2-bigdataviewer-the-n5-api-and-spark"], [60, "i2k2024-workshop-material-lazy-parallel-processing-and-visualization-of-large-data-with-imglib2-bigdataviewer-the-n5-api-and-spark"], [70, "i2k2024-workshop-material-lazy-parallel-processing-and-visualization-of-large-data-with-imglib2-bigdataviewer-the-n5-api-and-spark"], [114, "i2k2024-workshop-material-lazy-parallel-processing-and-visualization-of-large-data-with-imglib2-bigdataviewer-the-n5-api-and-spark"]], "I2K2024(virtual) - Bio-Image Analysis Code Generation": [[29, "i2k2024-virtual-bio-image-analysis-code-generation"], [48, "i2k2024-virtual-bio-image-analysis-code-generation"], [49, "i2k2024-virtual-bio-image-analysis-code-generation"], [57, "i2k2024-virtual-bio-image-analysis-code-generation"], [70, "i2k2024-virtual-bio-image-analysis-code-generation"], [80, "i2k2024-virtual-bio-image-analysis-code-generation"], [91, "i2k2024-virtual-bio-image-analysis-code-generation"]], "I3D bio \u2013 Information Infrastructure for BioImage Data - Bioimage Metadata": [[3, "i3d-bio-information-infrastructure-for-bioimage-data-bioimage-metadata"], [43, "i3d-bio-information-infrastructure-for-bioimage-data-bioimage-metadata"], [87, "i3d-bio-information-infrastructure-for-bioimage-data-bioimage-metadata"], [92, "i3d-bio-information-infrastructure-for-bioimage-data-bioimage-metadata"], [101, "i3d-bio-information-infrastructure-for-bioimage-data-bioimage-metadata"], [111, "i3d-bio-information-infrastructure-for-bioimage-data-bioimage-metadata"]], "I3D:bio list of online training material": [[43, "i3d-bio-list-of-online-training-material"], [87, "i3d-bio-list-of-online-training-material"], [111, "i3d-bio-list-of-online-training-material"]], "I3D:bio\u2019s OMERO training material: Re-usable, adjustable, multi-purpose slides for local user training": [[3, "i3d-bio-s-omero-training-material-re-usable-adjustable-multi-purpose-slides-for-local-user-training"], [8, "i3d-bio-s-omero-training-material-re-usable-adjustable-multi-purpose-slides-for-local-user-training"], [23, "i3d-bio-s-omero-training-material-re-usable-adjustable-multi-purpose-slides-for-local-user-training"], [33, "i3d-bio-s-omero-training-material-re-usable-adjustable-multi-purpose-slides-for-local-user-training"], [34, "i3d-bio-s-omero-training-material-re-usable-adjustable-multi-purpose-slides-for-local-user-training"], [35, "i3d-bio-s-omero-training-material-re-usable-adjustable-multi-purpose-slides-for-local-user-training"], [37, "i3d-bio-s-omero-training-material-re-usable-adjustable-multi-purpose-slides-for-local-user-training"], [55, "i3d-bio-s-omero-training-material-re-usable-adjustable-multi-purpose-slides-for-local-user-training"], [58, "i3d-bio-s-omero-training-material-re-usable-adjustable-multi-purpose-slides-for-local-user-training"], [66, "i3d-bio-s-omero-training-material-re-usable-adjustable-multi-purpose-slides-for-local-user-training"], [74, "i3d-bio-s-omero-training-material-re-usable-adjustable-multi-purpose-slides-for-local-user-training"], [75, "i3d-bio-s-omero-training-material-re-usable-adjustable-multi-purpose-slides-for-local-user-training"], [81, "i3d-bio-s-omero-training-material-re-usable-adjustable-multi-purpose-slides-for-local-user-training"], [105, "i3d-bio-s-omero-training-material-re-usable-adjustable-multi-purpose-slides-for-local-user-training"], [106, "i3d-bio-s-omero-training-material-re-usable-adjustable-multi-purpose-slides-for-local-user-training"], [111, "i3d-bio-s-omero-training-material-re-usable-adjustable-multi-purpose-slides-for-local-user-training"]], "IAFIG-RMS Python for Bioimage Analysis Course": [[49, "iafig-rms-python-for-bioimage-analysis-course"], [70, "iafig-rms-python-for-bioimage-analysis-course"], [91, "iafig-rms-python-for-bioimage-analysis-course"]], "ICS/IDS stitched file": [[66, "ics-ids-stitched-file"], [75, "ics-ids-stitched-file"], [81, "ics-ids-stitched-file"]], "ITKElastix Examples": [[70, "itkelastix-examples"], [91, "itkelastix-examples"]], "Ibiology. Bioimage Analysis Course. The Life Cycle of an Image Data Set": [[43, "ibiology-bioimage-analysis-course-the-life-cycle-of-an-image-data-set"], [58, "ibiology-bioimage-analysis-course-the-life-cycle-of-an-image-data-set"], [91, "ibiology-bioimage-analysis-course-the-life-cycle-of-an-image-data-set"]], "If you license it, it\u2019ll be harder to steal it. Why we should license our work": [[29, "if-you-license-it-itll-be-harder-to-steal-it-why-we-should-license-our-work"], [40, "if-you-license-it-itll-be-harder-to-steal-it-why-we-should-license-our-work"], [68, "if-you-license-it-itll-be-harder-to-steal-it-why-we-should-license-our-work"], [81, "if-you-license-it-itll-be-harder-to-steal-it-why-we-should-license-our-work"], [100, "if-you-license-it-itll-be-harder-to-steal-it-why-we-should-license-our-work"], [111, "if-you-license-it-itll-be-harder-to-steal-it-why-we-should-license-our-work"]], "Image Analysis Training Resources": [[41, "image-analysis-training-resources"], [81, "image-analysis-training-resources"], [91, "image-analysis-training-resources"], [104, "image-analysis-training-resources"]], "Image Data Resources": [[43, "image-data-resources"], [44, "image-data-resources"], [53, "image-data-resources"], [73, "image-data-resources"]], "Image Data Services at Euro-BioImaging: Community efforts towards FAIR Image Data and Analysis Services": [[55, "image-data-services-at-euro-bioimaging-community-efforts-towards-fair-image-data-and-analysis-services"], [65, "image-data-services-at-euro-bioimaging-community-efforts-towards-fair-image-data-and-analysis-services"], [87, "image-data-services-at-euro-bioimaging-community-efforts-towards-fair-image-data-and-analysis-services"]], "Image Processing with Python": [[7, "image-processing-with-python"], [11, "image-processing-with-python"], [36, "image-processing-with-python"], [43, "image-processing-with-python"], [57, "image-processing-with-python"], [70, "image-processing-with-python"], [81, "image-processing-with-python"], [91, "image-processing-with-python"], [109, "image-processing-with-python"], [112, "image-processing-with-python"], [114, "image-processing-with-python"]], "Image Repository Decision Tree - Where do I deposit my imaging data": [[14, "image-repository-decision-tree-where-do-i-deposit-my-imaging-data"], [66, "image-repository-decision-tree-where-do-i-deposit-my-imaging-data"], [75, "image-repository-decision-tree-where-do-i-deposit-my-imaging-data"], [81, "image-repository-decision-tree-where-do-i-deposit-my-imaging-data"]], "Image analysis and visualization in Python with scikit-image, napari, and friends": [[49, "image-analysis-and-visualization-in-python-with-scikit-image-napari-and-friends"], [70, "image-analysis-and-visualization-in-python-with-scikit-image-napari-and-friends"], [80, "image-analysis-and-visualization-in-python-with-scikit-image-napari-and-friends"], [91, "image-analysis-and-visualization-in-python-with-scikit-image-napari-and-friends"], [109, "image-analysis-and-visualization-in-python-with-scikit-image-napari-and-friends"]], "Image analysis course material": [[4, "image-analysis-course-material"], [70, "image-analysis-course-material"], [85, "image-analysis-course-material"]], "Image analysis in Galaxy": [[1, "image-analysis-in-galaxy"], [55, "image-analysis-in-galaxy"], [65, "image-analysis-in-galaxy"], [87, "image-analysis-in-galaxy"], [91, "image-analysis-in-galaxy"]], "Image data management (14)": [[97, null]], "Image processing for beginners": [[13, "image-processing-for-beginners"], [49, "image-processing-for-beginners"], [70, "image-processing-for-beginners"], [80, "image-processing-for-beginners"], [91, "image-processing-for-beginners"], [109, "image-processing-for-beginners"]], "Image processing with Python": [[13, "image-processing-with-python"], [49, "image-processing-with-python"], [70, "id1"], [85, "image-processing-with-python"], [109, "id1"]], "Image-based Profiling Handbook": [[41, "image-based-profiling-handbook"], [70, "image-based-profiling-handbook"], [83, "image-based-profiling-handbook"], [91, "image-based-profiling-handbook"]], "ImageJ Macro Introduction": [[42, "imagej-macro-introduction"], [55, "imagej-macro-introduction"], [70, "imagej-macro-introduction"], [87, "imagej-macro-introduction"], [91, "imagej-macro-introduction"], [104, "imagej-macro-introduction"]], "ImageJ tool for percentage estimation of pneumonia in lungs": [[20, "imagej-tool-for-percentage-estimation-of-pneumonia-in-lungs"], [66, "imagej-tool-for-percentage-estimation-of-pneumonia-in-lungs"], [75, "imagej-tool-for-percentage-estimation-of-pneumonia-in-lungs"], [81, "imagej-tool-for-percentage-estimation-of-pneumonia-in-lungs"]], "ImageJ2 API-beating": [[29, "imagej2-api-beating"], [55, "imagej2-api-beating"], [69, "imagej2-api-beating"], [80, "imagej2-api-beating"], [91, "imagej2-api-beating"], [98, "imagej2-api-beating"], [104, "imagej2-api-beating"]], "Imagej (19)": [[98, null]], "Imaris Tutorials": [[43, "imaris-tutorials"], [58, "imaris-tutorials"], [78, "imaris-tutorials"], [91, "imaris-tutorials"]], "Implantation of abdominal imaging windows on the mouse kidney": [[22, "implantation-of-abdominal-imaging-windows-on-the-mouse-kidney"], [66, "implantation-of-abdominal-imaging-windows-on-the-mouse-kidney"], [75, "implantation-of-abdominal-imaging-windows-on-the-mouse-kidney"]], "Implantation of abdominal imaging windows on the mouse kidney - short version": [[22, "implantation-of-abdominal-imaging-windows-on-the-mouse-kidney-short-version"], [66, "implantation-of-abdominal-imaging-windows-on-the-mouse-kidney-short-version"], [75, "implantation-of-abdominal-imaging-windows-on-the-mouse-kidney-short-version"]], "Implantation of abdominal imaging windows on the mouse liver": [[22, "implantation-of-abdominal-imaging-windows-on-the-mouse-liver"], [66, "implantation-of-abdominal-imaging-windows-on-the-mouse-liver"], [75, "implantation-of-abdominal-imaging-windows-on-the-mouse-liver"]], "Implantation of abdominal imaging windows on the mouse liver - short version": [[22, "implantation-of-abdominal-imaging-windows-on-the-mouse-liver-short-version"], [66, "implantation-of-abdominal-imaging-windows-on-the-mouse-liver-short-version"], [75, "implantation-of-abdominal-imaging-windows-on-the-mouse-liver-short-version"]], "Impressum": [[77, "impressum"]], "Imprint": [[77, null]], "Inclusion criteria": [[62, "inclusion-criteria"]], "Ink in a dish": [[66, "ink-in-a-dish"], [75, "ink-in-a-dish"]], "Insights and Impact From Five Cycles of Essential Open Source Software for Science": [[53, "insights-and-impact-from-five-cycles-of-essential-open-source-software-for-science"], [75, "insights-and-impact-from-five-cycles-of-essential-open-source-software-for-science"], [81, "insights-and-impact-from-five-cycles-of-essential-open-source-software-for-science"], [108, "insights-and-impact-from-five-cycles-of-essential-open-source-software-for-science"]], "Insights from Acquiring Open Medical Imaging Datasets for Foundation Model Development": [[66, "insights-from-acquiring-open-medical-imaging-datasets-for-foundation-model-development"], [75, "insights-from-acquiring-open-medical-imaging-datasets-for-foundation-model-development"], [81, "insights-from-acquiring-open-medical-imaging-datasets-for-foundation-model-development"]], "Insights from Acquiring Open Medical Imaging Datasets for Foundation Model Development": [[66, "id1"], [75, "id1"], [81, "id1"]], "Institutionalization and Collaboration as a Way of Addressing the Challenges Open Science Presents to Libraries: The University of Konstanz as a National Pioneer": [[66, "institutionalization-and-collaboration-as-a-way-of-addressing-the-challenges-open-science-presents-to-libraries-the-university-of-konstanz-as-a-national-pioneer"], [75, "institutionalization-and-collaboration-as-a-way-of-addressing-the-challenges-open-science-presents-to-libraries-the-university-of-konstanz-as-a-national-pioneer"], [81, "institutionalization-and-collaboration-as-a-way-of-addressing-the-challenges-open-science-presents-to-libraries-the-university-of-konstanz-as-a-national-pioneer"]], "Interactive Image Data Flow Graphs": [[20, "interactive-image-data-flow-graphs"], [66, "interactive-image-data-flow-graphs"], [75, "interactive-image-data-flow-graphs"], [81, "interactive-image-data-flow-graphs"]], "Intravital microscopy contrasting agents for application - Database": [[22, "intravital-microscopy-contrasting-agents-for-application-database"], [66, "intravital-microscopy-contrasting-agents-for-application-database"], [75, "intravital-microscopy-contrasting-agents-for-application-database"], [81, "intravital-microscopy-contrasting-agents-for-application-database"]], "Intro napari slides": [[56, "intro-napari-slides"], [85, "intro-napari-slides"], [103, "intro-napari-slides"]], "Introduction to Bioimage Analysis": [[41, "introduction-to-bioimage-analysis"], [49, "introduction-to-bioimage-analysis"], [81, "introduction-to-bioimage-analysis"], [91, "introduction-to-bioimage-analysis"], [98, "introduction-to-bioimage-analysis"], [109, "introduction-to-bioimage-analysis"]], "Introduction to Deep Learning for Microscopy": [[49, "introduction-to-deep-learning-for-microscopy"], [70, "introduction-to-deep-learning-for-microscopy"], [85, "introduction-to-deep-learning-for-microscopy"], [95, "introduction-to-deep-learning-for-microscopy"], [109, "introduction-to-deep-learning-for-microscopy"], [112, "introduction-to-deep-learning-for-microscopy"]], "Introduction to Image Analysis with Fiji": [[29, "introduction-to-image-analysis-with-fiji"], [70, "introduction-to-image-analysis-with-fiji"], [91, "introduction-to-image-analysis-with-fiji"], [98, "introduction-to-image-analysis-with-fiji"]], "Introduction to ImageJ macro programming, Scientific Computing Facility, MPI CBG Dresden": [[29, "introduction-to-imagej-macro-programming-scientific-computing-facility-mpi-cbg-dresden"], [55, "introduction-to-imagej-macro-programming-scientific-computing-facility-mpi-cbg-dresden"], [69, "introduction-to-imagej-macro-programming-scientific-computing-facility-mpi-cbg-dresden"], [87, "introduction-to-imagej-macro-programming-scientific-computing-facility-mpi-cbg-dresden"], [91, "introduction-to-imagej-macro-programming-scientific-computing-facility-mpi-cbg-dresden"], [98, "introduction-to-imagej-macro-programming-scientific-computing-facility-mpi-cbg-dresden"]], "Introduction to Research Data Management and Open Research": [[56, "introduction-to-research-data-management-and-open-research"], [75, "introduction-to-research-data-management-and-open-research"], [81, "introduction-to-research-data-management-and-open-research"], [107, "introduction-to-research-data-management-and-open-research"], [111, "introduction-to-research-data-management-and-open-research"]], "Introduction to light-microscopy / Widefield microscopy": [[66, "introduction-to-light-microscopy-widefield-microscopy"], [75, "introduction-to-light-microscopy-widefield-microscopy"]], "Isabel kemmer (4)": [[14, null]], "JIPipe: visual batch processing for ImageJ": [[46, "jipipe-visual-batch-processing-for-imagej"], [53, "jipipe-visual-batch-processing-for-imagej"], [73, "jipipe-visual-batch-processing-for-imagej"], [98, "jipipe-visual-batch-processing-for-imagej"], [116, "jipipe-visual-batch-processing-for-imagej"]], "Jean-marie burel (6)": [[15, null]], "Jens wendt (7)": [[16, null]], "Josh moore (27)": [[17, null]], "Jupyter for interactive cloud computing": [[13, "jupyter-for-interactive-cloud-computing"], [55, "jupyter-for-interactive-cloud-computing"], [65, "jupyter-for-interactive-cloud-computing"], [87, "jupyter-for-interactive-cloud-computing"], [91, "jupyter-for-interactive-cloud-computing"], [104, "jupyter-for-interactive-cloud-computing"]], "KNIME Image Processing": [[46, "knime-image-processing"], [50, "knime-image-processing"], [57, "knime-image-processing"], [92, "knime-image-processing"], [98, "knime-image-processing"], [106, "knime-image-processing"], [115, "knime-image-processing"]], "Key-Value pair template for annotation in OMERO for light microscopy data acquired with AxioScan7 - Core Facility Cellular Imaging (CFCI)": [[32, "key-value-pair-template-for-annotation-in-omero-for-light-microscopy-data-acquired-with-axioscan7-core-facility-cellular-imaging-cfci"], [37, "key-value-pair-template-for-annotation-in-omero-for-light-microscopy-data-acquired-with-axioscan7-core-facility-cellular-imaging-cfci"], [66, "key-value-pair-template-for-annotation-in-omero-for-light-microscopy-data-acquired-with-axioscan7-core-facility-cellular-imaging-cfci"], [75, "key-value-pair-template-for-annotation-in-omero-for-light-microscopy-data-acquired-with-axioscan7-core-facility-cellular-imaging-cfci"], [81, "key-value-pair-template-for-annotation-in-omero-for-light-microscopy-data-acquired-with-axioscan7-core-facility-cellular-imaging-cfci"]], "Key-Value pair template for annotation of datasets in OMERO (PERIKLES study)": [[3, "key-value-pair-template-for-annotation-of-datasets-in-omero-perikles-study"], [23, "key-value-pair-template-for-annotation-of-datasets-in-omero-perikles-study"], [32, "key-value-pair-template-for-annotation-of-datasets-in-omero-perikles-study"], [37, "key-value-pair-template-for-annotation-of-datasets-in-omero-perikles-study"], [66, "key-value-pair-template-for-annotation-of-datasets-in-omero-perikles-study"], [75, "key-value-pair-template-for-annotation-of-datasets-in-omero-perikles-study"], [81, "key-value-pair-template-for-annotation-of-datasets-in-omero-perikles-study"]], "Key-Value pair template for annotation of datasets in OMERO for light- and electron microscopy data within the research group of Prof. M\u00fcller-Reichert": [[3, "key-value-pair-template-for-annotation-of-datasets-in-omero-for-light-and-electron-microscopy-data-within-the-research-group-of-prof-muller-reichert"], [22, "key-value-pair-template-for-annotation-of-datasets-in-omero-for-light-and-electron-microscopy-data-within-the-research-group-of-prof-muller-reichert"], [23, "key-value-pair-template-for-annotation-of-datasets-in-omero-for-light-and-electron-microscopy-data-within-the-research-group-of-prof-muller-reichert"], [32, "key-value-pair-template-for-annotation-of-datasets-in-omero-for-light-and-electron-microscopy-data-within-the-research-group-of-prof-muller-reichert"], [37, "key-value-pair-template-for-annotation-of-datasets-in-omero-for-light-and-electron-microscopy-data-within-the-research-group-of-prof-muller-reichert"], [66, "key-value-pair-template-for-annotation-of-datasets-in-omero-for-light-and-electron-microscopy-data-within-the-research-group-of-prof-muller-reichert"], [75, "key-value-pair-template-for-annotation-of-datasets-in-omero-for-light-and-electron-microscopy-data-within-the-research-group-of-prof-muller-reichert"], [81, "key-value-pair-template-for-annotation-of-datasets-in-omero-for-light-and-electron-microscopy-data-within-the-research-group-of-prof-muller-reichert"]], "Key-Value pairs scripts": [[43, "key-value-pairs-scripts"], [46, "key-value-pairs-scripts"], [87, "key-value-pairs-scripts"], [106, "key-value-pairs-scripts"]], "Kollaboratives Arbeiten und Versionskontrolle mit Git": [[29, "kollaboratives-arbeiten-und-versionskontrolle-mit-git"], [56, "kollaboratives-arbeiten-und-versionskontrolle-mit-git"], [66, "kollaboratives-arbeiten-und-versionskontrolle-mit-git"], [75, "kollaboratives-arbeiten-und-versionskontrolle-mit-git"], [81, "kollaboratives-arbeiten-und-versionskontrolle-mit-git"], [96, "kollaboratives-arbeiten-und-versionskontrolle-mit-git"], [111, "kollaboratives-arbeiten-und-versionskontrolle-mit-git"]], "Kontakt": [[77, "kontakt"]], "Kota miura (11)": [[18, null]], "Kreshuk Lab\u2019s EMBL EIPP predoc course teaching material": [[57, "kreshuk-lab-s-embl-eipp-predoc-course-teaching-material"], [70, "kreshuk-lab-s-embl-eipp-predoc-course-teaching-material"], [87, "kreshuk-lab-s-embl-eipp-predoc-course-teaching-material"], [90, "kreshuk-lab-s-embl-eipp-predoc-course-teaching-material"]], "LEO: Linking ELN with OMERO": [[66, "leo-linking-eln-with-omero"], [75, "leo-linking-eln-with-omero"], [81, "leo-linking-eln-with-omero"]], "LSM example J. Dubrulle": [[66, "lsm-example-j-dubrulle"], [75, "lsm-example-j-dubrulle"], [81, "lsm-example-j-dubrulle"], [117, "lsm-example-j-dubrulle"]], "LZ4-compressed Imaris ims example datasets.": [[66, "lz4-compressed-imaris-ims-example-datasets"], [75, "lz4-compressed-imaris-ims-example-datasets"], [81, "lz4-compressed-imaris-ims-example-datasets"]], "Large Language Models: An Introduction for Life Scientists": [[29, "large-language-models-an-introduction-for-life-scientists"], [66, "large-language-models-an-introduction-for-life-scientists"], [75, "large-language-models-an-introduction-for-life-scientists"], [81, "large-language-models-an-introduction-for-life-scientists"], [90, "large-language-models-an-introduction-for-life-scientists"]], "Large language models (5)": [[99, null]], "Large tiling confocal acquisition (rat brain)": [[66, "large-tiling-confocal-acquisition-rat-brain"], [75, "large-tiling-confocal-acquisition-rat-brain"], [81, "large-tiling-confocal-acquisition-rat-brain"]], "LauLauThom/MaskFromRois-Fiji: Masks from ROIs plugins for Fiji - initial release": [[66, "laulauthom-maskfromrois-fiji-masks-from-rois-plugins-for-fiji-initial-release"], [75, "laulauthom-maskfromrois-fiji-masks-from-rois-plugins-for-fiji-initial-release"], [85, "laulauthom-maskfromrois-fiji-masks-from-rois-plugins-for-fiji-initial-release"]], "Lecture Applied Bioimage Analysis 2020": [[29, "lecture-applied-bioimage-analysis-2020"], [55, "lecture-applied-bioimage-analysis-2020"], [69, "lecture-applied-bioimage-analysis-2020"], [91, "lecture-applied-bioimage-analysis-2020"], [98, "lecture-applied-bioimage-analysis-2020"]], "Lecture-materials of the DeepLife course": [[20, "lecture-materials-of-the-deeplife-course"], [48, "lecture-materials-of-the-deeplife-course"], [49, "lecture-materials-of-the-deeplife-course"], [56, "lecture-materials-of-the-deeplife-course"], [70, "lecture-materials-of-the-deeplife-course"], [87, "lecture-materials-of-the-deeplife-course"], [93, "lecture-materials-of-the-deeplife-course"]], "Leitfaden zur digitalen Datensparsamkeit (mit Praxisbeispielen)": [[66, "leitfaden-zur-digitalen-datensparsamkeit-mit-praxisbeispielen"], [75, "leitfaden-zur-digitalen-datensparsamkeit-mit-praxisbeispielen"], [81, "leitfaden-zur-digitalen-datensparsamkeit-mit-praxisbeispielen"]], "Leitlinie? Grunds\u00e4tze? Policy? Richtlinie? \u2013 Forschungsdaten-Policies an deutschen Universit\u00e4ten": [[53, "leitlinie-grundsatze-policy-richtlinie-forschungsdaten-policies-an-deutschen-universitaten"], [81, "leitlinie-grundsatze-policy-richtlinie-forschungsdaten-policies-an-deutschen-universitaten"], [96, "leitlinie-grundsatze-policy-richtlinie-forschungsdaten-policies-an-deutschen-universitaten"], [111, "leitlinie-grundsatze-policy-richtlinie-forschungsdaten-policies-an-deutschen-universitaten"]], "License": [[88, "license"]], "Licensing (6)": [[100, null]], "LimeSeg Test Datasets": [[24, "limeseg-test-datasets"], [66, "limeseg-test-datasets"], [75, "limeseg-test-datasets"], [81, "limeseg-test-datasets"]], "Linked (Open) Data for Microbial Population Biology": [[66, "linked-open-data-for-microbial-population-biology"], [75, "linked-open-data-for-microbial-population-biology"], [81, "linked-open-data-for-microbial-population-biology"]], "Liver Micrometastases area quantification using QuPath and pixel classifier": [[25, "liver-micrometastases-area-quantification-using-qupath-and-pixel-classifier"], [31, "liver-micrometastases-area-quantification-using-qupath-and-pixel-classifier"], [66, "liver-micrometastases-area-quantification-using-qupath-and-pixel-classifier"], [75, "liver-micrometastases-area-quantification-using-qupath-and-pixel-classifier"], [81, "liver-micrometastases-area-quantification-using-qupath-and-pixel-classifier"]], "Lund Declaration on Maximising the Benefits of Research Data": [[45, "lund-declaration-on-maximising-the-benefits-of-research-data"], [111, "lund-declaration-on-maximising-the-benefits-of-research-data"]], "MDEmic: a metadata annotation tool to facilitate management of FAIR image data in the bioimaging community": [[3, "mdemic-a-metadata-annotation-tool-to-facilitate-management-of-fair-image-data-in-the-bioimaging-community"], [33, "mdemic-a-metadata-annotation-tool-to-facilitate-management-of-fair-image-data-in-the-bioimaging-community"], [34, "mdemic-a-metadata-annotation-tool-to-facilitate-management-of-fair-image-data-in-the-bioimaging-community"], [53, "mdemic-a-metadata-annotation-tool-to-facilitate-management-of-fair-image-data-in-the-bioimaging-community"], [73, "mdemic-a-metadata-annotation-tool-to-facilitate-management-of-fair-image-data-in-the-bioimaging-community"], [78, "mdemic-a-metadata-annotation-tool-to-facilitate-management-of-fair-image-data-in-the-bioimaging-community"], [101, "mdemic-a-metadata-annotation-tool-to-facilitate-management-of-fair-image-data-in-the-bioimaging-community"], [111, "mdemic-a-metadata-annotation-tool-to-facilitate-management-of-fair-image-data-in-the-bioimaging-community"]], "Machine Learning - Deep Learning. Applications to Bioimage Analysis": [[10, "machine-learning-deep-learning-applications-to-bioimage-analysis"], [55, "machine-learning-deep-learning-applications-to-bioimage-analysis"], [87, "machine-learning-deep-learning-applications-to-bioimage-analysis"], [90, "machine-learning-deep-learning-applications-to-bioimage-analysis"], [91, "machine-learning-deep-learning-applications-to-bioimage-analysis"]], "Machine Learning Workflow for Imaging Flow Cytometry (IFC)": [[57, "machine-learning-workflow-for-imaging-flow-cytometry-ifc"], [70, "machine-learning-workflow-for-imaging-flow-cytometry-ifc"], [87, "machine-learning-workflow-for-imaging-flow-cytometry-ifc"], [91, "machine-learning-workflow-for-imaging-flow-cytometry-ifc"]], "Machine and Deep Learning on the cloud: Segmentation": [[55, "machine-and-deep-learning-on-the-cloud-segmentation"], [65, "machine-and-deep-learning-on-the-cloud-segmentation"], [87, "machine-and-deep-learning-on-the-cloud-segmentation"], [90, "machine-and-deep-learning-on-the-cloud-segmentation"], [91, "machine-and-deep-learning-on-the-cloud-segmentation"], [104, "machine-and-deep-learning-on-the-cloud-segmentation"]], "Maintenance of contributions": [[62, "maintenance-of-contributions"]], "Making the most of bioimaging data through interdisciplinary interactions": [[17, "making-the-most-of-bioimaging-data-through-interdisciplinary-interactions"], [53, "making-the-most-of-bioimaging-data-through-interdisciplinary-interactions"], [81, "making-the-most-of-bioimaging-data-through-interdisciplinary-interactions"], [91, "making-the-most-of-bioimaging-data-through-interdisciplinary-interactions"], [102, "making-the-most-of-bioimaging-data-through-interdisciplinary-interactions"], [107, "making-the-most-of-bioimaging-data-through-interdisciplinary-interactions"]], "Making your package available on conda-forge": [[46, "making-your-package-available-on-conda-forge"], [81, "making-your-package-available-on-conda-forge"], [109, "making-your-package-available-on-conda-forge"]], "Making your project citable": [[57, "making-your-project-citable"], [111, "making-your-project-citable"], [113, "making-your-project-citable"]], "Managing Scientific Python environments using Conda, Mamba and friends": [[29, "managing-scientific-python-environments-using-conda-mamba-and-friends"], [40, "managing-scientific-python-environments-using-conda-mamba-and-friends"], [68, "managing-scientific-python-environments-using-conda-mamba-and-friends"], [81, "managing-scientific-python-environments-using-conda-mamba-and-friends"], [109, "managing-scientific-python-environments-using-conda-mamba-and-friends"]], "Mara lampert (8)": [[19, null]], "Martin sch\u00e4tz (6)": [[20, null]], "Martin weigert (8)": [[21, null]], "Materials for EMBL Coding Club Mini-Tutorials": [[11, "materials-for-embl-coding-club-mini-tutorials"], [42, "materials-for-embl-coding-club-mini-tutorials"], [49, "materials-for-embl-coding-club-mini-tutorials"], [70, "materials-for-embl-coding-club-mini-tutorials"], [87, "materials-for-embl-coding-club-mini-tutorials"], [109, "materials-for-embl-coding-club-mini-tutorials"]], "Measuring reporter activity domain in EPI aggregates and Gastruloids.ijm": [[25, "measuring-reporter-activity-domain-in-epi-aggregates-and-gastruloids-ijm"], [31, "measuring-reporter-activity-domain-in-epi-aggregates-and-gastruloids-ijm"], [66, "measuring-reporter-activity-domain-in-epi-aggregates-and-gastruloids-ijm"], [75, "measuring-reporter-activity-domain-in-epi-aggregates-and-gastruloids-ijm"], [81, "measuring-reporter-activity-domain-in-epi-aggregates-and-gastruloids-ijm"]], "Meeting in the Middle: Towards Successful Multidisciplinary Bioimage Analysis Collaboration": [[29, "meeting-in-the-middle-towards-successful-multidisciplinary-bioimage-analysis-collaboration"], [53, "meeting-in-the-middle-towards-successful-multidisciplinary-bioimage-analysis-collaboration"], [81, "meeting-in-the-middle-towards-successful-multidisciplinary-bioimage-analysis-collaboration"], [91, "meeting-in-the-middle-towards-successful-multidisciplinary-bioimage-analysis-collaboration"]], "Melbourne Advanced Microscopy Facility": [[43, "melbourne-advanced-microscopy-facility"], [58, "melbourne-advanced-microscopy-facility"], [74, "melbourne-advanced-microscopy-facility"], [91, "melbourne-advanced-microscopy-facility"]], "Metabolic networks modelling with COBRApy": [[49, "metabolic-networks-modelling-with-cobrapy"], [70, "metabolic-networks-modelling-with-cobrapy"], [87, "metabolic-networks-modelling-with-cobrapy"], [109, "metabolic-networks-modelling-with-cobrapy"]], "Metadata (15)": [[101, null]], "Metadata Annotation Workflow for OMERO with Tabbles": [[66, "metadata-annotation-workflow-for-omero-with-tabbles"], [75, "metadata-annotation-workflow-for-omero-with-tabbles"], [81, "metadata-annotation-workflow-for-omero-with-tabbles"]], "Metadata matters: access to image data in the real world": [[11, "metadata-matters-access-to-image-data-in-the-real-world"], [53, "metadata-matters-access-to-image-data-in-the-real-world"], [101, "metadata-matters-access-to-image-data-in-the-real-world"]], "Methods in bioimage analysis": [[4, "methods-in-bioimage-analysis"], [50, "methods-in-bioimage-analysis"], [55, "methods-in-bioimage-analysis"], [58, "methods-in-bioimage-analysis"], [66, "methods-in-bioimage-analysis"], [72, "methods-in-bioimage-analysis"], [81, "methods-in-bioimage-analysis"], [91, "methods-in-bioimage-analysis"]], "MethodsJ2: a software tool to capture metadata and generate comprehensive microscopy methods text": [[53, "methodsj2-a-software-tool-to-capture-metadata-and-generate-comprehensive-microscopy-methods-text"], [73, "methodsj2-a-software-tool-to-capture-metadata-and-generate-comprehensive-microscopy-methods-text"], [101, "methodsj2-a-software-tool-to-capture-metadata-and-generate-comprehensive-microscopy-methods-text"]], "Metrics Reloaded - A framework for trustworthy image analysis validation": [[43, "metrics-reloaded-a-framework-for-trustworthy-image-analysis-validation"], [59, "metrics-reloaded-a-framework-for-trustworthy-image-analysis-validation"], [87, "metrics-reloaded-a-framework-for-trustworthy-image-analysis-validation"], [91, "metrics-reloaded-a-framework-for-trustworthy-image-analysis-validation"]], "MiToBo - A Toolbox for Image Processing and Analysis": [[46, "mitobo-a-toolbox-for-image-processing-and-analysis"], [53, "mitobo-a-toolbox-for-image-processing-and-analysis"], [98, "mitobo-a-toolbox-for-image-processing-and-analysis"], [116, "mitobo-a-toolbox-for-image-processing-and-analysis"]], "Michael gerlach (12)": [[22, null]], "Michele bortolomeazzi (9)": [[23, null]], "Micro-Meta App: an interactive tool for collecting microscopy metadata based on community specifications": [[11, "micro-meta-app-an-interactive-tool-for-collecting-microscopy-metadata-based-on-community-specifications"], [53, "micro-meta-app-an-interactive-tool-for-collecting-microscopy-metadata-based-on-community-specifications"], [66, "micro-meta-app-an-interactive-tool-for-collecting-microscopy-metadata-based-on-community-specifications"], [101, "micro-meta-app-an-interactive-tool-for-collecting-microscopy-metadata-based-on-community-specifications"]], "MicroSam-Talks": [[5, "microsam-talks"], [56, "microsam-talks"], [66, "microsam-talks"], [75, "microsam-talks"], [81, "microsam-talks"], [91, "microsam-talks"], [95, "microsam-talks"]], "Microscopy data analysis: machine learning and the BioImage Archive": [[7, "microscopy-data-analysis-machine-learning-and-the-bioimage-archive"], [21, "microscopy-data-analysis-machine-learning-and-the-bioimage-archive"], [39, "microscopy-data-analysis-machine-learning-and-the-bioimage-archive"], [56, "microscopy-data-analysis-machine-learning-and-the-bioimage-archive"], [58, "microscopy-data-analysis-machine-learning-and-the-bioimage-archive"], [72, "microscopy-data-analysis-machine-learning-and-the-bioimage-archive"], [81, "microscopy-data-analysis-machine-learning-and-the-bioimage-archive"], [95, "microscopy-data-analysis-machine-learning-and-the-bioimage-archive"], [102, "microscopy-data-analysis-machine-learning-and-the-bioimage-archive"], [109, "microscopy-data-analysis-machine-learning-and-the-bioimage-archive"]], "Microscopy image analysis (15)": [[102, null]], "Microscopy-BIDS - An Extension to the Brain Imaging Data Structure for Microscopy Data": [[11, "microscopy-bids-an-extension-to-the-brain-imaging-data-structure-for-microscopy-data"], [53, "microscopy-bids-an-extension-to-the-brain-imaging-data-structure-for-microscopy-data"], [81, "microscopy-bids-an-extension-to-the-brain-imaging-data-structure-for-microscopy-data"], [92, "microscopy-bids-an-extension-to-the-brain-imaging-data-structure-for-microscopy-data"], [97, "microscopy-bids-an-extension-to-the-brain-imaging-data-structure-for-microscopy-data"], [111, "microscopy-bids-an-extension-to-the-brain-imaging-data-structure-for-microscopy-data"]], "MicroscopyDB": [[43, "microscopydb"], [78, "microscopydb"]], "Mit (24)": [[85, null]], "Modeling community standards for metadata as templates makes data FAIR": [[53, "modeling-community-standards-for-metadata-as-templates-makes-data-fair"], [73, "modeling-community-standards-for-metadata-as-templates-makes-data-fair"], [81, "modeling-community-standards-for-metadata-as-templates-makes-data-fair"], [94, "modeling-community-standards-for-metadata-as-templates-makes-data-fair"], [96, "modeling-community-standards-for-metadata-as-templates-makes-data-fair"], [101, "modeling-community-standards-for-metadata-as-templates-makes-data-fair"]], "Models and Applications for BioImage.IO": [[11, "models-and-applications-for-bioimage-io"], [70, "models-and-applications-for-bioimage-io"], [87, "models-and-applications-for-bioimage-io"]], "Modular training resources for bioimage analysis": [[4, "modular-training-resources-for-bioimage-analysis"], [7, "modular-training-resources-for-bioimage-analysis"], [9, "modular-training-resources-for-bioimage-analysis"], [36, "modular-training-resources-for-bioimage-analysis"], [66, "modular-training-resources-for-bioimage-analysis"], [75, "modular-training-resources-for-bioimage-analysis"], [81, "modular-training-resources-for-bioimage-analysis"], [117, "modular-training-resources-for-bioimage-analysis"]], "ModularImageAnalysis (MIA): Assembly of modularisedimage and object analysis workflows in ImageJ": [[46, "modularimageanalysis-mia-assembly-of-modularisedimage-and-object-analysis-workflows-in-imagej"], [53, "modularimageanalysis-mia-assembly-of-modularisedimage-and-object-analysis-workflows-in-imagej"], [66, "modularimageanalysis-mia-assembly-of-modularisedimage-and-object-analysis-workflows-in-imagej"], [98, "modularimageanalysis-mia-assembly-of-modularisedimage-and-object-analysis-workflows-in-imagej"], [116, "modularimageanalysis-mia-assembly-of-modularisedimage-and-object-analysis-workflows-in-imagej"]], "MorphoLibJ documentation": [[45, "morpholibj-documentation"], [91, "morpholibj-documentation"]], "Morphological analysis of neural cells with WEKA and SNT Fiji plugins": [[66, "morphological-analysis-of-neural-cells-with-weka-and-snt-fiji-plugins"], [75, "morphological-analysis-of-neural-cells-with-weka-and-snt-fiji-plugins"], [81, "morphological-analysis-of-neural-cells-with-weka-and-snt-fiji-plugins"]], "Most downloaded training materials in the last week": [[88, "most-downloaded-training-materials-in-the-last-week"]], "Multi-Template-Matching for object-detection (slides)": [[66, "multi-template-matching-for-object-detection-slides"], [75, "multi-template-matching-for-object-detection-slides"], [81, "multi-template-matching-for-object-detection-slides"]], "Multi-view fusion": [[29, "multi-view-fusion"], [55, "multi-view-fusion"], [69, "multi-view-fusion"], [80, "multi-view-fusion"], [91, "multi-view-fusion"], [98, "multi-view-fusion"], [104, "multi-view-fusion"]], "Multimodal large language models for bioimage analysis": [[53, "multimodal-large-language-models-for-bioimage-analysis"], [73, "multimodal-large-language-models-for-bioimage-analysis"], [91, "multimodal-large-language-models-for-bioimage-analysis"], [96, "multimodal-large-language-models-for-bioimage-analysis"], [99, "multimodal-large-language-models-for-bioimage-analysis"], [115, "multimodal-large-language-models-for-bioimage-analysis"]], "Multiplexed histology of COVID-19 post-mortem lung samples - CONTROL CASE 1 FOV1": [[66, "multiplexed-histology-of-covid-19-post-mortem-lung-samples-control-case-1-fov1"], [75, "multiplexed-histology-of-covid-19-post-mortem-lung-samples-control-case-1-fov1"], [81, "multiplexed-histology-of-covid-19-post-mortem-lung-samples-control-case-1-fov1"]], "Multiplexed tissue imaging - tools and approaches": [[48, "multiplexed-tissue-imaging-tools-and-approaches"], [56, "multiplexed-tissue-imaging-tools-and-approaches"], [60, "multiplexed-tissue-imaging-tools-and-approaches"], [65, "multiplexed-tissue-imaging-tools-and-approaches"], [70, "multiplexed-tissue-imaging-tools-and-approaches"], [81, "multiplexed-tissue-imaging-tools-and-approaches"], [91, "multiplexed-tissue-imaging-tools-and-approaches"], [102, "multiplexed-tissue-imaging-tools-and-approaches"]], "My Journey Through Bioimage Analysis Teaching Methods From Classroom to Cloud": [[9, "my-journey-through-bioimage-analysis-teaching-methods-from-classroom-to-cloud"], [56, "my-journey-through-bioimage-analysis-teaching-methods-from-classroom-to-cloud"], [66, "my-journey-through-bioimage-analysis-teaching-methods-from-classroom-to-cloud"], [75, "my-journey-through-bioimage-analysis-teaching-methods-from-classroom-to-cloud"], [81, "my-journey-through-bioimage-analysis-teaching-methods-from-classroom-to-cloud"]], "NEUBIAS Academy @HOME: Interactive Bioimage Analysis with Python and Jupyter": [[49, "neubias-academy-home-interactive-bioimage-analysis-with-python-and-jupyter"], [70, "neubias-academy-home-interactive-bioimage-analysis-with-python-and-jupyter"], [80, "neubias-academy-home-interactive-bioimage-analysis-with-python-and-jupyter"], [91, "neubias-academy-home-interactive-bioimage-analysis-with-python-and-jupyter"], [104, "neubias-academy-home-interactive-bioimage-analysis-with-python-and-jupyter"], [109, "neubias-academy-home-interactive-bioimage-analysis-with-python-and-jupyter"]], "NEUBIAS Analyst School 2018": [[42, "neubias-analyst-school-2018"], [49, "neubias-analyst-school-2018"], [55, "neubias-analyst-school-2018"], [70, "neubias-analyst-school-2018"], [87, "neubias-analyst-school-2018"], [91, "neubias-analyst-school-2018"], [104, "neubias-analyst-school-2018"]], "NEUBIAS Bioimage Analyst Course 2017": [[11, "neubias-bioimage-analyst-course-2017"], [18, "neubias-bioimage-analyst-course-2017"], [55, "neubias-bioimage-analyst-course-2017"], [57, "neubias-bioimage-analyst-course-2017"], [70, "neubias-bioimage-analyst-course-2017"], [87, "neubias-bioimage-analyst-course-2017"], [91, "neubias-bioimage-analyst-course-2017"], [104, "neubias-bioimage-analyst-course-2017"]], "NEUBIAS Bioimage Analyst School 2019": [[11, "neubias-bioimage-analyst-school-2019"], [18, "neubias-bioimage-analyst-school-2019"], [29, "neubias-bioimage-analyst-school-2019"], [42, "neubias-bioimage-analyst-school-2019"], [49, "neubias-bioimage-analyst-school-2019"], [55, "neubias-bioimage-analyst-school-2019"], [70, "neubias-bioimage-analyst-school-2019"], [87, "neubias-bioimage-analyst-school-2019"], [91, "neubias-bioimage-analyst-school-2019"], [104, "neubias-bioimage-analyst-school-2019"]], "NEUBIAS Bioimage Analyst School 2020": [[10, "neubias-bioimage-analyst-school-2020"], [11, "neubias-bioimage-analyst-school-2020"], [18, "neubias-bioimage-analyst-school-2020"], [42, "neubias-bioimage-analyst-school-2020"], [49, "neubias-bioimage-analyst-school-2020"], [55, "neubias-bioimage-analyst-school-2020"], [70, "neubias-bioimage-analyst-school-2020"], [87, "neubias-bioimage-analyst-school-2020"], [91, "neubias-bioimage-analyst-school-2020"], [104, "neubias-bioimage-analyst-school-2020"]], "NEUBIAS YouTube Channel": [[43, "neubias-youtube-channel"], [58, "neubias-youtube-channel"], [74, "neubias-youtube-channel"], [104, "neubias-youtube-channel"]], "NFDI - Daten als gemeinsames Gut f\u00fcr exzellente Forschung, organisiert durch die Wissenschaft in Deutschland.": [[59, "nfdi-daten-als-gemeinsames-gut-fur-exzellente-forschung-organisiert-durch-die-wissenschaft-in-deutschland"], [87, "nfdi-daten-als-gemeinsames-gut-fur-exzellente-forschung-organisiert-durch-die-wissenschaft-in-deutschland"], [105, "nfdi-daten-als-gemeinsames-gut-fur-exzellente-forschung-organisiert-durch-die-wissenschaft-in-deutschland"], [111, "nfdi-daten-als-gemeinsames-gut-fur-exzellente-forschung-organisiert-durch-die-wissenschaft-in-deutschland"]], "NFDI4BIOIMAGE": [[56, "nfdi4bioimage"], [66, "nfdi4bioimage"], [75, "nfdi4bioimage"], [81, "nfdi4bioimage"], [91, "nfdi4bioimage"], [96, "nfdi4bioimage"], [105, "nfdi4bioimage"], [111, "nfdi4bioimage"]], "NFDI4BIOIMAGE - An Initiative for a National Research Data Infrastructure for Microscopy Data": [[3, "nfdi4bioimage-an-initiative-for-a-national-research-data-infrastructure-for-microscopy-data"], [8, "nfdi4bioimage-an-initiative-for-a-national-research-data-infrastructure-for-microscopy-data"], [51, "nfdi4bioimage-an-initiative-for-a-national-research-data-infrastructure-for-microscopy-data"], [53, "nfdi4bioimage-an-initiative-for-a-national-research-data-infrastructure-for-microscopy-data"], [55, "nfdi4bioimage-an-initiative-for-a-national-research-data-infrastructure-for-microscopy-data"], [66, "nfdi4bioimage-an-initiative-for-a-national-research-data-infrastructure-for-microscopy-data"], [82, "nfdi4bioimage-an-initiative-for-a-national-research-data-infrastructure-for-microscopy-data"], [92, "nfdi4bioimage-an-initiative-for-a-national-research-data-infrastructure-for-microscopy-data"], [97, "nfdi4bioimage-an-initiative-for-a-national-research-data-infrastructure-for-microscopy-data"], [105, "nfdi4bioimage-an-initiative-for-a-national-research-data-infrastructure-for-microscopy-data"], [111, "nfdi4bioimage-an-initiative-for-a-national-research-data-infrastructure-for-microscopy-data"]], "NFDI4BIOIMAGE - National Research Data Infrastructure for Microscopy and BioImage Analysis - Online Kick-Off 2023": [[33, "nfdi4bioimage-national-research-data-infrastructure-for-microscopy-and-bioimage-analysis-online-kick-off-2023"], [56, "nfdi4bioimage-national-research-data-infrastructure-for-microscopy-and-bioimage-analysis-online-kick-off-2023"], [66, "nfdi4bioimage-national-research-data-infrastructure-for-microscopy-and-bioimage-analysis-online-kick-off-2023"], [81, "nfdi4bioimage-national-research-data-infrastructure-for-microscopy-and-bioimage-analysis-online-kick-off-2023"], [91, "nfdi4bioimage-national-research-data-infrastructure-for-microscopy-and-bioimage-analysis-online-kick-off-2023"], [96, "nfdi4bioimage-national-research-data-infrastructure-for-microscopy-and-bioimage-analysis-online-kick-off-2023"], [105, "nfdi4bioimage-national-research-data-infrastructure-for-microscopy-and-bioimage-analysis-online-kick-off-2023"], [111, "nfdi4bioimage-national-research-data-infrastructure-for-microscopy-and-bioimage-analysis-online-kick-off-2023"]], "NFDI4BIOIMAGE - National Research Data Infrastructure for Microscopy and BioImage Analysis [conference talk: The Pelagic Imaging Consortium meets Helmholtz Imaging, 5.10.2023, Hamburg]": [[28, "nfdi4bioimage-national-research-data-infrastructure-for-microscopy-and-bioimage-analysis-conference-talk-the-pelagic-imaging-consortium-meets-helmholtz-imaging-5-10-2023-hamburg"], [56, "nfdi4bioimage-national-research-data-infrastructure-for-microscopy-and-bioimage-analysis-conference-talk-the-pelagic-imaging-consortium-meets-helmholtz-imaging-5-10-2023-hamburg"], [75, "nfdi4bioimage-national-research-data-infrastructure-for-microscopy-and-bioimage-analysis-conference-talk-the-pelagic-imaging-consortium-meets-helmholtz-imaging-5-10-2023-hamburg"], [81, "nfdi4bioimage-national-research-data-infrastructure-for-microscopy-and-bioimage-analysis-conference-talk-the-pelagic-imaging-consortium-meets-helmholtz-imaging-5-10-2023-hamburg"], [91, "nfdi4bioimage-national-research-data-infrastructure-for-microscopy-and-bioimage-analysis-conference-talk-the-pelagic-imaging-consortium-meets-helmholtz-imaging-5-10-2023-hamburg"], [105, "nfdi4bioimage-national-research-data-infrastructure-for-microscopy-and-bioimage-analysis-conference-talk-the-pelagic-imaging-consortium-meets-helmholtz-imaging-5-10-2023-hamburg"], [111, "nfdi4bioimage-national-research-data-infrastructure-for-microscopy-and-bioimage-analysis-conference-talk-the-pelagic-imaging-consortium-meets-helmholtz-imaging-5-10-2023-hamburg"]], "NFDI4BIOIMAGE - National Research Data Infrastructure for Microscopy and Bioimage Analysis": [[66, "nfdi4bioimage-national-research-data-infrastructure-for-microscopy-and-bioimage-analysis"], [75, "nfdi4bioimage-national-research-data-infrastructure-for-microscopy-and-bioimage-analysis"], [81, "nfdi4bioimage-national-research-data-infrastructure-for-microscopy-and-bioimage-analysis"]], "NFDI4BIOIMAGE data management illustrations by Henning Falk": [[66, "nfdi4bioimage-data-management-illustrations-by-henning-falk"], [75, "nfdi4bioimage-data-management-illustrations-by-henning-falk"], [81, "nfdi4bioimage-data-management-illustrations-by-henning-falk"]], "NFDI4BIOIMAGE: Perspective for a national bioimaging standard": [[17, "nfdi4bioimage-perspective-for-a-national-bioimaging-standard"], [34, "nfdi4bioimage-perspective-for-a-national-bioimaging-standard"], [53, "nfdi4bioimage-perspective-for-a-national-bioimaging-standard"], [81, "nfdi4bioimage-perspective-for-a-national-bioimaging-standard"], [105, "nfdi4bioimage-perspective-for-a-national-bioimaging-standard"]], "NFDI4BioImage Training Materials": [[88, null]], "NFDI4Bioimage - TA3-Hackathon - UoC-2023 (Cologne Hackathon)": [[0, "nfdi4bioimage-ta3-hackathon-uoc-2023-cologne-hackathon"], [23, "nfdi4bioimage-ta3-hackathon-uoc-2023-cologne-hackathon"], [37, "nfdi4bioimage-ta3-hackathon-uoc-2023-cologne-hackathon"], [38, "nfdi4bioimage-ta3-hackathon-uoc-2023-cologne-hackathon"], [46, "nfdi4bioimage-ta3-hackathon-uoc-2023-cologne-hackathon"], [47, "nfdi4bioimage-ta3-hackathon-uoc-2023-cologne-hackathon"], [53, "nfdi4bioimage-ta3-hackathon-uoc-2023-cologne-hackathon"], [66, "nfdi4bioimage-ta3-hackathon-uoc-2023-cologne-hackathon"], [70, "nfdi4bioimage-ta3-hackathon-uoc-2023-cologne-hackathon"], [81, "nfdi4bioimage-ta3-hackathon-uoc-2023-cologne-hackathon"], [105, "nfdi4bioimage-ta3-hackathon-uoc-2023-cologne-hackathon"], [106, "nfdi4bioimage-ta3-hackathon-uoc-2023-cologne-hackathon"], [109, "nfdi4bioimage-ta3-hackathon-uoc-2023-cologne-hackathon"], [111, "nfdi4bioimage-ta3-hackathon-uoc-2023-cologne-hackathon"]], "NFDI4Bioimage - TA3-Hackathon - UoC-2023 (Cologne-Hackathon-2023, GitHub repository)": [[0, "nfdi4bioimage-ta3-hackathon-uoc-2023-cologne-hackathon-2023-github-repository"], [16, "nfdi4bioimage-ta3-hackathon-uoc-2023-cologne-hackathon-2023-github-repository"], [23, "nfdi4bioimage-ta3-hackathon-uoc-2023-cologne-hackathon-2023-github-repository"], [37, "nfdi4bioimage-ta3-hackathon-uoc-2023-cologne-hackathon-2023-github-repository"], [38, "nfdi4bioimage-ta3-hackathon-uoc-2023-cologne-hackathon-2023-github-repository"], [48, "nfdi4bioimage-ta3-hackathon-uoc-2023-cologne-hackathon-2023-github-repository"], [75, "nfdi4bioimage-ta3-hackathon-uoc-2023-cologne-hackathon-2023-github-repository"], [81, "nfdi4bioimage-ta3-hackathon-uoc-2023-cologne-hackathon-2023-github-repository"], [91, "nfdi4bioimage-ta3-hackathon-uoc-2023-cologne-hackathon-2023-github-repository"], [96, "nfdi4bioimage-ta3-hackathon-uoc-2023-cologne-hackathon-2023-github-repository"], [105, "nfdi4bioimage-ta3-hackathon-uoc-2023-cologne-hackathon-2023-github-repository"], [111, "nfdi4bioimage-ta3-hackathon-uoc-2023-cologne-hackathon-2023-github-repository"]], "NFDI4Bioimage Calendar 2024 October; original image": [[66, "nfdi4bioimage-calendar-2024-october-original-image"], [75, "nfdi4bioimage-calendar-2024-october-original-image"], [81, "nfdi4bioimage-calendar-2024-october-original-image"]], "NGFF Converter": [[84, "ngff-converter"], [92, "ngff-converter"], [108, "ngff-converter"]], "NLP Course": [[48, "nlp-course"], [70, "nlp-course"], [85, "nlp-course"]], "Napari (13)": [[103, null]], "Neubias (26)": [[104, null]], "Neubias Academy 2020: Introduction to Nuclei Segmentation with StarDist": [[21, "neubias-academy-2020-introduction-to-nuclei-segmentation-with-stardist"], [25, "neubias-academy-2020-introduction-to-nuclei-segmentation-with-stardist"], [49, "neubias-academy-2020-introduction-to-nuclei-segmentation-with-stardist"], [55, "neubias-academy-2020-introduction-to-nuclei-segmentation-with-stardist"], [70, "neubias-academy-2020-introduction-to-nuclei-segmentation-with-stardist"], [87, "neubias-academy-2020-introduction-to-nuclei-segmentation-with-stardist"], [90, "neubias-academy-2020-introduction-to-nuclei-segmentation-with-stardist"], [91, "neubias-academy-2020-introduction-to-nuclei-segmentation-with-stardist"], [104, "neubias-academy-2020-introduction-to-nuclei-segmentation-with-stardist"], [109, "neubias-academy-2020-introduction-to-nuclei-segmentation-with-stardist"]], "NeubiasPasteur2023_AdvancedCellPose": [[48, "neubiaspasteur2023-advancedcellpose"], [70, "neubiaspasteur2023-advancedcellpose"], [80, "neubiaspasteur2023-advancedcellpose"], [112, "neubiaspasteur2023-advancedcellpose"]], "New Kid on the (NFDI) Block: NFDI4BIOIMAGE - A National Initiative for FAIR Data Management in Bioimaging and Bioimage Analysis": [[6, "new-kid-on-the-nfdi-block-nfdi4bioimage-a-national-initiative-for-fair-data-management-in-bioimaging-and-bioimage-analysis"], [66, "new-kid-on-the-nfdi-block-nfdi4bioimage-a-national-initiative-for-fair-data-management-in-bioimaging-and-bioimage-analysis"], [75, "new-kid-on-the-nfdi-block-nfdi4bioimage-a-national-initiative-for-fair-data-management-in-bioimaging-and-bioimage-analysis"], [81, "new-kid-on-the-nfdi-block-nfdi4bioimage-a-national-initiative-for-fair-data-management-in-bioimaging-and-bioimage-analysis"]], "New report highlights the scientific impact of open source software": [[87, "new-report-highlights-the-scientific-impact-of-open-source-software"]], "NextFlow core": [[43, "nextflow-core"], [109, "nextflow-core"]], "NextFlow documentation": [[46, "nextflow-documentation"], [116, "nextflow-documentation"]], "Nextflow Demo Pipelines for Image Processing": [[42, "nextflow-demo-pipelines-for-image-processing"], [70, "nextflow-demo-pipelines-for-image-processing"], [87, "nextflow-demo-pipelines-for-image-processing"]], "Nextflow: Scalable and reproducible scientific workflows": [[55, "nextflow-scalable-and-reproducible-scientific-workflows"], [66, "nextflow-scalable-and-reproducible-scientific-workflows"], [75, "nextflow-scalable-and-reproducible-scientific-workflows"], [81, "nextflow-scalable-and-reproducible-scientific-workflows"], [116, "nextflow-scalable-and-reproducible-scientific-workflows"]], "Nfdi4bioimage (23)": [[105, null]], "Nicolas chiaruttini (7)": [[24, null]], "Notebook (55)": [[49, null]], "OME Documentation": [[46, "ome-documentation"], [81, "ome-documentation"], [106, "ome-documentation"]], "OME Event Database": [[43, "ome-event-database"], [47, "ome-event-database"], [106, "ome-event-database"], [111, "ome-event-database"]], "OME-NGFF: a next-generation file format for expanding bioimaging data-access strategies": [[2, "ome-ngff-a-next-generation-file-format-for-expanding-bioimaging-data-access-strategies"], [4, "ome-ngff-a-next-generation-file-format-for-expanding-bioimaging-data-access-strategies"], [5, "ome-ngff-a-next-generation-file-format-for-expanding-bioimaging-data-access-strategies"], [15, "ome-ngff-a-next-generation-file-format-for-expanding-bioimaging-data-access-strategies"], [17, "ome-ngff-a-next-generation-file-format-for-expanding-bioimaging-data-access-strategies"], [53, "ome-ngff-a-next-generation-file-format-for-expanding-bioimaging-data-access-strategies"], [73, "ome-ngff-a-next-generation-file-format-for-expanding-bioimaging-data-access-strategies"], [81, "ome-ngff-a-next-generation-file-format-for-expanding-bioimaging-data-access-strategies"], [105, "ome-ngff-a-next-generation-file-format-for-expanding-bioimaging-data-access-strategies"], [111, "ome-ngff-a-next-generation-file-format-for-expanding-bioimaging-data-access-strategies"]], "OME-Zarr course": [[4, "ome-zarr-course"], [57, "ome-zarr-course"], [87, "ome-zarr-course"]], "OME2024 NGFF Challenge Results": [[17, "ome2024-ngff-challenge-results"], [66, "ome2024-ngff-challenge-results"], [75, "ome2024-ngff-challenge-results"], [81, "ome2024-ngff-challenge-results"]], "OMERO - HCS analysis pipeline using Jupyter Notebooks": [[28, "omero-hcs-analysis-pipeline-using-jupyter-notebooks"], [48, "omero-hcs-analysis-pipeline-using-jupyter-notebooks"], [70, "omero-hcs-analysis-pipeline-using-jupyter-notebooks"], [85, "omero-hcs-analysis-pipeline-using-jupyter-notebooks"], [91, "omero-hcs-analysis-pipeline-using-jupyter-notebooks"], [106, "omero-hcs-analysis-pipeline-using-jupyter-notebooks"], [109, "omero-hcs-analysis-pipeline-using-jupyter-notebooks"]], "OMERO - QuPath": [[50, "omero-qupath"], [91, "omero-qupath"], [106, "omero-qupath"]], "OMERO Guide": [[43, "omero-guide"], [79, "omero-guide"], [106, "omero-guide"]], "OMERO documentation": [[46, "omero-documentation"], [87, "omero-documentation"], [106, "omero-documentation"]], "OMERO for microscopy research data management": [[16, "omero-for-microscopy-research-data-management"], [35, "omero-for-microscopy-research-data-management"], [53, "omero-for-microscopy-research-data-management"], [78, "omero-for-microscopy-research-data-management"], [105, "omero-for-microscopy-research-data-management"], [106, "omero-for-microscopy-research-data-management"], [111, "omero-for-microscopy-research-data-management"]], "OMERO walkthrough example": [[45, "omero-walkthrough-example"], [79, "omero-walkthrough-example"], [106, "omero-walkthrough-example"]], "OMERO walkthrough for facility managers": [[45, "omero-walkthrough-for-facility-managers"], [79, "omero-walkthrough-for-facility-managers"], [106, "omero-walkthrough-for-facility-managers"]], "ONBI Image Analysis Course": [[49, "onbi-image-analysis-course"], [70, "onbi-image-analysis-course"], [84, "onbi-image-analysis-course"], [91, "onbi-image-analysis-course"], [109, "onbi-image-analysis-course"]], "Object Tracking and Track Analysis using TrackMate and CellTracksColab": [[48, "object-tracking-and-track-analysis-using-trackmate-and-celltrackscolab"], [56, "object-tracking-and-track-analysis-using-trackmate-and-celltrackscolab"], [57, "object-tracking-and-track-analysis-using-trackmate-and-celltrackscolab"], [60, "object-tracking-and-track-analysis-using-trackmate-and-celltrackscolab"], [70, "object-tracking-and-track-analysis-using-trackmate-and-celltrackscolab"], [91, "object-tracking-and-track-analysis-using-trackmate-and-celltrackscolab"], [114, "object-tracking-and-track-analysis-using-trackmate-and-celltrackscolab"]], "Olivier burri (6)": [[25, null]], "Omero (31)": [[106, null]], "Omero Deployment examples": [[43, "omero-deployment-examples"], [70, "omero-deployment-examples"], [79, "omero-deployment-examples"], [106, "omero-deployment-examples"]], "Omero-tools": [[48, "omero-tools"], [64, "omero-tools"], [81, "omero-tools"], [91, "omero-tools"], [106, "omero-tools"]], "Online tutorial (10)": [[50, null]], "Online_R_learning": [[48, "online-r-learning"], [70, "online-r-learning"], [83, "online-r-learning"]], "Open Image Data Handbook": [[41, "open-image-data-handbook"], [49, "open-image-data-handbook"], [81, "open-image-data-handbook"], [91, "open-image-data-handbook"], [103, "open-image-data-handbook"], [104, "open-image-data-handbook"], [109, "open-image-data-handbook"], [111, "open-image-data-handbook"]], "Open Micoscropy Environment (OME) Youtube Channel": [[43, "open-micoscropy-environment-ome-youtube-channel"], [58, "open-micoscropy-environment-ome-youtube-channel"], [74, "open-micoscropy-environment-ome-youtube-channel"], [81, "open-micoscropy-environment-ome-youtube-channel"], [92, "open-micoscropy-environment-ome-youtube-channel"], [102, "open-micoscropy-environment-ome-youtube-channel"], [108, "open-micoscropy-environment-ome-youtube-channel"]], "Open Microscopy Environment YouTube channel": [[43, "open-microscopy-environment-youtube-channel"], [58, "open-microscopy-environment-youtube-channel"], [74, "open-microscopy-environment-youtube-channel"], [106, "open-microscopy-environment-youtube-channel"]], "Open Science, Sharing & Licensing": [[29, "open-science-sharing-licensing"], [56, "open-science-sharing-licensing"], [66, "open-science-sharing-licensing"], [75, "open-science-sharing-licensing"], [81, "open-science-sharing-licensing"], [96, "open-science-sharing-licensing"], [100, "open-science-sharing-licensing"], [111, "open-science-sharing-licensing"]], "Open data": [[76, null]], "Open microscopy in the life sciences: quo vadis?": [[29, "open-microscopy-in-the-life-sciences-quo-vadis"], [53, "open-microscopy-in-the-life-sciences-quo-vadis"], [66, "open-microscopy-in-the-life-sciences-quo-vadis"], [78, "open-microscopy-in-the-life-sciences-quo-vadis"]], "Open science (9)": [[107, null]], "Open source software (7)": [[108, null]], "Optimisation and Validation of a Swarm Intelligence based Segmentation Algorithm for low Contrast Positron Emission Tomography": [[29, "optimisation-and-validation-of-a-swarm-intelligence-based-segmentation-algorithm-for-low-contrast-positron-emission-tomography"], [66, "optimisation-and-validation-of-a-swarm-intelligence-based-segmentation-algorithm-for-low-contrast-positron-emission-tomography"], [75, "optimisation-and-validation-of-a-swarm-intelligence-based-segmentation-algorithm-for-low-contrast-positron-emission-tomography"], [81, "optimisation-and-validation-of-a-swarm-intelligence-based-segmentation-algorithm-for-low-contrast-positron-emission-tomography"]], "Optimized cranial window implantation for subcellular and functional imaging in vivo": [[66, "optimized-cranial-window-implantation-for-subcellular-and-functional-imaging-in-vivo"], [75, "optimized-cranial-window-implantation-for-subcellular-and-functional-imaging-in-vivo"], [81, "optimized-cranial-window-implantation-for-subcellular-and-functional-imaging-in-vivo"], [117, "optimized-cranial-window-implantation-for-subcellular-and-functional-imaging-in-vivo"]], "Overview of the Galaxy OMERO-suite - Upload images and metadata in OMERO using Galaxy": [[28, "overview-of-the-galaxy-omero-suite-upload-images-and-metadata-in-omero-using-galaxy"], [57, "overview-of-the-galaxy-omero-suite-upload-images-and-metadata-in-omero-using-galaxy"], [81, "overview-of-the-galaxy-omero-suite-upload-images-and-metadata-in-omero-using-galaxy"], [101, "overview-of-the-galaxy-omero-suite-upload-images-and-metadata-in-omero-using-galaxy"], [106, "overview-of-the-galaxy-omero-suite-upload-images-and-metadata-in-omero-using-galaxy"]], "Parallelization and heterogeneous computing: from pure CPU to GPU-accelerated image processing": [[29, "parallelization-and-heterogeneous-computing-from-pure-cpu-to-gpu-accelerated-image-processing"], [55, "parallelization-and-heterogeneous-computing-from-pure-cpu-to-gpu-accelerated-image-processing"], [66, "parallelization-and-heterogeneous-computing-from-pure-cpu-to-gpu-accelerated-image-processing"], [67, "parallelization-and-heterogeneous-computing-from-pure-cpu-to-gpu-accelerated-image-processing"], [81, "parallelization-and-heterogeneous-computing-from-pure-cpu-to-gpu-accelerated-image-processing"]], "Photonic data analysis in 2050": [[53, "photonic-data-analysis-in-2050"], [66, "photonic-data-analysis-in-2050"], [81, "photonic-data-analysis-in-2050"], [96, "photonic-data-analysis-in-2050"], [111, "photonic-data-analysis-in-2050"]], "Pia voigt (6)": [[26, null]], "Plants & Python - A series of lessons in coding, plant biology, computation, and bioinformatics": [[53, "plants-python-a-series-of-lessons-in-coding-plant-biology-computation-and-bioinformatics"]], "Plugin \u201comero-batch-plugin\u201d": [[48, "plugin-omero-batch-plugin"], [70, "plugin-omero-batch-plugin"], [84, "plugin-omero-batch-plugin"], [98, "plugin-omero-batch-plugin"], [106, "plugin-omero-batch-plugin"]], "Plugin \u201comero-cli-transfer\u201d": [[11, "plugin-omero-cli-transfer"], [15, "plugin-omero-cli-transfer"], [48, "plugin-omero-cli-transfer"], [70, "plugin-omero-cli-transfer"], [84, "plugin-omero-cli-transfer"], [106, "plugin-omero-cli-transfer"]], "Plugin \u201csimple-omero-client\u201d": [[15, "plugin-simple-omero-client"], [48, "plugin-simple-omero-client"], [70, "plugin-simple-omero-client"], [84, "plugin-simple-omero-client"], [106, "plugin-simple-omero-client"]], "PoL Bio-Image Analysis Early Career Bio-image Analysis Training School": [[47, "pol-bio-image-analysis-early-career-bio-image-analysis-training-school"], [49, "pol-bio-image-analysis-early-career-bio-image-analysis-training-school"], [64, "pol-bio-image-analysis-early-career-bio-image-analysis-training-school"], [91, "pol-bio-image-analysis-early-career-bio-image-analysis-training-school"], [109, "pol-bio-image-analysis-early-career-bio-image-analysis-training-school"]], "PoL Bio-Image Analysis GPU Accelerated Image Analysis Training School": [[47, "pol-bio-image-analysis-gpu-accelerated-image-analysis-training-school"], [49, "pol-bio-image-analysis-gpu-accelerated-image-analysis-training-school"], [64, "pol-bio-image-analysis-gpu-accelerated-image-analysis-training-school"], [91, "pol-bio-image-analysis-gpu-accelerated-image-analysis-training-school"], [109, "pol-bio-image-analysis-gpu-accelerated-image-analysis-training-school"]], "PoL Bio-Image Analysis Training School on GPU-Accelerated Image Analysis": [[29, "pol-bio-image-analysis-training-school-on-gpu-accelerated-image-analysis"], [49, "pol-bio-image-analysis-training-school-on-gpu-accelerated-image-analysis"], [70, "pol-bio-image-analysis-training-school-on-gpu-accelerated-image-analysis"], [81, "pol-bio-image-analysis-training-school-on-gpu-accelerated-image-analysis"], [109, "pol-bio-image-analysis-training-school-on-gpu-accelerated-image-analysis"]], "Poster (10)": [[51, null]], "Practical Guide to the International Alignment of Research Data Management - Extended Edition": [[41, "practical-guide-to-the-international-alignment-of-research-data-management-extended-edition"], [66, "practical-guide-to-the-international-alignment-of-research-data-management-extended-edition"], [81, "practical-guide-to-the-international-alignment-of-research-data-management-extended-edition"]], "Preprint (5)": [[52, null]], "Preprint: \u201cBe Sustainable\u201d, Recommendations for FAIR Resources in Life Sciences research: EOSC-Life\u2019s Lessons": [[1, "preprint-be-sustainable-recommendations-for-fair-resources-in-life-sciences-research-eosc-life-s-lessons"], [15, "preprint-be-sustainable-recommendations-for-fair-resources-in-life-sciences-research-eosc-life-s-lessons"], [39, "preprint-be-sustainable-recommendations-for-fair-resources-in-life-sciences-research-eosc-life-s-lessons"], [66, "preprint-be-sustainable-recommendations-for-fair-resources-in-life-sciences-research-eosc-life-s-lessons"], [75, "preprint-be-sustainable-recommendations-for-fair-resources-in-life-sciences-research-eosc-life-s-lessons"], [81, "preprint-be-sustainable-recommendations-for-fair-resources-in-life-sciences-research-eosc-life-s-lessons"]], "Prerequisites": [[63, "prerequisites"]], "Promoting Data Management at the Nikon Imaging Center and Cell Biology Microscopy Facility": [[40, "promoting-data-management-at-the-nikon-imaging-center-and-cell-biology-microscopy-facility"], [111, "promoting-data-management-at-the-nikon-imaging-center-and-cell-biology-microscopy-facility"]], "Prompt Engineering in Bio-image Analysis": [[19, "prompt-engineering-in-bio-image-analysis"], [40, "prompt-engineering-in-bio-image-analysis"], [68, "prompt-engineering-in-bio-image-analysis"], [91, "prompt-engineering-in-bio-image-analysis"], [109, "prompt-engineering-in-bio-image-analysis"]], "Prompt-Engineering-LLMs-Course": [[48, "prompt-engineering-llms-course"], [57, "prompt-engineering-llms-course"], [70, "prompt-engineering-llms-course"], [85, "prompt-engineering-llms-course"]], "Publication (61)": [[53, null]], "Python (70)": [[109, null]], "Python BioImage Analysis Tutorial": [[70, "python-bioimage-analysis-tutorial"], [85, "python-bioimage-analysis-tutorial"], [91, "python-bioimage-analysis-tutorial"], [109, "python-bioimage-analysis-tutorial"]], "Python Workshop - Image Processing": [[42, "python-workshop-image-processing"], [49, "python-workshop-image-processing"], [70, "python-workshop-image-processing"], [87, "python-workshop-image-processing"], [109, "python-workshop-image-processing"]], "Python for Microscopists": [[43, "python-for-microscopists"], [49, "python-for-microscopists"], [70, "python-for-microscopists"], [85, "python-for-microscopists"], [91, "python-for-microscopists"], [109, "python-for-microscopists"]], "QI 2024 Analysis Lab Manual": [[12, "qi-2024-analysis-lab-manual"], [49, "qi-2024-analysis-lab-manual"], [81, "qi-2024-analysis-lab-manual"], [109, "qi-2024-analysis-lab-manual"], [112, "qi-2024-analysis-lab-manual"]], "QM Course Lectures on Bio-Image Analysis with napari 2024": [[49, "qm-course-lectures-on-bio-image-analysis-with-napari-2024"], [81, "qm-course-lectures-on-bio-image-analysis-with-napari-2024"], [103, "qm-course-lectures-on-bio-image-analysis-with-napari-2024"], [109, "qm-course-lectures-on-bio-image-analysis-with-napari-2024"]], "QUAREP-LiMi: A community-driven initiative to establish guidelines for quality assessment and reproducibility for instruments and images in light microscopy": [[11, "quarep-limi-a-community-driven-initiative-to-establish-guidelines-for-quality-assessment-and-reproducibility-for-instruments-and-images-in-light-microscopy"], [53, "quarep-limi-a-community-driven-initiative-to-establish-guidelines-for-quality-assessment-and-reproducibility-for-instruments-and-images-in-light-microscopy"], [81, "quarep-limi-a-community-driven-initiative-to-establish-guidelines-for-quality-assessment-and-reproducibility-for-instruments-and-images-in-light-microscopy"]], "QuPath for Python programmers": [[43, "qupath-for-python-programmers"], [48, "qupath-for-python-programmers"], [49, "qupath-for-python-programmers"], [70, "qupath-for-python-programmers"], [87, "qupath-for-python-programmers"], [91, "qupath-for-python-programmers"], [108, "qupath-for-python-programmers"], [109, "qupath-for-python-programmers"]], "QuPath: Open source software for analysing (awkward) images": [[55, "qupath-open-source-software-for-analysing-awkward-images"], [66, "qupath-open-source-software-for-analysing-awkward-images"], [75, "qupath-open-source-software-for-analysing-awkward-images"], [81, "qupath-open-source-software-for-analysing-awkward-images"], [91, "qupath-open-source-software-for-analysing-awkward-images"]], "Quality assurance of segmentation results": [[19, "quality-assurance-of-segmentation-results"], [40, "quality-assurance-of-segmentation-results"], [68, "quality-assurance-of-segmentation-results"], [91, "quality-assurance-of-segmentation-results"], [103, "quality-assurance-of-segmentation-results"], [109, "quality-assurance-of-segmentation-results"]], "Quick contributing short-cut:": [[62, "quick-contributing-short-cut"]], "RDF as a bridge to domain-platforms like OMERO, or There and back again.": [[17, "rdf-as-a-bridge-to-domain-platforms-like-omero-or-there-and-back-again"], [34, "rdf-as-a-bridge-to-domain-platforms-like-omero-or-there-and-back-again"], [56, "rdf-as-a-bridge-to-domain-platforms-like-omero-or-there-and-back-again"], [75, "rdf-as-a-bridge-to-domain-platforms-like-omero-or-there-and-back-again"], [81, "rdf-as-a-bridge-to-domain-platforms-like-omero-or-there-and-back-again"], [91, "rdf-as-a-bridge-to-domain-platforms-like-omero-or-there-and-back-again"], [96, "rdf-as-a-bridge-to-domain-platforms-like-omero-or-there-and-back-again"], [111, "rdf-as-a-bridge-to-domain-platforms-like-omero-or-there-and-back-again"]], "RDM Starter Kit": [[59, "rdm-starter-kit"], [81, "rdm-starter-kit"], [111, "rdm-starter-kit"]], "RDM4Mic Presentations": [[43, "rdm4mic-presentations"], [70, "rdm4mic-presentations"], [81, "rdm4mic-presentations"], [111, "rdm4mic-presentations"]], "RDM4mic": [[43, "rdm4mic"], [58, "rdm4mic"], [74, "rdm4mic"], [87, "rdm4mic"], [106, "rdm4mic"], [111, "rdm4mic"]], "RDMBites BioImage metadata": [[43, "rdmbites-bioimage-metadata"], [58, "rdmbites-bioimage-metadata"], [74, "rdmbites-bioimage-metadata"]], "RDMKit Training Resources": [[43, "rdmkit-training-resources"], [81, "rdmkit-training-resources"], [111, "rdmkit-training-resources"]], "RDMO - Research Data Management Organiser": [[50, "rdmo-research-data-management-organiser"], [59, "rdmo-research-data-management-organiser"], [87, "rdmo-research-data-management-organiser"], [108, "rdmo-research-data-management-organiser"], [111, "rdmo-research-data-management-organiser"]], "RDM_system_connector": [[48, "rdm-system-connector"], [70, "rdm-system-connector"], [87, "rdm-system-connector"], [97, "rdm-system-connector"], [111, "rdm-system-connector"]], "REMBI - Recommended Metadata for Biological Images\u2014enabling reuse of microscopy data in biology": [[11, "rembi-recommended-metadata-for-biological-imagesenabling-reuse-of-microscopy-data-in-biology"], [39, "rembi-recommended-metadata-for-biological-imagesenabling-reuse-of-microscopy-data-in-biology"], [53, "rembi-recommended-metadata-for-biological-imagesenabling-reuse-of-microscopy-data-in-biology"], [66, "rembi-recommended-metadata-for-biological-imagesenabling-reuse-of-microscopy-data-in-biology"], [73, "rembi-recommended-metadata-for-biological-imagesenabling-reuse-of-microscopy-data-in-biology"], [87, "rembi-recommended-metadata-for-biological-imagesenabling-reuse-of-microscopy-data-in-biology"], [92, "rembi-recommended-metadata-for-biological-imagesenabling-reuse-of-microscopy-data-in-biology"], [97, "rembi-recommended-metadata-for-biological-imagesenabling-reuse-of-microscopy-data-in-biology"], [101, "rembi-recommended-metadata-for-biological-imagesenabling-reuse-of-microscopy-data-in-biology"], [111, "rembi-recommended-metadata-for-biological-imagesenabling-reuse-of-microscopy-data-in-biology"]], "REMBI Overview": [[43, "rembi-overview"], [72, "rembi-overview"], [83, "rembi-overview"], [92, "rembi-overview"], [96, "rembi-overview"], [97, "rembi-overview"], [101, "rembi-overview"], [111, "rembi-overview"]], "RESEARCH DATA MANAGEMENT on Campus and in NFDI4BIOIMAGE": [[6, "research-data-management-on-campus-and-in-nfdi4bioimage"], [66, "research-data-management-on-campus-and-in-nfdi4bioimage"], [75, "research-data-management-on-campus-and-in-nfdi4bioimage"], [81, "research-data-management-on-campus-and-in-nfdi4bioimage"]], "Recently added (10)": [[117, null]], "Rechtsfragen bei Open Science - Ein Leitfaden": [[41, "rechtsfragen-bei-open-science-ein-leitfaden"], [81, "rechtsfragen-bei-open-science-ein-leitfaden"], [107, "rechtsfragen-bei-open-science-ein-leitfaden"]], "Reference Collection to push back against \u201cCommon Statistical Myths\u201d": [[87, "reference-collection-to-push-back-against-common-statistical-myths"]], "Report on a pilot study: Implementation of OMERO for microscopy data management": [[32, "report-on-a-pilot-study-implementation-of-omero-for-microscopy-data-management"], [66, "report-on-a-pilot-study-implementation-of-omero-for-microscopy-data-management"], [75, "report-on-a-pilot-study-implementation-of-omero-for-microscopy-data-management"], [81, "report-on-a-pilot-study-implementation-of-omero-for-microscopy-data-management"]], "Reporting and reproducibility in microscopy": [[43, "reporting-and-reproducibility-in-microscopy"], [73, "reporting-and-reproducibility-in-microscopy"], [87, "reporting-and-reproducibility-in-microscopy"], [92, "reporting-and-reproducibility-in-microscopy"], [101, "reporting-and-reproducibility-in-microscopy"]], "Rescaling images and pixel (an)isotropy": [[19, "rescaling-images-and-pixel-an-isotropy"], [40, "rescaling-images-and-pixel-an-isotropy"], [68, "rescaling-images-and-pixel-an-isotropy"], [91, "rescaling-images-and-pixel-an-isotropy"], [103, "rescaling-images-and-pixel-an-isotropy"], [109, "rescaling-images-and-pixel-an-isotropy"]], "Research Data Management Seminar - Slides": [[55, "research-data-management-seminar-slides"], [66, "research-data-management-seminar-slides"], [75, "research-data-management-seminar-slides"], [81, "research-data-management-seminar-slides"], [111, "research-data-management-seminar-slides"]], "Research Data Managemet and how not to get overwhelmed with data": [[20, "research-data-managemet-and-how-not-to-get-overwhelmed-with-data"], [66, "research-data-managemet-and-how-not-to-get-overwhelmed-with-data"], [75, "research-data-managemet-and-how-not-to-get-overwhelmed-with-data"], [81, "research-data-managemet-and-how-not-to-get-overwhelmed-with-data"]], "Research Data Reusability - Conceptual Foundations, Barriers and Enabling Technologies": [[53, "research-data-reusability-conceptual-foundations-barriers-and-enabling-technologies"], [81, "research-data-reusability-conceptual-foundations-barriers-and-enabling-technologies"], [107, "research-data-reusability-conceptual-foundations-barriers-and-enabling-technologies"], [111, "research-data-reusability-conceptual-foundations-barriers-and-enabling-technologies"]], "Research data - what are the key issues to consider when publishing this kind of material?": [[57, "research-data-what-are-the-key-issues-to-consider-when-publishing-this-kind-of-material"], [87, "research-data-what-are-the-key-issues-to-consider-when-publishing-this-kind-of-material"], [96, "research-data-what-are-the-key-issues-to-consider-when-publishing-this-kind-of-material"], [100, "research-data-what-are-the-key-issues-to-consider-when-publishing-this-kind-of-material"], [111, "research-data-what-are-the-key-issues-to-consider-when-publishing-this-kind-of-material"]], "Research data management (110)": [[111, null]], "Research data management for bioimaging - the 2021 NFDI4BIOIMAGE community survey": [[3, "research-data-management-for-bioimaging-the-2021-nfdi4bioimage-community-survey"], [8, "research-data-management-for-bioimaging-the-2021-nfdi4bioimage-community-survey"], [11, "research-data-management-for-bioimaging-the-2021-nfdi4bioimage-community-survey"], [17, "research-data-management-for-bioimaging-the-2021-nfdi4bioimage-community-survey"], [53, "research-data-management-for-bioimaging-the-2021-nfdi4bioimage-community-survey"], [67, "research-data-management-for-bioimaging-the-2021-nfdi4bioimage-community-survey"], [81, "research-data-management-for-bioimaging-the-2021-nfdi4bioimage-community-survey"], [92, "research-data-management-for-bioimaging-the-2021-nfdi4bioimage-community-survey"], [97, "research-data-management-for-bioimaging-the-2021-nfdi4bioimage-community-survey"], [111, "research-data-management-for-bioimaging-the-2021-nfdi4bioimage-community-survey"]], "Research data management for bioimaging: the 2021 NFDI4BIOIMAGE community survey": [[3, "id1"], [8, "id1"], [17, "id1"], [33, "research-data-management-for-bioimaging-the-2021-nfdi4bioimage-community-survey"], [53, "id1"], [67, "id1"], [81, "id2"], [105, "research-data-management-for-bioimaging-the-2021-nfdi4bioimage-community-survey"], [111, "id1"]], "Riccardo massei (12)": [[28, null]], "Robert haase (56)": [[29, null]], "Roland nitschke (5)": [[30, null]], "Romain guiet (10)": [[31, null]], "Round Table Workshop 1 - Sample Stabilization in intravital Imaging": [[22, "round-table-workshop-1-sample-stabilization-in-intravital-imaging"], [66, "round-table-workshop-1-sample-stabilization-in-intravital-imaging"], [75, "round-table-workshop-1-sample-stabilization-in-intravital-imaging"], [81, "round-table-workshop-1-sample-stabilization-in-intravital-imaging"]], "Round Table Workshop 2 - Correction of Drift and Movement": [[66, "round-table-workshop-2-correction-of-drift-and-movement"], [75, "round-table-workshop-2-correction-of-drift-and-movement"], [81, "round-table-workshop-2-correction-of-drift-and-movement"]], "Running Deep-Learning Scripts in the BiA-PoL Omero Server": [[40, "running-deep-learning-scripts-in-the-bia-pol-omero-server"], [64, "running-deep-learning-scripts-in-the-bia-pol-omero-server"], [81, "running-deep-learning-scripts-in-the-bia-pol-omero-server"], [90, "running-deep-learning-scripts-in-the-bia-pol-omero-server"], [91, "running-deep-learning-scripts-in-the-bia-pol-omero-server"], [109, "running-deep-learning-scripts-in-the-bia-pol-omero-server"]], "Running the Training Materials Submission App": [[63, "running-the-training-materials-submission-app"]], "SWC/GCNU Software Skills": [[43, "swc-gcnu-software-skills"], [57, "swc-gcnu-software-skills"], [81, "swc-gcnu-software-skills"], [114, "swc-gcnu-software-skills"]], "SciAugment": [[20, "sciaugment"], [66, "sciaugment"], [75, "sciaugment"]], "Scientific Visualization: Python + Matplotlib": [[41, "scientific-visualization-python-matplotlib"], [70, "scientific-visualization-python-matplotlib"], [109, "scientific-visualization-python-matplotlib"]], "Scipy Cookbook": [[48, "scipy-cookbook"], [70, "scipy-cookbook"], [80, "scipy-cookbook"], [91, "scipy-cookbook"]], "Scripts_FilopodyanR - a case study for the NEUBIAS TS7 in Szeged": [[42, "scripts-filopodyanr-a-case-study-for-the-neubias-ts7-in-szeged"], [70, "scripts-filopodyanr-a-case-study-for-the-neubias-ts7-in-szeged"], [87, "scripts-filopodyanr-a-case-study-for-the-neubias-ts7-in-szeged"], [91, "scripts-filopodyanr-a-case-study-for-the-neubias-ts7-in-szeged"], [104, "scripts-filopodyanr-a-case-study-for-the-neubias-ts7-in-szeged"]], "Segmentation (5)": [[112, null]], "Setting up a data management infrastructure for bioimaging": [[34, "setting-up-a-data-management-infrastructure-for-bioimaging"], [53, "setting-up-a-data-management-infrastructure-for-bioimaging"], [66, "setting-up-a-data-management-infrastructure-for-bioimaging"], [87, "setting-up-a-data-management-infrastructure-for-bioimaging"], [105, "setting-up-a-data-management-infrastructure-for-bioimaging"], [111, "setting-up-a-data-management-infrastructure-for-bioimaging"]], "Setting up a remote desktop to use Napari in a browser": [[57, "setting-up-a-remote-desktop-to-use-napari-in-a-browser"], [70, "setting-up-a-remote-desktop-to-use-napari-in-a-browser"], [87, "setting-up-a-remote-desktop-to-use-napari-in-a-browser"]], "Sharing (12)": [[113, null]], "Sharing Your Poster on Figshare: A Community Guide to How-To and Why": [[40, "sharing-your-poster-on-figshare-a-community-guide-to-how-to-and-why"], [68, "sharing-your-poster-on-figshare-a-community-guide-to-how-to-and-why"], [111, "sharing-your-poster-on-figshare-a-community-guide-to-how-to-and-why"], [113, "sharing-your-poster-on-figshare-a-community-guide-to-how-to-and-why"]], "Sharing and licensing material": [[29, "sharing-and-licensing-material"], [55, "sharing-and-licensing-material"], [67, "sharing-and-licensing-material"], [81, "sharing-and-licensing-material"], [111, "sharing-and-licensing-material"], [113, "sharing-and-licensing-material"]], "Sharing research data with Zenodo": [[29, "sharing-research-data-with-zenodo"], [40, "sharing-research-data-with-zenodo"], [68, "sharing-research-data-with-zenodo"], [81, "sharing-research-data-with-zenodo"], [111, "sharing-research-data-with-zenodo"], [113, "sharing-research-data-with-zenodo"]], "Silke tulok (6)": [[32, null]], "SimpleITK-Notebooks": [[48, "simpleitk-notebooks"], [70, "simpleitk-notebooks"], [91, "simpleitk-notebooks"]], "Slide (41)": [[55, null]], "Slides (42)": [[56, null]], "Slides about FLUTE: a Python GUI for interactive phasor analysis of FLIM data": [[66, "slides-about-flute-a-python-gui-for-interactive-phasor-analysis-of-flim-data"], [75, "slides-about-flute-a-python-gui-for-interactive-phasor-analysis-of-flim-data"], [81, "slides-about-flute-a-python-gui-for-interactive-phasor-analysis-of-flim-data"]], "Snakemake Documentation": [[46, "snakemake-documentation"], [109, "snakemake-documentation"], [116, "snakemake-documentation"]], "So geschlossen wie n\u00f6tig, so offen wie m\u00f6glich - Datenschutz beim Umgang mit Forschungsdaten": [[26, "so-geschlossen-wie-notig-so-offen-wie-moglich-datenschutz-beim-umgang-mit-forschungsdaten"], [56, "so-geschlossen-wie-notig-so-offen-wie-moglich-datenschutz-beim-umgang-mit-forschungsdaten"], [66, "so-geschlossen-wie-notig-so-offen-wie-moglich-datenschutz-beim-umgang-mit-forschungsdaten"], [75, "so-geschlossen-wie-notig-so-offen-wie-moglich-datenschutz-beim-umgang-mit-forschungsdaten"], [81, "so-geschlossen-wie-notig-so-offen-wie-moglich-datenschutz-beim-umgang-mit-forschungsdaten"], [96, "so-geschlossen-wie-notig-so-offen-wie-moglich-datenschutz-beim-umgang-mit-forschungsdaten"], [111, "so-geschlossen-wie-notig-so-offen-wie-moglich-datenschutz-beim-umgang-mit-forschungsdaten"]], "Software Citation with CITATION.cff": [[57, "software-citation-with-citation-cff"], [111, "software-citation-with-citation-cff"], [113, "software-citation-with-citation-cff"]], "Source Control Using Git and GitHub": [[57, "source-control-using-git-and-github"], [70, "source-control-using-git-and-github"], [87, "source-control-using-git-and-github"]], "SpatialData: an open and universal data framework for spatial omics": [[17, "spatialdata-an-open-and-universal-data-framework-for-spatial-omics"], [52, "spatialdata-an-open-and-universal-data-framework-for-spatial-omics"], [53, "spatialdata-an-open-and-universal-data-framework-for-spatial-omics"], [71, "spatialdata-an-open-and-universal-data-framework-for-spatial-omics"], [81, "spatialdata-an-open-and-universal-data-framework-for-spatial-omics"], [109, "spatialdata-an-open-and-universal-data-framework-for-spatial-omics"]], "Stackview sliceplot example data": [[29, "stackview-sliceplot-example-data"], [66, "stackview-sliceplot-example-data"], [75, "stackview-sliceplot-example-data"], [81, "stackview-sliceplot-example-data"]], "Standard and Super-Resolution Bioimaging Data Analysis: A Primer": [[41, "standard-and-super-resolution-bioimaging-data-analysis-a-primer"]], "Statistical Rethinking": [[48, "statistical-rethinking"], [70, "statistical-rethinking"], [74, "statistical-rethinking"], [87, "statistical-rethinking"]], "Statistics": [[89, null]], "Stefanie weidtkamp-peters (11)": [[33, null]], "Step-by-step tutorial": [[62, "step-by-step-tutorial"]], "Structuring of Data and Metadata in Bioimaging: Concepts and technical Solutions in the Context of Linked Data": [[16, "structuring-of-data-and-metadata-in-bioimaging-concepts-and-technical-solutions-in-the-context-of-linked-data"], [34, "structuring-of-data-and-metadata-in-bioimaging-concepts-and-technical-solutions-in-the-context-of-linked-data"], [35, "structuring-of-data-and-metadata-in-bioimaging-concepts-and-technical-solutions-in-the-context-of-linked-data"], [58, "structuring-of-data-and-metadata-in-bioimaging-concepts-and-technical-solutions-in-the-context-of-linked-data"], [66, "structuring-of-data-and-metadata-in-bioimaging-concepts-and-technical-solutions-in-the-context-of-linked-data"], [75, "structuring-of-data-and-metadata-in-bioimaging-concepts-and-technical-solutions-in-the-context-of-linked-data"], [81, "structuring-of-data-and-metadata-in-bioimaging-concepts-and-technical-solutions-in-the-context-of-linked-data"], [105, "structuring-of-data-and-metadata-in-bioimaging-concepts-and-technical-solutions-in-the-context-of-linked-data"], [111, "structuring-of-data-and-metadata-in-bioimaging-concepts-and-technical-solutions-in-the-context-of-linked-data"]], "Studentsourcing - aggregating and re-using data from a practical cell biology course": [[52, "studentsourcing-aggregating-and-re-using-data-from-a-practical-cell-biology-course"], [71, "studentsourcing-aggregating-and-re-using-data-from-a-practical-cell-biology-course"], [113, "studentsourcing-aggregating-and-re-using-data-from-a-practical-cell-biology-course"]], "Submitting data to the BioImage Archive": [[57, "submitting-data-to-the-bioimage-archive"], [58, "submitting-data-to-the-bioimage-archive"], [72, "submitting-data-to-the-bioimage-archive"], [83, "submitting-data-to-the-bioimage-archive"], [92, "submitting-data-to-the-bioimage-archive"], [97, "submitting-data-to-the-bioimage-archive"], [111, "submitting-data-to-the-bioimage-archive"]], "Susanne kunis (16)": [[34, null]], "Sustainable Data Stewardship": [[56, "sustainable-data-stewardship"], [66, "sustainable-data-stewardship"], [75, "sustainable-data-stewardship"], [81, "sustainable-data-stewardship"], [94, "sustainable-data-stewardship"], [111, "sustainable-data-stewardship"]], "TESS Event database": [[43, "tess-event-database"], [47, "tess-event-database"], [93, "tess-event-database"]], "TESS training materials": [[43, "tess-training-materials"], [87, "tess-training-materials"], [93, "tess-training-materials"]], "Teaching Bioimage Analysis with Python": [[57, "teaching-bioimage-analysis-with-python"], [70, "teaching-bioimage-analysis-with-python"], [85, "teaching-bioimage-analysis-with-python"], [91, "teaching-bioimage-analysis-with-python"], [109, "teaching-bioimage-analysis-with-python"]], "Teaching ImageJ FIJI": [[57, "teaching-imagej-fiji"], [70, "teaching-imagej-fiji"], [85, "teaching-imagej-fiji"], [91, "teaching-imagej-fiji"]], "Ten simple rules for making training materials FAIR": [[11, "ten-simple-rules-for-making-training-materials-fair"], [53, "ten-simple-rules-for-making-training-materials-fair"], [81, "ten-simple-rules-for-making-training-materials-fair"], [93, "ten-simple-rules-for-making-training-materials-fair"], [96, "ten-simple-rules-for-making-training-materials-fair"], [101, "ten-simple-rules-for-making-training-materials-fair"], [114, "ten-simple-rules-for-making-training-materials-fair"]], "Terminology service for research data management and knowledge discovery in low-temperature plasma physics": [[66, "terminology-service-for-research-data-management-and-knowledge-discovery-in-low-temperature-plasma-physics"], [75, "terminology-service-for-research-data-management-and-knowledge-discovery-in-low-temperature-plasma-physics"], [81, "terminology-service-for-research-data-management-and-knowledge-discovery-in-low-temperature-plasma-physics"]], "Tess Search for Data Life Cycle": [[43, "tess-search-for-data-life-cycle"], [87, "tess-search-for-data-life-cycle"], [111, "tess-search-for-data-life-cycle"]], "Test Dataset for Whole Slide Image Registration": [[24, "test-dataset-for-whole-slide-image-registration"], [31, "test-dataset-for-whole-slide-image-registration"], [66, "test-dataset-for-whole-slide-image-registration"], [75, "test-dataset-for-whole-slide-image-registration"], [81, "test-dataset-for-whole-slide-image-registration"]], "The BioImage Archive \u2013 Building a Home for Life-Sciences Microscopy Data": [[39, "the-bioimage-archive-building-a-home-for-life-sciences-microscopy-data"], [53, "the-bioimage-archive-building-a-home-for-life-sciences-microscopy-data"], [66, "the-bioimage-archive-building-a-home-for-life-sciences-microscopy-data"], [87, "the-bioimage-archive-building-a-home-for-life-sciences-microscopy-data"], [92, "the-bioimage-archive-building-a-home-for-life-sciences-microscopy-data"], [97, "the-bioimage-archive-building-a-home-for-life-sciences-microscopy-data"], [111, "the-bioimage-archive-building-a-home-for-life-sciences-microscopy-data"]], "The Digital Cell: Cell Biology as a Data Science": [[41, "the-digital-cell-cell-biology-as-a-data-science"], [91, "the-digital-cell-cell-biology-as-a-data-science"]], "The FAIR Guiding Principles for scientific data management and stewardship": [[53, "the-fair-guiding-principles-for-scientific-data-management-and-stewardship"], [66, "the-fair-guiding-principles-for-scientific-data-management-and-stewardship"], [73, "the-fair-guiding-principles-for-scientific-data-management-and-stewardship"], [81, "the-fair-guiding-principles-for-scientific-data-management-and-stewardship"], [96, "the-fair-guiding-principles-for-scientific-data-management-and-stewardship"], [111, "the-fair-guiding-principles-for-scientific-data-management-and-stewardship"]], "The FAIR guiding principles for data stewardship - fair enough?": [[53, "the-fair-guiding-principles-for-data-stewardship-fair-enough"], [73, "the-fair-guiding-principles-for-data-stewardship-fair-enough"], [81, "the-fair-guiding-principles-for-data-stewardship-fair-enough"], [94, "the-fair-guiding-principles-for-data-stewardship-fair-enough"], [96, "the-fair-guiding-principles-for-data-stewardship-fair-enough"], [113, "the-fair-guiding-principles-for-data-stewardship-fair-enough"]], "The Fiji Updater": [[29, "the-fiji-updater"], [53, "the-fiji-updater"], [78, "the-fiji-updater"], [98, "the-fiji-updater"]], "The Information Infrastructure for BioImage Data (I3D:bio) project to advance FAIR microscopy data management for the community": [[3, "the-information-infrastructure-for-bioimage-data-i3d-bio-project-to-advance-fair-microscopy-data-management-for-the-community"], [8, "the-information-infrastructure-for-bioimage-data-i3d-bio-project-to-advance-fair-microscopy-data-management-for-the-community"], [23, "the-information-infrastructure-for-bioimage-data-i3d-bio-project-to-advance-fair-microscopy-data-management-for-the-community"], [30, "the-information-infrastructure-for-bioimage-data-i3d-bio-project-to-advance-fair-microscopy-data-management-for-the-community"], [33, "the-information-infrastructure-for-bioimage-data-i3d-bio-project-to-advance-fair-microscopy-data-management-for-the-community"], [34, "the-information-infrastructure-for-bioimage-data-i3d-bio-project-to-advance-fair-microscopy-data-management-for-the-community"], [37, "the-information-infrastructure-for-bioimage-data-i3d-bio-project-to-advance-fair-microscopy-data-management-for-the-community"], [66, "the-information-infrastructure-for-bioimage-data-i3d-bio-project-to-advance-fair-microscopy-data-management-for-the-community"], [75, "the-information-infrastructure-for-bioimage-data-i3d-bio-project-to-advance-fair-microscopy-data-management-for-the-community"], [81, "the-information-infrastructure-for-bioimage-data-i3d-bio-project-to-advance-fair-microscopy-data-management-for-the-community"]], "The Open Microscopy Environment (OME) Data Model and XML file - open tools for informatics and quantitative analysis in biological imaging": [[2, "the-open-microscopy-environment-ome-data-model-and-xml-file-open-tools-for-informatics-and-quantitative-analysis-in-biological-imaging"], [15, "the-open-microscopy-environment-ome-data-model-and-xml-file-open-tools-for-informatics-and-quantitative-analysis-in-biological-imaging"], [53, "the-open-microscopy-environment-ome-data-model-and-xml-file-open-tools-for-informatics-and-quantitative-analysis-in-biological-imaging"], [66, "the-open-microscopy-environment-ome-data-model-and-xml-file-open-tools-for-informatics-and-quantitative-analysis-in-biological-imaging"], [81, "the-open-microscopy-environment-ome-data-model-and-xml-file-open-tools-for-informatics-and-quantitative-analysis-in-biological-imaging"], [91, "the-open-microscopy-environment-ome-data-model-and-xml-file-open-tools-for-informatics-and-quantitative-analysis-in-biological-imaging"], [102, "the-open-microscopy-environment-ome-data-model-and-xml-file-open-tools-for-informatics-and-quantitative-analysis-in-biological-imaging"]], "The Turing Way: Guide for reproducible research": [[41, "the-turing-way-guide-for-reproducible-research"], [81, "the-turing-way-guide-for-reproducible-research"], [85, "the-turing-way-guide-for-reproducible-research"]], "The role of Helmholtz Centers in NFDI4BIOIMAGE - A national consortium enhancing FAIR data management for microscopy and bioimage analysis": [[3, "the-role-of-helmholtz-centers-in-nfdi4bioimage-a-national-consortium-enhancing-fair-data-management-for-microscopy-and-bioimage-analysis"], [8, "the-role-of-helmholtz-centers-in-nfdi4bioimage-a-national-consortium-enhancing-fair-data-management-for-microscopy-and-bioimage-analysis"], [23, "the-role-of-helmholtz-centers-in-nfdi4bioimage-a-national-consortium-enhancing-fair-data-management-for-microscopy-and-bioimage-analysis"], [28, "the-role-of-helmholtz-centers-in-nfdi4bioimage-a-national-consortium-enhancing-fair-data-management-for-microscopy-and-bioimage-analysis"], [66, "the-role-of-helmholtz-centers-in-nfdi4bioimage-a-national-consortium-enhancing-fair-data-management-for-microscopy-and-bioimage-analysis"], [75, "the-role-of-helmholtz-centers-in-nfdi4bioimage-a-national-consortium-enhancing-fair-data-management-for-microscopy-and-bioimage-analysis"], [81, "the-role-of-helmholtz-centers-in-nfdi4bioimage-a-national-consortium-enhancing-fair-data-management-for-microscopy-and-bioimage-analysis"]], "Thinking data management on different scales": [[34, "thinking-data-management-on-different-scales"], [55, "thinking-data-management-on-different-scales"], [56, "thinking-data-management-on-different-scales"], [66, "thinking-data-management-on-different-scales"], [75, "thinking-data-management-on-different-scales"], [81, "thinking-data-management-on-different-scales"], [105, "thinking-data-management-on-different-scales"], [111, "thinking-data-management-on-different-scales"]], "Thomas zobel (7)": [[35, null]], "Toby hodges (4)": [[36, null]], "Tom boissonnet (13)": [[37, null]], "Torsten st\u00f6ter (5)": [[38, null]], "Towards Preservation of Life Science Data with NFDI4BIOIMAGE": [[29, "towards-preservation-of-life-science-data-with-nfdi4bioimage"], [66, "towards-preservation-of-life-science-data-with-nfdi4bioimage"], [75, "towards-preservation-of-life-science-data-with-nfdi4bioimage"], [81, "towards-preservation-of-life-science-data-with-nfdi4bioimage"]], "Towards Transparency and Knowledge Exchange in AI-assisted Data Analysis Code Generation": [[29, "towards-transparency-and-knowledge-exchange-in-ai-assisted-data-analysis-code-generation"], [66, "towards-transparency-and-knowledge-exchange-in-ai-assisted-data-analysis-code-generation"], [75, "towards-transparency-and-knowledge-exchange-in-ai-assisted-data-analysis-code-generation"], [81, "towards-transparency-and-knowledge-exchange-in-ai-assisted-data-analysis-code-generation"]], "Towards community-driven metadata standards for light microscopy - tiered specifications extending the OME model": [[11, "towards-community-driven-metadata-standards-for-light-microscopy-tiered-specifications-extending-the-ome-model"], [53, "towards-community-driven-metadata-standards-for-light-microscopy-tiered-specifications-extending-the-ome-model"], [87, "towards-community-driven-metadata-standards-for-light-microscopy-tiered-specifications-extending-the-ome-model"], [92, "towards-community-driven-metadata-standards-for-light-microscopy-tiered-specifications-extending-the-ome-model"], [97, "towards-community-driven-metadata-standards-for-light-microscopy-tiered-specifications-extending-the-ome-model"], [101, "towards-community-driven-metadata-standards-for-light-microscopy-tiered-specifications-extending-the-ome-model"], [102, "towards-community-driven-metadata-standards-for-light-microscopy-tiered-specifications-extending-the-ome-model"]], "Tracking Theory, TrackMate, and Mastodon": [[29, "tracking-theory-trackmate-and-mastodon"], [55, "tracking-theory-trackmate-and-mastodon"], [69, "tracking-theory-trackmate-and-mastodon"], [80, "tracking-theory-trackmate-and-mastodon"], [91, "tracking-theory-trackmate-and-mastodon"], [98, "tracking-theory-trackmate-and-mastodon"], [104, "tracking-theory-trackmate-and-mastodon"]], "Tracking in napari": [[19, "tracking-in-napari"], [40, "tracking-in-napari"], [68, "tracking-in-napari"], [91, "tracking-in-napari"], [103, "tracking-in-napari"], [109, "tracking-in-napari"]], "Tracking of mitochondria and capturing mitoflashes": [[57, "tracking-of-mitochondria-and-capturing-mitoflashes"], [81, "tracking-of-mitochondria-and-capturing-mitoflashes"], [91, "tracking-of-mitochondria-and-capturing-mitoflashes"], [93, "tracking-of-mitochondria-and-capturing-mitoflashes"]], "Train-the-Trainer Concept on Research Data Management": [[41, "train-the-trainer-concept-on-research-data-management"], [66, "train-the-trainer-concept-on-research-data-management"], [75, "train-the-trainer-concept-on-research-data-management"], [81, "train-the-trainer-concept-on-research-data-management"], [111, "train-the-trainer-concept-on-research-data-management"]], "Training (12)": [[114, null]], "Training Deep Learning Models for Vision - Compact Course": [[5, "training-deep-learning-models-for-vision-compact-course"], [70, "training-deep-learning-models-for-vision-compact-course"], [87, "training-deep-learning-models-for-vision-compact-course"], [90, "training-deep-learning-models-for-vision-compact-course"], [91, "training-deep-learning-models-for-vision-compact-course"]], "Tutorial (42)": [[57, null]], "Ugis sarkans (5)": [[39, null]], "Ultrack I2K 2024 Workshop Materials": [[48, "ultrack-i2k-2024-workshop-materials"], [57, "ultrack-i2k-2024-workshop-materials"], [60, "ultrack-i2k-2024-workshop-materials"], [70, "ultrack-i2k-2024-workshop-materials"], [91, "ultrack-i2k-2024-workshop-materials"], [112, "ultrack-i2k-2024-workshop-materials"], [114, "ultrack-i2k-2024-workshop-materials"]], "Understanding metric-related pitfalls in image analysis validation": [[11, "understanding-metric-related-pitfalls-in-image-analysis-validation"], [52, "understanding-metric-related-pitfalls-in-image-analysis-validation"], [53, "understanding-metric-related-pitfalls-in-image-analysis-validation"], [91, "understanding-metric-related-pitfalls-in-image-analysis-validation"]], "Unknown (89)": [[87, null]], "Upcoming Image Analysis Events": [[11, "upcoming-image-analysis-events"], [25, "upcoming-image-analysis-events"], [31, "upcoming-image-analysis-events"], [43, "upcoming-image-analysis-events"], [47, "upcoming-image-analysis-events"], [60, "upcoming-image-analysis-events"], [87, "upcoming-image-analysis-events"], [91, "upcoming-image-analysis-events"], [102, "upcoming-image-analysis-events"]], "Urheberrecht": [[77, "urheberrecht"]], "User friendly Image metadata annotation tool/workflow for OMERO": [[16, "user-friendly-image-metadata-annotation-tool-workflow-for-omero"], [101, "user-friendly-image-metadata-annotation-tool-workflow-for-omero"], [106, "user-friendly-image-metadata-annotation-tool-workflow-for-omero"], [115, "user-friendly-image-metadata-annotation-tool-workflow-for-omero"]], "Using Glittr.org to find, compare and re-use online training materials": [[52, "using-glittr-org-to-find-compare-and-re-use-online-training-materials"], [53, "using-glittr-org-to-find-compare-and-re-use-online-training-materials"], [71, "using-glittr-org-to-find-compare-and-re-use-online-training-materials"], [81, "using-glittr-org-to-find-compare-and-re-use-online-training-materials"], [91, "using-glittr-org-to-find-compare-and-re-use-online-training-materials"], [111, "using-glittr-org-to-find-compare-and-re-use-online-training-materials"], [114, "using-glittr-org-to-find-compare-and-re-use-online-training-materials"]], "Using the Training Materials Submission App": [[63, null]], "V4SDB_Winter_School_2025": [[43, "v4sdb-winter-school-2025"], [48, "v4sdb-winter-school-2025"], [70, "v4sdb-winter-school-2025"], [91, "v4sdb-winter-school-2025"], [117, "v4sdb-winter-school-2025"]], "Verantwortlich f\u00fcr den Inhalt nach \u00a7 55 Abs. 2 RStV:": [[77, "verantwortlich-fur-den-inhalt-nach-55-abs-2-rstv"]], "Video (24)": [[58, null]], "Virtual-I2K-2024-multiview-stitcher": [[48, "virtual-i2k-2024-multiview-stitcher"], [57, "virtual-i2k-2024-multiview-stitcher"], [70, "virtual-i2k-2024-multiview-stitcher"], [80, "virtual-i2k-2024-multiview-stitcher"]], "Website (9)": [[59, null]], "Welcome to BioImage Town": [[17, "welcome-to-bioimage-town"], [56, "welcome-to-bioimage-town"], [75, "welcome-to-bioimage-town"], [81, "welcome-to-bioimage-town"], [91, "welcome-to-bioimage-town"], [105, "welcome-to-bioimage-town"], [106, "welcome-to-bioimage-town"]], "What is Bioimage Analysis? An Introduction": [[18, "what-is-bioimage-analysis-an-introduction"], [55, "what-is-bioimage-analysis-an-introduction"], [87, "what-is-bioimage-analysis-an-introduction"], [91, "what-is-bioimage-analysis-an-introduction"], [104, "what-is-bioimage-analysis-an-introduction"]], "What is Open Data?": [[11, "what-is-open-data"], [43, "what-is-open-data"], [81, "what-is-open-data"], [107, "what-is-open-data"]], "What to contribute": [[62, "what-to-contribute"]], "Who you gonna call? - Data Stewards to the rescue": [[6, "who-you-gonna-call-data-stewards-to-the-rescue"], [16, "who-you-gonna-call-data-stewards-to-the-rescue"], [28, "who-you-gonna-call-data-stewards-to-the-rescue"], [51, "who-you-gonna-call-data-stewards-to-the-rescue"], [66, "who-you-gonna-call-data-stewards-to-the-rescue"], [75, "who-you-gonna-call-data-stewards-to-the-rescue"], [81, "who-you-gonna-call-data-stewards-to-the-rescue"], [91, "who-you-gonna-call-data-stewards-to-the-rescue"], [94, "who-you-gonna-call-data-stewards-to-the-rescue"], [105, "who-you-gonna-call-data-stewards-to-the-rescue"], [111, "who-you-gonna-call-data-stewards-to-the-rescue"]], "Workflow (10)": [[115, null]], "Workflow engine (13)": [[116, null]], "WorkflowHub": [[11, "workflowhub"], [43, "workflowhub"], [80, "workflowhub"], [93, "workflowhub"], [109, "workflowhub"], [115, "workflowhub"], [116, "workflowhub"]], "Working Group Charter. RDM Helpdesk Network": [[66, "working-group-charter-rdm-helpdesk-network"], [75, "working-group-charter-rdm-helpdesk-network"], [81, "working-group-charter-rdm-helpdesk-network"]], "Working with objects in 2D and 3D": [[29, "working-with-objects-in-2d-and-3d"], [55, "working-with-objects-in-2d-and-3d"], [69, "working-with-objects-in-2d-and-3d"], [80, "working-with-objects-in-2d-and-3d"], [91, "working-with-objects-in-2d-and-3d"], [98, "working-with-objects-in-2d-and-3d"], [104, "working-with-objects-in-2d-and-3d"]], "Working with pixels": [[29, "working-with-pixels"], [55, "working-with-pixels"], [69, "working-with-pixels"], [80, "working-with-pixels"], [91, "working-with-pixels"], [98, "working-with-pixels"], [104, "working-with-pixels"]], "Workshop (12)": [[60, null]], "Workshop-June2024-Madrid": [[43, "workshop-june2024-madrid"], [60, "workshop-june2024-madrid"], [70, "workshop-june2024-madrid"], [85, "workshop-june2024-madrid"], [91, "workshop-june2024-madrid"]], "Www.biorxiv.org (5)": [[71, null]], "Www.ebi.ac.uk (10)": [[72, null]], "Www.nature.com (16)": [[73, null]], "Www.youtube.com (22)": [[74, null]], "YMIA - Python-Based Event Series Training Material": [[28, "ymia-python-based-event-series-training-material"], [29, "ymia-python-based-event-series-training-material"], [48, "ymia-python-based-event-series-training-material"], [56, "ymia-python-based-event-series-training-material"], [70, "ymia-python-based-event-series-training-material"], [85, "ymia-python-based-event-series-training-material"], [91, "ymia-python-based-event-series-training-material"], [99, "ymia-python-based-event-series-training-material"], [102, "ymia-python-based-event-series-training-material"], [109, "ymia-python-based-event-series-training-material"]], "YML format": [[61, null]], "ZEN & Python workshop": [[43, "zen-python-workshop"], [49, "zen-python-workshop"], [70, "zen-python-workshop"], [91, "zen-python-workshop"], [103, "zen-python-workshop"], [109, "zen-python-workshop"]], "ZIDAS 2020 Introduction to Deep Learning": [[10, "zidas-2020-introduction-to-deep-learning"], [55, "zidas-2020-introduction-to-deep-learning"], [70, "zidas-2020-introduction-to-deep-learning"], [87, "zidas-2020-introduction-to-deep-learning"], [90, "zidas-2020-introduction-to-deep-learning"], [91, "zidas-2020-introduction-to-deep-learning"]], "Zeiss AxioZoom Stage Adapter": [[22, "zeiss-axiozoom-stage-adapter"], [66, "zeiss-axiozoom-stage-adapter"], [75, "zeiss-axiozoom-stage-adapter"], [81, "zeiss-axiozoom-stage-adapter"]], "Zeiss AxioZoom Stage Adapter - 12/6Well Plate": [[22, "zeiss-axiozoom-stage-adapter-12-6well-plate"], [66, "zeiss-axiozoom-stage-adapter-12-6well-plate"], [75, "zeiss-axiozoom-stage-adapter-12-6well-plate"], [81, "zeiss-axiozoom-stage-adapter-12-6well-plate"]], "Zeiss AxioZoom Stage Adapter - EM block holder": [[22, "zeiss-axiozoom-stage-adapter-em-block-holder"], [66, "zeiss-axiozoom-stage-adapter-em-block-holder"], [75, "zeiss-axiozoom-stage-adapter-em-block-holder"], [81, "zeiss-axiozoom-stage-adapter-em-block-holder"]], "Zeiss AxioZoom Stage Adapter - Microscope slides": [[22, "zeiss-axiozoom-stage-adapter-microscope-slides"], [66, "zeiss-axiozoom-stage-adapter-microscope-slides"], [75, "zeiss-axiozoom-stage-adapter-microscope-slides"], [81, "zeiss-axiozoom-stage-adapter-microscope-slides"]], "Zenodo.org (172)": [[75, null]], "ZeroCostDL4Mic: exploiting Google Colab to develop a free and open-source toolbox for Deep-Learning in microscopy": [[12, "zerocostdl4mic-exploiting-google-colab-to-develop-a-free-and-open-source-toolbox-for-deep-learning-in-microscopy"], [43, "zerocostdl4mic-exploiting-google-colab-to-develop-a-free-and-open-source-toolbox-for-deep-learning-in-microscopy"], [49, "zerocostdl4mic-exploiting-google-colab-to-develop-a-free-and-open-source-toolbox-for-deep-learning-in-microscopy"], [66, "zerocostdl4mic-exploiting-google-colab-to-develop-a-free-and-open-source-toolbox-for-deep-learning-in-microscopy"], [70, "zerocostdl4mic-exploiting-google-colab-to-develop-a-free-and-open-source-toolbox-for-deep-learning-in-microscopy"], [73, "zerocostdl4mic-exploiting-google-colab-to-develop-a-free-and-open-source-toolbox-for-deep-learning-in-microscopy"], [85, "zerocostdl4mic-exploiting-google-colab-to-develop-a-free-and-open-source-toolbox-for-deep-learning-in-microscopy"], [91, "zerocostdl4mic-exploiting-google-colab-to-develop-a-free-and-open-source-toolbox-for-deep-learning-in-microscopy"]], "[BINA CC] Scalable strategies for a next-generation of FAIR bioimaging": [[17, "bina-cc-scalable-strategies-for-a-next-generation-of-fair-bioimaging"], [66, "bina-cc-scalable-strategies-for-a-next-generation-of-fair-bioimaging"], [75, "bina-cc-scalable-strategies-for-a-next-generation-of-fair-bioimaging"], [81, "bina-cc-scalable-strategies-for-a-next-generation-of-fair-bioimaging"]], "[CIDAS] Scalable strategies for a next-generation of FAIR bioimaging": [[17, "cidas-scalable-strategies-for-a-next-generation-of-fair-bioimaging"], [66, "cidas-scalable-strategies-for-a-next-generation-of-fair-bioimaging"], [75, "cidas-scalable-strategies-for-a-next-generation-of-fair-bioimaging"], [81, "cidas-scalable-strategies-for-a-next-generation-of-fair-bioimaging"], [117, "cidas-scalable-strategies-for-a-next-generation-of-fair-bioimaging"]], "[CMCB] Scalable strategies for a next-generation of FAIR bioimaging": [[17, "cmcb-scalable-strategies-for-a-next-generation-of-fair-bioimaging"], [66, "cmcb-scalable-strategies-for-a-next-generation-of-fair-bioimaging"], [75, "cmcb-scalable-strategies-for-a-next-generation-of-fair-bioimaging"], [81, "cmcb-scalable-strategies-for-a-next-generation-of-fair-bioimaging"], [117, "cmcb-scalable-strategies-for-a-next-generation-of-fair-bioimaging"]], "[CORDI 2023] Zarr: A Cloud-Optimized Storage for Interactive Access of Large Arrays": [[17, "cordi-2023-zarr-a-cloud-optimized-storage-for-interactive-access-of-large-arrays"], [51, "cordi-2023-zarr-a-cloud-optimized-storage-for-interactive-access-of-large-arrays"], [75, "cordi-2023-zarr-a-cloud-optimized-storage-for-interactive-access-of-large-arrays"], [81, "cordi-2023-zarr-a-cloud-optimized-storage-for-interactive-access-of-large-arrays"], [91, "cordi-2023-zarr-a-cloud-optimized-storage-for-interactive-access-of-large-arrays"], [111, "cordi-2023-zarr-a-cloud-optimized-storage-for-interactive-access-of-large-arrays"]], "[Community Meeting 2024] Overview Team Image Data Analysis and Management": [[34, "community-meeting-2024-overview-team-image-data-analysis-and-management"], [35, "community-meeting-2024-overview-team-image-data-analysis-and-management"], [66, "community-meeting-2024-overview-team-image-data-analysis-and-management"], [75, "community-meeting-2024-overview-team-image-data-analysis-and-management"], [81, "community-meeting-2024-overview-team-image-data-analysis-and-management"]], "[Community Meeting 2024] Supporting and financing RDM projects within GerBI": [[3, "community-meeting-2024-supporting-and-financing-rdm-projects-within-gerbi"], [17, "community-meeting-2024-supporting-and-financing-rdm-projects-within-gerbi"], [30, "community-meeting-2024-supporting-and-financing-rdm-projects-within-gerbi"], [33, "community-meeting-2024-supporting-and-financing-rdm-projects-within-gerbi"], [34, "community-meeting-2024-supporting-and-financing-rdm-projects-within-gerbi"], [35, "community-meeting-2024-supporting-and-financing-rdm-projects-within-gerbi"], [66, "community-meeting-2024-supporting-and-financing-rdm-projects-within-gerbi"], [75, "community-meeting-2024-supporting-and-financing-rdm-projects-within-gerbi"], [81, "community-meeting-2024-supporting-and-financing-rdm-projects-within-gerbi"]], "[ELMI 2024] AI\u2019s Dirty Little Secret: Without": [[17, "elmi-2024-ai-s-dirty-little-secret-without"], [34, "elmi-2024-ai-s-dirty-little-secret-without"], [66, "elmi-2024-ai-s-dirty-little-secret-without"], [75, "elmi-2024-ai-s-dirty-little-secret-without"], [81, "elmi-2024-ai-s-dirty-little-secret-without"]], "[ELMI 2024] AI\u2019s Dirty Little Secret: Without FAIR Data, It\u2019s Just Fancy Math": [[17, "elmi-2024-ai-s-dirty-little-secret-without-fair-data-it-s-just-fancy-math"], [34, "elmi-2024-ai-s-dirty-little-secret-without-fair-data-it-s-just-fancy-math"], [51, "elmi-2024-ai-s-dirty-little-secret-without-fair-data-it-s-just-fancy-math"], [75, "elmi-2024-ai-s-dirty-little-secret-without-fair-data-it-s-just-fancy-math"], [81, "elmi-2024-ai-s-dirty-little-secret-without-fair-data-it-s-just-fancy-math"], [91, "elmi-2024-ai-s-dirty-little-secret-without-fair-data-it-s-just-fancy-math"], [96, "elmi-2024-ai-s-dirty-little-secret-without-fair-data-it-s-just-fancy-math"], [105, "elmi-2024-ai-s-dirty-little-secret-without-fair-data-it-s-just-fancy-math"], [111, "elmi-2024-ai-s-dirty-little-secret-without-fair-data-it-s-just-fancy-math"]], "[GBI EOE VII] Five (or ten) must-have items for making IT infrastructure for managing bioimage data": [[17, "gbi-eoe-vii-five-or-ten-must-have-items-for-making-it-infrastructure-for-managing-bioimage-data"], [66, "gbi-eoe-vii-five-or-ten-must-have-items-for-making-it-infrastructure-for-managing-bioimage-data"], [75, "gbi-eoe-vii-five-or-ten-must-have-items-for-making-it-infrastructure-for-managing-bioimage-data"], [81, "gbi-eoe-vii-five-or-ten-must-have-items-for-making-it-infrastructure-for-managing-bioimage-data"]], "[GBI EoE IX] NFDI4BIOIMAGE": [[17, "gbi-eoe-ix-nfdi4bioimage"], [66, "gbi-eoe-ix-nfdi4bioimage"], [75, "gbi-eoe-ix-nfdi4bioimage"], [81, "gbi-eoe-ix-nfdi4bioimage"]], "[I2K] Scalable strategies for a next-generation of FAIR bioimaging": [[17, "i2k-scalable-strategies-for-a-next-generation-of-fair-bioimaging"], [66, "i2k-scalable-strategies-for-a-next-generation-of-fair-bioimaging"], [75, "i2k-scalable-strategies-for-a-next-generation-of-fair-bioimaging"], [81, "i2k-scalable-strategies-for-a-next-generation-of-fair-bioimaging"]], "[N4BI AHM] Welcome to BioImage Town": [[17, "n4bi-ahm-welcome-to-bioimage-town"], [56, "n4bi-ahm-welcome-to-bioimage-town"], [66, "n4bi-ahm-welcome-to-bioimage-town"], [75, "n4bi-ahm-welcome-to-bioimage-town"], [81, "n4bi-ahm-welcome-to-bioimage-town"], [111, "n4bi-ahm-welcome-to-bioimage-town"]], "[SWAT4HCLS 2023] NFDI4BIOIMAGE: Perspective for a national bioimage standard": [[17, "swat4hcls-2023-nfdi4bioimage-perspective-for-a-national-bioimage-standard"], [34, "swat4hcls-2023-nfdi4bioimage-perspective-for-a-national-bioimage-standard"], [51, "swat4hcls-2023-nfdi4bioimage-perspective-for-a-national-bioimage-standard"], [75, "swat4hcls-2023-nfdi4bioimage-perspective-for-a-national-bioimage-standard"], [81, "swat4hcls-2023-nfdi4bioimage-perspective-for-a-national-bioimage-standard"], [96, "swat4hcls-2023-nfdi4bioimage-perspective-for-a-national-bioimage-standard"], [105, "swat4hcls-2023-nfdi4bioimage-perspective-for-a-national-bioimage-standard"], [111, "swat4hcls-2023-nfdi4bioimage-perspective-for-a-national-bioimage-standard"]], "[Short Talk] NFDI4BIOIMAGE - A consortium in the National Research Data Infrastructure": [[3, "short-talk-nfdi4bioimage-a-consortium-in-the-national-research-data-infrastructure"], [56, "short-talk-nfdi4bioimage-a-consortium-in-the-national-research-data-infrastructure"], [66, "short-talk-nfdi4bioimage-a-consortium-in-the-national-research-data-infrastructure"], [75, "short-talk-nfdi4bioimage-a-consortium-in-the-national-research-data-infrastructure"], [81, "short-talk-nfdi4bioimage-a-consortium-in-the-national-research-data-infrastructure"], [91, "short-talk-nfdi4bioimage-a-consortium-in-the-national-research-data-infrastructure"], [105, "short-talk-nfdi4bioimage-a-consortium-in-the-national-research-data-infrastructure"], [111, "short-talk-nfdi4bioimage-a-consortium-in-the-national-research-data-infrastructure"]], "[Workshop Material] Fit for OMERO - How imaging facilities and IT departments work together to enable RDM for bioimaging, October 16-17, 2024, Heidelberg": [[3, "workshop-material-fit-for-omero-how-imaging-facilities-and-it-departments-work-together-to-enable-rdm-for-bioimaging-october-16-17-2024-heidelberg"], [33, "workshop-material-fit-for-omero-how-imaging-facilities-and-it-departments-work-together-to-enable-rdm-for-bioimaging-october-16-17-2024-heidelberg"], [34, "workshop-material-fit-for-omero-how-imaging-facilities-and-it-departments-work-together-to-enable-rdm-for-bioimaging-october-16-17-2024-heidelberg"], [37, "workshop-material-fit-for-omero-how-imaging-facilities-and-it-departments-work-together-to-enable-rdm-for-bioimaging-october-16-17-2024-heidelberg"], [66, "workshop-material-fit-for-omero-how-imaging-facilities-and-it-departments-work-together-to-enable-rdm-for-bioimaging-october-16-17-2024-heidelberg"], [75, "workshop-material-fit-for-omero-how-imaging-facilities-and-it-departments-work-together-to-enable-rdm-for-bioimaging-october-16-17-2024-heidelberg"], [81, "workshop-material-fit-for-omero-how-imaging-facilities-and-it-departments-work-together-to-enable-rdm-for-bioimaging-october-16-17-2024-heidelberg"]], "[Workshop] Bioimage data management and analysis with OMERO": [[3, "workshop-bioimage-data-management-and-analysis-with-omero"], [23, "workshop-bioimage-data-management-and-analysis-with-omero"], [28, "workshop-bioimage-data-management-and-analysis-with-omero"], [66, "workshop-bioimage-data-management-and-analysis-with-omero"], [75, "workshop-bioimage-data-management-and-analysis-with-omero"], [81, "workshop-bioimage-data-management-and-analysis-with-omero"]], "[Workshop] FAIR data handling for microscopy: Structured metadata annotation in OMERO": [[3, "workshop-fair-data-handling-for-microscopy-structured-metadata-annotation-in-omero"], [37, "workshop-fair-data-handling-for-microscopy-structured-metadata-annotation-in-omero"], [66, "workshop-fair-data-handling-for-microscopy-structured-metadata-annotation-in-omero"], [75, "workshop-fair-data-handling-for-microscopy-structured-metadata-annotation-in-omero"], [81, "workshop-fair-data-handling-for-microscopy-structured-metadata-annotation-in-omero"]], "[Workshop] Research Data Management for Microscopy and BioImage Analysis": [[3, "workshop-research-data-management-for-microscopy-and-bioimage-analysis"], [23, "workshop-research-data-management-for-microscopy-and-bioimage-analysis"], [37, "workshop-research-data-management-for-microscopy-and-bioimage-analysis"], [66, "workshop-research-data-management-for-microscopy-and-bioimage-analysis"], [75, "workshop-research-data-management-for-microscopy-and-bioimage-analysis"], [81, "workshop-research-data-management-for-microscopy-and-bioimage-analysis"], [117, "workshop-research-data-management-for-microscopy-and-bioimage-analysis"]], "arivis Vision4D Tutorials": [[43, "arivis-vision4d-tutorials"], [58, "arivis-vision4d-tutorials"], [74, "arivis-vision4d-tutorials"], [78, "arivis-vision4d-tutorials"], [91, "arivis-vision4d-tutorials"]], "bioformats2raw Converter": [[2, "bioformats2raw-converter"], [11, "bioformats2raw-converter"], [17, "bioformats2raw-converter"], [48, "bioformats2raw-converter"], [70, "bioformats2raw-converter"], [84, "bioformats2raw-converter"], [92, "bioformats2raw-converter"], [108, "bioformats2raw-converter"]], "cba-support-template": [[4, "cba-support-template"], [7, "cba-support-template"], [11, "cba-support-template"], [57, "cba-support-template"], [85, "cba-support-template"], [111, "cba-support-template"], [115, "cba-support-template"]], "i2k-2020-s3-zarr-workshop": [[4, "i2k-2020-s3-zarr-workshop"], [5, "i2k-2020-s3-zarr-workshop"], [48, "i2k-2020-s3-zarr-workshop"], [70, "i2k-2020-s3-zarr-workshop"], [87, "i2k-2020-s3-zarr-workshop"], [109, "i2k-2020-s3-zarr-workshop"]], "ilastik: interactive machine learning for (bio)image analysis": [[7, "ilastik-interactive-machine-learning-for-bio-image-analysis"], [55, "ilastik-interactive-machine-learning-for-bio-image-analysis"], [75, "ilastik-interactive-machine-learning-for-bio-image-analysis"], [81, "ilastik-interactive-machine-learning-for-bio-image-analysis"], [90, "ilastik-interactive-machine-learning-for-bio-image-analysis"], [91, "ilastik-interactive-machine-learning-for-bio-image-analysis"]], "introduction-to-generative-ai": [[48, "introduction-to-generative-ai"], [57, "introduction-to-generative-ai"], [70, "introduction-to-generative-ai"], [81, "introduction-to-generative-ai"], [90, "introduction-to-generative-ai"]], "introduction-to-image-analysis": [[48, "introduction-to-image-analysis"], [70, "introduction-to-image-analysis"], [82, "introduction-to-image-analysis"], [91, "introduction-to-image-analysis"]], "martinschatz-cz/SciCount: v1.0.0 with reusable example notebooks": [[20, "martinschatz-cz-scicount-v1-0-0-with-reusable-example-notebooks"], [66, "martinschatz-cz-scicount-v1-0-0-with-reusable-example-notebooks"], [75, "martinschatz-cz-scicount-v1-0-0-with-reusable-example-notebooks"]], "microlist": [[43, "microlist"], [78, "microlist"]], "nextflow-workshop": [[48, "nextflow-workshop"], [57, "nextflow-workshop"], [70, "nextflow-workshop"], [81, "nextflow-workshop"], [115, "nextflow-workshop"]], "numpy pandas course": [[13, "numpy-pandas-course"], [49, "numpy-pandas-course"], [70, "numpy-pandas-course"], [80, "numpy-pandas-course"], [109, "numpy-pandas-course"]], "ome-ngff-validator": [[17, "ome-ngff-validator"], [48, "ome-ngff-validator"], [70, "ome-ngff-validator"], [79, "ome-ngff-validator"], [92, "ome-ngff-validator"]], "ome2024-ngff-challenge": [[15, "ome2024-ngff-challenge"], [17, "ome2024-ngff-challenge"], [37, "ome2024-ngff-challenge"], [38, "ome2024-ngff-challenge"], [48, "ome2024-ngff-challenge"], [70, "ome2024-ngff-challenge"], [80, "ome2024-ngff-challenge"], [113, "ome2024-ngff-challenge"]], "patho_prompt_injection": [[48, "patho-prompt-injection"], [49, "patho-prompt-injection"], [70, "patho-prompt-injection"], [91, "patho-prompt-injection"]], "quantixed/TheDigitalCell: First complete code set": [[42, "quantixed-thedigitalcell-first-complete-code-set"], [66, "quantixed-thedigitalcell-first-complete-code-set"], [70, "quantixed-thedigitalcell-first-complete-code-set"], [75, "quantixed-thedigitalcell-first-complete-code-set"], [91, "quantixed-thedigitalcell-first-complete-code-set"]], "raw2ometiff Converter": [[2, "raw2ometiff-converter"], [17, "raw2ometiff-converter"], [48, "raw2ometiff-converter"], [70, "raw2ometiff-converter"], [84, "raw2ometiff-converter"], [92, "raw2ometiff-converter"], [108, "raw2ometiff-converter"]], "re3data.org - registry of Research Data Repositories": [[59, "re3data-org-registry-of-research-data-repositories"], [81, "re3data-org-registry-of-research-data-repositories"], [111, "re3data-org-registry-of-research-data-repositories"]], "rse-skills-workshop": [[48, "rse-skills-workshop"], [56, "rse-skills-workshop"], [70, "rse-skills-workshop"]], "scanpy-tutorials": [[48, "scanpy-tutorials"], [70, "scanpy-tutorials"], [91, "scanpy-tutorials"]], "scikit-learn MOOC": [[48, "scikit-learn-mooc"], [70, "scikit-learn-mooc"], [81, "scikit-learn-mooc"], [91, "scikit-learn-mooc"]], "skimage-tutorials": [[48, "skimage-tutorials"], [70, "skimage-tutorials"], [91, "skimage-tutorials"]], "training-resources": [[4, "training-resources"], [5, "training-resources"], [7, "training-resources"], [9, "training-resources"], [36, "training-resources"], [48, "training-resources"], [70, "training-resources"], [81, "training-resources"]], "\u201cZENODO und Co.\u201d Was bringt und wer braucht ein Repositorium?": [[56, "zenodo-und-co-was-bringt-und-wer-braucht-ein-repositorium"], [66, "zenodo-und-co-was-bringt-und-wer-braucht-ein-repositorium"], [75, "zenodo-und-co-was-bringt-und-wer-braucht-ein-repositorium"], [81, "zenodo-und-co-was-bringt-und-wer-braucht-ein-repositorium"], [111, "zenodo-und-co-was-bringt-und-wer-braucht-ein-repositorium"]]}, "docnames": ["authors/astrid_schauss", "authors/beatriz_serrano-solano", "authors/chris_allan", "authors/christian_schmidt", "authors/christian_tischer", "authors/constantin_pape", "authors/cornelia_wetzker", "authors/dominik_kutra", "authors/elisa_ferrando-may", "authors/elnaz_fazeli", "authors/estibaliz_g\u00f3mez-de-mariscal", "authors/et_al.", "authors/florian_jug", "authors/guillaume_witz", "authors/isabel_kemmer", "authors/jean-marie_burel", "authors/jens_wendt", "authors/josh_moore", "authors/kota_miura", "authors/mara_lampert", "authors/martin_sch\u00e4tz", "authors/martin_weigert", "authors/michael_gerlach", "authors/michele_bortolomeazzi", "authors/nicolas_chiaruttini", "authors/olivier_burri", "authors/pia_voigt", "authors/readme", "authors/riccardo_massei", "authors/robert_haase", "authors/roland_nitschke", "authors/romain_guiet", "authors/silke_tulok", "authors/stefanie_weidtkamp-peters", "authors/susanne_kunis", "authors/thomas_zobel", "authors/toby_hodges", "authors/tom_boissonnet", "authors/torsten_st\u00f6ter", "authors/ugis_sarkans", "content_types/blog", "content_types/book", "content_types/code", "content_types/collection", "content_types/data", "content_types/document", "content_types/documentation", "content_types/event", "content_types/github repository", "content_types/notebook", "content_types/online tutorial", "content_types/poster", "content_types/preprint", "content_types/publication", "content_types/readme", "content_types/slide", "content_types/slides", "content_types/tutorial", "content_types/video", "content_types/website", "content_types/workshop", "contributing/format", "contributing/index", "contributing/submit_app", "domain/biapol.github.io", "domain/docs.google.com", "domain/doi.org", "domain/f1000research.com", "domain/focalplane.biologists.com", "domain/git.mpi-cbg.de", "domain/github.com", "domain/www.biorxiv.org", "domain/www.ebi.ac.uk", "domain/www.nature.com", "domain/www.youtube.com", "domain/zenodo.org", "export/readme", "imprint", "licenses/all_rights_reserved", "licenses/bsd-2-clause", "licenses/bsd-3-clause", "licenses/cc-by-4.0", "licenses/cc-by-sa-4.0", "licenses/cc0-1.0", "licenses/gpl-2.0", "licenses/mit", "licenses/readme", "licenses/unknown", "readme", "statistics/readme", "tags/artificial_intelligence", "tags/bioimage_analysis", "tags/bioimage_data", "tags/bioinformatics", "tags/data_stewardship", "tags/deep_learning", "tags/fair-principles", "tags/image_data_management", "tags/imagej", "tags/large_language_models", "tags/licensing", "tags/metadata", "tags/microscopy_image_analysis", "tags/napari", "tags/neubias", "tags/nfdi4bioimage", "tags/omero", "tags/open_science", "tags/open_source_software", "tags/python", "tags/readme", "tags/research_data_management", "tags/segmentation", "tags/sharing", "tags/training", "tags/workflow", "tags/workflow_engine", "whats_new"], "envversion": {"sphinx": 62, "sphinx.domains.c": 3, "sphinx.domains.changeset": 1, "sphinx.domains.citation": 1, "sphinx.domains.cpp": 9, "sphinx.domains.index": 1, "sphinx.domains.javascript": 3, "sphinx.domains.math": 2, "sphinx.domains.python": 4, "sphinx.domains.rst": 2, "sphinx.domains.std": 2, "sphinx.ext.intersphinx": 1}, "filenames": ["authors/astrid_schauss.md", "authors/beatriz_serrano-solano.md", "authors/chris_allan.md", "authors/christian_schmidt.md", "authors/christian_tischer.md", "authors/constantin_pape.md", "authors/cornelia_wetzker.md", "authors/dominik_kutra.md", "authors/elisa_ferrando-may.md", "authors/elnaz_fazeli.md", "authors/estibaliz_g\u00f3mez-de-mariscal.md", "authors/et_al..md", "authors/florian_jug.md", "authors/guillaume_witz.md", "authors/isabel_kemmer.md", "authors/jean-marie_burel.md", "authors/jens_wendt.md", "authors/josh_moore.md", "authors/kota_miura.md", "authors/mara_lampert.md", "authors/martin_sch\u00e4tz.md", "authors/martin_weigert.md", "authors/michael_gerlach.md", "authors/michele_bortolomeazzi.md", "authors/nicolas_chiaruttini.md", "authors/olivier_burri.md", "authors/pia_voigt.md", "authors/readme.md", "authors/riccardo_massei.md", "authors/robert_haase.md", "authors/roland_nitschke.md", "authors/romain_guiet.md", "authors/silke_tulok.md", "authors/stefanie_weidtkamp-peters.md", "authors/susanne_kunis.md", "authors/thomas_zobel.md", "authors/toby_hodges.md", "authors/tom_boissonnet.md", "authors/torsten_st\u00f6ter.md", "authors/ugis_sarkans.md", "content_types/blog.md", "content_types/book.md", "content_types/code.md", "content_types/collection.md", "content_types/data.md", "content_types/document.md", "content_types/documentation.md", "content_types/event.md", "content_types/github repository.md", "content_types/notebook.md", "content_types/online tutorial.md", "content_types/poster.md", "content_types/preprint.md", "content_types/publication.md", "content_types/readme.md", "content_types/slide.md", "content_types/slides.md", "content_types/tutorial.md", "content_types/video.md", "content_types/website.md", "content_types/workshop.md", "contributing/format.md", "contributing/index.md", "contributing/submit_app.md", "domain/biapol.github.io.md", "domain/docs.google.com.md", "domain/doi.org.md", "domain/f1000research.com.md", "domain/focalplane.biologists.com.md", "domain/git.mpi-cbg.de.md", "domain/github.com.md", "domain/www.biorxiv.org.md", "domain/www.ebi.ac.uk.md", "domain/www.nature.com.md", "domain/www.youtube.com.md", "domain/zenodo.org.md", "export/readme.md", "imprint.md", "licenses/all_rights_reserved.md", "licenses/bsd-2-clause.md", "licenses/bsd-3-clause.md", "licenses/cc-by-4.0.md", "licenses/cc-by-sa-4.0.md", "licenses/cc0-1.0.md", "licenses/gpl-2.0.md", "licenses/mit.md", "licenses/readme.md", "licenses/unknown.md", "readme.md", "statistics/readme.md", "tags/artificial_intelligence.md", "tags/bioimage_analysis.md", "tags/bioimage_data.md", "tags/bioinformatics.md", "tags/data_stewardship.md", "tags/deep_learning.md", "tags/fair-principles.md", "tags/image_data_management.md", "tags/imagej.md", "tags/large_language_models.md", "tags/licensing.md", "tags/metadata.md", "tags/microscopy_image_analysis.md", "tags/napari.md", "tags/neubias.md", "tags/nfdi4bioimage.md", "tags/omero.md", "tags/open_science.md", "tags/open_source_software.md", "tags/python.md", "tags/readme.md", "tags/research_data_management.md", "tags/segmentation.md", "tags/sharing.md", "tags/training.md", "tags/workflow.md", "tags/workflow_engine.md", "whats_new.md"], "indexentries": {}, "objects": {}, "objnames": {}, "objtypes": {}, "terms": {"": [0, 10, 11, 12, 16, 18, 28, 30, 38, 46, 47, 48, 63, 67, 73, 76, 78, 88, 92, 93, 97, 101, 104, 107, 108, 109, 113], "0": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 55, 56, 57, 58, 59, 60, 64, 65, 67, 68, 70, 71, 72, 73, 74, 77, 80, 85, 87, 88, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 111, 112, 113, 114, 115, 116, 117], "00": [3, 4, 5, 7, 9, 15, 17, 36, 37, 38, 41, 43, 48, 49, 56, 57, 60, 66, 70, 75, 80, 81, 82, 83, 85, 87, 90, 91, 93, 106, 111, 113, 115, 117], "000": [41, 66, 81, 111], "00001": [4, 50, 55, 58, 66, 72, 81, 91], "00029489": [3, 8, 55, 66, 92, 97, 105, 111], "0004000112": [29, 53, 78, 98], "0004000267": [16, 35, 53, 78, 105, 106, 111], "00050003": [18, 41, 78, 91, 104], "001": [20, 66, 75, 81], "002": [29, 66, 75, 81], "00_opening_pag": 53, "01": [3, 4, 6, 7, 11, 16, 17, 19, 28, 29, 30, 33, 40, 43, 47, 48, 53, 55, 56, 57, 66, 68, 69, 70, 72, 74, 75, 81, 85, 87, 88, 91, 98, 103, 107, 109, 111, 113, 115, 117], "01166": [11, 14, 39, 53, 56, 57, 66, 73, 75, 81, 87, 91, 92, 96, 97, 101, 111], "01288": [3, 33, 34, 53, 73, 78, 101, 111], "01290": [53, 73, 101], "01315": [11, 53, 66, 101], "01326": [2, 4, 5, 15, 17, 53, 73, 81, 105, 111], "0160": [53, 73, 81, 94, 96, 113], "01602": [29, 53, 66, 78], "017": [25, 31, 66, 75, 81], "01744": [46, 53, 73, 98, 116], "01790v3": [11, 52, 53, 91], "018": [39, 53, 73, 81, 94, 96, 111, 113], "01815": [53, 73, 81, 94, 96, 101], "0182672": [3, 23, 32, 37, 66, 75, 81], "019": [11, 29, 53, 66, 78, 91, 98], "0195": [39, 53, 73, 111], "01987": [4, 12, 18, 30, 53, 73, 78], "01_example_pipelin": [46, 48, 57, 70, 80, 91, 102, 103], "01_marcello_uliverpool_cci_zeiss_axioobserz1_lsm710": [30, 33, 66, 75, 81], "02": [0, 9, 19, 20, 25, 28, 29, 31, 32, 40, 56, 57, 66, 68, 74, 75, 81, 87, 88, 91, 101, 103, 106, 109, 111, 113], "021": [2, 3, 4, 5, 11, 12, 14, 15, 17, 33, 34, 39, 43, 49, 53, 56, 57, 66, 70, 73, 75, 78, 81, 85, 87, 91, 92, 96, 97, 101, 105, 111], "022": [29, 46, 53, 66, 73, 78, 81, 94, 96, 98, 101, 116], "02203": [53, 111], "02267597": [51, 81, 91], "023": [4, 12, 18, 30, 53, 73, 78, 81, 92, 96, 97, 101, 111], "02334": [53, 73, 91, 96, 99, 115], "02367": [53, 73, 81, 92, 96, 97, 101, 111], "024": [53, 73, 91, 96, 99, 115], "03": [1, 2, 3, 4, 6, 7, 8, 9, 14, 15, 16, 17, 19, 23, 26, 28, 29, 30, 33, 34, 35, 36, 37, 38, 40, 43, 45, 46, 48, 49, 53, 55, 56, 57, 66, 68, 70, 73, 75, 81, 85, 87, 91, 92, 94, 96, 99, 100, 101, 102, 103, 107, 109, 111, 113, 115, 117], "0304": [34, 53, 66, 87, 105, 111], "03427242": [41, 70, 109], "04": [3, 4, 5, 7, 8, 9, 11, 14, 19, 22, 23, 24, 26, 29, 30, 31, 33, 34, 36, 37, 40, 41, 42, 43, 48, 53, 55, 56, 57, 66, 68, 70, 75, 81, 87, 91, 92, 93, 96, 97, 100, 103, 105, 107, 109, 111, 113, 115], "04105": 77, "04t12": [43, 48, 70, 93, 117], "05": [2, 3, 5, 11, 15, 17, 19, 20, 23, 25, 26, 28, 29, 31, 33, 34, 37, 39, 40, 41, 48, 52, 53, 55, 56, 66, 68, 70, 71, 73, 75, 80, 81, 82, 87, 91, 92, 93, 94, 95, 96, 97, 100, 101, 102, 103, 107, 109, 111, 113, 114], "06": [1, 3, 6, 8, 11, 14, 19, 20, 22, 23, 25, 28, 29, 31, 32, 37, 39, 40, 43, 48, 49, 52, 53, 56, 57, 66, 68, 70, 71, 75, 80, 81, 82, 87, 90, 91, 92, 93, 96, 97, 99, 100, 103, 107, 109, 111, 113], "0650": [11, 29, 53, 66, 78, 91, 98], "07": [1, 3, 11, 16, 19, 20, 24, 25, 28, 29, 31, 35, 40, 48, 53, 55, 56, 57, 66, 67, 68, 70, 73, 75, 81, 82, 83, 85, 87, 91, 92, 95, 96, 97, 101, 102, 106, 107, 109, 111, 113], "08": [1, 6, 9, 12, 15, 17, 20, 24, 28, 29, 34, 35, 37, 38, 40, 48, 52, 53, 55, 58, 66, 68, 70, 71, 74, 75, 80, 81, 87, 90, 91, 92, 97, 105, 106, 109, 111, 113, 114], "08t08": [48, 49, 70, 91], "09": [0, 1, 3, 8, 10, 11, 15, 17, 20, 22, 23, 24, 25, 28, 29, 31, 32, 37, 39, 40, 43, 48, 52, 53, 57, 66, 67, 68, 70, 71, 75, 79, 81, 83, 84, 85, 90, 91, 92, 93, 97, 102, 106, 107, 111, 113, 117], "09t06": [48, 70, 83], "0x1000000": [66, 75, 81], "0x7": [66, 75, 81], "1": [3, 4, 6, 11, 15, 16, 17, 20, 23, 24, 25, 28, 29, 30, 31, 33, 34, 37, 38, 41, 43, 44, 48, 49, 50, 53, 55, 57, 58, 67, 70, 72, 73, 77, 78, 80, 85, 88, 91, 92, 94, 95, 96, 97, 98, 101, 102, 106, 107, 109, 113, 115, 117], "10": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 17, 18, 20, 22, 23, 24, 25, 26, 29, 30, 32, 33, 34, 35, 36, 37, 38, 39, 41, 43, 45, 46, 47, 48, 49, 52, 53, 55, 57, 58, 67, 70, 71, 73, 74, 77, 78, 80, 82, 85, 87, 90, 92, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 106, 107, 108, 109, 113, 114, 116], "100": [24, 31, 66, 75, 81], "10008464": [17, 56, 75, 81, 91, 105, 106], "10008465": [17, 56, 66, 75, 81, 111], "1002": [4, 9, 16, 18, 29, 35, 41, 53, 78, 81, 91, 98, 104, 105, 106, 111], "10054": [30, 33, 66, 75, 81], "10069368": [33, 38, 66, 75, 81], "1007": [53, 111], "1007854": [11, 53, 81, 93, 96, 101, 114], "10083555": [6, 55, 66, 75, 81, 92, 105, 111], "101024": [53, 66, 81, 91, 97, 106, 115], "10103316": [32, 66, 75, 81], "1016": [39, 53, 66, 81, 87, 91, 92, 96, 97, 106, 111, 115], "1017": [53, 66, 70, 75, 80, 81, 91], "1022": [60, 91, 106, 111], "1024x1024": [66, 75, 81], "1024x1024x62": [31, 66, 75, 81], "103": [46, 53, 88, 98, 116], "103685": [53, 66, 81, 96, 111], "1038": [11, 12, 14, 25, 29, 31, 39, 43, 49, 53, 56, 57, 66, 70, 73, 75, 78, 81, 85, 87, 91, 92, 96, 97, 98, 101, 111], "10460434": [29, 66, 75, 81], "1054": [29, 55, 67, 81, 91, 105, 111], "10609770": [0, 16, 23, 37, 38, 46, 47, 48, 53, 66, 70, 75, 81, 91, 96, 105, 106, 109, 111], "1063": [3, 23, 32, 37, 66, 75, 81], "10679054": [9, 56, 66, 75, 81], "10679318": [66, 75, 81], "10687658": [17, 34, 56, 75, 81, 91, 96, 111], "10730423": [51, 91, 94, 105, 111], "10730424": [6, 16, 28, 66, 75, 81], "10793699": [56, 111], "10793700": [28, 66, 75, 81], "10796364": [34, 35, 66, 75, 81], "108": [66, 75, 81], "10805204": [3, 8, 23, 30, 33, 34, 37, 66, 75, 81], "10808486": [66, 75, 81], "10815329": [29, 55, 66, 75, 81, 99, 109], "10816895": [29, 56, 66, 75, 81, 91, 96, 100, 107, 111], "10839310": [66, 75, 81], "10886749": [56, 91], "10886750": [6, 66, 75, 81], "10889694": [3, 17, 30, 33, 34, 35, 66, 75, 81], "109020": [20, 66, 75, 81], "10925217": [66, 75, 81], "1093": [4, 24, 53, 66, 75, 81, 91, 111], "10939519": [56, 91, 105, 111], "10939520": [3, 66, 75, 81], "10942559": [56, 66, 75, 81, 94, 111], "10970869": [29, 56, 66, 75, 81, 111], "10972692": [29, 56, 66, 75, 81, 96, 111], "10990107": [29, 56, 66, 75, 81, 96, 100, 111], "10k": [55, 66, 75, 81, 116], "10t15": [48, 70, 85], "10th": [3, 56, 66, 75, 81, 91, 105, 111], "10x": [29, 66, 75, 81], "11": [0, 1, 3, 6, 8, 11, 12, 17, 22, 23, 26, 28, 29, 30, 32, 34, 35, 36, 37, 38, 40, 41, 48, 49, 53, 55, 56, 57, 58, 66, 68, 70, 73, 74, 75, 80, 81, 87, 90, 91, 92, 93, 94, 95, 96, 97, 99, 101, 105, 106, 111, 117], "1101": [17, 43, 49, 52, 53, 70, 71, 80, 81, 85, 91, 109, 111, 113, 114], "11031746": [56, 91, 96, 105, 111], "11031747": [66, 75, 81], "110385": [3, 37, 66, 75, 81], "11065": [66, 75, 81], "11066250": [29, 56, 66, 75, 81, 96, 100, 111], "11107798": [26, 56, 66, 75, 81, 111], "11109616": [3, 37, 66, 75, 81], "1111": [3, 8, 11, 29, 30, 33, 34, 37, 46, 53, 66, 81, 91, 98, 111, 116], "11146807": [66, 75, 81], "1119026": [29, 55, 66, 67, 81, 91], "1119154": [29, 55, 66, 67, 81], "11201216": [53, 75, 81, 108], "11234863": [66, 75, 81], "11235512": [17, 34, 51, 75, 81, 91, 96, 105, 111], "11235513": [17, 34, 66, 75, 81], "11261115": [6, 28, 37, 56, 66, 75, 81, 111], "11265038": [5, 56, 66, 75, 81, 91, 95], "11318151": [17, 66, 75, 81], "11350689": [3, 23, 28, 66, 75, 81], "11382341": [6, 28, 37, 56, 66, 75, 81, 111], "11396199": [26, 56, 66, 75, 81, 96, 111], "11445843": [66, 75, 81], "11472148": [56, 66, 75, 81, 100, 111], "11473803": [14, 43, 57, 66, 75, 81, 91, 96, 111], "11474407": [14, 56, 57, 66, 75, 81, 91, 96, 111], "11501662": [3, 8, 23, 28, 66, 75, 81], "11503289": [66, 75, 81], "11521029": [66, 75, 82], "11548617": [6, 28, 37, 56, 66, 75, 81, 111], "11588": [3, 8, 55, 66, 92, 97, 105, 111], "11637422": [66, 75, 81], "1171": [29, 55, 66, 67, 81], "1186": [2, 15, 53, 66, 81, 91, 102], "1192": [29, 66, 75, 81], "1195": [29, 66, 75, 81], "11t07": [48, 57, 70, 85], "12": [1, 3, 4, 7, 9, 11, 15, 16, 20, 23, 24, 25, 29, 30, 31, 33, 35, 36, 37, 39, 40, 41, 43, 48, 49, 53, 55, 56, 57, 67, 68, 70, 73, 85, 87, 90, 91, 92, 93, 94, 96, 101, 105, 106, 109, 111, 115, 116, 117], "12166710": [22, 66, 75, 81], "12252": [30, 33, 66, 75, 81], "12344": [11, 53, 66, 87, 93, 107, 111, 113], "1242": [29, 66, 75, 81], "12533989": [66, 75], "12546808": [3, 22, 23, 32, 37, 66, 75, 81], "12547566": [3, 22, 23, 32, 37, 66, 75, 81], "12578084": [3, 22, 23, 32, 37, 66, 75, 81], "12623730": [29, 56, 75, 81, 91, 95, 102, 109], "12688": [3, 11, 20, 37, 53, 66, 75, 81, 87, 93, 107, 111, 113], "12699637": [66, 75, 81], "12744715": [41, 75, 81, 111], "12773657": [24, 66, 75, 81], "1282": [41, 91], "128x128x62": [31, 66, 75, 81], "13": [1, 3, 8, 15, 17, 19, 23, 28, 29, 30, 33, 34, 35, 39, 40, 51, 53, 55, 56, 58, 66, 67, 68, 74, 75, 81, 91, 95, 96, 99, 102, 105, 106, 109, 111, 117], "13041": [11, 53, 81], "13168693": [66, 75, 81], "13192": [29, 53, 81, 91, 111], "13227": [46, 53, 66, 98, 116], "13317": [3, 8, 30, 33, 34, 37, 53, 111], "13325226": [66, 75, 81], "13331351": [9, 66, 75, 81], "13371196": [29, 66, 75, 81], "13379394": 90, "13380289": [66, 75, 81], "13640979": [29, 66, 75, 81], "13642395": [66, 75], "13680725": [24, 66, 75, 81], "13682928": [22, 66, 75], "13683167": [22, 66, 75], "13684187": [6, 66, 75, 81], "13692297": [66, 75, 81], "137": [17, 53, 81, 91, 102, 107], "1371": [11, 43, 53, 66, 80, 81, 93, 96, 101, 114], "13736218": [22, 66, 75], "13736240": [22, 66, 75], "13739137": [10, 29, 66, 75, 81], "13752220": [66, 75, 81], "13807114": [0, 32, 66, 75, 81], "13810879": [0, 32, 66, 75, 81], "13831274": [17, 66, 75, 81], "13837146": [66, 75, 81], "13861026": [3, 23, 37, 66, 75, 81, 117], "13908108": [29, 66, 75, 81, 90, 91], "13908480": [28, 66, 75, 81], "13928832": [29, 66, 75, 81], "13945179": [14, 66, 75, 81], "13991322": [17, 66, 75, 81], "13t08": [43, 48, 70, 91, 117], "13th": [3, 23, 28, 48, 66, 70, 75, 81, 85, 91, 106, 109], "14": [11, 15, 22, 24, 29, 30, 31, 33, 43, 48, 56, 66, 70, 74, 75, 81, 83, 84, 87, 90, 91, 92, 95, 96, 99, 100, 102, 106, 107, 111, 114], "140": [41, 46, 50, 57, 66, 81, 92, 98, 106, 111, 115], "14001044": [29, 66, 75, 81], "14001388": [17, 66, 75, 81], "14006558": [6, 66, 75, 81], "14014252": [16, 35, 66, 75, 81], "14030307": [29, 66, 75, 81], "14032908": [66, 75, 81], "14035822": [66, 75, 81], "14044640": [1, 28, 66, 75, 81], "141": [66, 75, 81], "14113714": [66, 75, 81], "14161289": [22, 66, 75, 81], "14161633": [66, 75, 81], "14168603": [22, 66, 75, 81], "14178789": [3, 33, 34, 37, 66, 75, 81], "1418": [14, 56, 57, 66, 75, 81, 91, 96, 111], "14186100": [66, 75, 81], "14186101": [66, 75, 81], "14197622": [66, 75, 81], "1422": [14, 56, 57, 66, 75, 81, 91, 96, 111], "14234608": [17, 66, 75, 81], "14235726": [66, 75, 81], "14264885": [4, 7, 9, 36, 66, 75, 81, 117], "14268554": [66, 75, 81], "14278058": [29, 66, 75, 81], "14281237": [66, 75, 81], "14287640": [66, 75, 81], "14324": [41, 66, 81, 111], "14381522": [66, 75, 81], "14418209": [29, 66, 75, 81], "14451": [4, 9, 29, 53, 81, 91], "14510432": [66, 75, 81, 117], "146": [66, 75, 81], "14602075": [1, 15, 39, 66, 75, 81], "14626054": [29, 66, 75, 81, 117], "14641777": [66, 75, 81, 117], "14650434": [17, 66, 75, 81, 117], "14675120": [66, 75, 117], "147": [56, 67, 81, 95, 96, 102], "14716546": [17, 66, 75, 81, 117], "1472859": [24, 66, 75, 81], "1480": [66, 75, 81], "1481": [66, 75, 81], "15": [3, 22, 23, 28, 29, 30, 31, 33, 37, 40, 45, 53, 56, 66, 68, 73, 75, 81, 96, 111, 113, 117], "1515": [34, 53, 66, 87, 105, 111], "152": [66, 75, 81], "15252": [1, 15, 39, 66, 75, 81], "1546": [66, 75, 81], "1546596": [41, 66, 81, 111], "1552": [3, 22, 23, 32, 37, 66, 75, 81], "1555": [3, 22, 23, 32, 37, 66, 75, 81], "1557": [3, 23, 32, 37, 66, 75, 81], "156": [66, 75, 81], "15pct": [24, 66, 75, 81], "16": [9, 17, 20, 30, 31, 51, 55, 56, 60, 91, 96, 105, 106, 111, 117], "161": [24, 31, 66, 75, 81], "162": [17, 18, 29, 51, 53, 67, 81, 101], "163": [66, 75, 81], "167505": [39, 53, 66, 87, 92, 97, 111], "16koa013a": [66, 75, 81], "16mcm": [20, 66, 75, 81], "16t03": [48, 70, 91], "16th": [20, 66, 75, 81], "17": [20, 24, 42, 53, 55, 56, 60, 70, 73, 91, 92, 94, 96, 97, 101, 106, 107, 111, 113, 116, 117], "1700309488232283050": [41, 91], "17192": [53, 66, 81, 111], "17204": [25, 31, 66, 75, 81], "1725": [24, 31, 66, 75, 81], "1787": 87, "17th": [24, 66, 75, 81], "18": [3, 10, 14, 19, 24, 29, 33, 34, 37, 40, 53, 55, 56, 57, 66, 68, 73, 75, 81, 87, 91, 96, 100, 109, 111, 113], "1873": [4, 9, 29, 53, 81, 91], "1883436974": [66, 75, 81], "189": [11, 53, 101], "18f": [29, 66, 75, 81], "18th": [56, 66, 75, 81, 91, 96, 105, 111], "19": [9, 10, 11, 20, 22, 26, 29, 53, 56, 58, 74, 91, 92, 97, 106, 109, 111, 116], "195": [29, 66, 75, 81], "19778": [53, 73, 91, 96, 99, 115], "1henpidtpht3bc1y26altitahj2c5xcol": [55, 65, 87], "1m": [55, 66, 75, 81, 116], "1mhuqfkhzcyu3bchwmqogiybkjamu5msg": [4, 50, 55, 58, 66, 72, 81, 91], "1ojoy9ghmuusmuwckps_injf_wzazmllunvk1fueb4pa": [55, 65, 87, 90, 91, 104], "1ppvuumi5w2ojw_sabzsqvaxuuiktq7ma": [87, 91], "1q8q1xe": [13, 55, 65, 87, 91, 104], "1r9": [48, 56, 60, 65, 70, 81, 91, 102], "1st": [3, 22, 23, 32, 37, 66, 75, 81], "1th": [3, 23, 32, 37, 66, 75, 81], "1wg_4307xmksgfwt3taxmvx2rzig1k0sm1e7saenjqki": [1, 55, 65, 87, 91], "1x": [24, 31, 66, 75, 81], "1z": [31, 66, 75, 81], "2": [2, 3, 11, 15, 17, 20, 23, 24, 25, 28, 29, 30, 31, 33, 37, 43, 45, 48, 49, 50, 53, 55, 60, 70, 73, 85, 87, 91, 92, 93, 94, 96, 98, 99, 101, 104, 106, 107, 108, 109, 113, 114, 115], "20": [3, 8, 10, 11, 17, 22, 24, 29, 43, 49, 52, 53, 57, 66, 67, 70, 71, 75, 81, 85, 91, 93, 97, 102, 107, 111, 114], "200": [24, 31, 66, 75, 81], "2005": [2, 15, 17, 34, 53, 56, 66, 75, 81, 91, 96, 102, 111], "200m": [30, 33, 66, 75, 81], "2010": [11, 31, 53, 66, 75, 81, 101], "2011": [66, 75, 81], "2012": [30, 33, 66, 75, 81], "2014": [4, 18, 29, 30, 33, 55, 66, 70, 75, 81, 87, 91, 116], "2015": [66, 75, 81], "2016": [3, 22, 23, 32, 37, 43, 66, 73, 75, 94, 96, 111], "2017": [25, 30, 31, 33, 41, 53, 66, 75, 81, 107, 111], "2018": [11, 24, 29, 41, 48, 53, 66, 73, 75, 81, 93, 94, 96, 107, 111, 113], "2018h2s1": [53, 81, 96, 111], "2019": [30, 33, 45, 66, 75, 81, 111], "2020": [7, 9, 26, 30, 33, 36, 41, 45, 46, 56, 57, 75, 78, 93, 96, 107, 111, 114, 116], "2020aprilgitcours": [57, 70, 87], "2021": [4, 7, 14, 18, 24, 26, 29, 30, 31, 39, 40, 41, 43, 50, 56, 57, 59, 66, 68, 73, 75, 80, 85, 96, 101, 104, 107, 113, 115], "2021_jamali_biologicalimag": [53, 66, 70, 80, 91], "2022": [3, 4, 5, 8, 11, 16, 17, 20, 25, 29, 30, 31, 33, 34, 35, 37, 39, 40, 43, 48, 50, 53, 55, 58, 67, 68, 72, 73, 78, 79, 92, 94, 96, 97, 101, 102, 105, 111, 113, 114], "20220923_defragmentationt": [18, 55, 87, 91, 104], "2023": [1, 3, 6, 7, 8, 11, 14, 15, 19, 20, 22, 24, 29, 32, 35, 39, 40, 52, 55, 57, 58, 68, 71, 72, 73, 74, 80, 82, 83, 85, 92, 93, 95, 97, 99, 100, 101, 102, 103, 107, 113, 115], "2023115008": [1, 15, 39, 66, 75, 81], "2023_imageanalysissurvei": [52, 53, 70, 71, 80, 91], "2024": [0, 1, 4, 5, 6, 7, 8, 9, 10, 11, 14, 15, 16, 19, 22, 23, 24, 26, 28, 32, 36, 38, 40, 41, 43, 46, 52, 53, 55, 56, 65, 68, 71, 72, 82, 84, 85, 87, 90, 92, 93, 94, 99, 100, 101, 107, 108, 113, 117], "2025": [17, 43, 48, 66, 70, 75, 81, 88, 91, 117], "205": [41, 81, 107], "2080": [25, 31, 66, 75, 81], "2083": [43, 44, 73], "20club": [11, 42, 49, 70, 87, 109], "20materi": [43, 72, 93, 114], "20tutori": [11, 42, 49, 70, 87, 109], "20x": [25, 29, 31], "21": [11, 17, 22, 31, 34, 39, 53, 56, 66, 73, 75, 81, 87, 92, 93, 96, 97, 101, 111, 114], "210": [66, 75, 81], "22": [3, 14, 23, 24, 29, 37, 39, 43, 48, 53, 55, 66, 67, 75, 81, 85, 91, 92, 97, 111, 117], "22518": [12, 43, 49, 66, 70, 73, 85, 91], "22t17": [48, 56, 70], "23": [5, 6, 17, 20, 26, 45, 46, 48, 49, 53, 56, 57, 66, 75, 81, 87, 95, 102, 107, 108, 109, 111, 117], "2302": [11, 52, 53, 91], "2304": [53, 81, 107, 111], "2314": [66, 75, 81], "23t07": [4, 5, 7, 9, 36, 48, 70, 81], "23t14": [48, 70, 82, 91], "24": [17, 34, 46, 48, 56, 66, 74, 75, 81, 87, 92, 93, 95, 102, 105, 106, 111, 117], "2407": [53, 73, 91, 96, 99, 115], "2409": [66, 75, 81], "240t": [31, 66, 75, 81], "244": [66, 75, 81], "25": [1, 6, 17, 25, 29, 31, 41, 56, 66, 75, 77, 81, 94, 96, 100, 107, 111], "2520": [46, 109, 116], "25g": [22, 66, 75, 81], "26": [3, 6, 17, 22, 23, 28, 32, 37, 40, 56, 66, 68, 75, 81, 87, 91, 111, 113, 117], "261950": [29, 66, 75, 81], "26275": [29, 66, 75, 81], "2643411": [42, 66, 70, 75, 91], "265": [29, 66, 75, 81], "267": [66, 75, 81], "26_learn_guidance_137": [41, 66, 81, 111], "27": [1, 6, 15, 24, 34, 39, 41, 48, 53, 66, 70, 75, 81, 83, 90, 105, 111], "273": [29, 66, 75, 81], "27t14": [48, 57, 70, 81, 90], "28": [3, 17, 29, 30, 32, 33, 34, 35, 37, 46, 48, 57, 66, 70, 75, 80, 81, 91, 102, 103, 109, 116], "281": [66, 75, 81], "285": [17, 34, 53, 66, 81, 105, 111], "28th": [3, 22, 23, 29, 32, 37, 43, 48, 49, 57, 66, 70, 75, 80, 81, 91, 117], "29": [3, 6, 8, 17, 20, 43, 48, 55, 56, 66, 68, 70, 75, 79, 81, 87, 91, 92, 95, 97, 105, 111, 117], "290322": [46, 109, 116], "29489": [51, 53, 82], "299": [31, 66, 75, 81], "29t10": [48, 57, 70, 81, 115], "29th": [3, 37, 66, 75, 81], "2_t": [66, 75, 81], "2d": [25, 31], "2d_enteric_ganglia_model_qa": [29, 66, 75, 81], "2d_enteric_neuron_model_qa": [29, 66, 75, 81], "2d_enteric_neuron_subtype_model_qa": [29, 66, 75, 81], "2d_enteric_neuron_subtype_v4": [29, 66, 75, 81], "2d_enteric_neuron_v4_1": [29, 66, 75, 81], "2d_ganglia_rgb_v2": [29, 66, 75, 81], "2dbkuwkk4": [43, 58, 74, 91], "2nd": [0, 16, 23, 37, 38, 48, 56, 66, 75, 81, 91, 96, 105, 111], "3": [3, 11, 13, 15, 16, 17, 22, 23, 24, 25, 26, 28, 29, 30, 31, 32, 33, 35, 37, 38, 41, 42, 43, 45, 46, 48, 49, 52, 53, 55, 56, 57, 58, 60, 66, 69, 70, 71, 73, 75, 78, 81, 85, 90, 91, 93, 94, 96, 98, 101, 102, 103, 104, 106, 109, 111, 112, 113, 114, 115, 116], "30": [3, 16, 19, 23, 26, 35, 37, 40, 48, 56, 57, 66, 68, 70, 75, 81, 85, 91, 96, 103, 109, 111, 117], "300": [24, 31, 66, 75, 81], "3002167": [43, 53, 66, 80], "302": [29, 53, 67, 81, 104], "30t07": [48, 57, 70, 80], "30t12": [15, 17, 37, 38, 48, 70, 80, 113], "30th": [3, 29, 37, 48, 49, 57, 66, 70, 75, 80, 81, 91], "31": [1, 14, 55, 66, 75, 81], "316": [29, 66, 75, 81], "31st": [43, 48, 70, 91, 117], "32": [24, 48, 49, 66, 70, 75, 81, 91], "320": [24, 31, 66, 75, 81], "325": [66, 75, 81], "326": [66, 75, 81], "32nd": [3, 33, 66, 75, 81], "33": [29, 66, 75, 81], "33248": [32, 37, 66, 75, 81], "334": [18, 29, 31, 53, 67, 81, 91, 104], "337": [66, 75, 81], "3389": [11, 29, 53, 81, 91, 92, 97, 111], "34": [48, 53, 70, 85], "3415": [17, 34, 53, 81, 105], "345": [25, 31, 66, 75, 81], "3468": [4, 9, 29, 53, 81, 91], "3490058": [45, 66, 75, 81, 111], "35": [66, 75, 81], "35828": [11, 53, 101], "362478": [17, 53, 81, 91, 102, 107], "36371407": [53, 73, 81, 94, 96, 101], "37": [24, 31, 66, 75, 81], "3778431": [26, 56, 66, 75, 81, 111], "38": [4, 5, 7, 9, 36, 48, 57, 70, 80, 81, 90], "39": [30, 33, 48, 56, 66, 70, 75, 81], "3_20200323": [45, 46, 57, 111], "3_ch": [66, 75, 81], "3_z": [66, 75, 81], "3a": 41, "3d": [22, 24, 25, 30, 33], "3dihub": [39, 53, 66, 87, 92, 97, 111], "3r": [22, 66, 75], "3x": [30, 33, 66, 75, 81], "4": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 28, 29, 30, 31, 32, 33, 34, 35, 37, 38, 39, 40, 41, 42, 43, 45, 46, 47, 48, 49, 50, 51, 52, 53, 55, 56, 57, 58, 59, 60, 64, 65, 67, 68, 70, 71, 72, 73, 74, 77, 80, 85, 88, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 111, 112, 113, 114, 115, 116, 117], "40": [29, 48, 57, 66, 70, 75, 81, 115], "405": [24, 66, 75, 81], "4071471": [41, 66, 75, 81, 111], "40x": [24, 31, 66, 75, 81], "42": [48, 70, 91], "420": [24, 66, 75, 81], "4249": [66, 75, 81], "4317149": [36, 55, 66, 75, 81], "4326": [43, 44, 53, 73], "4328911": [55, 66, 75, 81, 91], "4330625": [7, 55, 75, 81, 90, 91], "4334697": [55, 66, 75, 81, 116], "4461261": [56, 66, 75, 81, 111], "45": [3, 23, 28, 29, 37, 57, 66, 70, 75, 81, 85, 91, 106, 109], "46": [6, 28, 37, 48, 56, 66, 70, 75, 81, 88, 91, 111], "4630788": [26, 56, 66, 75, 81, 111], "4748510": [26, 56, 66, 75, 81, 111], "4748534": [26, 56, 66, 75, 81, 111], "4778265": [56, 75, 81, 107, 111], "48": [48, 56, 70], "48550": [66, 75, 81], "488": [31, 66, 75, 81], "4915861": [41, 66, 81], "496963457": [66, 75, 81], "498": [66, 75, 81], "4d": [30, 33, 66, 75, 81], "4dn": [11, 30, 33, 53, 66, 75, 81, 87, 92, 97, 101, 102], "4lxamtyuyfzptmdr85sjnlspzhvz8": [48, 56, 60, 65, 70, 81, 91, 102], "4th": 22, "4v8nsap1z24cappupxg8jzyqegz6mvw_dh": [57, 87, 114], "4x8": [24, 66, 75, 81], "5": [3, 11, 15, 16, 17, 22, 23, 24, 25, 31, 32, 33, 34, 35, 37, 51, 53, 60, 66, 73, 96, 101, 102, 106, 107, 117], "50": [24, 66, 75, 81], "501864659": [6, 28, 37, 56, 66, 75, 81, 88, 111], "51": [4, 5, 7, 9, 36, 48, 53, 57, 70, 75, 81, 90, 108], "5101351": [31, 66, 75, 81], "5121890": [66, 75, 85], "519": [29, 55, 67, 81, 111, 113], "5281": [0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 14, 15, 16, 17, 20, 22, 23, 24, 25, 26, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 41, 42, 43, 45, 46, 47, 48, 51, 53, 55, 56, 57, 58, 66, 70, 74, 75, 81, 82, 85, 90, 91, 92, 94, 95, 96, 99, 100, 105, 106, 107, 109, 111, 116, 117], "5282": [66, 75, 81], "52825": [17, 34, 53, 66, 81, 105, 111], "53": [15, 17, 37, 38, 48, 70, 80, 113], "5334": [46, 53, 98, 116], "539647v1": [17, 52, 53, 71, 81, 109], "53j": [66, 75, 85], "540x540": [31, 66, 75, 81], "543701v1": [52, 53, 70, 71, 80, 91], "546": 88, "55": [48, 70, 82, 91], "555": [24, 31, 66, 75, 81], "55e7d8fbf6df4a54ac56942b98d94e4f": [45, 111], "561": [24, 66, 75, 81], "561479v1": [52, 71, 113], "563252v2": [43, 49, 70, 71, 85], "5675686": [24, 31, 66, 75, 81], "568": [29, 66, 75, 81], "5805939": [20, 66, 75, 81], "5847477": [30, 33, 66, 75, 81], "58h": [29, 66, 75, 81], "5996883": [25, 31, 66, 75, 81], "5_ch": [66, 75, 81], "5abw3cvxrhpobg4": [18, 55, 87, 91, 104], "5c": [31, 66, 75, 81], "6": [2, 3, 10, 22, 23, 24, 28, 29, 30, 31, 33, 37, 53, 66, 91, 102], "60": [24, 31, 66, 75, 81, 88], "60018": [11, 25, 31, 43, 47, 60, 87, 91, 102], "6019": [4, 50, 55, 58, 66, 72, 81, 91], "6023317": [25, 31, 66, 75, 81], "6036": [66, 75, 81], "608021v1": [52, 53, 71, 81, 91, 111, 114], "6139958": [25, 31, 66, 75, 81], "6140064": [25, 31, 66, 75, 81], "6140111": [25, 31, 66, 75, 81], "62": [66, 75, 81], "6267": [29, 66, 75, 81], "630": [24, 66, 75, 81], "634": [66, 75, 81], "638": [3, 8, 11, 17, 33, 53, 67, 81, 92, 97, 105, 111], "647": [24, 66, 75, 81], "64x64x62": [31, 66, 75, 81], "6523649": [25, 31, 66, 75, 81], "653203": [17, 66, 75, 81, 117], "6535296": [66, 75], "6554166": [66, 75, 81], "6602101": [55, 66, 75, 81, 111], "6628764": 53, "6645978": [31, 66, 75, 81], "6646128": [31, 66, 75, 81], "67": [11, 25, 31, 43, 47, 60, 87, 91, 102], "6775": [53, 81, 107, 111], "6821815": [34, 58, 105, 111], "6834214": [66, 75, 81], "6905931": [66, 75], "6953610": [20, 66, 75], "6991106": [20, 66, 75], "6th": [3, 33, 66, 75, 81], "7": [3, 10, 29, 30, 32, 33, 37, 53, 77, 81, 111], "70": [22, 66, 75, 81], "701": [29, 66, 75, 81], "7015307": [66, 75, 81], "7018750": [16, 35, 66, 75, 81], "7018928": [16, 35, 66, 75, 81], "7018929": [34, 58, 105, 111], "703": [66, 75, 81], "710": [30, 33, 66, 75, 81], "7209862": [29, 66, 75, 81], "7215114": [20, 66, 75, 81], "7257808": [41, 75], "7409423": [25, 31, 66, 75, 81], "7409525": [12, 29, 51, 53, 98], "7447491": [66, 75, 81], "746": [29, 55, 66, 67, 81, 91], "7490": [29, 55, 66, 67, 81, 91], "75": [24, 25, 31, 66, 75, 81], "750": [66, 75, 81], "77": [66, 75, 81], "777": [11, 53, 101], "7788899": [1, 14, 66, 75, 81], "78": [66, 75, 81], "7885379": [20, 66, 75, 81], "7890311": [3, 33, 66, 75, 81], "7919117": [66, 75, 81], "7928332": [17, 34, 51, 75, 81, 96, 105, 111], "7944877": [22, 66, 75, 81], "7945": [53, 66, 81, 111], "7945018": [22, 66, 75, 81], "7963006": [22, 66, 75, 81], "7963020": [22, 66, 75, 81], "7th": [1, 17, 66, 75, 81], "8": [3, 11, 14, 16, 20, 22, 23, 30, 31, 32, 33, 35, 37, 39, 53, 56, 57, 66, 73, 75, 77, 81, 87, 91, 92, 96, 97, 101, 108, 111], "8070038": [33, 56, 66, 81, 91, 96, 105, 111], "8139353": [16, 35, 43, 75, 81, 91, 106], "8146412": [66, 75, 81], "8153907": [24, 66, 75, 81], "8182154": [1, 66, 75, 81], "8305531": [24, 66, 75, 81], "8314968": [66, 75, 81], "8323588": [3, 8, 23, 33, 34, 35, 37, 55, 58, 66, 74, 75, 81, 105, 106, 111], "8329305": [34, 56, 75, 81, 105, 111], "8329306": [55, 66], "8338931": [1, 15, 39, 66, 75, 81], "8340247": [17, 51, 75, 81, 91, 111], "8349562": [0, 38, 51, 75, 81, 96, 111], "8372703": [20, 66, 75, 81], "83784": [24, 66, 75, 81], "8414318": [28, 56, 75, 81, 91, 105, 111], "8434325": [28, 51, 66, 81, 91], "86304": [83, 92, 102], "871228": [11, 53, 81, 92, 97, 111], "87925": [16, 101, 106, 115], "88": [29, 66, 75, 81], "880": [29, 66, 75, 81], "889755": [29, 53, 81, 91], "8_tf1": [31, 66, 75, 81], "8zd4kty": [43, 58, 74, 81, 91, 111], "9": [3, 4, 12, 17, 18, 28, 29, 30, 33, 51, 53, 60, 66, 67, 73, 75, 78, 81, 91, 101, 106, 111], "91b": [0, 32, 66, 75, 81], "924": [29, 66, 75, 81], "93": [66, 75, 81], "96": [31, 66, 75, 81], "971": [29, 55, 67, 81, 90, 91, 109], "9781119096900": 41, "98": 88, "99": [66, 75, 81], "A": [0, 10, 15, 16, 18, 22, 25, 31, 32, 35, 38, 47, 48, 50, 58, 61, 67, 74, 78, 84, 85, 88, 90, 92, 93, 94, 95, 96, 99, 101, 104, 109], "AT": [66, 75], "And": [3, 48, 57, 70, 87, 89, 111], "As": [3, 8, 11, 17, 24, 29, 32, 53, 66, 67, 75, 81, 92, 96, 97, 111], "BUT": [43, 72, 93, 114], "BY": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 45, 46, 47, 48, 49, 50, 51, 52, 53, 55, 56, 57, 58, 59, 60, 64, 65, 66, 67, 68, 70, 71, 72, 73, 74, 75, 77, 80, 81, 82, 85, 88, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 111, 112, 113, 114, 115, 116, 117], "Being": [66, 75, 81], "By": [1, 3, 9, 22, 23, 28, 29, 32, 37, 43, 49, 52, 53, 66, 71, 75, 81, 91, 111, 114], "For": [3, 8, 17, 20, 23, 29, 30, 33, 34, 37, 51, 66, 75, 76, 81, 89, 91, 111], "IN": [1, 15, 39, 66, 75, 81], "IT": [6, 28, 56], "If": [22, 25, 30, 31, 33, 62, 63, 66, 75], "In": [1, 3, 8, 9, 11, 16, 17, 23, 28, 29, 33, 34, 35, 36, 37, 38, 40, 41, 43, 48, 49, 50, 55, 56, 57, 59, 61, 62, 63, 66, 68, 70, 74, 75, 81, 82, 83, 85, 87, 90, 91, 92, 94, 95, 96, 99, 100, 102, 103, 105, 106, 107, 109, 111, 113, 114, 115], "It": [3, 6, 9, 13, 20, 22, 23, 29, 32, 33, 37, 49, 55, 59, 62, 66, 70, 82, 85, 90, 92, 109, 116], "Its": [59, 87, 111], "No": [20, 66, 75, 81], "ON": [43, 72, 93, 114], "On": [3, 8, 23, 33, 34, 35, 37, 55, 58, 63, 66, 74, 75, 81, 105, 106, 111], "One": [20, 29, 66, 75, 81, 83, 92, 102], "Such": [48, 64, 66, 75, 81, 82, 91, 106], "That": [3, 22, 23, 32, 37, 66, 75, 81], "The": [1, 6, 7, 11, 16, 20, 21, 22, 24, 25, 31, 32, 35, 42, 45, 46, 48, 49, 50, 55, 57, 59, 60, 62, 63, 70, 72, 74, 80, 82, 83, 93, 95, 100, 103, 106, 107, 108, 109, 114, 115, 116], "There": [46, 66, 92], "These": [1, 3, 24, 28, 29, 31, 33, 43, 48, 49, 56, 57, 58, 61, 62, 66, 70, 72, 75, 81, 83, 87, 88, 91, 92, 94, 95, 96, 97, 100, 102, 108, 109, 111], "To": [0, 3, 14, 29, 31, 33, 38, 48, 49, 51, 57, 58, 66, 70, 72, 75, 80, 81, 83, 91, 92, 96, 97], "Will": [2, 4, 5, 11, 15, 17, 37, 38, 48, 53, 70, 73, 79, 80, 81, 84, 92, 105, 106, 111, 113], "With": [3, 22, 23, 32, 37, 45, 46, 57, 63, 66, 75, 81, 111], "_a": [30, 33, 66, 75, 81], "_bf": [25, 31, 66, 75, 81], "_cxbfab": [43, 58, 74, 91], "_cyto": [25, 31, 66, 75, 81], "_dpc": [25, 31, 66, 75, 81], "_flow": [25, 31, 66, 75, 81], "_nuclei": [25, 31, 66, 75, 81], "_sqrdpc": [25, 31, 66, 75, 81], "a1": [3, 22, 23, 32, 37, 66, 75, 81], "a2": [3, 22, 23, 32, 37, 66, 75, 81], "a4po9z61tm": [43, 58, 74, 109], "aalbersberg": [53, 66, 73, 81, 96, 111], "aastha": [1, 4, 12, 14, 18, 30, 53, 55, 65, 66, 73, 75, 78, 81, 87, 111], "aayush": [3, 22, 23, 32, 37, 66, 75, 81], "ab": [11, 43, 44, 48, 52, 53, 70, 72, 73, 91, 96, 99, 115], "abbasi": [41, 70, 83, 91], "abdelfattah": [30, 33, 66, 75, 81], "abdelmoneim": [66, 75, 82], "abdrabb": [0, 16, 23, 37, 38, 46, 47, 48, 53, 66, 70, 75, 81, 91, 96, 105, 106, 109, 111], "abif": [30, 33, 66, 75, 81], "abil": [48, 56, 70], "abl": [3, 22, 23, 30, 32, 33, 37, 66, 75, 81], "abolfazl": [48, 70, 91], "about": [3, 4, 5, 9, 11, 14, 29, 30, 33, 34, 37, 40, 43, 47, 48, 49, 52, 53, 55, 56, 58, 67, 68, 70, 73, 74, 78, 80, 85, 91, 94, 95, 96, 98, 100, 105, 106, 107, 111, 113], "abov": [29, 66, 75, 81], "abstract": [3, 8, 17, 33, 34, 52, 53, 55, 66, 71, 75, 92, 97, 105, 109], "abstractdmp4nfdi": [45, 81, 111], "ac": [4, 7, 21, 30, 33, 39, 41, 43, 44, 49, 50, 53, 55, 56, 57, 58, 66, 75, 81, 83, 91, 92, 93, 95, 96, 97, 98, 101, 102, 107, 109, 111, 113, 114], "academ": [46, 53, 59, 66, 75, 81, 109, 111, 116], "academi": [66, 75, 81], "academia": [55, 66, 75, 81, 116], "acardona": [49, 83, 91, 98], "acceler": [3, 20, 33, 75], "accept": [3, 37, 66, 75, 81], "access": [1, 3, 8, 14, 20, 23, 28, 29, 30, 33, 37, 41, 43, 45, 46, 48, 50, 56, 57, 59, 62, 63, 70, 72, 76, 85, 88, 94, 95, 96, 98, 99, 100, 102, 106, 107, 108, 109, 113, 116], "accomod": [22, 66, 75, 81], "accompani": [48, 57, 70, 80], "accord": [14, 24, 29, 31, 43, 50, 56, 57, 59, 66, 75, 81, 91, 96, 100, 107, 111], "accordingli": [14, 43, 57, 66, 75, 81, 91, 96, 111], "account": [29, 57, 58, 63, 66, 72, 75, 81, 83, 92, 97, 111], "accumul": [66, 75, 81], "acess": [29, 56, 66, 75, 81, 91, 96, 100, 107, 111], "achiev": [3, 8, 23, 28, 33, 66, 75, 81, 90], "acid": [66, 75, 81], "acommod": [22, 66, 75, 81], "acquif": [66, 75, 81], "acquir": [3, 22, 23, 24, 25, 29, 30, 31, 33], "acquisit": [2, 3, 15, 23, 24, 25, 28, 29, 30, 31, 32, 33, 37, 53, 91, 102], "across": [1, 3, 17, 28, 29, 33, 34, 37, 51, 55, 66, 75, 81, 91, 111, 116], "action": [29, 31, 66, 75, 81], "activ": [3, 8, 23, 28, 33, 34, 35, 38, 56, 85, 94, 111], "actor": [1, 14, 66, 75, 81], "acut": [22, 66, 75], "ad": [1, 10, 20, 28, 29, 43, 46, 50, 57, 62, 63, 83, 87, 88, 106, 111, 113, 115], "ada": [29, 66, 75, 81], "adam": [4, 10, 12, 18, 29, 30, 48, 53, 66, 70, 73, 75, 78, 81, 91], "adapet": [3, 22, 23, 32, 37, 66, 75, 81], "adapt": [11, 25, 29, 31, 45, 53, 93, 96, 101, 111, 114], "add": [61, 62, 63], "addit": [3, 8, 23, 24, 25, 29, 31, 33, 34, 35, 37, 41, 43, 49, 55, 58, 62, 66, 74, 75, 81, 82, 91, 105, 106, 111], "addition": [3, 37, 53, 66, 75, 81, 108], "address": [0, 1, 3, 8, 11, 14, 23, 28, 29, 33, 38, 39, 45, 51, 53, 55, 57, 59, 73, 82, 87, 92, 96, 97, 101, 111], "adequ": [45, 66, 75, 81, 111], "adher": [0, 38, 51, 52, 53, 71, 73, 75, 81, 91, 94, 96, 101, 111, 114], "adjust": 88, "admin": [3, 33, 34, 37, 66, 75, 81], "administr": [3, 33, 34, 37, 66, 75, 81], "administrativen": [0, 32, 66, 75, 81], "adopt": [3, 8, 17, 23, 28, 30, 33, 34, 35, 37, 51, 53, 55, 58, 66, 74, 75, 81, 91, 105, 106, 108, 111], "adressen": 77, "adrian": [5, 57, 70, 87, 90, 91], "advanc": [1, 13, 20, 28, 48, 49, 53, 70, 80, 85, 87, 108, 109, 112], "advantag": [66, 75], "adventur": [17, 34, 56, 75, 81, 91, 96, 111], "advertis": 62, "advic": [45, 57, 66, 75, 81, 82, 87, 96, 100, 111], "aenderungen": [29, 56, 66, 75, 81, 96, 111], "afford": [66, 75, 81], "afmboornr5grqog": [57, 87, 114], "after": [17, 24, 29, 30, 31, 33, 34, 41, 56, 62, 63, 66, 75, 81, 91, 96, 111], "afterward": [29, 66, 75, 81], "ag": [3, 30, 33, 34, 37, 41, 66, 75, 81, 111], "again": 62, "agenc": [3, 32, 57, 66, 75, 81, 87, 111], "aggreg": [53, 91, 111, 114], "aghajanian": [30, 33, 66, 75, 81], "agpl": [45, 46, 49, 57, 70, 90, 109, 111], "agre": [3, 22, 23, 32, 37, 66, 75, 81], "agust\u00edn": [48, 56, 60, 65, 70, 81, 91, 102], "ahead": [29, 55, 66, 75, 81, 99, 109], "ahklemm": [42, 55, 70, 87, 91, 104], "ahmadi": [6, 16, 28, 33, 38, 51, 66, 75, 81, 91, 94, 105, 111], "ahmet": [12, 43, 49, 66, 70, 73, 85, 91], "ai": [3, 6, 8, 23, 28, 37, 46, 49, 77, 80, 85, 88, 116], "ai4lif": [1, 53], "aic": [30, 33, 66, 75, 81], "aim": [3, 8, 9, 20, 23, 28, 29, 36, 45, 55, 66, 75, 81, 82, 88, 111], "airyscan": [66, 75, 81], "ajinkya": [12, 48, 70, 91], "aktiven": [0, 32, 66, 75, 81], "aktualisiert": [26, 56, 66, 75, 81, 111], "aktualit\u00e4t": 77, "aktuel": [26, 56, 66, 75, 81, 111], "al": [2, 3, 4, 7, 8, 10, 14, 15, 17, 18, 25, 29, 30, 31, 33, 36, 37, 39, 41, 42, 43, 46, 47, 48, 49, 51, 52, 53, 55, 56, 57, 60, 66, 67, 70, 73, 75, 77, 78, 80, 81, 84, 85, 91, 92, 93, 96, 97, 98, 101, 102, 104, 106, 107, 108, 109, 112, 113, 114, 115, 116], "alain": [12, 43, 49, 66, 70, 73, 85, 91], "alan": [11, 20, 43, 48, 49, 66, 70, 75, 80, 81, 87, 91, 93, 108, 109, 115, 116], "alban": [11, 17, 18, 25, 29, 31, 43, 47, 51, 53, 60, 67, 81, 87, 91, 101, 102], "albert": [21, 29, 30, 33, 43, 48, 49, 57, 66, 70, 75, 80, 81, 83, 87, 90, 91, 98, 109], "alberto": [11, 53, 81, 92, 97, 111], "aleksej": [49, 70, 87, 109], "alessandro": [11, 30, 33, 53, 66, 75, 81, 87, 88, 92, 97, 101, 102], "alex": [1, 4, 12, 15, 18, 30, 33, 39, 48, 53, 66, 70, 73, 75, 78, 81, 88, 91, 101], "alexa": [24, 31, 66, 75, 81], "alexand": [12, 43, 48, 49, 57, 66, 70, 73, 75, 81, 85, 90, 91, 115], "alexandr": [11, 17, 18, 29, 51, 53, 55, 57, 67, 70, 81, 87, 91, 101, 104], "alexandra": [49, 70, 91], "algorithm": [11, 20, 46, 52, 53, 91, 98, 116], "ali": [4, 12, 18, 30, 53, 73, 78], "aliaksandr": [4, 5, 7, 9, 18, 36, 48, 66, 70, 75, 81, 87, 91, 117], "align": [1, 3, 8, 9, 23, 24, 28, 33, 55, 59, 75, 87, 92, 97, 105, 111], "alison": [4, 12, 18, 30, 53, 73, 78], "all": [3, 4, 11, 12, 16, 17, 18, 20, 22, 23, 24, 25, 26, 28, 29, 30, 31, 32, 33, 34, 35, 37, 41, 43, 45, 50, 53, 58, 62, 70, 73, 74, 82, 88, 90, 91, 98, 101, 104, 105, 106, 107], "all_training_resourc": [43, 81, 111], "allan": [4, 5, 11, 15, 17, 43, 48, 53, 57, 66, 70, 73, 81, 84, 87, 91, 92, 102, 105, 106, 108, 111, 114], "allen": [59, 66, 75, 81, 87, 105, 111], "aller": [50, 59, 87, 108, 111], "allgemeinen": 77, "allianc": [66, 75, 81, 82], "allianz": [66, 75, 81], "allow": [3, 8, 14, 22, 23, 28, 29, 33, 34, 35, 37, 43, 46, 48, 50, 55, 57, 58, 66, 70, 72, 74, 75, 81, 88, 90, 91, 92, 96, 98, 105, 106, 107, 111, 113, 115], "alltag": [26, 56, 66, 75, 81, 111], "allyson": [1, 15, 39, 66, 75, 81], "alon": [66, 75, 81], "along": [66, 75, 81], "alphafold": 87, "alreadi": [6, 28, 37, 56, 66, 75, 81, 111], "also": [1, 3, 9, 11, 14, 16, 20, 23, 28, 29, 34, 35, 37, 43, 45, 46, 49, 53, 55, 57, 58, 62, 66, 73, 75, 81, 82, 89, 91, 96, 99, 103, 105, 109, 111, 115, 116], "altern": [49, 81, 103, 109], "although": [53, 75, 81, 108], "althous": 87, "alvi": [39, 53, 66, 73, 87, 92, 97, 111], "alwai": [3, 22, 23, 32, 37, 66, 75, 81], "alyona": [21, 49, 57, 70, 87, 90, 109], "am": [3, 23, 28, 56, 60, 91, 106, 111], "amer": [66, 75, 82], "america": [30, 33, 66, 75, 81], "amherst": [30, 33, 66, 75, 81], "amin": [30, 33, 66, 75, 81], "among": [3, 29, 33, 34, 37, 66, 75, 81, 89], "amount": [22, 66, 75, 81], "an": [1, 2, 4, 6, 9, 12, 14, 15, 16, 20, 22, 23, 24, 25, 26, 28, 30, 31, 32, 33, 34, 35, 36, 37, 39, 46, 48, 49, 50, 56, 57, 60, 63, 67, 70, 72, 73, 74, 77, 80, 83, 84, 85, 93, 94, 95, 98, 100, 102, 106, 107, 108, 113, 114, 115, 116], "analy": [29, 66, 75, 81], "analys": [1, 6, 15, 25, 28, 31, 39, 46, 50, 56, 57, 83, 89, 109, 115, 116], "analysi": [0, 13, 14, 17, 20, 22, 24, 30, 32, 38, 42, 44, 45, 62, 79, 88, 93, 94, 101, 107, 108], "analyst": [56, 74, 90, 95, 99], "analyt": [5, 46, 49, 56, 66, 70, 75, 81, 85, 91, 95, 109, 112, 116], "analyticalsci": [16, 18, 29, 35, 41, 53, 78, 91, 98, 104, 105, 106, 111], "analyz": [20, 25, 29, 31, 46, 49, 52, 53, 55, 66, 71, 75, 81, 91, 98, 99, 103, 109, 111, 114, 116], "anatol": [17, 18, 29, 51, 53, 67, 81, 101], "anatomi": [30, 33, 66, 75, 81], "anbiet": [56, 66, 75, 77, 81, 111], "anca": [6, 56, 66, 75, 81, 91], "anchor": [22, 66, 75, 81], "anderem": [41, 81, 107], "andi": [3, 22, 23, 32, 37, 66, 75, 81], "andor": [30, 31, 33, 81], "andra": [17, 34, 56, 75, 81, 91, 96, 111], "andrea": [0, 1, 2, 15, 16, 23, 37, 38, 39, 46, 47, 48, 51, 53, 66, 70, 75, 81, 91, 96, 102, 105, 106, 109, 111], "andrew": [43, 53, 66, 80, 87], "andrii": [7, 21, 39, 56, 58, 72, 81, 95, 102, 109], "andr\u00e9": [48, 56, 60, 65, 70, 81, 91, 102], "anett": [3, 22, 23, 32, 37, 66, 75, 81], "anforderungen": [26, 56, 66, 75, 81, 111], "angab": 77, "angaben": [26, 56, 66, 75, 81, 111], "angebot": 77, "angeboten": [26, 56, 66, 75, 81, 111], "angefordert": 77, "angewendet": [26, 56, 66, 75, 81, 96, 111], "angl": [24, 66, 75, 81], "angrenzend": [29, 66, 75, 81], "anhaltspunkt": 77, "anhand": [26, 56, 66, 75, 81, 111], "ani": [29, 32, 46, 48, 57, 62, 63, 66, 70, 75, 80, 81, 88, 91, 102, 103], "anim": [22, 29, 66, 75, 81], "anirban": [87, 91], "anisotropi": [19, 31, 40, 66, 68, 75, 81, 91, 103, 109], "anja": [3, 22, 23, 32, 37, 66, 75, 81], "anjali": [29, 53, 81, 91], "ankerhold": [66, 75, 81], "ann": [29, 41, 43, 44, 53, 66, 67, 70, 73, 80, 81, 91, 104], "anna": [4, 7, 9, 18, 21, 29, 31, 39, 42, 43, 49, 53, 55, 56, 57, 58, 66, 67, 70, 72, 75, 81, 87, 90, 91, 95, 102, 103, 104, 107, 109, 113], "annavonbachmann": [20, 48, 49, 56, 70, 87, 93], "annelien": [53, 73, 81, 94, 96, 113], "anniek": [4, 7, 9, 36, 66, 75, 81, 117], "anniekstok": [4, 5, 7, 9, 36, 48, 70, 81], "annika": [11, 52, 53, 91], "annoat": [66, 75, 81], "annot": [0, 1, 2, 8, 15, 20, 25, 30, 35, 38, 46, 50, 55, 57, 58, 74, 83, 87, 96, 102, 105], "annotations_biop": [31, 66, 75, 81], "announc": [1, 53, 87, 90, 116], "anoth": [29, 66, 75, 81], "anschlie\u00dfend": [56, 66, 75, 81, 111], "anschrift": 77, "answer": [0, 29, 38, 51, 66, 75, 81, 96, 111, 117], "anti": [24, 31, 66, 75, 81], "antibodi": [29, 66, 75, 81], "antj": [1, 14, 15, 39, 53, 66, 75, 81, 111], "antonio": [1, 4, 5, 7, 9, 15, 36, 39, 48, 66, 70, 75, 81, 117], "antragsbewilligung": [0, 32, 66, 75, 81], "antragststellung": [0, 32, 66, 75, 81], "antragsvorbereitung": [0, 32, 66, 75, 81], "antti": [11, 43, 81, 107], "antuono": [18, 29, 31, 53, 67, 81, 91, 104], "antwortfoli": [26, 56, 66, 75, 81, 111], "anymor": [66, 75, 81], "anyth": [5, 56, 66, 75, 81, 91, 95], "anythign": [5, 56, 66, 75, 81, 91, 95], "apach": [48, 49, 60, 70, 91, 106, 114], "aphaia": [3, 6, 16, 22, 23, 28, 32, 37, 51, 66, 75, 81, 91, 94, 105, 111], "api": [16, 35, 43, 46, 63, 75, 81, 85, 95, 106, 109, 116], "apismichel": [3, 23, 37, 66, 75, 81, 117], "apo": [29, 66, 75, 81], "apoc": [49, 81, 103, 109], "apotom": [30, 33, 66, 75, 81], "app": [29, 41, 57, 85, 88, 111, 113, 117], "appear": [29, 66, 75, 81], "appleton": [53, 66, 73, 81, 96, 111], "appli": [6, 16, 22, 28, 30, 33, 35, 37, 43, 45, 46, 48, 50, 56, 57, 66, 70, 75, 81, 83, 92, 99, 106, 109, 111, 115], "applic": [2, 3, 8, 17, 23, 29, 33, 34, 35, 37, 48, 49, 51, 53, 58, 73, 74, 79, 82, 84, 85, 92, 95, 96, 99, 105, 106, 108, 109, 111, 112, 115], "apprais": [53, 73, 81, 94, 96, 113], "approach": [1, 7, 9, 14, 17, 21, 22, 28, 29, 34, 36, 39, 55, 58, 72, 73, 75, 94, 95, 96, 101, 109], "appropri": [1, 9, 14, 28, 43, 59, 66, 75, 81, 87, 91], "approv": [22, 66, 75], "approxim": [29, 31, 66, 75, 81], "appsubmitt": 63, "april": [3, 5, 29, 37, 48, 56, 66, 70, 75, 81, 91, 95, 96, 102, 105, 109, 111], "apurv": [29, 49, 70, 81, 109], "ar": [0, 1, 3, 11, 14, 15, 20, 22, 23, 24, 25, 28, 29, 31, 32, 33, 34, 37, 38, 39, 43, 45, 46, 48, 49, 50, 51, 53, 55, 56, 58, 59, 61, 62, 66, 67, 70, 72, 75, 81, 83, 85, 88, 89, 90, 91, 92, 95, 97, 99, 101, 102, 105, 106, 107, 108, 109], "arbeit": [29, 56, 66, 75, 81, 96, 100, 111], "arbeitspapi": [66, 75, 81], "arc": [23, 37, 46, 47, 53, 66, 70, 105, 106, 109], "architect": [3, 33, 34, 37, 66, 75, 81], "archiv": [3, 6, 11, 23, 28, 37, 51, 75, 82, 96, 101, 117], "archivieren": [29, 56, 66, 75, 81, 96, 111], "archivierung": [26, 56, 66, 75, 81, 111], "ardan": [39, 53, 66, 73, 87, 92, 97, 111], "area": [29, 33, 45, 56, 91, 96, 105, 111], "arganda": [10, 11, 18, 29, 42, 49, 55, 65, 70, 87, 90, 91, 104], "argu": [32, 66, 75, 81], "arhnhk07t3q": [43, 58, 74], "ariann": [10, 11, 18, 42, 49, 55, 70, 87, 91, 104], "arif": [4, 7, 9, 11, 36, 57, 66, 75, 81, 85, 111, 115, 117], "arina": [1, 14, 15, 39, 53, 66, 75, 81, 111], "aris": [29, 53, 66, 75, 81, 91, 111], "arn": [4, 12, 18, 30, 53, 73, 78], "arndt": [66, 75, 82], "arrai": [3, 8, 23, 30, 33, 37], "arrat": [10, 17, 18, 29, 51, 53, 66, 67, 75, 81, 101], "arrest": [56, 74, 87, 95, 102], "arriv": [3, 37, 66, 75, 81], "art": [0, 3, 8, 11, 17, 29, 32, 53, 66, 67, 75, 77, 78, 81, 92, 97, 111], "articl": [2, 3, 4, 5, 8, 9, 11, 12, 15, 17, 18, 22, 23, 29, 30, 31, 32, 33, 34, 37, 39, 43, 44, 46, 49, 51, 52, 53, 66, 67, 70, 72, 73, 75, 78, 81, 85, 87, 91, 92, 93, 94, 96, 97, 98, 99, 101, 102, 104, 105, 107, 109, 111, 113, 114, 115, 116], "artif": [68, 87, 95], "artifici": [1, 5, 7, 10, 13, 21, 25, 40, 41, 43, 46, 48, 49, 53, 56, 57, 64, 65, 68, 70, 73, 85, 95, 96, 99, 104, 111, 115, 116, 117], "artikel": [66, 75, 81], "arxiv": [11, 52, 53, 66, 73, 75, 81, 91, 96, 99, 115], "asadipour": [66, 75, 81], "ashi": [4, 24, 53, 66, 81, 91, 111], "ask": [29, 32, 55, 66, 75, 81, 99, 109], "aslihan": [4, 12, 18, 30, 53, 73, 78], "aspect": [4, 6, 9, 28, 29, 37, 41, 43, 53, 56, 66, 75, 81, 91, 107, 111, 114], "aspekt": [26, 29, 41, 56, 66, 75, 81, 96, 100, 107, 111], "aspekten": [26, 56, 66, 75, 81, 111], "assaf": [17, 42, 49, 53, 55, 70, 81, 87, 91, 102, 104, 107], "assembl": [46, 53, 66, 98, 116], "assembli": [30, 33, 50, 57, 75, 81, 83, 115], "assess": [3, 8, 17, 36, 55, 66, 67, 75, 92, 97, 111], "asset": [3, 8, 10, 23, 33, 34, 35, 37, 43, 55, 58, 66, 74, 75, 87, 90, 94, 96, 105, 106, 111], "assist": [3, 8, 23, 30, 33, 34, 37, 49, 53, 73, 94, 96, 101, 103, 109], "associ": [24, 29, 31, 43, 48, 49, 66, 70, 75, 81, 87, 91, 108, 109], "assunta": [1, 15, 39, 66, 75, 81], "assur": [29, 66, 75, 81, 90], "astrid": [16, 23, 32, 37, 38, 46, 47, 48, 51, 53, 66, 70, 75, 81, 91, 96, 105, 106, 109, 111], "athar": [7, 21, 39, 56, 58, 72, 81, 95, 102, 109], "atkinson": [48, 56, 70], "attach": [16, 35, 66, 75, 81], "attende": [36, 55, 66, 75, 81], "attribut": [1, 28, 66, 75, 81, 82], "attributionpleas": [66, 75, 81], "au": [0, 32, 40, 41, 50, 57, 66, 75, 81, 83, 87, 107, 111, 113, 115], "auch": [26, 56, 66, 75, 77, 81, 111], "audergon": [1, 15, 39, 66, 75, 81], "audienc": [3, 6, 9, 28, 29, 33, 34, 37, 56, 66, 75, 81, 111], "audrei": [1, 15, 39, 66, 75, 81], "auf": [26, 29, 56, 66, 75, 77, 81, 96, 111], "aufbewahren": [26, 56, 66, 75, 81, 96, 111], "aufgaben": [0, 26, 32, 50, 56, 59, 66, 75, 81, 87, 108, 111], "aufloesen": [29, 56, 66, 75, 81, 96, 111], "aufl\u00f6sung": [26, 56, 66, 75, 81, 111], "aufmerksam": 77, "aufteilen": [0, 32, 66, 75, 81], "aufweisen": 77, "aufzeigen": [26, 56, 66, 75, 81, 111], "aug": [3, 22, 23, 32, 37, 66, 75, 81], "augment": [20, 29, 66, 75, 81], "august": [17, 66, 75, 81, 117], "auml": [30, 33, 66, 75, 81], "aurelien": [49, 70, 91], "ausdr\u00fccklich": 77, "ausgab": [66, 75, 81], "ausgesprochen": [66, 75, 81], "austausch": [0, 32, 66, 75, 81], "author": [3, 8, 11, 17, 22, 29, 39, 40, 43, 53, 58, 61, 62, 63, 66, 67, 68, 73, 74, 75, 81, 87, 90, 91, 92, 93, 94, 96, 97, 99, 101, 104, 106, 107, 111, 113, 114, 115], "auto": [25, 31, 66, 75, 81], "autom": [1, 24, 28, 29, 31, 51, 66], "automat": [1, 25, 29, 31, 57, 62, 75, 76, 88, 111, 113], "automatisiert": [29, 56, 66, 75, 81, 96, 111], "autonoma": [30, 33, 66, 75, 81], "autostain": [24, 31, 66, 75, 81], "au\u00dferhalb": 77, "av": [66, 75, 81], "avail": [1, 3, 6, 8, 14, 15, 20, 22, 23, 28, 29, 32, 33, 34, 35, 37, 39, 40, 52, 53, 55, 56, 58, 61, 66, 71, 74, 75, 87, 91, 96, 105, 106, 111, 114], "avanzada": [30, 33, 66, 75, 81], "averag": [29, 66, 75, 81], "avilov": [4, 12, 18, 30, 53, 73, 78], "avoid": [3, 22, 23, 32, 37, 66, 75, 81], "awai": [7, 21, 39, 56, 58, 72, 81, 95, 102, 109], "await": [45, 66, 75, 81, 111], "axial": [66, 75, 81], "axio": [29, 30, 33, 66, 75, 81], "axioscan": [32, 37], "axioscan7": [3, 22, 23], "axiovert": [30, 33, 66, 75, 81], "axton": [53, 66, 73, 81, 96, 111], "ayala": [30, 33, 66, 75, 81], "ayam": [10, 29, 66, 75, 81], "aybukeki": [15, 17, 37, 38, 48, 70, 80, 113], "ayoub": [1, 14, 53, 66, 75, 81, 111], "ays": [4, 12, 18, 30, 53, 73, 78], "azid": [24, 31, 66, 75, 81], "b": [11, 18, 29, 30, 33, 53, 55, 57, 66, 70, 75, 77, 81, 87, 91, 104], "b1": [3, 23, 32, 37, 66, 75, 81], "babaki": [0, 16, 23, 37, 38, 46, 47, 48, 53, 66, 70, 75, 81, 91, 96, 105, 106, 109, 111], "bacal": [11, 43, 80, 93, 109, 115, 116], "back": [55, 66, 116], "background": [29, 30, 33, 49, 53, 66, 70, 75, 78, 81, 85, 95, 98, 109, 112], "backward": [36, 55, 66, 75, 81], "bacteri": [30, 33, 50, 57, 66, 75, 81, 83, 115], "bad": [48, 70, 85], "baden": [66, 75, 81], "baecker": [55, 70, 79, 91, 104], "baker": [24, 31, 66, 75, 81], "bakker": [4, 12, 18, 30, 53, 73, 78], "balanc": [24, 31, 66, 75, 81], "bankhead": [25, 29, 31, 41, 43, 49, 53, 55, 66, 67, 75, 80, 81, 91, 98, 104, 109], "bar": [66, 75, 81, 88], "barbara": [26, 43, 53, 56, 66, 75, 80, 81, 111], "barbora": [29, 53, 66, 78], "barbotin": [49, 70, 91], "barcel\u00f3": [46, 87, 90, 91, 116], "barlow": [39, 53, 73, 111], "barranco": [11, 46, 109, 116], "barri": [48, 52, 53, 70, 71, 80, 82, 91], "barrier": [1, 15, 39, 66, 75], "barrutia": [10, 17, 18, 29, 51, 53, 66, 67, 75, 81, 101], "barwil": [20, 48, 49, 56, 70, 87, 93], "base": [0, 1, 3, 8, 13, 15, 20, 22, 23, 30, 32, 33, 34, 35, 37, 38, 39, 46, 50, 51, 52, 55, 58, 71, 74, 79, 80, 82, 87, 93, 96, 98, 105, 106, 107, 113, 116], "base4nfdi": [45, 66, 75, 81, 111], "basel": [17, 34, 51, 75, 81, 96, 105, 111], "basi": [3, 8, 23, 33, 34, 35, 37, 55, 58, 66, 74, 75, 77, 81, 105, 106, 111], "basic": [3, 6, 7, 11, 13, 20, 23, 28, 29, 36, 37, 43, 49, 55, 56, 57, 66, 75, 85, 89, 99, 109, 112, 114], "bastien": [55, 70, 79, 91, 104], "batch": [10, 29, 57, 66, 75, 80, 81, 91, 102, 103], "batchconvert_cli": [66, 75, 81], "batut": [11, 53, 81, 93, 96, 101, 114], "baum": [66, 75, 81], "baustein": [53, 66, 81, 111], "bayesian": [48, 70], "bbbc": [43, 44, 73], "bdv": [24, 31, 66, 75, 81], "bea": [53, 81, 96, 111], "beachten": [26, 56, 66, 75, 81, 111], "beachtet": 77, "beam": [24, 66, 75, 81], "beantragung": [0, 32, 66, 75, 81], "beantwortet": [41, 81, 107], "bearbeitung": 77, "beatriz": [14, 15, 28, 39, 53, 55, 65, 66, 75, 81, 87, 90, 91, 111, 116], "becaus": [3, 6, 28, 29, 33, 34, 37, 56, 66, 75, 81, 111], "becker": [7, 11, 33, 36, 38, 41, 57, 66, 70, 75, 81, 83, 91, 109, 112, 114], "becom": [3, 8, 23, 33, 34, 35, 37, 53, 55, 58, 66, 73, 74, 75, 81, 94, 96, 105, 106, 111, 113], "bedarfsanmeldung": [0, 32, 66, 75, 81], "bedeutung": [26, 56, 66, 75, 81, 111], "bedingungen": [26, 56, 66, 75, 81, 96, 111], "been": [3, 6, 8, 17, 22, 23, 28, 29, 51, 53, 63, 66, 73, 75, 81, 88, 91, 96, 99, 108, 111, 115], "befor": [24, 29, 31, 66, 75, 81], "begin": [3, 11, 17, 29, 33, 34, 37, 39, 45, 51, 53, 66, 73, 75, 81, 87, 92, 96, 97, 101, 105, 111], "beginn": [0, 3, 8, 23, 32, 33, 34, 35, 37, 55, 58, 66, 74, 75, 81, 105, 106, 111], "begriff": [29, 56, 66, 75, 81, 96, 100, 111], "begr\u00fc\u00dfenswert": [66, 75, 81], "begun": [3, 8, 23, 30, 33, 34, 37, 66, 75, 81], "begutachtungsverfahren": [41, 81, 107], "behalten": [26, 56, 66, 75, 77, 81, 111], "behandelt": [0, 26, 32, 56, 66, 75, 81, 111], "behavior": [30, 33, 66, 75, 81], "behind": [53, 62, 66, 73, 81, 96, 111], "bei": [50, 56, 59, 66, 75, 77, 87, 100, 108, 111], "beiden": [26, 56, 66, 75, 81, 111], "beigetragen": [29, 56, 66, 75, 81, 96, 111], "being": [1, 6, 14, 28, 37, 56, 66, 75, 81, 111], "beispiel": [26, 56, 66, 75, 81, 111], "beispielsweis": 77, "beitrag": [0, 32, 66, 75, 81], "beitr\u00e4gen": [0, 32, 66, 75, 81], "bekanntwerden": 77, "bekemei": [6, 28, 37, 56, 66, 75, 81, 111], "bekeschu": [33, 38, 66, 75, 81], "beleuchtet": [29, 56, 66, 75, 81, 111], "belevich": [4, 9, 29, 53, 81, 91], "belgium": [22, 66, 75, 81], "believ": [43, 49, 91, 111], "below": [3, 30, 33, 37, 66, 75, 81], "ben": [66, 75, 81, 117], "benchmark": [43, 44, 73], "benedict": [1, 15, 29, 39, 53, 66, 75, 78, 81], "benefit": [3, 6, 17, 28, 33, 34, 37, 40, 52, 53, 56, 66, 71, 75, 81, 87, 91, 96, 113, 114], "benjamin": [21, 49, 55, 70, 79, 87, 90, 91, 104, 109], "benoit": [29, 55, 69, 70, 87, 91, 98], "benthamiana": [30, 33, 66, 75, 81], "beratungsstammtisch": [56, 66, 75, 81, 94, 111], "bercowski": [10, 11, 18, 42, 49, 55, 70, 87, 91, 104], "bereich": [26, 56, 111], "bereit": [29, 56, 66, 75, 81, 96, 111], "berlin": [45, 66, 75, 81, 111], "berndt": [1, 28, 66, 75, 81], "bernhardt": [3, 8, 23, 30, 33, 34, 37, 53, 66, 75, 81, 87, 105, 111], "bert": [11, 43, 80, 93, 109, 115, 116], "bertinetti": [6, 28, 37, 56, 66, 75, 81, 111], "bertocchi": [4, 12, 18, 30, 53, 73, 78], "bertolini": [53, 66, 81, 91, 97, 106, 115], "ber\u00fccksichtigen": [56, 66, 75, 81, 100, 111], "beschaffungsprozess": [0, 32, 66, 75, 81], "besch\u00e4ftigt": [0, 32, 66, 75, 81], "besond": [56, 66, 75, 81, 100, 111], "besprechen": [26, 56, 66, 75, 81, 111], "besson": [2, 4, 5, 11, 15, 17, 48, 53, 70, 73, 79, 81, 84, 92, 105, 108, 111], "best": [1, 3, 8, 15, 20, 23, 28, 29, 33, 34, 35, 37, 39, 41, 45, 55, 56, 58, 74, 75, 81, 85, 94, 105, 106], "beteiligt": [56, 66, 75, 81, 100, 111], "beth": [4, 7, 12, 18, 21, 30, 39, 41, 43, 49, 52, 53, 56, 58, 66, 70, 71, 72, 73, 78, 80, 81, 83, 91, 95, 102, 109, 111, 112], "bethac07": [12, 49, 81, 109, 112], "betreib": 77, "betroffenen": [41, 81, 107], "better": [1, 15, 39, 57, 66, 75, 81, 87, 114], "bettina": [3, 33, 34, 37, 66, 75, 81], "between": [3, 9, 11, 15, 23, 28, 29, 37, 48, 56, 64, 66, 70, 75, 81, 84, 91, 95, 102, 106, 109], "beyond": [3, 8, 23, 28, 45, 55, 59, 66, 75, 81, 87, 111], "bfdm": [53, 66, 81, 111], "bhattiprolu": [43, 49, 58, 70, 74, 85, 91, 109], "bi": 77, "bia23": [47, 91], "biabob": [19, 28, 29, 40, 48, 49, 56, 57, 68, 70, 80, 85, 91, 99, 102, 109], "biapol": [19, 29, 40, 47, 48, 49, 70, 81, 90, 91, 103, 106, 109], "bias_nucleus_segment": [18, 41, 57, 70, 87, 91, 104], "bib": [53, 66, 75, 81, 96, 111], "bibliotheken": [41, 81, 107], "bid": 49, "bielefeld": [0, 16, 23, 37, 38, 48, 75, 81, 91, 96, 105, 111], "biernacka": [41, 45, 66, 75, 81, 111], "bierwirth": [41, 66, 75, 81, 111], "bietet": [0, 26, 32, 56, 66, 75, 81, 111], "big": [1, 3, 4, 5, 13, 14, 33, 48, 49, 57, 66, 70, 75, 80, 81, 85, 87, 91, 109], "bigstich": [31, 66, 75, 81], "biifsweden": [48, 56, 60, 65, 70, 81, 91, 102], "biii": [43, 91], "bildanalys": [0, 32, 66, 75, 81], "bildern": [56, 66, 75, 81, 100, 111], "bina": [11, 30, 33, 53, 87, 92, 97, 101, 102], "binari": [17, 34, 51, 56, 66, 75, 81, 85, 91, 96, 105, 111], "bind": [16, 35, 43, 75, 81, 91, 106], "bio": [11, 20, 22, 24, 28, 31, 32, 42, 46, 62, 88, 104, 107], "biochristian": [3, 23, 37, 66, 75, 81, 117], "biocompat": [3, 23, 32, 37, 66, 75, 81], "biocur": [43, 57, 72, 81, 107, 113], "bioformat": [24, 66, 75], "biohackathon": [0, 16, 23, 37, 38, 48, 75, 81, 91, 96, 105, 111], "bioimag": [0, 12, 19, 20, 22, 24, 25, 32, 38, 40, 45, 48, 52, 59, 64, 68, 71, 79, 84, 88, 93, 94, 100, 108, 112, 114], "bioimage_analysis_icob_2023": [43, 60, 70, 91], "bioimage_team": [29, 55, 69, 87, 91, 98], "bioimageanalysi": [4, 5, 7, 9, 36, 48, 57, 70, 80, 81], "bioimageanalysis22": [4, 50, 55, 58, 66, 72, 81, 91], "bioimageanalysisnotebook": [41, 49, 80, 81, 91, 109], "bioimagebook": [41, 49, 81, 91, 98, 109], "bioimagedataanalysi": [18, 41, 78, 91, 104], "bioimagelist": [43, 87, 111], "bioimaging_and_optics_platform_biop": [24, 31, 66, 75, 81], "bioimagingguid": [43, 53, 66, 80], "bioimagingnorthamerica": [17, 66, 75, 81], "bioinformat": [4, 11, 20, 24, 39, 43, 46, 47, 48, 49, 56, 57, 66, 70, 72, 80, 81, 87, 91, 92, 96, 97, 101, 107, 109, 111, 113, 114, 115, 116, 117], "bioinformatician": [3, 23, 28, 66, 75, 81], "biolog": [1, 3, 9, 14, 17, 23, 28, 29, 33, 34, 37, 38, 43, 51, 56, 57, 58, 72, 74, 75, 83, 96, 105, 108], "biologi": [3, 14, 30, 33, 34, 37, 42, 43, 56, 57, 58, 85, 96, 99, 115], "biologist": [17, 19, 29, 40, 48, 56, 74, 91, 95, 99, 100, 102, 103, 107, 111, 113], "biomateri": [3, 23, 32, 37, 66, 75, 81], "biomed": [6, 29, 30, 33, 43, 46, 53, 55, 56, 59, 66, 75, 81, 87, 99, 108, 109, 116], "biomedcentr": [2, 15, 53, 66, 81, 91, 102], "biomolecular": [43, 48, 70, 93, 117], "biop": [50, 91, 106], "bioport": [66, 75, 81], "biorxiv": [17, 30, 33, 43, 49, 52, 53, 66, 70, 75, 80, 81, 85, 91, 109, 111, 113, 114], "biotec": [29, 49, 70, 81, 91, 103, 109], "biotechnologi": [30, 33, 66, 75, 81], "biotin": [24, 31, 66, 75, 81], "birgit": [46, 53, 98, 116], "birgitta": [29, 66, 75, 81], "birnei": [39, 53, 73, 111], "birt": [66, 75, 82], "bischof": [1, 4, 12, 14, 18, 30, 53, 66, 73, 75, 78, 81, 111], "bit": [3, 20, 22, 23, 31, 32, 37, 66, 75, 81], "bitten": 77, "bjorn": [1, 15, 39, 66, 75, 81], "bj\u00f6rn": [1, 28, 55, 57, 65, 81, 87, 91, 101, 106], "black": [24, 31, 66, 75, 81], "bleiben": 77, "blick": [66, 75, 81], "blog": [19, 29, 61, 62, 64, 68, 81, 87, 90, 91, 95, 99, 100, 103, 106, 109, 111, 113], "blomberg": [1, 15, 39, 66, 75, 81], "blood": [20, 66, 75], "bmbf": [26, 56, 66, 75, 81, 111], "bnsreenu": [43, 49, 70, 85, 91, 109], "bob": [53, 73, 92, 96, 97, 101, 111], "bocklitz": [53, 66, 81, 96, 111], "bodi": [29, 30, 33, 59, 66, 75, 81, 111], "boeckhout": [53, 73, 81, 94, 96, 113], "boehm": [4, 11, 12, 18, 30, 33, 53, 66, 73, 75, 78, 81, 87, 88, 92, 97, 101, 102], "bogov": [17, 18, 29, 51, 53, 67, 81, 101], "bohr": [1, 28, 51, 66, 75, 81, 91], "boissonnet": [0, 3, 6, 8, 15, 16, 17, 22, 23, 28, 30, 32, 33, 34, 35, 38, 46, 47, 48, 53, 55, 56, 58, 66, 70, 74, 75, 80, 81, 88, 91, 96, 105, 106, 109, 111, 113, 117], "boiten": [1, 15, 39, 66, 75, 81], "bombrun": [4, 57, 70, 87, 91, 104], "book": [12, 18, 42, 43, 49, 57, 66, 70, 75, 78, 80, 81, 83, 85, 87, 88, 90, 91, 98, 103, 104, 107, 109, 111, 112], "bornholdt": [41, 70, 87, 90, 91], "bortolomeazzi": [0, 3, 8, 16, 22, 28, 30, 32, 33, 34, 35, 37, 38, 46, 47, 48, 53, 55, 58, 66, 70, 74, 75, 81, 88, 91, 96, 105, 106, 109, 111, 117], "bot": [15, 17, 37, 38, 48, 70, 80, 91, 113], "both": [0, 3, 22, 23, 29, 32, 37, 38, 51, 53, 57, 59, 66, 73, 75, 81, 82, 94, 96, 101, 111], "bottleneck": [66, 75, 81], "bottom": 62, "botzki": [48, 57, 70, 81, 90], "boundari": [66, 75, 81], "bourget": [11, 53, 81, 92, 97, 111], "box": [10, 29, 66, 75, 81], "bo\u00dfe": [66, 75, 82], "bq8zccqc": [30, 33, 66, 75, 81], "bragantini": [48, 57, 60, 70, 91, 112, 114], "brain": [0, 24, 38, 51, 96], "branch": 62, "brazma": [39, 53, 66, 73, 87, 92, 97, 111], "brdu": [24, 31, 66, 75, 81], "break": [3, 16, 30, 33, 35, 37, 66, 75, 81], "bredenoord": [53, 73, 81, 94, 96, 113], "bret": [57, 87, 114], "brian": [29, 49, 70, 81, 109], "brick": [55, 66, 75, 81, 111], "brideau": [43, 53, 66, 80], "bridg": [0, 16, 23, 37, 38, 48, 97, 105, 106, 115], "brief": [53, 73, 91, 96, 99, 115], "brighfield": [66, 75, 81], "bright": [66, 75], "brightfield": [30, 33], "bring": [1, 3, 8, 15, 23, 28, 30, 33, 34, 37, 39, 43, 57, 66, 72, 75, 81, 107, 113], "brisanz": [66, 75, 81], "britni": [43, 48, 70, 93, 117], "britta": [4, 12, 18, 20, 30, 48, 49, 53, 56, 70, 73, 78, 87, 93], "broad": [3, 6, 8, 23, 28, 33, 34, 35, 37, 43, 44, 55, 56, 58, 66, 73, 74, 75, 81, 105, 106, 111], "broaddu": [29, 66, 75, 81], "broader": [1, 28, 52, 53, 66, 71, 75, 81, 91, 111, 114], "broadinstitut": [43, 44, 73], "brocher": [4, 12, 18, 30, 53, 73, 78], "brook": [10, 29, 66, 75, 81], "brought": [1, 14, 33, 38, 66, 75, 81], "brown": [30, 33, 53, 66, 73, 75, 81, 88, 101], "brows": [14, 56, 57, 66, 75, 91, 96, 111], "browser": [34, 58, 105, 111], "browsing_idr": [29, 40, 64, 81, 106, 109], "bruna": [48, 57, 70, 81, 90, 115], "bsd": [11, 13, 15, 17, 29, 37, 38, 41, 43, 45, 46, 48, 49, 50, 52, 53, 55, 57, 60, 66, 69, 70, 71, 81, 91, 92, 93, 98, 102, 103, 104, 106, 109, 111, 112, 113, 114, 115, 116], "bsd3": [48, 57, 60, 70, 91, 112, 114], "bsg": [24, 66, 75, 81], "btab106": [4, 24, 53, 66, 81, 91, 111], "bu1": [24, 31, 66, 75, 81], "buchholz": [4, 5, 7, 9, 12, 36, 41, 43, 48, 49, 66, 70, 73, 75, 81, 85, 91, 111, 117], "budapest": [43, 48, 70, 91, 117], "bug": [10, 29, 66, 75, 81], "buglakova": [21, 49, 57, 70, 87, 90, 109], "bugra": [1, 4, 7, 9, 14, 36, 57, 66, 75, 81, 87, 117], "bugraoezdemir": [4, 5, 7, 9, 36, 48, 66, 70, 75, 81], "build": [11, 30, 33, 46, 50, 57, 73, 83, 96, 99, 108, 109, 114, 115, 116], "built": [43, 57, 72, 81, 107, 109, 113], "bumberg": [1, 3, 8, 23, 28, 51, 66, 75, 81, 91], "bunch": [10, 29, 48, 66, 70, 75, 80, 81, 91], "bundl": [45, 66, 75, 81, 111], "burden": [17, 51, 75, 81, 91, 111], "burdett": [66, 75, 81], "burel": [1, 2, 4, 5, 11, 17, 37, 38, 39, 48, 53, 66, 70, 73, 75, 80, 81, 84, 91, 102, 105, 106, 111, 113], "burk": [11, 53, 81, 93, 96, 101, 114], "burri": [11, 21, 24, 31, 43, 47, 49, 55, 60, 66, 70, 75, 81, 87, 90, 91, 102, 104, 109], "burst": [30, 33, 66, 75, 81], "busch": [1, 28, 51, 66, 75, 81, 91], "busi": 62, "butler": [42, 49, 55, 70, 87, 91, 104], "button": 62, "bu\u011fra": [53, 111], "bwfdm": [66, 75, 81], "byod": [60, 91, 111], "b\u00e9r\u00e9nice": [11, 53, 81, 93, 96, 101, 114], "c": [4, 11, 12, 18, 24, 29, 30, 31, 33, 39, 43, 48, 53, 58, 66, 70, 73, 74, 75, 78, 81, 83, 85, 87, 91, 92, 93, 97, 101, 107, 111, 113], "c35tvcsrxzay98s2uywwxpp0cfcljbmmfpco": [13, 55, 65, 87, 91, 104], "c4scienc": [24, 31, 66, 75, 81], "ca": [30, 33, 66, 75, 81], "cai": [30, 33, 66, 75, 81], "caicedo": [41, 70, 87, 90, 91], "calbindin": [29, 66, 75, 81], "calcul": [22, 66, 75, 81], "calf": [31, 66, 75, 81], "call": [1, 11, 15, 46, 48, 70, 84, 106, 109, 116], "callaghan": [43, 48, 49, 70, 87, 91, 108, 109], "callum": [41, 70, 83, 91], "calretinin": [29, 66, 75, 81], "cam": [49, 83, 91, 98], "camacho": [57, 70, 85, 91, 109], "camachodejai": [57, 70, 85, 91, 109], "camera": [20, 24, 25, 31], "cameron": [53, 73, 101], "camp": [66, 75, 81], "campu": [17, 28, 30, 33, 37, 55, 56, 92, 105, 111, 117], "can": [1, 3, 8, 12, 14, 15, 17, 20, 22, 23, 24, 25, 28, 29, 30, 31, 32, 33, 34, 37, 38, 40, 43, 46, 48, 49, 50, 53, 55, 56, 57, 58, 61, 62, 63, 64, 66, 68, 70, 72, 73, 74, 75, 76, 81, 82, 83, 84, 85, 87, 88, 89, 90, 91, 93, 94, 96, 99, 101, 105, 106, 107, 109, 111, 113, 114, 115], "cancer": [3, 23, 25, 28, 31, 33, 34, 37, 66, 75, 81, 117], "candid": [20, 66, 75, 81], "cannot": [17, 34, 56, 66, 75, 81, 91, 96, 111], "canva": [50, 57, 83, 115], "capabl": [1, 28, 66, 75, 81], "capolupo": [25, 31, 66, 75, 81], "captur": [0, 38, 51, 66, 75, 96, 111], "carbon": [10, 29, 66, 75, 81], "cardiac": [3, 23, 32, 37, 66, 75, 81], "cardiovascular": [3, 23, 32, 37, 66, 75, 81], "cardona": [49, 83, 91, 98], "care": [17, 34, 51, 62, 75, 81, 96, 105, 111], "career": [3, 9, 23, 28, 37, 40, 66, 75, 81, 87, 111, 113], "carl": [3, 20, 23, 24, 29, 30, 32, 33, 37, 48, 49, 52, 53, 56, 70, 71, 80, 87, 91, 93], "carla": [1, 15, 39, 66, 75, 81], "carli": [53, 75, 81, 108], "carol": [1, 15, 39, 66, 75, 81], "carolin": [26, 41, 56, 66, 75, 81, 111], "carolina": [4, 30, 33, 57, 66, 70, 75, 81, 87, 91, 104], "carpent": [29, 43, 44, 53, 66, 67, 70, 73, 80, 81, 91, 104], "carpenti": [43, 48, 70, 93, 117], "carpentri": [36, 55, 66, 75], "carrera": [10, 11, 18, 29, 42, 49, 55, 65, 70, 87, 90, 91, 104], "carsten": [3, 8, 23, 33, 34, 35, 37, 55, 56, 58, 66, 74, 75, 81, 88, 91, 96, 105, 106, 111], "cart": [41, 91], "caru": [3, 23, 29, 32, 37, 66, 75, 81], "carvalho": [4, 12, 18, 30, 53, 73, 78], "case": [0, 1, 3, 6, 8, 16, 23, 24, 28, 29, 33, 34, 35, 37, 38, 48, 53, 55, 56, 58, 62, 74, 78, 96, 105, 106, 111], "castillia": [3, 4, 12, 18, 30, 33, 34, 53, 66, 73, 75, 78, 81, 88, 101, 111], "catalin": [4, 12, 18, 30, 53, 73, 78], "catalog": [41, 81, 107], "catalogu": [56, 75, 81, 107, 111], "categor": [9, 66, 75, 81], "caterina": [3, 4, 12, 18, 30, 33, 34, 46, 53, 66, 73, 75, 78, 81, 87, 88, 90, 91, 101, 111, 116], "caus": [48, 70], "causal": [48, 70], "cavanagh": [66, 75], "cbg": [80, 104], "cc": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 25, 26, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 45, 46, 47, 48, 49, 50, 51, 52, 53, 55, 56, 57, 58, 59, 60, 64, 65, 67, 68, 70, 71, 72, 73, 74, 77, 80, 85, 88, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 111, 112, 113, 114, 115, 116, 117], "cc0": [41, 43, 44, 48, 49, 50, 57, 58, 70, 72, 91, 92, 93, 95, 96, 97, 98, 101, 102, 111, 114, 115], "cc1": [24, 31, 66, 75, 81], "cci": [30, 33, 66, 75, 81], "cck": [66, 75, 81], "ccy": [3, 8, 55, 66, 92, 97, 105, 111], "cd": 63, "cedar": [53, 73, 81, 94, 96, 101], "cell": [1, 3, 5, 22, 23, 24, 25, 28, 29, 30, 32, 33, 37, 42, 48, 49, 53, 55, 69, 70, 73, 80, 85, 92, 96, 97, 98, 101, 104, 109, 112], "cellcarri": [31, 66, 75, 81], "cellmigrationlab": [43, 48, 56, 57, 60, 70, 91, 114, 117], "cellpos": [48, 70, 80, 112], "cellprofil": [1, 28, 66], "cellprofiler_introduct": [55, 70, 87, 91, 104], "celltrackingchalleng": [43, 44, 87, 91], "celltrackscolab": [43, 49, 71, 85], "cellulair": [30, 33, 66, 75, 81], "cellular": [3, 22, 23], "cenalmor": [68, 87, 95], "center": [6, 30, 33, 34, 37, 56, 77, 117], "centr": [28, 30, 33, 43, 56, 57, 66, 75, 81, 91, 105, 111, 114], "central": [3, 6, 33, 34, 37, 66, 75, 81], "cerebr": [30, 33, 66, 75, 81], "certain": [14, 29, 43, 48, 53, 57, 66, 70, 75, 81, 85, 91, 96, 111], "ceur": [17, 34, 53, 66, 75, 81, 105], "cfci": [3, 22, 23], "cgrp": [29, 66, 75, 81], "ch": [24, 31, 50, 66, 75, 81, 91, 106], "cha": [49, 70, 91], "chaeroni": [66, 75, 81], "chain": [50, 57, 83, 115], "challeng": [0, 1, 3, 6, 8, 9, 14, 20, 23, 28, 30, 33, 34, 36, 39, 51, 56, 96, 99, 100, 107, 109], "cham": [29, 66, 75, 81], "chamber": [11, 24, 30, 33, 53, 66, 75, 81, 87, 88, 92, 97, 101, 102], "chamier": [12, 29, 43, 49, 66, 70, 73, 75, 81, 85, 91], "champion": [66, 75, 81], "chan": [25, 31, 53, 66, 75, 81, 108], "chan2": [25, 31, 66, 75, 81], "chanc": [3, 33, 34, 37, 66, 75, 81], "chang": [10, 16, 20, 29, 35, 53, 55, 62, 66, 75, 81, 90, 99, 108, 109], "changelog": [10, 29, 66, 75, 81], "channel": [20, 24, 29, 31, 56, 96, 100, 107, 111], "charact": [3, 22, 23, 32, 37, 66, 75, 81], "characterist": [14, 56, 57, 66, 75, 81, 91, 96, 111], "charg": [66, 75, 81], "charl": [39, 53, 73, 111], "charlott": [17, 18, 29, 51, 53, 67, 81, 101], "chat": 29, "chatgpt": 56, "check": [22, 62, 66, 75], "checkalina": [49, 70, 85, 90, 109], "checklist": [3, 37, 66, 75, 81], "chen": [53, 73, 91, 96, 99, 115], "chessel": [17, 18, 29, 51, 53, 67, 81, 101], "chiang": [57, 81, 91, 93], "chiara": [6, 56, 66, 75, 81, 91], "chiaruttini": [4, 31, 53, 66, 75, 81, 91, 111], "chiesa": [55, 56, 66, 75, 81, 94, 111], "chines": [30, 33, 66, 75, 81], "chiritescu": [4, 12, 18, 30, 53, 73, 78], "chitwood": 53, "chiu": [11, 14, 39, 53, 56, 57, 66, 73, 75, 81, 87, 91, 92, 96, 97, 101, 111], "chong": [11, 17, 18, 29, 42, 49, 51, 53, 55, 67, 70, 81, 87, 91, 101, 104], "choos": [4, 9, 29, 36, 40, 53, 55, 63, 66, 68, 75, 81, 91, 100], "chosen": [6, 28, 37, 56, 66, 75, 81, 111], "chri": [4, 5, 11, 15, 17, 48, 53, 57, 66, 70, 73, 81, 84, 92, 102, 105, 106, 108, 111, 114], "chrishavlin": [43, 58, 74, 87, 91, 103, 109], "christi": [10, 29, 66, 75, 81], "christian": [1, 2, 5, 7, 8, 9, 11, 12, 15, 17, 18, 22, 23, 24, 28, 29, 30, 32, 33, 34, 35, 36, 37, 39, 43, 48, 50, 51, 53, 55, 56, 57, 58, 66, 67, 70, 72, 73, 74, 75, 78, 81, 82, 85, 87, 88, 91, 92, 97, 101, 104, 105, 106, 109, 111, 115, 117], "christoph": [0, 4, 11, 12, 15, 16, 18, 23, 30, 37, 38, 43, 46, 47, 48, 49, 53, 56, 66, 70, 73, 75, 78, 81, 83, 84, 85, 87, 91, 92, 96, 102, 105, 106, 109, 111], "chromogen": [24, 31, 66, 75, 81], "chronic": [22, 66, 75, 81], "chukwuebuka": [3, 22, 23, 32, 37, 66, 75, 81], "chum": [30, 33, 66, 75, 81], "chumontr": [30, 33, 66, 75, 81], "chungyoun": [43, 48, 70, 93, 117], "chunk": [17, 51, 75, 81, 91, 111], "ci": [48, 70, 91], "cimini": [4, 7, 12, 18, 21, 30, 39, 41, 43, 49, 52, 53, 56, 58, 66, 70, 71, 72, 73, 78, 80, 81, 83, 91, 95, 102, 109, 111, 112], "ciminilab": [53, 66, 70, 80, 91], "circul": [29, 56, 66, 74, 75, 81, 87, 91, 95, 96, 100, 102, 107, 111], "circumst": [22, 66, 75], "cisa": [30, 33, 66, 75, 81], "cite": [29, 57, 66, 75, 81, 111, 113], "clair": [30, 33, 53, 66, 73, 75, 81, 88, 101], "clariti": [10, 29, 66, 75, 81], "class": [14, 20, 22, 66, 75, 81], "classic": 63, "classif": [46, 49, 50, 57, 70, 80, 85, 91, 92, 95, 98, 106, 109, 112, 115], "claudia": [1, 15, 39, 66, 75, 81], "claus": [11, 13, 15, 17, 29, 37, 38, 41, 43, 45, 46, 48, 49, 50, 52, 53, 55, 57, 60, 66, 69, 70, 71, 81, 91, 92, 93, 98, 102, 103, 104, 106, 109, 112, 113, 114, 115, 116], "clausthal": [45, 46, 57, 111], "clear": [24, 66, 75, 81, 82, 87], "clearli": [9, 66, 75, 81], "clemen": [4, 18, 26, 56, 66, 70, 75, 81, 87, 91, 111], "clermont": [15, 48, 70, 84, 98, 106], "clesperanto": [81, 109], "clesperanto_workshop_i2k24": [29, 48, 49, 57, 60, 70, 80, 91, 114, 115], "click": [62, 63], "clij2": [20, 66, 75, 81], "clinic": [29, 66, 75, 81], "clone": [24, 31, 66, 75, 81], "close": [3, 8, 23, 28, 66, 75, 81, 82], "closer": [41, 75, 81, 111], "cloud": [1, 15, 28, 29, 39, 116], "cluster": [25, 31, 48, 55, 66, 70, 75, 81, 88, 106, 116], "cmcb": [6, 28, 29, 37, 55, 56, 69, 91, 98, 111], "cmd": 63, "cnr": [30, 33, 66, 75, 81], "co": [30, 33, 43, 109], "coars": [24, 66, 75, 81], "coba": [52, 53, 70, 71, 80], "cobacenterforopenbioimagea1864": [43, 58, 74, 91], "cochin": [30, 33, 66, 75, 81], "code": [3, 6, 10, 12, 18, 20, 22, 23, 25, 28, 31, 32, 37, 55, 56, 85, 99, 104, 111], "coderefineri": [57, 111, 113], "codingclub": [11, 42, 49, 70, 87, 109], "coffe": [3, 37, 66, 75, 81], "cohort": [1, 66, 75, 81], "coia": [41, 70, 83, 91], "colab": [20, 29, 48, 75, 81, 93, 117], "colarusso": [43, 53, 66, 80], "coleman": [30, 33, 66, 75, 81, 88], "collabor": [0, 1, 3, 8, 15, 16, 17, 23, 28, 33, 34, 37, 38, 39, 45, 48, 50, 51, 52, 59, 71, 94, 96, 105, 108, 114], "collect": [2, 3, 12, 14, 16, 20, 25, 29, 31, 35, 44, 45, 46, 47, 48, 49, 50, 57, 58, 59, 60, 61, 62, 64, 71, 73, 74, 78, 79, 80, 81, 83, 88, 89, 92, 94, 95, 96, 97, 98, 102, 103, 104, 106, 107, 108, 109, 111, 113, 115, 116, 117], "colleg": [30, 33, 66, 75, 81], "collinson": [11, 14, 39, 53, 56, 57, 66, 73, 75, 81, 87, 91, 92, 96, 97, 101, 111], "coloc": [30, 33, 66, 75, 81], "cologn": [3, 33, 34], "colombelli": [17, 18, 29, 31, 51, 53, 67, 81, 91, 101, 104], "colon": [29, 66, 75, 81], "coloni": [20, 66, 75], "color": [24, 25, 31, 66, 75, 81], "colorect": [25, 31, 66, 75, 81], "column": [3, 23, 32, 37, 66, 75, 81, 82], "com": [1, 2, 3, 4, 5, 8, 9, 10, 11, 12, 13, 15, 16, 17, 18, 19, 23, 29, 30, 31, 33, 34, 35, 37, 39, 40, 41, 43, 44, 46, 48, 49, 50, 51, 53, 55, 56, 57, 58, 60, 66, 72, 75, 78, 81, 84, 85, 87, 90, 91, 92, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 111, 113, 114, 115, 116], "combin": [3, 17, 23, 28, 29, 33, 34, 40, 53, 56, 66, 68, 90, 91, 99], "come": [3, 23, 28, 37, 66, 75, 81], "comlinetool": [46, 81, 92], "comma": [3, 22, 23, 32, 37, 66, 75, 81], "command": [25, 31, 48, 57, 63, 64, 66, 75, 91, 106, 111, 113], "comment": [29, 53, 66, 73, 75, 78, 81, 82, 96, 111], "commerci": [53, 62, 66, 73, 75, 81, 91, 96, 99, 115], "commit": [48, 62, 70, 91], "common": [1, 3, 14, 17, 20, 29, 33, 34, 45, 53, 56, 67, 77, 91, 96, 104, 111], "commonli": [4, 9, 16, 29, 35, 43, 53, 58, 66, 74, 75, 81, 91, 104, 106], "commun": [0, 9, 15, 19, 28, 29, 38, 39, 45, 46, 50, 51, 57, 82, 98, 99, 106, 108, 109, 114, 115, 116], "compani": [43, 87], "companion": [43, 49, 91, 111], "compar": [9, 10, 29, 31, 55, 56, 66, 75, 82, 89, 96, 99, 109], "compat": [2, 11, 16, 17, 29, 34, 35, 48, 51, 66, 70, 75, 81, 84, 92, 96, 105, 108, 111], "compens": [66, 75, 81], "compet": [45, 55, 66, 75, 81, 82, 111], "complement": [3, 8, 23, 31, 33, 34, 35, 37, 41, 55, 58, 66, 74, 75, 81, 105, 106, 111], "complet": 81, "complex": [0, 1, 3, 6, 8, 9, 11, 14, 23, 28, 30, 33, 34, 37, 38, 51, 53, 55, 66, 75, 81, 87, 91, 92, 96, 97, 101, 102, 105, 111], "compli": [3, 8, 23, 30, 33, 34, 37, 66, 75, 81], "complianc": [55, 66, 75, 81, 111], "compliant": [45, 46, 57, 111], "complic": [20, 66, 75, 81], "compon": [56, 66, 74, 75, 81, 87, 95, 102], "compos": [57, 87, 114], "compound": [66, 75, 81], "comprehens": [11, 43, 49, 59, 66, 75, 81, 87, 91, 92, 97, 102, 111], "comput": [1, 5, 11, 15, 20, 25, 30, 31, 33, 39, 43, 48, 52, 56, 57, 71, 80, 85, 93, 95, 96, 97, 99, 106, 107, 111, 112, 113, 114, 115, 116], "concentr": [22, 66, 75, 81], "concept": [1, 6, 15, 28, 29, 37, 39, 56, 74, 87, 90, 91, 95, 99], "conceptu": [48, 70], "concern": [83, 92, 102], "conclus": [66, 75, 81], "concret": [66, 75, 81], "cond": [4, 12, 18, 30, 53, 73, 78], "conda": [19, 31, 64], "conda_forge_packag": [46, 81, 109], "condit": [24, 31, 56, 66, 74, 75, 81, 87, 95, 102], "conduct": [3, 8, 11, 17, 23, 32, 37, 53, 66, 67, 75, 81, 92, 97, 111], "confer": [3, 6, 8, 17, 29, 34, 47, 48, 49, 53, 55, 57, 70, 80, 92, 97], "configur": [66, 75], "conflict": [29, 53, 81, 91, 111], "confoc": [24, 29, 30, 31, 33], "confront": [45, 66, 75, 81, 111], "confus": [25, 31, 66, 75, 81, 87], "congress": [17, 20, 66, 75, 81], "connect": [1, 3, 23, 28, 48, 53, 66, 70, 75, 81, 85, 91, 97, 106, 109, 115], "conni": [6, 56, 66, 75, 81, 91], "connor": [53, 73, 81, 94, 96, 101], "conrad": [7, 21, 39, 56, 58, 72, 81, 95, 102, 109], "consequ": [66, 75, 81], "conserv": [66, 75], "consid": [3, 4, 9, 29, 33, 34, 37, 53, 62, 66, 75, 81, 88, 91], "consider": [17, 34, 56, 57, 75, 81, 87, 91, 96, 100, 111], "consist": [29, 49, 66, 70, 75, 81, 85, 95, 109, 112], "consorti": [66, 75, 81], "consortia": [17, 45, 51, 66, 75, 81, 91, 111], "consortium": [1, 6, 15, 16, 17, 29, 33, 34, 35, 37, 38, 39, 51, 55, 58, 74, 92, 94, 96, 106], "constanc": [17, 66, 75, 81, 117], "constantin": [2, 4, 7, 9, 15, 17, 21, 36, 48, 49, 53, 55, 56, 66, 70, 73, 75, 81, 85, 87, 90, 91, 95, 105, 109, 111], "constantinpap": [5, 70, 85, 87, 90, 91], "constantli": [66, 75, 81], "construct": [1, 28, 66, 75, 81], "construct_cli": [66, 75, 81], "consult": [30, 33, 48, 57, 66, 70, 75, 81, 82, 85], "contact": [22, 66, 75, 81], "contain": [3, 5, 12, 16, 20, 22, 23, 24, 25, 29, 30, 31, 32, 33, 35, 37, 41, 46, 48, 49, 56, 57, 61, 62, 64, 66, 70, 75, 80, 81, 84, 88, 91, 95, 97, 102, 106, 109, 111, 112, 116], "content": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 23, 24, 25, 26, 29, 30, 31, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 52, 55, 56, 57, 58, 59, 60, 61, 62, 64, 65, 67, 68, 69, 70, 71, 72, 74, 78, 79, 80, 82, 83, 84, 85, 87, 88, 90, 93, 94, 95, 98, 99, 100, 102, 103, 104, 105, 106, 107, 108, 109, 112, 113, 114, 115, 116, 117], "contentasset": [45, 111], "context": [0, 29, 38, 51, 53, 55, 62, 67, 78, 88, 90, 91, 96, 107], "contextu": [0, 38, 51, 75, 81, 96, 111], "continu": [14, 43, 57, 66, 75, 81, 91, 96, 111], "contour": [29, 66, 75, 81], "contract": [66, 75, 81], "contribut": [0, 1, 3, 6, 8, 10, 16, 20, 23, 28, 29, 33, 34, 35, 37, 38, 48, 53, 55, 56, 58, 63, 66, 74, 75, 81, 82, 91, 96, 105, 106, 108, 111], "contributor": [10, 29, 66, 75, 81], "control": [1, 11, 25, 28, 30, 31, 33, 40, 49, 53, 68, 80, 91, 92, 97, 101, 102, 111, 113], "convei": [66, 75, 82], "conveni": [49, 76, 81, 103, 109], "convenin": [31, 66, 75, 81], "convent": [6, 20, 28, 37, 56, 66, 75, 81, 111], "convers": [2, 17, 48, 70, 80, 84, 91, 92, 108], "convert": [24, 31, 66, 75, 81], "convex": [29, 66, 75, 81], "cook": [39, 53, 73, 111], "cooper": [66, 75, 81], "coordin": [3, 23, 28, 66, 75, 81], "cope": [48, 70], "copi": [3, 22, 23, 32, 37, 66, 75, 81], "coppen": [1, 15, 39, 66, 75, 81], "copyright": [29, 41, 56, 66, 75, 81, 96, 100, 107, 111], "corbat": [48, 56, 60, 65, 70, 81, 91, 102], "cord": [41, 66, 75, 81, 111], "cordeli\u00e8r": [18, 29, 31, 53, 67, 81, 91, 104], "cordi": [34, 53, 66, 105], "core": [0, 16, 20, 22, 23, 24, 31, 35, 56, 58, 74, 82, 88, 96, 106], "core_facil": [30, 33, 66, 75, 81], "cornelia": [16, 28, 37, 51, 55, 56, 66, 75, 81, 91, 92, 94, 105, 111], "corner": 63, "cornerston": [53, 73, 81, 94, 96, 113], "correct": [10, 14, 24, 29, 31], "correspond": [3, 22, 23, 25, 31, 32, 37, 66, 75, 81, 88], "cortez": [45, 66, 75, 81, 111], "corvellec": [43, 70], "costantin": [49, 70, 85, 95, 109, 112], "costantino": [53, 81, 107, 111], "could": [22, 29, 53, 61, 66, 73, 75, 81, 88, 91, 96, 99, 115], "count": [20, 55, 66, 75, 81, 116], "counterstain": [24, 31, 66, 75, 81], "countri": [66, 75, 81], "couponcod": [57, 87, 114], "cours": [7, 9, 12, 28, 29, 39, 60, 66, 72, 74, 75, 95, 102, 106, 107, 112, 114, 115], "course_script": [4, 57, 87], "courseintrotoia": [29, 70, 91, 98], "coursematerialimageanalysi": [29, 55, 69, 87, 91, 98], "courtesi": [24, 66, 75, 81], "courtnei": [43, 48, 70, 93, 117], "cover": [6, 28, 37, 41, 43, 48, 50, 56, 57, 59, 66, 70, 72, 75, 81, 83, 85, 93, 107, 111, 113, 115, 117], "covid": 20, "cr": [30, 33, 66, 75, 81], "craig": [43, 46, 53, 66, 80, 85, 95, 109, 116], "crbm": [30, 33, 66, 75, 81], "crchum": [30, 33, 66, 75, 81], "creager": [2, 15, 53, 66, 81, 91, 102], "creat": [1, 3, 8, 11, 14, 15, 20, 22, 23, 25, 28, 30, 31, 32, 33, 34, 35, 37, 46, 48, 53, 58, 62, 63, 70, 74, 76, 82, 83, 84, 87, 92, 96, 97, 101, 102, 105, 106, 109, 113, 116], "creation": [22, 66, 75, 81, 85], "creativ": [20, 66, 75, 77, 81], "cristina": [4, 12, 18, 30, 53, 73, 78], "critera": [14, 66, 75, 81], "critic": [53, 56, 66, 73, 74, 75, 81, 87, 94, 95, 96, 102, 108, 111, 113], "crockett": [1, 15, 39, 66, 75, 81], "crop": [17, 31, 51, 66, 75, 81, 91, 111], "crop_ds441": [31, 66, 75, 81], "crops_biop_v1": [31, 66, 75, 81], "cross": [1, 3, 8, 15, 22, 23, 28, 29, 32, 37, 39, 46, 49, 53, 66, 70, 75, 81, 91, 98, 116], "crtd": [17, 66, 75, 81, 117], "crucial": [0, 1, 14, 28, 29, 38, 51, 56, 66, 74, 75, 81, 87, 91, 95, 96, 102, 111], "cryosect": [30, 33, 66, 75, 81], "csaba": [42, 49, 55, 70, 87, 91, 104], "cseresny\u00e9": [46, 53, 73, 98, 116], "cshl": [12, 49, 81, 109, 112], "cshlpress": [41, 91], "csv": [3, 16, 20, 22, 23, 25, 31, 32, 35, 37, 66, 75, 76, 81, 82], "csv_axioscan": [32, 37, 66, 75, 81], "csv_tem": [3, 22, 23, 32, 37, 66, 75, 81], "csv_tomo": [3, 22, 23, 32, 37, 66, 75, 81], "cullei": [4, 9, 11, 18, 21, 25, 29, 42, 49, 53, 55, 70, 81, 87, 90, 91, 104, 109], "culmin": [0, 16, 23, 37, 38, 48, 75, 81, 91, 96, 105, 111], "curat": [3, 8, 11, 20, 23, 33, 34, 35, 37, 42, 43, 49, 55, 58, 66, 70, 74, 75, 81, 87, 105, 106, 109, 111], "current": [1, 5, 6, 14, 29, 32, 53, 55, 56, 66, 75, 78, 81, 91, 92, 95, 96, 100, 105, 107, 111], "curti": [11, 12, 18, 25, 29, 31, 43, 47, 51, 53, 55, 57, 60, 70, 87, 91, 98, 102, 104], "custom": [29, 30, 33, 66, 75, 81], "cut": [24, 29, 31, 66, 75, 81], "cxli233": [48, 70, 83, 85], "cycl": [0, 38, 51, 96], "cylindr": [22, 66, 75, 81], "cyto": [25, 31, 66, 75, 81], "cyto2": [25, 31, 66, 75, 81], "cyto_from_bf": [25, 31, 66, 75, 81], "cyto_from_dpc": [25, 31, 66, 75, 81], "cyto_from_sqrdpc": [25, 31, 66, 75, 81], "cytomin": [41, 70, 83, 87, 90, 91], "cytoskeleton": [30, 33, 66, 75, 81], "cz": 81, "czi": [31, 55, 91], "c\u00b3rdm": [3, 56, 66, 75, 81, 91, 105, 111], "d": [1, 4, 13, 18, 29, 30, 31, 33, 46, 48, 50, 53, 55, 56, 58, 59, 60, 65, 66, 67, 70, 72, 73, 75, 81, 87, 90, 91, 94, 96, 98, 102, 104, 111, 116], "da": [0, 29, 30, 32, 33, 41, 59, 87, 96, 105, 107], "dab": [24, 31], "dabei": [26, 29, 56, 66, 75, 81, 96, 100, 111], "dabmap": [24, 31, 66, 75, 81], "dachorganis": [59, 87, 105, 111], "dafour": [11, 18, 55, 57, 70, 87, 91, 104], "daf\u00fcr": [29, 56, 66, 75, 81, 111], "dahm": [0, 16, 23, 37, 38, 46, 47, 48, 53, 66, 70, 75, 81, 91, 96, 105, 106, 109, 111], "dai": [3, 22, 31, 33, 34, 37, 53, 73, 91, 96, 99, 115], "daili": [3, 8, 23, 33, 34, 35, 37, 55, 58, 66, 74, 75, 81, 105, 106, 111], "daim": [48, 49, 70, 82, 91], "damag": [20, 66, 75, 81], "damit": [26, 56, 66, 75, 81, 111], "dan": 53, "daniel": [10, 11, 12, 17, 18, 29, 43, 46, 49, 50, 51, 53, 55, 57, 59, 66, 67, 70, 73, 75, 81, 82, 85, 87, 91, 101, 104, 106, 107, 109, 116], "danni": [46, 53, 98, 116], "danta": [48, 57, 70, 81, 115], "dapi": [24, 31, 66, 75, 81], "dapi_mcherry_22lambda": [24, 66, 75, 81], "dapi_mcherry_4t_5lambda": [24, 66, 75, 81], "dapi_mcherry_4z_5lambda": [24, 66, 75, 81], "darauf": 77, "dario": [1, 15, 20, 39, 48, 49, 53, 56, 66, 70, 75, 81, 87, 93, 108], "darm": [66, 75, 81], "darrow": [11, 39, 53, 66, 73, 87, 92, 97, 101, 111], "dar\u00fcber": [0, 32, 66, 75, 81], "dask": [29, 81], "daskcours": [13, 49, 70, 87, 91, 109], "dass": 77, "data": [13, 25, 26, 31, 46, 47, 61, 62, 69, 77, 79, 80, 84, 88, 98, 115, 116], "data_life_cycl": [43, 50, 59, 81, 111], "databas": [11, 29, 39, 45, 46, 53, 57, 73, 78, 87, 88, 92, 97, 101], "datacarpentri": [7, 11, 36, 42, 43, 57, 70, 81, 85, 91, 109, 112, 114], "dataksenia": [3, 23, 37, 66, 75, 81, 117], "datamanag": [40, 59, 87, 111], "datamethod": 87, "datapl": [0, 16, 23, 37, 38, 46, 47, 48, 51, 53, 66, 70, 75, 91, 96, 105, 106, 109], "dataset": [0, 1, 9, 11, 14, 17, 20, 25, 28, 29, 30, 33, 38, 48, 51, 53, 56, 57, 64, 93, 96, 106, 107, 111, 113, 117], "date": [3, 23, 37, 66, 75, 81, 117], "datei": [29, 56, 66, 75, 81, 96, 111], "daten": [26, 29, 56, 66, 75, 77, 81, 96, 100], "datenmanag": [96, 100], "datenmanagementplan": [26, 29, 56, 66, 75, 81, 111], "datenproblem": [26, 56, 66, 75, 81, 111], "datenschutzrechtlich": [26, 56, 66, 75, 81, 96, 111], "daten\u00fcbertragung": 77, "dave": [48, 70, 82, 91], "davi": [48, 57, 70, 81, 115], "david": [1, 2, 4, 5, 7, 9, 11, 12, 15, 17, 18, 20, 29, 30, 36, 39, 42, 43, 48, 49, 51, 52, 53, 55, 56, 57, 66, 67, 70, 71, 73, 75, 78, 80, 81, 84, 87, 90, 91, 92, 93, 97, 101, 104, 105, 108, 109, 111, 112, 114], "dcimg": [66, 75, 81], "de": [0, 3, 4, 7, 11, 12, 15, 16, 17, 18, 21, 22, 23, 25, 26, 29, 30, 31, 32, 33, 34, 37, 38, 39, 41, 42, 43, 45, 46, 47, 48, 49, 50, 51, 53, 55, 56, 57, 58, 59, 60, 61, 66, 67, 70, 72, 73, 75, 77, 78, 80, 81, 82, 85, 87, 88, 90, 91, 92, 95, 96, 97, 98, 100, 101, 102, 104, 105, 106, 107, 108, 109, 111, 113, 115, 116, 117], "deal": [45, 66, 75, 81, 87, 111], "dean": [17, 18, 29, 51, 53, 67, 81, 101], "deborah": [12, 29, 51, 53, 98], "debri": [66, 75, 81], "decad": [17, 51, 66, 75, 81, 91, 111], "decemb": [55, 66, 75, 81, 91], "decid": [66, 75, 81], "decim": [3, 22, 23, 32, 37, 66, 75, 81], "decis": [29, 45, 55, 67, 91, 111], "deck": [3, 8, 23, 29, 33, 34, 35, 37, 55, 58, 66, 74, 75, 81, 105, 106, 111, 117], "decod": [3, 22, 23, 32, 37, 66, 75, 81], "decontextu": [22, 66, 75], "dedic": [17, 30, 32, 33, 34, 51, 66, 75, 81, 91, 96, 105, 111], "deed": [43, 58, 91], "deep": [7, 29, 39, 44, 46, 48, 58, 72, 75, 83, 93, 97, 99, 116, 117], "deep_vision_and_graph": [49, 70, 85, 90, 109], "deepak": [29, 49, 70, 81, 109], "deepbac": [56, 67, 81, 95, 96, 102], "deeper": [29, 66, 75, 81], "deepimagej": [29, 56, 66, 67, 75, 81, 95, 96, 102], "deeplearningbasedsegmentationforbiologist": [49, 70, 90, 109], "deeplife4eu": [20, 48, 49, 56, 70, 87, 93], "default": [25, 31, 41, 66, 75, 81, 91], "defin": [1, 2, 9, 15, 28, 53, 66, 75, 81, 91, 102], "definieren": [29, 56, 66, 75, 81, 96, 100, 111], "definit": [17, 34, 51, 66, 75, 81, 82, 96, 105, 111], "deform": [66, 75, 81], "defragmentation_trainingschool_eosc": [55, 70, 79, 91, 104], "degre": [29, 66, 75, 81], "deinert": [56, 66, 75, 81, 111], "delet": [25, 31, 66, 75, 81], "delimit": [3, 22, 23, 32, 37, 66, 75, 81], "delin": [29, 66, 75, 81], "delisl": [1, 28, 66, 75, 81], "deliv": [29, 43, 57, 66, 72, 75, 81, 93, 114], "deliver": [17, 34, 51, 75, 81, 96, 105, 111], "della": [55, 56, 66, 75, 81, 94, 111], "delmic": [40, 111], "delv": [56, 66, 75, 81, 94, 111], "dem": [0, 26, 32, 56, 66, 75, 77, 81, 111], "demand": [3, 20, 33, 34, 37, 43, 57, 66, 72, 75, 81, 87, 93, 111, 114], "demo": [24, 29, 48, 49, 57, 60, 66, 75, 80, 81, 91, 114, 115], "democratis": [29, 66, 75, 81], "demograph": [53, 75, 81, 108], "demonstr": [17, 29, 40, 53, 66, 68, 73, 75, 81, 90, 91, 96, 99, 111, 115], "den": [26, 41, 56, 66, 75, 81, 107, 111], "denis": [6, 28, 37, 56, 66, 75, 81, 111], "depart": [20, 23, 30, 32], "depend": [3, 37, 43, 66, 72, 75, 81, 93, 114], "dependabot": [15, 17, 37, 38, 48, 70, 80, 113], "depict": [66, 75, 81], "deploi": [56, 67, 81, 95, 96, 102], "deploy": [46, 81, 85, 95, 109, 116], "deppen": [43, 70], "der": [0, 26, 32, 41, 50, 53, 56, 59, 77, 87, 96, 100, 107, 108, 111], "derartig": 77, "deren": [26, 53, 56, 66, 75, 77, 81, 100, 111], "deriv": [30, 33, 66, 75, 81], "deschamp": [12, 48, 70, 91], "describ": [1, 11, 15, 22, 30, 33, 39, 43, 46, 66, 75, 80, 81, 87, 93, 106, 109, 115, 116], "descript": [3, 23, 25, 28, 29, 30, 31, 33, 34, 37, 40, 61, 63, 66, 68, 75, 81, 82, 90, 99, 111], "deshalb": 77, "design": [3, 20, 23, 29, 32, 36, 37, 41, 43, 46, 48, 55, 59, 66, 70, 72, 75, 81, 85, 93, 95, 109, 111, 114, 116, 117], "desir": [66, 75, 81], "desktop": [29, 66, 75, 81, 117], "desouki": [66, 75, 82], "despit": [56, 74, 87, 95, 102], "detail": [3, 11, 25, 29, 31, 33, 37, 38, 52, 53, 57, 62, 66, 75, 81, 87, 91, 96, 100, 111], "detailed_worflow": [25, 31, 66, 75, 81], "detect": [20, 22, 24, 29, 31, 57, 91, 93], "determin": [29, 43, 49, 66, 75, 81, 91, 111], "deutsch": [0, 6, 32, 66, 75, 81, 88], "deutschen": 77, "dev": [43, 57, 81, 114], "develop": [0, 1, 3, 6, 8, 14, 17, 20, 22, 23, 32, 33, 34, 36, 37, 38, 45, 46, 48, 55, 56, 57, 58, 63, 74, 82, 92, 94, 95, 96, 98, 101, 102, 108, 109, 113, 116], "devic": [53, 66, 81, 96, 111], "dewax": [24, 31, 66, 75, 81], "dfc": [24, 31, 66, 75, 81], "dfc350fxr2": [24, 31, 66, 75, 81], "dfg": [0, 6, 16, 23, 26, 28, 37, 38, 48, 56, 66, 75, 81, 88, 91, 96, 105, 111], "di": [55, 66, 75, 77, 81, 116], "diagnost": [24, 31, 66, 75, 81], "dialog": [10, 29, 66, 75, 81], "dialogu": [29, 66, 75, 81], "diamond": [66, 75, 81], "diana": [57, 81, 91, 93], "diataxi": [57, 59, 82], "diaz": [43, 53, 66, 75, 80, 81], "dickscheid": [0, 3, 8, 23, 28, 38, 51, 66, 75, 81, 96, 111], "didact": [41, 66, 75, 81, 111], "didier": [25, 31, 66, 75, 81], "die": [0, 26, 32, 41, 53, 56, 77, 96, 100, 107], "diederich": [29, 53, 66, 78], "diel": [2, 4, 5, 15, 17, 53, 73, 81, 105, 111], "dienst": [29, 66, 75, 81], "diensteanbiet": 77, "dies": [0, 26, 29, 32, 41, 56, 66, 75, 77, 81, 96, 100, 107, 111], "diesbez\u00fcglich": 77, "diesem": [26, 29, 41, 56, 66, 75, 81, 96, 107, 111], "diesen": 77, "dieser": [0, 26, 29, 32, 56, 66, 75, 77, 81, 111], "dietrich": [11, 43, 81, 107], "diez": [4, 12, 18, 30, 53, 73, 78], "dif": [66, 75, 82], "differ": [1, 3, 9, 14, 24, 25, 28, 29, 31, 33, 37, 38, 43, 45, 46, 48, 50, 57, 59, 70, 72, 87, 89, 92, 93, 97, 98, 99, 106, 109, 114, 115], "difficult": [3, 8, 23, 30, 33, 34, 37, 66, 75, 81], "difficulti": [1, 14, 66, 75, 81], "diffract": [66, 75], "diffus": [66, 75], "digit": [1, 15, 29, 39, 42, 43, 46, 53, 56, 94, 96, 98, 100, 107, 111, 116], "digital": [66, 75, 81], "digitalen": [26, 56, 111], "digitalisierung": [41, 81, 107], "digitalit\u00e4t": [66, 75, 81], "dilut": [24, 31, 66, 75, 81], "dimens": [24, 31, 48, 70], "dimension": [3, 8, 17, 23, 28, 29, 30, 33, 34, 37, 51, 66, 75, 81, 91, 96, 105, 111], "dinesh": [48, 70, 91], "dini": [41, 66, 75, 81, 111], "dir": [25, 31, 66, 75, 81], "direct": [1, 3, 8, 14, 22, 23, 28, 33, 34, 35, 37, 55, 58, 66, 74, 75, 81, 105, 106, 111], "directli": [22, 32, 37, 48, 50, 66, 70, 75, 81, 91, 106], "directori": [2, 17, 48, 70, 84, 92, 108], "direkt": [41, 81, 107], "dirk": [0, 38, 51, 66, 75, 81, 96, 111], "disc": [24, 66, 75, 81], "disciplin": [1, 28, 29, 59, 66, 75, 81, 111], "disciplinari": [1, 3, 8, 15, 23, 28, 39, 66, 75, 81], "discours": 87, "discov": [43, 57, 72, 81, 107, 113], "discover": [66, 75, 81], "discoveri": [1, 24, 28, 31, 41, 51, 53, 91, 108, 111], "discuss": [0, 3, 16, 23, 28, 29, 37, 38, 48, 53, 55, 66, 73, 75, 81, 87, 91, 94, 96, 99, 105, 107, 111, 113, 115], "diseas": [30, 33, 66, 75, 81], "disk": [30, 33, 66, 75, 81], "diskutieren": [29, 56, 66, 75, 81, 96, 100, 111], "distinct": [57, 59, 82], "distribut": [22, 30, 33, 66, 75, 81, 89], "disziplinen": [29, 66, 75, 81], "dive": [29, 48, 49, 56, 66, 70, 75, 81, 91, 95, 97, 102, 109, 117], "divers": [0, 9, 11, 38, 39, 51, 53, 73, 75, 87, 92, 96, 97, 101, 108, 111], "divis": [30, 33, 66, 75, 81], "djiciihhjh": [43, 73, 87, 92, 101], "dkfz": [3, 23, 28, 33, 34, 37, 43, 59, 60, 66, 75, 81, 87, 91, 106, 111, 117], "dl": [18, 21, 67, 81, 85, 96, 102, 104, 109, 112], "dl4mia": [87, 91], "dl4mic": [29, 66, 75, 81], "dl4miceverywher": [56, 67, 96, 102], "dlimag": [13, 49, 70, 87, 90, 91, 109], "dm5500": [24, 30, 31, 33, 66, 75, 81], "dm5500b": [30, 33, 66, 75, 81], "dmem": [31, 66, 75, 81], "dmi6000b": [30, 33, 66, 75, 81], "dmi8": [30, 33, 66, 75, 81], "dmp": [14, 29, 40, 43, 45, 55, 56, 57, 66, 68, 75, 81, 90, 91, 96, 99, 111], "dmp4nfdi": [45, 81, 111], "dna": [30, 33, 66, 75, 81], "do": [7, 11, 13, 16, 18, 20, 25, 29, 31, 35, 36, 41, 43, 49, 53, 57, 62, 70, 78, 85, 90, 91, 98, 104, 105, 106, 109, 111, 112, 114], "dobson": [53, 66, 70, 80, 91], "doc": [1, 13, 41, 46, 48, 50, 55, 56, 60, 70, 79, 81, 87, 90, 91, 98, 102, 104, 106], "document": [0, 3, 11, 16, 23, 24, 25, 28, 29, 31, 32, 37, 38, 41, 43, 47, 48, 50, 51, 53, 62, 70, 73, 79, 80, 88, 90, 92, 96, 98, 102, 103, 105, 111, 115], "doe": [3, 57, 66, 75, 87, 111], "doesn": [31, 66, 75, 81], "dofunct": [66, 75, 81], "dohl": [3, 8, 23, 30, 33, 34, 35, 37, 53, 55, 58, 66, 74, 75, 81, 88, 105, 106, 111], "doi": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 17, 20, 22, 23, 24, 25, 26, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 41, 42, 43, 45, 46, 47, 48, 49, 50, 51, 53, 55, 56, 57, 58, 61, 67, 70, 72, 73, 74, 75, 78, 80, 81, 82, 85, 87, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 105, 106, 107, 109, 111, 113, 115, 116, 117], "dolzycka": [41, 66, 75, 81, 111], "domain": [1, 28, 43, 51], "domart": [17, 18, 29, 51, 53, 67, 81, 101], "domin": [17, 18, 29, 49, 51, 53, 67, 70, 81, 84, 91, 101, 109], "dominguez": [21, 49, 70, 87, 90, 109], "dominik": [2, 4, 5, 9, 11, 15, 17, 21, 36, 39, 48, 53, 55, 56, 57, 58, 66, 70, 72, 73, 75, 81, 85, 90, 91, 95, 102, 105, 109, 111, 112, 114, 115, 117], "dominika": [41, 66, 75, 81, 111], "don": [48, 62, 63, 66, 70, 75, 81, 85], "donati": [29, 66, 75, 81], "done": [1, 15, 25, 31, 32, 39, 48, 49, 66, 70, 75, 81, 82, 103, 109], "donkei": [24, 31, 66, 75, 81], "donor": [66, 75, 81], "doolin": [29, 66, 75, 81], "dor": [29, 66, 75, 81], "dornier": [50, 91, 106], "dorun": [66, 75, 81], "dosag": [22, 66, 75, 81], "dose": [1, 28, 29, 66, 75, 81], "dostat": [66, 75, 81], "doub": [4, 9, 29, 53, 66, 75, 81, 91], "doubl": [30, 33, 66, 75, 81], "doug": [2, 15, 53, 66, 81, 91, 102], "dougla": [4, 12, 18, 30, 53, 73, 78], "down": [17, 51, 75, 81, 91, 111], "download": [30, 33, 55, 66, 75, 76, 81, 89, 116], "downsampl": [31, 66, 75, 81], "downscal": [31, 66, 75, 81], "dozuki": 43, "dpc": [31, 66, 75, 81], "dr": [3, 23, 28, 33, 34, 37, 66, 75, 77, 81], "draft": [11, 39, 53, 66, 73, 87, 92, 97, 101, 111], "dragon": [30, 33, 66, 75, 81], "draw": [66, 75, 81], "dreht": [26, 56, 66, 75, 81, 111], "drei": [0, 32, 66, 75, 81], "dresden": [3, 17, 22, 23, 28, 32, 34, 37, 49, 56, 70, 77, 88, 103, 109, 117], "dresden16th": [17, 66, 75, 81, 117], "dritt": 77, "dritter": 77, "drive": [4, 50, 55, 58, 66, 72, 75, 81, 87, 91], "driven": [1, 15, 29, 30, 33, 39, 66, 75, 96, 111], "droesbek": [11, 43, 80, 93, 109, 115, 116], "dropbox": [18, 55, 87, 91, 104], "drosophila": [24, 66, 75, 81], "drosophilaeggchamb": [24, 66, 75, 81], "drosopoulo": [30, 33, 66, 75, 81], "dtc": [49, 70, 84, 91, 109], "dtl": [60, 91, 111], "du": [30, 33, 66, 75, 81], "dublin": [66, 75, 82], "duc": [25, 31, 66, 75, 81], "due": [3, 23, 28, 37, 66, 75, 81], "dufour": [17, 18, 29, 51, 53, 67, 81, 101], "dumonti": [53, 66, 73, 81, 96, 111], "duodenum": [24, 31, 66, 75, 81], "durch": 77, "dure": [3, 17, 20, 22, 23, 28, 29, 32, 34, 37, 45, 51, 56, 66, 75, 81, 96, 105, 111], "dust": [66, 75, 81], "dvoretskii": [66, 75, 81], "dwaith": [49, 70, 84, 91, 109], "dwyer": [4, 12, 18, 30, 53, 73, 78], "dynam": [22, 66, 75], "d\u00e9v\u00e9dec": [4, 12, 18, 30, 53, 73, 78, 81, 92, 96, 97, 101, 111], "d\u00f6rfel": [6, 28, 37, 56, 66, 75, 81, 111], "d\u00fcrfen": [26, 56, 66, 75, 81, 96, 111], "d\u00fcsseldorf": [3, 17, 23, 33, 34, 37, 56, 66, 75, 81, 91, 105, 106, 111, 117], "e": [0, 1, 3, 11, 14, 16, 17, 23, 28, 29, 30, 32, 33, 34, 35, 37, 39, 43, 44, 53, 56, 58, 62, 66, 67, 70, 73, 74, 75, 76, 77, 80, 81, 87, 88, 89, 91, 92, 93, 96, 97, 101, 104, 105, 106, 107, 111, 113, 117], "e1": 53, "each": [3, 24, 29, 30, 33, 34, 37, 43, 48, 57, 66, 70, 72, 75, 81, 87, 97, 107, 111, 113], "eacut": [30, 33, 66, 75, 81], "earli": [3, 9, 33, 34, 37, 45, 66, 75, 81, 88, 111], "earlier": [29, 66, 75, 81], "earliest": [66, 75, 81], "eas": [17, 31, 51, 66, 75, 81, 91, 111], "easi": [29, 45, 46, 53, 66, 73, 75, 81, 84, 90, 91, 92, 98, 108, 111, 116], "easier": [3, 37, 52, 53, 66, 71, 75, 81, 91, 111, 114], "easili": [56, 63, 66, 67, 75, 81, 82, 87, 95, 96, 102], "easur": [30, 33, 66, 75, 81], "ebi": [4, 7, 21, 39, 44, 50, 53, 55, 56, 57, 58, 66, 75, 81, 83, 87, 91, 92, 95, 96, 97, 101, 102, 107, 109, 111, 113], "ebner": [4, 12, 18, 30, 53, 73, 78], "echo": 63, "ecker": [4, 12, 18, 30, 53, 73, 78], "eclips": [30, 33, 66, 75, 81], "ecol": [50, 78, 91], "econom": [66, 75, 81], "ecosystem": [55, 116], "edg": [24, 29, 66, 75, 81], "edit": [1, 13, 16, 35, 43, 46, 48, 55, 56, 60, 62, 65, 70, 75, 87, 90, 91, 102, 104, 106], "editor": 41, "edu": [24, 30, 31, 33, 40, 66, 75, 81, 87, 111, 113], "eduardo": [4, 12, 18, 30, 53, 73, 78], "educ": [3, 8, 20, 23, 33, 34, 35, 37, 41, 45, 55, 58, 62, 74, 82, 105, 106, 111], "eduroam": [3, 23, 28, 66, 75, 81], "edward": [43, 53, 66, 80], "edwardson": [30, 33, 66, 75, 81], "edx": [50, 78, 91], "effect": [1, 9, 15, 28, 29, 33, 38, 39, 51, 66, 75, 81, 91], "effektiv": [29, 56, 66, 75, 81, 96, 111], "effici": [1, 9, 28, 29, 46, 51, 57, 59, 87, 91], "effizi": [26, 56, 66, 75, 81, 111], "effort": [1, 3, 8, 14, 23, 28, 29, 30, 33, 34, 37, 43, 66, 75, 81, 109], "egfp": [30, 31, 33, 66, 75, 81], "egg": [24, 66, 75, 81], "egling": [4, 5, 7, 9, 36, 48, 70, 81], "ehrlich": [3, 23, 37, 66, 75, 81, 117], "eigen": [29, 56, 66, 75, 77, 81, 96, 100, 111], "eigenen": [26, 56, 66, 75, 81, 111], "eija": [4, 9, 29, 53, 81, 91], "eik": [0, 16, 23, 37, 38, 46, 47, 48, 53, 66, 70, 75, 81, 91, 96, 105, 106, 109, 111], "ein": [26, 29, 59, 77, 87, 96, 100, 105], "einblick": [29, 96], "einen": [26, 29, 53, 56, 77, 111], "einer": [26, 56, 75, 77], "einfluss": 77, "einf\u00fchrung": [26, 56, 66, 75, 81, 111], "einhaltung": 77, "einstein": [30, 33, 66, 75, 81], "einsteinm": [30, 33, 66, 75, 81], "einstieg": [26, 56, 66, 75, 81, 111], "einzelnen": [26, 56, 66, 75, 81, 111], "either": [66, 75], "el": [1, 14, 53, 66, 75, 81, 111], "elabftw": [6, 28, 37, 56, 66, 75, 81], "elastix": [4, 70, 85], "elastix_tutori": [43, 49], "electron": [6, 11, 20, 24, 28, 39, 43, 53, 56, 73, 78, 87, 92, 97, 101], "element": [26, 41, 56, 66, 75, 81, 82, 87, 111], "eleni": [12, 43, 49, 66, 70, 73, 85, 91], "elfi": [56, 66, 75, 81, 111], "elia": [12, 43, 49, 66, 70, 73, 85, 91], "eliceiri": [4, 12, 18, 29, 30, 43, 52, 53, 66, 67, 70, 71, 73, 78, 80, 81, 91, 104], "elimin": [29, 66, 75, 81], "elisa": [3, 11, 17, 23, 28, 30, 33, 34, 35, 37, 51, 53, 55, 58, 66, 67, 74, 75, 81, 82, 88, 92, 97, 105, 106, 111], "elisabeth": [29, 40, 53, 68, 81, 91, 111, 113], "elixir": [43, 47, 50, 59, 81, 87, 93, 111], "ellen": [53, 66, 70, 80, 91], "ellenberg": [11, 39, 53, 66, 73, 87, 92, 97, 101, 111], "elmar": [0, 32, 66, 75, 81], "elmer": [24, 31, 66, 75, 81], "elmi2024": [17, 34, 51, 66, 75, 81, 91, 96, 105, 111], "elmi23_cfd": [56, 82, 91, 97, 107, 111], "elnaz": [4, 5, 7, 29, 36, 48, 53, 56, 66, 70, 75, 81, 91, 117], "elodi": [20, 48, 49, 56, 70, 87, 93], "elt": [43, 48, 70, 91, 117], "elya": [17, 52, 53, 71, 81, 109], "elz": [26, 56, 66, 75, 81, 111], "em": 24, "email": 77, "embed": [29, 56, 66, 75, 81, 94, 111], "embj": [1, 15, 39, 66, 75, 81], "embl": [4, 7, 39, 47, 53, 66, 75, 81, 85, 91, 92, 97, 111, 115], "embo": [1, 5, 15, 39, 56, 66, 75, 81, 95], "embo_2022": [21, 49, 70, 87, 90, 91, 109], "embopress": [1, 15, 39, 66, 75, 81], "embrac": [1, 28, 66, 75, 81], "emil": [2, 43, 57, 87, 106, 114], "emilia": [66, 75, 81], "emiss": 24, "emma": [1, 15, 39, 66, 75, 81], "emmanuel": [29, 53, 66, 78], "empfehlung": [53, 66, 81, 111], "empfehlungen": [66, 75, 81], "emphas": [0, 16, 23, 37, 38, 48, 56, 66, 75, 81, 91, 94, 96, 105, 111], "emphasi": [43, 73, 87, 92, 101], "emploi": [16, 29, 35, 43, 66, 75, 81, 91, 106], "empow": [9, 66, 75, 81], "en": [11, 17, 30, 33, 41, 43, 45, 46, 50, 57, 59, 66, 75, 79, 81, 87, 91, 92, 96, 98, 100, 106, 107, 109, 111, 116, 117], "enabl": [1, 8, 9, 14, 22, 23, 28, 29, 43, 46, 55, 56, 57, 59, 72, 83, 90, 93, 94, 96, 114, 116], "encod": [66, 75, 81], "encompass": [66, 75, 81], "encount": [3, 22, 23, 32, 37, 66, 75, 81], "encourag": [3, 14, 23, 28, 37, 43, 57, 66, 75, 81, 91, 96, 111], "end": [41, 60, 62, 66, 75, 81, 91, 106, 111], "endeavor": [3, 17, 33, 34, 51, 66, 75, 81, 96, 105, 111], "endl": [0, 32, 66, 75, 81], "endotheli": [56, 74, 87, 95, 102], "engag": [53, 75, 81, 108], "engel": [29, 66, 75, 81], "engelska": [45, 111], "engin": [1, 11, 28, 29, 43, 46, 53, 55, 56, 73, 80, 81, 87, 90, 93, 95, 98, 99, 102, 115], "englisch": [66, 75, 81], "english": [41, 66, 75, 81, 111], "enhanc": [9, 29, 37, 45, 53, 57, 59, 82, 108, 111], "enicolai": [28, 29, 48, 56, 70, 85, 91, 99, 102, 109], "enorm": [1, 14, 66, 75, 81], "enrich": [1, 3, 23, 28, 37, 66, 75, 81], "ensur": [1, 11, 14, 28, 29, 30, 33, 43, 53, 56, 57, 59, 63, 66, 67, 75, 81, 87, 91, 92, 95, 96, 97, 101, 102, 111], "enter": [1, 14, 62, 63, 88], "entfernen": 77, "entfernung": 77, "enthalten": [0, 26, 32, 56, 66, 75, 81, 111], "enthaltenen": [0, 32, 66, 75, 81], "enth\u00e4lt": [26, 56, 66, 75, 77, 81, 111], "entir": [66, 75, 81], "entitl": [30, 33, 66, 75, 81], "entri": [25, 29, 31, 61, 62, 66, 75, 81], "entsprechenden": 77, "entstand": [41, 81, 107], "entstehen": [29, 56, 66, 75, 81, 111], "entsteht": [59, 87, 105, 111], "entstehung": [56, 66, 75, 81, 100, 111], "entwerfen": [29, 56, 66, 75, 81, 111], "entwicklung": [66, 75, 81], "environ": [3, 8, 22, 23, 25, 30, 31, 32, 33, 34, 35, 37, 55, 63, 97, 105, 111, 115], "environment": [3, 23, 28, 56, 66, 75, 81, 91, 105, 111], "envisag": [66, 75, 81], "eosin": [25, 31, 66, 75, 81], "eoss": [43, 53, 55, 66, 75, 81, 91, 108], "epfl": [24, 31, 50, 66, 75, 81, 91, 106], "epi": [30, 33], "epifluoresc": [30, 33, 66, 75, 81], "eprint": [41, 66, 81, 111], "epsilon": [30, 33, 66, 75, 81], "eqskudatarq": [41, 91], "equip": [30, 33, 66, 75, 81], "equipment_710": [30, 33, 66, 75, 81], "equiti": [53, 75, 81, 108], "era": [1, 14, 66, 75, 81], "erarbeitet": [41, 81, 107], "eraslan": [48, 70, 91], "erfahren": [56, 66, 75, 81, 100, 111], "erfahrungen": [56, 66, 75, 81, 111], "erfahrungsbericht": [53, 66, 81, 111], "erfolgt": 77, "erf\u00fcllt": [26, 56, 66, 75, 81, 96, 111], "erg\u00e4nzend": [26, 56, 66, 75, 81, 111], "erhalten": [26, 56, 66, 75, 81, 96, 111], "erhebung": [26, 56, 66, 75, 81, 111], "erhoben": 77, "eric": [14, 15, 17, 37, 38, 43, 48, 53, 56, 57, 70, 80, 91, 96, 97, 106, 111, 113, 115], "erick": [11, 15, 48, 70, 84], "erik": [53, 73, 81, 94, 96, 101], "eriksson": [1, 14, 53, 66, 75, 81, 111], "erin": [2, 4, 5, 7, 11, 15, 17, 36, 53, 57, 73, 81, 91, 102, 105, 107, 109, 111, 112, 114], "erkennbar": 77, "erkennen": [56, 66, 75, 81, 100, 111], "erlaubt": [29, 56, 66, 75, 81, 96, 111], "erl\u00e4utert": [56, 66, 75, 81, 111], "erl\u00e4uterungen": [26, 56, 66, 75, 81, 111], "erm\u00f6glicht": [41, 81, 107], "error": [62, 66, 75, 81], "erschienen": [66, 75, 81], "erst": 77, "erstel": [26, 56, 66, 75, 81, 111], "erstellt": [26, 56, 66, 75, 77, 81, 111], "erstellten": 77, "ersten": [26, 56, 66, 75, 81, 111], "erua": [66, 75, 81], "erwarten": [26, 56, 66, 75, 81, 111], "escobar": [66, 75, 81], "esfri": [66, 75, 81], "esgomezm": [10, 55, 70, 87, 90, 91], "especi": [1, 15, 17, 22, 28, 29, 39, 49, 66, 75, 81, 82, 103, 109], "essenti": [3, 28, 33, 41, 51, 66, 91, 96, 111], "establish": [1, 3, 8, 14, 15, 17, 23, 28, 29, 33, 34, 37, 39, 51, 55, 66, 75, 92, 96, 97, 105, 111], "estibali": [7, 21, 39, 56, 58, 72, 81, 95, 102, 109], "estibaliz": [11, 17, 18, 29, 42, 49, 51, 53, 55, 56, 66, 67, 70, 75, 81, 87, 90, 91, 95, 96, 101, 102, 104], "est\u00e8v": [48, 70, 81, 91], "et": [2, 3, 4, 7, 8, 10, 14, 15, 17, 18, 25, 29, 30, 31, 33, 36, 37, 39, 41, 42, 43, 46, 47, 48, 49, 51, 52, 53, 55, 56, 57, 60, 66, 67, 70, 73, 75, 78, 80, 81, 84, 85, 87, 91, 92, 93, 96, 97, 98, 101, 102, 104, 106, 107, 108, 109, 111, 112, 113, 114, 115, 116], "etc": [48, 61, 64, 66, 75, 81, 91, 106], "ethic": [1, 15, 39, 66, 75, 81], "ethisch": [26, 56, 66, 75, 81, 111], "etwa": [56, 66, 75, 77, 81, 100, 111], "eu": [1, 11, 14, 17, 26, 30, 33, 43, 46, 53, 56, 57, 66, 75, 80, 81, 87, 90, 91, 93, 96, 109, 111, 115, 116], "eugen": [12, 29, 51, 53, 98], "euniwel": [66, 75, 81], "euro": [53, 73, 92, 97, 101], "eurobioimag": [1, 14, 17, 46, 53, 56, 57, 66, 75, 81, 87, 90, 91, 96, 111, 116], "eurobioimagingcommun": [43, 58, 74], "europ": [43, 47, 50, 59, 66, 75, 81, 87, 93, 111], "european": [1, 15, 17, 34, 39, 45, 51, 53, 66, 75, 81, 87, 91, 92, 96, 97, 105, 111], "evalu": [20, 53, 56, 66, 67, 73, 75, 81, 94, 95, 96, 101, 102, 111], "evan": [43, 53, 55, 66, 75, 80, 81, 116], "evanbiederstedt": [48, 70, 91], "even": [1, 17, 20, 28, 29, 59, 66, 75, 81, 87, 111], "event": [0, 3, 4, 16, 17, 23, 37, 38, 46, 49, 50, 53, 55, 58, 62, 64, 66, 72, 75, 81, 96, 105], "ever": [1, 14, 66, 75, 81], "everi": [3, 22, 23, 31, 32, 37, 66, 75, 81], "everydai": [3, 8, 23, 28, 30, 33, 34, 37, 66, 75, 81], "everyth": 62, "evid": [48, 70], "ewan": [39, 53, 73, 111], "ewbank": [1, 15, 39, 66, 75, 81], "exact": [3, 22, 23, 32, 37, 66, 75, 81], "exactli": [11, 43, 81, 107], "exampl": [2, 3, 9, 16, 22, 23, 28, 31, 32, 35, 37, 53, 55, 56, 62, 73, 78, 82, 85, 87, 88, 90, 92, 94, 96, 97, 101, 105, 109, 111, 114], "example_facility_manag": [45, 79, 106], "exc2068": 88, "excel": [3, 22, 23, 32, 37, 88], "except": [24, 62, 66, 75, 81], "exchang": [17, 34, 56, 91, 96, 111], "excit": [24, 66, 75, 81], "execut": [25, 31, 46, 66, 75, 81, 85, 95, 109, 116], "exemplar": [53, 66, 73, 81, 96, 111], "exemplari": [6, 28, 37, 56, 66, 75, 81, 111], "exercis": [14, 29, 56, 57, 66, 69, 75, 80, 81, 85, 95, 96, 98, 111, 112], "exist": [3, 6, 8, 16, 20, 23, 28, 33, 34, 35, 37, 43, 50, 53, 55, 56, 57, 58, 66, 73, 74, 75, 81, 82, 83, 88, 91, 92, 94, 96, 97, 101, 105, 106, 111, 115], "expand": [66, 75], "expect": [3, 33, 34, 37, 66, 75, 81], "experi": [1, 2, 3, 8, 11, 15, 17, 22, 23, 28, 32, 34, 35, 37, 45, 46, 55, 57, 58, 73, 74, 87, 88, 92, 96, 97, 101, 102, 105, 106, 111], "experienc": [29, 66, 75, 81], "experiment": [11, 22, 53, 66, 75, 87, 88, 92, 97, 101, 102], "expert": [3, 6, 28, 33, 34, 37, 43, 49, 56, 66, 72, 75, 81, 91, 93, 111, 114], "expertis": [3, 8, 23, 28, 33, 34, 37, 45, 66, 75, 81, 111], "explain": [29, 43, 53, 55, 57, 58, 66, 72, 74, 75, 78, 81, 83, 92, 97, 98, 99, 104, 109, 111, 113], "explan": [57, 59, 62, 66, 75, 82], "exploit": [3, 33, 75, 81], "explor": [0, 3, 16, 23, 28, 37, 38, 43, 48, 49, 66, 75, 81, 88, 91, 96, 103, 105, 109, 111, 114], "exploratori": 87, "export": [3, 23, 28, 29, 31, 37, 63, 66, 75, 76, 81], "expos": [13, 49, 70, 85, 109], "express": [25, 30, 31, 33, 66, 75, 81], "extend": [0, 30, 33, 38, 51, 75, 96, 111], "extens": [3, 28, 37, 46, 50, 51, 57, 61, 62, 75, 98], "exter": [1, 15, 39, 66, 75, 81], "extern": [6, 22, 28, 37, 56, 66, 75, 81, 111], "externen": 77, "extra": [24, 66, 75, 81], "extract": [9, 17, 46, 50, 57, 66, 75, 81, 90, 92, 98, 106, 115], "extravas": [56, 74, 87, 95, 102], "ezomero": [16, 35, 43, 75, 81, 91, 106], "e\u00f6tv\u00f6": [43, 48, 70, 91, 117], "f": [0, 11, 12, 16, 23, 25, 29, 31, 37, 38, 43, 45, 46, 47, 48, 49, 53, 57, 60, 66, 70, 73, 75, 81, 85, 87, 91, 96, 102, 105, 106, 109, 111], "f1000research": [3, 8, 11, 17, 18, 20, 29, 31, 33, 37, 51, 53, 55, 56, 66, 75, 81, 87, 90, 91, 92, 93, 95, 96, 97, 101, 102, 104, 105, 107, 109, 111, 113], "f1000reserach": [20, 66, 75, 81], "f100research": [3, 37, 66, 75, 81], "f505": [25, 31, 66, 75, 81], "fabian": [17, 52, 53, 71, 81, 109], "fabiann": [17, 18, 29, 51, 53, 67, 81, 101], "fabig": [3, 22, 23, 32, 37, 66, 75, 81], "fabric": [18, 29, 31, 53, 67, 81, 91, 104], "fabrizio": [43, 91, 103, 109], "fabriziomusacchio": [43, 91, 103, 109], "face": [43, 48, 49, 66, 70, 75, 81, 91, 111], "facet": [43, 72, 93, 114], "facil": [0, 16, 20, 22, 23, 24, 31, 35, 88], "facilit": [1, 8, 9, 15, 23, 28, 29, 35, 37, 39, 55, 58, 66, 74, 75, 81, 82, 91, 96, 99, 105, 106, 115], "factor": [66, 75, 81], "faculti": [3, 20, 23, 30, 32, 33, 37, 66, 75, 81], "fahren": [1, 15, 39, 66, 75, 81], "fair": [0, 16, 20, 26, 29, 38, 48, 50, 52, 59, 60, 67, 70, 71, 72, 76, 82, 83, 88, 95, 99, 100, 102, 106, 107, 115], "fairdata": [32, 66, 75, 81], "fairif": [3, 33, 56, 66, 75, 81, 91, 96, 105, 111], "fairwar": [53, 73, 81, 94, 96, 101], "faisal": [12, 74, 87, 90], "faklari": [30, 33, 66, 75, 81, 88], "falconi": [2, 15, 53, 66, 81, 91, 102], "fall": 77, "fallen": [66, 75, 81], "fallesen": [49, 70, 91], "familiar": [46, 48, 57, 70, 80, 91, 102, 103], "fanci": 66, "fand": [56, 66, 75, 81, 111], "faq": [57, 87, 96, 100, 111], "far": [32, 66, 75, 81], "fashion": [1, 28, 66, 75, 81], "fast": [55, 66, 75, 81, 116], "faster": [1, 14, 20, 66, 75, 81], "fastq": [50, 57, 83, 115], "favorit": [62, 88], "faze": [4, 5, 7, 29, 36, 48, 53, 56, 66, 70, 75, 81, 91, 117], "fbi": [56, 82, 91, 97, 107, 111], "fbinf": [29, 53, 81, 91], "fchi": [20, 66, 75, 81], "fdm": [6, 45, 46, 53, 57, 66, 75, 81, 111], "fdmentor": [41, 45, 66, 75, 81, 111], "fdo": [16, 35, 66, 75, 81], "featur": [3, 22, 30, 33, 37, 43, 46, 50, 57, 66, 73, 75, 81, 87, 92, 98, 101, 106, 115], "feb": [4, 9, 17, 28, 29, 34, 51, 53, 56, 66, 75, 81, 91, 96, 105, 111], "februari": [3, 6, 33, 56, 66, 75, 81, 91], "feder": [45, 66, 75, 81, 111], "federal": [50, 78, 91], "fedor": [49, 70, 85, 90, 109], "feed": [66, 75], "feedback": [3, 8, 17, 23, 30, 33, 34, 36, 37, 45, 51, 55, 66, 75, 81, 82, 88, 96, 105, 111], "feel": [62, 63], "felder": [66, 75, 82], "feldhau": [66, 75, 81], "feldman": [29, 66, 75, 81], "felix": [4, 5, 7, 9, 11, 36, 48, 57, 66, 70, 75, 81, 85, 111, 115, 117], "feriel": [14, 66, 75, 81], "fermi": [40, 111], "fernandez": [4, 12, 18, 30, 33, 53, 66, 73, 75, 78, 81], "ferrando": [3, 11, 17, 23, 28, 30, 33, 34, 35, 37, 51, 53, 55, 58, 66, 67, 74, 75, 81, 82, 88, 92, 97, 105, 106, 111], "fertigstellung": [0, 32, 66, 75, 81], "fertil": [66, 75, 81], "festigen": [26, 56, 66, 75, 81, 111], "fetal": [31, 66, 75, 81], "fetch": [1, 28, 66, 75, 81], "few": [66, 75], "fib": [24, 66, 75, 81], "fibroblast": [30, 33], "fibsem": [24, 66, 75, 81], "fictiv": [29, 40, 68, 81, 90, 99, 111], "fidel": [48, 70, 91], "field": [1, 3, 8, 14, 23, 28, 29, 31, 43, 53, 56, 57, 62, 66, 73, 75, 81, 82, 90, 91, 96, 99, 100, 107, 108, 111, 115], "figg": [46, 53, 73, 98, 116], "figshar": [12, 29, 51, 53, 98], "figur": [3, 23, 28, 37, 43, 49, 66, 75, 81, 91, 111], "fiji": [3, 15, 16, 20, 21, 23, 28, 31, 35, 37, 46, 48, 55, 69, 80, 84, 87, 90, 104, 116], "file": [0, 3, 8, 11, 16, 22, 23, 25, 28, 29, 31, 32, 34, 35, 37, 38, 43, 46, 48, 49, 50, 51, 55, 57, 58, 61, 62, 63, 70, 72, 74, 76, 79, 83, 84, 87, 88, 92, 96, 97, 106, 108, 109, 113], "fileadmin": [45, 46, 57, 111], "filenam": [66, 75, 81], "filippo": [24, 31, 66, 75, 81], "fill": [3, 22, 23, 29, 32, 37, 56, 66, 75, 81, 91, 96, 100, 107, 111], "fillabl": [14, 43, 57, 66, 75, 81, 91, 96, 111], "filter": [22, 66, 75, 81], "finaci": [0, 32, 66, 75, 81], "final": [1, 3, 15, 22, 23, 25, 28, 29, 31, 37, 39, 66, 75, 81], "financi": [0, 1, 15, 32, 39, 66, 75, 81], "find": [1, 11, 15, 28, 39, 49, 62, 66, 75, 88, 93, 96, 101], "findabl": [1, 3, 8, 14, 23, 28, 29, 30, 33, 34, 37, 43, 53, 56, 66, 73, 75, 81, 88, 94, 96, 100, 111, 113], "finden": [26, 56, 66, 75, 81, 111], "findet": [26, 56, 66, 75, 81, 111], "finish": [46, 48, 57, 70, 80, 91, 102, 103], "finn": [11, 43, 80, 93, 109, 115, 116], "fiona": [3, 6, 16, 22, 23, 28, 32, 37, 51, 66, 75, 81, 91, 94, 105, 111], "first": [0, 6, 10, 16, 17, 20, 22, 23, 28, 29, 34, 37, 38, 45, 48, 53, 56, 73, 81, 96, 105, 111], "fish": [66, 75, 81], "fisher": [24, 31, 46, 53, 57, 66, 75, 81, 87, 98, 114, 116], "fit": [8, 23, 30], "five": [20, 45], "fix": [10, 22, 24, 29, 31, 66, 75, 81], "fiz": [66, 75, 81], "flagship": [53, 73, 81, 92, 96, 97, 101, 111], "flexibl": [46, 53, 66, 81, 85, 95, 96, 109, 111, 116], "fli": [66, 75, 81], "flim": [49, 103, 109], "float": [66, 75, 81], "floden": [55, 66, 75, 81, 116], "florian": [4, 11, 17, 18, 29, 30, 43, 48, 49, 51, 53, 55, 57, 66, 67, 70, 73, 74, 78, 81, 85, 87, 90, 91, 98, 101, 104, 109, 112], "flow": [14, 25, 31, 43, 56, 74, 95, 96, 102, 111], "flowchart": [9, 66, 75, 81], "fluor": [24, 31, 66, 75, 81], "fluoresc": [6, 22, 24, 30, 31, 33, 38, 50, 56, 91, 106], "fluorobrit": [31, 66, 75, 81], "fluoromisonidazol": [29, 66, 75, 81], "fluotar": [24, 31, 66, 75, 81], "fly": [17, 30, 33, 51, 66, 75, 81, 91, 111], "fmiso": [29, 66, 75, 81], "fnin": [11, 53, 81, 92, 97, 111], "focalplan": [19, 29, 40, 81, 87, 90, 91, 95, 99, 100, 103, 109, 111, 113], "focu": [3, 8, 11, 20, 23, 28, 29, 30, 33, 34, 37, 43, 53, 59, 66, 73, 75, 81, 87, 92, 93, 101, 107, 111, 113], "focus": [1, 7, 9, 21, 24, 28, 29, 39, 48, 56, 57, 58, 66, 70, 72, 75, 81, 87, 90, 91, 95, 96, 100, 102, 107, 109, 111], "focuss": [29, 48, 49, 57, 60, 66, 70, 75, 80, 81, 91, 114, 115], "foix": [7, 21, 39, 56, 58, 72, 81, 95, 102, 109], "foku": [0, 32], "folder": [20, 25, 29, 31, 63, 66, 75, 81], "folgen": [26, 56, 66, 75, 81, 111], "foliensatz": [26, 56, 66, 75, 81, 96, 111], "follow": [3, 23, 28, 29, 30, 31, 33, 43, 49, 63, 66, 75, 76, 81, 88, 91, 111], "folter": [15, 17, 37, 38, 48, 70, 80, 113], "foong": [10, 29, 66, 75, 81], "forc": [66, 75, 81], "form": [6, 36, 45, 55, 57, 58, 66, 72, 75, 81, 82, 83, 92, 97, 105, 111], "formal": [53, 66, 73, 81, 94, 96, 101, 111], "format": [3, 8, 9, 11, 16, 20, 22, 23, 24, 28, 30, 31, 32, 33, 34, 35, 37, 43, 46, 48, 56, 58, 62, 70, 74, 84, 87, 92, 95, 102, 106, 108], "formatread": [66, 75, 81], "formul": [45, 66, 75, 81, 111], "forschen": 77, "forschend": [26, 56, 66, 75, 81, 96, 111], "forschungsdaten": [6, 28, 29, 37, 41], "forschungsdateninfrastruktur": [6, 55, 92, 105, 111], "forschungsdatenlebenszyklu": [66, 75, 81], "forschungsdatenmanag": [50, 53, 59, 87, 105, 108], "forschungsf\u00f6rder": [26, 56, 66, 75, 81, 111], "forschungsgemeinschaft": [6, 66, 75, 81, 88], "forschungsgro\u00dfger\u00e4ten": [0, 32, 66, 75, 81], "forschungspraxi": [26, 56, 66, 75, 81, 111], "forschungsprojekt": [26, 50, 56, 59, 66, 75, 81, 87, 108, 111], "forschungsvorhaben": [56, 66, 75, 81, 100, 111], "fortbildungen": [41, 66, 75, 81, 111], "fortmann": [3, 8, 23, 33, 34, 35, 37, 55, 56, 58, 66, 74, 75, 81, 88, 91, 96, 105, 106, 111], "forum": [11, 16, 24, 25, 31, 43, 47, 60, 66, 75, 81, 83, 87, 91, 92, 101, 102, 106, 115], "forward": [32, 66, 75, 81], "foster": [1, 3, 8, 9, 14, 23, 28, 29, 30, 33, 34, 37, 38, 53, 66, 75, 81, 108], "foti": [11, 53, 81, 93, 96, 101, 114], "found": [17, 20, 29, 53, 66, 75, 81, 91, 111], "foundat": [1, 5, 15, 39, 56, 88, 91, 95], "foundinggid": [17, 66, 75, 81], "four": [31, 45, 57, 59, 66, 75, 81, 82, 111], "fov": [66, 75, 81], "foyl": [66, 75, 117], "fr": [30, 33, 56, 57, 59, 66, 75, 81, 82, 91, 97, 107, 111], "frage": [56, 66, 75, 81, 100, 111], "fragen": [26, 29, 41, 56, 66, 75, 81, 96, 107, 111], "frame": [1, 28], "framework": [0, 1, 2, 9, 15, 16, 28, 29, 32, 33, 34, 35, 38, 39, 46, 48, 56, 57, 58, 70, 74, 75, 82, 95, 98, 101, 102, 105, 107, 111, 116], "framwork": [0, 32, 66, 75, 81], "franc": [3, 7, 21, 33, 34, 39, 53, 58, 72, 73, 78, 81, 95, 101, 102, 109], "franco": [11, 46, 109, 116], "frank": [66, 75, 82], "frankfurt": [3, 23, 37, 45, 66, 75, 81, 111, 117], "fratelli": [1, 15, 39, 66, 75, 81], "fraunhof": [66, 75, 81], "fred": [66, 75, 81, 117], "frederik": [1, 15, 39, 66, 75, 81], "free": [6, 28, 31, 37, 50, 56, 62, 63, 74, 75, 79, 87, 95, 98, 102, 106], "freeli": [45, 66, 75, 81, 111], "freiburg": [28, 30, 33, 56, 66, 75, 81, 111], "freie": [45, 66, 75, 81, 111], "freien": [41, 81, 107], "freiwillig": 77, "fremd": 77, "fremden": 77, "frerick": [26, 56, 66, 75, 81, 111], "fri": [22, 66, 75, 81], "friend": [48, 85], "friendli": [20, 29, 66, 75, 81], "from": [1, 3, 14, 15, 16, 17, 20, 22, 23, 24, 30, 32, 33, 34, 35, 38, 39, 43, 46, 48, 49, 50, 51, 58, 59, 70, 72, 73, 76, 78, 80, 83, 84, 90, 92, 96, 97, 98, 101, 102, 103, 105, 106, 107, 109, 115], "fromlink": [41, 91], "frontiersin": [11, 29, 53, 81, 91, 92, 97, 111], "fr\u00fchzeitig": [56, 66, 75, 81, 100, 111], "fuch": [0, 3, 6, 16, 22, 23, 28, 32, 37, 38, 46, 47, 48, 51, 53, 66, 70, 75, 81, 91, 94, 96, 105, 106, 109, 111], "fuer": [29, 56, 66, 75, 81, 96, 111], "fuhrman": [66, 75, 82], "fulfil": [29, 53, 81, 91, 111], "full": [0, 1, 4, 9, 10, 11, 14, 18, 22, 24, 28, 29, 30, 31, 33, 38, 41, 51, 53, 66, 75, 78, 81, 91, 92, 96, 97, 104, 111], "full_model_path": [25, 31, 66, 75, 81], "fulli": [16, 24, 31, 32, 35, 43, 66, 75, 81, 91, 106], "function": [1, 6, 11, 15, 16, 28, 29, 30, 33, 35, 37, 39, 48, 53, 56, 70, 78, 80, 88, 91, 98, 111, 112], "fund": [0, 3, 6, 16, 17, 20, 23, 28, 32, 33, 34, 37, 38, 45, 48, 51, 53, 56, 57, 59, 66, 75, 81, 87, 88, 91, 96, 105, 108, 111], "fundament": [43, 66, 75, 81, 111, 114], "fundamentals_image_process": [13, 49, 70, 87, 91], "funder": [3, 8, 14, 23, 28, 43, 57, 66, 75, 81, 91, 96, 111], "funk": [5, 49, 55, 70, 87, 90, 91], "furth": [29, 66, 75, 81], "further": [3, 14, 22, 23, 29, 32, 37, 41, 43, 57, 66, 75, 81, 91, 96, 111], "furthermor": [1, 3, 22, 23, 28, 29, 32, 37, 41, 56, 66, 67, 75, 81, 95, 96, 102, 111, 117], "fuse": [24, 66, 75, 81], "fuster": [46, 87, 90, 91, 116], "futur": [0, 16, 23, 32, 37, 38, 48, 75, 81, 96, 105, 111], "f\u00f6rstner": [29, 66, 75, 81], "f\u00fchrt": [66, 75, 81], "f\u00fcr": [0, 26, 32], "g": [2, 3, 4, 12, 15, 16, 18, 23, 29, 30, 31, 32, 33, 35, 37, 43, 53, 62, 66, 67, 73, 75, 76, 78, 81, 88, 89, 91, 102, 104, 106], "ga456d5535c_2_53": [13, 55, 65, 87, 91, 104], "gabeau79": [43, 48, 70, 93, 117], "gabriel": [4, 12, 18, 29, 30, 31, 53, 66, 67, 73, 75, 78, 81, 91, 96, 104, 111], "gaell": [48, 70, 80, 112], "gai": [11, 15, 48, 70, 84, 106], "gaignard": [17, 18, 29, 51, 53, 67, 81, 101], "galaxyproject": [1, 28, 43, 50, 56, 57, 66, 70, 75, 81, 85, 91, 93, 101, 106, 115], "galluss": [21, 49, 70, 87, 90, 109], "ganglia": 10, "gaol": [53, 73, 91, 96, 99, 115], "gap": [29, 56, 66, 75, 81, 91, 96, 100, 107, 111], "garcia": [11, 53, 66, 75, 81, 93, 96, 101, 114], "garc\u00eda": [29, 66, 75, 81], "gase": [66, 75, 81], "gat": [29, 66, 75, 81], "gatewai": [15, 48, 66, 70, 75, 81, 84, 106], "gather": [3, 8, 16, 23, 33, 34, 35, 37, 55, 58, 66, 74, 75, 81, 105, 106, 111], "gatsbi": [43, 57, 81, 114], "gaudreault": [4, 12, 18, 30, 53, 73, 78], "gault": [2, 4, 5, 11, 15, 17, 48, 53, 70, 73, 81, 84, 92, 105, 108, 111], "gayoso": [48, 70, 91], "gb": [2, 15, 53, 66, 81, 91, 102], "ge3a24e733b_0_54": [55, 65, 87, 90, 91, 104], "geben": [29, 53, 66, 75, 81, 111], "geert": [52, 53, 71, 81, 91, 111, 114], "geest": [52, 53, 71, 81, 91, 111, 114], "geforc": [25, 31, 66, 75, 81], "gegebenenfal": [29, 56, 66, 75, 81, 111], "gehrig": [66, 75, 81], "geht": [0, 26, 32, 56, 66, 75, 81, 111], "geh\u00f6ren": [29, 56, 66, 75, 81, 100, 111], "gei": [26, 56, 66, 75, 81, 111], "geiger": [66, 75, 82], "gekennzeichnet": 77, "gelernt": [26, 56, 66, 75, 81, 111], "gelman": [4, 12, 18, 30, 53, 73, 78], "gemacht": [26, 29, 56, 66, 75, 81, 96, 111], "gemeinsam": [26, 29, 56, 66, 75, 81, 96], "gener": [1, 3, 6, 16, 20, 22, 23, 24, 25, 28, 31, 35, 37, 38, 39, 43, 51, 56, 58, 63, 74, 84, 85, 92, 99, 106, 108, 113], "genom": [6, 28, 37, 50, 55, 56, 57, 66, 75, 81, 83, 111, 115, 116], "genomebiologi": [2, 15, 53, 66, 81, 91, 102], "gentzel": [6, 28, 37, 56, 66, 75, 81, 111], "geoffrei": [1, 15, 39, 66, 75, 81], "georg": [17, 66, 75, 81, 117], "gerald": [53, 66, 81, 111], "gerard": [39, 53, 66, 87, 92, 97, 111], "gerbi": [6, 56, 57, 91, 92, 101, 111], "geregelt": [0, 32, 66, 75, 81], "gerhard": [53, 73, 81, 94, 96, 113], "gerlach": [3, 23, 32, 37, 66, 75, 81], "german": [3, 6, 17, 23, 28, 29, 33, 34, 35, 37, 41, 43, 45, 51, 53, 55, 56, 66, 70, 75, 81, 87, 88, 91, 92, 96, 105, 111, 117], "germanbioimag": [0, 32, 66, 75, 81], "germani": [1, 3, 8, 11, 17, 23, 28, 33, 34, 35, 37, 53, 55, 56, 58, 66, 67, 74, 75, 81, 88, 91, 92, 97, 105, 106, 111], "gerst": [46, 53, 73, 98, 116], "gerstung": [17, 52, 53, 71, 81, 109], "gert": [4, 12, 18, 30, 53, 73, 78], "gesellschaft": [0, 32, 59, 66, 75, 81, 87, 105, 111], "gesetzen": 77, "gespeichert": 77, "gestalten": [26, 56, 111], "gestaltung": [53, 66, 81, 111], "gestattet": 77, "get": [3, 22, 29, 30, 31, 33, 41, 45, 46, 48, 57, 59, 70, 76, 80, 87, 90, 91, 102, 103, 111], "getrieben": [29, 56, 66, 75, 81, 96, 111], "getting_started_with_mambaforge_and_python": [19, 40, 64, 81, 109], "gew\u00e4hr": 77, "gfp": [24, 30, 33, 66, 75, 81], "gg": [0, 32, 66, 75, 81], "ggf": [29, 56, 66, 75, 81, 111], "ghadraoui": [1, 14, 53, 66, 75, 81, 111], "ghga": [66, 75, 81], "ghosh": [11, 53, 81, 92, 97, 111], "giacomo": [11, 53, 81, 92, 97, 111], "gian": [24, 31, 66, 75, 81], "giant": [24, 66, 75, 81], "gibco": [31, 66, 75, 81], "gide": [17, 66, 75, 81], "gilet": [49, 70, 87, 90, 91], "gilloteaux": [17, 18, 29, 31, 51, 53, 67, 81, 91, 101, 104], "giovanni": [17, 48, 52, 53, 70, 71, 81, 91, 109], "girgin": [25, 31, 66, 75, 81], "git": [4, 6, 7, 11, 28, 37, 52, 53, 55, 71, 80, 85, 91, 98, 104, 114, 115], "github": [2, 4, 5, 7, 9, 10, 11, 12, 15, 17, 19, 20, 28, 29, 36, 40, 41, 43, 46, 47, 49, 50, 53, 55, 56, 59, 60, 61, 62, 63, 65, 66, 68, 76, 79, 80, 82, 83, 84, 85, 88, 90, 92, 93, 95, 97, 98, 99, 102, 103, 104, 106, 108, 109, 112, 113, 114, 115, 116, 117], "github_api_kei": 63, "githubusercont": [10, 48, 55, 57, 70, 81, 87, 90, 91, 115], "give": [1, 4, 6, 9, 11, 28, 29, 32, 36, 45, 52, 53, 55, 66, 73, 75, 81, 83, 91, 92, 96, 99, 102, 105, 111, 115], "given": [5, 17, 25, 31, 34, 55, 56, 66, 75, 81, 87, 91, 95, 96, 105, 111, 117], "gkr469": [66, 75, 81], "glass": [22, 66, 75, 81], "gla\u00df": [46, 53, 98, 116], "gleichzeitig": [29, 56, 66, 75, 81, 96, 111], "glencoesoftwar": [2, 11, 17, 43, 48, 57, 70, 84, 87, 92, 106, 108, 114], "glennan": [10, 29, 66, 75, 81], "gletort": [48, 70, 80, 112], "glob": [66, 75, 81], "global": [30, 33, 59, 66, 75, 81, 111], "globalbioimag": [17, 43, 47, 58, 66, 74, 75, 81, 91], "glutamax": [31, 66, 75, 81], "glycogen": [30, 33, 66, 75, 81], "glyn": [11, 30, 33, 53, 66, 75, 81, 88], "gmb": [0, 3, 32, 33, 43, 57, 66, 75, 81, 87, 92, 101, 111], "gmhydnm1rsm": [58, 74, 91, 97, 106], "gnu": [48, 49, 56, 70, 91], "go": [20, 43, 59, 63, 66, 75, 81, 90, 94, 96, 111], "goal": [1, 3, 6, 15, 24, 28, 33, 34, 37, 39, 43, 46, 48, 49, 55, 57, 66, 70, 75, 80, 81, 85, 90, 91, 92, 102, 103, 105, 106, 109, 111], "gobl": [1, 15, 39, 66, 75, 81], "goedhart": [41, 52, 71, 75, 87, 113], "goettingen": [17, 66, 75, 81, 117], "gogoberidz": [7, 21, 39, 56, 58, 72, 81, 95, 102, 109], "golani": [17, 18, 29, 31, 51, 53, 67, 81, 91, 101, 104], "gold": [48, 70, 91], "goldberg": [2, 15, 53, 66, 81, 91, 102], "gomez": [7, 21, 39, 56, 58, 72, 81, 95, 102, 109], "gonzal": [43, 53, 66, 80], "gonzalez": [4, 5, 7, 9, 11, 36, 48, 57, 66, 70, 75, 81, 85, 111, 115, 117], "googl": [1, 4, 13, 20, 29, 48, 50, 55, 56, 58, 60, 72, 75, 81, 87, 90, 102, 104], "gormann": [1, 15, 39, 66, 75, 81], "got": 62, "gottlieb": [66, 75, 81], "gottschal": [0, 38, 51, 75, 81, 96, 111], "gould": [29, 66, 75, 81], "gov": [3, 11, 22, 23, 32, 37, 39, 53, 66, 73, 75, 81, 87, 92, 94, 96, 97, 101, 102, 111], "govern": [55, 66, 75, 81, 111, 116], "gpl": [2, 11, 15, 17, 42, 43, 48, 49, 56, 57, 60, 66, 70, 75, 91, 92, 98, 103, 106, 108, 109, 114], "gplv3": [46, 50, 57, 92, 98, 106, 115], "gpu": [20, 25, 31, 75], "grade": [31, 66, 75, 81], "graduat": [1, 66, 75, 81], "graham": [29, 66, 75, 81], "grai": [11, 43, 48, 70, 81, 93, 107, 117], "grain": [24, 66, 75, 81], "grammar": [10, 29, 66, 75, 81], "grant": [6, 20, 28, 37, 56, 66, 75, 81, 111], "grantner": [1, 15, 39, 66, 75, 81], "graph": [16, 35, 82], "graphic": [11, 29, 53, 66, 78, 87, 91, 98], "graybeal": [53, 73, 81, 94, 96, 101], "graylab": [43, 48, 70, 93, 117], "grayscal": [66, 75, 81], "great": [66, 75, 81], "gred": [15, 48, 70, 84, 98, 106], "gregor": [48, 70, 91], "gregori": [41, 70, 83, 91], "greifbar": [41, 81, 107], "greiner": [22, 66, 75, 81], "grenzen": 77, "gribbon": [1, 15, 39, 66, 75, 81], "grid": [31, 66, 75, 81], "griffin": [11, 53, 66, 87, 93, 107, 111, 113], "grimm": [30, 33, 66, 75, 81], "grinic": [4, 5, 7, 9, 36, 48, 70, 81], "gritti": [4, 7, 9, 36, 66, 75, 81, 117], "gro": [4, 5, 7, 9, 36, 48, 70, 81], "grob": [0, 32, 66, 75, 81], "groovi": [25, 31, 66, 75, 81], "grote": [3, 8, 23, 33, 34, 35, 37, 55, 56, 58, 66, 74, 75, 81, 88, 91, 96, 105, 106, 111], "ground": [3, 33, 66, 75, 81], "groundbreak": [45, 66, 75, 81, 111], "group": [8, 17, 28, 30, 33, 34, 35, 41, 55, 58, 74, 89, 105, 106, 111], "grow": [55, 66, 75, 81, 116], "growth": [53, 75, 81, 108], "gro\u00dfger\u00e4tebeschaffung": [0, 32, 66, 75, 81], "grp": [4, 7, 11, 57, 85, 111, 115], "grundlagen": [26, 56, 66, 75, 81, 111], "grundlegend": [66, 75, 81], "grundlegenden": [26, 56, 66, 75, 81, 111], "grunds\u00e4tzen": [26, 56, 66, 75, 81, 111], "grunds\u00e4tzlich": [56, 66, 75, 81, 111], "grune": [1, 15, 39, 66, 75, 81], "grunwald": [4, 11, 12, 18, 30, 39, 53, 66, 73, 78, 87, 92, 97, 101, 111], "gruppenarbeit": [29, 56, 66, 75, 81, 111], "gr\u00f6\u00dfere": [66, 75, 81], "gr\u00f6\u00dfter": 77, "gr\u00fcning": [1, 28, 55, 57, 65, 81, 87, 91, 101, 106], "gt": [3, 22, 23, 32, 37, 66, 75, 81], "gu": [4, 12, 18, 30, 53, 73, 78], "guerrero": [66, 75, 81], "gui": [84, 92, 108], "guid": [11, 23, 35, 36, 45, 46, 49, 50, 55, 58, 59, 62, 70, 74, 82, 87, 98, 105, 107, 114], "guidanc": [43, 49, 55, 75, 91], "guidelin": [3, 8, 23, 30, 33, 34, 37, 39, 43, 45, 59, 66, 72, 73, 75, 83, 87, 92, 94, 96, 97, 101, 111], "guiet": [11, 18, 24, 25, 29, 43, 47, 53, 60, 66, 67, 75, 81, 87, 91, 102, 104], "guijacquemet": [43, 49, 70, 71, 85], "guilbert": [30, 33, 66, 75, 81, 88], "guillaum": [7, 11, 12, 15, 21, 39, 43, 48, 49, 55, 58, 65, 66, 70, 71, 72, 73, 80, 81, 84, 85, 90, 91, 104, 106, 109], "guillermo": [53, 73, 101], "guiwitz": [13, 49, 70, 80, 85, 87, 90, 91, 104, 109], "gunar": [3, 22, 23, 32, 37, 66, 75, 81], "guo": [53, 66, 81, 96, 111], "gurwitz": [1, 15, 39, 66, 75, 81], "gustafsson": [11, 43, 80, 93, 109, 115, 116], "gustav": [3, 23, 29, 32, 37, 66, 75, 81], "gutanalysistoolbox": [10, 29, 66, 75, 81], "guten": [26, 56, 66, 75, 81, 111], "guyad": [4, 12, 18, 30, 53, 73, 78], "g\u00f3mez": [11, 17, 18, 29, 42, 49, 51, 53, 55, 56, 66, 67, 70, 75, 81, 87, 90, 91, 95, 96, 101, 102, 104], "g\u00f6k\u00e7en": [48, 70, 91], "g\u00f6ttingen": [6, 17, 66, 75, 81, 117], "h": [0, 16, 18, 23, 29, 30, 31, 33, 37, 38, 46, 47, 48, 53, 66, 67, 70, 75, 81, 88, 91, 96, 104, 105, 106, 109, 111], "h22": [3, 23, 37, 66, 75, 81, 117], "h2b": [31, 66, 75, 81], "h5": [31, 66, 75, 81], "ha": [1, 3, 6, 8, 9, 14, 15, 17, 20, 22, 23, 29, 32, 33, 34, 35, 37, 39, 43, 45, 51, 53, 55, 57, 58, 63, 66, 74, 75, 81, 88, 90, 91, 96, 99, 105, 106, 108, 109, 111], "haas": [4, 6, 9, 10, 11, 12, 17, 18, 20, 28, 31, 37, 40, 41, 42, 48, 49, 51, 53, 55, 56, 57, 60, 64, 66, 67, 68, 69, 70, 75, 77, 78, 80, 81, 85, 87, 88, 90, 91, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 109, 111, 113, 114, 115, 117], "haben": [29, 56, 66, 75, 77, 81, 96, 111], "habing": [66, 75, 81], "hackathon": [28, 56], "had": [53, 66, 75, 81, 108], "haeflig": [52, 53, 71, 81, 91, 111, 114], "haesleinhuepf": [29, 41, 48, 49, 57, 66, 70, 75, 80, 81, 91, 109], "hagen": [3, 33, 34, 37, 66, 75, 81], "hahn": [53, 66, 81, 111], "hal": [41, 51, 70, 81, 91, 109], "halavatyi": [4, 5, 7, 9, 18, 36, 48, 66, 70, 75, 81, 87, 91, 117], "halchenko": [17, 48, 70, 79, 92], "half": [2, 17, 48, 70, 84, 92, 108], "halfwai": [49, 70, 80, 91, 109], "halidi": [4, 12, 18, 30, 53, 73, 78], "hall": [3, 23, 37, 46, 53, 66, 75, 81, 98, 116, 117], "halo": [30, 33, 66, 75, 81], "hamburg": [41, 107], "hamdah": [41, 70, 83, 91], "hammer": [4, 11, 12, 18, 30, 53, 73, 78, 87, 92, 97, 101, 102], "hamnett": [10, 29, 66, 75, 81], "hamper": [1, 14, 66, 75, 81], "hamster": [30, 33, 66, 75, 81], "han": [1, 10, 15, 22, 29, 39, 41, 66, 70, 75, 81, 87, 90, 91], "hancock": [1, 15, 39, 66, 75, 81], "hand": [3, 14, 17, 22, 29, 32, 33, 34, 37, 48, 49, 56, 57, 60, 66, 70, 75, 80, 81, 91, 96, 105, 106, 111, 114, 115], "handbook": [11, 43, 107], "handhabbar": [26, 56, 66, 75, 81, 111], "handl": [1, 6, 8, 14, 16, 23, 24, 28, 30, 32, 33, 34, 35, 51, 53, 56, 91, 96], "hann": [1, 3, 8, 11, 17, 23, 28, 30, 33, 34, 37, 51, 53, 66, 67, 75, 81, 91, 92, 97, 105, 111], "hanna": [3, 22, 23, 32, 37, 66, 75, 81], "hannib": [30, 33, 66, 75, 81], "harald": [1, 15, 17, 39, 52, 53, 66, 71, 75, 81, 109], "hardi": [53, 73, 81, 94, 96, 101], "hardwar": [29, 30, 33, 53, 66, 75, 78, 81], "harmon": [1, 3, 8, 14, 23, 28, 66, 75, 81], "harmoni": [31, 66, 75, 81], "harmonis": [1, 15, 39, 66, 75, 81], "harms": [1, 15, 39, 66, 75, 81], "haro": [29, 66, 75, 81], "harri": [24, 31, 66, 75, 81], "hartlei": [4, 12, 17, 18, 30, 39, 53, 66, 73, 78, 81, 87, 91, 92, 97, 102, 107, 111], "hartmann": [11, 42, 49, 70, 85, 87, 91, 109], "harvard": [30, 33, 40, 66, 75, 81, 111], "hat": [29, 41, 56, 66, 75, 81, 96, 107, 111], "hau": [3, 23, 37, 66, 75, 81, 117], "haugwitz": [66, 75, 82], "hauser": [66, 75, 81], "have": [3, 8, 9, 11, 14, 22, 23, 28, 29, 30, 33, 34, 36, 37, 39, 43, 45, 49, 53, 55, 56, 57, 61, 62, 63, 72, 73, 87, 88, 91, 92, 94, 96, 97, 99, 101, 107, 108, 111, 113, 115], "hc": [24, 29, 53, 73, 92, 96, 97, 101, 111], "hca": [30, 33, 66, 75, 81], "head": [3, 16, 30, 33, 34, 35, 37, 48, 57, 66, 70, 75, 81, 90], "header": [66, 75, 82], "health": [17, 34, 51, 75, 81, 96, 105, 111], "healthcar": [66, 75, 81], "hearth": [30, 33, 66, 75, 81], "heat": [24, 31, 66, 75, 81], "heavili": [13, 49, 66, 70, 75, 85, 109], "heber": [66, 75, 81], "hedrich": [0, 32, 66, 75, 81], "heidari": [17, 52, 53, 71, 81, 109], "heidelberg": [23, 28, 48, 51, 53, 70, 82, 85, 91, 106, 109, 117], "heidi": [41, 75, 81, 111], "heidok": [3, 8, 55, 66, 92, 97, 105, 111], "heilemann": [12, 43, 49, 66, 70, 73, 85, 91], "heiligenstein": [17, 18, 29, 51, 53, 67, 81, 101], "hein": [3, 23, 30, 33, 34, 37, 66, 75, 81, 117], "heinrich": [3, 23, 30, 33, 34, 37, 66, 75, 81, 117], "hela": [24, 25, 30, 33], "helacel": [24, 66, 75, 81], "helbig": [41, 45, 53, 66, 75, 81, 111], "held": [3, 4, 6, 12, 18, 23, 28, 30, 48, 53, 56, 66, 70, 73, 75, 78, 81, 85, 91, 106, 109], "helen": [1, 15, 39, 66, 75, 81], "helena": [4, 12, 18, 30, 43, 53, 66, 73, 75, 78, 80, 81], "helfen": [26, 56, 66, 75, 81, 111], "helfrich": [4, 5, 7, 9, 36, 48, 70, 81], "hell": [66, 75, 81], "hellen": [66, 75, 81], "helmholtzai": [48, 57, 70, 85], "help": [3, 8, 11, 16, 23, 24, 28, 35, 37, 43, 53, 57, 59, 63, 72, 83, 92, 93, 96, 97, 101, 107, 111, 113, 114], "helpdesk": [6, 16, 28, 51, 91, 94, 105, 111], "hematoxylin": [24, 25, 31, 66, 75, 81], "hen": [41, 107], "henni": [4, 7, 9, 36, 66, 75, 81, 117], "henriett": [1, 15, 39, 66, 75, 81], "henriqu": [12, 41, 43, 49, 66, 70, 73, 85, 91], "henriqueslab": [11, 12, 43, 49, 66, 70, 73, 81, 85, 91], "hensel": [34, 53, 66, 87, 105, 111], "herausforderungen": [26, 29, 56, 66, 75, 81, 96, 111], "here": [1, 3, 8, 11, 14, 15, 22, 23, 28, 29, 32, 37, 39, 46, 48, 53, 56, 57, 58, 61, 62, 63, 66, 70, 72, 73, 75, 81, 83, 87, 91, 92, 95, 96, 97, 99, 101, 102, 109, 111, 115], "herearii": [49, 70, 87, 90, 91], "hereariim": [49, 70, 87, 90, 91], "herich": [1, 15, 39, 66, 75, 81], "hern\u00e1ndez": [12, 29, 43, 49, 66, 70, 73, 75, 81, 85, 91], "herrenbr\u00fcck": [66, 75, 81], "herrmann": [20, 48, 49, 56, 70, 87, 93], "herstellen": [56, 66, 75, 81, 100, 111], "hertweck": [53, 75, 81, 108], "herv\u00e9": [17, 18, 29, 51, 53, 67, 81, 101], "hess": [56, 66, 75, 81, 111], "heterogen": [3, 33, 34, 37, 75], "hettn": [17, 34, 56, 75, 81, 91, 96, 111], "heuckeroth": [29, 66, 75, 81], "hhu": [30, 33, 66, 75, 81], "hi": [66, 75, 81], "hidalgo": [11, 43, 49, 68, 70, 81, 87, 91, 95], "hiemenz": [53, 81, 96, 111], "hier": [0, 32, 56, 66, 75, 81, 100, 111], "hierf\u00fcr": [26, 56, 66, 75, 81, 111], "hiermit": 77, "hiervon": 77, "hierzu": [26, 56, 66, 75, 81, 111], "high": [3, 22, 23, 29, 31, 37, 46, 48, 56, 74, 90, 95, 102, 109, 115, 116], "higher": [29, 32, 45, 66, 75, 81, 111], "highest": [66, 75, 81], "highli": [22, 66, 75], "highlight": [1, 3, 6, 14, 28, 33, 37, 56, 66, 75, 90, 111], "hilf": [26, 29, 56, 66, 75, 81, 96, 100, 111], "hilft": [26, 56, 66, 75, 81, 111], "hilo": [30, 33, 66, 75, 81], "hilsenstein": [57, 70, 87], "hin": 77, "hinau": [0, 32, 66, 75, 81], "hinder": [29, 66, 75, 81], "hindranc": [66, 75, 81], "hinter": [26, 56, 66, 75, 81, 111], "hintergrund": [66, 75, 81], "hinwei": 77, "hinweis": [26, 56, 66, 75, 81, 111], "hinweisen": 77, "hip": [3, 8, 23, 28, 66, 75, 81], "histologi": [24, 31], "histopathologi": [48, 49, 70, 91], "histor": [53, 73, 91, 96, 99, 115], "histori": [50, 57, 83, 115], "hiv": [30, 33, 66, 75, 81], "hki": [46, 53, 73, 98, 116], "hm": [30, 33, 40, 66, 75, 81, 111], "hmc": [3, 8, 23, 28, 66, 75, 81], "hochgeladen": [29, 56, 66, 75, 81, 96, 111], "hodg": [4, 5, 7, 9, 11, 43, 48, 55, 57, 66, 70, 75, 81, 91, 109, 112, 114, 117], "hoeb": [53, 66, 81, 91, 97, 106, 115], "hoevel": [0, 16, 23, 37, 38, 46, 47, 48, 53, 66, 70, 75, 81, 91, 96, 105, 106, 109, 111], "hoffmann": [26, 56, 66, 75, 81, 111], "hohlbein": [29, 53, 66, 78], "hoksza": [20, 48, 49, 56, 70, 87, 93], "hoku": [66, 75, 117], "hold": [66, 75, 81], "holden": [12, 29, 43, 49, 53, 66, 70, 73, 75, 78, 81, 85, 91], "holgerhennig": [57, 70, 87, 91], "holub": [1, 15, 39, 66, 75, 81], "hope": [43, 49, 91, 111], "hopp": [66, 75, 81], "horizon2020": [66, 75, 81], "hospit": [3, 20, 23, 29, 37, 66, 75, 81, 117], "hospitali": [30, 33, 66, 75, 81], "hossein": [11, 18, 29, 42, 49, 55, 70, 87, 91, 104], "hosseini": [53, 73, 81, 92, 96, 97, 101, 111], "host": [3, 29, 30, 33, 37, 49, 66, 70, 75, 81, 89, 109], "hot": [29, 66, 75, 81], "hoth": [0, 32, 66, 75, 81], "hour": [24, 31, 66, 75, 81], "hover": [14, 43, 57, 66, 75, 81, 91, 96, 111], "how": [1, 7, 11, 12, 13, 14, 15, 17, 22, 23, 25, 28, 29, 30, 31, 32, 35, 36, 39, 48, 49, 53, 55, 57, 59, 63, 67, 70, 73, 78, 80, 82, 85, 87, 89, 90, 96, 98, 99, 104, 112, 114, 115], "how_to": [46, 81, 109], "howard": [29, 30, 33, 66, 75, 81], "howev": [1, 3, 17, 28, 29, 33, 34, 38, 45, 56, 62, 66, 75, 81, 91, 96, 111], "hpc": [48, 53, 55, 66, 70, 75, 81, 91, 97, 106, 115, 116], "hrovatin": [48, 70, 91], "hsz": [34, 53, 66, 87, 105, 111], "htmanalysiscours": [4, 18, 70, 87, 91], "html": [7, 11, 12, 17, 19, 28, 29, 30, 33, 36, 40, 41, 43, 44, 45, 46, 47, 49, 50, 53, 56, 57, 64, 66, 70, 72, 75, 79, 80, 81, 82, 83, 87, 90, 91, 92, 93, 95, 97, 98, 101, 103, 104, 106, 107, 109, 111, 112, 113, 114, 117], "http": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 55, 56, 57, 58, 59, 60, 61, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 78, 79, 80, 81, 82, 83, 84, 85, 87, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 111, 112, 113, 114, 115, 116, 117], "htw": [56, 66, 75, 81, 111], "hu": [29, 66, 75, 81], "huang": [4, 5, 7, 9, 36, 48, 53, 70, 73, 81, 91, 96, 99, 115], "huber": [17, 52, 53, 71, 81, 109], "huerta": [29, 66, 75, 81], "hugger": [57, 70, 87, 90], "hugh": [30, 33, 66, 75, 81], "huijben": [48, 57, 60, 70, 91, 112, 114], "huisken": [29, 53, 81, 91], "huisman": [11, 53, 87, 92, 97, 101, 102], "human": [5, 14, 25, 29, 30, 31, 33, 48, 49, 56, 57, 60, 70, 74, 80, 87, 90, 91, 95, 102, 114, 115], "humantechnopol": [17, 66, 75, 81], "humboldstra\u00df": 77, "humboldt": [45, 66, 75, 81, 111], "humenick": [10, 29, 66, 75, 81], "hundt": [26, 56, 66, 75, 81, 111], "hungari": [43, 48, 70, 91, 117], "hup": [41, 81, 107], "hutchinson": [66, 75, 81, 117], "huygenspro": [31, 66, 75, 81], "hypox": [29, 66, 75, 81], "hypoxia": [29, 66, 75, 81], "hzdr": [56, 66, 75, 81, 111], "h\u00e4nsch": [3, 33, 34, 53, 73, 78, 101, 111], "h\u00e4ufig": [26, 56, 66, 75, 81, 111], "h\u00e9l\u00e8ne": [11, 53, 81, 92, 97, 111], "h\u00f6rl": [17, 18, 29, 51, 53, 67, 81, 101], "i": [1, 2, 3, 6, 8, 9, 13, 16, 17, 20, 22, 23, 24, 25, 28, 29, 30, 31, 32, 33, 34, 35, 37, 39, 40, 41, 45, 46, 48, 49, 50, 51, 52, 53, 56, 57, 59, 60, 61, 62, 63, 68, 70, 71, 73, 78, 79, 80, 82, 83, 84, 85, 88, 90, 92, 94, 95, 96, 97, 98, 100, 102, 103, 105, 106, 108, 109, 111, 113, 114, 115, 116], "i2k": [43, 56, 65, 102, 108], "i2k2024": [56, 65, 81, 102, 112], "i2k_2024": [48, 56, 57, 60, 70, 91, 114], "i2k_poster_haase_v6_imagej_repro_pdf": [12, 29, 51, 53, 98], "i3d": [88, 117], "i3dbio": [3, 8, 23, 33, 34, 35, 37, 43, 55, 57, 58, 66, 74, 75, 81, 87, 92, 101, 105, 106, 111], "i7dl": [29, 66, 75, 81], "ial": [30, 33, 66, 75, 81], "ic": [30, 33], "icr1": [24, 31, 66, 75, 81], "icsread": [66, 75, 81], "id": [1, 11, 13, 41, 48, 53, 55, 56, 60, 65, 70, 87, 90, 91, 93, 96, 101, 102, 104, 111, 114], "ideal": [43, 49, 91, 111], "idealerweis": [26, 56, 66, 75, 81, 111], "ident": [29, 66, 75, 81], "identif": [3, 33, 34, 37, 66, 75, 81], "identifi": [1, 28, 36, 55, 66, 75, 81], "idr": [1, 28, 43, 44, 53, 58, 66, 73, 74, 75, 81, 106], "ifetim": [66, 75, 81], "iglesia": [48, 49, 70, 80, 91, 109], "ignacio": [10, 11, 18, 29, 42, 49, 55, 65, 70, 87, 90, 91, 104], "ihda": [66, 75, 81], "ihnen": [26, 29, 56, 66, 75, 81, 96, 111], "ihr": [26, 56, 66, 75, 77, 81, 100, 111], "ihren": [26, 56, 66, 75, 81, 96, 111], "ii": [66, 75, 81], "iii": [43, 53, 66, 75, 80, 81], "ij": [66, 75, 81], "ij_textbook1": [18, 41, 70, 78, 91], "ijsbrand": [53, 66, 73, 81, 96, 111], "ilan": [48, 70, 91], "illumin": [24, 30, 33, 66, 75, 81], "illustr": [1, 9, 15, 39, 55, 111], "ilya": [2, 4, 9, 15, 29, 53, 66, 81, 91, 102], "im": [0, 30, 32, 33, 41, 59, 77, 87, 96, 105, 107], "imag": [0, 8, 17, 23, 45, 62, 72, 79, 88, 93, 107, 108], "image_1_2023_08_18__14_32_31_964": [24, 66, 75, 81], "image_analysi": 43, "image_analysis_with_python": [29, 49, 70, 81, 91, 103, 109], "imageinlif": [66, 75, 81], "imagej": [3, 11, 23, 24, 25, 28, 31, 37, 41, 45, 48, 49, 50, 78, 79, 80, 83, 84, 92, 106, 109, 115], "imagejmacro_24h_2017": [29, 55, 69, 87, 91, 98], "imagejmacro_introduct": [42, 55, 70, 87, 91, 104], "imageri": [30, 33, 66, 75, 81], "images_for_classifier_train": [25, 31, 66, 75, 81], "imcf": [66, 75, 81], "img_filt": [25, 31, 66, 75, 81], "imjoi": [11, 70, 87], "immedi": [17, 34, 56, 75, 81, 91, 96, 111], "immer": [26, 56, 66, 75, 81, 111], "immun": [56, 74, 87, 95, 102], "immunofluoresc": [30, 33, 66, 75, 81], "immunolabel": [29, 66, 75, 81], "immunoresearch": [24, 31, 66, 75, 81], "immunostain": [66, 75, 81], "impact": [29, 55, 66, 90, 99, 109], "imperfect": [48, 70], "implement": [2, 3, 6, 8, 12, 15, 22, 23, 28, 29, 33, 34, 37, 38, 43, 48, 51, 53, 56, 70, 73, 91, 92, 94, 96, 97, 101, 102, 111, 114], "import": [3, 22, 23, 29, 30, 32, 33, 37, 40, 55, 56, 66, 68, 74, 75, 81, 87, 95, 100, 102, 111], "importprocess": [66, 75, 81], "impressumspflicht": 77, "improv": [1, 3, 8, 22, 23, 28, 29, 30, 33, 34, 37, 43, 48, 53, 56, 66, 70, 73, 75, 81, 87, 92, 94, 96, 101, 108, 111], "includ": [1, 2, 3, 6, 9, 11, 14, 15, 16, 17, 20, 24, 25, 28, 29, 30, 31, 33, 34, 37, 39, 48, 51, 53, 55, 56, 57, 59, 62, 66, 70, 73, 75, 81, 82, 84, 87, 91, 92, 94, 96, 102, 105, 107, 108, 111], "inclus": [53, 75, 81, 108], "incomplet": [56, 74, 87, 95, 102], "inconveni": [3, 8, 23, 30, 33, 34, 37, 66, 75, 81], "incorpor": [29, 33, 38, 66, 75, 81, 82], "increas": [1, 14, 15, 28, 29, 39, 53, 66, 75, 81, 108], "increasingli": [1, 14, 45, 66, 75, 81, 111], "incub": [24, 31, 66, 75, 81], "independ": [6, 28, 29, 37, 56, 66, 75, 81, 111], "index": [41, 46, 49, 50, 52, 53, 57, 71, 79, 81, 88, 92, 98, 106, 109, 111, 114], "indic": [30, 33, 66, 75, 81], "indico": [60, 91, 106, 111], "indispens": [66, 75, 81], "individu": [24, 31, 62, 88], "individualis": [29, 66, 75, 81], "individuel": [26, 56, 66, 75, 81, 111], "industri": [1, 3, 15, 33, 39, 55, 66, 75, 81, 116], "infect": [30, 33, 66, 75, 81], "infiltr": [24, 31, 66, 75, 81], "influenc": [29, 33, 38, 55, 66, 67, 75, 81, 91], "info": [47, 66, 75, 81, 91], "inform": [1, 9, 14, 17, 24, 28, 29, 35, 49, 50, 53, 55, 56, 57, 58, 59, 70, 73, 74, 90, 91, 96, 97, 105, 106, 109], "informatik": [46, 53, 98, 116], "informationen": 77, "informationsmaterialien": 77, "infrasstructur": 88, "infrastructur": [1, 11, 14, 15, 29, 35, 39, 58, 67, 74, 90, 106, 116], "infrastruktur": [29, 66, 75, 81], "infuerung": [29, 56, 66, 75, 81, 96, 111], "inhalt": [29, 56, 66, 75, 81, 96, 111], "inhalten": [29, 56, 66, 75, 81, 96, 111], "inhaltlich": 77, "inhibit": [22, 66, 75], "initfil": [66, 75, 81], "initi": [0, 16, 17, 23, 28, 29, 30, 33, 34, 37, 38, 48, 67, 87, 91, 96, 101, 102, 108], "initializefil": [66, 75, 81], "initialvortrag": [0, 32, 66, 75, 81], "inject": 88, "innat": [56, 74, 87, 95, 102], "innerhalb": [0, 32, 66, 75, 81], "innov": [1, 3, 28, 29, 33, 66, 75, 81], "inp": [66, 75, 81], "input": [3, 37, 50, 57, 66, 75, 81, 83, 115], "inria": [41, 48, 70, 81, 91, 109], "insbesonder": [41, 77, 81, 107], "inserm": [30, 33, 66, 75, 81], "insert": [3, 23, 32, 37, 66, 75, 81], "insid": [66, 75, 81], "insight": [22, 29, 52, 56, 71, 91, 94, 111, 114], "insightsoftwareconsortium": [48, 70, 91], "inspect": [31, 75], "instal": [3, 20, 30, 33, 34, 37, 63, 66, 75, 81, 85], "instanc": [3, 8, 12, 23, 28, 33, 34, 35, 37, 48, 55, 58, 66, 70, 74, 75, 81, 85, 91, 105, 106, 109, 111], "institut": [0, 3, 8, 17, 22, 23, 28, 30, 32, 33, 34, 35, 37, 39, 41, 53, 55, 56, 58, 59, 74, 87, 92, 94, 97, 105, 106, 117], "institutcochin": [30, 33, 66, 75, 81], "instruct": [3, 8, 14, 23, 30, 33, 34, 35, 37, 43, 55, 57, 58, 66, 74, 75, 81, 83, 88, 91, 92, 96, 102, 105, 106, 111], "instrument": [30, 33, 66, 75], "integr": [0, 1, 3, 16, 17, 28, 29, 30, 33, 34, 35, 37, 38, 43, 51, 56, 57, 66, 74, 75, 81, 82, 87, 90, 91, 94, 95, 96, 102, 105, 106, 111, 113, 117], "integrieren": [29, 56, 66, 75, 81, 96, 100, 111], "intellig": [1, 5, 7, 10, 13, 21, 25, 40, 41, 43, 46, 48, 49, 53, 56, 57, 64, 65, 68, 70, 73, 85, 95, 96, 99, 104, 111, 115, 116, 117], "intend": [3, 29, 33, 34, 37, 43, 46, 48, 57, 66, 70, 75, 80, 81, 87, 91, 94, 96, 97, 102, 103, 111], "intens": [1, 3, 8, 23, 24, 25, 28, 31, 33, 34, 35, 37, 46, 55, 58, 66, 74, 75, 81, 105, 106, 111, 116], "intent": [11, 53, 87, 92, 97, 101, 102], "inter": [29, 66, 75, 81], "interact": [1, 14, 29, 36, 46, 56, 57, 96, 103, 116], "interaktiv": [26, 56, 66, 75, 81, 111], "interconnect": [66, 75, 81], "interdisciplinari": [0, 1, 28, 29, 38, 51, 66, 75, 96, 111], "interessierten": [26, 56, 66, 75, 81, 111], "interest": [1, 17, 28, 29, 32, 40, 51, 53, 66, 75, 81, 87, 91, 111, 113], "interfac": [16, 35, 43, 46, 48, 63, 64, 75, 81, 87, 91, 106], "interfer": [29, 66, 75, 81], "intermedi": [2, 11, 17, 48, 70, 84, 92, 108], "intern": [3, 6, 8, 23, 28, 29, 30, 33, 34, 37, 43, 45, 47, 53, 56, 75, 91, 96, 111], "internation": [3, 33, 34, 37, 66, 75, 81], "internet": [3, 23, 28, 29, 56, 61, 66, 75, 77, 81, 96, 111], "interoper": [0, 1, 3, 8, 14, 15, 16, 23, 28, 29, 30, 33, 34, 37, 38, 39, 43, 48, 53, 56, 66, 73, 75, 81, 82, 88, 91, 94, 96, 100, 105, 111, 113], "interpret": [28, 43, 49, 51, 66, 75, 81, 91, 111], "interrog": [3, 33, 34, 37, 66, 75, 81], "intersect": [66, 75, 81], "intervent": [29, 66, 75, 81], "intracellular": [66, 75, 81], "intravit": 117, "intro": [12, 29, 41, 43, 47, 49, 64, 80, 91, 104, 109, 111, 112], "introduc": [3, 6, 7, 21, 28, 29, 33, 37, 39, 53, 55, 56, 58, 66, 67, 72, 75, 81, 83, 90, 91, 92, 95, 96, 97, 99, 100, 102, 105, 106, 107, 109, 111, 115, 117], "introducemi": [9, 56, 66, 75, 81], "introduct": [3, 23, 37, 40, 45, 67, 68, 96, 102, 103, 113, 117], "intuit": [1, 28, 66, 75, 81], "invert": [66, 75, 81], "investig": [3, 22, 23, 28, 30, 33, 37, 53, 66, 75, 81, 89, 96, 108, 111], "invit": [17, 34, 51, 75, 81, 96, 105, 111], "invok": [29, 66, 75, 81], "involv": [1, 28, 32, 45, 66, 75, 81, 111], "io": [10, 12, 17, 19, 29, 40, 41, 43, 45, 46, 47, 48, 49, 50, 53, 55, 56, 57, 59, 60, 66, 75, 78, 79, 80, 81, 83, 85, 90, 91, 92, 95, 96, 98, 102, 103, 104, 106, 108, 109, 111, 112, 113, 114, 115, 116], "ioer": [56, 66, 75, 81, 94, 111], "ion": [24, 66, 75, 81], "ioniz": [66, 75, 81], "iota": [30, 33, 66, 75, 81], "ipynb": [31, 66, 75, 81], "ipython": [48, 70, 80, 91], "irizarri": 87, "irradi": [29, 66, 75, 81], "isa": [16, 35, 66, 75, 81], "isaac": [17, 48, 52, 53, 70, 71, 81, 91, 109], "isabel": [1, 15, 39, 43, 53, 56, 57, 66, 75, 81, 91, 96, 111], "ishikawa": [66, 75, 81], "ison": [17, 18, 29, 51, 53, 67, 81, 101], "isotrop": [24, 66, 75, 81], "issu": [3, 8, 17, 22, 23, 24, 28, 29, 30, 32, 33, 37, 43, 45, 51, 61, 62, 66, 73, 75, 81, 88, 91, 92, 101, 117], "ist": [0, 26, 29, 32, 56, 66, 75, 77, 81, 111], "isyntax": [2, 17, 48, 70, 84, 92, 108], "itali": [29, 48, 49, 57, 60, 70, 80, 91, 114, 115], "itano": [30, 33, 53, 66, 73, 75, 81, 88, 101], "iter": [3, 8, 23, 30, 33, 34, 37, 66, 75, 81], "itk": [31, 43, 49, 66, 75, 81], "itll": [29, 40, 68, 81, 100, 111], "itn": [66, 75, 81], "its": [3, 6, 8, 23, 28, 29, 33, 45, 55, 66, 75, 81, 92, 105, 111], "itself": [66, 75, 81], "iudin": [7, 21, 39, 56, 58, 72, 81, 95, 102, 109], "ivana": [17, 18, 29, 51, 53, 67, 81, 101], "iv\u00e1n": [11, 43, 49, 68, 70, 81, 87, 91, 95], "ix71": [29, 30, 33, 66, 75, 81], "j": [11, 17, 18, 24, 29, 30, 31, 33, 39, 46, 52, 53, 55, 57, 70, 71, 73, 80, 87, 88, 91, 92, 94, 96, 97, 98, 101, 102, 104, 106, 109, 111, 115, 116], "jablonski": [66, 75], "jack": [48, 56, 70], "jackson": [24, 31, 66, 75, 81], "jacob": [43, 70], "jacquemet": [7, 12, 21, 39, 43, 49, 58, 66, 70, 71, 72, 73, 81, 85, 91, 109], "jagusch": [53, 66, 81, 111], "jahr": [29, 53, 66, 78], "jaim": [10, 29, 30, 33, 66, 75, 81, 88], "jakob": [12, 66, 74, 75, 81, 82, 87, 90], "jamali": [43, 53, 66, 70, 80, 91], "jambor": [4, 12, 18, 30, 43, 53, 66, 73, 78, 80], "jame": [11, 30, 33, 53, 66, 75, 81, 87, 88, 92, 97, 101, 102], "jan": [1, 3, 4, 5, 7, 8, 9, 11, 12, 15, 18, 20, 23, 28, 29, 30, 36, 39, 48, 49, 51, 53, 55, 56, 66, 70, 73, 75, 78, 81, 82, 87, 90, 91, 92, 96, 97, 101, 111], "jana": [0, 4, 12, 18, 30, 32, 53, 66, 73, 75, 78, 81], "janabraung": [20, 48, 49, 56, 70, 87, 93], "janclusmann": [48, 49, 70, 91], "janelia": [30, 33, 66, 75, 81], "janeliascicomp": [42, 57, 70, 87], "janina": [3, 8, 11, 17, 23, 30, 33, 34, 37, 53, 66, 67, 75, 81, 92, 97, 105, 111], "janna": [41, 66, 75, 81, 111], "jannasch": [3, 22, 23, 32, 37, 66, 75, 81], "januari": [17, 43, 48, 66, 70, 75, 81, 91, 117], "japan": [17, 66, 75, 81], "jason": [2, 4, 5, 15, 17, 39, 43, 53, 66, 73, 80, 81, 87, 92, 97, 105, 111], "jatkinson1000": [48, 56, 70], "java": [2, 11, 15, 17, 46, 48, 66, 70, 75, 81, 84, 92, 106, 108], "jax": [56, 85, 103], "jc": [17, 29, 53, 66, 75, 81, 91, 102, 107], "jcb": [11, 53, 101], "jcs262139": [17, 53, 81, 91, 102, 107], "je": [0, 32, 66, 75, 81], "jean": [1, 2, 4, 5, 11, 17, 18, 29, 31, 37, 38, 39, 42, 48, 49, 50, 53, 55, 57, 66, 67, 70, 73, 75, 80, 81, 84, 87, 91, 102, 104, 105, 106, 111, 113], "jede": 77, "jedoch": 77, "jeffrei": [43, 48, 70, 93, 117], "jen": [0, 6, 23, 28, 35, 37, 38, 43, 46, 47, 48, 49, 51, 53, 66, 70, 75, 78, 81, 84, 91, 94, 96, 101, 105, 106, 109, 111, 115], "jena": [46, 53, 73, 98, 116], "jenkinson": [49, 70, 84, 91, 109], "jennif": [40, 111], "jenswendt": [15, 17, 37, 38, 48, 70, 80, 113], "jepson": [46, 53, 66, 98, 116], "jeremi": [1, 46, 53, 85, 87, 90, 95, 109, 116], "jessica": [1, 15, 39, 53, 66, 75, 81, 111], "jeweil": [26, 56, 66, 75, 81, 111], "jeweilig": 77, "jh": [10, 29, 66, 75, 81], "jhenni": [4, 5, 7, 9, 36, 48, 70, 81], "jianxu": [53, 73, 91, 96, 99, 115], "jing": [1, 15, 39, 66, 75, 81], "jlrumberg": [5, 49, 55, 70, 87, 90, 91], "jmb": [39, 53, 66, 87, 92, 97, 111], "jmi": [3, 8, 11, 29, 30, 33, 34, 37, 46, 53, 66, 81, 91, 98, 111, 116], "jn": [28, 48, 70, 85, 91, 106, 109], "jnguyenvan": [20, 48, 49, 56, 70, 87, 93], "jni": [49, 70, 80, 91, 109], "jo": [1, 28, 66, 75, 81], "joachim": [41, 52, 71, 75, 87, 113], "joachimgoedhart": [41, 75], "joakim": [17, 18, 29, 51, 53, 67, 81, 101], "joanna": [43, 48, 56, 57, 60, 70, 91, 114, 117], "job": [40, 111], "jochen": [66, 75, 81], "joel": [4, 5, 7, 9, 30, 33, 36, 48, 53, 66, 70, 73, 75, 81, 88, 101], "johan": [11, 43, 80, 93, 109, 115, 116], "johann": [29, 48, 49, 53, 57, 64, 66, 70, 78, 81, 87, 90, 91, 103, 106, 109, 111], "johanna": [1, 4, 12, 14, 18, 30, 43, 49, 53, 66, 70, 73, 75, 78, 81, 85, 91, 111], "john": [1, 14, 15, 17, 18, 29, 39, 51, 53, 66, 67, 73, 75, 81, 94, 96, 101, 111], "join": [66, 75, 81], "joint": [66, 75, 81], "jointli": [45, 66, 75, 81, 111], "jokitalo": [4, 9, 29, 53, 81, 91], "jon": [17, 18, 29, 51, 53, 67, 81, 101], "jona": [11, 42, 48, 49, 56, 60, 65, 70, 81, 85, 87, 91, 102, 109], "jonathan": [1, 11, 15, 39, 43, 66, 75, 81, 82, 107], "jone": [12, 17, 18, 29, 43, 48, 49, 51, 52, 53, 66, 67, 70, 71, 73, 80, 81, 82, 85, 91, 101], "joost": [15, 17, 37, 38, 48, 53, 70, 73, 80, 81, 92, 96, 97, 101, 111, 113], "jor": [46, 53, 98, 116], "joran": [12, 48, 70, 91], "jordan": [36, 46, 53, 55, 66, 75, 81, 98, 116], "jord\u00e3o": [48, 57, 60, 70, 91, 112, 114], "josef": [53, 73, 81, 94, 96, 101], "josh": [2, 3, 4, 5, 8, 11, 15, 18, 29, 30, 33, 34, 35, 37, 38, 48, 51, 52, 53, 56, 66, 67, 70, 71, 73, 75, 79, 80, 81, 84, 91, 92, 96, 97, 101, 102, 105, 106, 107, 108, 109, 111, 113, 117], "joshua": [0, 16, 23, 37, 38, 46, 47, 48, 53, 66, 70, 75, 81, 91, 96, 105, 106, 109, 111], "journal": [1, 3, 11, 14, 15, 17, 37, 39, 43, 53, 56, 57, 66, 75, 80, 81, 91, 92, 93, 96, 97, 101, 102, 107, 111, 114], "json": [16, 35, 88], "juan": [41, 48, 49, 70, 80, 87, 90, 91, 109], "jubrail": [30, 33, 66, 75, 81], "judith": [4, 12, 18, 29, 30, 33, 53, 66, 73, 75, 78, 81, 88], "jug": [4, 18, 29, 30, 43, 48, 49, 51, 53, 66, 70, 73, 74, 78, 81, 85, 87, 90, 91, 98, 109, 112], "juglab": [12, 48, 70, 91], "jukkala": [12, 29, 43, 49, 66, 70, 73, 75, 81, 85, 91], "juli": [24, 29, 48, 56, 66, 70, 75, 81, 91, 95, 102, 109], "julia": [0, 3, 4, 8, 10, 12, 16, 18, 23, 28, 29, 30, 32, 33, 34, 35, 37, 38, 46, 47, 48, 53, 55, 58, 66, 70, 73, 74, 75, 78, 81, 88, 91, 96, 105, 106, 109, 111], "julian": [0, 4, 7, 9, 32, 36, 66, 75, 81, 82, 117], "julien": [17, 18, 25, 29, 31, 51, 53, 66, 67, 75, 81, 91, 101, 104], "juliu": [11, 18, 29, 42, 49, 55, 70, 87, 91, 104], "jupp": [66, 75, 81], "jupyt": [3, 19, 23, 29, 31, 40, 57, 66, 68, 75, 81, 88, 90, 103], "jurado": [57, 81, 91, 93], "jurenait": [29, 49, 70, 81, 109], "just": [56, 61, 62, 66], "juti": [1, 15, 39, 66, 75, 81], "jyoti": [11, 53, 66, 87, 93, 107, 111, 113], "j\u00fcngst": [66, 75, 81], "k": [10, 29, 66, 75, 81], "k40": [25, 31, 66, 75, 81], "kala": [51, 81, 91], "kala\u0161": [17, 18, 29, 51, 53, 67, 81, 101], "kaltschmidt": [10, 29, 66, 75, 81], "kamentski": [11, 53, 81, 92, 97, 111], "kamvar": [36, 55, 66, 75, 81], "kandpal": [0, 3, 8, 16, 23, 33, 34, 35, 37, 38, 46, 47, 48, 51, 53, 55, 58, 66, 70, 74, 75, 81, 88, 91, 96, 105, 106, 109, 111], "kann": [26, 29, 56, 66, 75, 77, 81, 96, 111], "kapitel": [66, 75, 81], "kapoor": [4, 12, 18, 30, 53, 73, 78], "karaica": [17, 18, 29, 51, 53, 67, 81, 101], "karen": [3, 8, 23, 30, 33, 34, 37, 53, 66, 75, 81, 87, 105, 111], "kari": [36, 55, 66, 75, 81], "karim": [1, 15, 39, 66, 75, 81], "karin": [12, 29, 42, 43, 48, 49, 66, 70, 73, 75, 81, 85, 87, 91, 109], "karinsasaki": [42, 49, 70, 87, 109], "karlsruh": [66, 75, 81], "karnbach": [1, 15, 39, 66, 75, 81], "karolin": [66, 75, 82], "karolina": [20, 66, 75], "karoun": [1, 15, 39, 66, 75, 81], "karreman": [17, 18, 29, 51, 53, 67, 81, 101], "katarzyna": [41, 45, 66, 75, 81, 111], "kate": [11, 53, 66, 75, 81, 87, 93, 107, 108, 111, 113], "kather": [12, 74, 87, 90], "katherin": [17, 34, 43, 44, 53, 56, 73, 75, 81, 91, 92, 96, 97, 101, 111], "katherlab": [48, 49, 70, 91], "katrin": [45, 66, 75, 81, 111], "katrina": [1, 15, 39, 66, 75, 81], "kedar": [7, 21, 39, 56, 58, 72, 81, 95, 102, 109], "keep": [11, 16, 35, 53, 66, 75, 81, 87, 92, 97, 101, 102], "kei": [1, 6, 7, 11, 15, 17, 28, 33, 34, 36, 39, 51, 56, 63, 105, 109, 112, 114], "kein": 77, "keinen": 77, "keith": [10, 29, 66, 75, 81], "kekunn": [15, 48, 70, 84, 106], "kemmer": [1, 43, 53, 56, 57, 66, 75, 81, 91, 96, 111], "kenntni": 77, "keppler": [1, 14, 15, 39, 53, 66, 75, 81, 111], "kerstin": [41, 45, 53, 66, 75, 81, 111], "kevin": [2, 4, 5, 12, 15, 17, 18, 29, 30, 41, 43, 46, 49, 52, 53, 66, 67, 70, 71, 73, 78, 80, 81, 91, 103, 104, 105, 109, 111], "kevinyamauchi": [41, 46, 49, 81, 91, 103, 104, 109, 111], "keynot": [17, 56, 66, 75, 81, 111], "keyval_from_csv": [16, 35, 66, 75, 81], "keyword": [66, 75, 81], "khadak": [11, 53, 66, 87, 93, 107, 111, 113], "khan": [4, 7, 9, 11, 36, 57, 66, 75, 81, 85, 111, 115, 117], "kheop": [24, 31, 66, 75, 81], "kidnei": [30, 33, 81], "kiepa": [30, 33, 66, 75, 81], "kim": [1, 15, 39, 66, 75, 81], "kimberli": [43, 70], "kind": [29, 46, 48, 50, 61, 66, 70, 75, 81, 88, 92, 98, 106, 115], "king": [10, 29, 66, 75, 81], "kirk": [43, 53, 66, 80], "kirschmann": [17, 18, 29, 51, 53, 67, 81, 101], "kirti": [29, 53, 66, 78], "kit": [24, 31, 43, 50, 66, 75], "kitchen": [29, 56, 66, 75, 81, 91, 96, 100, 107, 111], "klara": [4, 12, 18, 30, 53, 73, 78], "klau": [4, 5, 7, 9, 36, 48, 70, 81], "klemeier": [1, 15, 39, 66, 75, 81], "klemm": [4, 9, 18, 29, 31, 42, 53, 55, 67, 70, 81, 87, 91, 104], "kleywegt": [39, 53, 66, 87, 92, 97, 111], "kneip": [66, 75, 81], "know": [61, 62, 66, 75], "knowledg": [16, 35, 45, 48, 49, 55, 57, 70, 72, 80, 82, 91, 107, 113], "knowledgebas": [43, 81, 111, 114], "known": [3, 8, 23, 29, 33, 34, 35, 37, 46, 50, 55, 57, 58, 66, 74, 75, 81, 92, 98, 105, 106, 111, 115], "koala": [66, 75, 81], "kobe": [48, 57, 70, 81, 115], "koeckert": [3, 22, 23, 32, 37, 66, 75, 81], "koksoi": [4, 12, 18, 30, 53, 73, 78], "kommen": [41, 81, 107], "kommunik": [29, 56, 66, 75, 77, 81, 96, 100, 111], "komplex": [0, 32, 66, 75, 81], "kondata": [66, 75, 81], "konflikten": [29, 56, 66, 75, 81, 96, 111], "konkret": 77, "konkreten": [66, 75, 77, 81], "konrad": [29, 66, 75, 81], "kontaktdaten": 77, "kontaktstel": [6, 28, 37, 56, 66, 75, 81, 111], "kontext": [41, 81, 107], "kontrol": 77, "kop": [66, 75, 81], "korten": [29, 49, 70, 81, 109], "kostina": [66, 75, 81], "kostrykin": [57, 81, 91, 93], "kota": [4, 9, 10, 11, 12, 17, 29, 30, 31, 41, 42, 49, 51, 53, 55, 57, 66, 67, 70, 73, 75, 78, 81, 87, 91, 101, 104], "koushouu": [43, 60, 70, 91], "kozlowski": [2, 4, 5, 15, 17, 53, 73, 81, 105, 111], "kozubek": [11, 18, 55, 57, 70, 87, 91, 104], "kralj": [66, 75, 81], "krawczyk": [53, 66, 81, 91, 97, 106, 115], "krempp": [56, 74, 87, 90, 91, 95, 99], "krentzel": [12, 29, 43, 49, 66, 70, 73, 75, 81, 85, 91], "kreshuk": [7, 21, 29, 39, 42, 49, 53, 55, 56, 58, 67, 72, 75, 81, 91, 95, 102, 104, 109], "kreshuklab": [5, 21, 49, 57, 70, 87, 90, 109], "kreutzer": [41, 81, 107], "kri": [48, 57, 70, 81, 115], "kristina": [17, 34, 56, 75, 81, 91, 96, 111], "krist\u00edna": [48, 56, 60, 65, 70, 81, 91, 102], "kriterien": [66, 75, 81], "kroo\u00df": [3, 23, 37, 66, 75, 81, 117], "krull": [12, 43, 49, 66, 70, 73, 85, 91], "krzysztof": [48, 70, 91], "kr\u00e1lovsk\u00e9": [20, 66, 75, 81], "ksenia": [3, 23, 37, 66, 75, 81, 117], "ktdzbgscyjq": [56, 74, 87, 95, 102], "kuberek": [53, 81, 96, 111], "kugel": [32, 66, 75, 81], "kugler": [29, 40, 53, 68, 81, 91, 111, 113], "kulkarni": [12, 29, 48, 49, 70, 81, 91, 109], "kulturwandel": [41, 81, 107], "kundegorski": [49, 70, 91], "kuni": [3, 8, 17, 23, 30, 33, 35, 37, 51, 53, 55, 56, 58, 66, 73, 74, 75, 78, 81, 87, 88, 91, 96, 101, 105, 106, 111], "kur": [26, 56, 66, 75, 81, 111], "kutra": [4, 5, 9, 11, 21, 36, 39, 48, 55, 56, 57, 58, 66, 70, 72, 75, 81, 85, 90, 91, 95, 102, 109, 111, 112, 114, 115, 117], "kuzak": [11, 53, 81, 93, 96, 101, 114], "kvpair": [43, 46, 87, 106], "kxhm4xht7uuvgwwvsu": [74, 87], "kxp": [66, 75, 81], "kyoto": 25, "k\u00f6nig": [29, 66, 75, 81], "k\u00f6nnen": [26, 29, 56, 66, 75, 77, 81, 96, 100, 111], "k\u00f6nnten": [29, 66, 75, 81], "k\u00fcchengespr\u00e4ch": [56, 66, 75, 81, 111], "k\u00fchne": [26, 56, 66, 75, 81, 111], "k\u00fchnel": [66, 75, 81], "k\u00fcrzere": [66, 75, 81], "l": [1, 3, 8, 11, 12, 14, 15, 23, 29, 33, 34, 35, 37, 39, 43, 44, 49, 52, 53, 55, 56, 57, 58, 66, 70, 71, 73, 74, 75, 80, 81, 85, 91, 93, 94, 96, 101, 105, 106, 111, 113, 114], "la": [11, 24, 25, 31, 43, 47, 60, 66, 75, 81, 87, 91, 102], "lab": [3, 6, 23, 24, 28, 29, 37, 45, 46, 56, 66, 91, 111, 117], "label": [56, 74, 87, 95, 102], "laboratori": [20, 24, 30, 31, 33, 66, 75, 81], "laboratorio": [30, 33, 66, 75, 81], "laboratoryopticsbiosci": [66, 75, 81], "lack": [66, 75, 81], "lacost": [4, 12, 18, 30, 33, 53, 66, 73, 75, 78, 81, 88], "lahmann": [41, 81, 107], "laia": [25, 31, 66, 75, 81], "laid": [1, 15, 39, 66, 75, 81], "lain": [12, 20, 29, 43, 48, 49, 56, 66, 70, 73, 75, 81, 85, 87, 91, 93], "lakner": [4, 18, 70, 87, 91], "lalit": [12, 48, 70, 91], "lampert": [40, 64, 68, 81, 91, 103, 109], "lancelotti": [1, 15, 39, 66, 75, 81], "landesdirekt": [22, 66, 75], "landmark": [66, 75, 81], "landscap": [66, 75, 81, 82], "landwehr": [66, 75, 81], "lang": [66, 75, 81, 82], "languag": [16, 28, 35, 40, 43, 46, 48, 56, 68, 70, 74, 85, 87, 95, 98, 102, 106, 111, 116], "laps": [30, 31, 33, 66, 75, 81], "laptop": [3, 23, 28, 66, 75, 81], "larg": [0, 1, 3, 6, 8, 15, 23, 28, 30, 31, 33, 37, 38, 39, 56, 68, 74, 85, 87, 95, 102], "larger": [29, 31, 66, 75, 81], "largest": [66, 75], "larva": [1, 28, 66, 75, 81], "laser": [24, 66, 75, 81], "lassi": [17, 18, 29, 51, 53, 67, 81, 101], "last": [3, 20, 23, 28, 66, 75, 81], "later": [32, 37, 66, 75, 81], "latest": [43, 45, 46, 50, 66, 75, 79, 85, 87, 98, 106], "lattic": [3, 22, 23, 32, 37, 66, 75, 81], "laud": [30, 33, 66, 75, 81, 88], "laufzeit": [41, 81, 107], "laur": [4, 12, 17, 18, 29, 30, 51, 53, 67, 73, 78, 81, 101], "laura": [17, 18, 25, 29, 31, 51, 53, 66, 67, 75, 81, 101], "laurent": [4, 12, 18, 30, 53, 66, 73, 75, 78, 81, 85], "laus": [48, 70, 91], "lausann": [50, 78, 91], "lavaert": [48, 57, 70, 81, 115], "layer": [56, 74, 87, 95, 102], "lazari": [11, 53, 81, 92, 97, 111], "ld": [16, 35, 66, 75, 81], "le": [4, 12, 18, 30, 53, 73, 78, 81, 92, 96, 97, 101, 111], "lead": [1, 14, 15, 29, 39, 50, 59, 66, 75, 81, 87, 94, 96, 111], "leandro": [17, 18, 29, 51, 53, 67, 81, 101], "learn": [1, 3, 15, 20, 23, 25, 28, 30, 31, 33, 34, 36, 37, 41, 44, 46, 50, 53, 59, 78, 82, 83, 93, 97, 99, 107, 111, 113, 114, 115, 116, 117], "least": [66, 75, 81], "leav": [30, 33, 36, 55, 66, 75, 81], "lebenszyklu": [29, 56, 66, 75, 81, 111], "lectur": [3, 8, 23, 33, 34, 35, 37, 66, 80, 85, 88, 97, 104, 105, 106, 111, 112, 117], "lecture_applied_bioimage_analysi": [29, 55, 69, 91, 98], "lecture_imagej2_dev": [29, 55, 69, 80, 91, 98, 104], "lecture_multiview_registr": [29, 55, 69, 80, 91, 98, 104], "lecture_tracking_trackm": [29, 55, 69, 80, 91, 98, 104], "lecture_working_with_objects_in_2d_and_3d": [29, 55, 69, 80, 91, 98, 104], "lecture_working_with_pixel": [29, 55, 69, 80, 91, 98, 104], "led": [17, 34, 53, 56, 75, 81, 91, 96, 108, 111], "lee": [11, 53, 81, 92, 97, 111], "leenderts": [66, 75, 81], "left": [63, 66, 75, 81, 88], "legal": [0, 1, 15, 32, 39, 45, 53, 57, 66, 75, 81, 87, 96, 100, 107, 111], "legend": [66, 75, 82], "legland": [4, 9, 29, 53, 66, 75, 81, 91], "legra": [1, 15, 39, 66, 75, 81], "leibniz": [56, 66, 75, 81, 94, 111], "leica": [24, 29, 30, 31, 33, 66, 75, 81], "leicht": [26, 56, 66, 75, 81, 111], "leiden": [53, 73, 81, 92, 96, 97, 101, 111], "leipzig": [3, 6, 23, 26, 28, 37, 48, 49, 66, 70, 77, 88, 97, 105, 111], "leistungsschutzrecht": [56, 66, 75, 81, 100, 111], "lemai": [11, 53, 66, 87, 93, 107, 111, 113], "lemair": [66, 75, 81, 82], "lemk": [29, 66, 75, 81, 82], "len": [53, 73, 91, 96, 99, 115], "lenz": [48, 49, 70, 91], "leomil": [29, 49, 70, 81, 91, 103, 109], "leonid": [57, 81, 91, 93], "leopoldina": [66, 75, 81], "leplat": [43, 48, 49, 70, 87, 91, 108, 109], "lerch": [12, 29, 43, 49, 66, 70, 73, 75, 81, 85, 91], "lernen": [26, 29, 56, 66, 75, 81, 96, 100, 111], "leroi": [66, 75, 81], "less": [66, 75, 82], "lesson": [7, 11, 36, 55, 57, 91, 109, 112, 114], "let": [48, 61, 62, 66, 70, 75, 85], "leterri": [12, 43, 49, 66, 70, 73, 85, 91], "letort": [48, 70, 80, 112], "leukocyt": [56, 74, 87, 95, 102], "leuven": [22, 66, 75, 81], "level": [3, 23, 28, 33, 37, 38, 45, 66, 75, 81, 111], "leverag": [3, 33, 34, 37, 66, 75, 81], "levet": [11, 17, 18, 29, 51, 53, 55, 57, 67, 70, 81, 87, 91, 101, 104], "lewi": [11, 53, 66, 87, 93, 107, 111, 113], "leyla": [11, 53, 81, 93, 96, 101, 114], "li": [48, 70, 83, 85], "liascript": [48, 57, 70, 81, 90, 115], "librari": [3, 8, 11, 15, 23, 24, 30, 33, 34, 37, 40, 43, 46, 63, 72, 84, 87, 92, 93, 103, 106, 109, 111, 113, 116], "librarian": [66, 75, 81], "lic": [30, 33, 66, 75, 81], "licens": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 28, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 57, 58, 59, 60, 61, 63, 64, 65, 69, 70, 71, 72, 73, 74, 78, 79, 80, 82, 83, 84, 85, 87, 90, 91, 92, 93, 94, 95, 97, 98, 99, 101, 102, 103, 104, 105, 106, 107, 108, 109, 112, 114, 115, 116, 117], "lidayov\u00e1": [48, 56, 60, 65, 70, 81, 91, 102], "lienhard": [45, 46, 57, 111], "lif": [30, 33, 66, 75, 81], "life": [6, 17, 28, 30, 33, 34, 37, 46, 48, 51, 52, 56, 57, 70, 71, 72, 73, 74, 80, 88, 94, 95, 96, 99, 105, 114, 117], "life_2022": [55, 70, 79, 91, 104], "lifecycl": [3, 8, 55, 66, 75, 81, 92, 97, 105, 111], "lifetim": [6, 56, 66, 75, 81, 91], "light": [17, 25, 30, 31, 33, 34, 39, 43, 51, 73, 78, 91, 94, 96, 105, 111, 113], "lighter": [66, 75], "lightn": [29, 66, 75, 81], "lightsheet": [24, 66, 75, 81], "like": [0, 1, 16, 23, 24, 28, 29, 37, 38, 46, 48, 50, 57, 61, 62, 66, 82, 87, 88, 92, 98, 100, 105, 106, 115], "likewis": [45, 66, 75, 81, 111], "lim": [30, 33, 66, 75, 81], "limi": [30, 33, 43, 49, 66, 75, 91, 111], "limit": [29, 53, 62, 66, 75, 81, 96, 108, 111], "lindblad": [17, 18, 29, 51, 53, 67, 81, 101], "lindner": [2, 4, 5, 15, 17, 53, 73, 81, 105, 111], "lindst\u00e4dt": [66, 75, 82], "line": [1, 3, 25, 28, 31, 33, 34, 37, 48, 57, 64, 66, 75, 91, 106, 111, 113], "link": [1, 24, 26, 28, 48, 53, 56, 61, 62, 70, 88, 97], "linkact": [41, 91], "linkert": [2, 4, 5, 11, 15, 17, 48, 53, 70, 73, 81, 84, 92, 101, 105, 108, 111], "linksortbi": [41, 91], "lipid": [24, 66, 75, 81], "lisa": [53, 73, 101], "lish": [24, 66, 75, 81], "list": [3, 6, 8, 22, 23, 29, 30, 33, 34, 35, 37, 46, 55, 57, 58, 59, 61, 62, 63, 66, 72, 74, 75, 78, 81, 83, 88, 91, 92, 97, 103, 105, 106, 109], "lister": [1, 15, 39, 66, 75, 81], "literaci": [66, 75, 82], "literaturhinweis": [26, 56, 66, 75, 81, 111], "liu": [4, 12, 18, 24, 30, 53, 66, 73, 75, 78, 81], "liv": [30, 33, 66, 75, 81], "live": [30, 33, 50, 59, 66, 75, 81, 94, 96, 111], "liverpool": [17, 30, 33, 34, 51, 66, 75, 81, 91, 96, 105, 111], "livio": [1, 15, 39, 66, 75, 81], "lizenz": 77, "lizenzbedingungen": 77, "lizenzierung": [26, 29, 56, 66, 75, 81, 96, 100, 111], "ljosa": [43, 44, 73], "ll": [57, 58, 72, 83, 92, 97], "llm": [29, 55, 66, 75, 81, 90, 99, 109], "llopi": [4, 12, 18, 30, 33, 43, 53, 66, 73, 75, 78, 80, 81, 88, 101], "lmb": [49, 83, 91, 98], "lmu": [6, 56, 66, 75, 81, 91], "lnma": [30, 33, 66, 75, 81], "load": [3, 37, 66, 75, 81], "local": [1, 22, 28, 88, 116], "locat": [3, 29, 33, 34, 37, 62, 66, 75, 81], "loci": [66, 75, 81], "lociimport": [66, 75, 81], "loesung": [29, 56, 66, 75, 81, 96, 111], "loic": [11, 12, 29, 43, 49, 53, 66, 70, 73, 78, 85, 91, 98], "lokal": [56, 66, 75, 81, 111], "lombardot": [29, 55, 69, 70, 87, 91, 98], "long": [66, 75, 81], "longitudin": [22, 66, 75], "longo": [1, 15, 39, 66, 75, 81], "look": [29, 30, 33, 53, 66, 73, 75, 81, 88, 94, 96, 113], "lookup": [66, 75, 81], "lopez": [1, 28, 29, 66, 75, 81], "lorenzo": [24, 66, 75, 81], "lor\u00e1nd": [43, 48, 70, 91, 117], "lot": [29, 56, 66, 75, 81, 91, 96, 100, 107, 111], "louveaux": [10, 11, 18, 29, 31, 42, 49, 53, 55, 67, 70, 81, 87, 91, 104], "low": 31, "lower": [66, 75, 81], "lox": [30, 33, 66, 75, 81], "lo\u00efc": [48, 70, 81, 91], "lsen": [24, 66, 75, 81], "lsm": [30, 33], "lsm700": [66, 75, 81], "lsm710": [31, 66, 75, 81], "ltain19007": [20, 66, 75, 81], "ltima": [66, 75, 81], "ltp": [66, 75, 81], "luc": [1, 15, 39, 66, 75, 81], "luca": [4, 6, 12, 17, 18, 28, 30, 37, 43, 49, 52, 53, 56, 66, 70, 71, 73, 75, 78, 81, 85, 91, 109, 111], "luci": [11, 39, 43, 53, 66, 73, 80, 87, 92, 97, 101, 111], "lucil": [1, 28, 66, 75, 81], "ludwig": [1, 15, 39, 66, 75, 81], "luik": [48, 53, 66, 70, 81, 91, 97, 106, 115], "luis": [1, 28, 66, 75, 81], "luka": [0, 16, 23, 37, 38, 46, 47, 48, 53, 66, 70, 75, 81, 91, 96, 105, 106, 109, 111], "luke": [10, 29, 66, 75, 81], "luk\u00e1\u0161": [20, 66, 75], "lunch": [3, 37, 66, 75, 81], "lundberg": [29, 66, 75, 81], "luoresc": [66, 75, 81], "lutolf": [25, 31, 66, 75, 81], "l\u00e4sst": [0, 32, 66, 75, 81], "l\u00e9o": [43, 48, 49, 70, 87, 91, 108, 109], "l\u00f3pez": [29, 66, 75, 81], "l\u00f6schen": [56, 66, 75, 81, 111], "l\u00f6sungen": [26, 56, 66, 75, 81, 111], "l\u00fcckenlos": 77, "l\u00fcthi": [4, 5, 7, 9, 36, 48, 70, 81], "m": [0, 3, 10, 22, 23, 25, 29, 30, 31, 32, 33, 37, 38, 43, 46, 47, 48, 49, 52, 53, 57, 66, 70, 71, 73, 75, 80, 81, 88, 91, 97, 101, 105, 106, 109, 111, 114, 115, 117], "m2": [29, 66, 75, 81], "macdonald": [29, 53, 81, 91], "machado": [24, 66, 75, 81], "machen": [41, 81, 107], "machin": [1, 3, 28, 33, 34, 37, 43, 48, 53, 59, 66, 74, 96, 99, 111, 114], "machinelearning_spaomworkshop_publ": [10, 55, 87, 90, 91], "maco": 63, "macro": [3, 16, 20, 25, 31, 35, 37, 48, 66, 75, 81, 84, 106], "macrophag": [30, 33, 66, 75, 81], "macrorunn": [66, 75, 81], "maddalena": [1, 15, 39, 66, 75, 81], "made": [10, 17, 20, 24, 29, 45, 53, 55, 66, 73, 75, 76, 81, 94, 96, 101, 111, 116], "madon": [1, 15, 39, 66, 75, 81], "maduni\u0107": [17, 18, 29, 51, 53, 67, 81, 101], "magnif": [29, 66, 75, 81], "mahdavian": [10, 29, 66, 75, 81], "mahmood": [12, 74, 87, 90], "mai": [3, 5, 7, 11, 17, 21, 23, 28, 29, 30, 33, 34, 35, 37, 39, 43, 48, 49, 51, 53, 55, 56, 57, 58, 66, 67, 70, 72, 74, 75, 81, 82, 85, 87, 88, 91, 92, 95, 97, 102, 105, 106, 109, 111], "maik": [41, 66, 75, 81, 111], "mail": 77, "main": [1, 15, 28, 29, 39, 48, 56, 57, 66, 70, 75, 81, 82, 85, 90, 91, 97, 106, 107, 109, 111, 115], "maintain": [1, 28, 43, 57, 66, 75, 81, 91], "mainten": [53, 75, 81, 108], "major": [16, 35, 55, 66, 75, 81, 116], "make": [1, 3, 8, 23, 25, 28, 29, 30, 31, 33, 34, 37, 40, 41, 43, 48, 52, 55, 56, 63, 67, 70, 71, 72, 82, 85, 87, 88, 92, 97], "malchiodi": [20, 48, 49, 56, 70, 87, 93], "mallm": [3, 8, 23, 28, 66, 75, 81], "mamba": [19, 64], "man": [3, 26, 29, 33, 56, 66, 75, 81, 96, 111], "manag": [0, 1, 2, 4, 5, 7, 15, 20, 24, 26, 39, 46, 47, 48, 52, 60, 62, 71, 72, 83, 85, 88, 93, 94, 95, 100, 102, 103, 104, 113, 114, 115, 116], "managementtom": [3, 23, 37, 66, 75, 81, 117], "manan": [12, 48, 70, 91], "mancini": [24, 31, 66, 75, 81], "mandatori": 61, "mani": [41, 45, 53, 66, 73, 75, 81, 82, 91, 96, 99, 111, 115], "manifold": [29, 45, 66, 75, 81, 111], "manipul": [43, 58, 74, 81, 92, 102, 108], "manner": [56, 67, 81, 95, 96, 102], "mant": [21, 49, 70, 87, 90, 109], "manual": [3, 8, 23, 25, 29, 31, 33, 34, 35, 37, 43, 55, 58, 66, 74, 75, 105, 106, 111, 114], "manual_elab_v0": [45, 46, 57, 111], "manufactur": [22, 30, 33, 66, 75, 81], "manuscript": [3, 30, 33, 37, 66, 75, 81], "manz": [2, 4, 5, 15, 17, 53, 73, 81, 105, 111], "maohan": [66, 75, 81], "map": [0, 16, 23, 37, 38, 48, 66, 75, 81, 91, 96, 105, 111], "maplotlib": [13, 49, 70, 85, 109], "mar": [28, 56, 66, 75, 81, 111], "mara": [40, 64, 68, 81, 91, 103, 109], "mara_lampert": [19, 40, 64, 81, 109], "marc": [6, 28, 37, 46, 53, 56, 66, 73, 75, 81, 82, 98, 111, 116], "marcel": [48, 57, 70, 81, 115], "marcella": [17, 52, 53, 71, 81, 109], "marcello": [30, 33, 66, 75, 81, 88], "marcelo": [6, 28, 29, 37, 40, 49, 56, 64, 66, 70, 75, 81, 90, 91, 103, 109, 111], "marcelo_zoccol": [40, 64, 81, 90, 91, 109], "march": [48, 57, 70, 81, 115], "marco": [30, 33, 53, 66, 73, 75, 81, 88, 94, 96, 101], "marconato": [17, 52, 53, 71, 81, 109], "marcotti": [4, 5, 7, 9, 36, 48, 52, 53, 66, 70, 71, 75, 80, 81, 82, 91, 117], "marcu": [66, 75, 81], "mare\u0161": [20, 66, 75, 81], "margareta": [3, 22, 23, 32, 37, 66, 75, 81], "margineanu": [6, 56, 66, 75, 81, 91], "mari": [1, 2, 4, 5, 11, 12, 17, 18, 29, 30, 37, 38, 39, 48, 51, 53, 66, 67, 70, 73, 75, 78, 80, 81, 84, 91, 92, 97, 101, 102, 105, 106, 111, 113], "maria": [1, 3, 15, 22, 23, 32, 37, 39, 66, 75, 81], "mariana": [4, 12, 18, 30, 53, 73, 78], "mariann": [43, 70], "marin": [66, 75, 81], "marina": [66, 75, 81, 82], "mario": [66, 75, 81], "marion": [10, 11, 18, 29, 31, 42, 49, 53, 55, 67, 70, 81, 87, 91, 104], "marionlouveaux": [42, 70, 87, 91, 104], "marisc": [7, 11, 17, 18, 21, 29, 39, 42, 49, 51, 53, 55, 56, 58, 66, 67, 70, 72, 75, 81, 87, 90, 91, 95, 96, 101, 102, 104, 109], "mark": [7, 11, 36, 46, 49, 53, 57, 66, 70, 73, 81, 84, 91, 94, 96, 98, 101, 109, 111, 112, 114, 116], "marker": [29, 66, 75, 81], "marku": [33, 38, 46, 53, 66, 75, 81, 98, 116], "marqu\u00e9": [53, 73, 101], "marsikova": [29, 53, 66, 78], "martha": [66, 75, 81], "martin": [1, 3, 4, 5, 7, 9, 11, 12, 15, 17, 18, 22, 23, 25, 29, 30, 31, 32, 36, 37, 39, 43, 48, 49, 51, 52, 53, 55, 56, 66, 67, 70, 71, 72, 73, 75, 78, 80, 81, 82, 84, 85, 87, 90, 93, 94, 95, 96, 101, 102, 104, 109, 113, 117], "martina": [12, 43, 49, 66, 70, 73, 85, 91], "mart\u00ednez": [53, 73, 81, 94, 96, 101], "marvin": [29, 43, 49, 70, 81, 109], "marzia": [1, 15, 39, 66, 75, 81], "mask": [31, 81], "mask_filt": [25, 31, 66, 75, 81], "mass": [33, 38, 66, 75, 81], "massachusett": [30, 33, 66, 75, 81], "massei": [1, 3, 6, 8, 16, 23, 29, 37, 48, 51, 56, 57, 66, 70, 75, 81, 85, 91, 94, 99, 101, 102, 105, 106, 109, 111], "massimi": [1, 15, 39, 66, 75, 81], "massiv": [66, 75, 81], "master": [10, 29, 55, 69, 87, 90, 91, 98], "mat": [43, 87, 111], "match": [14, 56, 57, 91, 96, 111], "materi": [6, 7, 9, 15, 17, 22, 38, 39, 41, 45, 61, 62, 65, 69, 80, 82, 84, 95, 98, 107, 115, 117], "materialien": [41, 81, 107], "mateusz": [11, 53, 81, 93, 96, 101, 114], "math": 66, "mathemat": [30, 33, 66, 75, 81], "mathia": [4, 11, 12, 18, 30, 53, 73, 78, 87, 92, 97, 101, 102], "mathur": [1, 4, 12, 14, 18, 30, 53, 55, 65, 66, 73, 75, 78, 81, 87, 111], "matser": [1, 15, 39, 66, 75, 81], "matt": [4, 5, 7, 9, 36, 48, 70, 81], "matteoni": [1, 15, 39, 66, 75, 81], "matter": [66, 75, 81], "mattern": [33, 38, 66, 75, 81], "matthew": [4, 12, 17, 18, 30, 39, 53, 66, 73, 78, 81, 87, 91, 92, 97, 102, 107, 111], "matthia": [1, 17, 18, 25, 28, 29, 31, 51, 53, 66, 67, 75, 81, 101], "matthij": [53, 73, 81, 92, 96, 97, 101, 111], "mattila": [12, 29, 43, 49, 66, 70, 73, 75, 81, 85, 91], "mattyrowei": [10, 29, 66, 75, 81], "matu": [51, 81, 91], "mat\u00fa\u0161": [17, 18, 29, 51, 53, 67, 81, 101], "maulakhan": [4, 5, 7, 9, 36, 48, 70, 81], "maven": [15, 48, 70, 84, 106], "maweigert": [21, 25, 49, 55, 70, 87, 90, 91, 104, 109], "max": [3, 33, 34, 37, 66, 75, 81], "maxim": [4, 57, 70, 87, 91, 104], "maximiliaan": [11, 53, 87, 92, 97, 101, 102], "maximilian": [6, 16, 28, 51, 66, 75, 81, 91, 94, 105, 111], "mayrhof": [1, 15, 39, 66, 75, 81], "mazzamuto": [11, 53, 81, 92, 97, 111], "mccormick": [4, 5, 7, 9, 36, 48, 70, 81], "mcelreath": [48, 70, 74, 87], "mcgill": [30, 33, 66, 75, 81], "mcherri": [24, 31, 66, 75, 81], "mcherry_3t_4z_5lambda": [24, 66, 75, 81], "mcnamara": [11, 43, 81, 107], "mcquad": [10, 29, 66, 75, 81], "md": [48, 57, 70, 81, 90, 115], "mdemic": [66, 75], "mdpi": [53, 81, 107, 111], "me": [66, 75], "mea": [42, 49, 55, 70, 87, 91, 104], "mean": [1, 15, 29, 39, 66, 75, 81], "meaning": [9, 62, 66, 75, 81], "measur": [1, 3, 15, 22, 33, 39], "mechan": [17, 34, 36, 55, 56, 66, 74, 75, 81, 87, 91, 95, 96, 102, 111], "med": [3, 22, 23, 30, 32, 33, 37, 56, 66, 75, 81, 91, 105, 111], "media": [2, 31, 43, 57, 66, 75, 81, 87, 106, 114], "medic": [1, 15, 29, 30, 33, 39, 77], "medicin": [3, 23, 30, 32, 37, 56, 91, 105, 111], "meechan": [43, 70], "meester": [3, 8, 11, 17, 33, 53, 67, 81, 92, 97, 105, 111], "meet": [5, 14, 51, 55, 95, 96, 106], "mehmet": [25, 31, 66, 75, 81], "mehrer": [29, 56, 66, 75, 81, 96, 100, 111], "mehrnaz": [0, 16, 23, 37, 38, 46, 47, 48, 53, 66, 70, 75, 81, 91, 96, 105, 106, 109, 111], "mehrten": [66, 75, 81], "mein": 26, "melbourneadvancedmicroscop2617": [43, 58, 74, 91], "melissa": [2, 4, 5, 11, 15, 17, 48, 53, 70, 73, 81, 84, 92, 93, 96, 101, 105, 108, 111, 114], "member": [3, 6, 8, 17, 22, 23, 32, 33, 36, 37, 41, 43, 49, 53, 55, 57, 66, 67, 75, 81, 91, 92, 105, 111, 114], "membran": [24, 66, 75, 81], "mengen": [66, 75, 81], "mention": 88, "mentor": [1, 66, 75, 81], "mercier": [24, 66, 75, 81], "merg": [29, 56, 62, 66, 75, 81, 96, 111], "messag": 62, "meta": [56, 61, 88, 91, 96, 105, 111], "metadata": [0, 1, 6, 8, 14, 17, 18, 20, 22, 23, 24, 29, 31, 32, 38, 55, 56, 67, 72, 82, 83, 88, 93, 114], "metadaten": [66, 75, 81], "metajnl": [46, 53, 98, 116], "metastas": [25, 31, 66, 75, 81], "method": [3, 12, 14, 22, 29, 30, 33, 34, 37, 41, 43, 46, 48, 49, 57, 70, 74, 87, 90, 92, 95, 96, 98, 99, 106, 111, 115], "methoden": [29, 56, 66, 75, 81, 96, 100, 111], "methodolog": [53, 81, 96, 111], "methodologi": [66, 75, 81], "metric": [25, 31, 66, 71, 75, 81, 111, 114], "metuarea": [49, 70, 87, 90, 91], "metz": [1, 46, 53, 85, 87, 90, 95, 109, 116], "mexico": [30, 33, 66, 75, 81], "meysenburg": [7, 11, 36, 57, 81, 91, 109, 112, 114], "meystr": [66, 75, 81], "miacellavi": [30, 33, 66, 75, 81], "mianalysi": [46, 53, 66, 98, 116], "miao": [48, 70, 91], "miap": [30, 33, 66, 75, 81], "mice": [22, 66, 75, 81], "michael": [1, 3, 4, 6, 9, 12, 15, 18, 23, 29, 30, 32, 34, 37, 39, 41, 43, 48, 53, 66, 70, 73, 75, 78, 81, 82, 87, 90, 91, 93, 105, 111, 117], "michaela": [1, 15, 39, 66, 75, 81], "michal": [11, 18, 55, 57, 70, 87, 91, 104], "michel": [0, 3, 8, 11, 16, 22, 28, 30, 32, 33, 34, 35, 37, 38, 39, 46, 47, 48, 53, 55, 58, 66, 70, 73, 74, 75, 81, 87, 88, 91, 92, 96, 97, 101, 105, 106, 109, 111, 117], "micpy_workshop_2022": [13, 49, 70, 87, 91, 109], "micro": [5, 49, 56, 70, 85, 88, 91, 95, 109, 112], "microcopi": [3, 22, 23, 32, 37, 66, 75, 81], "microfluid": [56, 74, 87, 95, 102], "micron": [24, 25, 30, 31, 33, 66, 75, 81], "microscop": [0, 3, 24, 25, 29, 30, 31, 33, 38, 46, 51, 53, 96, 98, 111, 116], "microscopi": [5, 6, 17, 20, 24, 25, 31, 46, 47, 48, 57, 60, 65, 67, 80, 83, 88, 99, 103, 107, 108], "microscopia": [30, 33, 66, 75, 81], "microscopist": 78, "microsoft": [3, 22, 23, 32, 37, 66, 75, 81], "microsycopi": [68, 87, 95], "microsystem": [30, 33, 66, 75, 81], "might": [3, 16, 22, 23, 25, 31, 32, 35, 37, 66, 75, 81], "mihcsm": [53, 73, 81, 92, 96, 97, 101, 111], "mikautsch": [66, 75, 81], "mike": [12, 43, 49, 66, 70, 73, 85, 91], "mikolaj": [49, 70, 91], "mikroskopi": [0, 32, 66, 75, 81], "milan": [29, 48, 49, 57, 60, 70, 80, 91, 114, 115], "mild": [24, 31, 66, 75, 81], "mileston": [53, 73, 91, 96, 99, 115], "million": [53, 75, 81, 108], "mimic": [66, 75, 81], "mind": [16, 35, 66, 75, 81], "minim": [43, 49, 91, 111], "minimis": [66, 75], "minimum": [53, 73, 81, 92, 96, 97, 101, 111], "ministri": [20, 45, 66, 75, 81, 111], "minut": [31, 66, 75, 81], "miroscopi": [22, 66, 75, 81], "mirror": [66, 75, 81], "misiak": [46, 53, 98, 116], "misinterpret": 87, "mission": [33, 43, 56, 59, 66, 81, 87, 91, 96, 105, 111], "misus": [22, 66, 75], "mit": [0, 4, 5, 7, 11, 12, 13, 28, 32, 41, 42, 43, 46, 48, 49, 57, 60, 70, 71, 73, 77, 90, 91, 95, 99, 100, 102, 103, 106, 109, 112, 115, 116], "mitarbeit": [29, 56, 66, 75, 81, 96, 111], "mitglied": [0, 32, 66, 75, 81], "mithilf": [26, 56, 66, 75, 81, 111], "mitot": [30, 33, 66, 75, 81], "miura": [4, 9, 10, 11, 12, 17, 29, 30, 31, 41, 42, 49, 51, 53, 55, 57, 66, 67, 70, 73, 75, 78, 81, 87, 91, 101, 104], "mm": [22, 66, 75, 81], "mmunofluoresc": [30, 33, 66, 75, 81], "modal": [29, 33, 38, 66, 75, 81], "model": [1, 3, 8, 16, 22, 23, 28, 30, 33, 34, 35, 37, 39, 40, 41, 46, 48, 56, 58, 67, 68, 74, 85, 95, 105, 111, 116], "modern": [45, 46, 57, 111], "modif": [62, 66, 75, 81], "modifi": [29, 62, 66, 75, 81], "modul": [3, 23, 28, 48, 50, 55, 57, 66, 70, 75, 81, 83, 85, 91, 106, 109, 111, 115], "modular": [46, 53, 98, 116], "moehl": [11, 15, 48, 70, 84, 106], "moham": [0, 16, 23, 37, 38, 46, 47, 48, 53, 66, 70, 75, 81, 91, 96, 105, 106, 109, 111], "mohsen": [6, 16, 28, 33, 38, 51, 66, 75, 81, 91, 94, 105, 111], "molecul": [1, 3, 28, 33, 66, 75, 81], "molecular": [30, 33, 66, 75, 81], "molnar": [42, 49, 55, 70, 87, 91, 104], "moman": [3, 33, 66, 75, 81], "moment": [26, 29, 56, 66, 75, 81, 111], "mondai": [3, 33, 66, 75, 81], "monica": [0, 16, 23, 37, 38, 46, 47, 48, 51, 53, 66, 70, 75, 81, 91, 96, 105, 106, 109, 111], "monika": [53, 81, 96, 111], "moniqu": [52, 53, 71, 81, 91, 111, 114], "monko": [46, 48, 57, 70, 80, 91, 102, 103], "montero": [4, 12, 18, 30, 33, 43, 53, 66, 73, 75, 78, 80, 81, 88, 101], "monthli": [66, 75, 81], "montpelli": [30, 33, 66, 75, 81], "montr": [30, 33, 66, 75, 81], "montreal": [30, 33, 66, 75, 81], "moor": [0, 2, 3, 4, 5, 8, 11, 15, 16, 18, 23, 29, 30, 33, 34, 35, 37, 38, 46, 47, 48, 51, 52, 53, 56, 66, 67, 70, 71, 73, 75, 79, 80, 81, 84, 91, 92, 96, 97, 101, 102, 105, 106, 107, 108, 109, 111, 113, 117], "mor": [29, 66, 75, 81], "more": [1, 3, 6, 9, 17, 28, 29, 30, 33, 34, 37, 38, 40, 43, 46, 49, 50, 56, 57, 61, 66, 75, 81, 87, 91, 92, 96, 98, 106, 111, 113, 115], "moreov": [3, 29, 37, 66, 75, 81], "morgan": [5, 49, 55, 70, 87, 90, 91], "moritz": [0, 16, 17, 18, 23, 29, 37, 38, 46, 47, 48, 51, 52, 53, 66, 67, 70, 71, 75, 81, 91, 96, 101, 105, 106, 109, 111], "moshkov": [41, 70, 87, 90, 91], "most": [43, 55, 57, 66, 72, 75, 111, 113], "mostli": [13, 15, 43, 48, 49, 62, 66, 70, 72, 75, 81, 84, 85, 93, 103, 106, 109, 114], "moth": [66, 75, 81], "motion": [66, 75, 81], "motivationen": [66, 75, 81], "mous": [24, 25, 29, 30, 31, 33, 81], "move": [3, 23, 28, 37, 66, 75, 81], "mpg": [56, 66, 75, 81, 91, 96, 105, 111], "mpi": [80, 104], "mpicbg": [29, 70, 91, 98], "mraz\u00edk": [20, 66, 75], "mrc": [49, 83, 91, 98], "mri": [30, 33, 66, 75, 81], "mrx": [2, 11, 17, 48, 70, 84, 92, 108], "mtiworkshop": [48, 56, 60, 65, 70, 81, 91, 102], "mu": [30, 33, 66, 75, 81], "much": [17, 48, 66, 70, 75, 81], "mueller": [3, 22, 23, 32, 37, 66, 75, 81], "multi": [24, 28, 31, 88], "multidisciplinari": [55, 66, 75, 111], "multin": [20, 66, 75, 81], "multipl": [17, 30, 33, 51, 53, 61, 66, 73, 75, 81, 91, 92, 96, 97, 101, 111], "multiresolut": [24, 31, 66, 75, 81], "multiview": [29, 55, 69, 91, 98, 104], "munck": [17, 18, 29, 51, 53, 67, 81, 101], "munich": [48, 57, 66, 70, 75, 81, 85], "musacchio": [43, 91, 103, 109], "musen": [53, 66, 73, 75, 81, 94, 96, 101], "must": [3, 23, 28, 37, 45, 61, 111], "mutli": [24, 66, 75, 81], "mutual": [9, 66, 75, 81], "mutunduw": [10, 29, 66, 75, 81], "muylderman": [48, 57, 70, 81, 115], "mu\u00f1oz": [10, 17, 18, 29, 51, 53, 66, 67, 75, 81, 101], "mx": [30, 33, 66, 75, 81], "myat": [10, 29, 66, 75, 81], "myer": [12, 29, 51, 53, 66, 75, 81, 98], "myle": [53, 66, 73, 81, 96, 111], "myocardium": [30, 33, 66, 75, 81], "m\u00e1hlerova": [20, 66, 75], "m\u00e9nager": [17, 18, 29, 51, 53, 67, 81, 101], "m\u00f6chten": [26, 56, 66, 75, 81, 111], "m\u00f6glich": 77, "m\u00f6glichkeit": [0, 32, 66, 75, 81], "m\u00f6glichkeiten": [53, 66, 81, 111], "m\u00f6hl": [0, 16, 23, 37, 38, 46, 47, 48, 53, 66, 70, 75, 81, 91, 96, 105, 106, 109, 111], "m\u00f6ller": [46, 53, 98, 116], "m\u00fchlhau": [0, 38, 51, 75, 81, 96, 111], "m\u00fcller": [6, 16, 24, 28, 29, 31, 49, 51, 56, 70, 91, 94, 103, 105, 109, 111], "m\u00fcnchen": [6, 56, 66, 75, 81, 91], "m\u00fcnster": [16, 35, 53, 66, 75, 78, 81, 105, 106, 111], "m\u00fcssen": [26, 56, 66, 75, 81, 96, 111], "n": [3, 8, 17, 23, 24, 25, 30, 31, 33, 34, 37, 51, 66, 75, 81, 91, 96, 105, 111], "na": [29, 66, 75, 81], "nach": [0, 29, 32, 56, 66, 75, 81, 96, 111], "nachfolgenden": [0, 32, 66, 75, 81], "nachhaltigkeit": [66, 75, 81], "nacion": [30, 33, 66, 75, 81], "nadia": [4, 12, 18, 30, 53, 73, 78], "nadin": [66, 75, 81], "nagi": [0, 32, 66, 75, 81], "naji": [29, 66, 75, 81], "name": [3, 6, 20, 22, 23, 24, 25, 28, 30, 31, 32, 33, 37, 56, 61, 63, 66, 75, 77, 81, 111], "nanguag": [46, 85, 95, 109, 116], "nanoparticl": [30, 33, 66, 75, 81], "nanosecond": [66, 75, 81], "napari": [29, 41, 46, 48, 58, 74, 102, 104, 111], "nar": [66, 75, 81], "narayan": [7, 21, 39, 56, 58, 72, 81, 95, 102, 109], "narg": [10, 29, 66, 75, 81], "nasim": [43, 53, 66, 70, 80, 91], "nat": [14, 56, 57, 66, 75, 81, 91, 96, 111], "nata\u0161a": [17, 18, 29, 51, 53, 67, 81, 101], "nathali": [4, 12, 18, 24, 30, 31, 53, 66, 73, 75, 78, 81], "nation": [1, 11, 67, 88], "national": [6, 55, 66, 75, 81, 92, 105, 111], "nationalen": 29, "nativ": [17, 34, 51, 56, 75, 81, 91, 96, 111], "natur": [2, 3, 4, 5, 11, 12, 15, 17, 18, 23, 28, 29, 30, 33, 34, 37, 39, 43, 44, 46, 48, 49, 53, 66, 70, 75, 78, 81, 85, 87, 91, 92, 94, 96, 97, 98, 99, 101, 105, 111, 113, 115, 116], "navaroli": [30, 33, 66, 75, 81], "navig": [9, 63], "nbi": [0, 16, 23, 37, 38, 48, 75, 81, 91, 96, 105, 111], "nbsp": [1, 15, 24, 30, 33, 39, 66, 75, 81], "nc": [12, 29, 41, 43, 48, 50, 53, 58, 70, 73, 75, 91, 96, 98, 99, 106, 115], "ncbi": [3, 11, 22, 23, 32, 37, 39, 53, 66, 73, 75, 81, 87, 92, 94, 96, 97, 101, 102, 111], "ncl": [30, 33, 66, 75, 81], "nd": [22, 41, 43, 49, 58, 66, 70, 75, 81, 91, 103, 109], "ndev": [46, 48, 57, 70, 80, 91, 102, 103], "nearli": [24, 66, 75, 81], "necessari": [3, 8, 16, 23, 28, 29, 33, 35, 63, 66, 75, 81, 82], "necessarili": [66, 75, 81], "neckel": [10, 29, 66, 75, 81], "need": [3, 8, 11, 17, 23, 28, 29, 30, 31, 33, 34, 37, 39, 43, 49, 57, 58, 59, 67, 72, 73, 75, 76, 81, 82, 83, 87, 88, 92, 97, 101, 111], "nehm": [12, 29, 43, 49, 66, 70, 73, 75, 81, 85, 91], "nelson": [4, 11, 12, 18, 30, 33, 49, 52, 53, 66, 70, 71, 73, 75, 78, 80, 81, 88, 91], "neringa": [29, 49, 70, 81, 109], "nervou": [29, 66, 75, 81], "nestor": [41, 66, 75, 81, 111], "net": [24, 31, 43, 44, 45, 46, 57, 66, 75, 81, 87, 91, 111], "netherland": [53, 73, 81, 92, 96, 97, 101, 111], "netlifi": [41, 57, 81, 85, 111, 113], "network": [3, 6, 16, 23, 28, 33, 34, 35, 37, 53, 56, 73, 78, 92, 96, 97, 101, 105, 106, 111], "netzwerk": [0, 32, 66, 75, 81], "neubia": [5, 7, 9, 13, 36, 41, 48, 65, 69, 78, 79, 98, 103, 111], "neubias2018_ts7": [42, 70, 87, 91, 104], "neubias_academy_biapi": [49, 70, 80, 91, 104, 109], "neubias_academy_stardist": [21, 25, 49, 55, 70, 87, 90, 91, 104, 109], "neubias_analystschool2018": [42, 49, 55, 70, 87, 91, 104], "neubias_analystschool2019": [11, 18, 29, 42, 49, 55, 70, 87, 91, 104], "neubias_analystschool2020": [10, 11, 18, 42, 49, 55, 70, 87, 91, 104], "neubias_bioimage_analyst_course2017": [11, 18, 55, 57, 70, 87, 91, 104], "neubias_chapter_dl_2020": [10, 55, 70, 87, 90, 91], "neue": [29, 56, 66, 75, 81, 96, 111], "neumann": [41, 66, 75, 81, 111], "neurit": [66, 75, 81], "neuro": [30, 33, 66, 75, 81], "neurobia": [4, 5, 7, 9, 36, 48, 70, 81], "neurofila": [29, 66, 75, 81], "neuroglanc": [0, 16, 23, 37, 38, 48, 75, 81, 91, 96, 105, 111], "neuroimag": [0, 11, 38, 51, 53, 75, 81, 92, 96, 97, 111], "neuroinformat": [43, 57, 81, 114], "neurosci": [11, 30, 33, 43, 53, 57, 66, 75, 81, 92, 97, 111, 114], "nevertheless": [3, 33, 34, 37, 66, 75, 81], "new": [1, 3, 10, 14, 17, 22, 23, 25, 28, 29, 31, 32, 37, 39, 40, 53, 56, 57, 62, 63, 90, 91, 92, 96, 97, 111, 117], "newbi": [66, 75, 81], "newcastl": [30, 33, 66, 75, 81], "newli": [3, 6, 17, 23, 32, 34, 37, 41, 51, 55, 66, 75, 81, 92, 96, 105, 111], "next": [24, 31, 43, 58, 62, 74, 84, 92, 106, 108], "nf": [42, 43, 70, 87, 109], "nfdi": [3, 8, 17, 23, 28, 33, 37, 38, 51, 55, 56, 82, 88, 91, 92, 96], "nfdi46": [66, 75, 81], "nfdi4bioimag": [1, 2, 4, 5, 15, 35, 58, 59, 61, 73, 74, 78, 87, 94, 117], "nfdi4biomag": [66, 75, 81], "nfdi4plant": [43, 81, 111, 114], "ngff": [11, 43, 58, 74, 106], "nichola": [10, 29, 66, 75, 81], "nicht": [26, 29, 56, 66, 75, 77, 81, 96, 111], "nick": [1, 15, 33, 38, 39, 66, 75, 81], "nicola": [4, 7, 9, 31, 36, 41, 48, 53, 57, 66, 70, 75, 81, 91, 109, 111, 115, 117], "nicol\u00e1": [17, 18, 29, 51, 53, 67, 81, 101], "night": [20, 66, 75], "nih": [3, 11, 22, 23, 32, 37, 39, 52, 53, 66, 70, 71, 73, 75, 80, 81, 87, 91, 92, 94, 96, 97, 101, 102, 111], "nikita": [41, 70, 87, 90, 91], "nikla": [1, 15, 39, 66, 75, 81], "nikola": [12, 74, 87, 90], "nikon": [30, 33, 66, 75, 81], "nil": [4, 24, 53, 66, 81, 91, 111], "nima": [4, 5, 7, 9, 36, 48, 66, 70, 75, 81, 117], "nina": [0, 32, 66, 75, 81], "niraj": [0, 3, 8, 16, 23, 33, 34, 35, 37, 38, 46, 47, 48, 51, 53, 55, 58, 66, 70, 74, 75, 81, 88, 91, 96, 105, 106, 109, 111], "nitschk": [3, 4, 8, 12, 17, 18, 23, 33, 34, 35, 37, 53, 66, 73, 75, 78, 81, 88, 111], "nl": [48, 60, 70, 91, 106, 111], "nlm": [3, 11, 22, 23, 32, 37, 39, 53, 66, 73, 75, 81, 87, 92, 94, 96, 97, 101, 102, 111], "nlp_cours": [48, 70, 85], "nm": [24, 31, 66, 75, 81], "nmeth": [43, 44, 53, 73], "nno": [29, 66, 75, 81], "no_npi": [25, 31, 66, 75, 81], "nobi": [66, 75, 81], "nobst": [3, 22, 23, 32, 37, 66, 75, 81], "nodar": [7, 21, 39, 56, 58, 72, 81, 95, 102, 109], "node": [53, 73, 81, 92, 96, 97, 101, 111], "noe": [10, 29, 66, 75, 81], "noi": [66, 75, 81], "nois": [24, 66, 75, 81], "non": [43, 49, 66, 75, 81, 91, 111], "none": [28, 29, 43, 46, 48, 49, 50, 53, 56, 57, 58, 60, 66, 70, 74, 81, 85, 91, 92, 97, 98, 99, 102, 106, 108, 109, 114, 115], "norlin": [4, 24, 53, 66, 81, 91, 111], "normal": [66, 75, 81], "norman": [15, 17, 37, 38, 48, 70, 80, 113], "normox": [29, 66, 75, 81], "north": [4, 12, 18, 30, 33, 53, 66, 73, 75, 78, 81], "northan": [29, 49, 70, 81, 109], "note": [3, 17, 22, 23, 25, 29, 31, 32, 37, 66, 75, 81], "notebook": [3, 5, 6, 10, 11, 12, 13, 18, 21, 23, 25, 29, 31, 37, 42, 47, 53, 55, 56, 57, 60, 64, 71, 73, 82, 83, 84, 87, 90, 95, 97, 98, 102, 103, 104, 108, 111, 112, 114, 115], "noth": [48, 70], "novel": [1, 3, 8, 28, 55, 56, 66, 74, 75, 81, 87, 92, 95, 97, 102, 105, 111], "novo": [3, 33, 34, 37, 66, 75, 81], "now": [1, 14, 16, 17, 35, 51, 62, 66, 75, 81, 91, 111], "nowadai": [3, 33, 66, 75, 81], "nr": [30, 33, 66, 75, 81], "nrg": [24, 66, 75, 81], "nsch": [30, 33, 66, 75, 81], "nsg": [25, 31, 66, 75, 81], "nuclei": [24, 28, 48, 85, 106], "nuclei_from_bf": [25, 31, 66, 75, 81], "nuclei_from_dpc": [25, 31, 66, 75, 81], "nuclei_from_sqrdpc": [25, 31, 66, 75, 81], "nucleic": [66, 75, 81], "nucleom": [30, 33, 66, 75, 81], "nucleu": [30, 33, 66, 75, 81], "null": [24, 66, 75, 81], "nullpointerexcept": [66, 75, 81], "number": [6, 20, 22, 28, 37, 56, 66, 75, 81, 87, 111], "numer": [28, 51, 66, 81, 91], "numpi": 85, "numpypandas_cours": [13, 49, 70, 80, 109], "nun": [41, 81, 107], "nunez": [30, 33, 48, 49, 66, 70, 75, 80, 81, 91, 109], "nur": [29, 56, 66, 75, 81, 96, 111], "nutzer": [56, 66, 75, 81, 111], "nutzt": [0, 32, 66, 75, 81], "nutzung": [0, 32, 66, 75, 77, 81], "nutzungsm\u00f6glichkeiten": [59, 87, 105, 111], "nutzungsrechten": [26, 56, 66, 75, 81, 111], "nvidia": [25, 31, 66, 75, 81], "nyffel": [1, 28, 66, 75, 81], "nyula": [66, 75, 81], "n\u00e4her": [29, 56, 66, 75, 81, 111], "n\u00f8rrelykk": [11, 25, 31, 43, 47, 60, 87, 91, 102], "n\u00fcst": [29, 66, 75, 81], "n\u00fctzlichen": [26, 56, 66, 75, 81, 111], "o": [43, 48, 49, 53, 66, 70, 73, 75, 81, 87, 91, 94, 96, 101, 108, 109, 111], "oa": [41, 66, 75, 81, 107], "oan": [4, 5, 7, 9, 36, 48, 70, 81], "object": [1, 3, 6, 8, 9, 17, 20, 23, 24, 25, 28, 31, 33, 34, 35, 36, 37, 51, 58, 74, 96, 105, 106, 111], "observ": [9, 29, 30, 33, 53, 66, 75, 81, 88, 108], "observatori": [53, 73, 81, 92, 96, 97, 101, 111], "obtain": [1, 22, 25, 28, 31, 66, 75, 81], "oct": [60, 91, 106, 111], "octob": [17, 29, 48, 49, 56, 57, 70, 80, 91, 105, 106, 111], "od": [66, 75, 82], "odc": [43, 91], "odebrecht": [41, 66, 75, 81, 111], "oder": [26, 41, 45, 77, 107], "oer": [66, 75, 81], "oezdemir": [4, 7, 9, 36, 57, 66, 75, 81, 87, 117], "offen": [41, 107], "offer": [1, 6, 11, 16, 28, 29, 43, 45, 48, 51, 53, 56, 57, 66, 70, 73, 75, 81, 85, 87, 91, 92, 93, 94, 96, 99, 100, 101, 102, 105, 109, 111, 114], "offic": [3, 20, 22, 23, 32, 37, 47, 66, 75, 81, 91], "ofra": [17, 18, 29, 31, 51, 53, 67, 81, 91, 101, 104], "oft": [26, 56, 66, 75, 81, 96, 111], "often": [3, 8, 23, 28, 29, 33, 34, 35, 37, 45, 55, 58, 66, 74, 75, 81, 88, 105, 106, 111], "oh3vki7era": [58, 74], "ohmann": [1, 15, 39, 66, 75, 81], "ohn": 77, "oil": [24, 31, 66, 75, 81], "okafornta": [3, 22, 23, 32, 37, 66, 75, 81], "okazaki": [17, 66, 75, 81], "ola": [49, 70, 91], "oladazimi": [66, 75, 81], "olaf": [4, 12, 18, 30, 53, 73, 78], "old": [3, 22, 23, 32, 37, 66, 75, 81], "older": [16, 35, 66, 75, 81], "oleg": [53, 66, 81, 96, 111], "olga": [20, 66, 75, 81], "oliv": [4, 5, 7, 9, 12, 17, 36, 43, 48, 49, 52, 53, 66, 70, 71, 73, 75, 81, 85, 91, 109, 117], "olivi": [11, 21, 24, 31, 43, 47, 49, 55, 60, 66, 70, 75, 81, 87, 90, 91, 102, 104, 109], "olympu": [25, 29, 30, 31, 33, 66, 75, 81], "om": [1, 3, 8, 22, 23, 24, 28, 30, 31, 32, 33, 34, 35, 37, 38, 51, 55, 75, 80, 84, 96, 113], "om9tl4dh2yw": [12, 74, 87, 90], "omero": [0, 1, 2, 6, 29, 30, 38, 47, 82, 88, 92, 97, 107, 114, 117], "omero_batch": [48, 70, 84, 98, 106], "omero_script": [40, 64, 81, 90, 91, 109], "omfreder": [48, 56, 60, 65, 70, 81, 91, 102], "omic": [11, 66, 75, 87, 93, 107, 111, 113], "omp2info": [24, 66, 75, 81], "onbi_image_analysi": [49, 70, 84, 91, 109], "onc": 63, "one": [3, 8, 14, 23, 28, 29, 31, 33, 34, 35, 37, 50, 55, 57, 58, 66, 74, 75, 81, 83, 87, 92, 102, 105, 106, 111, 115], "ones": [66, 75, 81], "ongo": [3, 8, 23, 28, 53, 66, 73, 75, 81, 94, 96, 113], "onli": [1, 22, 24, 28, 53, 62, 66, 73, 75, 81, 82, 89, 91, 96, 99, 115], "onlin": [3, 4, 5, 9, 14, 29, 46, 48, 55, 57, 58, 59, 67, 70, 72, 75, 78, 79, 82, 83, 88, 92, 93, 94, 95, 98, 106, 107, 108, 113, 115], "onlinelibrari": [3, 4, 8, 9, 11, 29, 30, 33, 34, 37, 53, 81, 91, 111], "onlineplatform": [29, 56, 66, 75, 81, 96, 111], "ontologi": [3, 8, 14, 16, 23, 28, 35, 37, 56, 57, 66, 75, 96, 111], "ontologieentwicklung": [66, 75, 81], "oop_titl": [41, 91], "open": [1, 3, 8, 14, 20, 22, 23, 25, 28, 30, 31, 32, 33, 34, 35, 37, 39, 45, 46, 48, 50, 57, 59, 63, 72, 79, 82, 84, 93, 94, 95, 97, 98, 105, 113, 116], "openai": [29, 66, 75, 81], "opendatahandbook": [11, 43, 81, 107], "openli": [3, 8, 23, 29, 33, 34, 35, 37, 40, 55, 56, 58, 62, 66, 74, 75, 81, 87, 91, 96, 100, 105, 106, 107, 111, 113], "openlink": [66, 75], "openmicroscopi": [24, 43, 44, 46, 47, 53, 66, 73, 75, 81, 106, 111], "openmicroscopyenviron": [43, 58, 74, 81, 92, 102, 106, 108], "openresearchsoftwar": [46, 53, 98, 116], "opensci": [59, 87, 111], "oper": [3, 8, 23, 28, 31, 66, 75], "operetta": 31, "operetta_import": [31, 66, 75, 81], "opportun": [3, 33, 34, 37, 66, 75], "opt": [66, 75, 81], "optic": [22, 30, 33, 38], "optim": [3, 37, 56, 94, 96], "option": [3, 6, 10, 20, 23, 28, 29, 37, 56, 61, 66, 75, 111], "oral": [3, 33, 66, 75, 81], "orchard": [11, 53, 66, 87, 93, 107, 111, 113], "order": [22, 25, 31, 33, 38, 45, 53, 66, 73, 75, 81, 94, 96, 111, 113], "oresti": [30, 33, 66, 75, 81, 88], "org": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 17, 20, 22, 23, 24, 25, 26, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 55, 56, 57, 58, 67, 68, 70, 72, 73, 74, 78, 80, 82, 85, 87, 88, 90, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 105, 106, 107, 108, 109, 112, 113, 115, 116, 117], "organ": [1, 3, 6, 8, 11, 20, 22, 23, 28, 30, 33, 34, 35, 37, 53, 55, 56, 57, 58, 59, 66, 74, 82, 87, 91, 92, 94, 97, 105, 106], "organigram_team": [30, 33, 66, 75, 81], "organis": [1, 15, 39, 43, 57, 58, 72, 83, 92, 97], "organisatorisch": [66, 75, 81], "organisatorischen": [66, 75, 81], "organoid": [30, 33, 66, 75, 81], "orient": [3, 37, 66, 75, 81, 82], "origin": [3, 20, 57, 87, 111], "orign": [22, 66, 75, 81], "orkg": [66, 75, 81], "osman": [7, 21, 39, 56, 58, 72, 81, 95, 102, 109], "osnabr\u00fcck": [3, 33, 34, 37, 66, 75, 81], "oss": [53, 75, 81, 108], "other": [1, 3, 8, 11, 17, 20, 23, 28, 29, 33, 34, 37, 43, 45, 48, 51, 53, 55, 56, 57, 62, 66, 69, 70, 72, 75, 76, 81, 83, 87, 88, 91, 92, 93, 96, 97, 98, 101, 102, 105, 107, 111, 113, 114, 117], "otherwis": [3, 22, 23, 32, 37, 66, 75, 81, 88], "oup": [46, 53, 109, 116], "our": [1, 3, 9, 28, 30, 32, 33, 41, 57, 58, 62, 63, 66, 72, 75, 76, 83, 88, 89, 92, 97], "out": [3, 23, 29, 32, 37, 66, 75, 81], "outcom": [45, 66, 75, 81, 111], "outlin": [1, 6, 14, 28, 29, 43, 53, 55, 56, 57, 66, 67, 75, 78, 81, 91, 92, 96, 99, 104, 105, 109, 111], "outlook": [3, 37, 66, 75, 81], "output": [25, 31, 53, 66, 75, 81, 90, 108], "outreach": [66, 75, 81], "ouyang": [1, 11, 29, 46, 53, 66, 70, 75, 81, 85, 87, 90, 95, 109, 116], "ovari": [30, 33, 66, 75, 81], "over": [14, 43, 53, 55, 57, 66, 75, 81, 91, 96, 108, 111, 116], "overcom": [3, 8, 23, 28, 66, 75, 81], "overnight": [24, 31, 66, 75, 81], "overview": [3, 4, 5, 6, 9, 11, 14, 16, 17, 20, 29, 30, 33, 37, 52, 53, 55, 56, 58, 67, 73, 87, 91, 93, 94, 95, 99, 105, 107, 113, 115], "own": [3, 8, 23, 33, 34, 35, 37, 55, 58, 66, 74, 75, 81, 105, 106], "oxford": [49, 66, 70, 75, 84, 91, 109], "oxinst": [43, 58, 78, 91], "oxncaxrqu4xqdujzwfhvwlf06pvdvm": [43, 58, 74, 81, 91, 111], "oyi": [43, 58, 74, 81, 91, 111], "p": [1, 3, 4, 10, 12, 18, 23, 25, 29, 30, 31, 37, 41, 53, 55, 65, 66, 67, 70, 73, 75, 78, 81, 87, 91, 104, 109, 117], "p1": [48, 55, 56, 60, 65, 70, 81, 87, 91, 102], "pa": [45, 111], "paavolainen": [17, 18, 29, 51, 53, 67, 81, 101], "packag": [13, 48, 49, 50, 70, 79, 85, 91, 95, 98, 106, 116], "packet": [11, 15, 48, 66, 70, 75, 84, 106], "page": [17, 29, 48, 59, 62, 63, 66, 70, 75, 79, 81, 88, 92, 111, 117], "pair": [1, 28], "palagi": [52, 53, 71, 81, 91, 111, 114], "paland": 53, "palett": 87, "palla": [17, 48, 52, 53, 70, 71, 81, 91, 109], "palli": [48, 70, 91], "palmquist": [7, 11, 36, 43, 57, 70, 81, 91, 109, 112, 114], "pan": [29, 66, 75, 81], "panagiotopoul": [1, 15, 39, 66, 75, 81], "pancreat": [56, 74, 87, 95, 102], "panda": 63, "panel": [66, 75], "paolo": [1, 15, 39, 55, 66, 75, 81, 116], "pape": [2, 4, 7, 9, 15, 17, 21, 36, 48, 49, 53, 55, 56, 66, 70, 73, 75, 81, 85, 87, 90, 91, 95, 105, 109, 111, 112], "paper": [9, 17, 20, 29, 34, 43, 49, 53, 73, 83, 91, 92, 101, 102, 105], "paper_29": [66, 75, 81], "paraffin": [24, 31, 66, 75, 81], "paramet": [2, 15, 20, 29, 32, 33, 37, 38, 53, 66, 75, 81, 91, 102], "pari": [30, 33, 66, 75, 81], "parker": [6, 28, 37, 56, 66, 75, 81, 111], "parkinson": [1, 15, 39, 66, 75, 81], "parse_s3_filenam": [66, 75, 81], "parslow": [4, 12, 18, 30, 53, 73, 78], "part": [3, 5, 6, 13, 20, 23, 24, 28, 29, 30, 33, 41, 49, 55, 56, 66, 70, 75, 81, 85, 91, 92, 95, 105, 109, 111], "partial": [66, 75, 81], "particip": [3, 23, 28, 33, 34, 37, 66, 75, 81, 82], "particl": [30, 33], "particlar": 62, "particular": [1, 3, 6, 11, 24, 28, 37, 43, 53, 56, 62, 66, 75, 81, 87, 93, 107, 111, 113], "particularli": [1, 6, 9, 28, 37, 53, 56, 66, 75, 81, 108, 111], "partner": [3, 23, 28, 33, 37, 45, 66, 75, 81, 111], "pascual": [66, 75, 81], "pass": [62, 66, 75, 81], "passiert": [29, 56, 66, 75, 81, 111], "past": [3, 22, 23, 32, 37, 62, 66, 75, 81], "patch": [31, 66, 75, 81], "patent": [66, 75, 81], "patents4sci": [66, 75, 81], "path": [1, 3, 14, 23, 25, 28, 31, 37, 66, 75, 81], "patholog": [22, 66, 75], "pathologi": [25, 31, 66, 75, 81], "patient": [6, 28, 29, 30, 33, 37, 56, 66, 75, 81, 111], "patricia": [52, 53, 71, 81, 91, 111, 114], "patrick": [66, 75, 81], "patter": [53, 66, 81, 91, 97, 106, 115], "pattern": [52, 53, 71, 81, 91, 111, 114], "patwardhan": [39, 53, 66, 73, 87, 92, 97, 111], "paul": [3, 17, 18, 23, 29, 31, 37, 51, 53, 66, 67, 75, 81, 91, 101, 104, 117], "paula": [4, 12, 17, 18, 29, 30, 33, 43, 51, 53, 66, 67, 73, 75, 78, 80, 81, 88, 101], "pauli": [48, 70, 80, 91], "paulin": [1, 15, 39, 66, 75, 81], "pave": [3, 33, 66, 75, 81], "pavel": [12, 29, 51, 53, 98], "pavi": [55, 70, 79, 91, 104], "paxillin": [30, 33, 66, 75, 81], "payn": [4, 12, 18, 30, 53, 73, 78], "paywal": 62, "pb": [15, 17, 24, 31, 37, 38, 48, 66, 70, 75, 80, 81, 113], "pbio": [43, 53, 66, 80], "pcbi": [11, 53, 81, 93, 96, 101, 114], "pcna": [30, 33, 66, 75, 81], "pdac": [56, 74, 87, 95, 102], "pdf": [10, 14, 17, 18, 25, 31, 34, 41, 43, 45, 46, 53, 55, 57, 66, 75, 78, 81, 82, 87, 90, 91, 96, 104, 105, 111], "peer": [41, 81, 107], "pejamn": [11, 18, 29, 42, 49, 55, 70, 87, 91, 104], "pejman": [21, 49, 70, 87, 90, 91, 109], "penghuan": [4, 12, 18, 30, 53, 73, 78], "pengo": [43, 53, 66, 73, 80, 101], "peopl": 89, "pepperkok": [4, 24, 53, 66, 81, 91, 111], "per": [25, 31, 66, 75, 77, 81], "perfectli": [66, 75, 81], "perform": [1, 3, 17, 24, 28, 29, 31, 34, 48, 49, 55, 56, 59, 70, 85, 87, 88, 91, 96, 97, 99, 103, 106, 109, 111, 115], "pericardi": [3, 23, 32, 37, 66, 75, 81], "perikl": 22, "period": [17, 34, 51, 75, 81, 96, 105, 111], "perkin": [24, 31, 66, 75, 81], "perkinelm": [31, 66, 75, 81], "perlman": [15, 17, 37, 38, 48, 70, 80, 113], "perman": [59, 81, 111], "permanent": 77, "permeabil": [24, 31, 66, 75, 81], "permiss": [66, 75, 81], "perrin": [17, 18, 29, 31, 51, 53, 66, 67, 75, 81, 91, 101, 104], "perseil": [1, 15, 39, 66, 75, 81], "person": [3, 9, 33, 34, 37, 46, 48, 56, 57, 63, 66, 70, 75, 80, 81, 91, 102, 103], "personen": [56, 66, 75, 81, 100, 111], "personenbezogen": [26, 56, 66, 75, 77, 81, 96, 111], "personenbezogenen": [26, 56, 66, 75, 81, 96, 111], "personnel": [32, 66, 75, 81], "perspect": [3, 22, 23, 30, 32, 33, 37, 38, 66], "pet": [29, 66, 75, 81], "pete": [41, 49, 81, 91, 98, 109], "peter": [0, 3, 8, 10, 16, 17, 23, 29, 30, 34, 35, 37, 38, 43, 46, 47, 48, 49, 51, 53, 55, 56, 58, 66, 67, 70, 73, 74, 75, 78, 80, 81, 85, 88, 91, 96, 101, 103, 104, 105, 106, 109, 111], "petersen": [24, 66, 75, 81], "petr": [1, 7, 15, 21, 39, 56, 58, 66, 72, 75, 81, 95, 102, 109], "petra": [41, 66, 75, 81, 82, 111], "peuckert": [22, 66, 75, 81], "pfa": [24, 31, 66, 75, 81], "pfander": [1, 15, 39, 66, 75, 81], "ph488": [31, 66, 75, 81], "phalloidin": [31, 66, 75, 81], "phase": [3, 33, 34, 37], "phasen": [66, 75, 81], "phasor": [49, 103, 109], "phd": [66, 75, 81], "phenol": [31, 66, 75, 81], "philip": [1, 15, 39, 66, 75, 81], "philipp": [1, 3, 8, 15, 23, 28, 33, 38, 39, 48, 66, 70, 75, 81, 91], "philippa": [11, 53, 66, 87, 93, 107, 111, 113], "photo": 63, "photon": [30, 33, 75], "physic": 88, "physiolog": [22, 56, 66, 74, 75, 87, 95, 102], "pia": [56, 66, 75, 81, 96, 111], "piereck": [48, 57, 70, 81, 90, 115], "pierr": [15, 48, 66, 70, 75, 84, 85, 106], "pieruschka": [1, 15, 39, 66, 75, 81], "pieta": [12, 43, 49, 66, 70, 73, 85, 91], "pietzsch": [48, 49, 60, 70, 114], "pii": [39, 43, 44, 53, 66, 72, 87, 92, 97, 111], "pike": [25, 31, 66, 75, 81], "pilliod": [30, 33, 66, 75, 81], "pilot": [41, 53, 73, 92, 96, 97, 101, 111], "pimentel": [30, 33, 66, 75, 81, 88], "pina": [43, 53, 66, 80], "pip": [20, 63, 66, 75], "pipelin": [29, 43, 49, 53, 60, 73, 98, 114, 115], "pitfal": [43, 59, 87], "pivot": [66, 75, 81], "pixel": [24, 49, 50, 70, 106], "pixel2": [29, 66, 75, 81], "pl": [24, 29, 31, 66, 75, 81], "pl2k": [3, 8, 23, 33, 34, 35, 37, 55, 58, 66, 74, 75, 81, 105, 106, 111], "pl5edc1v41fyclfzbbclo41zfo": [43, 58, 74, 91], "pl5esqnfm5lc7samsteu082ivw4bdmvd0u": [43, 58, 74, 91], "pla": [1, 15, 17, 18, 29, 39, 51, 53, 66, 67, 75, 81, 101], "place": [3, 22, 33, 34, 37, 66, 75, 81], "plai": [1, 28, 66, 75, 81], "plan": [3, 15, 17, 20, 23, 28, 31, 33, 34, 36, 37, 38, 41, 48, 50, 55, 56, 59, 70, 94, 113], "planck": [3, 33, 34, 37, 66, 75, 81], "plane": [24, 66, 75, 81], "plant": [43, 66, 75, 81, 111, 114], "plantard": [4, 12, 17, 18, 29, 30, 51, 53, 67, 73, 78, 81, 101], "plantsandpython": 53, "planung": [50, 59, 87, 108, 111], "plasmatechnologi": [66, 75, 81], "plate": 31, "plateform": [30, 33, 66, 75, 81], "platform": [1, 3, 8, 23, 28, 29, 30, 33, 37, 43, 46, 48, 50, 52, 53, 58, 59, 70, 71, 74, 82, 87, 89, 94, 97, 106, 109, 114, 115, 116], "plath": [33, 38, 66, 75, 81], "playlist": [3, 8, 23, 33, 34, 35, 37, 43, 55, 58, 66, 74, 75, 78, 81, 87, 91, 103, 105, 106, 109, 111], "plcell": 53, "pldcum9us4xdpz": [74, 87], "pleas": [16, 22, 29, 30, 33, 35, 62, 66, 75, 81], "plenti": [3, 33, 66, 75, 81], "plo": [11, 53, 81, 93, 96, 101, 114], "ploscompbiol": [11, 53, 81, 93, 96, 101, 114], "plotter": [49, 103, 109], "plqbhamyzu5kxuacnnbixybkivueikswq1": [43, 58, 74, 87, 91, 103, 109], "plrc9dt9lezh_svrc4g5moovghuvjpwmv0": [43, 58, 74, 78, 91], "plugin": [3, 20, 31, 37, 43, 45, 46, 49, 58, 74, 103, 109, 116], "plw": [43, 58, 74, 81, 91, 111], "plycntv": [43, 58, 74], "pm": [3, 23, 28, 48, 60, 66, 70, 75, 81, 85, 91, 106, 109, 111], "pmc": [3, 11, 22, 23, 32, 37, 39, 53, 66, 73, 75, 81, 87, 92, 97, 101, 102, 111], "pmc8606015": [3, 11, 22, 23, 32, 37, 39, 53, 66, 73, 75, 81, 87, 92, 97, 101, 111], "pmc9271325": [11, 53, 87, 92, 97, 101, 102], "pml": [30, 33, 66, 75, 81], "png": [29, 66, 75, 81], "png_umap": [29, 66, 75, 81], "poddar": [3, 22, 23, 32, 37, 66, 75, 81], "poetsch": [29, 49, 70, 81, 91, 103, 109], "poikola": [11, 43, 81, 107], "point": [7, 11, 24, 31, 36, 45, 57, 62, 66, 75, 81, 91, 109, 111, 112, 114], "pointer": [24, 66, 75, 81], "pol": [3, 6, 22, 23, 28, 32, 34, 37, 55, 56, 66, 75, 105, 111], "polanski": [48, 70, 91], "polici": [14, 45, 55, 56, 57, 75, 91, 107], "policlin": [29, 66, 75, 81], "policymak": [3, 8, 23, 28, 66, 75, 81], "politi": [4, 5, 7, 9, 36, 48, 66, 70, 75, 81, 117], "pollock": [11, 43, 81, 107], "polygon": [29, 66, 75, 81], "polym": [30, 33, 66, 75, 81], "polytechniqu": [50, 78, 91], "pool": [10, 29, 66, 75, 81], "poon": [10, 29, 66, 75, 81], "popular": [1, 28, 50, 66, 75, 79, 81, 98, 106], "popz": [30, 33, 66, 75, 81], "portabl": [55, 66, 75, 81, 116], "portal": [43, 111], "portfolio": [3, 8, 23, 33, 34, 35, 37, 55, 58, 66, 74, 75, 81, 105, 106, 111], "posch": [46, 53, 98, 116], "pose": [17, 51, 75, 81, 91, 111], "posit": [24, 29, 66, 75, 81], "possibl": [1, 3, 11, 17, 22, 23, 28, 32, 34, 37, 45, 53, 56, 66, 75, 81, 87, 91, 92, 96, 97, 101, 102, 111], "post": [11, 16, 20, 24, 25, 29, 31, 40, 43, 47, 60, 61, 62, 68, 83, 87, 90, 91, 92, 95, 99, 100, 101, 102, 106, 111, 113, 115], "poster": [0, 6, 12, 17, 18, 28, 29, 34, 38, 53, 58, 62, 66, 67, 75, 81, 82, 91, 94, 96, 98, 101, 105], "potenti": [1, 3, 11, 14, 17, 28, 29, 33, 39, 48, 51, 53, 56, 61, 66, 70, 73, 75, 81, 85, 87, 88, 91, 92, 96, 97, 99, 100, 101, 106, 107, 109, 111, 115], "potsdam": [45, 66, 75, 81, 111], "pouchin": [3, 15, 37, 48, 66, 70, 75, 81, 84, 106], "power": [45, 46, 48, 57, 70, 87, 111], "pp": [10, 29, 66, 75, 81], "ppt": [66, 75], "pptx": [29, 56, 75, 81, 91, 95, 102, 109], "pr4deepr": [10, 29, 66, 75, 81], "practic": [1, 15, 20, 23, 28, 29, 35, 36, 39, 43, 55, 58, 67, 74, 85, 94, 105, 106, 108, 114], "pradeep": [10, 29, 66, 75, 81], "prakash": [29, 53, 66, 78], "praktisch": [29, 56, 66, 75, 81, 96, 100, 111], "praxi": [26, 56, 66, 75, 81, 111], "praxisorientiert": [26, 56, 66, 75, 81, 111], "pre": [48, 66, 70, 75, 91], "precis": [48, 70], "preclin": [14, 66, 75, 81], "predict": [43, 48, 70, 93, 117], "preliminari": 88, "premis": [3, 23, 28, 66, 75, 81], "prepar": [1, 3, 4, 5, 7, 8, 9, 17, 20, 23, 24, 28, 29, 31, 33, 34, 35, 36, 37, 46, 48, 51, 55, 57, 58, 66, 70, 72, 74, 75, 81, 83, 91, 92, 97, 105, 106, 109, 111, 116, 117], "preparatori": [0, 16, 23, 37, 38, 48, 66, 75, 81, 91, 96, 105, 111], "preprint": [11, 17, 53, 70, 71, 80, 91, 109, 111, 113, 114], "preprocess": [24, 46, 50, 57, 66, 75, 81, 92, 98, 106, 115], "prerequisit": [3, 23, 28, 66, 75, 81], "presenc": [20, 66, 75, 81], "present": [1, 3, 4, 8, 13, 15, 16, 17, 20, 23, 29, 33, 34, 35, 39, 48, 49, 51, 55, 57, 58, 60, 65, 67, 74, 80, 82, 87, 90, 91, 92, 94, 95, 96, 97, 102, 104, 105, 106, 107, 114, 115], "press": [1, 15, 39, 63, 66, 75, 81], "pretrain": [25, 31, 66, 75, 81], "pretrained_model": [25, 31, 66, 75, 81], "pretreat": [24, 31, 66, 75, 81], "pretti": [66, 75, 81], "preview": [66, 75], "previou": [25, 31, 66, 75, 81, 82], "previous": [1, 17, 28, 51, 66, 75, 81, 91, 111], "primari": [24, 31], "primarili": [56, 75, 81, 107, 111], "primer": [56, 75, 81, 107, 111], "princip": [53, 75, 81, 108], "principl": [0, 1, 3, 8, 11, 14, 16, 17, 20, 23, 26, 28, 29, 30, 32, 33, 34, 37, 38, 43, 48, 50, 51, 52, 55, 56, 57, 59, 67, 71, 72, 75, 76, 83, 87, 88, 91, 92, 93, 95, 97, 99, 100, 101, 102, 105, 107, 109, 114, 115], "printabl": [22, 66, 75, 81], "prinzipien": [29, 56, 66, 75, 81, 96, 100, 111], "priorit": [48, 55, 66, 70, 75, 81, 116], "prioriti": [45, 66, 75, 81, 111], "privat": [29, 56, 66, 75, 81, 91, 96, 100, 107, 111], "probe": [66, 75], "problem": [43, 45, 49, 59, 66, 70, 75, 81, 85, 87, 91, 95, 109, 111, 112], "procedur": [1, 3, 15, 20, 23, 24, 25, 28, 31, 33, 37, 38, 39, 66, 75, 81], "process": [1, 3, 9, 14, 20, 22, 23, 25, 28, 31, 33, 34, 37, 56, 58, 59, 62, 63, 74, 75, 79, 82, 93, 94, 102, 103, 107, 113], "procida": [57, 59, 82], "produc": [29, 31, 40, 68, 88, 90, 99, 111], "product": [62, 84, 92, 108], "prof": [33, 34], "profession": [66, 75, 81], "profil": [63, 66, 75, 82], "prognosi": [29, 66, 75, 81], "program": [20, 30, 33, 46, 53, 73, 108, 116], "programm": [3, 20, 28, 37, 41, 56, 66, 75, 81, 105, 107, 111], "programmat": [7, 21, 39, 56, 58, 72, 81, 95, 102, 109], "project": [0, 1, 4, 6, 9, 14, 15, 16, 20, 22, 24, 25, 28, 29, 31, 32, 38, 39, 40, 41, 43, 45, 48, 53, 55, 56, 58, 59, 60, 68, 74, 80, 82, 84, 88, 96, 99, 105, 106, 108, 114, 115], "project_for_classifier_train": [25, 31, 66, 75, 81], "projektbezogenen": [26, 56, 66, 75, 81, 111], "promin": [66, 75, 81], "promis": [29, 66, 75, 81], "promot": [29, 66, 75, 81], "prompt": [10, 28, 29, 55, 56, 63, 66, 75, 81, 90, 99, 102], "proper": [24, 66, 75, 81], "properli": [17, 51, 59, 75, 81, 87, 91, 111], "properti": [29, 61, 66, 75, 81], "propos": [1, 9, 11, 28, 29, 39, 43, 49, 53, 56, 62, 66, 73, 75, 81, 87, 91, 92, 96, 97, 100, 101, 102, 107, 111], "proprietari": [3, 8, 23, 28, 30, 33, 34, 37, 66, 75, 81], "protect": [6, 26, 28, 37, 53, 56, 66, 75, 81, 96, 107, 111], "protein": [24, 66, 75, 81], "protocol": [1, 17, 28, 31, 34, 56, 66, 81, 91, 96, 111], "protoplast": [30, 33, 66, 75, 81], "prototyp": [22, 88], "proven": [30, 33, 45, 66, 75, 81, 111], "provid": [0, 1, 3, 6, 8, 9, 11, 14, 15, 16, 20, 23, 25, 28, 29, 31, 34, 35, 37, 38, 39, 41, 43, 45, 46, 51, 52, 53, 55, 57, 58, 59, 66, 71, 72, 75, 81, 82, 83, 87, 88, 91, 92, 93, 94, 96, 97, 98, 101, 105, 107, 108, 111, 113, 114, 116], "prozess": [0, 32, 66, 75, 81], "przemek": [53, 66, 81, 91, 97, 106, 115], "pr\u00e4sentat": [26, 56, 66, 75, 81, 111], "psf": [31, 66, 75, 81], "psfgen_wf_wbpsf": [31, 66, 75, 81], "psfgen_wfsquare_wbpsf": [31, 66, 75, 81], "psfhuygens_confocal_theopsf": [31, 66, 75, 81], "psomopoulo": [11, 53, 81, 93, 96, 101, 114], "ptbiop": [24, 66, 75, 81], "pub": [41, 81, 107], "public": [0, 1, 2, 3, 4, 5, 8, 9, 11, 12, 15, 16, 17, 18, 22, 23, 24, 28, 29, 30, 31, 32, 33, 34, 35, 37, 38, 41, 43, 44, 46, 47, 48, 49, 51, 52, 56, 57, 62, 66, 67, 70, 71, 72, 75, 78, 80, 81, 82, 83, 87, 90, 91, 92, 93, 94, 96, 97, 98, 99, 101, 102, 104, 105, 106, 107, 108, 109, 113, 114, 115, 116, 117], "publication_d": 61, "publicli": [29, 56, 66, 75, 91, 96, 100, 111], "publik": [26, 56, 66, 75, 81, 111], "publish": [0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 14, 15, 16, 17, 20, 22, 23, 24, 25, 26, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 45, 46, 48, 52, 55, 56, 58, 59, 60, 66, 67, 68, 70, 71, 72, 74, 75, 79, 80, 81, 82, 84, 85, 90, 93, 94, 95, 97, 98, 99, 101, 103, 104, 105, 106, 107, 108, 109, 113, 114, 115, 116, 117], "publisso": [57, 87, 96, 100, 111], "publizieren": [29, 56, 66, 75, 81, 96, 100, 111], "pubm": [53, 73, 81, 94, 96, 101], "pull": [29, 56, 62, 63, 66, 75, 81, 96, 111], "purpos": 88, "pursu": [66, 75, 81], "push": [32, 66, 75, 81], "puv": [43, 48, 70, 93, 117], "py": [16, 35, 63, 66, 75, 81], "pyclesperanto": [49, 81, 103, 109], "pygithub": 63, "pyimagecourse_beginn": [13, 49, 70, 80, 91, 109], "pylv\u00e4n\u00e4inen": [43, 48, 56, 57, 60, 70, 91, 114, 117], "pyramid": [2, 17, 48, 70, 84, 92, 108], "python": [0, 4, 5, 12, 16, 17, 21, 23, 25, 31, 35, 37, 38, 39, 47, 52, 55, 58, 63, 67, 71, 72, 74, 84, 90, 93, 95, 97, 98, 105, 111, 115], "python_for_microscopist": [43, 49, 70, 85, 91, 109], "python_image_process": [13, 49, 70, 85, 109], "pythoncod": [25, 31, 66, 75, 81], "pytorch": [85, 95, 112], "pyyaml": 63, "p\u00e9rez": [12, 29, 43, 49, 66, 70, 73, 75, 81, 85, 91], "q": [29, 43, 66, 75, 81, 87, 111], "qc": [30, 33, 66, 75, 81], "qc_notebook": [31, 66, 75, 81], "qi_2024_analysis_lab_manu": [12, 49, 81, 109, 112], "qian": [30, 33, 66, 75, 81], "qin": [57, 70, 87, 90], "qm_course_bio_image_analysis_with_napari_2024": [49, 81, 103, 109], "qptdat": [66, 75, 81], "quad": [30, 33, 66, 75, 81], "quadrant": [31, 66, 75, 81], "qualifi": [45, 66, 75, 81, 111], "qualiti": [1, 14, 25, 28, 29, 30, 31, 33, 49, 66, 70, 75, 80, 87, 90, 92, 97, 101, 102], "qualit\u00e4tssicherung": [66, 75, 81], "quantit": [3, 12, 23, 28, 29, 37, 49, 75, 109, 112], "quareo": [11, 53, 81], "quarep": [30, 33, 43, 49, 66, 75, 91, 111], "quelloffen": [41, 81, 107], "queri": [17, 34, 43, 56, 66, 72, 75, 81, 91, 93, 96, 111, 114], "question": [0, 3, 14, 29, 36, 38, 43, 48, 51, 55, 57, 66, 70, 75, 81, 87, 91, 96, 111], "quick": [66, 75, 81], "quota": [66, 75, 81], "qupath": [24, 29], "r": [2, 4, 5, 11, 15, 17, 29, 39, 41, 43, 46, 48, 49, 53, 63, 66, 70, 73, 75, 80, 81, 83, 87, 88, 90, 92, 93, 97, 98, 105, 109, 111, 115, 116], "r2": [24, 31, 66, 75, 81], "r47": [2, 15, 53, 66, 81, 91, 102], "rachel": [10, 29, 66, 75, 81], "radbruch": [66, 75, 81], "radiat": [29, 66, 75, 81], "radiochemotherapi": [29, 66, 75, 81], "radiolog": [20, 66, 75, 81], "radiooncolog": [29, 66, 75, 81], "radiooncologi": [29, 66, 75, 81], "radiorestist": [29, 66, 75, 81], "radiotherapi": [29, 66, 75, 81], "raess": [1, 15, 39, 66, 75, 81], "raf": [17, 18, 29, 51, 53, 67, 81, 101], "rafael": [1, 15, 39, 57, 66, 70, 75, 81, 85, 87, 91, 109], "rahmen": [56, 66, 75, 77, 81, 111], "rahul": [17, 52, 53, 71, 81, 109], "rai": [87, 91], "rainer": [4, 24, 53, 66, 81, 91, 111], "rajasekhar": [10, 29, 66, 75, 81], "rama": [10, 11, 18, 42, 49, 55, 70, 87, 91, 104], "ramirez": [48, 70, 91], "ran": [7, 21, 25, 31, 39, 56, 58, 66, 72, 75, 81, 95, 102, 109], "random": [31, 66, 75, 81], "rang": [1, 3, 14, 22, 23, 28, 32, 37, 41, 66, 75, 81, 111], "raphaelbuzzi": [48, 70, 91], "rare": [29, 56, 66, 75, 81, 91, 96, 100, 107, 111], "rasband": [12, 29, 51, 53, 98], "rasti": [11, 18, 21, 29, 42, 49, 55, 70, 87, 90, 91, 104, 109], "rat": [24, 29], "ratamero": [11, 15, 48, 70, 84], "rather": [29, 66, 75, 81], "rational": [53, 66, 73, 81, 96, 111], "ratnikov": [49, 70, 85, 90, 109], "rauber": [1, 15, 39, 66, 75, 81], "ravindran": [4, 24, 53, 66, 81, 91, 111], "raw": [1, 6, 10, 28, 31, 48, 50, 55, 56, 57, 66, 70, 75, 81, 85, 87, 90, 91, 106, 109, 115], "raw_larg": [31, 66, 75, 81], "rawdata": [66, 75, 81], "rdf": [16, 35, 51, 66, 105], "rdm": [6, 8, 11, 23, 28, 38, 43, 45, 53, 55, 56, 58, 67, 74, 82, 92, 94, 97, 101, 105, 107, 114], "rdm4mic": [3, 8, 23, 33, 34, 35, 37, 55, 66, 75, 105], "rdmkit": [50, 59], "rdmorganis": [50, 59, 87, 108, 111], "rdornier": [15, 48, 70, 84, 106], "re": [11, 43, 62, 73, 87, 88, 92, 93, 96, 101, 109], "reach": [29, 66, 75, 81], "read": [6, 40, 43, 46, 50, 56, 57, 83, 87, 91, 92, 98, 106, 111, 113, 114, 115], "readabl": [1, 28, 66, 75, 81], "reader": [24, 66, 75, 81], "readi": [3, 23, 28, 66, 75, 81], "readm": [19, 20, 29, 40, 48, 57, 64, 66, 70, 75, 81, 85, 90, 91, 106, 109, 115], "readthedoc": [11, 43, 45, 46, 50, 79, 81, 87, 92, 98, 106, 109, 116], "real": [14, 56, 57, 66, 75, 81, 91, 96, 111], "realiz": [1, 14, 28, 51, 66, 75, 81, 91], "reason": [20, 29, 53, 66, 75, 81, 91], "rebecca": [1, 15, 39, 41, 43, 53, 66, 70, 75, 80, 81, 87, 90, 91], "receiv": [36, 55, 66, 75, 81], "recent": [30, 33, 66, 75, 81, 88], "recherch": [30, 33, 66, 75, 81], "rechtlich": [26, 41, 56, 66, 75, 77, 81, 96, 107, 111], "rechtsbereich": [26, 56, 66, 75, 81, 111], "rechtsgebiet": [41, 81, 107], "rechtssicherheit": [56, 66, 75, 81, 100, 111], "rechtsverletzung": 77, "rechtsverletzungen": 77, "rechtsverst\u00f6\u00df": 77, "rechtswidrig": 77, "recogn": [53, 66, 73, 75, 81, 91, 96, 99, 115], "recommend": [3, 8, 14, 22, 23, 29, 30, 32, 33, 34, 37, 43, 49, 56, 57, 61, 72, 83, 91, 96], "record": [0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 14, 15, 16, 17, 20, 22, 23, 24, 25, 26, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 41, 42, 43, 45, 53, 55, 56, 57, 58, 62, 66, 70, 74, 75, 81, 82, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 99, 100, 101, 102, 105, 106, 107, 108, 109, 111, 116, 117], "rector": [66, 75, 81], "red": [31, 66, 75, 81], "rediscov": [17, 34, 56, 75, 81, 91, 96, 111], "reduc": [29, 66, 75, 81], "reduct": [22, 66, 75], "ref": [48, 57, 70, 81, 90], "refer": [3, 22, 23, 29, 30, 32, 33, 37, 41, 57, 59, 66, 75, 81, 82, 111], "reflect": [66, 75], "reform": [66, 75, 81], "regard": [6, 22, 28, 37, 56, 66, 75, 81, 111], "regardless": [14, 43, 57, 66, 75, 81, 91, 96, 111], "regel": 77, "regelm\u00e4\u00dfig": [0, 32, 66, 75, 81], "reger": [1, 28, 66, 75, 81], "regeringen": [45, 111], "region": [1, 28, 66, 75, 81], "regist": [24, 31, 57, 58, 66, 72, 75, 81, 83, 92, 97, 111], "registr": [4, 43, 49, 70, 85], "registri": [11, 43, 80, 93, 109, 115, 116], "reguant": [66, 75, 81], "rehabilit": [20, 66, 75, 81], "rehydr": [24, 31, 66, 75, 81], "reih": [0, 26, 32, 56, 66, 75, 81, 111], "reink": [11, 52, 53, 91], "reit": [53, 66, 81, 91, 97, 106, 115], "reither": [4, 24, 53, 66, 75, 81, 91, 111], "relat": [1, 10, 15, 29, 39, 41, 43, 59, 62, 66, 75, 81, 85, 87, 88, 117], "relationship": [66, 75, 81], "releas": [17, 34, 56, 81, 88, 91, 96, 111], "relev": [9, 29, 55, 62, 66, 75, 81, 111], "releva": [32, 66, 75, 81], "relevant": [26, 56, 66, 75, 81, 111], "relevanten": [26, 56, 66, 75, 81, 111], "reli": [66, 75, 81], "reliabl": [29, 66, 75, 81], "remain": [3, 8, 9, 16, 23, 28, 29, 35, 56, 66, 74, 75, 81, 87, 95, 102], "rembi": [0, 3, 14, 16, 22, 23, 32, 37, 38, 48, 56, 57, 75, 81, 91, 105], "remix": [22, 66, 75], "remot": [1, 3, 8, 23, 28, 33, 34, 35, 37, 48, 55, 58, 66, 74, 75, 81, 105, 106, 111], "remov": [43, 49, 62, 91, 111], "renku": [25, 31, 66, 75, 81], "renown": [53, 66, 81, 91, 97, 106, 115], "repeat": [20, 29, 66, 75, 81], "replac": [66, 75, 81], "report": [0, 16, 23, 29, 30, 33, 37, 38, 48, 53, 91, 94, 96, 105, 111], "repositori": [1, 2, 4, 5, 6, 7, 8, 9, 11, 15, 17, 20, 24, 25, 28, 29, 31, 36, 43, 46, 49, 52, 53, 56, 60, 61, 62, 63, 64, 65, 71, 76, 79, 80, 82, 83, 84, 85, 88, 90, 92, 93, 95, 97, 98, 99, 100, 102, 103, 106, 107, 108, 109, 112, 113, 114, 115, 117], "repositorien": [56, 66, 75, 81, 111], "repres": [1, 15, 39, 66, 75, 81], "represent": [2, 15, 17, 34, 51, 53, 66, 75, 81, 91, 96, 102, 105, 111], "reproduc": [1, 3, 22, 23, 28, 30, 33, 37, 40, 46, 48, 49, 56, 57, 67, 70, 80, 91, 93, 96, 97, 102, 103, 106, 107, 109, 113, 117], "reproducibli": 90, "request": [29, 56, 62, 63, 66, 75, 81, 96, 111], "requir": [0, 1, 3, 6, 8, 11, 14, 17, 23, 28, 30, 33, 34, 35, 37, 38, 39, 43, 49, 51, 53, 55, 56, 57, 58, 66, 73, 74, 75, 81, 82, 87, 91, 92, 96, 97, 101, 102, 105, 106, 111], "rer": 77, "resav": [31, 66, 75, 81], "rescal": [29, 66, 75, 81], "research": [2, 4, 5, 7, 9, 24, 26, 46, 47, 48, 49, 52, 58, 60, 62, 70, 71, 72, 73, 74, 83, 88, 93, 94, 101, 103, 104, 109, 114, 115, 116], "reserv": [3, 4, 11, 12, 16, 18, 29, 30, 33, 34, 35, 41, 43, 50, 53, 58, 62, 66, 70, 73, 74, 91, 98, 101, 104, 105, 106, 111], "reshuffl": [20, 66, 75, 81], "resin": [22, 66, 75, 81], "resolut": [3, 23, 28, 29, 30, 33, 37, 66, 75, 81], "resourc": [3, 8, 11, 17, 23, 28, 30, 33, 34, 35, 37, 49, 51, 52, 55, 56, 58, 59, 61, 62, 67, 71, 72, 74, 76, 78, 82, 85, 89, 92, 93, 95, 97, 99, 101, 102, 105, 113, 114], "respect": [29, 66, 75, 81, 90], "respons": [1, 14, 28, 43, 56, 57, 66, 74, 75, 81, 87, 91, 95, 96, 102, 111], "responsibli": [29, 59, 66, 75, 81, 87, 111], "restrict": [43, 57, 72, 81, 107, 113], "result": [2, 3, 15, 20, 23, 25, 30, 31, 33, 34, 37, 45, 96, 102], "results_csv": [20, 66, 75, 81], "resuspend": [31, 66, 75, 81], "retriev": [1, 3, 17, 23, 28, 29, 34, 37, 56, 66, 75, 81, 91, 96, 111], "reuasabl": [29, 56, 66, 75, 81, 96, 100, 111], "reus": [1, 14, 15, 43, 56, 57, 75, 81, 91, 94, 96], "reusabl": [1, 3, 8, 11, 14, 23, 28, 29, 30, 33, 34, 35, 37, 55, 56, 58, 73, 74, 88, 91, 92, 94, 96, 97, 100, 105, 106, 113], "revel": [24, 31, 66, 75, 81], "reversibli": [22, 66, 75, 81], "review": [20, 22, 41, 56, 66, 74, 75, 81, 87, 90, 91, 95, 99, 107], "revolution": [66, 75, 81], "revolutionari": [53, 73, 91, 96, 99, 115], "rex": [53, 66, 81, 111], "rey": [11, 43, 80, 93, 109, 115, 116], "reynaud": [29, 53, 66, 78], "rfc": [56, 91, 96, 105, 111], "rgb": [50, 66, 75, 81, 91, 106], "rhaas": [29, 55, 69, 80, 91, 98, 104], "rhinoviru": [30, 33, 66, 75, 81], "rhode": [24, 66, 75, 81], "ri": [1, 15, 29, 39, 66, 75, 81], "ribeiro": [48, 57, 70, 81, 115], "ricardo": [12, 41, 43, 49, 66, 70, 73, 85, 91], "riccardo": [1, 3, 6, 8, 16, 23, 29, 37, 48, 51, 56, 57, 66, 70, 75, 81, 85, 91, 94, 99, 101, 102, 105, 106, 109, 111], "rich": [3, 23, 28, 37, 66, 75, 81], "richard": [1, 15, 29, 39, 42, 48, 49, 53, 55, 66, 70, 74, 75, 81, 87, 91, 103, 104, 109, 111], "richtigkeit": 77, "rigano": [11, 30, 33, 53, 66, 73, 75, 81, 87, 88, 92, 97, 101, 102], "rigaud": [29, 48, 49, 57, 60, 70, 80, 81, 91, 109, 114, 115], "right": [3, 4, 11, 12, 16, 18, 29, 30, 33, 34, 35, 41, 43, 50, 53, 55, 58, 62, 63, 66, 70, 73, 74, 75, 81, 91, 98, 99, 101, 104, 105, 106, 109, 111], "rigor": [11, 53, 87, 92, 97, 101, 102], "rise": [66, 75, 81], "rittscher": [49, 70, 84, 91, 109], "riudalba": [25, 31, 66, 75, 81], "rizwan": [4, 12, 18, 30, 53, 73, 78], "rm": [48, 82], "rmassei": [28, 29, 48, 56, 70, 85, 91, 99, 102, 106, 109], "rmcelreath": [48, 70], "roadmap": [45, 66, 75, 81, 111], "robert": [4, 6, 9, 10, 11, 12, 17, 18, 20, 28, 30, 31, 33, 37, 38, 40, 41, 42, 43, 48, 49, 51, 53, 55, 56, 57, 60, 64, 66, 67, 68, 69, 70, 75, 77, 78, 80, 81, 85, 87, 88, 90, 91, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 109, 111, 113, 114, 115, 117], "robert_haas": [29, 40, 64, 81, 106, 109], "robust": [29, 66, 75, 81], "rocco": [18, 29, 31, 53, 67, 81, 91, 104], "roccod": [55, 70, 79, 91, 104], "roch": [24, 31, 66, 75, 81], "rodent": [22, 66, 75, 81], "rodrigo": [53, 66, 75, 81, 91, 97, 106, 115], "rodriguez": [4, 12, 18, 30, 53, 73, 78], "rohola": [53, 73, 81, 92, 96, 97, 101, 111], "rohrer": [43, 53, 66, 80], "roimanag": [66, 75, 85], "roland": [1, 3, 4, 8, 12, 15, 17, 18, 23, 33, 34, 35, 37, 39, 53, 66, 73, 75, 78, 81, 88, 111], "role": 1, "roll": [29, 56, 66, 75, 81, 96, 100, 111], "romain": [1, 11, 12, 15, 18, 24, 25, 29, 39, 43, 47, 49, 53, 60, 66, 67, 70, 73, 75, 81, 85, 87, 91, 102, 104], "roman": [66, 75, 81], "romano": [1, 15, 39, 66, 75, 81], "romdhan": [14, 66, 75, 81], "romero": [7, 21, 39, 53, 56, 58, 72, 73, 81, 94, 95, 96, 101, 102, 109], "romi": [26, 56, 66, 75, 81, 111], "ron": [53, 66, 81, 91, 97, 106, 115], "ronja": [66, 75, 81], "ronni": [26, 56, 66, 75, 81, 111], "root": [66, 75, 81], "rosa": [53, 66, 81, 91, 97, 106, 115], "rosato": [1, 15, 39, 66, 75, 81], "rot": [26, 56, 66, 75, 81, 111], "rotkreuz": [24, 31, 66, 75, 81], "rougier": [41, 70, 109], "round": [24, 31, 82], "rousseau": [11, 18, 29, 42, 49, 55, 70, 87, 90, 91, 104], "rout": [22, 66, 75, 81], "routin": [3, 8, 23, 33, 34, 35, 37, 55, 58, 66, 74, 75, 81, 105, 106, 111], "royer": [11, 12, 29, 43, 49, 53, 66, 70, 73, 78, 85, 91, 98], "royerlab": [48, 57, 60, 70, 91, 112, 114], "royl": [41, 42, 66, 70, 75, 91], "rozbicki": [2, 43, 57, 87, 106, 114], "rpc": [46, 85, 95, 109, 116], "rtx": [25, 31, 66, 75, 81], "rube\u0161ov\u00e1": [20, 66, 75, 81], "rudolf": [1, 15, 39, 66, 75, 81], "rueden": [11, 12, 18, 25, 29, 31, 43, 47, 51, 53, 55, 57, 60, 70, 87, 91, 98, 102, 104], "rufu": [11, 43, 81, 107], "ruman": [46, 53, 73, 98, 116], "run": [1, 25, 29, 31, 32, 48, 53, 56, 62, 66, 70, 75, 80, 84, 85, 87, 98, 103, 106, 112, 116], "run_convers": [66, 75, 81], "runcommand": [66, 75, 81], "rund": [0, 32, 66, 75, 81], "runplugin": [66, 75, 81], "runuserplugin": [66, 75, 81], "rupert": [4, 12, 18, 30, 53, 73, 78], "rupress": [11, 53, 101], "russel": [46, 85, 95, 109, 116], "ruth": [66, 75, 81], "ryabchykov": [53, 66, 81, 96, 111], "ryan": [4, 7, 10, 12, 18, 21, 29, 30, 33, 39, 53, 56, 58, 66, 72, 73, 75, 78, 81, 88, 91, 95, 101, 102, 109], "rybakov": [48, 70, 91], "rybina": [1, 14, 15, 39, 53, 66, 75, 81, 111], "rzepka": [15, 17, 37, 38, 48, 70, 80, 113], "r\u00e9my": [50, 91, 106], "r\u00f6der": [66, 75, 82], "r\u00f6ssler": [17, 18, 29, 51, 53, 67, 81, 101], "s0022283622000791": [39, 43, 44, 53, 66, 72, 87, 92, 97, 111], "s00418": [53, 111], "s10nm": [24, 66, 75, 81], "s2633903x21000039": [53, 66, 70, 80, 91], "s2633903x23000211": [66, 75, 81], "s3": [66, 75, 81], "s41431": [53, 73, 81, 94, 96, 113], "s41467": [12, 43, 49, 66, 70, 73, 85, 91], "s41592": [2, 3, 4, 5, 11, 12, 14, 15, 17, 18, 29, 30, 33, 34, 39, 46, 53, 56, 57, 66, 73, 75, 78, 81, 87, 91, 92, 96, 97, 98, 99, 101, 105, 111, 115, 116], "s41597": [53, 73, 81, 92, 94, 96, 97, 101, 111], "s41598": [25, 31, 66, 75, 81], "s50nm": [24, 66, 75, 81], "sa": [3, 8, 41, 43, 48, 50, 51, 53, 55, 56, 57, 59, 66, 70, 73, 75, 91, 92, 96, 97, 99, 103, 105, 106, 107, 109, 111, 115], "saalfeld": [48, 49, 60, 70, 114], "saalfeldlab": [48, 49, 60, 70, 114], "sabin": [4, 24, 53, 66, 75, 81, 91, 111], "sabrina": [10, 29, 66, 75, 81], "sachsen": [22, 66, 75], "sack": [66, 75, 81], "sage": [11, 17, 18, 29, 51, 53, 55, 57, 66, 67, 70, 75, 81, 87, 91, 101, 104], "sai": [48, 70], "saibotmagd": [48, 70, 87, 97, 111], "sainsburi": [43, 57, 81, 114], "saito": [10, 29, 66, 75, 81], "salama": [66, 75, 81, 117], "salih": [7, 21, 39, 56, 58, 72, 81, 95, 102, 109], "sam": [5, 56, 66, 75, 81, 91, 95], "same": [3, 31, 33, 34, 37, 66, 75, 81], "sampaio": [17, 18, 29, 51, 53, 67, 81, 101], "sampl": [3, 14, 17, 25, 28, 29, 31, 37, 43, 51, 57, 91, 96, 111], "sanchez": [1, 15, 39, 66, 75, 81], "sander": [33, 38, 53, 66, 73, 75, 81, 101], "sandra": [11, 53, 66, 87, 93, 107, 111, 113], "sanson": [1, 15, 39, 66, 75, 81], "sara": [1, 12, 15, 39, 43, 49, 66, 70, 73, 75, 81, 85, 91], "sarah": [16, 24, 35, 43, 53, 66, 75, 78, 81, 91, 105, 106, 111], "sarkan": [1, 7, 11, 14, 15, 21, 53, 56, 57, 58, 66, 72, 73, 75, 81, 87, 91, 92, 95, 96, 97, 101, 102, 109, 111], "sasaki": [42, 49, 70, 87, 109], "sass": [66, 75, 81], "sathya": [4, 12, 18, 30, 53, 73, 78], "satisfi": [14, 43, 57, 66, 75, 81, 91, 96, 111], "satrajit": [11, 53, 81, 92, 97, 111], "save": [3, 22, 23, 31, 32, 37, 62, 66, 75, 81], "save_tif": [25, 31, 66, 75, 81], "saxfdm": [29, 56, 66, 75, 81, 91, 94, 96, 100, 107, 111], "sc": [11, 16, 24, 25, 31, 43, 47, 60, 66, 75, 81, 83, 87, 91, 92, 101, 102, 106, 115], "scad": [6, 28, 37, 48, 49, 66, 70, 77, 88, 97, 111], "scalabl": [46, 109], "scale": [1, 11, 15, 17, 32, 39, 46, 51, 53, 87, 91, 92, 97, 101, 102, 109, 116], "scan": [30, 32, 33, 37], "scanner": [25, 30, 31, 33, 66, 75, 81], "scatter": [66, 75], "scene": [66, 75, 81], "schauss": [16, 23, 32, 37, 38, 46, 47, 48, 51, 53, 66, 70, 75, 81, 91, 96, 105, 106, 109, 111], "schemat": [66, 75, 82], "scheme": [14, 22, 56, 57, 66, 75, 81, 91, 96, 111], "schiklenk": [4, 18, 70, 87, 91], "schiller": [43, 70], "schlaeppi": [29, 53, 81, 91], "schlierf": [6, 66, 75, 81], "schlussbericht": [66, 75, 81], "schmi": [4, 12, 18, 30, 53, 73, 78, 83, 92, 102], "schmidt": [8, 11, 12, 17, 21, 22, 23, 25, 28, 29, 30, 32, 33, 34, 35, 37, 43, 49, 51, 53, 55, 56, 57, 58, 66, 67, 70, 73, 74, 75, 78, 81, 82, 87, 88, 90, 91, 92, 97, 98, 101, 104, 105, 106, 109, 111, 117], "schmitz": [31, 66, 75, 81], "schneider": [0, 4, 7, 9, 11, 29, 36, 38, 51, 57, 66, 75, 81, 85, 96, 111, 115, 117], "schnike": [28, 51, 66, 81, 91], "scholarli": [59, 81, 111], "scholten": [66, 75, 81], "scholz": [1, 17, 18, 28, 29, 51, 53, 66, 67, 75, 81, 91, 101], "school": [30, 33, 43, 48, 66, 75, 85, 117], "schorb": [4, 5, 7, 9, 29, 36, 48, 53, 66, 70, 75, 81, 91, 117], "schrader": [0, 16, 23, 37, 38, 46, 47, 48, 51, 53, 66, 70, 75, 81, 91, 96, 105, 106, 109, 111], "schritt": [59, 77, 87, 105, 111], "schroth": [4, 12, 18, 30, 53, 73, 78], "schult": [53, 73, 81, 94, 96, 101], "schulungen": [41, 66, 75, 81, 111], "schulz": [0, 32, 66, 75, 81], "schutz": 77, "schwartz": [5, 49, 55, 70, 87, 90, 91], "schwarzbach": [3, 22, 23, 32, 37, 66, 75, 81], "schwerpunkt": [66, 75, 81], "sch\u00e4tz": [48, 49, 56, 66, 70, 75, 81, 87, 93], "sch\u00fctz": [4, 12, 18, 30, 53, 73, 78], "sci": [29, 66, 75, 81], "scicomp": [29, 55, 69, 70, 87, 91, 98], "scienc": [3, 6, 17, 19, 23, 28, 30, 33, 34, 37, 40, 44, 46, 48, 51, 52, 57, 59, 68, 71, 72, 73, 77, 82, 88, 90, 94, 105, 114, 117], "science23rd": [17, 66, 75, 81, 117], "sciencedirect": [39, 43, 44, 53, 66, 72, 87, 92, 97, 111], "scienceeurop": [41, 66, 81], "scientif": [3, 8, 11, 14, 20, 23, 25, 28, 30, 31, 33, 34, 37, 43, 46, 48, 51, 72, 80, 82, 83, 90, 92, 93, 94, 97, 99, 101, 107, 108, 115], "scientist": [3, 6, 9, 11, 23, 28, 33, 34, 37, 48, 56, 70, 74, 87, 92, 95, 97, 99, 101, 102, 111], "scikit": [7, 11, 13, 36, 57, 85, 103, 112, 114], "scipi": [49, 109], "scope": [14, 25], "scott": [4, 12, 18, 30, 53, 73, 78], "script": [3, 15, 16, 22, 23, 25, 31, 32, 35, 37, 48, 55, 63, 66, 69, 70, 75, 84, 92], "scvers": [48, 70, 91], "sd": [29, 30, 33, 66, 75, 81], "sdata": [53, 66, 73, 81, 96, 111], "sdata201618": [53, 66, 73, 81, 96, 111], "se": [45, 55, 70, 79, 91, 104, 111], "seamless": [1, 28, 29, 66, 75, 81], "seamlessli": [55, 66, 75, 81, 116], "search": [14, 56, 57, 66, 75, 81, 82, 88, 91, 96, 114], "searchabl": [43, 78], "sebastian": [3, 4, 5, 7, 9, 10, 11, 17, 18, 24, 26, 29, 33, 34, 36, 48, 49, 51, 53, 56, 57, 66, 67, 70, 73, 75, 78, 81, 85, 101, 109, 111, 115, 117], "sebastien": [11, 18, 55, 57, 70, 87, 91, 104], "second": [2, 17, 22, 24, 31, 48, 62, 66, 70, 75, 80, 81, 84, 91, 92, 108], "section": [17, 24, 29, 31, 43, 50, 51, 56, 59, 66, 75, 81, 85, 91, 103, 111], "section_a01_g001_": [24, 66, 75, 81], "secur": [3, 33, 34, 37, 45, 46, 57, 66, 75, 81, 111], "see": [3, 22, 23, 32, 37, 63, 66, 75, 81, 85, 90], "seed": [1, 31, 66, 75, 81], "seek": [29, 46, 48, 57, 66, 70, 75, 80, 81, 91, 102, 103], "seem": [66, 75, 81], "seen": [3, 22, 23, 32, 37, 56, 66, 75, 81, 107, 111], "segment": [5, 7, 10, 11, 12, 24, 28, 31, 36, 43, 46, 48, 50, 56, 57, 59, 60, 80, 85, 92, 95, 98, 106, 114, 115], "seibold": [41, 75, 81, 111], "sein": [26, 29, 56, 66, 75, 81, 96, 111], "seit": [26, 56, 66, 75, 77, 81, 111], "seiten": 77, "seitenbetreib": 77, "seitz": [4, 12, 18, 30, 53, 73, 78], "selbst": [26, 56, 66, 75, 81, 111], "selchow": [4, 12, 18, 30, 53, 73, 78], "select": [1, 3, 14, 15, 20, 22, 23, 32, 37, 39, 43, 57, 59, 63, 66, 75, 81, 82, 87, 90, 91, 96, 100, 111], "self": [3, 8, 23, 33, 34, 35, 37, 55, 58, 66, 74, 75, 81, 105, 106, 111], "sem": [24, 66, 75, 81], "semant": [1, 15, 17, 34, 39, 51, 66, 75, 81, 85, 96, 105, 111], "semest": [42, 57, 81, 85], "semi": [1, 75], "semin": [66, 75, 81], "seminar": [3, 17, 33, 87, 91, 117], "seminarstechnisch": [17, 66, 75, 81, 117], "semisupervis": [20, 66, 75], "send": [62, 63], "senft": [41, 43, 53, 66, 70, 80, 87, 90, 91], "sengupta": [29, 66, 75, 81], "sens": [66, 75, 81], "sensit": [1, 15, 39, 66, 75, 81], "sensor": [20, 66, 75], "separ": [3, 22, 23, 32, 37, 53, 66, 73, 75, 81, 94, 96, 101], "seper": [3, 14, 23, 32, 37, 66, 75, 81], "sept": [3, 22, 23, 32, 37, 66, 75, 81, 117], "septemb": [6, 29, 66, 75, 81], "sequenc": [6, 28, 37, 50, 56, 57, 66, 75, 81, 83, 111, 115], "sequenti": [66, 75, 81], "sergei": [48, 70, 91], "sergii": [4, 12, 18, 30, 53, 73, 78], "seri": [13, 41, 43, 49, 66, 73, 75, 81, 87, 92, 101, 111], "serotec": [24, 31, 66, 75, 81], "serrano": [14, 15, 28, 39, 53, 55, 65, 66, 75, 81, 87, 90, 91, 111, 116], "serum": [31, 66, 75, 81], "serv": [2, 3, 8, 15, 23, 33, 34, 35, 37, 46, 48, 53, 55, 57, 58, 59, 62, 66, 70, 74, 75, 80, 81, 91, 102, 103, 105, 106, 111], "server": [1, 3, 11, 15, 17, 28, 33, 34, 37, 48, 51, 66, 70, 75, 84, 106, 111], "servic": [1, 3, 6, 8, 16, 28, 29, 30, 33, 34, 37, 43, 51, 53, 91, 92, 94, 97, 105, 114], "session": [0, 3, 16, 23, 29, 36, 37, 38, 48, 55, 56, 66, 69, 75, 80, 81, 91, 96, 98, 104, 105, 111], "set": [3, 8, 20, 22, 23, 24, 25, 29, 30, 31, 32, 33, 35, 37, 45, 49, 55, 59, 63, 74, 83, 92, 94, 96, 102, 106, 109, 113], "setid": [66, 75, 81], "setup": [3, 32, 33, 34, 37, 66, 75, 81], "setx": 63, "sever": [1, 3, 14, 22, 23, 29, 32, 37, 46, 66, 75, 81, 82, 92], "severina": [4, 5, 7, 9, 36, 48, 70, 81], "sf4af": [57, 87, 114], "sh": [66, 75, 81], "shade": [24, 31, 66, 75, 81], "shah": [66, 75, 81], "shall": [66, 75, 81], "shanghang": [53, 73, 91, 96, 99, 115], "shanmugasundaram": [56, 75, 81, 107, 111], "shantanu": [41, 70, 83, 91], "shape": [1, 9, 14, 66, 75, 81], "share": [0, 1, 3, 6, 8, 11, 14, 15, 17, 23, 28, 30, 33, 34, 37, 38, 39, 43, 48, 51, 52, 53, 57, 59, 70, 71, 72, 73, 80, 83, 91, 92, 93, 94, 95, 97, 101, 102, 107, 109, 115, 116], "sharma": [4, 12, 18, 30, 53, 73, 78], "shechtman": [12, 43, 49, 66, 70, 73, 85, 91], "sheer": [1, 28, 66, 75, 81], "sheet": [3, 22, 23, 32, 37, 66, 75, 81], "shenoi": [29, 66, 75, 81], "sherwoodf": [15, 17, 37, 38, 48, 70, 80, 113], "short": [6, 41, 55, 92], "shorten": [22, 66, 75], "shortli": [29, 66, 75, 81], "should": [3, 22, 23, 28, 32, 37, 57, 66, 75, 87, 113], "show": [1, 3, 7, 11, 15, 22, 23, 28, 32, 36, 37, 39, 48, 49, 53, 57, 66, 70, 73, 75, 81, 85, 91, 92, 94, 95, 96, 97, 101, 106, 109, 111, 112, 114], "showcas": [16, 35, 56, 66, 75, 81, 94, 111], "showdialog": [66, 75, 81], "shown": [29, 66, 75, 81], "shrestha": [17, 52, 53, 71, 81, 109], "shuxia": [53, 66, 81, 96, 111], "sibarta": [11, 18, 55, 57, 70, 87, 91, 104], "sich": [0, 26, 32, 56, 66, 75, 77, 81, 100, 111], "sicher": [26, 56, 66, 75, 81, 96, 111], "sicherheitsl\u00fccken": 77, "sicht": [0, 32, 66, 75, 81], "sidebar": 63, "sie": [26, 29, 56, 66, 75, 77, 81, 96, 100, 111], "siffa": [66, 75, 81], "signal": [30, 33, 66, 75, 81], "signific": [1, 3, 8, 11, 17, 23, 28, 39, 51, 53, 66, 73, 75, 81, 87, 91, 92, 97, 101, 111], "significantli": [29, 53, 66, 75, 81, 108], "silk": [0, 3, 22, 23, 37, 66, 75, 81], "silva": [30, 33, 66, 75, 81], "similar": [20, 29, 66, 75, 81], "simon": [6, 7, 10, 11, 21, 25, 28, 29, 31, 37, 39, 43, 47, 56, 58, 60, 66, 72, 75, 81, 87, 91, 95, 102, 109, 111], "simona": [10, 29, 66, 75, 81], "simpl": [16, 22, 28, 35, 61, 66, 75, 85, 91, 109], "simplifi": [9, 66, 75, 81], "simplystatist": 87, "simul": [24, 66, 75, 81], "sim\u00f3": [25, 31, 66, 75, 81], "sinc": [3, 8, 23, 33, 34, 35, 37, 55, 58, 66, 74, 75, 81, 105, 106, 111, 116], "sind": [56, 66, 75, 77, 81, 100, 111], "singer": [30, 33, 66, 75, 81], "singh": [41, 70, 83, 91], "singl": [1, 3, 23, 28, 30, 33, 48, 66, 70, 75, 81, 91], "site": [30, 33, 66, 75, 81, 85], "sivagurunathan": [7, 21, 39, 52, 53, 56, 58, 70, 71, 72, 80, 81, 91, 95, 102, 109], "size": [1, 3, 14, 23, 25, 28, 31, 33, 34, 37, 75], "si\u00e2n": [4, 9, 11, 18, 21, 25, 29, 42, 49, 53, 55, 70, 81, 87, 90, 91, 104, 109], "skill": [36, 41, 55, 66, 75, 82, 85], "skimag": [49, 80, 109], "skin": [30, 33, 66, 75, 81], "sladoj": [17, 18, 29, 51, 53, 67, 81, 101], "slater": [52, 53, 70, 71, 80, 91], "slice": [24, 25, 31, 66, 75, 81], "slide": [1, 4, 5, 6, 7, 9, 10, 11, 13, 14, 17, 18, 20, 21, 25, 26, 28, 29, 32, 36, 39, 42, 48, 49, 50, 57, 60, 62, 65, 67, 69, 70, 72, 79, 80, 82, 87, 88, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 102, 104, 107, 109, 113, 114, 116, 117], "slidedeck": [29, 66, 75, 81], "slightli": [16, 35, 66, 75, 81], "small": [32, 66, 75, 81], "smaller": [3, 23, 28, 37, 66, 75, 81], "smallest": [66, 75, 81], "smart": [16, 35, 66, 75, 81], "smear": [20, 66, 75], "smooth": [66, 75, 82], "snap": [31, 66, 75, 81], "so": [3, 32, 57, 87, 88], "sobolewski": [56, 85, 103], "social": [48, 70], "societ": [66, 75, 81], "societi": [17, 66, 75, 81, 117], "softli": [29, 56, 66, 75, 81, 91, 96, 100, 107, 111], "softwar": [1, 3, 8, 11, 15, 17, 20, 23, 24, 25, 28, 30, 31, 32, 33, 34, 35, 37, 39, 46, 48, 49, 50, 58, 59, 62, 70, 74, 84, 92, 94, 96, 102, 103, 105, 109, 116, 117], "softwarecontext": [29, 56, 66, 75, 81, 96, 111], "softwareentwicklern": [29, 56, 66, 75, 81, 96, 111], "soiland": [11, 43, 80, 93, 109, 115, 116], "sokolnicki": [43, 44, 73], "solak": [12, 43, 49, 66, 70, 73, 85, 91], "solano": [14, 15, 28, 39, 53, 55, 65, 66, 75, 81, 87, 90, 91, 111, 116], "solar": [66, 75, 81], "solch": [29, 56, 66, 75, 77, 81, 96, 111], "solchen": [26, 56, 66, 75, 81, 111], "soll": [26, 41, 56, 66, 75, 81, 107, 111], "sollt": [26, 56, 66, 75, 81, 111], "sollten": 77, "soltwedel": [29, 48, 49, 53, 64, 70, 81, 91, 106, 109, 111], "solut": [1, 3, 8, 14, 15, 23, 24, 29, 30, 31, 33, 37, 38, 39, 43, 45, 48, 55, 56, 70, 85, 92, 96, 97, 100, 106, 107, 109], "soma": [66, 75, 81], "somasundharam": [7, 21, 39, 56, 58, 72, 81, 95, 102, 109], "some": [11, 13, 16, 24, 35, 40, 43, 45, 49, 53, 56, 62, 66, 70, 73, 75, 76, 81, 85, 87, 88, 89, 91, 93, 94, 96, 101, 103, 109, 111, 113, 114], "sondern": [66, 75, 81], "sonja": [66, 75, 81, 82], "soon": [1, 15, 16, 17, 34, 35, 39, 51, 66, 75, 81, 96, 105, 111], "sophi": [29, 66, 75, 81, 82], "sophist": [33, 38, 66, 75, 81], "sorensen": [10, 29, 66, 75, 81], "sorgfalt": 77, "sort": [6, 11, 28, 37, 43, 56, 66, 75, 81, 107, 111], "sourabh": 53, "sourc": [1, 2, 3, 8, 11, 17, 22, 23, 25, 28, 31, 33, 34, 35, 36, 37, 41, 46, 48, 50, 58, 59, 74, 79, 84, 92, 94, 95, 96, 98, 100, 102, 103, 105, 106, 107, 109, 116], "sousa": [4, 12, 18, 30, 53, 73, 78], "soweit": 77, "sowi": [26, 53, 56, 66, 75, 81, 111], "sp8": [29, 30, 33, 66, 75, 81], "space": [1, 11, 15, 39, 53, 66, 75, 81, 87, 93, 107, 111, 113], "spahn": [12, 29, 43, 49, 66, 70, 73, 75, 81, 85, 91], "spam": 77, "spaom2018": [10, 55, 87, 90, 91], "sparc": [29, 66, 75, 81], "spark": [20, 66, 75, 81], "sparql": [16, 35, 66, 75, 81], "spatial": [3, 23, 28, 37, 66, 75], "spatiotempor": [66, 75, 81], "speak": [66, 75, 81], "speci": [29, 66, 75, 81], "special": [66, 75, 82], "specialis": [66, 75, 81], "specialist": [3, 23, 28, 33, 34, 37, 66, 75, 81], "specif": [1, 2, 4, 9, 17, 20, 22, 28, 29, 30, 33, 40, 48, 62, 68, 70, 75, 76, 81, 84, 85, 90, 91, 99, 106, 108, 109, 111], "spectral": [66, 75, 81], "spectrometri": [33, 38, 66, 75, 81], "spectroscopi": [53, 66, 81, 96, 111], "speed": [56, 74, 87, 95, 102], "speichern": [26, 56, 66, 75, 81, 96, 111], "speichert": [26, 56, 66, 75, 81, 111], "speicherung": [26, 56, 66, 75, 81, 111], "spermatocyt": [30, 33, 66, 75, 81], "sperrung": 77, "spielen": [29, 56, 66, 75, 81, 96, 100, 111], "spin": [24, 30, 33, 66, 75, 81], "spital": [4, 12, 18, 30, 53, 73, 78], "sponsor": 87, "sport": [20, 66, 75, 81], "sprecherin": [56, 66, 75, 81, 111], "spring": [56, 85, 103], "springer": [29, 53, 66, 75, 81, 111], "sql": [17, 34, 56, 75, 81, 91, 96, 111], "squar": [31, 66, 75, 81], "sreeni": [43, 58, 74, 109], "sreeniva": [43, 49, 70, 85, 91, 109], "sreev": [43, 48, 70, 93, 117], "srinivasan": [4, 12, 18, 30, 53, 73, 78], "sriram": [7, 21, 39, 56, 58, 72, 81, 95, 102, 109], "srsltid": [57, 87, 114], "ss": [26, 56, 66, 75, 81, 111], "sseldorf": [30, 33, 66, 75, 81], "ssh": [48, 70, 106], "sst": [29, 66, 75, 81], "st2mt110724bnew": [57, 87, 114], "stabil": [30, 33], "stabl": [29, 46, 66, 75, 81, 87, 106, 109, 116], "stack": 85, "staff": [3, 8, 22, 23, 28, 32, 33, 34, 35, 37, 45, 55, 58, 66, 74, 75, 81, 105, 106, 111], "stage": [36, 43, 50, 55, 59, 111], "stain": [24, 25, 31], "stakehold": [3, 33, 34, 37, 66, 75, 81], "stand": [0, 32, 66, 75, 81], "standalon": [46, 81, 92], "standard": [0, 3, 13, 22, 23, 24, 28, 29, 31, 32, 37, 38, 43, 49, 58, 66, 70, 74, 82, 85, 91, 106, 108, 109], "standardis": [66, 75], "star": [29, 66, 75, 81], "stardist": 29, "stardist0": [31, 66, 75, 81], "stardist3d": [31, 66, 75, 81], "stark": [29, 49, 70, 81, 109], "start": [3, 23, 24, 29, 30, 33, 37, 38, 46, 48, 57, 59, 60, 70, 80, 90, 91, 102, 103, 106], "stat_rethinking_2024": [48, 70], "state": [3, 8, 11, 17, 29, 53, 66, 67, 75, 78, 81, 92, 97, 111], "statist": [83, 88], "statnew": 87, "statt": [26, 56, 66, 75, 81, 111], "stattfindend": [0, 32, 66, 75, 81], "steckt": [26, 56, 66, 75, 81, 111], "sted": [30, 33, 66, 75, 81], "stefan": [1, 4, 5, 7, 9, 26, 28, 36, 46, 48, 51, 53, 56, 66, 70, 75, 81, 91, 98, 111, 116], "stefani": [3, 8, 17, 23, 30, 34, 35, 37, 38, 53, 55, 56, 58, 66, 67, 73, 74, 75, 78, 81, 88, 91, 96, 101, 105, 106, 111], "stefania": [4, 5, 7, 9, 36, 48, 52, 53, 66, 70, 71, 75, 80, 81, 82, 91, 117], "stefano": [55, 56, 66, 75, 81, 94, 111], "steff": [48, 57, 70, 81, 115], "stegl": [17, 52, 53, 71, 81, 109], "steigen": [26, 56, 66, 75, 81, 111], "steinbach": [29, 49, 70, 81, 109], "steiner": [66, 75, 82], "stellmach": [66, 75, 81], "stellt": [26, 29, 56, 66, 75, 81, 96, 100, 111], "stem": [66, 75, 81], "step": [3, 14, 24, 30, 31, 33, 34, 37, 50, 56, 57, 58, 63, 66, 72, 74, 83, 87, 92, 95, 97, 102, 115], "stephan": [26, 29, 48, 49, 56, 57, 60, 66, 70, 75, 80, 81, 91, 100, 109, 111, 114, 115], "stephen": [41, 42, 46, 49, 53, 66, 70, 75, 91, 98, 116], "stereomicroscop": [22, 66, 75, 81], "stet": 77, "stetig": [26, 56, 66, 75, 81, 111], "steuerungsgremium": [66, 75, 81], "steward": [3, 8, 23, 32, 37], "stewardship": [6, 43, 51, 91, 101, 105], "stian": [11, 43, 80, 93, 109, 115, 116], "stich": [24, 66, 75, 81], "still": [3, 29, 33, 56, 66, 75, 81, 91, 96, 100, 107, 111], "stitch_a01_g001": [24, 66, 75, 81], "stitch_a01_g001_00001": [24, 66, 75, 81], "stokkerman": [4, 7, 9, 36, 66, 75, 81, 117], "stoma": [21, 49, 70, 87, 90, 109], "storag": [0, 3, 6, 28, 30, 33, 37, 38, 43, 56, 58, 59, 74, 92, 96, 102, 108], "store": [1, 3, 8, 17, 23, 24, 28, 33, 34, 35, 37, 55, 56, 58, 59, 61, 66, 74, 75, 81, 87, 91, 96, 105, 106, 111], "strambio": [3, 4, 12, 18, 30, 33, 34, 53, 66, 73, 75, 78, 81, 88, 101, 111], "strand": [30, 33, 66, 75, 81], "strasser": [53, 75, 81, 108], "strateg": [48, 56, 57, 66, 70, 75, 81, 90, 94, 111], "strategi": [3, 33, 34, 37, 45, 88], "strategien": [26, 56, 66, 75, 81, 96, 111], "strau\u00df": [45, 46, 57, 111], "streamlin": [1, 3, 28, 37, 63, 66, 75, 81], "streamlit": 63, "stress": [43, 49, 91, 111], "stretch": [29, 66, 75, 81], "strigaud": [29, 48, 49, 57, 60, 70, 80, 91, 114, 115], "stringari": [6, 56, 66, 75, 81, 91], "strongli": [14, 66, 75, 81], "structur": [2, 8, 9, 17, 22, 23, 28, 30, 33, 43, 48, 61, 70, 82, 84, 93, 108, 117], "strukturiert": [26, 56, 66, 75, 81, 111], "stucchi": [66, 75, 81], "student": [29, 43, 48, 49, 56, 70, 75, 81, 91, 95, 97, 102, 109, 117], "studi": [1, 22, 28], "stufen": [0, 32, 66, 75, 81], "sturm": [48, 70, 91], "st\u00e9phane": [10, 11, 18, 42, 49, 55, 70, 87, 91, 104], "st\u00f6ter": [0, 15, 16, 17, 23, 33, 37, 46, 47, 48, 51, 53, 66, 70, 75, 80, 81, 91, 96, 105, 106, 109, 111, 113], "su": [66, 75, 81], "sub": [41, 66, 75, 81, 107, 111], "subject": [66, 75, 81], "submiss": [57, 58, 66, 72, 75, 81, 83, 92, 97, 111], "submit": [14, 43, 62, 63, 66, 75, 81, 91, 96], "subscript": [66, 75, 81], "subsequ": [22, 29, 66, 75, 81, 82], "subset": [66, 75], "substanc": [22, 66, 75, 81], "substanti": [53, 62, 63, 75, 81, 108], "substitut": [3, 23, 32, 37, 66, 75, 81], "success": [66, 75], "suchodoletz": [0, 38, 51, 66, 75, 81, 96, 111], "suffix": [30, 33, 66, 75, 81], "suganya": [7, 21, 39, 52, 53, 56, 58, 70, 71, 72, 80, 81, 91, 95, 102, 109], "suit": [1, 66, 75], "suitabl": [3, 23, 28, 37, 66, 75, 81, 87], "sum": [20, 66, 75, 81], "summer": [3, 8, 11, 17, 53, 67, 81, 92, 97, 111], "sun": [4, 5, 7, 9, 36, 48, 66, 70, 75, 81, 117], "sundar": [7, 21, 39, 56, 58, 72, 81, 95, 102, 109], "super": [30, 33, 66, 75, 81], "superhydrophob": [30, 33, 66, 75, 81], "supervis": [66, 75, 81], "supplement": [41, 66, 75, 81, 111], "support": [1, 6, 8, 15, 20, 23, 25, 28, 29, 31, 37, 39, 43, 45, 50, 52, 53, 56, 71, 88, 91, 106, 108, 114], "suppos": [59, 81, 111], "sure": [1, 15, 25, 31, 39, 63, 66, 75, 81, 88], "surfac": [66, 75, 81], "surgeri": [3, 20, 23, 32, 37, 66, 75, 81], "surgic": [22, 66, 75], "survei": 75, "susann": [3, 8, 17, 23, 30, 33, 35, 37, 51, 53, 55, 56, 58, 66, 73, 74, 75, 78, 81, 82, 87, 88, 91, 96, 101, 105, 106, 111], "susanna": [1, 15, 39, 66, 75, 81], "sustain": [3, 8, 23, 28, 29, 33, 34, 37, 53, 108], "suzanna": [11, 53, 66, 87, 93, 107, 111, 113], "sven": [1, 15, 39, 66, 75, 81], "swan": [43, 57, 72, 81, 107, 113], "swedlow": [2, 4, 5, 15, 17, 39, 53, 66, 73, 81, 87, 92, 97, 105, 111], "swift": [43, 53, 66, 80], "swim": [30, 33, 66, 75, 81], "swissbia": [5, 56, 66, 75, 81, 91, 95], "switzerland": [17, 24, 31, 34, 51, 66, 75, 81, 96, 105, 111], "sylvi": [4, 12, 18, 30, 53, 73, 78], "sylvia": [4, 12, 18, 30, 53, 73, 78, 81, 92, 96, 97, 101, 111], "symposium": [3, 6, 22, 23, 28, 29, 32, 34, 37, 49, 55, 56, 66, 70, 75, 81, 105, 109, 111], "syn": [49, 83, 91, 98], "synergi": [1, 14, 66, 75, 81], "syntact": [66, 75], "synthet": [24, 66, 75, 81], "system": [0, 1, 3, 8, 22, 23, 28, 29, 32, 33, 34, 35, 37, 38, 45, 46, 51, 53, 55, 57, 58, 66, 73, 74, 75, 81, 94, 96, 101, 105, 106, 109, 111, 116], "systemat": [9, 11, 39, 53, 66, 73, 75, 81, 87, 92, 97, 101, 111], "systematisch": [41, 81, 107], "szymon": [21, 49, 70, 87, 90, 109], "s\u00e1enz": [17, 18, 29, 51, 53, 67, 81, 101], "s\u00e9amu": [12, 29, 43, 49, 53, 66, 70, 73, 78, 85, 91], "s\u00e9bastien": [2, 4, 5, 11, 15, 17, 48, 53, 70, 73, 79, 81, 84, 92, 105, 108, 111], "t": [3, 4, 11, 12, 16, 18, 22, 23, 24, 25, 29, 30, 31, 32, 33, 37, 41, 43, 47, 48, 49, 53, 60, 62, 63, 64, 66, 70, 73, 75, 78, 80, 81, 83, 85, 87, 91, 92, 97, 101, 102, 106, 109, 115], "ta5": [56, 91, 96, 105, 111], "taatj": [4, 12, 18, 30, 53, 73, 78], "tabl": [30, 33, 82], "tabular": [66, 75, 82], "tach": [29, 66, 75, 81], "tackl": [29, 66, 75, 81], "taelman": [48, 57, 70, 81, 115], "tag": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 23, 24, 25, 26, 28, 29, 30, 31, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 55, 56, 57, 58, 59, 60, 61, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 78, 79, 80, 81, 82, 83, 84, 85, 87, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 111, 112, 113, 114, 115, 116, 117], "tailor": [3, 8, 14, 16, 22, 23, 32, 33, 34, 35, 37, 43, 55, 57, 58, 66, 74, 75, 81, 91, 96, 105, 106, 111], "take": [1, 28, 29, 30, 33, 62, 66, 75, 81], "taken": [24, 31, 66, 75, 81], "talk": [6, 11, 17, 29, 37, 43, 55, 74, 87, 90, 99, 102, 107, 109, 117], "tal\u00e0": [24, 66, 75, 81], "tamara": [1, 15, 39, 66, 75, 81], "tang": [1, 15, 39, 66, 75, 81], "tanoli": [1, 15, 39, 66, 75, 81], "taraborelli": [53, 75, 81, 108], "target": [3, 22, 23, 28, 29, 33, 34, 37, 38, 66, 75, 81], "tarkowska": [49, 70, 91], "task": [3, 8, 23, 29, 30, 33, 34, 37, 55, 92, 97, 99, 105, 109], "tau": [30, 33, 66, 75, 81], "tauchen": [29, 56, 66, 75, 81, 96, 100, 111], "tc": [29, 30, 33, 66, 75, 81], "teach": [4, 7, 21, 24, 28, 29, 31, 36, 48, 49, 55, 72, 82, 95, 99, 102, 103, 106, 107, 111, 112, 113, 114, 117], "teaching_bioimage_analysi": [43, 91, 103, 109], "team": [0, 3, 6, 8, 11, 16, 23, 28, 30, 32, 33, 36, 37, 51, 55, 58, 70, 74, 94, 105, 106, 111], "technic": [1, 3, 15, 37, 39, 53, 108], "techniqu": [16, 20, 29, 34, 35, 53, 58, 66, 75, 81, 90, 96, 105, 111], "technisch": [3, 23, 26, 32, 37, 45, 56, 66, 75, 81, 111], "technischen": [66, 75, 81], "technolog": [53, 75, 107, 111], "technologi": [1, 14, 56, 66, 67, 75, 95, 96, 102], "technologien": [41, 81, 107], "technopol": [5, 29, 48, 49, 56, 57, 60, 66, 70, 75, 80, 81, 91, 95, 114, 115], "tedd": [1, 15, 39, 66, 75, 81], "teil": [41, 107], "teilen": [26, 56, 66, 75, 81, 96, 111], "teilten": [56, 66, 75, 81, 111], "teilvorhaben": [66, 75, 81], "templat": [25, 31, 55, 82], "tempor": [3, 23, 28, 37, 66, 75, 81], "terabyt": [17, 51, 75, 81, 91, 111], "term": [1, 14, 22, 66, 75, 81, 88], "termin": [17, 63, 66, 75, 81, 117], "terminologi": [3, 29, 37, 117], "tesm": [30, 33, 66, 75, 81], "test": [20, 25, 29, 32, 57, 62, 85, 87, 114], "test_dir": [25, 31, 66, 75, 81], "test_grai": [24, 66, 75, 81], "teun": [48, 57, 60, 70, 91, 112, 114], "text": [29, 62, 66, 75, 81, 90], "textdaten": [56, 66, 75, 81, 100, 111], "th": [1, 15, 39, 66, 75, 81], "than": [1, 14, 29, 46, 50, 57, 66, 75, 81, 92, 98, 106, 115], "thank": [66, 75, 81, 82], "thano": [53, 81, 107, 111], "thecolon": [29, 66, 75, 81], "thei": [1, 3, 17, 22, 23, 24, 28, 29, 32, 36, 37, 43, 48, 49, 52, 53, 55, 62, 66, 70, 71, 73, 75, 81, 85, 88, 91, 94, 96, 101, 109, 111], "thejacksonlaboratori": [56, 85, 103], "them": [3, 9, 11, 30, 33, 37, 49, 53, 56, 62, 63, 66, 67, 73, 75, 81, 93, 95, 96, 101, 102, 103, 109, 111, 114], "thema": [0, 26, 32, 56, 66, 75, 81, 111], "themat": [14, 66, 75, 81], "themen": [26, 56, 66, 75, 81, 111], "themselv": [6, 16, 28, 51, 66, 75, 81, 91, 94, 105, 111], "thenod": 87, "theoret": [31, 66, 75, 81], "theoretischen": [26, 56, 66, 75, 81, 111], "therapeut": [29, 66, 75, 81], "therapi": [29, 66, 75, 81], "therebi": [9, 66, 75, 81], "therefor": [45, 66, 75, 81, 111], "thereof": [6, 28, 37, 56, 66, 75, 81, 111], "thermo": [24, 31, 66, 75, 81], "thermofish": [66, 75], "thesi": [29, 66, 75, 81], "thi": [0, 1, 2, 3, 4, 6, 7, 8, 9, 11, 12, 14, 16, 17, 20, 22, 23, 24, 25, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 43, 46, 48, 49, 50, 51, 52, 53, 55, 56, 58, 59, 61, 62, 64, 66, 67, 68, 70, 72, 73, 74, 75, 76, 78, 80, 81, 82, 83, 84, 85, 89, 90, 91, 92, 95, 97, 99, 101, 102, 103, 104, 105, 106, 107, 108, 109, 112, 113, 114, 115, 117], "thilo": [46, 53, 73, 98, 116], "thing": [66, 75, 81], "think": [62, 63], "thoennissen": [3, 8, 23, 28, 66, 75, 81], "thoma": [3, 8, 16, 17, 22, 23, 28, 30, 32, 33, 34, 37, 43, 48, 51, 52, 53, 55, 58, 66, 70, 71, 73, 74, 75, 78, 80, 81, 85, 88, 91, 93, 96, 101, 105, 106, 111, 117], "thorough": [30, 33, 66, 75, 81], "thoroughli": [43, 46, 87, 106], "those": [1, 6, 14, 28, 37, 48, 56, 66, 70, 75, 81, 111], "thread": [66, 75, 81], "three": [3, 6, 8, 14, 23, 28, 31, 36, 37, 55, 56, 66, 75, 81, 88, 111], "thrive": [66, 75, 82], "through": [0, 3, 8, 14, 16, 20, 23, 36, 38, 48, 70, 73, 90, 94, 96, 99, 105, 106, 115], "throughout": [3, 8, 14, 23, 28, 37, 43, 53, 55, 57, 59, 66, 73, 75, 81, 87, 91, 92, 96, 97, 101, 105, 111], "thu": [29, 66, 75, 81], "thursdai": [3, 23, 37, 66, 75, 81, 117], "th\u00f6nni\u00dfen": [0, 16, 23, 37, 38, 46, 47, 48, 53, 66, 70, 75, 81, 91, 96, 105, 106, 109, 111], "ti": [30, 33, 66, 75, 81], "ti2": [30, 33, 66, 75, 81], "tiejun": [53, 73, 91, 96, 99, 115], "tier": [30, 33, 66, 75, 81], "tif": [24, 31, 66, 75, 81], "tiff": [2, 3, 8, 17, 23, 24, 30, 31, 33, 34, 37, 48, 66, 70, 75, 81, 84, 92, 108], "tile": [2, 17, 31, 48, 70, 84, 92, 108], "till": [29, 41, 49, 70, 81, 107, 109], "tim": [4, 5, 7, 9, 11, 12, 17, 36, 41, 43, 46, 48, 49, 52, 53, 57, 66, 70, 71, 73, 75, 80, 81, 83, 85, 91, 102, 103, 107, 109, 117], "time": [3, 20, 31, 33, 34, 37, 62, 66, 75, 81, 88], "timepoint": [24, 66, 75, 81], "timmonko": [46, 48, 57, 70, 80, 91, 102, 103], "timo": [0, 3, 8, 23, 28, 38, 51, 66, 75, 81, 96, 111], "tinevez": [11, 18, 29, 31, 42, 49, 53, 55, 57, 67, 70, 81, 87, 91, 104], "tingt": [4, 12, 18, 30, 53, 73, 78], "tip": [22, 66, 75, 81], "tirado": [4, 5, 7, 9, 36, 48, 66, 70, 75, 81, 117], "tirf": [30, 33, 66, 75, 81], "tischer": [2, 5, 7, 9, 11, 12, 15, 17, 18, 24, 29, 30, 36, 48, 50, 53, 55, 57, 58, 66, 70, 72, 73, 75, 78, 81, 85, 87, 91, 104, 105, 109, 111, 115, 117], "tischi": [4, 5, 48, 55, 57, 70, 85, 87, 91, 104, 109], "tissu": [3, 23, 29, 32, 37, 66, 75], "titanium": [22, 66, 75, 81], "titl": [56, 61, 82, 91, 97, 107, 111], "tjxnmiwtdac": [56, 74, 87, 90, 91, 95, 99], "tobi": [4, 5, 7, 9, 11, 43, 48, 55, 57, 66, 70, 75, 81, 91, 109, 112, 114, 117], "tobia": [0, 1, 3, 8, 15, 23, 30, 33, 34, 37, 38, 39, 48, 49, 51, 53, 60, 66, 70, 75, 81, 96, 111, 114], "todai": [66, 75, 81], "todd": [49, 70, 91], "togeth": [1, 14, 15, 32, 38, 39, 43, 45, 57, 72, 107, 113], "token": 63, "tol": [4, 50, 55, 58, 66, 72, 81, 91], "toler": [22, 66, 75], "tom": [0, 3, 6, 8, 15, 16, 17, 22, 23, 28, 30, 32, 33, 34, 35, 38, 46, 47, 48, 53, 55, 56, 58, 66, 70, 74, 75, 80, 81, 88, 91, 96, 105, 106, 109, 111, 113, 117], "tomancak": [12, 29, 51, 53, 98], "tommaso": [55, 66, 75, 81, 116], "tomograph": [29, 66, 75, 81], "tomographi": 20, "too": [3, 57, 62, 66, 75, 85, 87, 111], "tool": [1, 8, 17, 23, 26, 28, 29, 30, 35, 36, 37, 39, 41, 50, 51, 55, 57, 58, 67, 74, 83, 85, 95, 96, 97, 99, 104, 105, 108, 109, 114, 116, 117], "toolkit": [41, 66, 81, 111], "top": [3, 8, 23, 33, 34, 35, 37, 55, 58, 66, 74, 75, 81, 105, 106, 111], "topic": [0, 3, 6, 16, 20, 23, 28, 33, 34, 37, 38, 41, 43, 45, 48, 56, 57, 62, 66, 72, 75, 85, 87, 89, 93, 96, 105, 106, 107, 111, 113, 114], "torec": [48, 53, 66, 70, 81, 91, 97, 106, 115], "torsten": [0, 15, 16, 17, 23, 33, 37, 46, 47, 48, 51, 53, 66, 70, 75, 80, 81, 91, 96, 105, 106, 109, 111, 113], "tosi": [11, 18, 55, 57, 70, 79, 87, 91, 104], "toth": [17, 52, 53, 71, 81, 109], "touch": 76, "toward": [1, 3, 14, 33, 37, 90], "toxicolog": [1, 28, 66, 75, 81], "tpecot": [49, 70, 90, 109], "tpl": [41, 91], "trace": 88, "traceabl": [14, 43, 57, 66, 75, 81, 91, 96, 111], "tracer": [29, 66, 75, 81], "track": [30, 33, 45, 46, 47, 49, 50, 64, 66, 73, 75, 89, 92, 101, 106, 111, 115, 117], "trackem2": [24, 66, 75, 81], "tradit": [17, 34, 56, 75, 81, 91, 96, 111], "train": [1, 2, 15, 21, 25, 30, 32, 39, 45, 50, 60, 61, 62, 72, 80, 83, 95, 97, 112, 113, 115], "trainer": [3, 11, 23, 28, 33, 34, 37, 53, 93, 96, 101, 114], "training_materi": 76, "training_notebook": [31, 66, 75, 81], "transcript": [66, 75, 81], "transfer": [25, 31, 64, 66, 75, 81, 82, 91], "transform": [3, 9, 22, 23, 25, 28, 31, 32, 37, 66, 75, 81], "translat": [3, 8, 23, 30, 33, 34, 37, 41, 66, 75, 81, 111], "transloc": [49, 70, 80, 91], "transmit": [30, 33, 66, 75, 81], "transpar": 82, "transparent": [41, 81, 107], "transpos": [3, 22, 23, 32, 37, 66, 75, 81], "trap": [20, 66, 75], "treat": [33, 38, 66, 75, 81], "treatment": [29, 33, 38, 66, 75, 81], "tree": [29, 55, 69, 87, 91, 98], "treffen": [0, 32, 66, 75, 81], "trehin": [66, 75, 85], "trei": [17, 52, 53, 71, 81, 109], "trello": [30, 33, 66, 75, 81], "trevor": [2, 4, 5, 15, 17, 53, 73, 81, 105, 111], "tri": [66, 75, 81], "trial": [29, 66, 75, 81], "trick": [22, 66, 75, 81], "tripl": [17, 34, 56, 75, 81, 91, 96, 111], "triton": [24, 31, 66, 75, 81], "troman": [41, 70, 83, 91], "trono": [25, 31, 66, 75, 81], "trotzdem": 77, "troubleshoot": [66, 75], "troublshoot": [66, 75], "truth": [48, 70], "try": [29, 66, 75, 81], "ts4nfdi": [66, 75, 81], "tu": [3, 17, 22, 23, 28, 29, 32, 37, 45, 46, 56, 57, 69, 87, 88, 91, 98, 117], "tubulin": [31, 66, 75, 81], "tudorach": [66, 75, 81], "tulok": [0, 3, 22, 23, 37, 66, 75, 81], "tumour": [29, 56, 66, 74, 75, 81, 87, 95, 102], "tune": 90, "ture": [57, 111, 113], "turner": [43, 70], "tutori": [2, 3, 4, 7, 8, 14, 18, 20, 23, 28, 29, 30, 33, 34, 35, 36, 37, 41, 45, 55, 56, 59, 60, 66, 72, 75, 79, 81, 82, 92, 93, 94, 96, 97, 100, 101, 104, 105, 106, 107, 108, 111, 112, 113, 114, 115], "tuur": [48, 57, 70, 81, 115], "two": [3, 5, 6, 20, 22, 23, 28, 29, 30, 32, 33, 34, 37, 56, 66, 75, 81, 83, 91, 92, 95, 102, 111], "txt": [25, 31, 66, 75, 81], "type": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 78, 79, 80, 81, 82, 83, 84, 85, 87, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 111, 112, 113, 114, 115, 116, 117], "t\u00e4tigkeit": 77, "u": [3, 14, 23, 26, 28, 29, 37, 41, 43, 56, 57, 61, 62, 66, 72, 75, 81, 88, 91, 96, 107, 111, 113], "uag": [41, 66, 75, 81, 111], "ub": [51, 53, 82], "ubvujf7wausq5q7v7pi1vepi1": [43, 58, 74], "uc": [3, 22, 23, 32, 37, 66, 75, 81], "ucl": [41, 43, 57, 66, 81, 111, 114], "udemi": [57, 87, 114], "ufz": [1, 3, 23, 28, 56, 66, 75, 81, 91, 105, 111], "ugent": [59, 87, 111], "ugi": [1, 7, 11, 15, 21, 53, 56, 58, 66, 72, 73, 75, 81, 87, 92, 95, 97, 101, 102, 109, 111], "uhlmann": [17, 43, 53, 66, 80, 81, 91, 102, 107], "uk": [4, 7, 21, 30, 33, 39, 41, 43, 44, 49, 50, 53, 55, 56, 57, 58, 66, 75, 81, 83, 91, 92, 93, 95, 96, 97, 98, 101, 102, 107, 109, 111, 113, 114], "ulf": [43, 70], "uli": [53, 66, 81, 111], "ulm": [3, 33, 66, 75, 81], "ulrich": [22, 66, 75, 81], "ulrik": [4, 11, 12, 18, 30, 33, 41, 53, 66, 73, 75, 78, 81, 87, 88, 92, 97, 101, 102, 111], "ultim": [1, 28, 66, 75, 81], "ultra": [31, 66, 75, 81], "um": [0, 26, 32, 56, 66, 75, 77, 81, 96, 100, 111], "umap": [29, 66, 75, 81], "umass": [30, 33, 66, 75, 81], "umfassend": [29, 56, 66, 75, 81, 96, 111], "umfeld": [41, 81, 107], "umgehend": 77, "umsetzung": [50, 59, 87, 108, 111], "umsetzungsvorschl\u00e4g": [66, 75, 81], "umst\u00e4nden": 77, "umzusetzen": [26, 56, 66, 75, 81, 111], "un": [29, 56, 66, 75, 81, 96, 111], "unam": [30, 33, 66, 75, 81], "unambigu": [14, 43, 57, 66, 75, 81, 91, 96, 111], "unannot": [66, 75, 81], "unbalanc": [53, 66, 81, 96, 111], "unber\u00fchrt": 77, "unc": [30, 33, 66, 75, 81], "uncertain": [66, 75, 81], "unchang": [16, 35, 66, 75, 81], "und": [0, 32, 41, 50, 59, 77, 87, 100, 105, 107, 108], "under": [22, 24, 25, 45, 56, 63, 74, 87, 88, 95, 102, 111], "undergo": [16, 35, 66, 75, 81], "underli": [15, 20, 48, 66, 70, 75, 81, 84, 106], "underlin": [66, 75, 81], "understand": [1, 9, 15, 33, 38, 39, 57, 59, 66, 75, 81, 82, 87], "understood": [56, 74, 87, 95, 102], "unet": [29, 66, 75, 81], "unfortun": [48, 70], "ung": [66, 75, 81], "uni": [6, 16, 17, 28, 35, 37, 41, 46, 48, 49, 51, 53, 66, 70, 77, 82, 88, 97, 98, 107, 111, 116, 117], "uniform": [29, 66, 75, 81], "unilamellar": [24, 66, 75, 81], "uniqu": [66, 75], "unit": [11, 29, 30, 33, 41, 43, 53, 57, 66, 75, 78, 81, 91, 98, 111, 114], "univer": [30, 33, 66, 75, 81], "univers": [3, 6, 8, 20, 23, 30, 33, 34, 35, 37, 43, 45, 48, 49, 58, 70, 74, 84, 92, 96, 105, 106, 117], "universidad": [30, 33, 66, 75, 81], "universit": [30, 33, 66, 75, 81], "universit\u00e4t": [3, 17, 23, 26, 32, 37, 45, 56, 66, 75, 81, 111, 117], "universit\u00e4ten": [41, 107], "unknown": [1, 2, 3, 4, 5, 10, 11, 12, 13, 18, 20, 21, 25, 29, 31, 34, 39, 40, 41, 42, 43, 44, 46, 47, 48, 49, 50, 53, 55, 56, 57, 58, 59, 60, 65, 66, 68, 69, 70, 73, 74, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 111, 113, 114, 116], "unkown": [50, 59, 94, 96, 111], "unless": 88, "unlock": [11, 39, 53, 66, 73, 75, 81, 87, 92, 97, 101, 111], "unmount": [24, 31, 66, 75, 81], "unseen": [25, 31, 66, 75, 81], "unser": [0, 29, 32, 56, 59, 66, 75, 77, 81, 87, 96, 100, 105, 111], "unseren": 77, "unter": [26, 41, 56, 66, 75, 77, 81, 96, 107, 111], "untereinand": [0, 32, 66, 75, 81], "unterliegen": 77, "unterschiedlich": [0, 26, 32, 56, 66, 75, 81, 111], "unterst\u00fctzt": [41, 50, 59, 81, 87, 107, 108, 111], "until": [48, 62, 70], "unverlangten": 77, "un\u00fcbersichtlich": [26, 56, 66, 75, 81, 111], "up": [3, 20, 22, 23, 24, 32, 33, 37, 55], "updat": [10, 41, 66, 75, 76, 85, 88, 111], "upload": [1, 22, 29, 32, 37, 48, 56, 58, 63, 66, 70, 72, 75, 83, 85, 91, 92, 96, 97, 100, 107, 109, 111], "uplsapo": [25, 31, 66, 75, 81], "upper": 63, "upright": [66, 75], "uptak": [3, 8, 23, 33, 34, 35, 37, 55, 58, 66, 74, 75, 81, 105, 106, 111], "upton": [30, 33, 66, 75, 81], "uq": [40, 87, 111, 113], "urbanc": [42, 49, 55, 70, 87, 91, 104], "urheb": [56, 66, 75, 81, 100, 111], "urheberrecht": 26, "urheberrechtsverletzung": 77, "url": [30, 33, 61, 63, 66, 75, 81], "uruguai": [17, 66, 75, 81], "us": [0, 3, 4, 6, 7, 8, 9, 11, 12, 14, 15, 16, 20, 21, 22, 23, 24, 32, 34, 35, 36, 37, 38, 39, 46, 58, 61, 62, 73, 74, 78, 80, 82, 83, 84, 89, 92, 93, 95, 96, 97, 98, 100, 102, 103, 104, 105, 108, 112, 115, 116], "usabl": [53, 73, 88, 94, 96, 101, 108], "usag": [2, 3, 22, 23, 32, 37, 43, 57, 66, 75, 81, 87, 106, 114], "use_gpu": [25, 31, 66, 75, 81], "usegalaxi": [46, 116], "user": [1, 10, 14, 20, 22, 24, 28, 29, 32, 43, 46, 48, 49, 57, 59, 60, 76, 82, 88, 92, 96, 102, 103, 114, 116], "usual": [66, 75, 81], "utf": [3, 22, 23, 32, 37, 66, 75, 81], "util": [30, 33, 46, 48, 53, 57, 66, 70, 73, 75, 80, 81, 91, 92, 96, 97, 101, 102, 103, 111], "utz": [66, 75, 81], "uuml": [30, 33, 66, 75, 81], "uw": [21, 25, 49, 55, 70, 87, 90, 91, 104, 109], "v": [0, 1, 3, 12, 15, 17, 32, 33, 34, 35, 39, 43, 56, 58, 66, 74, 75, 81, 87, 90, 91, 95, 97, 99, 102, 106, 109, 111, 117], "v0": [10, 20, 29], "v1": [18, 29, 31, 53, 67, 81, 91, 104], "v1i": [17, 34, 53, 66, 81, 105, 111], "v2": [3, 8, 11, 17, 53, 66, 67, 75, 81, 92, 97, 111], "v3": [29, 48, 49, 56, 66, 70, 75, 81, 91], "v4sdb": [43, 48, 70, 91, 117], "v8": [46, 81, 92], "vakili": [4, 5, 7, 9, 36, 48, 66, 70, 75, 81, 117], "val_rdm42": [31, 66, 75, 81], "valencia": [0, 16, 23, 37, 38, 46, 47, 48, 51, 53, 66, 70, 75, 81, 91, 96, 105, 106, 109, 111], "valentin": [49, 70, 87, 90, 91], "valentyna": [21, 49, 70, 87, 90, 109], "valid": 31, "valu": [1, 11, 28, 30, 33, 53, 92, 97, 101, 102], "valuabl": [1, 28, 56, 62, 66, 75, 81, 82, 94, 111], "van": [17, 18, 29, 51, 52, 53, 67, 71, 73, 81, 91, 92, 96, 97, 101, 111, 114], "vanessa": [0, 3, 6, 16, 22, 23, 28, 32, 37, 38, 46, 47, 48, 51, 53, 66, 70, 75, 81, 91, 94, 96, 105, 106, 109, 111], "vannari": [42, 49, 55, 70, 87, 91, 104], "vannieuwkerk": [48, 57, 70, 81, 115], "vari": [29, 66, 75, 81], "variabl": [3, 29, 33, 34, 37, 63, 66, 75, 81], "variat": [29, 53, 66, 75, 81, 96, 111], "varieti": [3, 23, 28, 37, 43, 57, 66, 72, 75, 81, 107, 113], "variou": [0, 1, 3, 8, 16, 22, 23, 28, 30, 33, 34, 37, 38, 48, 52, 53, 62, 71, 91, 96, 105, 107, 108, 111, 114], "varun": [4, 12, 18, 30, 53, 73, 78], "vasja": [42, 49, 55, 70, 87, 91, 104], "vebjorn": [43, 44, 73], "ved": [4, 12, 18, 30, 53, 73, 78], "veldhui": [10, 29, 66, 75, 81], "velten": [20, 48, 49, 56, 70, 87, 93], "vem": [43, 44, 72, 83, 91], "vendor": [29, 55, 66, 75, 81, 99, 109], "ventana": [24, 31, 66, 75, 81], "venu": [3, 23, 37, 66, 75, 81, 117], "vera": [1, 15, 39, 66, 75, 81], "veranstaltung": [26, 56, 66, 75, 81, 111], "veranstaltungsreih": [26, 56, 66, 75, 81, 111], "verantwortlich": [29, 56, 66, 75, 81, 111], "verarbeiten": [26, 56, 66, 75, 81, 96, 111], "verarbeitet": [26, 29, 56, 66, 75, 81, 96, 111], "verbessern": [59, 87, 105, 111], "verbos": [25, 31, 66, 75, 81], "verbreitung": 77, "verbunden": [26, 56, 66, 75, 81, 111], "verbundprojekt": [66, 75, 81], "verein": [59, 87, 105, 111], "vereinbarungen": [56, 66, 75, 81, 100, 111], "verfuegung": [29, 56, 66, 75, 81, 96, 111], "verger": [10, 11, 18, 42, 49, 55, 70, 87, 91, 104], "veri": [16, 35, 45, 62, 66, 75, 81, 88, 111], "verifi": 63, "verlauf": [66, 75, 81], "verlinkten": 77, "verlinkung": 77, "vermaerck": [66, 75, 81, 117], "vernetzung": [66, 75, 81], "verpflichtet": 77, "verpflichtungen": 77, "versatil": [45, 46, 57, 111], "verschiedenen": [26, 53, 56, 66, 75, 81, 111], "version": [3, 16, 17, 20, 23, 25, 26, 31, 32, 34, 35, 37, 40, 41, 56, 68, 82, 91, 96, 111, 113], "versionskontrollwerkzeug": [29, 56, 66, 75, 81, 96, 111], "vervielf\u00e4ltigung": 77, "verwaltung": [50, 59, 87, 108, 111], "verwenden": [26, 56, 66, 75, 81, 111], "verwertung": 77, "ver\u00e4ndern": [29, 56, 66, 75, 81, 96, 100, 111], "ver\u00f6ffentlichen": 29, "ver\u00f6ffentlichten": 77, "ver\u00f6ffentlichung": [26, 56, 66, 75, 81, 111], "ver\u00f6ffentlichungen": [41, 81, 107], "vesicl": [24, 66, 75, 81], "vesicular": [30, 33, 66, 75, 81], "via": [7, 21, 25, 31, 39, 43, 46, 49, 53, 56, 58, 66, 72, 75, 81, 85, 87, 92, 95, 97, 102, 103, 106, 109, 111, 116], "viabl": [1, 28, 66, 75, 81], "viadrina": [45, 66, 75, 81, 111], "vibbit": [48, 57, 70, 81, 90, 115], "vibrant": [66, 75, 81], "vibspec": [53, 66, 81, 96, 111], "victor": [49, 70, 85, 90, 109], "video": [2, 3, 4, 7, 8, 12, 16, 21, 22, 23, 30, 33, 34, 35, 37, 39, 43, 50, 55, 56, 57, 61, 66, 72, 74, 75, 78, 81, 83, 87, 90, 91, 92, 95, 97, 99, 100, 102, 103, 104, 105, 106, 107, 108, 109, 111, 113, 114], "viel": [26, 41, 56, 66, 75, 81, 107, 111], "view": [4, 30, 31, 33, 46, 48, 50, 53, 57, 58, 66, 70, 72, 75, 81, 87, 89, 96, 102, 103, 111], "viewer": [49, 66, 75, 81, 103, 109], "viktoriia": [49, 70, 85, 90, 109], "vilardel": [66, 75, 81], "villalobo": [30, 33, 66, 75, 81], "villegeorg": [11, 25, 31, 43, 47, 60, 87, 91, 102], "vincent": [24, 66, 75, 81], "vinohradi": [20, 66, 75, 81], "virgini": [17, 43, 53, 66, 80, 81, 91, 102, 107], "virshup": [17, 48, 52, 53, 70, 71, 81, 91, 109], "virtanen": [48, 70, 80, 91], "virtual": [3, 33, 34, 37, 66, 75, 81], "vision": [30, 33, 56, 66, 75, 81, 95], "visit": [29, 66, 75, 81], "vison": [5, 70, 87, 90, 91], "visual": [6, 9, 16, 24, 29, 30, 31, 33, 35, 50, 56, 57, 66, 81, 85, 103, 115], "visualis": [43, 44, 72, 83, 91], "vital": [29, 30, 33, 53, 66, 75, 81, 108], "vlasveld": [53, 73, 81, 92, 96, 97, 101, 111], "vocabulari": [66, 75, 81], "vogel": [3, 22, 23, 32, 37, 66, 75, 81], "vogelsang": [3, 22, 23, 32, 37, 66, 75, 81], "voic": [66, 75, 81], "voigt": [56, 66, 75, 81, 96, 111], "vol": [17, 34, 53, 66, 75, 81, 105], "volker": [55, 57, 70, 79, 87, 91, 104], "volkerh": [57, 70, 87], "vollst\u00e4ndigkeit": 77, "volltextserv": [51, 53, 82], "volum": [1, 28, 29, 66, 75, 81], "volumetr": [29, 66, 75, 81], "vom": 77, "von": [0, 12, 26, 29, 32, 38, 43, 49, 51, 56, 59, 66, 70, 73, 75, 77, 81, 85, 87, 91, 96, 100, 105, 111], "vor": [26, 29, 56, 66, 75, 77, 81, 96, 111], "voraussetzungen": [26, 56, 66, 75, 81, 96, 111], "vorfeld": [66, 75, 81], "vorgaben": [26, 56, 66, 75, 81, 111], "vorliegend": [41, 53, 66, 75, 81, 107, 111], "vorliegenden": [26, 56, 66, 75, 81, 111], "vorteil": [26, 56, 66, 75, 81, 111], "voxel": [24, 66, 75, 81], "vo\u00df": [66, 75, 82], "vpn": [3, 23, 28, 66, 75, 81], "vre": [66, 75, 81], "vrhovac": [17, 18, 29, 51, 53, 67, 81, 101], "vs120": [25, 31, 66, 75, 81], "vsi": [25, 31, 66, 75, 81], "v\u00f6hringer": [17, 52, 53, 71, 81, 109], "w": [2, 4, 5, 12, 14, 15, 17, 24, 29, 31, 34, 43, 50, 51, 52, 53, 55, 56, 57, 58, 66, 67, 70, 71, 72, 73, 75, 80, 81, 91, 92, 96, 97, 98, 101, 104, 105, 111], "w10nm": [24, 66, 75, 81], "w541": [66, 75, 81], "w545": [66, 75, 81], "wa": [1, 3, 6, 8, 16, 17, 18, 20, 23, 24, 25, 26, 28, 29, 30, 31, 32, 33, 34, 35, 37, 41, 53, 78, 82, 91, 96, 98, 104, 105, 106], "waagmeest": [17, 34, 56, 75, 81, 91, 96, 111], "wachsenden": [66, 75, 81], "wagen": [1, 15, 39, 66, 75, 81], "wagner": [33, 38, 66, 75, 81], "wah": [11, 39, 53, 66, 73, 87, 92, 97, 101, 111], "wai": [3, 11, 14, 29, 33, 40, 43, 49, 53, 55, 57, 59, 70, 83, 87, 90, 91, 92, 96, 97, 99, 103, 109, 111, 113], "waiger": [66, 75, 81], "wait": 62, "waith": [17, 18, 29, 49, 51, 53, 67, 70, 81, 84, 91, 101, 109], "walczysko": [7, 21, 39, 56, 58, 72, 81, 95, 102, 109], "walkthrough": [16, 34, 35, 58, 66, 75, 81, 105, 111], "wang": [29, 66, 75, 81], "wann": [29, 56, 66, 75, 81, 96, 111], "want": [1, 22, 28, 29, 30, 33, 48, 49, 56, 57, 60, 62, 63, 66, 70, 75, 80, 81, 82, 91, 95, 97, 102, 109, 114, 115], "waren": [56, 66, 75, 77, 81, 100, 111], "warp": [4, 70, 85], "warpi": [24, 31, 66, 75, 81], "watch": [12, 34, 43, 56, 58, 74, 81, 87, 90, 91, 95, 97, 99, 102, 105, 106, 109, 111], "water": [40, 53, 73, 81, 92, 96, 97, 101, 111], "watson": [30, 33, 66, 75, 81], "wayn": [12, 29, 51, 53, 98], "we": [1, 3, 6, 8, 11, 14, 15, 16, 17, 22, 23, 25, 28, 31, 32, 33, 34, 35, 37, 39, 43, 48, 49, 50, 51, 53, 55, 57, 58, 61, 62, 66, 70, 73, 74, 75, 76, 82, 83, 85, 88, 89, 90, 91, 92, 94, 96, 99, 102, 103, 105, 106, 107, 109, 115, 117], "weaken": [66, 75], "web": [1, 16, 17, 28, 30, 33, 34, 35, 40, 43, 46, 48, 51, 57, 58, 66, 70, 72, 75, 79, 81, 83, 87, 92, 96, 97, 105, 106, 111, 113, 116], "webclient": [3, 22, 23, 32, 37, 66, 75, 81], "webinar": [58, 72, 93, 104], "webseit": 77, "webseiten": [29, 56, 66, 75, 77, 81, 96, 111], "websit": [29, 30, 33, 34, 43, 50, 57, 58, 61, 66, 75, 76, 81, 82, 87, 91, 94, 96, 100, 105, 108, 111], "week": [26, 56, 66, 75, 81, 111], "wege": [53, 66, 81, 111], "wegewitz": [45, 46, 57, 111], "wei": [1, 11, 46, 53, 70, 85, 87, 90, 95, 109, 116], "weichenchu": [43, 60, 70, 91], "weidtkamp": [3, 8, 17, 23, 30, 34, 35, 37, 38, 53, 55, 56, 58, 66, 67, 73, 74, 75, 78, 81, 88, 91, 96, 101, 105, 106, 111], "weigert": [5, 7, 25, 29, 39, 49, 55, 56, 58, 66, 70, 72, 75, 81, 87, 90, 91, 95, 102, 104, 109], "weil": [0, 16, 23, 37, 38, 46, 47, 48, 53, 66, 70, 75, 81, 91, 96, 105, 106, 109, 111], "weimer": [66, 75, 81], "weiner": [26, 56, 66, 75, 81, 111], "weis": [1, 15, 39, 66, 75, 81], "weisbart": [17, 53, 81, 91, 102, 107], "weischer": [16, 35, 43, 66, 75, 81, 91, 106], "weischner": [16, 35, 53, 78, 105, 106, 111], "weisen": 77, "weiteren": [0, 32, 66, 75, 81], "weiterf\u00fchrend": [26, 56, 66, 75, 81, 111], "weitergeben": [56, 66, 75, 81, 100, 111], "weitergegeben": 77, "welch": [0, 26, 32, 56, 66, 75, 81, 96, 111], "welchen": [26, 56, 66, 75, 81, 96, 111], "welcom": [3, 37, 41, 62, 70, 87, 88, 90], "well": [1, 3, 16, 22, 24, 28, 29, 31, 33, 34, 35, 37, 38, 41, 43, 46, 50, 56, 57, 58, 59, 66, 74, 75, 81, 87, 90, 91, 92, 95, 98, 99, 105, 106, 111, 115], "wellcom": [43, 57, 81, 114], "welt": [29, 56, 66, 75, 81, 96, 100, 111], "weltweit": [29, 56, 66, 75, 81, 96, 111], "wem": [29, 56, 66, 75, 81, 100, 111], "wendt": [0, 6, 23, 28, 35, 37, 38, 43, 46, 47, 48, 51, 53, 66, 70, 75, 78, 81, 91, 94, 96, 101, 105, 106, 109, 111, 115], "wenn": [29, 56, 66, 75, 81, 96, 111], "wer": [29, 96], "werbeinformationen": 77, "werbung": 77, "werden": [0, 26, 29, 32, 41, 56, 66, 75, 77, 81, 96, 107, 111], "were": [0, 3, 6, 16, 20, 22, 23, 24, 25, 28, 29, 31, 32, 37, 38, 43, 48, 53, 56, 66, 75, 81, 88, 91, 94, 96, 105, 108, 111], "werk": 77, "werner": [0, 38, 51, 75, 81, 96, 111], "wernet": [3, 8, 23, 30, 33, 34, 37, 53, 66, 75, 81, 111], "wessen": [56, 66, 75, 81, 100, 111], "west": [66, 75, 117], "westerhoff": [1, 15, 39, 66, 75, 81], "wetzker": [16, 28, 37, 51, 55, 56, 66, 75, 81, 91, 92, 94, 105, 111], "weyend": [7, 21, 39, 56, 58, 72, 81, 95, 102, 109], "wg12_checklists_for_image_publish": [43, 49, 91, 111], "what": [10, 29, 36, 40, 48, 49, 66, 68, 70, 72, 75, 85, 88, 113], "wheeler": 41, "when": [4, 9, 11, 25, 29, 31, 40, 52, 53, 66, 67, 68, 75, 81, 91, 104], "where": [0, 1, 3, 8, 16, 23, 28, 33, 34, 35, 37, 38, 43, 48, 55, 57, 58, 63, 74, 87, 91, 96, 104, 105, 106, 111], "wherev": [66, 75, 81], "whetzel": [66, 75, 81], "which": [1, 3, 4, 7, 9, 14, 15, 20, 21, 22, 23, 24, 25, 29, 31, 32, 34, 37, 39, 41, 46, 48, 53, 55, 56, 58, 61, 62, 66, 70, 72, 75, 81, 82, 84, 85, 88, 91, 92, 95, 99, 102, 105, 106, 108, 109, 111], "while": [0, 3, 8, 23, 28, 29, 38, 51, 56, 62, 66, 74, 75, 81, 87, 95, 96, 102, 111], "white": [24, 31, 66, 75, 81], "who": [3, 23, 29, 33, 34, 37, 48, 49, 56, 57, 60, 70, 80, 82, 95, 97, 102, 109, 114, 115], "whoisjack": [11, 42, 49, 70, 85, 87, 91, 109], "whole": [1, 25, 28], "whole_ds441": [31, 66, 75, 81], "wholemount": [29, 66, 75, 81], "why": [3, 17, 30, 33, 34, 35, 48, 62, 66, 70, 75, 85, 87], "wibk": [1, 28, 51, 66, 75, 81, 91], "wichtig": [29, 56, 66, 75, 81, 96, 100, 111], "wide": [1, 14, 66, 75, 81], "widersprochen": 77, "widget": [46, 48, 57, 66, 70, 75, 80, 81, 91, 102, 103], "wie": [29, 100], "wiebk": [29, 53, 66, 78], "wiederzuverwenden": [29, 56, 66, 75, 81, 111], "wifi": [3, 23, 28, 66, 75, 81], "wiki": [43, 50, 81, 87, 91, 106, 111], "wildlif": [20, 66, 75], "wilei": [3, 4, 8, 9, 11, 16, 18, 29, 30, 33, 34, 35, 37, 41, 53, 78, 81, 91, 98, 104, 105, 106, 111], "wilj": [41, 66, 75, 81, 111], "wilkin": [29, 66, 75, 81], "wilkinson": [53, 66, 73, 81, 96, 111], "willem": [1, 15, 39, 66, 75, 81], "willems": [53, 73, 81, 92, 96, 97, 101, 111], "william": [11, 43, 80, 93, 109, 115, 116], "wilson": [29, 53, 81, 91], "win": 63, "windhag": [48, 56, 60, 65, 70, 81, 91, 102], "window": [24, 63], "winter": [43, 48, 70, 91, 117], "wir": [26, 29, 56, 59, 66, 75, 77, 81, 87, 96, 100, 105, 111], "wird": [26, 29, 56, 66, 75, 77, 81, 111], "wirkt": [26, 56, 66, 75, 81, 111], "wise": [20, 66, 75], "wissenschaft": [41, 66, 75, 81, 107], "wissenschaftlich": [26, 56, 66, 75, 81, 111], "wissenschaftlichen": [26, 29, 41, 56, 66, 75, 81, 96, 100, 107, 111], "wissenschaftszweigen": [59, 87, 105, 111], "wissensgraph": [66, 75, 81], "within": [1, 8, 11, 14, 15, 28, 29, 38, 39, 41, 53, 56, 67, 73, 82, 87, 91, 92, 94, 97, 101, 105, 108, 111], "without": [20, 24, 29, 32, 57, 82, 87, 113], "wittner": [1, 15, 39, 66, 75, 81], "witz": [49, 55, 65, 70, 80, 85, 87, 90, 91, 104, 109], "wizard": [24, 31, 66, 75, 81], "wo": [26, 56, 66, 75, 81, 111], "wolf": [48, 66, 70, 75, 81, 91], "wolfgang": [17, 52, 53, 71, 81, 109], "wolni": [5, 57, 70, 87, 90, 91], "wolstencroft": [17, 34, 53, 56, 73, 75, 81, 91, 92, 96, 97, 101, 111], "wong": [3, 7, 21, 33, 34, 39, 53, 56, 58, 72, 73, 78, 81, 95, 101, 102, 109, 111], "work": [0, 1, 6, 8, 14, 15, 16, 17, 20, 23, 28, 30, 38, 39, 41, 45, 46, 48, 53, 56, 57, 62, 64, 70, 78, 85, 87, 96, 102, 103, 105], "workbook": [16, 35, 66, 75, 81], "workflow": [0, 1, 2, 3, 4, 7, 11, 15, 23, 29, 33, 36, 37, 38, 39, 48, 49, 59, 60, 73, 80, 82, 85, 92, 93, 95, 96, 97, 99, 102, 103, 105, 109, 112, 114, 117], "workload": [66, 75], "workshop": [11, 14, 16, 20, 25, 26, 29, 31, 35, 41, 45, 47, 65, 80, 96, 102, 106, 108], "workshopthi": [3, 23, 28, 66, 75, 81], "world": [14, 16, 34, 35, 56, 57, 58, 66, 75, 81, 91, 96, 105, 111], "worldwid": [43, 55, 57, 66, 75, 81, 91, 116], "wors": [29, 66, 75, 81], "worth": [45, 66, 75, 81, 111], "would": [20, 61, 62, 66, 75, 81, 82], "wp": [30, 33, 66, 75, 81], "wrap": [3, 15, 37, 48, 66, 70, 75, 81, 84, 106], "wrapper": [15, 48, 70, 84, 106], "wright": [29, 66, 75, 81], "write": [29, 46, 48, 56, 62, 66, 70, 75, 81, 92], "wurd": [26, 56, 66, 75, 81, 111], "wurden": 77, "wuttk": [41, 66, 75, 81, 111], "www": [1, 2, 3, 4, 5, 7, 8, 11, 12, 14, 15, 17, 18, 20, 21, 22, 23, 24, 29, 30, 32, 33, 34, 35, 37, 39, 41, 43, 44, 45, 46, 47, 49, 50, 51, 52, 53, 55, 56, 57, 58, 59, 60, 66, 70, 75, 78, 80, 81, 82, 83, 84, 85, 87, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 111, 113, 114, 115, 116, 117], "w\u00e4hlby": [4, 57, 70, 87, 91, 104], "w\u00fcnsche": [26, 56, 66, 75, 81, 100, 111], "w\u00fcrttemberg": [66, 75, 81], "x": [24, 29, 31, 66, 75, 81], "xavier": [17, 18, 29, 51, 53, 67, 81, 101], "xiaoyan": [5, 49, 55, 70, 87, 90, 91], "xlsx": [20, 66, 75, 81], "xperiment": [30, 33, 66, 75, 81], "xplorer": [66, 75, 81], "xt": [24, 31, 66, 75, 81], "xyz": [31, 66, 75, 81], "xz": [24, 66, 75, 81], "xzt": [24, 66, 75, 81], "y": [29, 66, 75, 81], "yamauchi": [17, 41, 46, 49, 52, 53, 71, 81, 91, 103, 104, 109, 111], "yaml": 63, "yandex": [48, 70, 85], "yandexdataschool": [48, 49, 70, 85, 90, 109], "yaniv": [48, 70, 91], "yann": [52, 53, 71, 81, 91, 111, 114], "yannick": [56, 74, 87, 90, 91, 95, 99], "yanqi": [24, 66, 75, 81], "yaroslav": [17, 48, 70, 79, 92], "year": [17, 34, 51, 53, 56, 66, 75, 81, 91, 96, 105, 108, 111], "yedid": [42, 49, 55, 70, 87, 91, 104], "yet": [9, 53, 66, 75, 81, 108], "yi": [4, 5, 7, 9, 36, 48, 66, 70, 75, 81, 117], "yield": [25, 31, 66, 75, 81], "yimin": [48, 70, 91], "yishen": [48, 70, 91], "ymia": [66, 75, 81], "ymia_python_event_series_materi": [28, 29, 48, 56, 70, 85, 91, 99, 102, 109], "yml": [29, 31, 62, 66, 75, 81], "yoav": [12, 43, 49, 66, 70, 73, 85, 91], "yolo_v5": [20, 66, 75], "yolov5": [20, 66, 75], "you": [14, 22, 25, 30, 31, 33, 34, 41, 43, 45, 46, 48, 50, 57, 58, 61, 62, 63, 70, 72, 76, 83, 87, 88, 92, 97, 98, 106, 107, 113, 114, 115], "your": [3, 14, 32, 33, 34, 41, 45, 58, 62, 63, 72, 83, 88, 92, 97, 114], "your_github_api_kei": 63, "yourself": [62, 63], "youth": [20, 66, 75, 81], "youtub": [3, 8, 12, 23, 33, 34, 35, 37, 55, 56, 66, 75, 78, 90, 95, 97, 99, 105, 111], "ysda": [48, 70, 85], "yu": [41, 57, 70, 87, 90, 91], "yuan": [29, 66, 75, 81], "yurchenko": [49, 70, 85, 90, 109], "yve": [11, 18, 29, 31, 42, 49, 53, 55, 57, 67, 70, 81, 87, 91, 104], "z": [3, 11, 33, 34, 53, 66, 73, 75, 77, 78, 81, 101, 111], "z1": [24, 29, 30, 33, 66, 75, 81], "z2": [30, 33, 66, 75, 81], "zabal": [52, 53, 71, 81, 91, 111, 114], "zach": [66, 75, 81], "zahlreich": [26, 56, 66, 75, 81, 111], "zahn": [52, 53, 71, 81, 91, 111, 114], "zaritski": [17, 42, 49, 53, 55, 70, 81, 87, 91, 102, 104, 107], "zarr": [0, 2, 11, 15, 16, 23, 34, 37, 38, 56, 66, 80, 84, 92, 96, 105, 108, 113], "zb": [3, 56, 66, 75, 81, 91, 105, 111], "zebrafish": [1, 28, 66, 75, 81], "zeiss": [24, 29, 30, 31, 32, 33, 37], "zeissmicroscopi": [43, 49, 70, 91, 103, 109], "zeitpunkt": 77, "zeitschrift": [66, 75, 81], "zelezniak": [49, 70, 87, 109], "zen": [66, 75], "zen_python_oad_workshop": [43, 49, 70, 91, 103, 109], "zenodo": [0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 14, 15, 16, 17, 20, 22, 23, 24, 25, 26, 28, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 41, 42, 43, 45, 46, 47, 48, 51, 53, 55, 57, 58, 62, 70, 74, 82, 85, 88, 89, 90, 91, 92, 94, 95, 96, 99, 100, 102, 105, 106, 107, 108, 109, 116, 117], "zenti": [0, 3, 8, 16, 23, 33, 34, 35, 37, 38, 46, 47, 48, 51, 53, 55, 58, 66, 70, 74, 75, 81, 88, 91, 96, 105, 106, 109, 111], "zentral": [66, 75, 81], "zero": [66, 75, 117], "zerocost": [29, 66, 75, 81], "zerocostdl4m": [25, 29, 31, 56, 67, 75, 81, 95, 96, 102], "zhang": [11, 17, 18, 29, 42, 49, 51, 53, 55, 67, 70, 73, 81, 87, 91, 96, 99, 101, 104, 115], "zheng": [48, 70, 91], "zhian": [36, 55, 66, 75, 81], "ziaurrehman": [1, 15, 39, 66, 75, 81], "zidas2020_intro_dl": [10, 55, 70, 87, 90, 91], "zidas_2020_dl_intro_part_2": [21, 49, 70, 87, 90, 91], "zielhui": [53, 73, 81, 94, 96, 113], "zih": [56, 66, 75, 81, 111], "zimmermann": [66, 75, 81], "zinchenko": [21, 49, 70, 87, 90, 109], "zink": [66, 75, 81], "zip": [25, 29, 31, 66, 75, 81], "ziqiang": [4, 5, 7, 9, 36, 48, 70, 81], "ziv": [48, 70, 91], "zmb": 43, "zobel": [3, 8, 16, 17, 23, 30, 33, 34, 37, 43, 53, 55, 58, 66, 74, 75, 78, 81, 88, 91, 105, 106, 111], "zoccol": [6, 28, 29, 37, 40, 49, 56, 64, 66, 70, 75, 81, 90, 91, 103, 109, 111], "zolt\u00e1n": [46, 53, 73, 98, 116], "zoo": [1, 53, 56, 67, 81, 95, 96, 102, 116], "zu": [26, 29, 41, 45, 53, 56, 66, 75, 77, 81, 96, 100, 107, 111], "zuckerberg": [53, 75, 81, 108], "zudem": [26, 56, 66, 75, 81, 111], "zugang": [41, 81, 107], "zuge": [26, 56, 66, 75, 81, 111], "zugeh\u00f6rigen": [53, 66, 81, 111], "zugriff": 77, "zuletzt": [26, 56, 66, 75, 81, 111], "zum": [26, 29, 56, 77, 96], "zumutbar": 77, "zun\u00e4chst": [41, 56, 66, 75, 81, 107, 111], "zur": [26, 29, 56, 77, 96, 111], "zusammen": [29, 56, 66, 75, 81, 96, 111], "zusammenfuehren": [29, 56, 66, 75, 81, 96, 111], "zusammenwirken": [59, 87, 105, 111], "zuschratt": [0, 38, 51, 75, 81, 96, 111], "zusendung": 77, "zustimmung": 77, "zus\u00e4tzlich": [66, 75, 81], "zweiten": [0, 26, 32, 41, 56, 66, 75, 81, 107, 111], "zwpor7shjg1hhdiwlzj0mb_stlu": [3, 8, 23, 33, 34, 35, 37, 55, 58, 66, 74, 75, 81, 105, 106, 111], "zyla": [31, 66, 75, 81], "\u00b5": [3, 22, 23, 32, 37, 66, 75, 81], "\u00b5m": [29, 66, 75, 81], "\u00f6zdemir": [1, 14, 53, 66, 75, 81, 111], "\u00fcber": [0, 26, 32, 53, 56, 66, 75, 81, 111], "\u00fcberarbeitet": [26, 56, 66, 75, 81, 111], "\u00fcberblick": [26, 53, 56, 66, 75, 81, 111], "\u00fcberfordernd": [26, 56, 66, 75, 81, 111], "\u00fcbermittelt": 77, "\u00fcbernehmen": 77, "\u00fcberpr\u00fcft": 77, "\u00fcbersendung": 77, "\u00fcbersetzt": [66, 75, 81], "\u00fcberwachen": 77, "\u00fcbungsaufgaben": [26, 56, 66, 75, 81, 111], "\u03b1": [31, 66, 75, 81]}, "titles": ["Astrid schauss (5)", "Beatriz serrano-solano (6)", "Chris allan (5)", "Christian schmidt (19)", "Christian tischer (14)", "Constantin pape (8)", "Cornelia wetzker (6)", "Dominik kutra (6)", "Elisa ferrando-may (7)", "Elnaz fazeli (5)", "Estibaliz g\u00f3mez-de-mariscal (5)", "Et al. (25)", "Florian jug (6)", "Guillaume witz (8)", "Isabel kemmer (4)", "Jean-marie burel (6)", "Jens wendt (7)", "Josh moore (27)", "Kota miura (11)", "Mara lampert (8)", "Martin sch\u00e4tz (6)", "Martin weigert (8)", "Michael gerlach (12)", "Michele bortolomeazzi (9)", "Nicolas chiaruttini (7)", "Olivier burri (6)", "Pia voigt (6)", "<no title>", "Riccardo massei (12)", "Robert haase (56)", "Roland nitschke (5)", "Romain guiet (10)", "Silke tulok (6)", "Stefanie weidtkamp-peters (11)", "Susanne kunis (16)", "Thomas zobel (7)", "Toby hodges (4)", "Tom boissonnet (13)", "Torsten st\u00f6ter (5)", "Ugis sarkans (5)", "Blog (19)", "Book (19)", "Code (10)", "Collection (81)", "Data (8)", "Document (7)", "Documentation (19)", "Event (8)", "Github repository (45)", "Notebook (55)", "Online tutorial (10)", "Poster (10)", "Preprint (5)", "Publication (61)", "<no title>", "Slide (41)", "Slides (42)", "Tutorial (42)", "Video (24)", "Website (9)", "Workshop (12)", "YML format", "How to contribute", "Using the Training Materials Submission App", "Biapol.github.io (6)", "Docs.google.com (5)", "Doi.org (183)", "F1000research.com (11)", "Focalplane.biologists.com (14)", "Git.mpi-cbg.de (7)", "Github.com (123)", "Www.biorxiv.org (5)", "Www.ebi.ac.uk (10)", "Www.nature.com (16)", "Www.youtube.com (22)", "Zenodo.org (172)", "Open data", "Imprint", "All rights reserved (13)", "Bsd-2-clause (7)", "Bsd-3-clause (23)", "Cc-by-4.0 (249)", "Cc-by-sa-4.0 (5)", "Cc0-1.0 (10)", "Gpl-2.0 (7)", "Mit (24)", "<no title>", "Unknown (89)", "NFDI4BioImage Training Materials", "Statistics", "Artificial intelligence (32)", "Bioimage analysis (174)", "Bioimage data (20)", "Bioinformatics (10)", "Data stewardship (6)", "Deep learning (12)", "Fair-principles (27)", "Image data management (14)", "Imagej (19)", "Large language models (5)", "Licensing (6)", "Metadata (15)", "Microscopy image analysis (15)", "Napari (13)", "Neubias (26)", "Nfdi4bioimage (23)", "Omero (31)", "Open science (9)", "Open source software (7)", "Python (70)", "<no title>", "Research data management (110)", "Segmentation (5)", "Sharing (12)", "Training (12)", "Workflow (10)", "Workflow engine (13)", "Recently added (10)"], "titleterms": {"": [1, 3, 4, 8, 9, 14, 15, 17, 23, 29, 31, 33, 34, 35, 37, 39, 43, 51, 53, 55, 56, 57, 58, 66, 70, 74, 75, 80, 81, 87, 90, 91, 96, 105, 106, 111], "0": [20, 66, 75, 81, 82, 83, 84], "1": [0, 22, 26, 32, 56, 66, 75, 81, 83, 111], "10": [28, 31, 42, 50, 51, 56, 66, 72, 75, 81, 83, 91, 93, 105, 111, 115, 117], "11": [18, 33, 67], "110": 111, "12": [22, 28, 60, 66, 75, 81, 95, 113, 114], "123": 70, "13": [37, 78, 103, 116], "14": [4, 68, 97], "15": [101, 102], "16": [3, 33, 34, 37, 66, 73, 75, 81], "17": [3, 33, 34, 37, 66, 75, 81], "172": 75, "174": 91, "183": 66, "19": [3, 40, 41, 46, 66, 75, 81, 98], "2": [0, 26, 32, 56, 66, 75, 77, 79, 81, 84, 111], "20": 92, "2013": [18, 41, 57, 70, 87, 91, 104], "2016": [18, 29, 31, 53, 67, 81, 91, 104], "2017": [4, 11, 18, 55, 57, 70, 87, 91, 104], "2018": [42, 49, 55, 70, 87, 91, 104], "2019": [11, 18, 21, 29, 42, 49, 55, 70, 87, 90, 91, 104, 109], "2020": [4, 5, 10, 11, 17, 18, 21, 25, 29, 31, 42, 43, 48, 49, 51, 53, 55, 58, 66, 67, 69, 70, 74, 80, 81, 87, 90, 91, 98, 101, 104, 109], "2021": [3, 5, 8, 11, 17, 21, 33, 49, 53, 55, 67, 70, 81, 87, 90, 91, 92, 97, 105, 109, 111], "2022": [13, 21, 49, 66, 70, 75, 81, 87, 90, 91, 109], "2023": [0, 16, 17, 21, 23, 28, 33, 34, 37, 38, 43, 46, 47, 48, 49, 51, 53, 56, 60, 66, 70, 75, 81, 87, 90, 91, 96, 105, 106, 109, 111], "2024": [3, 12, 17, 29, 30, 33, 34, 35, 37, 48, 49, 51, 57, 58, 60, 66, 70, 74, 75, 80, 81, 91, 95, 96, 97, 102, 103, 105, 106, 109, 111, 112, 114, 115], "2050": [53, 66, 81, 96, 111], "20x": [66, 75, 81], "22": [5, 21, 49, 70, 74, 87, 90, 109], "23": [43, 60, 70, 80, 91, 105], "24": [43, 58, 60, 70, 85, 91], "249": 81, "25": 11, "26": 104, "27": [17, 96], "2d": [29, 43, 44, 55, 66, 69, 75, 80, 81, 87, 91, 98, 104], "3": 80, "31": 106, "32": 90, "3d": [19, 29, 31, 40, 43, 44, 55, 66, 68, 69, 75, 80, 81, 87, 91, 98, 103, 104, 109], "4": [14, 36, 66, 75, 81, 82], "4000": [24, 66, 75, 81], "41": 55, "42": [56, 57], "45": 48, "4pi": [66, 75, 81], "4th": [66, 75, 81], "5": [0, 2, 9, 10, 28, 30, 38, 39, 52, 56, 65, 71, 75, 77, 81, 82, 91, 99, 105, 111, 112], "55": [49, 77], "56": 29, "6": [1, 6, 7, 12, 15, 20, 25, 26, 32, 41, 64, 75, 81, 94, 100, 111], "61": 53, "6well": [22, 66, 75, 81], "7": [8, 16, 24, 35, 45, 66, 69, 75, 79, 84, 108], "70": 109, "8": [5, 13, 19, 21, 44, 47], "81": 43, "89": 87, "9": [23, 59, 107], "A": [3, 4, 6, 8, 9, 11, 12, 17, 23, 24, 28, 29, 30, 33, 34, 37, 39, 40, 41, 43, 46, 49, 51, 52, 53, 56, 57, 59, 66, 68, 70, 71, 73, 75, 80, 81, 82, 83, 87, 91, 97, 98, 105, 106, 111, 113, 115, 116], "Be": [1, 15, 39, 66, 75, 81], "For": [49, 70, 90, 109], "IT": [3, 17, 33, 34, 37, 60, 66, 75, 81, 91, 106, 111], "If": [29, 40, 68, 81, 100, 111], "It": [17, 34, 51, 75, 81, 91, 96, 105, 111], "The": [2, 3, 8, 15, 17, 18, 23, 28, 29, 30, 33, 34, 37, 39, 41, 43, 51, 53, 56, 58, 66, 67, 73, 75, 78, 81, 85, 87, 91, 92, 94, 96, 97, 98, 101, 102, 105, 111, 113], "There": [17, 34, 56, 75, 81, 91, 96, 111], "To": [40, 68, 111, 113], "ab": 77, "abdomin": [22, 66, 75, 81], "aberr": [66, 75, 81], "about": [66, 75, 81], "abstract": [45, 81, 111], "ac": 72, "academi": [21, 25, 49, 55, 70, 80, 87, 90, 91, 104, 109], "acceler": [11, 29, 47, 48, 49, 53, 55, 57, 60, 64, 66, 67, 70, 78, 80, 81, 91, 98, 109, 114, 115], "access": [2, 4, 5, 11, 15, 17, 34, 51, 53, 66, 73, 75, 81, 91, 101, 105, 111], "acknowledg": 88, "acquir": [32, 37, 66, 75, 81], "acquisit": [66, 75, 81], "actin": [66, 75, 117], "activ": [25, 31, 66, 75, 81], "ad": [16, 35, 55, 66, 70, 75, 79, 81, 91, 104, 117], "adapt": [22, 66, 75, 81], "address": [66, 75, 81], "adjust": [3, 8, 23, 33, 34, 35, 37, 55, 58, 66, 74, 75, 81, 105, 106, 111], "advanc": [3, 8, 23, 29, 30, 33, 34, 37, 43, 47, 50, 56, 57, 58, 66, 74, 75, 81, 91, 96, 100, 111, 115], "again": [17, 34, 56, 75, 81, 91, 96, 111], "against": 87, "agent": [22, 66, 75, 81], "aggreg": [25, 31, 52, 66, 71, 75, 81, 113], "ahm": [17, 56, 66, 75, 81, 111], "ai": [17, 29, 34, 43, 44, 48, 51, 56, 57, 66, 70, 72, 74, 75, 81, 83, 87, 90, 91, 95, 96, 99, 102, 105, 109, 111], "ai4lif": [46, 87, 90, 91, 116], "aiw": [22, 66, 75, 81], "al": [11, 59, 87, 105, 111], "algorithm": [29, 66, 75, 81], "align": [41, 66, 81], "all": [56, 66, 75, 78, 81, 100, 111], "allan": 2, "am": [0, 32, 66, 75, 81], "ambassador": [1, 66, 75, 81], "an": [3, 8, 11, 17, 18, 19, 29, 40, 41, 43, 51, 52, 53, 55, 58, 66, 68, 71, 75, 81, 82, 87, 90, 91, 92, 96, 97, 101, 103, 104, 105, 109, 111], "analys": [4, 12, 18, 30, 43, 49, 53, 55, 66, 73, 75, 78, 81, 91, 111], "analysi": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 15, 16, 18, 19, 21, 23, 25, 28, 29, 31, 33, 34, 35, 36, 37, 39, 40, 41, 43, 46, 47, 48, 49, 50, 51, 52, 53, 55, 56, 57, 58, 59, 60, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 78, 80, 81, 82, 83, 84, 85, 87, 90, 91, 92, 95, 96, 97, 98, 99, 100, 102, 103, 104, 105, 106, 109, 111, 112, 113, 114, 115, 116, 117], "analyst": [9, 10, 11, 18, 29, 31, 42, 49, 53, 55, 57, 66, 67, 70, 75, 81, 87, 91, 104], "andor": [66, 75, 117], "angaben": 77, "angebot": [29, 66, 75, 81], "anleitung": [45, 46, 57, 111], "annot": [3, 16, 19, 22, 23, 28, 31, 32, 33, 34, 37, 40, 43, 44, 51, 53, 66, 68, 73, 75, 78, 81, 91, 101, 103, 106, 109, 111, 115], "annual": [66, 75, 81], "api": [29, 48, 49, 55, 60, 69, 70, 80, 91, 98, 104, 114], "app": [11, 30, 33, 53, 63, 66, 75, 81, 101], "appli": [29, 55, 69, 91, 98], "applic": [10, 11, 22, 55, 66, 70, 75, 81, 87, 90, 91], "approach": [11, 48, 53, 56, 57, 59, 60, 65, 66, 70, 81, 82, 87, 91, 93, 102, 107, 111, 113], "ar": [57, 87, 96, 100, 111], "arbeiten": [29, 56, 66, 75, 81, 96, 111], "arc": [0, 38, 51, 75, 81, 96, 111], "archiv": [7, 21, 39, 43, 44, 53, 56, 57, 58, 66, 72, 73, 81, 83, 87, 91, 92, 95, 97, 102, 109, 111], "area": [25, 31, 66, 75, 81], "arivi": [43, 58, 74, 78, 91], "arrai": [17, 34, 51, 53, 66, 75, 81, 91, 105, 111], "artifici": [12, 29, 55, 66, 67, 74, 75, 81, 87, 90, 91, 109], "assembli": [46, 53, 66, 98, 116], "assess": [11, 53, 81], "asset": [18, 29, 31, 53, 67, 81, 91, 104], "assist": [29, 66, 75, 81], "assur": [19, 40, 68, 91, 103, 109], "astigmat": [66, 75, 81], "astrid": 0, "author": [57, 59, 82], "autom": [16, 35, 43, 75, 81, 91, 106], "automat": [28, 51, 66, 81, 91], "avail": [43, 46, 57, 72, 81, 107, 109, 113], "awkward": [55, 66, 75, 81, 91], "axioscan": [66, 75, 81], "axioscan7": [32, 37, 66, 75, 81], "axiozoom": [22, 66, 75, 81], "back": [17, 34, 56, 75, 81, 87, 91, 96, 111], "barrier": [53, 81, 107, 111], "base": [11, 28, 29, 41, 43, 48, 49, 53, 56, 66, 70, 75, 81, 83, 85, 90, 91, 99, 101, 102, 109, 111, 114], "basic": [18, 41, 45, 70, 78, 81, 91, 111], "batch": [46, 48, 53, 70, 73, 84, 98, 106, 116], "batchconvert": [66, 75, 81], "bead": [66, 75, 81], "beat": [29, 55, 69, 80, 91, 98, 104], "beatriz": 1, "bedarf": [0, 32, 66, 75, 81], "beginn": [13, 49, 70, 80, 91, 109], "bei": [41, 81, 107], "beim": [26, 56, 66, 75, 81, 96, 111], "benefit": [45, 111], "bereich": [29, 66, 75, 81], "best": [11, 53, 66, 87, 93, 107, 111, 113], "besten": [0, 32, 66, 75, 81], "bf": [66, 75, 81], "bi": [0, 32, 66, 75, 81], "bia": [29, 40, 64, 66, 75, 81, 90, 91, 109], "biaflow": [55, 70, 79, 91, 104], "biapi": [11, 46, 109, 116], "biapol": 64, "bid": [0, 11, 29, 38, 48, 51, 53, 70, 75, 81, 91, 92, 95, 96, 97, 102, 109, 111], "bigdataprocessor2": [4, 24, 53, 66, 81, 91, 111], "bigdataview": [48, 49, 60, 70, 114], "bina": [17, 66, 75, 81], "bio": [3, 4, 6, 7, 8, 9, 19, 23, 29, 30, 33, 34, 35, 37, 40, 43, 47, 48, 49, 53, 55, 56, 57, 58, 60, 64, 66, 67, 68, 70, 74, 75, 80, 81, 87, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 102, 103, 105, 106, 109, 111, 113], "bioengin": [1, 46, 53, 85, 87, 90, 95, 109, 116], "bioformat": [46, 81, 92], "bioformats2raw": [2, 11, 17, 48, 70, 84, 92, 108], "bioimag": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 21, 23, 28, 29, 30, 31, 33, 34, 35, 36, 37, 39, 41, 42, 43, 44, 46, 47, 49, 50, 51, 53, 55, 56, 57, 58, 60, 65, 66, 67, 69, 70, 72, 73, 74, 75, 78, 80, 81, 82, 83, 85, 87, 90, 91, 92, 95, 96, 97, 98, 99, 101, 102, 103, 104, 105, 106, 107, 109, 111, 115, 116, 117], "bioinformat": [53, 93], "biolog": [2, 11, 15, 39, 53, 66, 73, 81, 87, 91, 92, 97, 101, 102, 111], "biologi": [11, 21, 29, 39, 40, 41, 49, 52, 53, 66, 70, 71, 73, 75, 81, 87, 90, 91, 92, 97, 101, 103, 109, 111, 113], "biologist": [9, 42, 43, 49, 53, 57, 66, 68, 70, 75, 80, 81, 85, 87, 90, 109], "biomed": [5, 70, 85, 90, 91], "biomero": [48, 53, 66, 70, 81, 91, 97, 106, 115], "biorxiv": 71, "biostatist": [29, 49, 70, 81, 91, 103, 109], "blob": [66, 75, 81], "block": [6, 22, 66, 75, 81], "blog": 40, "bob": [29, 66, 75, 81, 90, 91], "boissonnet": 37, "book": 41, "bortolomeazzi": 23, "bpae": [66, 75, 117], "brain": [11, 31, 53, 66, 75, 81, 92, 97, 111], "braucht": [56, 66, 75, 81, 111], "bridg": [9, 17, 34, 52, 53, 56, 66, 70, 71, 75, 80, 81, 91, 96, 111], "brightfield": [25, 31, 66, 75, 81], "bring": [60, 91, 111], "bringt": [56, 66, 75, 81, 111], "brows": [29, 40, 64, 81, 106, 109], "browser": [57, 70, 87], "bsd": [79, 80], "bug": [19, 40, 68, 109], "build": [1, 10, 14, 28, 39, 53, 55, 66, 70, 75, 81, 87, 90, 91, 92, 97, 111], "burel": 15, "burri": 25, "calendar": [66, 75, 81], "call": [6, 16, 28, 39, 51, 53, 66, 73, 75, 81, 91, 94, 105, 111], "camera": [66, 75, 81], "campu": [6, 66, 75, 81], "cancer": [56, 74, 87, 95, 102], "captain": [57, 87, 114], "captur": [53, 57, 73, 81, 91, 93, 101], "care": [21, 49, 70, 87, 90, 91, 109], "career": [47, 49, 64, 91, 109], "carl": [66, 75, 81], "carpentri": [42, 57, 81, 85], "case": [42, 66, 70, 75, 81, 87, 91, 104], "cba": [4, 7, 11, 57, 85, 111, 115], "cbg": [29, 55, 69, 87, 91, 98], "cc": [17, 66, 75, 81, 82], "cc0": 83, "cell": [9, 31, 40, 41, 43, 44, 52, 56, 66, 71, 74, 75, 81, 87, 91, 95, 102, 111, 113, 117], "cellpos": [25, 31, 66, 75, 81], "cellprofil": [4, 16, 35, 43, 49, 55, 57, 70, 75, 80, 81, 87, 91, 104, 106], "celltrackcolab": [43, 49, 70, 71, 85], "celltrackscolab": [48, 56, 57, 60, 70, 91, 114], "cellular": [32, 37, 66, 75, 81], "center": [3, 8, 23, 28, 40, 43, 58, 66, 74, 75, 81, 91, 111], "cfci": [32, 37, 66, 75, 81], "cff": [57, 111, 113], "challeng": [15, 17, 29, 37, 38, 43, 44, 48, 53, 55, 66, 67, 68, 70, 75, 80, 81, 87, 91, 95, 104, 105, 111, 113], "channel": [43, 58, 66, 74, 75, 81, 87, 91, 92, 102, 103, 104, 106, 108, 109], "charter": [66, 75, 81], "chat": [0, 32, 66, 75, 81], "chatgpt": [29, 40, 66, 68, 75, 81, 90, 99, 111], "checklist": [4, 12, 18, 30, 43, 49, 53, 73, 78, 83, 91, 92, 102, 111], "chiaruttini": 24, "choos": [3, 57, 87, 111], "chri": [2, 43, 58, 74, 87, 91, 103, 109], "christian": [3, 4], "cida": [17, 66, 75, 81, 117], "citabl": [57, 111, 113], "citat": [57, 111, 113], "classifi": [25, 31, 66, 75, 81], "classroom": [9, 56, 66, 75, 81], "claus": [79, 80], "clesperanto": [29, 48, 49, 57, 60, 70, 80, 91, 114, 115], "cli": [11, 15, 46, 48, 70, 81, 84, 92, 106], "client": [15, 48, 70, 84, 106], "clij": [11, 29, 53, 66, 78, 91, 98], "cloud": [9, 13, 17, 34, 51, 53, 55, 56, 65, 66, 75, 81, 87, 90, 91, 104, 105, 111], "club": [11, 42, 49, 70, 87, 109], "cmcb": [17, 66, 75, 81, 117], "co": [56, 66, 75, 81, 111], "coba": [43, 58, 74, 91], "cobrapi": [49, 70, 87, 109], "code": [11, 29, 42, 48, 49, 53, 57, 66, 70, 75, 80, 81, 87, 90, 91, 109], "colab": [12, 43, 49, 58, 66, 70, 73, 74, 85, 91], "collabor": [29, 40, 53, 66, 68, 75, 81, 91, 111, 113, 117], "collect": [5, 11, 43, 53, 66, 70, 72, 75, 85, 87, 90, 91, 93, 101, 114], "cologn": [0, 16, 23, 37, 38, 46, 47, 48, 53, 66, 70, 75, 81, 91, 96, 105, 106, 109, 111], "color": 87, "com": [65, 67, 68, 70, 73, 74], "combin": [0, 38, 51, 75, 81, 96, 111], "command": [46, 81, 92], "common": [9, 66, 75, 81, 87], "commun": [1, 3, 4, 8, 11, 12, 14, 17, 18, 23, 30, 33, 34, 35, 37, 40, 43, 49, 52, 53, 55, 58, 65, 66, 67, 68, 70, 71, 73, 74, 75, 78, 80, 81, 87, 91, 92, 94, 96, 97, 101, 102, 105, 111, 113], "compact": [5, 70, 87, 90, 91], "compar": [52, 53, 71, 81, 91, 111, 114], "complet": [42, 66, 70, 75, 91], "comprehens": [53, 73, 101], "compress": [66, 75, 81], "comput": [13, 21, 29, 36, 49, 53, 55, 65, 66, 67, 69, 70, 75, 81, 87, 90, 91, 98, 103, 104, 109], "concept": [16, 34, 35, 41, 58, 66, 75, 81, 105, 111], "conceptu": [53, 81, 107, 111], "conda": [28, 29, 40, 46, 66, 68, 75, 81, 109], "confer": [28, 56, 66, 75, 81, 91, 105, 111], "confoc": [66, 75, 81, 117], "consid": [57, 87, 96, 100, 111], "consortium": [3, 8, 23, 28, 56, 66, 75, 81, 91, 105, 111], "constantin": 5, "content": [1, 28, 51, 53, 66, 73, 75, 81, 91, 92, 96, 97, 101, 111], "context": [16, 34, 35, 58, 66, 75, 81, 105, 111], "contrast": [22, 25, 29, 31, 66, 75, 81], "contribut": [62, 88], "control": [29, 57, 66, 70, 75, 81, 87, 117], "convers": [28, 56, 66, 75, 81, 111], "convert": [2, 11, 17, 48, 70, 84, 92, 108], "cookbook": [48, 70, 80, 91], "cordi": [17, 51, 75, 81, 91, 111], "core": [3, 6, 8, 29, 30, 32, 33, 34, 37, 43, 53, 55, 66, 67, 75, 81, 91, 92, 105, 109, 111], "cornelia": 6, "correct": [66, 75, 81], "cours": [4, 5, 11, 13, 18, 20, 21, 41, 43, 47, 48, 49, 52, 55, 56, 57, 58, 70, 71, 80, 81, 84, 85, 87, 90, 91, 93, 103, 104, 109, 113], "covid": [66, 75, 81], "cpu": [29, 55, 66, 67, 81], "cranial": [66, 75, 81, 117], "crashkur": [26, 56, 66, 75, 81, 111], "creat": [29, 36, 40, 50, 55, 57, 66, 68, 75, 81, 90, 99, 111, 115], "criteria": 62, "csbdeep": [21, 49, 70, 87, 90, 91, 109], "cultiv": [29, 56, 66, 75, 81, 91, 96, 100, 107, 111], "curricula": [36, 55, 66, 75, 81], "cut": 62, "cycl": [11, 43, 50, 53, 58, 59, 66, 75, 81, 87, 91, 93, 107, 108, 111, 113], "cytometri": [57, 70, 87, 91], "cz": [20, 66, 75], "czi": [24, 43, 66, 75, 81], "da": [26, 56, 66, 75, 81, 111], "dab": [66, 75, 81], "dai": [66, 75, 81], "dalia": [66, 75, 82], "darf": [26, 56, 66, 75, 81, 111], "dask": [13, 49, 70, 87, 91, 109], "data": [0, 1, 2, 3, 4, 5, 6, 7, 8, 11, 14, 15, 16, 17, 18, 20, 21, 22, 23, 24, 28, 29, 30, 32, 33, 34, 35, 37, 38, 39, 40, 41, 42, 43, 44, 45, 48, 49, 50, 51, 52, 53, 55, 56, 57, 58, 59, 60, 64, 65, 66, 67, 68, 70, 71, 72, 73, 74, 75, 76, 78, 81, 82, 83, 85, 87, 90, 91, 92, 93, 94, 95, 96, 97, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 111, 113, 114, 117], "databas": [22, 43, 47, 66, 75, 81, 91, 93, 106, 111], "datapl": [43, 81, 111, 114], "dataset": [3, 22, 23, 24, 31, 32, 37, 43, 44, 66, 75, 81, 87, 91], "dataviz": [41, 75], "daten": [59, 87, 105, 111], "datenmanag": [26, 29, 56, 66, 75, 81, 111], "datenmanagementpl\u00e4n": [26, 56, 66, 75, 81, 111], "datenschutz": [26, 56, 66, 75, 77, 81, 96, 111], "datensparsamkeit": [66, 75, 81], "de": [10, 69], "decis": [14, 66, 75, 81], "declar": [45, 111], "deconvolut": [31, 66, 75, 81], "deep": [5, 10, 12, 13, 21, 40, 43, 49, 55, 56, 64, 65, 66, 67, 68, 70, 73, 74, 81, 85, 87, 90, 91, 95, 96, 102, 104, 109, 112], "deeplif": [20, 48, 49, 56, 70, 87, 93], "deepprofil": [41, 70, 87, 90, 91], "demo": [42, 70, 87], "den": 77, "depart": [3, 33, 34, 37, 60, 66, 75, 81, 91, 106, 111], "deploy": [43, 70, 79, 106], "deposit": [14, 66, 75, 81], "der": [29, 66, 75, 81], "dermal": [66, 75, 81], "desktop": [57, 70, 87], "detect": [66, 75, 81], "deutschen": [53, 81, 96, 111], "deutschland": [59, 87, 105, 111], "develop": [4, 12, 18, 28, 29, 30, 41, 43, 49, 51, 53, 66, 67, 70, 73, 75, 78, 81, 85, 91, 104, 111], "die": [29, 59, 66, 75, 81, 87, 105, 111], "differ": [34, 55, 56, 66, 75, 81, 105, 111], "diffus": [87, 91], "digit": [12, 13, 25, 31, 41, 49, 66, 70, 74, 75, 81, 87, 90, 91], "digitalen": [66, 75, 81], "digitalsreeni": [43, 58, 74, 109], "dimens": [66, 75, 81], "directori": [0, 38, 51, 75, 81, 96, 111], "dirti": [17, 34, 51, 66, 75, 81, 91, 96, 105, 111], "discoveri": [66, 75, 81], "dish": [66, 75], "displai": [66, 75], "divers": [29, 55, 66, 67, 81, 91], "di\u00e1taxi": [57, 59, 82], "dl": [5, 49, 55, 56, 70, 74, 87, 90, 91, 95, 99], "dl4miceverywher": [11, 43, 49, 68, 70, 81, 87, 91, 95], "dl4protein": [43, 48, 70, 93, 117], "do": [14, 66, 75, 81], "doc": 65, "docker": [57, 87, 114], "document": [30, 33, 45, 46, 57, 59, 66, 75, 81, 82, 85, 87, 91, 95, 106, 109, 116], "doi": 66, "dokument": [45, 46, 57, 111], "domain": [17, 25, 31, 34, 56, 66, 75, 81, 91, 96, 111], "dominik": 7, "download": 88, "dr": [56, 74, 87, 95, 102], "dragonfli": [66, 75, 117], "dresden": [6, 29, 55, 66, 69, 75, 81, 87, 91, 92, 98, 105, 111], "drift": [66, 75, 81], "driven": [11, 53, 81, 87, 92, 97, 101, 102], "dubrul": [66, 75, 81, 117], "durch": [59, 87, 105, 111], "dynam": [18, 41, 57, 70, 87, 91, 104], "earli": [47, 49, 64, 91, 109], "ebi": [43, 72, 93, 114], "ecosystem": [1, 14, 53, 58, 66, 74, 75, 81, 91, 97, 106, 111], "edam": [17, 18, 29, 51, 53, 67, 81, 91, 101], "edit": [29, 40, 41, 66, 68, 81, 111, 113], "educ": [66, 75, 81], "effici": [45, 66, 75, 81, 111], "effort": [55, 65, 87], "ein": [41, 56, 66, 75, 81, 107, 111], "einblick": [26, 56, 66, 75, 81, 111], "einen": [0, 32, 66, 75, 81], "einer": [53, 66, 81, 111], "eipp": [57, 70, 87, 90], "elabftw": [45, 46, 57, 111], "elastix": [43, 49], "electron": [3, 22, 23, 32, 37, 40, 66, 75, 81, 111], "elektronischen": [45, 46, 57, 111], "elisa": 8, "elmi": [17, 34, 51, 66, 75, 81, 91, 96, 105, 111], "eln": [66, 75, 81], "elnaz": 9, "em": [22, 43, 44, 66, 72, 75, 81, 83, 91], "embedseg": [12, 48, 70, 91], "embl": [5, 11, 21, 42, 43, 49, 57, 70, 72, 87, 90, 93, 109, 114], "embo": [21, 47, 49, 70, 87, 90, 91, 109], "emiss": [29, 66, 75, 81], "enabl": [3, 11, 33, 34, 37, 39, 53, 60, 66, 73, 75, 81, 87, 91, 92, 97, 101, 106, 107, 111], "engin": [19, 40, 48, 57, 66, 68, 70, 75, 85, 91, 109, 116], "enhanc": [3, 8, 23, 28, 46, 66, 75, 81, 87, 90, 91, 116], "enough": [53, 73, 81, 94, 96, 113], "enter": [29, 66, 75, 81], "environ": [2, 15, 28, 29, 40, 43, 53, 58, 66, 68, 74, 75, 81, 91, 92, 102, 106, 108, 109], "eoe": [17, 66, 75, 81], "eosc": [1, 15, 39, 66, 75, 81], "epflx": [50, 78, 91], "epi": [25, 31, 66, 75, 81], "era": [56, 74, 87, 95, 102], "eric": [66, 75, 81], "erick": [58, 74, 91, 97, 106], "erstellen": [26, 56, 66, 75, 81, 111], "erstellung": [53, 66, 81, 111], "essenti": [53, 75, 81, 108], "establish": [11, 53, 81], "estibaliz": 10, "estim": [20, 66, 75, 81], "et": 11, "eubia": [18, 41, 57, 70, 87, 91, 104], "euro": [1, 14, 43, 55, 56, 57, 58, 65, 66, 74, 75, 81, 87, 91, 96, 111], "event": [11, 25, 28, 29, 31, 43, 47, 48, 56, 60, 70, 85, 87, 91, 93, 99, 102, 106, 109, 111], "everyon": [11, 29, 53, 66, 78, 91, 98], "evid": [24, 66, 75, 81], "exampl": [20, 24, 29, 30, 33, 43, 45, 46, 48, 57, 66, 70, 75, 79, 80, 81, 91, 102, 103, 106, 117], "excel": [16, 35, 66, 75, 81], "exchang": [29, 66, 75, 81], "exclus": 62, "exercis": [5, 21, 49, 55, 70, 87, 90, 91, 109], "expand": [2, 4, 5, 15, 17, 53, 58, 73, 74, 81, 91, 97, 105, 106, 111], "experi": [30, 33, 43, 53, 66, 70, 75, 80, 81, 91], "exploit": [12, 43, 49, 66, 70, 73, 85, 91], "extend": [11, 41, 53, 66, 81, 87, 92, 97, 101, 102], "extens": [11, 53, 66, 81, 91, 92, 97, 106, 111, 115], "extract": [19, 40, 68, 91, 103, 109], "exzellent": [59, 87, 105, 111], "f1000research": 67, "facil": [3, 6, 8, 29, 30, 32, 33, 34, 37, 40, 43, 45, 53, 55, 58, 60, 66, 67, 69, 74, 75, 79, 81, 87, 91, 92, 98, 105, 106, 111], "facilit": [3, 33, 34, 53, 73, 78, 101, 111], "fair": [1, 3, 6, 8, 11, 14, 15, 17, 23, 28, 30, 33, 34, 37, 39, 43, 51, 53, 55, 56, 57, 58, 65, 66, 73, 74, 75, 78, 81, 87, 91, 92, 93, 94, 96, 97, 101, 105, 111, 113, 114, 117], "fairi": [56, 67, 81, 95, 96, 102], "falk": [66, 75, 81], "fanci": [17, 34, 51, 75, 81, 91, 96, 105, 111], "faze": 9, "featur": [19, 40, 68, 91, 103, 109], "ferrando": 8, "fibroblast": [66, 75, 81], "figshar": [40, 68, 111, 113], "fiji": [4, 12, 24, 29, 43, 49, 50, 51, 53, 57, 58, 66, 70, 74, 75, 78, 79, 81, 83, 85, 91, 98, 106, 111], "file": [2, 4, 5, 15, 17, 24, 30, 33, 53, 66, 73, 75, 81, 91, 102, 105, 111, 117], "financ": [3, 17, 30, 33, 34, 35, 66, 75, 81], "find": [3, 43, 52, 53, 57, 71, 72, 81, 87, 91, 107, 111, 113, 114], "first": [42, 66, 70, 75, 91], "fit": [3, 33, 34, 37, 60, 66, 75, 81, 91, 106, 111], "five": [17, 40, 53, 66, 75, 81, 87, 108, 111, 113], "flim": [6, 56, 66, 75, 81, 91], "flimfit": [6, 56, 66, 75, 81, 91], "florian": 12, "flow": [20, 57, 66, 70, 75, 81, 87, 91], "fluoresc": [66, 75, 81], "flute": [6, 56, 66, 75, 81, 91], "fly": 87, "focalplan": 68, "foku": [26, 56, 66, 75, 81, 111], "forg": [46, 81, 109], "format": [2, 4, 5, 15, 17, 18, 29, 51, 53, 61, 66, 67, 73, 75, 81, 82, 91, 101, 105, 111, 117], "forschung": [29, 59, 66, 75, 81, 87, 105, 111], "forschungsdaten": [26, 43, 53, 56, 66, 75, 81, 96, 100, 111], "forschungsdateninfrastruktur": [66, 75, 81], "forschungsdatenmanag": [26, 56, 66, 75, 81, 111], "foundat": [53, 66, 75, 81, 107, 111], "fov1": [66, 75, 81], "fractal": [46, 109, 116], "frame": [66, 75, 81], "framework": [17, 43, 52, 53, 59, 66, 71, 81, 87, 91, 97, 106, 109, 115], "franc": [56, 82, 91, 97, 107, 111], "free": [4, 12, 24, 43, 49, 53, 66, 70, 73, 81, 85, 91, 111], "friend": [29, 40, 49, 68, 70, 80, 81, 91, 109], "friendli": [16, 101, 106, 115], "friendsdontletfriend": [48, 70, 85], "from": [6, 9, 18, 25, 28, 29, 31, 37, 52, 53, 55, 56, 57, 66, 67, 71, 75, 81, 85, 87, 91, 104, 108, 111, 113, 114], "function": [66, 75, 81, 117], "fundament": [13, 49, 70, 80, 87, 91, 109], "fusion": [29, 55, 69, 80, 91, 98, 104], "futur": [53, 66, 70, 80, 91], "fv": [24, 66, 75, 81], "f\u00fcr": [29, 56, 59, 66, 75, 77, 81, 87, 100, 105, 111], "galaxi": [1, 28, 43, 46, 50, 55, 56, 57, 65, 66, 70, 75, 81, 83, 85, 87, 90, 91, 101, 106, 111, 115, 116], "galleri": [43, 44, 72, 83, 91, 95], "ganglia": [29, 66, 75, 81], "gastruloid": [25, 31, 66, 75, 81], "gbi": [17, 66, 75, 81], "gcnu": [43, 57, 81, 114], "gemeinsam": [59, 87, 105, 111], "gem\u00e4\u00df": 77, "gener": [2, 4, 5, 15, 17, 29, 48, 49, 53, 55, 57, 66, 67, 70, 73, 75, 80, 81, 90, 91, 101, 105, 109, 111, 117], "gerbi": [0, 3, 17, 30, 32, 33, 34, 35, 43, 66, 75, 81, 87], "gerlach": 22, "geschlossen": [26, 56, 66, 75, 81, 96, 111], "gestalten": [66, 75, 81], "get": [19, 20, 28, 40, 58, 64, 66, 74, 75, 81, 109], "ghent": [59, 87, 111], "git": [29, 40, 56, 57, 66, 68, 69, 70, 75, 81, 87, 96, 111, 113, 117], "github": [0, 16, 23, 37, 38, 48, 57, 64, 70, 75, 81, 87, 91, 96, 105, 111], "glenco": [2, 43, 57, 87, 106, 114], "glimps": [6, 56, 66, 75, 81, 91], "glittr": [52, 53, 71, 81, 91, 111, 114], "global": [43, 47, 58, 74, 91], "gonna": [6, 16, 28, 51, 66, 75, 81, 91, 94, 105, 111], "googl": [12, 43, 49, 65, 66, 70, 73, 85, 91], "gpl": 84, "gpu": [11, 29, 47, 48, 49, 53, 55, 57, 60, 64, 66, 67, 70, 78, 80, 81, 91, 98, 109, 114, 115], "graph": [20, 66, 75, 81], "graphic": [49, 70, 85, 90, 109], "great": [40, 87, 111, 113], "group": [3, 22, 23, 32, 37, 66, 75, 81], "gro\u00dfegr\u00e4teantrag": [0, 32, 66, 75, 81], "gro\u00dfger\u00e4teantrag": [0, 32, 66, 75, 81], "grunds\u00e4tz": [53, 81, 96, 111], "gtn": [46, 87, 90, 91, 116], "gui": [66, 75, 81], "guid": [3, 4, 8, 9, 14, 29, 30, 33, 34, 37, 40, 41, 43, 53, 56, 57, 66, 68, 73, 75, 79, 80, 81, 85, 91, 94, 96, 106, 111, 113], "guidanc": [41, 66, 81, 111], "guidelin": [11, 53, 81], "guiet": 31, "guillaum": [13, 56, 74, 87, 95, 102], "gut": [10, 29, 59, 66, 75, 81, 87, 105, 111], "g\u00f3mez": 10, "haas": 29, "hackathon": [0, 16, 23, 37, 38, 46, 47, 48, 53, 66, 70, 75, 81, 91, 96, 105, 106, 109, 111], "hackaton": [28, 56, 66, 75, 81, 111], "haftung": 77, "haftungsausschluss": 77, "halvin": [43, 58, 74, 87, 91, 103, 109], "hamburg": [28, 56, 75, 81, 91, 105, 111], "handbook": [41, 49, 70, 81, 83, 87, 90, 91, 103, 104, 109, 111], "handl": [3, 37, 40, 66, 75, 81, 111], "harder": [29, 40, 68, 81, 100, 111], "have": [17, 66, 75, 81], "hc": [1, 28, 48, 51, 66, 70, 75, 81, 85, 91, 106, 109], "heidelberg": [3, 33, 34, 37, 66, 75, 81], "hela": [31, 66, 75, 81], "helmholtz": [3, 8, 23, 28, 56, 66, 75, 81, 91, 105, 111], "help": [29, 55, 66, 75, 81, 99, 109], "helpdesk": [66, 75, 81], "hen": [66, 75, 81], "heterogen": [29, 55, 66, 67, 81], "high": [1, 4, 16, 18, 28, 35, 43, 44, 51, 53, 66, 70, 73, 75, 81, 87, 91, 92, 96, 97, 101, 106, 111], "highlight": [18, 29, 31, 53, 67, 81, 87, 91, 104], "histologi": [66, 75, 81], "hitchhik": [4, 9, 29, 53, 55, 66, 67, 81, 91], "hodg": 36, "holder": [22, 66, 75, 81], "home": [39, 49, 53, 66, 70, 80, 87, 91, 92, 97, 104, 109, 111], "how": [3, 19, 20, 33, 34, 37, 40, 43, 58, 60, 62, 66, 68, 74, 75, 81, 88, 91, 106, 109, 111, 113], "hub": [29, 66, 75, 81, 117], "human": [66, 75, 81], "i": [11, 14, 18, 43, 55, 58, 66, 74, 75, 81, 87, 91, 104, 107], "i2k": [4, 5, 17, 21, 29, 48, 49, 57, 60, 66, 70, 75, 80, 81, 87, 90, 91, 109, 112, 114, 115], "i2k2024": [29, 48, 49, 57, 60, 70, 80, 91, 114], "i3d": [3, 8, 23, 30, 33, 34, 35, 37, 43, 55, 58, 66, 74, 75, 81, 87, 92, 101, 105, 106, 111], "iafig": [49, 70, 91], "ibiologi": [43, 58, 91], "ic": [66, 75, 81], "ich": [0, 26, 32, 56, 66, 75, 81, 111], "icob": [43, 60, 70, 91], "id": [66, 75, 81], "ifc": [57, 70, 87, 91], "ijm": [25, 31, 66, 75, 81], "ilastik": [7, 55, 75, 81, 90, 91], "illustr": [66, 75, 81], "im": [26, 29, 56, 66, 75, 81, 111, 117], "imag": [1, 2, 3, 4, 5, 6, 7, 9, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 24, 25, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53, 55, 56, 57, 58, 59, 60, 64, 65, 66, 67, 68, 70, 71, 73, 74, 75, 78, 80, 81, 82, 83, 84, 85, 87, 90, 91, 92, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 109, 111, 112, 113, 114, 115, 116, 117], "imagej": [12, 20, 29, 42, 43, 46, 51, 53, 55, 57, 58, 66, 69, 70, 73, 74, 75, 81, 85, 87, 91, 98, 104, 116], "imagej2": [29, 55, 69, 80, 91, 98, 104], "imari": [43, 58, 66, 75, 78, 81, 91], "imglib2": [48, 49, 60, 70, 114], "impact": [53, 75, 81, 87, 108], "implant": [22, 66, 75, 81, 117], "implement": [32, 66, 75, 81], "impressum": 77, "imprint": 77, "impuls": [66, 75, 81], "inclus": 62, "index": [43, 91], "individu": [30, 33, 66, 75, 81], "info": [43, 111], "inform": [3, 8, 23, 30, 33, 34, 37, 43, 66, 75, 81, 87, 92, 101, 111], "informat": [2, 15, 17, 18, 29, 43, 51, 53, 66, 67, 81, 91, 101, 102], "infrastructur": [3, 8, 17, 23, 28, 30, 33, 34, 37, 43, 51, 53, 55, 56, 66, 75, 81, 82, 87, 91, 92, 96, 97, 101, 105, 111], "inhalt": 77, "initi": [3, 6, 8, 11, 51, 53, 55, 66, 75, 81, 82, 85, 92, 97, 105, 111], "ink": [66, 75], "ins": [26, 56, 66, 75, 81, 111], "insight": [53, 66, 75, 81, 108], "inspect": [4, 24, 53, 66, 81, 91, 111], "institut": [45, 66, 75, 81, 111], "institution": [66, 75, 81], "institutionellen": [53, 66, 81, 111], "instrument": [11, 53, 81], "intellig": [12, 29, 55, 66, 67, 74, 75, 81, 87, 90, 91, 109], "intens": [18, 41, 57, 70, 87, 91, 104], "interact": [7, 11, 13, 17, 20, 34, 49, 51, 53, 55, 65, 66, 70, 75, 80, 81, 87, 90, 91, 101, 102, 104, 105, 107, 109, 111], "interchang": [66, 75, 82], "interdisciplinari": [17, 53, 81, 91, 102, 107], "intern": [41, 66, 81], "intravit": [22, 66, 75, 81], "intro": [28, 56, 66, 75, 81, 85, 103], "introduct": [10, 18, 21, 25, 29, 41, 42, 48, 49, 55, 56, 57, 66, 69, 70, 75, 81, 82, 85, 87, 90, 91, 95, 98, 104, 107, 109, 111, 112], "io": [11, 64, 70, 87], "isabel": 14, "isotropi": [19, 40, 68, 91, 103, 109], "issu": [57, 87, 96, 100, 111], "item": [17, 66, 75, 81], "itkelastix": [70, 91], "ix": [17, 66, 75, 81], "j": [66, 75, 81, 117], "jacquemet": [56, 74, 87, 95, 102], "jean": 15, "jen": 16, "jipip": [46, 53, 73, 98, 116], "josh": 17, "journei": [3, 9, 33, 56, 66, 75, 81], "json": [30, 33, 66, 75, 81], "jug": 12, "june2024": [43, 60, 70, 85, 91], "jupyt": [13, 28, 48, 49, 55, 58, 65, 70, 74, 80, 85, 87, 91, 104, 106, 109], "just": [17, 34, 43, 51, 58, 74, 75, 81, 91, 96, 105, 111], "kei": [3, 16, 18, 22, 23, 29, 31, 32, 35, 37, 43, 46, 53, 57, 66, 67, 75, 81, 87, 91, 96, 100, 104, 106, 111], "kemmer": 14, "kick": [33, 56, 66, 81, 91, 96, 105, 111], "kid": [6, 66, 75, 81], "kidnei": [22, 66, 75], "kind": [57, 87, 96, 100, 111], "kioto": [43, 60, 70, 91], "kit": [59, 81, 111], "kl\u00e4ren": [56, 66, 75, 81, 100, 111], "knime": [28, 46, 50, 56, 57, 66, 75, 81, 92, 98, 106, 111, 115], "knowledg": [29, 43, 66, 75, 81, 111, 114], "kollabor": [29, 56, 66, 75, 81, 96, 111], "konstanz": [66, 75, 81], "kontakt": 77, "kota": 18, "kreshuk": [57, 70, 87, 90], "kubernet": [57, 87, 114], "kuni": 34, "kutra": 7, "kyoto": [31, 66, 75, 81], "lab": [12, 41, 49, 57, 70, 75, 81, 87, 90, 109, 112], "label": [25, 31, 66, 75, 81], "laborbuch": [45, 46, 57, 111], "lambda": [24, 66, 75, 81], "lampert": 19, "languag": [9, 29, 53, 55, 66, 73, 75, 81, 90, 91, 96, 99, 109, 115], "larg": [17, 29, 34, 40, 48, 49, 51, 53, 55, 60, 66, 70, 73, 75, 81, 90, 91, 96, 99, 105, 109, 111, 114, 115], "last": 88, "laulauthom": [66, 75, 85], "lazi": [48, 49, 60, 70, 114], "learn": [5, 7, 10, 12, 13, 21, 29, 39, 40, 43, 48, 49, 55, 56, 57, 58, 64, 65, 66, 67, 68, 70, 72, 73, 74, 75, 81, 85, 87, 90, 91, 95, 96, 102, 103, 104, 109, 112], "lectur": [20, 29, 43, 48, 49, 55, 56, 58, 69, 70, 74, 75, 81, 87, 91, 93, 95, 98, 102, 103, 109], "leipzig": [29, 56, 75, 81, 91, 95, 102, 109], "leitfaden": [41, 66, 75, 81, 107], "leitlini": [53, 81, 96, 111], "leo": [66, 75, 81], "lesson": [1, 15, 39, 53, 66, 75, 81], "librari": [29, 48, 49, 57, 60, 66, 70, 75, 80, 81, 91, 114, 115], "licens": [29, 40, 55, 56, 66, 67, 68, 75, 81, 88, 96, 100, 111, 113], "life": [1, 11, 15, 18, 29, 31, 39, 43, 50, 53, 58, 59, 66, 67, 75, 78, 81, 87, 90, 91, 92, 93, 97, 104, 107, 111, 113], "light": [3, 11, 22, 23, 32, 37, 53, 66, 75, 81, 87, 92, 97, 101, 102], "like": [17, 34, 56, 75, 81, 91, 96, 111], "limeseg": [24, 66, 75, 81], "limi": [11, 53, 81], "line": [46, 81, 92], "link": [16, 33, 34, 35, 38, 43, 58, 66, 75, 77, 81, 87, 105, 111], "lisbon": [4, 57, 70, 87, 91, 104], "list": [43, 87, 111], "littl": [17, 34, 51, 66, 75, 81, 91, 96, 105, 111], "liver": [22, 25, 31, 66, 75, 81], "ll": [29, 40, 68, 81, 100, 111], "llm": [48, 57, 70, 85], "local": [3, 8, 23, 33, 34, 35, 37, 55, 58, 66, 74, 75, 81, 105, 106, 111], "long": [12, 29, 51, 53, 98], "low": [29, 66, 75, 81], "lsm": [66, 75, 81, 117], "lund": [45, 111], "lung": [20, 66, 75, 81], "lz4": [66, 75, 81], "machin": [7, 10, 21, 29, 39, 49, 55, 56, 57, 58, 65, 70, 72, 75, 81, 87, 90, 91, 95, 102, 103, 104, 109], "macro": [29, 42, 55, 69, 70, 87, 91, 98, 104], "madrid": [43, 60, 70, 85, 91], "mai": 8, "mainten": 62, "make": [11, 17, 46, 53, 57, 66, 73, 75, 81, 91, 93, 94, 96, 101, 102, 107, 109, 111, 113, 114], "mamba": [29, 40, 68, 81, 109], "mambaforg": [19, 40, 64, 81, 109], "manag": [3, 6, 8, 11, 14, 16, 17, 23, 28, 29, 30, 32, 33, 34, 35, 37, 38, 40, 41, 43, 45, 49, 50, 51, 53, 55, 56, 57, 58, 59, 66, 67, 68, 70, 73, 74, 75, 78, 79, 81, 82, 87, 90, 91, 92, 96, 97, 99, 101, 105, 106, 107, 108, 109, 111, 117], "managemet": [20, 66, 75, 81], "manual": [12, 49, 81, 109, 112], "mara": 19, "mari": 15, "marisc": 10, "martin": [20, 21, 58, 74, 91, 97, 106], "martinschatz": [20, 66, 75], "mask": [66, 75, 85], "maskfromroi": [66, 75, 85], "massei": 28, "masteri": [57, 87, 114], "mastodon": [29, 55, 69, 80, 91, 98, 104], "match": [66, 75, 81], "materi": [3, 4, 5, 8, 11, 20, 21, 23, 28, 29, 33, 34, 35, 37, 42, 43, 48, 49, 52, 53, 55, 56, 57, 58, 60, 63, 66, 67, 70, 71, 72, 74, 75, 81, 85, 87, 88, 90, 91, 93, 96, 99, 100, 101, 102, 105, 106, 109, 111, 112, 113, 114], "math": [17, 34, 51, 75, 81, 91, 96, 105, 111], "matplotlib": [41, 70, 109], "matter": [11, 53, 101], "maximis": [45, 111], "mbl": [5, 49, 55, 70, 87, 90, 91], "mdemic": [3, 33, 34, 53, 73, 78, 101, 111], "measur": [25, 31, 66, 75, 81], "medic": [66, 75, 81], "medicin": [33, 38, 66, 75, 81], "meet": [3, 17, 28, 29, 30, 33, 34, 35, 53, 56, 66, 75, 81, 91, 105, 111], "mein": [56, 66, 75, 81, 100, 111], "melbourn": [43, 58, 74, 91], "meta": [11, 30, 33, 53, 66, 75, 81, 101], "metabol": [49, 70, 87, 109], "metadata": [3, 11, 16, 28, 30, 33, 34, 35, 37, 39, 43, 51, 53, 57, 58, 66, 73, 74, 75, 78, 81, 87, 91, 92, 94, 96, 97, 101, 102, 105, 106, 111, 115], "metastasi": [56, 74, 87, 95, 102], "method": [4, 9, 47, 50, 53, 55, 56, 58, 66, 72, 73, 75, 81, 91, 101], "methodsj2": [53, 73, 101], "metric": [11, 43, 52, 53, 59, 87, 91], "mia": [46, 53, 66, 98, 116], "mic": [13, 49, 70, 87, 91, 109], "michael": 22, "michel": 23, "micoscropi": [43, 58, 74, 81, 92, 102, 108], "micro": [11, 30, 33, 53, 66, 75, 81, 101], "microbi": [66, 75, 81], "microlist": [43, 78], "micrometastas": [25, 31, 66, 75, 81], "microsam": [5, 56, 66, 75, 81, 91, 95], "microscop": [22, 66, 75, 81], "microscopi": [1, 2, 3, 4, 7, 8, 11, 12, 14, 15, 16, 18, 21, 22, 23, 28, 29, 30, 32, 33, 34, 35, 37, 39, 40, 43, 44, 49, 51, 53, 55, 56, 58, 64, 66, 68, 70, 72, 73, 74, 75, 78, 81, 82, 85, 87, 91, 92, 95, 96, 97, 101, 102, 105, 106, 109, 111, 112, 117], "microscopist": [43, 49, 70, 85, 91, 109], "microscopydb": [43, 78], "middl": [29, 53, 81, 91], "mini": [11, 42, 49, 70, 87, 109], "mit": [26, 29, 56, 66, 75, 81, 85, 96, 111], "mitobo": [46, 53, 98, 116], "mitochondria": [57, 81, 91, 93], "mitoflash": [57, 81, 91, 93], "miura": 18, "ml": [56, 74, 87, 90, 91, 95, 99], "model": [2, 5, 11, 15, 25, 29, 31, 43, 49, 53, 55, 66, 70, 73, 75, 81, 87, 90, 91, 92, 94, 96, 97, 99, 101, 102, 109, 115], "modular": [4, 7, 9, 36, 66, 75, 81, 117], "modularimageanalysi": [46, 53, 66, 98, 116], "modularisedimag": [46, 53, 66, 98, 116], "mooc": [48, 70, 81, 91], "moor": 17, "morpholibj": [45, 91], "morpholog": [66, 75, 81], "mortem": [66, 75, 81], "most": [17, 53, 81, 88, 91, 102, 107], "mous": [22, 66, 75], "movement": [66, 75, 81], "mpi": [29, 55, 69, 87, 91, 98], "multi": [3, 8, 23, 29, 33, 34, 35, 37, 55, 58, 66, 69, 74, 75, 80, 81, 91, 98, 104, 105, 106, 111], "multidisciplinari": [29, 53, 81, 91], "multimod": [0, 33, 38, 51, 53, 66, 73, 75, 81, 91, 96, 99, 111, 115], "multiplex": [48, 56, 60, 65, 66, 70, 75, 81, 91, 102], "multiview": [48, 57, 70, 80], "must": [17, 66, 75, 81], "my": [9, 14, 56, 66, 75, 81], "myth": 87, "m\u00f6glich": [26, 56, 66, 75, 81, 96, 111], "m\u00fcller": [3, 22, 23, 32, 37, 66, 75, 81], "n4bi": [17, 56, 66, 75, 81, 111], "n5": [48, 49, 60, 70, 114], "nach": 77, "napari": [6, 19, 40, 43, 49, 56, 57, 66, 68, 70, 75, 80, 81, 85, 87, 90, 91, 103, 109], "nation": [3, 6, 8, 17, 23, 28, 33, 34, 51, 53, 55, 56, 66, 75, 81, 82, 91, 92, 96, 97, 105, 111], "nationalen": [66, 75, 81], "natur": 73, "navig": [6, 28, 37, 56, 66, 75, 81, 111], "need": [53, 66, 70, 80, 91], "network": [46, 49, 66, 70, 75, 81, 87, 90, 91, 109, 116], "neubia": [4, 10, 11, 18, 21, 25, 29, 31, 42, 43, 49, 53, 55, 57, 58, 67, 70, 74, 80, 81, 87, 90, 91, 104, 109], "neubiaspasteur2023_advancedcellpos": [48, 70, 80, 112], "neural": [66, 75, 81], "neuron": [29, 66, 75, 81], "new": [6, 66, 75, 81, 87], "next": [2, 4, 5, 15, 17, 53, 66, 73, 75, 81, 105, 111, 117], "nextflow": [42, 43, 46, 48, 55, 57, 66, 70, 75, 81, 87, 109, 115, 116], "nfdi": [6, 29, 45, 59, 66, 75, 81, 87, 105, 111], "nfdi4bioimag": [0, 3, 6, 8, 11, 16, 17, 23, 28, 29, 33, 34, 37, 38, 46, 47, 48, 51, 53, 55, 56, 66, 67, 70, 75, 81, 82, 88, 91, 92, 96, 97, 105, 106, 109, 111], "ngff": [2, 4, 5, 15, 17, 37, 38, 48, 53, 66, 70, 73, 75, 79, 80, 81, 84, 92, 105, 108, 111, 113], "nicola": 24, "nikon": [40, 111], "nitschk": 30, "nlp": [48, 70, 85], "notebook": [20, 28, 41, 43, 48, 49, 66, 70, 75, 80, 81, 85, 91, 93, 106, 109, 117], "nuclei": [21, 25, 31, 49, 55, 66, 70, 75, 81, 87, 90, 91, 104, 109], "nucleu": [18, 41, 57, 70, 87, 91, 104], "numpi": [13, 49, 70, 80, 109], "n\u00f6tig": [26, 56, 66, 75, 81, 96, 111], "object": [29, 46, 48, 53, 55, 56, 57, 60, 66, 69, 70, 75, 80, 81, 91, 98, 104, 114, 116], "octob": [3, 33, 34, 37, 66, 75, 81], "oder": [56, 66, 75, 81, 100, 111], "off": [33, 56, 66, 81, 91, 96, 105, 111], "offen": [26, 56, 66, 75, 81, 96, 111], "oir": [24, 66, 75, 81], "olivi": 25, "om": [2, 4, 5, 11, 15, 17, 43, 46, 47, 48, 53, 57, 58, 66, 70, 73, 74, 79, 81, 87, 91, 92, 97, 101, 102, 105, 106, 108, 111], "ome2024": [15, 17, 37, 38, 48, 66, 70, 75, 80, 81, 113], "omero": [3, 8, 11, 15, 16, 17, 22, 23, 28, 32, 33, 34, 35, 37, 40, 43, 45, 46, 48, 50, 53, 55, 56, 57, 58, 60, 64, 66, 70, 74, 75, 78, 79, 81, 84, 85, 87, 90, 91, 96, 98, 101, 105, 106, 109, 111, 115], "omic": [17, 52, 53, 71, 81, 109], "onbi": [49, 70, 84, 91, 109], "onlin": [33, 43, 50, 52, 53, 56, 66, 71, 81, 87, 91, 96, 105, 111, 114], "online_r_learn": [48, 70, 83], "ontologi": [17, 18, 29, 51, 53, 67, 81, 91, 101], "open": [2, 4, 6, 11, 12, 15, 17, 24, 29, 36, 40, 41, 43, 49, 52, 53, 55, 56, 58, 64, 66, 67, 70, 71, 73, 74, 75, 76, 78, 81, 85, 87, 91, 92, 96, 100, 102, 103, 104, 106, 107, 108, 109, 111], "oper": [17, 18, 29, 51, 53, 67, 81, 91, 101], "operetta": [24, 66, 75, 81], "opportun": [29, 53, 55, 67, 81, 91, 104, 105, 111], "optic": [21, 49, 66, 70, 75, 81, 87, 90, 91, 109], "optim": [17, 34, 51, 53, 66, 75, 81, 91, 105, 111, 117], "optimis": [29, 66, 75, 81], "option": [50, 57, 81, 115], "org": [43, 52, 53, 59, 66, 71, 75, 81, 91, 111, 114], "organ": [0, 38, 51, 75, 81, 96, 111], "organis": [26, 50, 56, 59, 66, 75, 81, 87, 108, 111], "organisiert": [59, 87, 105, 111], "origin": [66, 75, 81], "our": [29, 40, 68, 81, 100, 111], "overcom": [68, 87, 95], "overview": [28, 34, 35, 43, 57, 66, 72, 75, 81, 83, 92, 96, 97, 101, 106, 111], "overwhelm": [20, 66, 75, 81], "own": [60, 91, 111], "packag": [46, 81, 109], "pair": [3, 16, 22, 23, 32, 35, 37, 43, 46, 66, 75, 81, 87, 106], "panda": [13, 49, 70, 80, 109], "pape": 5, "paper": [6, 28, 37, 56, 66, 75, 81, 87, 111], "parallel": [29, 48, 49, 55, 60, 66, 67, 70, 81, 114], "particl": [66, 75, 81], "patho_prompt_inject": [48, 49, 70, 91], "pathologi": [12, 74, 87, 90], "pelag": [28, 56, 75, 81, 91, 105, 111], "percentag": [20, 66, 75, 81], "perform": [30, 33, 43, 53, 66, 75, 80, 81], "perikl": [3, 23, 32, 37, 66, 75, 81], "peripheri": [18, 41, 57, 70, 87, 91, 104], "perspect": [17, 34, 51, 53, 75, 81, 96, 105, 111], "peter": 33, "phase": [25, 31, 66, 75, 81], "phasor": [6, 56, 66, 75, 81, 91], "photon": [53, 66, 81, 96, 111], "physic": [66, 75, 81], "pia": 26, "pilot": [32, 66, 75, 81], "pioneer": [66, 75, 81], "pipelin": [1, 11, 28, 42, 46, 48, 51, 57, 66, 70, 75, 80, 81, 85, 87, 91, 102, 103, 106, 109, 116], "pitfal": [11, 52, 53, 91], "pixel": [6, 9, 19, 25, 28, 29, 31, 37, 40, 55, 56, 66, 68, 69, 75, 80, 81, 91, 98, 103, 104, 109, 111], "plan": [14, 29, 40, 43, 45, 53, 57, 66, 68, 75, 80, 81, 90, 91, 96, 99, 111], "plant": 53, "plasma": [33, 38, 66, 75, 81], "plate": [22, 66, 75, 81], "platform": [17, 34, 56, 66, 75, 81, 91, 96, 111], "plot": 87, "plotter": [6, 56, 66, 75, 81, 91], "plugin": [4, 11, 15, 24, 48, 53, 66, 70, 75, 81, 84, 85, 91, 98, 106, 111], "pneumonia": [20, 66, 75, 81], "pol": [29, 40, 47, 49, 64, 70, 81, 90, 91, 109], "polici": [41, 53, 59, 66, 81, 87, 96, 111], "popul": [66, 75, 81], "positron": [29, 66, 75, 81], "post": [66, 75, 81], "poster": [40, 51, 68, 111, 113], "practic": [3, 4, 8, 11, 14, 21, 30, 33, 34, 37, 41, 47, 49, 52, 53, 56, 57, 66, 70, 71, 75, 81, 87, 90, 91, 93, 96, 104, 107, 109, 111, 113], "praxisbeispielen": [66, 75, 81], "predict": [25, 31, 66, 75, 81], "predoc": [57, 70, 87, 90], "preprint": [1, 15, 39, 52, 66, 75, 81], "prerequisit": 63, "present": [6, 28, 37, 43, 56, 66, 70, 75, 81, 111], "preserv": [29, 66, 75, 81], "pricipl": [43, 81, 94, 96, 111], "primari": [66, 75, 81], "primer": 41, "principl": [53, 66, 73, 81, 94, 96, 111, 113], "process": [4, 7, 11, 13, 18, 24, 29, 36, 41, 42, 43, 46, 48, 49, 50, 53, 55, 57, 60, 66, 67, 70, 73, 78, 80, 81, 85, 87, 91, 92, 98, 106, 109, 111, 112, 114, 115, 116], "produc": [30, 33, 66, 75, 81], "prof": [3, 22, 23, 32, 37, 66, 75, 81], "profil": [41, 70, 83, 91], "program": [1, 29, 43, 49, 55, 66, 69, 70, 75, 81, 87, 91, 98, 103, 109], "programm": [43, 48, 49, 70, 87, 91, 108, 109], "project": [3, 8, 17, 23, 30, 33, 34, 35, 37, 49, 57, 66, 70, 75, 81, 87, 90, 91, 111, 113], "promot": [40, 111], "prompt": [19, 40, 48, 57, 68, 70, 85, 91, 109], "protocol": [41, 75], "prototyp": [66, 75, 81], "public": [39, 53, 73, 111], "publicli": [43, 57, 72, 81, 107, 113], "publish": [4, 12, 18, 30, 43, 49, 53, 57, 73, 78, 83, 87, 91, 92, 96, 100, 102, 111], "pure": [29, 55, 66, 67, 81], "purpos": [3, 8, 23, 33, 34, 35, 37, 55, 58, 66, 74, 75, 81, 105, 106, 111], "push": 87, "put": 87, "python": [7, 11, 13, 19, 28, 29, 36, 40, 41, 42, 43, 46, 48, 49, 53, 56, 57, 64, 66, 68, 70, 75, 80, 81, 85, 87, 91, 99, 102, 103, 104, 106, 108, 109, 112, 114, 116], "pytorch": [13, 49, 70, 87, 90, 91, 109], "qi": [12, 49, 81, 109, 112], "qm": [49, 81, 103, 109], "qualiti": [11, 19, 40, 53, 68, 81, 91, 103, 109], "quantif": [25, 31, 66, 75, 81], "quantit": [2, 15, 43, 53, 66, 80, 81, 91, 102], "quantix": [42, 66, 70, 75, 91], "quarep": [11, 53, 81], "quick": 62, "quo": [29, 53, 66, 78], "qupath": [25, 31, 43, 48, 49, 50, 55, 66, 70, 75, 81, 87, 91, 106, 108, 109], "rat": [31, 66, 75, 81], "ratamero": [58, 74, 91, 97, 106], "raw2ometiff": [2, 17, 48, 70, 84, 92, 108], "rdf": [17, 34, 56, 75, 81, 91, 96, 111], "rdm": [3, 17, 30, 33, 34, 35, 37, 41, 59, 60, 66, 75, 81, 87, 91, 106, 111], "rdm4mic": [43, 58, 70, 74, 81, 87, 106, 111], "rdm_system_connector": [48, 70, 87, 97, 111], "rdmbite": [43, 58, 74], "rdmkit": [43, 81, 111], "rdmo": [50, 59, 87, 108, 111], "re": [3, 8, 23, 33, 34, 35, 37, 52, 53, 55, 58, 66, 71, 74, 75, 81, 91, 105, 106, 111, 113, 114], "re3data": [59, 81, 111], "read": [66, 75, 81], "real": [11, 53, 101], "realisierung": [53, 66, 81, 111], "reason": [40, 87, 111, 113], "recent": 117, "rechtsfragen": [26, 41, 56, 66, 75, 81, 107, 111], "recommend": [1, 11, 15, 39, 53, 66, 73, 75, 81, 87, 92, 97, 101, 111], "refer": 87, "registr": [24, 31, 66, 75, 81], "registri": [59, 81, 111], "reichert": [3, 22, 23, 32, 37, 66, 75, 81], "relat": [11, 52, 53, 91], "releas": [66, 75, 85], "reload": [43, 59, 87, 91], "rembi": [11, 39, 43, 53, 66, 72, 73, 83, 87, 92, 96, 97, 101, 111], "remot": [57, 70, 87], "report": [19, 25, 31, 32, 40, 43, 66, 68, 73, 75, 81, 87, 92, 101, 109], "repositori": [0, 3, 12, 14, 16, 23, 37, 38, 48, 57, 59, 66, 70, 75, 81, 87, 91, 96, 105, 111], "repositorium": [56, 66, 75, 81, 111], "reproduc": [11, 12, 29, 41, 43, 51, 53, 55, 66, 68, 73, 75, 81, 85, 87, 92, 95, 98, 101, 111, 116], "rescal": [19, 40, 68, 91, 103, 109], "rescu": [6, 16, 28, 51, 66, 75, 81, 91, 94, 105, 111], "research": [0, 1, 3, 6, 8, 11, 14, 15, 16, 17, 20, 22, 23, 28, 29, 30, 32, 33, 34, 35, 37, 38, 39, 40, 41, 43, 45, 50, 51, 53, 55, 56, 57, 59, 66, 67, 68, 75, 78, 81, 82, 85, 87, 90, 91, 92, 96, 97, 99, 100, 105, 106, 107, 108, 111, 113, 117], "reserv": 78, "resolut": 41, "resourc": [1, 4, 5, 7, 9, 15, 29, 36, 39, 40, 41, 43, 44, 46, 48, 53, 64, 66, 70, 73, 75, 81, 87, 88, 90, 91, 104, 106, 109, 111, 116, 117], "restor": [87, 91], "result": [12, 17, 19, 28, 29, 40, 51, 53, 56, 66, 68, 75, 81, 91, 98, 103, 109, 111], "rethink": [48, 70, 74, 87], "reus": [11, 39, 53, 66, 73, 87, 92, 97, 101, 111], "reusabl": [20, 53, 66, 75, 81, 107, 111], "riccardo": 28, "richtlini": [53, 81, 96, 111], "right": 78, "rm": [49, 70, 91], "robert": 29, "roi": [66, 75, 85], "roland": 30, "role": [3, 8, 23, 28, 66, 75, 81], "romain": 31, "round": [22, 66, 75, 81], "rse": [48, 56, 70], "rstv": 77, "rule": [11, 53, 81, 93, 96, 101, 114], "run": [40, 63, 64, 81, 90, 91, 109], "s3": [4, 5, 48, 70, 87, 109], "sa": 82, "sampl": [22, 24, 66, 75, 81], "sarkan": 39, "scad": [29, 56, 75, 81, 91, 95, 102, 109], "scalabl": [17, 53, 55, 66, 75, 81, 91, 97, 106, 115, 116, 117], "scale": [34, 40, 55, 56, 66, 75, 81, 105, 111], "scan": [24, 66, 75, 81], "scanpi": [48, 70, 91], "schauss": 0, "schmidt": 3, "school": [10, 11, 18, 29, 31, 42, 47, 49, 53, 55, 64, 67, 70, 81, 87, 91, 104, 109], "schreib": [0, 32, 66, 75, 81], "schreiben": [0, 32, 66, 75, 81], "sch\u00e4tz": 20, "sciaugment": [20, 66, 75], "scicount": [20, 66, 75], "scienc": [1, 11, 15, 29, 39, 41, 43, 49, 53, 56, 66, 70, 75, 78, 81, 87, 91, 92, 93, 95, 96, 97, 100, 102, 107, 108, 109, 111, 113], "scientif": [1, 29, 40, 41, 53, 55, 66, 68, 69, 70, 73, 75, 81, 87, 91, 96, 98, 109, 111, 116], "scientist": [18, 29, 31, 41, 50, 53, 66, 67, 75, 78, 81, 90, 91, 104], "scikit": [48, 49, 70, 80, 81, 91, 109], "scipi": [48, 58, 70, 74, 80, 91, 97, 106], "scope": [31, 66, 75, 81], "screen": [1, 28, 51, 53, 66, 73, 75, 81, 91, 92, 96, 97, 101, 111], "script": [29, 40, 43, 46, 49, 64, 68, 81, 83, 87, 90, 91, 98, 106, 109, 111, 113], "scripts_filopodyanr": [42, 70, 87, 91, 104], "search": [43, 87, 111], "secret": [17, 34, 51, 66, 75, 81, 91, 96, 105, 111], "segment": [19, 21, 25, 29, 40, 49, 55, 65, 66, 68, 70, 75, 81, 87, 90, 91, 103, 104, 109, 112], "semi": [28, 51, 66, 81, 91], "seminar": [55, 66, 75, 81, 111], "seri": [28, 29, 48, 53, 56, 70, 85, 91, 99, 102, 109], "serrano": 1, "server": [40, 64, 81, 90, 91, 109], "servic": [45, 55, 65, 66, 75, 81, 87, 111], "set": [28, 34, 42, 43, 44, 53, 57, 58, 66, 70, 73, 75, 81, 87, 91, 105, 111], "share": [29, 40, 55, 56, 66, 67, 68, 75, 81, 87, 96, 100, 111, 113], "short": [3, 22, 56, 62, 66, 75, 81, 91, 105, 111], "should": [29, 40, 68, 81, 100, 111], "silk": 32, "simpl": [11, 15, 48, 53, 70, 81, 84, 93, 96, 101, 106, 114], "simpleitk": [48, 70, 91], "size": [4, 24, 53, 66, 81, 91, 111], "skill": [43, 48, 56, 57, 70, 81, 114], "skimag": [48, 70, 91], "sliceplot": [29, 66, 75, 81], "slide": [3, 8, 22, 23, 24, 31, 33, 34, 35, 37, 55, 56, 58, 66, 74, 75, 81, 85, 103, 105, 106, 111], "snakemak": [46, 109, 116], "snt": [66, 75, 81], "so": [26, 56, 66, 75, 81, 96, 111], "softwar": [2, 4, 6, 9, 29, 43, 53, 55, 56, 57, 66, 67, 73, 75, 81, 87, 91, 101, 104, 106, 108, 111, 113, 114], "solano": 1, "solut": [16, 28, 34, 35, 51, 58, 66, 75, 81, 91, 105, 111], "sourc": [4, 6, 12, 24, 29, 43, 49, 53, 55, 56, 57, 66, 67, 70, 73, 75, 81, 85, 87, 91, 104, 108, 111], "spark": [48, 49, 60, 70, 114], "spatial": [17, 52, 53, 71, 81, 109], "spatialdata": [17, 52, 53, 71, 81, 109], "speaker": [6, 28, 37, 56, 66, 75, 81, 111], "specif": [11, 53, 66, 87, 92, 97, 101, 102], "speicherstrategien": [26, 56, 66, 75, 81, 111], "stabil": [22, 66, 75, 81], "stack": [66, 75, 81], "stackview": [29, 66, 75, 81], "stage": [22, 66, 75, 81], "stain": [66, 75, 81, 117], "standard": [11, 17, 34, 41, 51, 53, 73, 75, 81, 87, 92, 94, 96, 97, 101, 102, 105, 111], "stardist": [21, 25, 31, 49, 55, 66, 70, 75, 81, 87, 90, 91, 104, 109], "start": [19, 28, 40, 45, 58, 64, 66, 74, 75, 81, 109, 111], "starter": [59, 81, 111], "statist": [48, 70, 74, 87, 89], "steal": [29, 40, 68, 81, 100, 111], "stefani": 33, "step": [41, 62, 75, 81, 111], "steward": [6, 16, 28, 51, 66, 75, 81, 91, 94, 105, 111], "stewardship": [33, 38, 50, 53, 56, 59, 66, 73, 75, 81, 94, 96, 111, 113], "stitch": [24, 66, 75, 81], "stitcher": [48, 57, 70, 80], "stop": 87, "storag": [17, 34, 51, 53, 66, 75, 81, 91, 105, 111], "stori": [18, 29, 31, 53, 67, 81, 91, 104], "strategi": [2, 4, 5, 15, 17, 53, 66, 73, 75, 81, 105, 111, 117], "structur": [0, 3, 11, 16, 34, 35, 37, 38, 51, 53, 58, 66, 75, 81, 92, 96, 97, 105, 111], "strudel": [6, 55, 66, 75, 81, 92, 105, 111], "strukturevalu": [66, 75, 81], "studentsourc": [52, 71, 113], "studi": [3, 12, 23, 29, 32, 37, 42, 51, 53, 56, 66, 70, 74, 75, 81, 87, 91, 95, 98, 102, 104], "st\u00f6ter": 38, "subcellular": [66, 75, 81, 117], "submiss": 63, "submit": [57, 58, 72, 83, 92, 97, 111], "subtyp": [29, 66, 75, 81], "success": [18, 29, 31, 53, 67, 81, 91, 104], "suit": [28, 57, 81, 101, 106], "super": 41, "support": [3, 4, 7, 11, 17, 30, 33, 34, 35, 57, 59, 66, 75, 81, 85, 87, 111, 115], "survei": [3, 8, 11, 17, 33, 52, 53, 66, 67, 70, 71, 80, 81, 91, 92, 97, 105, 111], "susann": 34, "sustain": [1, 15, 39, 56, 66, 75, 81, 94, 111], "swarm": [29, 57, 66, 75, 81, 87, 114], "swat4hcl": [17, 34, 51, 75, 81, 96, 105, 111], "swc": [43, 57, 81, 114], "systemat": [57, 59, 82], "szege": [42, 70, 87, 91, 104], "ta3": [0, 16, 23, 37, 38, 46, 47, 48, 53, 66, 70, 75, 81, 91, 96, 105, 106, 109, 111], "tabbl": [66, 75, 81], "tabl": [22, 66, 75, 81], "taipei": [43, 60, 70, 91], "talk": [3, 5, 28, 56, 66, 75, 81, 91, 95, 105, 111], "task": [14, 56, 57, 66, 75, 81, 91, 96, 111], "tb": [4, 24, 53, 66, 81, 91, 111], "teach": [5, 9, 43, 56, 57, 66, 70, 75, 81, 85, 87, 90, 91, 109], "team": [34, 35, 46, 66, 75, 81, 87, 90, 91, 116], "technic": [16, 34, 35, 57, 58, 59, 66, 75, 81, 82, 105, 111], "technolog": [28, 51, 66, 81, 91], "technologi": [53, 81, 107, 111], "teil": [0, 26, 32, 56, 66, 75, 81, 111], "temperatur": [66, 75, 81], "templat": [3, 4, 7, 11, 14, 16, 22, 23, 32, 35, 37, 43, 53, 57, 66, 73, 75, 81, 85, 91, 94, 96, 101, 111, 115], "ten": [11, 17, 53, 66, 75, 81, 93, 96, 101, 114], "term": [12, 29, 51, 53, 98], "terminologi": [66, 75, 81], "tess": [43, 47, 87, 93, 111], "test": [24, 31, 66, 75, 81], "text": [53, 73, 101], "thedigitalcel": [42, 66, 70, 75, 91], "theori": [29, 55, 69, 80, 91, 98, 104], "thi": [57, 87, 88, 96, 100, 111], "think": [34, 55, 56, 66, 75, 81, 105, 111], "thoma": 35, "through": [4, 6, 9, 17, 28, 29, 37, 53, 55, 56, 66, 67, 75, 81, 91, 102, 107, 111], "throughput": [4, 16, 18, 35, 43, 44, 70, 73, 75, 81, 87, 91, 106], "tier": [11, 53, 87, 92, 97, 101, 102], "tile": [24, 66, 75, 81], "tischer": 4, "tissu": [48, 56, 60, 65, 70, 81, 91, 102], "tmg": 77, "tobi": 36, "togeth": [3, 33, 34, 37, 60, 66, 75, 81, 91, 106, 111], "tom": 37, "tomographi": [29, 66, 75, 81], "tool": [2, 3, 6, 11, 15, 16, 20, 33, 34, 38, 43, 46, 48, 49, 53, 56, 60, 64, 65, 66, 70, 73, 75, 78, 81, 87, 90, 91, 92, 93, 101, 102, 106, 111, 115], "toolbox": [10, 12, 29, 43, 46, 49, 53, 66, 70, 73, 75, 81, 85, 91, 98, 116], "topic": [17, 18, 29, 51, 53, 67, 81, 91, 101], "torsten": 38, "toward": [11, 29, 41, 53, 55, 65, 66, 75, 81, 87, 91, 92, 97, 101, 102, 111], "town": [17, 56, 66, 75, 81, 91, 105, 106, 111], "track": [19, 29, 40, 43, 44, 48, 55, 56, 57, 60, 68, 69, 70, 80, 81, 87, 91, 93, 98, 103, 104, 109, 114], "trackmat": [29, 48, 55, 56, 57, 60, 69, 70, 80, 91, 98, 104, 114], "train": [3, 4, 5, 7, 8, 9, 11, 18, 23, 28, 29, 31, 33, 34, 35, 36, 37, 41, 43, 46, 47, 48, 49, 52, 53, 55, 56, 57, 58, 63, 64, 66, 67, 70, 71, 74, 75, 81, 85, 87, 88, 90, 91, 93, 96, 99, 100, 101, 102, 104, 105, 106, 107, 109, 111, 114, 116, 117], "trainer": [41, 66, 75, 81, 111], "transfer": [11, 15, 48, 70, 84, 106], "transpar": [29, 66, 75, 81], "tree": [14, 66, 75, 81], "trustworthi": [43, 59, 87, 91], "ts7": [42, 70, 87, 91, 104], "tu": [6, 55, 66, 75, 81, 92, 105, 111], "tulok": 32, "ture": [41, 81, 85], "tutori": [11, 21, 42, 43, 46, 48, 49, 50, 57, 58, 62, 70, 74, 78, 80, 83, 85, 87, 90, 91, 98, 102, 103, 109], "ugi": 39, "uk": 72, "ultrack": [48, 57, 60, 70, 91, 112, 114], "umap": 87, "umgang": [26, 56, 66, 75, 81, 96, 111], "und": [26, 29, 45, 46, 53, 56, 57, 66, 75, 81, 96, 111], "under": [31, 66, 75, 81], "understand": [11, 52, 53, 91], "uni": [29, 56, 75, 81, 91, 95, 102, 109], "univers": [4, 9, 17, 29, 52, 53, 55, 59, 66, 67, 71, 75, 81, 87, 91, 109, 111], "universit\u00e4ten": [53, 81, 96, 111], "unknown": 87, "uoc": [0, 16, 23, 37, 38, 46, 47, 48, 53, 66, 70, 75, 81, 91, 96, 105, 106, 109, 111], "up": [28, 34, 46, 53, 57, 66, 70, 75, 81, 87, 90, 91, 105, 111, 116], "upcom": [11, 25, 31, 43, 47, 60, 87, 91, 102], "updat": [17, 18, 29, 51, 53, 67, 78, 81, 98, 101], "upload": [28, 57, 81, 101, 106], "urheberrecht": [56, 66, 75, 77, 81, 100, 111], "us": [1, 10, 13, 25, 28, 29, 30, 31, 33, 40, 43, 48, 49, 52, 53, 55, 56, 57, 60, 63, 66, 68, 70, 71, 72, 75, 81, 85, 87, 88, 90, 91, 99, 101, 106, 107, 109, 111, 113, 114], "usabl": [3, 8, 23, 33, 34, 35, 37, 55, 58, 66, 74, 75, 81, 105, 106, 111], "user": [3, 8, 16, 23, 33, 34, 35, 37, 52, 53, 55, 58, 66, 70, 71, 74, 75, 80, 81, 91, 101, 105, 106, 111, 115], "v0": [66, 75, 81], "v1": [20, 66, 75], "v4sdb_winter_school_2025": [43, 48, 70, 91, 117], "vadi": [29, 53, 66, 78], "valid": [11, 17, 29, 43, 44, 48, 52, 53, 59, 66, 70, 73, 75, 79, 81, 87, 91, 92], "valu": [3, 16, 22, 23, 32, 35, 37, 43, 46, 66, 75, 81, 87, 106], "variou": [66, 75, 81], "verantwortlich": 77, "version": [22, 29, 66, 75, 81, 117], "versionskontrol": [29, 56, 66, 75, 81, 96, 111], "ver\u00f6ffentlichen": [26, 56, 66, 75, 81, 111], "video": 58, "view": [29, 55, 69, 80, 91, 98, 104], "vii": [17, 66, 75, 81], "virtual": [29, 48, 49, 57, 70, 80, 91], "vision": [5, 49, 70, 85, 87, 90, 91, 109], "vision4d": [43, 58, 74, 78, 91], "visual": [41, 43, 44, 46, 48, 49, 53, 60, 70, 72, 73, 75, 80, 83, 87, 91, 98, 109, 114, 116], "vivo": [66, 75, 81, 117], "voigt": 26, "volum": [43, 44, 72, 83, 91], "vom": [0, 32, 66, 75, 81], "wa": [56, 66, 75, 81, 111], "wai": [41, 66, 75, 81, 85], "walkthrough": [45, 79, 106], "we": [29, 40, 68, 81, 100, 111], "webinar": [2, 43, 56, 57, 74, 87, 90, 91, 95, 99, 106, 114], "websit": 59, "week": 88, "weidtkamp": 33, "weigert": 21, "weka": [66, 75, 81], "welcom": [17, 56, 66, 75, 81, 91, 105, 106, 111], "wendt": 16, "wer": [56, 66, 75, 81, 111], "wet": [41, 75], "wetzker": 6, "what": [11, 18, 43, 55, 57, 62, 81, 87, 91, 96, 100, 104, 107, 111], "when": [57, 87, 96, 100, 111], "where": [14, 66, 75, 81], "who": [6, 16, 28, 51, 66, 75, 81, 91, 94, 105, 111], "whole": [24, 31, 66, 75, 81], "why": [29, 40, 68, 81, 100, 111, 113], "widefield": [66, 75], "wie": [0, 26, 32, 56, 66, 75, 81, 96, 111], "window": [22, 66, 75, 81, 117], "wissenschaft": [59, 87, 105, 111], "within": [3, 17, 22, 23, 30, 32, 33, 34, 35, 37, 66, 75, 81], "without": [17, 34, 51, 66, 75, 81, 91, 96, 105, 111], "witz": 13, "wizard": [50, 59, 94, 96, 111], "work": [3, 29, 33, 34, 37, 40, 55, 60, 66, 68, 69, 75, 80, 81, 91, 98, 100, 104, 106, 111, 117], "workflow": [10, 16, 28, 35, 43, 46, 50, 53, 55, 56, 57, 66, 70, 75, 79, 81, 83, 87, 90, 91, 98, 101, 104, 106, 111, 115, 116], "workflowhub": [11, 43, 80, 93, 109, 115, 116], "workshop": [3, 4, 5, 6, 13, 22, 23, 28, 33, 34, 37, 42, 43, 48, 49, 55, 56, 57, 60, 66, 70, 75, 81, 85, 87, 91, 92, 103, 105, 109, 111, 112, 114, 115, 117], "world": [11, 53, 101], "write": [19, 40, 68, 109], "www": [71, 72, 73, 74], "xml": [2, 15, 53, 66, 81, 91, 102], "ymia": [28, 29, 48, 56, 70, 85, 91, 99, 102, 109], "yml": 61, "you": [6, 16, 28, 29, 40, 51, 66, 68, 75, 81, 91, 94, 100, 105, 111], "your": [6, 28, 37, 40, 46, 56, 57, 60, 66, 68, 75, 81, 87, 91, 109, 111, 113], "youtub": [43, 58, 74, 81, 87, 91, 92, 102, 103, 104, 106, 108, 109], "zarr": [4, 5, 17, 48, 51, 57, 70, 75, 81, 87, 91, 109, 111], "zeiss": [22, 66, 75, 81], "zen": [43, 49, 70, 91, 103, 109], "zenodo": [29, 40, 56, 66, 68, 75, 81, 111, 113], "zerocostdl4m": [12, 43, 49, 66, 70, 73, 85, 91], "zida": [10, 55, 70, 87, 90, 91], "zobel": 35, "zoo": [43, 87, 90, 91], "zoologi": [29, 66, 75, 81], "zukunftsfest": [66, 75, 81], "zum": [0, 32, 45, 46, 57, 66, 75, 81, 111], "zur": [66, 75, 81]}}) \ No newline at end of file +Search.setIndex({"alltitles": {"10 frames of fluorescent particles": [[64, "frames-of-fluorescent-particles"], [73, "frames-of-fluorescent-particles"], [79, "frames-of-fluorescent-particles"]], "2020 BioImage Analysis Survey: Community experiences and needs for the future": [[52, "bioimage-analysis-survey-community-experiences-and-needs-for-the-future"], [64, "bioimage-analysis-survey-community-experiences-and-needs-for-the-future"], [68, "bioimage-analysis-survey-community-experiences-and-needs-for-the-future"], [78, "bioimage-analysis-survey-community-experiences-and-needs-for-the-future"], [90, "bioimage-analysis-survey-community-experiences-and-needs-for-the-future"]], "2022 MIC Workshop on Bioimage processing with Python": [[13, "mic-workshop-on-bioimage-processing-with-python"], [48, "mic-workshop-on-bioimage-processing-with-python"], [68, "mic-workshop-on-bioimage-processing-with-python"], [86, "mic-workshop-on-bioimage-processing-with-python"], [90, "mic-workshop-on-bioimage-processing-with-python"], [103, "mic-workshop-on-bioimage-processing-with-python"]], "3D Nuclei annotations and StarDist 3D model(s) (rat brain)": [[31, "d-nuclei-annotations-and-stardist-3d-model-s-rat-brain"], [64, "d-nuclei-annotations-and-stardist-3d-model-s-rat-brain"], [73, "d-nuclei-annotations-and-stardist-3d-model-s-rat-brain"], [79, "d-nuclei-annotations-and-stardist-3d-model-s-rat-brain"]], "6 Steps Towards Reproducible Research": [[40, "steps-towards-reproducible-research"], [73, "steps-towards-reproducible-research"], [79, "steps-towards-reproducible-research"], [105, "steps-towards-reproducible-research"]], "A Cloud-Optimized Storage for Interactive Access of Large Arrays": [[17, "a-cloud-optimized-storage-for-interactive-access-of-large-arrays"], [34, "a-cloud-optimized-storage-for-interactive-access-of-large-arrays"], [52, "a-cloud-optimized-storage-for-interactive-access-of-large-arrays"], [64, "a-cloud-optimized-storage-for-interactive-access-of-large-arrays"], [79, "a-cloud-optimized-storage-for-interactive-access-of-large-arrays"], [99, "a-cloud-optimized-storage-for-interactive-access-of-large-arrays"], [105, "a-cloud-optimized-storage-for-interactive-access-of-large-arrays"]], "A Fiji Scripting Tutorial": [[48, "a-fiji-scripting-tutorial"], [81, "a-fiji-scripting-tutorial"], [90, "a-fiji-scripting-tutorial"], [94, "a-fiji-scripting-tutorial"]], "A Glimpse of the Open-Source FLIM Analysis Software Tools FLIMfit, FLUTE and napari-flim-phasor-plotter": [[6, "a-glimpse-of-the-open-source-flim-analysis-software-tools-flimfit-flute-and-napari-flim-phasor-plotter"], [54, "a-glimpse-of-the-open-source-flim-analysis-software-tools-flimfit-flute-and-napari-flim-phasor-plotter"], [64, "a-glimpse-of-the-open-source-flim-analysis-software-tools-flimfit-flute-and-napari-flim-phasor-plotter"], [73, "a-glimpse-of-the-open-source-flim-analysis-software-tools-flimfit-flute-and-napari-flim-phasor-plotter"], [79, "a-glimpse-of-the-open-source-flim-analysis-software-tools-flimfit-flute-and-napari-flim-phasor-plotter"], [90, "a-glimpse-of-the-open-source-flim-analysis-software-tools-flimfit-flute-and-napari-flim-phasor-plotter"]], "A Hitchhiker\u2019s guide through the bio-image analysis software universe": [[4, "a-hitchhiker-s-guide-through-the-bio-image-analysis-software-universe"], [9, "a-hitchhiker-s-guide-through-the-bio-image-analysis-software-universe"], [29, "a-hitchhiker-s-guide-through-the-bio-image-analysis-software-universe"], [52, "a-hitchhiker-s-guide-through-the-bio-image-analysis-software-universe"], [79, "a-hitchhiker-s-guide-through-the-bio-image-analysis-software-universe"], [90, "a-hitchhiker-s-guide-through-the-bio-image-analysis-software-universe"]], "A biologist\u2019s guide to planning and performing quantitative bioimaging experiments": [[42, "a-biologists-guide-to-planning-and-performing-quantitative-bioimaging-experiments"], [52, "a-biologists-guide-to-planning-and-performing-quantitative-bioimaging-experiments"], [64, "a-biologists-guide-to-planning-and-performing-quantitative-bioimaging-experiments"], [78, "a-biologists-guide-to-planning-and-performing-quantitative-bioimaging-experiments"]], "A call for public archives for biological image data": [[38, "a-call-for-public-archives-for-biological-image-data"], [52, "a-call-for-public-archives-for-biological-image-data"], [71, "a-call-for-public-archives-for-biological-image-data"], [105, "a-call-for-public-archives-for-biological-image-data"]], "A journey to FAIR microscopy data": [[3, "a-journey-to-fair-microscopy-data"], [33, "a-journey-to-fair-microscopy-data"], [64, "a-journey-to-fair-microscopy-data"], [73, "a-journey-to-fair-microscopy-data"], [79, "a-journey-to-fair-microscopy-data"], [99, "a-journey-to-fair-microscopy-data"], [105, "a-journey-to-fair-microscopy-data"]], "A practical guide to bioimaging research data management in core facilities": [[3, "a-practical-guide-to-bioimaging-research-data-management-in-core-facilities"], [8, "a-practical-guide-to-bioimaging-research-data-management-in-core-facilities"], [30, "a-practical-guide-to-bioimaging-research-data-management-in-core-facilities"], [33, "a-practical-guide-to-bioimaging-research-data-management-in-core-facilities"], [34, "a-practical-guide-to-bioimaging-research-data-management-in-core-facilities"], [37, "a-practical-guide-to-bioimaging-research-data-management-in-core-facilities"], [52, "a-practical-guide-to-bioimaging-research-data-management-in-core-facilities"], [105, "a-practical-guide-to-bioimaging-research-data-management-in-core-facilities"]], "A study on long-term reproducibility of image analysis results on ImageJ and Fiji": [[12, "a-study-on-long-term-reproducibility-of-image-analysis-results-on-imagej-and-fiji"], [29, "a-study-on-long-term-reproducibility-of-image-analysis-results-on-imagej-and-fiji"], [50, "a-study-on-long-term-reproducibility-of-image-analysis-results-on-imagej-and-fiji"], [52, "a-study-on-long-term-reproducibility-of-image-analysis-results-on-imagej-and-fiji"], [94, "a-study-on-long-term-reproducibility-of-image-analysis-results-on-imagej-and-fiji"]], "AI ML DL in Bioimage Analysis - Webinar": [[54, "ai-ml-dl-in-bioimage-analysis-webinar"], [56, "ai-ml-dl-in-bioimage-analysis-webinar"], [72, "ai-ml-dl-in-bioimage-analysis-webinar"], [86, "ai-ml-dl-in-bioimage-analysis-webinar"], [89, "ai-ml-dl-in-bioimage-analysis-webinar"], [90, "ai-ml-dl-in-bioimage-analysis-webinar"]], "AI4Life teams up with Galaxy Training Network (GTN) to enhance training resources": [[45, "ai4life-teams-up-with-galaxy-training-network-gtn-to-enhance-training-resources"], [86, "ai4life-teams-up-with-galaxy-training-network-gtn-to-enhance-training-resources"], [89, "ai4life-teams-up-with-galaxy-training-network-gtn-to-enhance-training-resources"], [90, "ai4life-teams-up-with-galaxy-training-network-gtn-to-enhance-training-resources"], [108, "ai4life-teams-up-with-galaxy-training-network-gtn-to-enhance-training-resources"]], "Abdominal Imaging Window (AIW) for Intravital Imaging": [[22, "abdominal-imaging-window-aiw-for-intravital-imaging"], [64, "abdominal-imaging-window-aiw-for-intravital-imaging"], [73, "abdominal-imaging-window-aiw-for-intravital-imaging"], [79, "abdominal-imaging-window-aiw-for-intravital-imaging"]], "Aberrated Bead Stack": [[64, "aberrated-bead-stack"], [73, "aberrated-bead-stack"], [79, "aberrated-bead-stack"]], "Abstract - NFDI Basic Service for Data Management Plans": [[44, "abstract-nfdi-basic-service-for-data-management-plans"], [79, "abstract-nfdi-basic-service-for-data-management-plans"], [105, "abstract-nfdi-basic-service-for-data-management-plans"]], "Acknowledgements": [[87, "acknowledgements"]], "Adding a Workflow to BIAFLOWS": [[54, "adding-a-workflow-to-biaflows"], [68, "adding-a-workflow-to-biaflows"], [77, "adding-a-workflow-to-biaflows"], [90, "adding-a-workflow-to-biaflows"], [98, "adding-a-workflow-to-biaflows"]], "All rights reserved (13)": [[76, null]], "Alles meins \u2013 oder!? Urheberrechte kl\u00e4ren f\u00fcr Forschungsdaten": [[54, "alles-meins-oder-urheberrechte-klaren-fur-forschungsdaten"], [64, "alles-meins-oder-urheberrechte-klaren-fur-forschungsdaten"], [73, "alles-meins-oder-urheberrechte-klaren-fur-forschungsdaten"], [79, "alles-meins-oder-urheberrechte-klaren-fur-forschungsdaten"], [95, "alles-meins-oder-urheberrechte-klaren-fur-forschungsdaten"], [105, "alles-meins-oder-urheberrechte-klaren-fur-forschungsdaten"]], "Analysis of High-Throughput Microscopy Image Data": [[4, "analysis-of-high-throughput-microscopy-image-data"], [18, "analysis-of-high-throughput-microscopy-image-data"], [68, "analysis-of-high-throughput-microscopy-image-data"], [86, "analysis-of-high-throughput-microscopy-image-data"], [90, "analysis-of-high-throughput-microscopy-image-data"]], "Andor Dragonfly confocal image of BPAE cells stained for actin, IMS file format": [[64, "andor-dragonfly-confocal-image-of-bpae-cells-stained-for-actin-ims-file-format"], [73, "andor-dragonfly-confocal-image-of-bpae-cells-stained-for-actin-ims-file-format"], [81, "andor-dragonfly-confocal-image-of-bpae-cells-stained-for-actin-ims-file-format"], [109, "andor-dragonfly-confocal-image-of-bpae-cells-stained-for-actin-ims-file-format"]], "Angaben gem\u00e4\u00df \u00a7 5 TMG": [[75, "angaben-gemasz-5-tmg"]], "Angebote der NFDI f\u00fcr die Forschung im Bereich Zoologie": [[29, "angebote-der-nfdi-fur-die-forschung-im-bereich-zoologie"], [64, "angebote-der-nfdi-fur-die-forschung-im-bereich-zoologie"], [73, "angebote-der-nfdi-fur-die-forschung-im-bereich-zoologie"], [79, "angebote-der-nfdi-fur-die-forschung-im-bereich-zoologie"], [99, "angebote-der-nfdi-fur-die-forschung-im-bereich-zoologie"], [105, "angebote-der-nfdi-fur-die-forschung-im-bereich-zoologie"]], "Annotated high-throughput microscopy image sets for validation": [[42, "annotated-high-throughput-microscopy-image-sets-for-validation"], [43, "annotated-high-throughput-microscopy-image-sets-for-validation"], [71, "annotated-high-throughput-microscopy-image-sets-for-validation"]], "Annotating 3D images in napari": [[19, "annotating-3d-images-in-napari"], [39, "annotating-3d-images-in-napari"], [66, "annotating-3d-images-in-napari"], [90, "annotating-3d-images-in-napari"], [97, "annotating-3d-images-in-napari"], [103, "annotating-3d-images-in-napari"]], "Artificial Blobs and Labels image": [[64, "artificial-blobs-and-labels-image"], [73, "artificial-blobs-and-labels-image"], [79, "artificial-blobs-and-labels-image"]], "Artificial Intelligence for Digital Pathology": [[12, "artificial-intelligence-for-digital-pathology"], [56, "artificial-intelligence-for-digital-pathology"], [72, "artificial-intelligence-for-digital-pathology"], [86, "artificial-intelligence-for-digital-pathology"], [89, "artificial-intelligence-for-digital-pathology"]], "Artificial intelligence (44)": [[89, null]], "Astigmatic 4Pi bead stack": [[64, "astigmatic-4pi-bead-stack"], [73, "astigmatic-4pi-bead-stack"], [79, "astigmatic-4pi-bead-stack"]], "Astrid schauss (5)": [[0, null]], "Axioscan 7 fluorescent channels not displaying in QuPath": [[64, "axioscan-7-fluorescent-channels-not-displaying-in-qupath"], [73, "axioscan-7-fluorescent-channels-not-displaying-in-qupath"]], "BIDS-lecture-2024": [[29, "bids-lecture-2024"], [47, "bids-lecture-2024"], [68, "bids-lecture-2024"], [79, "bids-lecture-2024"], [89, "bids-lecture-2024"], [90, "bids-lecture-2024"], [103, "bids-lecture-2024"]], "BIOMERO - A scalable and extensible image analysis framework": [[52, "biomero-a-scalable-and-extensible-image-analysis-framework"], [64, "biomero-a-scalable-and-extensible-image-analysis-framework"], [79, "biomero-a-scalable-and-extensible-image-analysis-framework"], [90, "biomero-a-scalable-and-extensible-image-analysis-framework"], [100, "biomero-a-scalable-and-extensible-image-analysis-framework"], [107, "biomero-a-scalable-and-extensible-image-analysis-framework"]], "Basics of Image Processing and Analysis": [[18, "basics-of-image-processing-and-analysis"], [40, "basics-of-image-processing-and-analysis"], [68, "basics-of-image-processing-and-analysis"], [76, "basics-of-image-processing-and-analysis"], [90, "basics-of-image-processing-and-analysis"]], "Beatriz serrano-solano (6)": [[1, null]], "Best practice data life cycle approaches for the life sciences": [[11, "best-practice-data-life-cycle-approaches-for-the-life-sciences"], [52, "best-practice-data-life-cycle-approaches-for-the-life-sciences"], [64, "best-practice-data-life-cycle-approaches-for-the-life-sciences"], [86, "best-practice-data-life-cycle-approaches-for-the-life-sciences"], [91, "best-practice-data-life-cycle-approaches-for-the-life-sciences"], [101, "best-practice-data-life-cycle-approaches-for-the-life-sciences"], [105, "best-practice-data-life-cycle-approaches-for-the-life-sciences"], [106, "best-practice-data-life-cycle-approaches-for-the-life-sciences"]], "BiaPy: Bioimage analysis pipelines in Python": [[11, "biapy-bioimage-analysis-pipelines-in-python"], [45, "biapy-bioimage-analysis-pipelines-in-python"], [103, "biapy-bioimage-analysis-pipelines-in-python"], [108, "biapy-bioimage-analysis-pipelines-in-python"]], "Biapol.github.io (6)": [[62, null]], "BigDataProcessor2: A free and open-source Fiji plugin for inspection and processing of TB sized image data": [[4, "bigdataprocessor2-a-free-and-open-source-fiji-plugin-for-inspection-and-processing-of-tb-sized-image-data"], [24, "bigdataprocessor2-a-free-and-open-source-fiji-plugin-for-inspection-and-processing-of-tb-sized-image-data"], [52, "bigdataprocessor2-a-free-and-open-source-fiji-plugin-for-inspection-and-processing-of-tb-sized-image-data"], [64, "bigdataprocessor2-a-free-and-open-source-fiji-plugin-for-inspection-and-processing-of-tb-sized-image-data"], [79, "bigdataprocessor2-a-free-and-open-source-fiji-plugin-for-inspection-and-processing-of-tb-sized-image-data"], [90, "bigdataprocessor2-a-free-and-open-source-fiji-plugin-for-inspection-and-processing-of-tb-sized-image-data"], [105, "bigdataprocessor2-a-free-and-open-source-fiji-plugin-for-inspection-and-processing-of-tb-sized-image-data"]], "Bio Image Analysis": [[4, "bio-image-analysis"], [54, "bio-image-analysis"], [68, "bio-image-analysis"], [86, "bio-image-analysis"]], "Bio Image Analysis Lecture 2020": [[42, "bio-image-analysis-lecture-2020"], [56, "bio-image-analysis-lecture-2020"], [72, "bio-image-analysis-lecture-2020"], [90, "bio-image-analysis-lecture-2020"]], "Bio-Image Data Strudel for Workshop on Research Data Management in TU Dresden Core Facilities": [[6, "bio-image-data-strudel-for-workshop-on-research-data-management-in-tu-dresden-core-facilities"], [54, "bio-image-data-strudel-for-workshop-on-research-data-management-in-tu-dresden-core-facilities"], [64, "bio-image-data-strudel-for-workshop-on-research-data-management-in-tu-dresden-core-facilities"], [73, "bio-image-data-strudel-for-workshop-on-research-data-management-in-tu-dresden-core-facilities"], [79, "bio-image-data-strudel-for-workshop-on-research-data-management-in-tu-dresden-core-facilities"], [99, "bio-image-data-strudel-for-workshop-on-research-data-management-in-tu-dresden-core-facilities"], [105, "bio-image-data-strudel-for-workshop-on-research-data-management-in-tu-dresden-core-facilities"]], "Bio-image Analysis Code Generation": [[29, "bio-image-analysis-code-generation"], [64, "bio-image-analysis-code-generation"], [73, "bio-image-analysis-code-generation"], [79, "bio-image-analysis-code-generation"]], "Bio-image Analysis Code Generation using bia-bob": [[29, "bio-image-analysis-code-generation-using-bia-bob"], [64, "bio-image-analysis-code-generation-using-bia-bob"], [73, "bio-image-analysis-code-generation-using-bia-bob"], [79, "bio-image-analysis-code-generation-using-bia-bob"], [89, "bio-image-analysis-code-generation-using-bia-bob"], [90, "bio-image-analysis-code-generation-using-bia-bob"]], "Bio-image Analysis ICOB 2023": [[42, "bio-image-analysis-icob-2023"], [58, "bio-image-analysis-icob-2023"], [68, "bio-image-analysis-icob-2023"], [90, "bio-image-analysis-icob-2023"]], "Bio-image Analysis Workshop Kioto and Taipei 23/24": [[42, "bio-image-analysis-workshop-kioto-and-taipei-23-24"], [58, "bio-image-analysis-workshop-kioto-and-taipei-23-24"], [68, "bio-image-analysis-workshop-kioto-and-taipei-23-24"], [90, "bio-image-analysis-workshop-kioto-and-taipei-23-24"]], "Bio-image Analysis Workshop Taipei": [[42, "bio-image-analysis-workshop-taipei"], [58, "bio-image-analysis-workshop-taipei"], [68, "bio-image-analysis-workshop-taipei"], [90, "bio-image-analysis-workshop-taipei"]], "Bio-image Analysis with the Help of Large Language Models": [[29, "bio-image-analysis-with-the-help-of-large-language-models"], [54, "bio-image-analysis-with-the-help-of-large-language-models"], [64, "bio-image-analysis-with-the-help-of-large-language-models"], [73, "bio-image-analysis-with-the-help-of-large-language-models"], [79, "bio-image-analysis-with-the-help-of-large-language-models"], [89, "bio-image-analysis-with-the-help-of-large-language-models"], [103, "bio-image-analysis-with-the-help-of-large-language-models"]], "Bio-image Data Science": [[29, "bio-image-data-science"], [48, "bio-image-data-science"], [68, "bio-image-data-science"], [79, "bio-image-data-science"], [89, "bio-image-data-science"], [90, "bio-image-data-science"], [103, "bio-image-data-science"], [105, "bio-image-data-science"]], "Bio-image Data Science Lectures @ Uni Leipzig / ScaDS.AI": [[29, "bio-image-data-science-lectures-uni-leipzig-scads-ai"], [54, "bio-image-data-science-lectures-uni-leipzig-scads-ai"], [73, "bio-image-data-science-lectures-uni-leipzig-scads-ai"], [79, "bio-image-data-science-lectures-uni-leipzig-scads-ai"], [89, "bio-image-data-science-lectures-uni-leipzig-scads-ai"], [90, "bio-image-data-science-lectures-uni-leipzig-scads-ai"], [103, "bio-image-data-science-lectures-uni-leipzig-scads-ai"]], "Bio-image analysis, biostatistics, programming and machine learning for computational biology": [[29, "bio-image-analysis-biostatistics-programming-and-machine-learning-for-computational-biology"], [48, "bio-image-analysis-biostatistics-programming-and-machine-learning-for-computational-biology"], [68, "bio-image-analysis-biostatistics-programming-and-machine-learning-for-computational-biology"], [79, "bio-image-analysis-biostatistics-programming-and-machine-learning-for-computational-biology"], [90, "bio-image-analysis-biostatistics-programming-and-machine-learning-for-computational-biology"], [97, "bio-image-analysis-biostatistics-programming-and-machine-learning-for-computational-biology"], [103, "bio-image-analysis-biostatistics-programming-and-machine-learning-for-computational-biology"]], "Bio.tools database": [[42, "bio-tools-database"], [79, "bio-tools-database"], [91, "bio-tools-database"]], "BioEngine": [[1, "bioengine"], [52, "bioengine"], [86, "bioengine"], [89, "bioengine"], [108, "bioengine"]], "BioEngine Documentation": [[45, "bioengine-documentation"], [84, "bioengine-documentation"], [89, "bioengine-documentation"], [103, "bioengine-documentation"], [108, "bioengine-documentation"]], "BioFormats Command line (CLI) tools": [[45, "bioformats-command-line-cli-tools"], [79, "bioformats-command-line-cli-tools"]], "BioImage Analysis Notebooks": [[40, "bioimage-analysis-notebooks"], [48, "bioimage-analysis-notebooks"], [78, "bioimage-analysis-notebooks"], [79, "bioimage-analysis-notebooks"], [90, "bioimage-analysis-notebooks"], [103, "bioimage-analysis-notebooks"]], "BioImage Archive AI Gallery": [[42, "bioimage-archive-ai-gallery"], [43, "bioimage-archive-ai-gallery"], [70, "bioimage-archive-ai-gallery"], [81, "bioimage-archive-ai-gallery"], [89, "bioimage-archive-ai-gallery"], [90, "bioimage-archive-ai-gallery"]], "BioImage Archive Visual Gallery": [[42, "bioimage-archive-visual-gallery"], [43, "bioimage-archive-visual-gallery"], [70, "bioimage-archive-visual-gallery"], [81, "bioimage-archive-visual-gallery"], [90, "bioimage-archive-visual-gallery"]], "BioImage Archive Volume EM Gallery": [[42, "bioimage-archive-volume-em-gallery"], [43, "bioimage-archive-volume-em-gallery"], [70, "bioimage-archive-volume-em-gallery"], [81, "bioimage-archive-volume-em-gallery"], [90, "bioimage-archive-volume-em-gallery"]], "BioImage Data Analysis": [[18, "bioimage-data-analysis"], [40, "bioimage-data-analysis"], [76, "bioimage-data-analysis"], [90, "bioimage-data-analysis"], [98, "bioimage-data-analysis"]], "BioImage Informatics Index Training Materials": [[42, "bioimage-informatics-index-training-materials"], [90, "bioimage-informatics-index-training-materials"]], "Bioimage Archive": [[42, "bioimage-archive"], [43, "bioimage-archive"], [52, "bioimage-archive"], [70, "bioimage-archive"]], "Bioimage Model Zoo": [[42, "bioimage-model-zoo"], [86, "bioimage-model-zoo"], [89, "bioimage-model-zoo"], [90, "bioimage-model-zoo"]], "Bioimage analysis (183)": [[90, null]], "Bioimage analysis with Napari": [[42, "bioimage-analysis-with-napari"], [90, "bioimage-analysis-with-napari"], [97, "bioimage-analysis-with-napari"], [103, "bioimage-analysis-with-napari"]], "Bioinformatics (10)": [[91, null]], "Biologists, stop putting UMAP plots in your papers": [[39, "biologists-stop-putting-umap-plots-in-your-papers"], [86, "biologists-stop-putting-umap-plots-in-your-papers"]], "Biomero": [[47, "biomero"], [68, "biomero"], [100, "biomero"]], "Blog post (23)": [[39, null]], "Book (19)": [[40, null]], "Bridging Imaging Users to Imaging Analysis - A community survey": [[51, "bridging-imaging-users-to-imaging-analysis-a-community-survey"], [52, "bridging-imaging-users-to-imaging-analysis-a-community-survey"], [68, "bridging-imaging-users-to-imaging-analysis-a-community-survey"], [69, "bridging-imaging-users-to-imaging-analysis-a-community-survey"], [78, "bridging-imaging-users-to-imaging-analysis-a-community-survey"], [90, "bridging-imaging-users-to-imaging-analysis-a-community-survey"]], "Bring your own data workshops": [[58, "bring-your-own-data-workshops"], [90, "bring-your-own-data-workshops"], [105, "bring-your-own-data-workshops"]], "Browsing the Open Microscopy Image Data Resource with Python": [[29, "browsing-the-open-microscopy-image-data-resource-with-python"], [39, "browsing-the-open-microscopy-image-data-resource-with-python"], [62, "browsing-the-open-microscopy-image-data-resource-with-python"], [79, "browsing-the-open-microscopy-image-data-resource-with-python"], [100, "browsing-the-open-microscopy-image-data-resource-with-python"], [103, "browsing-the-open-microscopy-image-data-resource-with-python"]], "Bsd-2-clause (7)": [[77, null]], "Bsd-3-clause (26)": [[78, null]], "Building FAIR image analysis pipelines for high-content-screening (HCS) data using Galaxy": [[1, "building-fair-image-analysis-pipelines-for-high-content-screening-hcs-data-using-galaxy"], [28, "building-fair-image-analysis-pipelines-for-high-content-screening-hcs-data-using-galaxy"], [64, "building-fair-image-analysis-pipelines-for-high-content-screening-hcs-data-using-galaxy"], [73, "building-fair-image-analysis-pipelines-for-high-content-screening-hcs-data-using-galaxy"], [79, "building-fair-image-analysis-pipelines-for-high-content-screening-hcs-data-using-galaxy"]], "Building a Bioimage Analysis Workflow using Deep Learning": [[10, "building-a-bioimage-analysis-workflow-using-deep-learning"], [54, "building-a-bioimage-analysis-workflow-using-deep-learning"], [68, "building-a-bioimage-analysis-workflow-using-deep-learning"], [86, "building-a-bioimage-analysis-workflow-using-deep-learning"], [89, "building-a-bioimage-analysis-workflow-using-deep-learning"], [90, "building-a-bioimage-analysis-workflow-using-deep-learning"]], "Building a FAIR image data ecosystem for microscopy communities": [[1, "building-a-fair-image-data-ecosystem-for-microscopy-communities"], [14, "building-a-fair-image-data-ecosystem-for-microscopy-communities"], [52, "building-a-fair-image-data-ecosystem-for-microscopy-communities"], [64, "building-a-fair-image-data-ecosystem-for-microscopy-communities"], [73, "building-a-fair-image-data-ecosystem-for-microscopy-communities"], [79, "building-a-fair-image-data-ecosystem-for-microscopy-communities"], [105, "building-a-fair-image-data-ecosystem-for-microscopy-communities"]], "CARE/Stardist tutorials for EMBO Practical Course \u2014 Computational optical biology 2022": [[21, "care-stardist-tutorials-for-embo-practical-course-computational-optical-biology-2022"], [48, "care-stardist-tutorials-for-embo-practical-course-computational-optical-biology-2022"], [68, "care-stardist-tutorials-for-embo-practical-course-computational-optical-biology-2022"], [86, "care-stardist-tutorials-for-embo-practical-course-computational-optical-biology-2022"], [89, "care-stardist-tutorials-for-embo-practical-course-computational-optical-biology-2022"], [90, "care-stardist-tutorials-for-embo-practical-course-computational-optical-biology-2022"], [103, "care-stardist-tutorials-for-embo-practical-course-computational-optical-biology-2022"]], "CLIJ: GPU-accelerated image processing for everyone": [[11, "clij-gpu-accelerated-image-processing-for-everyone"], [29, "clij-gpu-accelerated-image-processing-for-everyone"], [52, "clij-gpu-accelerated-image-processing-for-everyone"], [64, "clij-gpu-accelerated-image-processing-for-everyone"], [76, "clij-gpu-accelerated-image-processing-for-everyone"], [90, "clij-gpu-accelerated-image-processing-for-everyone"], [94, "clij-gpu-accelerated-image-processing-for-everyone"]], "COBA: Center for Open Bioimage Analysis YouTube Channel": [[42, "coba-center-for-open-bioimage-analysis-youtube-channel"], [56, "coba-center-for-open-bioimage-analysis-youtube-channel"], [72, "coba-center-for-open-bioimage-analysis-youtube-channel"], [90, "coba-center-for-open-bioimage-analysis-youtube-channel"]], "CSBDeep and StarDist @ I2K 2020": [[21, "csbdeep-and-stardist-i2k-2020"], [48, "csbdeep-and-stardist-i2k-2020"], [68, "csbdeep-and-stardist-i2k-2020"], [86, "csbdeep-and-stardist-i2k-2020"], [89, "csbdeep-and-stardist-i2k-2020"], [90, "csbdeep-and-stardist-i2k-2020"], [103, "csbdeep-and-stardist-i2k-2020"]], "CZI (Carl Zeiss Image) dataset with artificial test camera images with various dimension for testing libraries reading": [[64, "czi-carl-zeiss-image-dataset-with-artificial-test-camera-images-with-various-dimension-for-testing-libraries-reading"], [73, "czi-carl-zeiss-image-dataset-with-artificial-test-camera-images-with-various-dimension-for-testing-libraries-reading"], [79, "czi-carl-zeiss-image-dataset-with-artificial-test-camera-images-with-various-dimension-for-testing-libraries-reading"]], "CZI file examples": [[24, "czi-file-examples"], [64, "czi-file-examples"], [73, "czi-file-examples"], [79, "czi-file-examples"]], "CZI: Open Science Program Collection": [[42, "czi-open-science-program-collection"], [73, "czi-open-science-program-collection"]], "Cc-by-4.0 (249)": [[79, null]], "Cc-by-sa-4.0 (5)": [[80, null]], "Cc0-1.0 (13)": [[81, null]], "Cell Tracking Challenge - 2D Datasets": [[42, "cell-tracking-challenge-2d-datasets"], [43, "cell-tracking-challenge-2d-datasets"], [86, "cell-tracking-challenge-2d-datasets"], [90, "cell-tracking-challenge-2d-datasets"]], "Cell Tracking Challenge - 3D Datasets": [[42, "cell-tracking-challenge-3d-datasets"], [43, "cell-tracking-challenge-3d-datasets"], [86, "cell-tracking-challenge-3d-datasets"], [90, "cell-tracking-challenge-3d-datasets"]], "CellProfiler Introduction": [[54, "cellprofiler-introduction"], [68, "cellprofiler-introduction"], [86, "cellprofiler-introduction"], [90, "cellprofiler-introduction"], [98, "cellprofiler-introduction"]], "CellProfiler Practical NeuBIAS Lisbon 2017": [[4, "cellprofiler-practical-neubias-lisbon-2017"], [55, "cellprofiler-practical-neubias-lisbon-2017"], [68, "cellprofiler-practical-neubias-lisbon-2017"], [86, "cellprofiler-practical-neubias-lisbon-2017"], [90, "cellprofiler-practical-neubias-lisbon-2017"], [98, "cellprofiler-practical-neubias-lisbon-2017"]], "CellProfiler tutorials": [[48, "cellprofiler-tutorials"], [68, "cellprofiler-tutorials"], [78, "cellprofiler-tutorials"], [90, "cellprofiler-tutorials"]], "CellTrackColab": [[42, "celltrackcolab"], [48, "celltrackcolab"], [68, "celltrackcolab"], [69, "celltrackcolab"], [84, "celltrackcolab"]], "Cellpose model for Digital Phase Contrast images": [[25, "cellpose-model-for-digital-phase-contrast-images"], [31, "cellpose-model-for-digital-phase-contrast-images"], [64, "cellpose-model-for-digital-phase-contrast-images"], [73, "cellpose-model-for-digital-phase-contrast-images"], [79, "cellpose-model-for-digital-phase-contrast-images"]], "Cellpose models for Label Prediction from Brightfield and Digital Phase Contrast images": [[25, "cellpose-models-for-label-prediction-from-brightfield-and-digital-phase-contrast-images"], [31, "cellpose-models-for-label-prediction-from-brightfield-and-digital-phase-contrast-images"], [64, "cellpose-models-for-label-prediction-from-brightfield-and-digital-phase-contrast-images"], [73, "cellpose-models-for-label-prediction-from-brightfield-and-digital-phase-contrast-images"], [79, "cellpose-models-for-label-prediction-from-brightfield-and-digital-phase-contrast-images"]], "Center for Microscopy and Image Analysis How-to Guides": [[42, "center-for-microscopy-and-image-analysis-how-to-guides"]], "Challenges and opportunities for bio-image analysis core-facilities": [[29, "challenges-and-opportunities-for-bio-image-analysis-core-facilities"], [54, "challenges-and-opportunities-for-bio-image-analysis-core-facilities"], [65, "challenges-and-opportunities-for-bio-image-analysis-core-facilities"], [79, "challenges-and-opportunities-for-bio-image-analysis-core-facilities"], [90, "challenges-and-opportunities-for-bio-image-analysis-core-facilities"], [99, "challenges-and-opportunities-for-bio-image-analysis-core-facilities"], [105, "challenges-and-opportunities-for-bio-image-analysis-core-facilities"]], "Challenges and opportunities for bioimage analysis core-facilities": [[29, "challenges-and-opportunities-for-bioimage-analysis-core-facilities"], [52, "challenges-and-opportunities-for-bioimage-analysis-core-facilities"], [79, "challenges-and-opportunities-for-bioimage-analysis-core-facilities"], [90, "challenges-and-opportunities-for-bioimage-analysis-core-facilities"], [105, "challenges-and-opportunities-for-bioimage-analysis-core-facilities"]], "ChatGPT for Image Analysis": [[29, "chatgpt-for-image-analysis"], [64, "chatgpt-for-image-analysis"], [73, "chatgpt-for-image-analysis"], [79, "chatgpt-for-image-analysis"]], "Checklists for publishing images and image analysis": [[81, "checklists-for-publishing-images-and-image-analysis"], [90, "checklists-for-publishing-images-and-image-analysis"]], "Chris Halvin YouTube channel": [[42, "chris-halvin-youtube-channel"], [56, "chris-halvin-youtube-channel"], [72, "chris-halvin-youtube-channel"], [86, "chris-halvin-youtube-channel"], [90, "chris-halvin-youtube-channel"], [97, "chris-halvin-youtube-channel"], [103, "chris-halvin-youtube-channel"]], "Chris allan (5)": [[2, null]], "Christian schmidt (19)": [[3, null]], "Christian tischer (14)": [[4, null]], "Code (10)": [[41, null]], "Collaborative Working and Version Control with git[hub]": [[29, "collaborative-working-and-version-control-with-git-hub"], [64, "collaborative-working-and-version-control-with-git-hub"], [73, "collaborative-working-and-version-control-with-git-hub"], [79, "collaborative-working-and-version-control-with-git-hub"], [99, "collaborative-working-and-version-control-with-git-hub"], [105, "collaborative-working-and-version-control-with-git-hub"], [109, "collaborative-working-and-version-control-with-git-hub"]], "Collaborative bio-image analysis script editing with git": [[29, "collaborative-bio-image-analysis-script-editing-with-git"], [39, "collaborative-bio-image-analysis-script-editing-with-git"], [66, "collaborative-bio-image-analysis-script-editing-with-git"], [79, "collaborative-bio-image-analysis-script-editing-with-git"], [105, "collaborative-bio-image-analysis-script-editing-with-git"], [106, "collaborative-bio-image-analysis-script-editing-with-git"]], "Collection (81)": [[42, null]], "Collection of teaching material for deep learning for (biomedical) image analysis": [[5, "collection-of-teaching-material-for-deep-learning-for-biomedical-image-analysis"], [68, "collection-of-teaching-material-for-deep-learning-for-biomedical-image-analysis"], [84, "collection-of-teaching-material-for-deep-learning-for-biomedical-image-analysis"], [89, "collection-of-teaching-material-for-deep-learning-for-biomedical-image-analysis"], [90, "collection-of-teaching-material-for-deep-learning-for-biomedical-image-analysis"]], "Combining the BIDS and ARC Directory Structures for Multimodal Research Data Organization": [[0, "combining-the-bids-and-arc-directory-structures-for-multimodal-research-data-organization"], [50, "combining-the-bids-and-arc-directory-structures-for-multimodal-research-data-organization"], [73, "combining-the-bids-and-arc-directory-structures-for-multimodal-research-data-organization"], [79, "combining-the-bids-and-arc-directory-structures-for-multimodal-research-data-organization"], [93, "combining-the-bids-and-arc-directory-structures-for-multimodal-research-data-organization"], [105, "combining-the-bids-and-arc-directory-structures-for-multimodal-research-data-organization"]], "Community-developed checklists for publishing images and image analyses": [[4, "community-developed-checklists-for-publishing-images-and-image-analyses"], [12, "community-developed-checklists-for-publishing-images-and-image-analyses"], [18, "community-developed-checklists-for-publishing-images-and-image-analyses"], [30, "community-developed-checklists-for-publishing-images-and-image-analyses"], [42, "community-developed-checklists-for-publishing-images-and-image-analyses"], [48, "community-developed-checklists-for-publishing-images-and-image-analyses"], [52, "community-developed-checklists-for-publishing-images-and-image-analyses"], [71, "community-developed-checklists-for-publishing-images-and-image-analyses"], [76, "community-developed-checklists-for-publishing-images-and-image-analyses"], [78, "community-developed-checklists-for-publishing-images-and-image-analyses"], [90, "community-developed-checklists-for-publishing-images-and-image-analyses"], [105, "community-developed-checklists-for-publishing-images-and-image-analyses"]], "Conference Slides - 4th Day of Intravital Microscopy": [[64, "conference-slides-4th-day-of-intravital-microscopy"], [73, "conference-slides-4th-day-of-intravital-microscopy"], [79, "conference-slides-4th-day-of-intravital-microscopy"]], "Constantin pape (8)": [[5, null]], "Contributing": [[87, "contributing"]], "Cornelia wetzker (6)": [[6, null]], "Course on Deep Learning for imaging using PyTorch": [[13, "course-on-deep-learning-for-imaging-using-pytorch"], [48, "course-on-deep-learning-for-imaging-using-pytorch"], [68, "course-on-deep-learning-for-imaging-using-pytorch"], [86, "course-on-deep-learning-for-imaging-using-pytorch"], [89, "course-on-deep-learning-for-imaging-using-pytorch"], [90, "course-on-deep-learning-for-imaging-using-pytorch"], [103, "course-on-deep-learning-for-imaging-using-pytorch"]], "Crashkurs Forschungsdatenmanagement": [[26, "crashkurs-forschungsdatenmanagement"], [54, "crashkurs-forschungsdatenmanagement"], [64, "crashkurs-forschungsdatenmanagement"], [73, "crashkurs-forschungsdatenmanagement"], [79, "crashkurs-forschungsdatenmanagement"], [105, "crashkurs-forschungsdatenmanagement"]], "Creating Workflows and Advanced Workflow Options": [[49, "creating-workflows-and-advanced-workflow-options"], [55, "creating-workflows-and-advanced-workflow-options"], [79, "creating-workflows-and-advanced-workflow-options"], [107, "creating-workflows-and-advanced-workflow-options"]], "Creating a Research Data Management Plan using chatGPT": [[29, "creating-a-research-data-management-plan-using-chatgpt"], [39, "creating-a-research-data-management-plan-using-chatgpt"], [66, "creating-a-research-data-management-plan-using-chatgpt"], [79, "creating-a-research-data-management-plan-using-chatgpt"], [89, "creating-a-research-data-management-plan-using-chatgpt"], [105, "creating-a-research-data-management-plan-using-chatgpt"]], "Creating open computational curricula": [[36, "creating-open-computational-curricula"], [54, "creating-open-computational-curricula"], [64, "creating-open-computational-curricula"], [73, "creating-open-computational-curricula"], [79, "creating-open-computational-curricula"]], "Cultivating Open Training": [[29, "cultivating-open-training"], [54, "cultivating-open-training"], [64, "cultivating-open-training"], [73, "cultivating-open-training"], [79, "cultivating-open-training"], [90, "cultivating-open-training"], [93, "cultivating-open-training"], [95, "cultivating-open-training"], [101, "cultivating-open-training"], [105, "cultivating-open-training"]], "Cultivating Open Training to advance Bio-image Analysis": [[29, "cultivating-open-training-to-advance-bio-image-analysis"], [54, "cultivating-open-training-to-advance-bio-image-analysis"], [64, "cultivating-open-training-to-advance-bio-image-analysis"], [73, "cultivating-open-training-to-advance-bio-image-analysis"], [79, "cultivating-open-training-to-advance-bio-image-analysis"], [93, "cultivating-open-training-to-advance-bio-image-analysis"], [95, "cultivating-open-training-to-advance-bio-image-analysis"], [105, "cultivating-open-training-to-advance-bio-image-analysis"]], "DALIA Interchange Format": [[64, "dalia-interchange-format"], [73, "dalia-interchange-format"], [80, "dalia-interchange-format"]], "DEEP NAPARI : Napari as a tool for deep learning project management": [[48, "deep-napari-napari-as-a-tool-for-deep-learning-project-management"], [68, "deep-napari-napari-as-a-tool-for-deep-learning-project-management"], [86, "deep-napari-napari-as-a-tool-for-deep-learning-project-management"], [89, "deep-napari-napari-as-a-tool-for-deep-learning-project-management"], [90, "deep-napari-napari-as-a-tool-for-deep-learning-project-management"]], "DL4MicEverywhere": [[11, "dl4miceverywhere"], [42, "dl4miceverywhere"], [48, "dl4miceverywhere"], [68, "dl4miceverywhere"], [79, "dl4miceverywhere"], [90, "dl4miceverywhere"]], "DL4MicEverywhere \u2013 Overcoming reproducibility challenges in deep learning microscopy imaging": [[39, "dl4miceverywhere-overcoming-reproducibility-challenges-in-deep-learning-microscopy-imaging"], [66, "dl4miceverywhere-overcoming-reproducibility-challenges-in-deep-learning-microscopy-imaging"], [86, "dl4miceverywhere-overcoming-reproducibility-challenges-in-deep-learning-microscopy-imaging"]], "DL4Proteins-notebooks": [[42, "dl4proteins-notebooks"], [47, "dl4proteins-notebooks"], [68, "dl4proteins-notebooks"], [91, "dl4proteins-notebooks"], [109, "dl4proteins-notebooks"]], "DL@MBL 2021 Exercises": [[5, "dl-mbl-2021-exercises"], [48, "dl-mbl-2021-exercises"], [54, "dl-mbl-2021-exercises"], [68, "dl-mbl-2021-exercises"], [86, "dl-mbl-2021-exercises"], [89, "dl-mbl-2021-exercises"], [90, "dl-mbl-2021-exercises"]], "Dask Course": [[13, "dask-course"], [48, "dask-course"], [68, "dask-course"], [86, "dask-course"], [90, "dask-course"], [103, "dask-course"]], "Data (8)": [[43, null]], "Data Carpentry for Biologists": [[41, "data-carpentry-for-biologists"], [55, "data-carpentry-for-biologists"], [79, "data-carpentry-for-biologists"], [84, "data-carpentry-for-biologists"]], "Data Stewardship Wizard": [[49, "data-stewardship-wizard"], [57, "data-stewardship-wizard"], [92, "data-stewardship-wizard"], [93, "data-stewardship-wizard"], [105, "data-stewardship-wizard"]], "Data Visualization with Flying Colors": [[39, "data-visualization-with-flying-colors"], [86, "data-visualization-with-flying-colors"]], "Data handling in large-scale electron microscopy": [[39, "data-handling-in-large-scale-electron-microscopy"], [105, "data-handling-in-large-scale-electron-microscopy"]], "Data life cycle": [[42, "data-life-cycle"], [49, "data-life-cycle"], [57, "data-life-cycle"], [79, "data-life-cycle"], [105, "data-life-cycle"]], "Data management at France BioImaging": [[54, "data-management-at-france-bioimaging"], [80, "data-management-at-france-bioimaging"], [90, "data-management-at-france-bioimaging"], [101, "data-management-at-france-bioimaging"], [105, "data-management-at-france-bioimaging"]], "Data stewardship (6)": [[92, null]], "Data stewardship and research data management tools for multimodal linking of imaging data in plasma medicine": [[33, "data-stewardship-and-research-data-management-tools-for-multimodal-linking-of-imaging-data-in-plasma-medicine"], [64, "data-stewardship-and-research-data-management-tools-for-multimodal-linking-of-imaging-data-in-plasma-medicine"], [73, "data-stewardship-and-research-data-management-tools-for-multimodal-linking-of-imaging-data-in-plasma-medicine"], [79, "data-stewardship-and-research-data-management-tools-for-multimodal-linking-of-imaging-data-in-plasma-medicine"]], "DataPLANT knowledge base": [[42, "dataplant-knowledge-base"], [79, "dataplant-knowledge-base"], [105, "dataplant-knowledge-base"]], "DataViz protocols - An introduction to data visualization protocols for wet lab scientists": [[40, "dataviz-protocols-an-introduction-to-data-visualization-protocols-for-wet-lab-scientists"], [73, "dataviz-protocols-an-introduction-to-data-visualization-protocols-for-wet-lab-scientists"]], "Datenmanagement": [[29, "datenmanagement"], [54, "datenmanagement"], [64, "datenmanagement"], [73, "datenmanagement"], [79, "datenmanagement"], [105, "datenmanagement"]], "Datenmanagement im Fokus: Organisation, Speicherstrategien und Datenschutz": [[26, "datenmanagement-im-fokus-organisation-speicherstrategien-und-datenschutz"], [54, "datenmanagement-im-fokus-organisation-speicherstrategien-und-datenschutz"], [64, "datenmanagement-im-fokus-organisation-speicherstrategien-und-datenschutz"], [73, "datenmanagement-im-fokus-organisation-speicherstrategien-und-datenschutz"], [79, "datenmanagement-im-fokus-organisation-speicherstrategien-und-datenschutz"], [105, "datenmanagement-im-fokus-organisation-speicherstrategien-und-datenschutz"]], "Datenmanagementpl\u00e4ne erstellen - Teil 1": [[26, "datenmanagementplane-erstellen-teil-1"], [54, "datenmanagementplane-erstellen-teil-1"], [64, "datenmanagementplane-erstellen-teil-1"], [73, "datenmanagementplane-erstellen-teil-1"], [79, "datenmanagementplane-erstellen-teil-1"], [105, "datenmanagementplane-erstellen-teil-1"]], "Datenmanagementpl\u00e4ne erstellen - Teil 2": [[26, "datenmanagementplane-erstellen-teil-2"], [54, "datenmanagementplane-erstellen-teil-2"], [64, "datenmanagementplane-erstellen-teil-2"], [73, "datenmanagementplane-erstellen-teil-2"], [79, "datenmanagementplane-erstellen-teil-2"], [105, "datenmanagementplane-erstellen-teil-2"]], "Datenschutz": [[75, "datenschutz"]], "Deconvolution Test Dataset": [[31, "deconvolution-test-dataset"], [64, "deconvolution-test-dataset"], [73, "deconvolution-test-dataset"], [79, "deconvolution-test-dataset"]], "Deep Learning Based Segmentation For Biologists": [[48, "deep-learning-based-segmentation-for-biologists"], [68, "deep-learning-based-segmentation-for-biologists"], [89, "deep-learning-based-segmentation-for-biologists"], [103, "deep-learning-based-segmentation-for-biologists"]], "Deep Learning for image analysis - Exercises": [[21, "deep-learning-for-image-analysis-exercises"], [48, "deep-learning-for-image-analysis-exercises"], [68, "deep-learning-for-image-analysis-exercises"], [86, "deep-learning-for-image-analysis-exercises"], [89, "deep-learning-for-image-analysis-exercises"], [90, "deep-learning-for-image-analysis-exercises"]], "Deep Vision and Graphics": [[48, "deep-vision-and-graphics"], [68, "deep-vision-and-graphics"], [84, "deep-vision-and-graphics"], [89, "deep-vision-and-graphics"], [103, "deep-vision-and-graphics"]], "DeepProfiler Handbook": [[40, "deepprofiler-handbook"], [68, "deepprofiler-handbook"], [86, "deepprofiler-handbook"], [89, "deepprofiler-handbook"], [90, "deepprofiler-handbook"]], "Developing (semi)automatic analysis pipelines and technological solutions for metadata annotation and management in high-content screening (HCS) bioimaging": [[28, "developing-semi-automatic-analysis-pipelines-and-technological-solutions-for-metadata-annotation-and-management-in-high-content-screening-hcs-bioimaging"], [50, "developing-semi-automatic-analysis-pipelines-and-technological-solutions-for-metadata-annotation-and-management-in-high-content-screening-hcs-bioimaging"], [64, "developing-semi-automatic-analysis-pipelines-and-technological-solutions-for-metadata-annotation-and-management-in-high-content-screening-hcs-bioimaging"], [79, "developing-semi-automatic-analysis-pipelines-and-technological-solutions-for-metadata-annotation-and-management-in-high-content-screening-hcs-bioimaging"], [90, "developing-semi-automatic-analysis-pipelines-and-technological-solutions-for-metadata-annotation-and-management-in-high-content-screening-hcs-bioimaging"]], "Developing open-source software for bioimage analysis: opportunities and challenges": [[29, "developing-open-source-software-for-bioimage-analysis-opportunities-and-challenges"], [52, "developing-open-source-software-for-bioimage-analysis-opportunities-and-challenges"], [65, "developing-open-source-software-for-bioimage-analysis-opportunities-and-challenges"], [79, "developing-open-source-software-for-bioimage-analysis-opportunities-and-challenges"], [98, "developing-open-source-software-for-bioimage-analysis-opportunities-and-challenges"]], "Development of a platform for advanced optics education, training and prototyping": [[64, "development-of-a-platform-for-advanced-optics-education-training-and-prototyping"], [73, "development-of-a-platform-for-advanced-optics-education-training-and-prototyping"], [79, "development-of-a-platform-for-advanced-optics-education-training-and-prototyping"]], "Diffusion Models for Image Restoration - An Introduction": [[86, "diffusion-models-for-image-restoration-an-introduction"], [90, "diffusion-models-for-image-restoration-an-introduction"]], "Digital Phase Contrast on Primary Dermal Human Fibroblasts cells": [[64, "digital-phase-contrast-on-primary-dermal-human-fibroblasts-cells"], [73, "digital-phase-contrast-on-primary-dermal-human-fibroblasts-cells"], [79, "digital-phase-contrast-on-primary-dermal-human-fibroblasts-cells"]], "DigitalSreeni YouTube Channel": [[42, "digitalsreeni-youtube-channel"], [56, "digitalsreeni-youtube-channel"], [72, "digitalsreeni-youtube-channel"], [103, "digitalsreeni-youtube-channel"]], "Di\u00e1taxis - A systematic approach to technical documentation authoring.": [[55, "diataxis-a-systematic-approach-to-technical-documentation-authoring"], [57, "diataxis-a-systematic-approach-to-technical-documentation-authoring"], [80, "diataxis-a-systematic-approach-to-technical-documentation-authoring"]], "Docker Mastery - with Kubernetes + Swarm from a Docker Captain": [[55, "docker-mastery-with-kubernetes-swarm-from-a-docker-captain"], [56, "docker-mastery-with-kubernetes-swarm-from-a-docker-captain"], [86, "docker-mastery-with-kubernetes-swarm-from-a-docker-captain"]], "Docs.google.com (5)": [[63, null]], "Document (7)": [[44, null]], "Documentation (19)": [[45, null]], "Doi.org (183)": [[64, null]], "Dokumentation und Anleitung zum elektronischen Laborbuch (eLabFTW)": [[44, "dokumentation-und-anleitung-zum-elektronischen-laborbuch-elabftw"], [45, "dokumentation-und-anleitung-zum-elektronischen-laborbuch-elabftw"], [55, "dokumentation-und-anleitung-zum-elektronischen-laborbuch-elabftw"], [105, "dokumentation-und-anleitung-zum-elektronischen-laborbuch-elabftw"]], "Dominik kutra (6)": [[7, null]], "Dr Guillaume Jacquemet on studying cancer cell metastasis in the era of deep learning for microscopy": [[54, "dr-guillaume-jacquemet-on-studying-cancer-cell-metastasis-in-the-era-of-deep-learning-for-microscopy"], [56, "dr-guillaume-jacquemet-on-studying-cancer-cell-metastasis-in-the-era-of-deep-learning-for-microscopy"], [72, "dr-guillaume-jacquemet-on-studying-cancer-cell-metastasis-in-the-era-of-deep-learning-for-microscopy"], [86, "dr-guillaume-jacquemet-on-studying-cancer-cell-metastasis-in-the-era-of-deep-learning-for-microscopy"], [89, "dr-guillaume-jacquemet-on-studying-cancer-cell-metastasis-in-the-era-of-deep-learning-for-microscopy"], [90, "dr-guillaume-jacquemet-on-studying-cancer-cell-metastasis-in-the-era-of-deep-learning-for-microscopy"]], "EDAM-bioimaging - The ontology of bioimage informatics operations, topics, data, and formats": [[50, "edam-bioimaging-the-ontology-of-bioimage-informatics-operations-topics-data-and-formats"], [79, "edam-bioimaging-the-ontology-of-bioimage-informatics-operations-topics-data-and-formats"], [90, "edam-bioimaging-the-ontology-of-bioimage-informatics-operations-topics-data-and-formats"]], "EDAM-bioimaging: The ontology of bioimage informatics operations, topics, data, and formats (update 2020)": [[17, "edam-bioimaging-the-ontology-of-bioimage-informatics-operations-topics-data-and-formats-update-2020"], [18, "edam-bioimaging-the-ontology-of-bioimage-informatics-operations-topics-data-and-formats-update-2020"], [29, "edam-bioimaging-the-ontology-of-bioimage-informatics-operations-topics-data-and-formats-update-2020"], [50, "edam-bioimaging-the-ontology-of-bioimage-informatics-operations-topics-data-and-formats-update-2020"], [52, "edam-bioimaging-the-ontology-of-bioimage-informatics-operations-topics-data-and-formats-update-2020"], [65, "edam-bioimaging-the-ontology-of-bioimage-informatics-operations-topics-data-and-formats-update-2020"], [79, "edam-bioimaging-the-ontology-of-bioimage-informatics-operations-topics-data-and-formats-update-2020"], [96, "edam-bioimaging-the-ontology-of-bioimage-informatics-operations-topics-data-and-formats-update-2020"]], "EMBL Deep Learning course 2019 exercises and materials": [[21, "embl-deep-learning-course-2019-exercises-and-materials"], [48, "embl-deep-learning-course-2019-exercises-and-materials"], [68, "embl-deep-learning-course-2019-exercises-and-materials"], [86, "embl-deep-learning-course-2019-exercises-and-materials"], [89, "embl-deep-learning-course-2019-exercises-and-materials"], [103, "embl-deep-learning-course-2019-exercises-and-materials"]], "EMBL Deep Learning course 2021/22 exercises and materials": [[5, "embl-deep-learning-course-2021-22-exercises-and-materials"], [21, "embl-deep-learning-course-2021-22-exercises-and-materials"], [48, "embl-deep-learning-course-2021-22-exercises-and-materials"], [68, "embl-deep-learning-course-2021-22-exercises-and-materials"], [86, "embl-deep-learning-course-2021-22-exercises-and-materials"], [89, "embl-deep-learning-course-2021-22-exercises-and-materials"], [103, "embl-deep-learning-course-2021-22-exercises-and-materials"]], "EMBL Deep Learning course 2023 exercises and materials": [[21, "embl-deep-learning-course-2023-exercises-and-materials"], [48, "embl-deep-learning-course-2023-exercises-and-materials"], [68, "embl-deep-learning-course-2023-exercises-and-materials"], [86, "embl-deep-learning-course-2023-exercises-and-materials"], [89, "embl-deep-learning-course-2023-exercises-and-materials"], [103, "embl-deep-learning-course-2023-exercises-and-materials"]], "EMBL-EBI material collection": [[42, "embl-ebi-material-collection"], [70, "embl-ebi-material-collection"], [91, "embl-ebi-material-collection"]], "EMBO Practical Course Advanced methods in bioimage analysis": [[46, "embo-practical-course-advanced-methods-in-bioimage-analysis"], [90, "embo-practical-course-advanced-methods-in-bioimage-analysis"]], "EPFLx: Image Processing and Analysis for Life Scientists": [[49, "epflx-image-processing-and-analysis-for-life-scientists"], [76, "epflx-image-processing-and-analysis-for-life-scientists"], [90, "epflx-image-processing-and-analysis-for-life-scientists"]], "Efficiently starting institutional research data management": [[44, "efficiently-starting-institutional-research-data-management"], [64, "efficiently-starting-institutional-research-data-management"], [73, "efficiently-starting-institutional-research-data-management"], [79, "efficiently-starting-institutional-research-data-management"], [105, "efficiently-starting-institutional-research-data-management"]], "Einblicke ins Forschungsdatenmanagement - Darf ich das ver\u00f6ffentlichen? Rechtsfragen im Umgang mit Forschungsdaten": [[26, "einblicke-ins-forschungsdatenmanagement-darf-ich-das-veroffentlichen-rechtsfragen-im-umgang-mit-forschungsdaten"], [54, "einblicke-ins-forschungsdatenmanagement-darf-ich-das-veroffentlichen-rechtsfragen-im-umgang-mit-forschungsdaten"], [64, "einblicke-ins-forschungsdatenmanagement-darf-ich-das-veroffentlichen-rechtsfragen-im-umgang-mit-forschungsdaten"], [73, "einblicke-ins-forschungsdatenmanagement-darf-ich-das-veroffentlichen-rechtsfragen-im-umgang-mit-forschungsdaten"], [79, "einblicke-ins-forschungsdatenmanagement-darf-ich-das-veroffentlichen-rechtsfragen-im-umgang-mit-forschungsdaten"], [105, "einblicke-ins-forschungsdatenmanagement-darf-ich-das-veroffentlichen-rechtsfragen-im-umgang-mit-forschungsdaten"]], "Elastix tutorial": [[42, "elastix-tutorial"], [48, "elastix-tutorial"], [78, "elastix-tutorial"]], "Elisa ferrando-may (7)": [[8, null]], "Elnaz fazeli (5)": [[9, null]], "EmbedSeg Repository": [[12, "embedseg-repository"], [47, "embedseg-repository"], [68, "embedseg-repository"], [90, "embedseg-repository"]], "Engineering a Software Environment for Research Data Management of Microscopy Image Data in a Core Facility": [[64, "engineering-a-software-environment-for-research-data-management-of-microscopy-image-data-in-a-core-facility"], [73, "engineering-a-software-environment-for-research-data-management-of-microscopy-image-data-in-a-core-facility"], [99, "engineering-a-software-environment-for-research-data-management-of-microscopy-image-data-in-a-core-facility"]], "Erick Martins Ratamero - Expanding the OME ecosystem for imaging data management | SciPy 2024": [[56, "erick-martins-ratamero-expanding-the-ome-ecosystem-for-imaging-data-management-scipy-2024"], [72, "erick-martins-ratamero-expanding-the-ome-ecosystem-for-imaging-data-management-scipy-2024"], [90, "erick-martins-ratamero-expanding-the-ome-ecosystem-for-imaging-data-management-scipy-2024"], [100, "erick-martins-ratamero-expanding-the-ome-ecosystem-for-imaging-data-management-scipy-2024"]], "Erstellung und Realisierung einer institutionellen Forschungsdaten-Policy": [[52, "erstellung-und-realisierung-einer-institutionellen-forschungsdaten-policy"], [64, "erstellung-und-realisierung-einer-institutionellen-forschungsdaten-policy"], [79, "erstellung-und-realisierung-einer-institutionellen-forschungsdaten-policy"], [105, "erstellung-und-realisierung-einer-institutionellen-forschungsdaten-policy"]], "Estibaliz g\u00f3mez-de-mariscal (5)": [[10, null]], "Et al. (25)": [[11, null]], "EuBIAS course 2013: Intensity Dynamics at the Periphery of Nucleus": [[18, "eubias-course-2013-intensity-dynamics-at-the-periphery-of-nucleus"], [40, "eubias-course-2013-intensity-dynamics-at-the-periphery-of-nucleus"], [55, "eubias-course-2013-intensity-dynamics-at-the-periphery-of-nucleus"], [68, "eubias-course-2013-intensity-dynamics-at-the-periphery-of-nucleus"], [86, "eubias-course-2013-intensity-dynamics-at-the-periphery-of-nucleus"], [90, "eubias-course-2013-intensity-dynamics-at-the-periphery-of-nucleus"], [98, "eubias-course-2013-intensity-dynamics-at-the-periphery-of-nucleus"]], "Euro-BioImaging Scientific Ambassadors Program": [[1, "euro-bioimaging-scientific-ambassadors-program"], [64, "euro-bioimaging-scientific-ambassadors-program"], [73, "euro-bioimaging-scientific-ambassadors-program"], [79, "euro-bioimaging-scientific-ambassadors-program"]], "Euro-BioImaging Communication YouTube Channel": [[42, "euro-bioimaging-communication-youtube-channel"], [56, "euro-bioimaging-communication-youtube-channel"], [72, "euro-bioimaging-communication-youtube-channel"]], "Euro-BioImaging ERIC Annual Report 2022": [[64, "euro-bioimaging-eric-annual-report-2022"], [73, "euro-bioimaging-eric-annual-report-2022"], [79, "euro-bioimaging-eric-annual-report-2022"]], "Euro-BioImaging/BatchConvert: v0.0.4": [[64, "euro-bioimaging-batchconvert-v0-0-4"], [73, "euro-bioimaging-batchconvert-v0-0-4"], [79, "euro-bioimaging-batchconvert-v0-0-4"]], "Euro-BioImaging\u2019s Guide to FAIR BioImage Data - Practical Tasks": [[14, "euro-bioimaging-s-guide-to-fair-bioimage-data-practical-tasks"], [54, "euro-bioimaging-s-guide-to-fair-bioimage-data-practical-tasks"], [55, "euro-bioimaging-s-guide-to-fair-bioimage-data-practical-tasks"], [64, "euro-bioimaging-s-guide-to-fair-bioimage-data-practical-tasks"], [73, "euro-bioimaging-s-guide-to-fair-bioimage-data-practical-tasks"], [79, "euro-bioimaging-s-guide-to-fair-bioimage-data-practical-tasks"], [90, "euro-bioimaging-s-guide-to-fair-bioimage-data-practical-tasks"], [93, "euro-bioimaging-s-guide-to-fair-bioimage-data-practical-tasks"], [105, "euro-bioimaging-s-guide-to-fair-bioimage-data-practical-tasks"]], "Euro-BioImaging\u2019s Template for Research Data Management Plans": [[14, "euro-bioimaging-s-template-for-research-data-management-plans"], [42, "euro-bioimaging-s-template-for-research-data-management-plans"], [55, "euro-bioimaging-s-template-for-research-data-management-plans"], [64, "euro-bioimaging-s-template-for-research-data-management-plans"], [73, "euro-bioimaging-s-template-for-research-data-management-plans"], [79, "euro-bioimaging-s-template-for-research-data-management-plans"], [90, "euro-bioimaging-s-template-for-research-data-management-plans"], [93, "euro-bioimaging-s-template-for-research-data-management-plans"], [105, "euro-bioimaging-s-template-for-research-data-management-plans"]], "Event (8)": [[46, null]], "Evident OIR sample files tiles + stitched image - FV 4000": [[24, "evident-oir-sample-files-tiles-stitched-image-fv-4000"], [64, "evident-oir-sample-files-tiles-stitched-image-fv-4000"], [73, "evident-oir-sample-files-tiles-stitched-image-fv-4000"], [79, "evident-oir-sample-files-tiles-stitched-image-fv-4000"]], "Evident OIR sample files with lambda scan - FV 4000": [[24, "evident-oir-sample-files-with-lambda-scan-fv-4000"], [64, "evident-oir-sample-files-with-lambda-scan-fv-4000"], [73, "evident-oir-sample-files-with-lambda-scan-fv-4000"], [79, "evident-oir-sample-files-with-lambda-scan-fv-4000"]], "Example Imaris ims datasets.": [[64, "example-imaris-ims-datasets"], [73, "example-imaris-ims-datasets"], [79, "example-imaris-ims-datasets"]], "Example Microscopy Metadata JSON files produced using Micro-Meta App to document example microscopy experiments performed at individual core facilities": [[30, "example-microscopy-metadata-json-files-produced-using-micro-meta-app-to-document-example-microscopy-experiments-performed-at-individual-core-facilities"], [33, "example-microscopy-metadata-json-files-produced-using-micro-meta-app-to-document-example-microscopy-experiments-performed-at-individual-core-facilities"], [64, "example-microscopy-metadata-json-files-produced-using-micro-meta-app-to-document-example-microscopy-experiments-performed-at-individual-core-facilities"], [73, "example-microscopy-metadata-json-files-produced-using-micro-meta-app-to-document-example-microscopy-experiments-performed-at-individual-core-facilities"], [79, "example-microscopy-metadata-json-files-produced-using-micro-meta-app-to-document-example-microscopy-experiments-performed-at-individual-core-facilities"]], "Example Operetta Dataset": [[24, "example-operetta-dataset"], [64, "example-operetta-dataset"], [73, "example-operetta-dataset"], [79, "example-operetta-dataset"]], "Example Pipeline Tutorial": [[45, "example-pipeline-tutorial"], [47, "example-pipeline-tutorial"], [55, "example-pipeline-tutorial"], [68, "example-pipeline-tutorial"], [78, "example-pipeline-tutorial"], [90, "example-pipeline-tutorial"], [97, "example-pipeline-tutorial"]], "Excel template for adding Key-Value Pairs to images": [[16, "excel-template-for-adding-key-value-pairs-to-images"], [35, "excel-template-for-adding-key-value-pairs-to-images"], [64, "excel-template-for-adding-key-value-pairs-to-images"], [73, "excel-template-for-adding-key-value-pairs-to-images"], [79, "excel-template-for-adding-key-value-pairs-to-images"]], "Exclusion criteria": [[60, "exclusion-criteria"]], "F1000research.com (11)": [[65, null]], "FAIR BioImage Data": [[42, "fair-bioimage-data"], [56, "fair-bioimage-data"], [72, "fair-bioimage-data"], [79, "fair-bioimage-data"], [90, "fair-bioimage-data"], [105, "fair-bioimage-data"]], "FAIR High Content Screening in Bioimaging": [[52, "fair-high-content-screening-in-bioimaging"], [71, "fair-high-content-screening-in-bioimaging"], [79, "fair-high-content-screening-in-bioimaging"], [93, "fair-high-content-screening-in-bioimaging"], [96, "fair-high-content-screening-in-bioimaging"], [105, "fair-high-content-screening-in-bioimaging"]], "FAIR Priciples": [[42, "fair-priciples"], [79, "fair-priciples"], [92, "fair-priciples"], [93, "fair-priciples"], [105, "fair-priciples"]], "FAIRy deep-learning for bioImage analysis": [[54, "fairy-deep-learning-for-bioimage-analysis"], [65, "fairy-deep-learning-for-bioimage-analysis"], [79, "fairy-deep-learning-for-bioimage-analysis"], [89, "fairy-deep-learning-for-bioimage-analysis"], [90, "fairy-deep-learning-for-bioimage-analysis"], [93, "fairy-deep-learning-for-bioimage-analysis"]], "Fair-principles (27)": [[93, null]], "Feature extraction in napari": [[19, "feature-extraction-in-napari"], [39, "feature-extraction-in-napari"], [66, "feature-extraction-in-napari"], [90, "feature-extraction-in-napari"], [97, "feature-extraction-in-napari"], [103, "feature-extraction-in-napari"]], "Fiji": [[49, "fiji"], [77, "fiji"], [94, "fiji"], [100, "fiji"]], "Fiji Is Just ImageJ Tutorials": [[42, "fiji-is-just-imagej-tutorials"], [56, "fiji-is-just-imagej-tutorials"], [72, "fiji-is-just-imagej-tutorials"], [90, "fiji-is-just-imagej-tutorials"]], "Finding and Choosing a Data Repository": [[3, "finding-and-choosing-a-data-repository"], [55, "finding-and-choosing-a-data-repository"], [86, "finding-and-choosing-a-data-repository"], [105, "finding-and-choosing-a-data-repository"]], "Finding and using publicly available data": [[42, "finding-and-using-publicly-available-data"], [55, "finding-and-using-publicly-available-data"], [56, "finding-and-using-publicly-available-data"], [70, "finding-and-using-publicly-available-data"], [79, "finding-and-using-publicly-available-data"], [101, "finding-and-using-publicly-available-data"], [106, "finding-and-using-publicly-available-data"]], "Fit for OMERO: How imaging facilities and IT departments work together to enable RDM for bioimaging": [[58, "fit-for-omero-how-imaging-facilities-and-it-departments-work-together-to-enable-rdm-for-bioimaging"], [90, "fit-for-omero-how-imaging-facilities-and-it-departments-work-together-to-enable-rdm-for-bioimaging"], [100, "fit-for-omero-how-imaging-facilities-and-it-departments-work-together-to-enable-rdm-for-bioimaging"], [105, "fit-for-omero-how-imaging-facilities-and-it-departments-work-together-to-enable-rdm-for-bioimaging"]], "Five great reasons to share your research data": [[39, "five-great-reasons-to-share-your-research-data"], [86, "five-great-reasons-to-share-your-research-data"], [105, "five-great-reasons-to-share-your-research-data"], [106, "five-great-reasons-to-share-your-research-data"]], "Florian jug (6)": [[12, null]], "Focalplane.biologists.com (14)": [[66, null]], "Forschungsdaten.info": [[42, "forschungsdaten-info"], [105, "forschungsdaten-info"]], "Forschungsdaten.org": [[42, "forschungsdaten-org"], [79, "forschungsdaten-org"], [105, "forschungsdaten-org"]], "Forschungsdatenmanagement zukunftsfest gestalten \u2013 Impulse f\u00fcr die Strukturevaluation der Nationalen Forschungsdateninfrastruktur (NFDI)": [[64, "forschungsdatenmanagement-zukunftsfest-gestalten-impulse-fur-die-strukturevaluation-der-nationalen-forschungsdateninfrastruktur-nfdi"], [73, "forschungsdatenmanagement-zukunftsfest-gestalten-impulse-fur-die-strukturevaluation-der-nationalen-forschungsdateninfrastruktur-nfdi"], [79, "forschungsdatenmanagement-zukunftsfest-gestalten-impulse-fur-die-strukturevaluation-der-nationalen-forschungsdateninfrastruktur-nfdi"]], "Fractal Documentation": [[45, "fractal-documentation"], [103, "fractal-documentation"], [108, "fractal-documentation"]], "FriendsDontLetFriends": [[47, "friendsdontletfriends"], [68, "friendsdontletfriends"], [84, "friendsdontletfriends"]], "From Cells to Pixels: Bridging Biologists and Image Analysts Through a Common Language": [[9, "from-cells-to-pixels-bridging-biologists-and-image-analysts-through-a-common-language"], [64, "from-cells-to-pixels-bridging-biologists-and-image-analysts-through-a-common-language"], [73, "from-cells-to-pixels-bridging-biologists-and-image-analysts-through-a-common-language"], [79, "from-cells-to-pixels-bridging-biologists-and-image-analysts-through-a-common-language"]], "From Paper to Pixels: Navigation through your Research Data - presentations of speakers": [[6, "from-paper-to-pixels-navigation-through-your-research-data-presentations-of-speakers"], [28, "from-paper-to-pixels-navigation-through-your-research-data-presentations-of-speakers"], [37, "from-paper-to-pixels-navigation-through-your-research-data-presentations-of-speakers"], [54, "from-paper-to-pixels-navigation-through-your-research-data-presentations-of-speakers"], [64, "from-paper-to-pixels-navigation-through-your-research-data-presentations-of-speakers"], [73, "from-paper-to-pixels-navigation-through-your-research-data-presentations-of-speakers"], [79, "from-paper-to-pixels-navigation-through-your-research-data-presentations-of-speakers"], [105, "from-paper-to-pixels-navigation-through-your-research-data-presentations-of-speakers"]], "Fundamentals in digital image processing": [[13, "fundamentals-in-digital-image-processing"], [48, "fundamentals-in-digital-image-processing"], [68, "fundamentals-in-digital-image-processing"], [86, "fundamentals-in-digital-image-processing"], [90, "fundamentals-in-digital-image-processing"]], "Fundamentals of image analysis in Python with scikit-image, napari, and friends": [[48, "fundamentals-of-image-analysis-in-python-with-scikit-image-napari-and-friends"], [68, "fundamentals-of-image-analysis-in-python-with-scikit-image-napari-and-friends"], [78, "fundamentals-of-image-analysis-in-python-with-scikit-image-napari-and-friends"], [90, "fundamentals-of-image-analysis-in-python-with-scikit-image-napari-and-friends"], [103, "fundamentals-of-image-analysis-in-python-with-scikit-image-napari-and-friends"]], "Galaxy Documentation": [[45, "galaxy-documentation"], [108, "galaxy-documentation"]], "Galaxy Training": [[42, "galaxy-training"], [55, "galaxy-training"], [79, "galaxy-training"], [90, "galaxy-training"]], "Galaxy Training Material": [[54, "galaxy-training-material"], [55, "galaxy-training-material"], [68, "galaxy-training-material"], [84, "galaxy-training-material"]], "Galaxy workflows": [[49, "galaxy-workflows"], [55, "galaxy-workflows"], [81, "galaxy-workflows"], [107, "galaxy-workflows"]], "Generative artificial intelligence for bio-image analysis": [[29, "generative-artificial-intelligence-for-bio-image-analysis"], [54, "generative-artificial-intelligence-for-bio-image-analysis"], [65, "generative-artificial-intelligence-for-bio-image-analysis"], [79, "generative-artificial-intelligence-for-bio-image-analysis"], [89, "generative-artificial-intelligence-for-bio-image-analysis"], [90, "generative-artificial-intelligence-for-bio-image-analysis"], [103, "generative-artificial-intelligence-for-bio-image-analysis"]], "GerBI Teaching Resources Link List": [[42, "gerbi-teaching-resources-link-list"], [86, "gerbi-teaching-resources-link-list"]], "GerBI-Chat: Teil 1 - Vom Bedarf bis zum Gro\u00dfger\u00e4teantrag-Schreiben": [[0, "gerbi-chat-teil-1-vom-bedarf-bis-zum-groszgerateantrag-schreiben"], [32, "gerbi-chat-teil-1-vom-bedarf-bis-zum-groszgerateantrag-schreiben"], [64, "gerbi-chat-teil-1-vom-bedarf-bis-zum-groszgerateantrag-schreiben"], [73, "gerbi-chat-teil-1-vom-bedarf-bis-zum-groszgerateantrag-schreiben"], [79, "gerbi-chat-teil-1-vom-bedarf-bis-zum-groszgerateantrag-schreiben"]], "GerBI-Chat: Teil 2 - Wie schreibe ich am besten einen Gro\u00dfegr\u00e4teantrag": [[0, "gerbi-chat-teil-2-wie-schreibe-ich-am-besten-einen-groszegrateantrag"], [32, "gerbi-chat-teil-2-wie-schreibe-ich-am-besten-einen-groszegrateantrag"], [64, "gerbi-chat-teil-2-wie-schreibe-ich-am-besten-einen-groszegrateantrag"], [73, "gerbi-chat-teil-2-wie-schreibe-ich-am-besten-einen-groszegrateantrag"], [79, "gerbi-chat-teil-2-wie-schreibe-ich-am-besten-einen-groszegrateantrag"]], "Getting started with Mambaforge and Python": [[19, "getting-started-with-mambaforge-and-python"], [39, "getting-started-with-mambaforge-and-python"], [62, "getting-started-with-mambaforge-and-python"], [79, "getting-started-with-mambaforge-and-python"], [103, "getting-started-with-mambaforge-and-python"]], "Getting started with Python: intro and set-up a conda environment": [[28, "getting-started-with-python-intro-and-set-up-a-conda-environment"], [64, "getting-started-with-python-intro-and-set-up-a-conda-environment"], [73, "getting-started-with-python-intro-and-set-up-a-conda-environment"], [79, "getting-started-with-python-intro-and-set-up-a-conda-environment"]], "Ghent University Research Data Management (RDM) - policy and support": [[57, "ghent-university-research-data-management-rdm-policy-and-support"], [86, "ghent-university-research-data-management-rdm-policy-and-support"], [105, "ghent-university-research-data-management-rdm-policy-and-support"]], "Git.mpi-cbg.de (7)": [[67, null]], "Github repository (45)": [[47, null]], "Github.com (123)": [[68, null]], "Glencoe Software Webinars": [[2, "glencoe-software-webinars"], [42, "glencoe-software-webinars"], [55, "glencoe-software-webinars"], [56, "glencoe-software-webinars"], [86, "glencoe-software-webinars"], [100, "glencoe-software-webinars"]], "Global BioImaging Training Database": [[42, "global-bioimaging-training-database"], [46, "global-bioimaging-training-database"], [90, "global-bioimaging-training-database"]], "Global BioImaging YouTube channel": [[42, "global-bioimaging-youtube-channel"], [56, "global-bioimaging-youtube-channel"], [72, "global-bioimaging-youtube-channel"]], "Gpl-2.0 (7)": [[82, null]], "Gpl-3.0 (7)": [[83, null]], "Guidance for Developing a Research Data Management (RDM) Policy": [[40, "guidance-for-developing-a-research-data-management-rdm-policy"], [64, "guidance-for-developing-a-research-data-management-rdm-policy"], [79, "guidance-for-developing-a-research-data-management-rdm-policy"], [105, "guidance-for-developing-a-research-data-management-rdm-policy"]], "Guillaume witz (8)": [[13, null]], "Gut Analysis Toolbox": [[10, "gut-analysis-toolbox"], [29, "gut-analysis-toolbox"], [64, "gut-analysis-toolbox"], [73, "gut-analysis-toolbox"], [79, "gut-analysis-toolbox"]], "Gut Analysis Toolbox: Training data and 2D models for segmenting enteric neurons, neuronal subtypes and ganglia": [[29, "gut-analysis-toolbox-training-data-and-2d-models-for-segmenting-enteric-neurons-neuronal-subtypes-and-ganglia"], [64, "gut-analysis-toolbox-training-data-and-2d-models-for-segmenting-enteric-neurons-neuronal-subtypes-and-ganglia"], [73, "gut-analysis-toolbox-training-data-and-2d-models-for-segmenting-enteric-neurons-neuronal-subtypes-and-ganglia"], [79, "gut-analysis-toolbox-training-data-and-2d-models-for-segmenting-enteric-neurons-neuronal-subtypes-and-ganglia"]], "Hackaton Results - Conversion of KNIME image analysis workflows to Galaxy": [[28, "hackaton-results-conversion-of-knime-image-analysis-workflows-to-galaxy"], [54, "hackaton-results-conversion-of-knime-image-analysis-workflows-to-galaxy"], [64, "hackaton-results-conversion-of-knime-image-analysis-workflows-to-galaxy"], [73, "hackaton-results-conversion-of-knime-image-analysis-workflows-to-galaxy"], [79, "hackaton-results-conversion-of-knime-image-analysis-workflows-to-galaxy"], [105, "hackaton-results-conversion-of-knime-image-analysis-workflows-to-galaxy"]], "Haftung f\u00fcr Inhalte": [[75, "haftung-fur-inhalte"]], "Haftung f\u00fcr Links": [[75, "haftung-fur-links"]], "Haftungsausschluss": [[75, "haftungsausschluss"]], "HeLa \u201cKyoto\u201d cells under the scope": [[31, "hela-kyoto-cells-under-the-scope"], [64, "hela-kyoto-cells-under-the-scope"], [73, "hela-kyoto-cells-under-the-scope"], [79, "hela-kyoto-cells-under-the-scope"]], "High throughput & automated data analysis and data management workflow with Cellprofiler and OMERO": [[16, "high-throughput-automated-data-analysis-and-data-management-workflow-with-cellprofiler-and-omero"], [35, "high-throughput-automated-data-analysis-and-data-management-workflow-with-cellprofiler-and-omero"], [42, "high-throughput-automated-data-analysis-and-data-management-workflow-with-cellprofiler-and-omero"], [73, "high-throughput-automated-data-analysis-and-data-management-workflow-with-cellprofiler-and-omero"], [79, "high-throughput-automated-data-analysis-and-data-management-workflow-with-cellprofiler-and-omero"], [90, "high-throughput-automated-data-analysis-and-data-management-workflow-with-cellprofiler-and-omero"], [100, "high-throughput-automated-data-analysis-and-data-management-workflow-with-cellprofiler-and-omero"]], "Highlights from the 2016-2020 NEUBIAS training schools for Bioimage Analysts: a success story and key asset for analysts and life scientists": [[18, "highlights-from-the-2016-2020-neubias-training-schools-for-bioimage-analysts-a-success-story-and-key-asset-for-analysts-and-life-scientists"], [29, "highlights-from-the-2016-2020-neubias-training-schools-for-bioimage-analysts-a-success-story-and-key-asset-for-analysts-and-life-scientists"], [31, "highlights-from-the-2016-2020-neubias-training-schools-for-bioimage-analysts-a-success-story-and-key-asset-for-analysts-and-life-scientists"], [52, "highlights-from-the-2016-2020-neubias-training-schools-for-bioimage-analysts-a-success-story-and-key-asset-for-analysts-and-life-scientists"], [65, "highlights-from-the-2016-2020-neubias-training-schools-for-bioimage-analysts-a-success-story-and-key-asset-for-analysts-and-life-scientists"], [79, "highlights-from-the-2016-2020-neubias-training-schools-for-bioimage-analysts-a-success-story-and-key-asset-for-analysts-and-life-scientists"], [90, "highlights-from-the-2016-2020-neubias-training-schools-for-bioimage-analysts-a-success-story-and-key-asset-for-analysts-and-life-scientists"], [98, "highlights-from-the-2016-2020-neubias-training-schools-for-bioimage-analysts-a-success-story-and-key-asset-for-analysts-and-life-scientists"]], "Hitchhiking through a diverse Bio-image Analysis Software Universe": [[29, "hitchhiking-through-a-diverse-bio-image-analysis-software-universe"], [54, "hitchhiking-through-a-diverse-bio-image-analysis-software-universe"], [64, "hitchhiking-through-a-diverse-bio-image-analysis-software-universe"], [65, "hitchhiking-through-a-diverse-bio-image-analysis-software-universe"], [79, "hitchhiking-through-a-diverse-bio-image-analysis-software-universe"], [90, "hitchhiking-through-a-diverse-bio-image-analysis-software-universe"]], "How to contribute": [[60, null]], "How to get started with Jupyter and Colab": [[56, "how-to-get-started-with-jupyter-and-colab"], [72, "how-to-get-started-with-jupyter-and-colab"]], "How to use this resource": [[87, "how-to-use-this-resource"]], "How to write a bug report": [[19, "how-to-write-a-bug-report"], [39, "how-to-write-a-bug-report"], [66, "how-to-write-a-bug-report"], [103, "how-to-write-a-bug-report"]], "Human DAB staining Axioscan BF 20x": [[64, "human-dab-staining-axioscan-bf-20x"], [73, "human-dab-staining-axioscan-bf-20x"], [79, "human-dab-staining-axioscan-bf-20x"]], "I2K 2024: clEsperanto - GPU-Accelerated Image Processing Library": [[29, "i2k-2024-clesperanto-gpu-accelerated-image-processing-library"], [47, "i2k-2024-clesperanto-gpu-accelerated-image-processing-library"], [48, "i2k-2024-clesperanto-gpu-accelerated-image-processing-library"], [55, "i2k-2024-clesperanto-gpu-accelerated-image-processing-library"], [58, "i2k-2024-clesperanto-gpu-accelerated-image-processing-library"], [68, "i2k-2024-clesperanto-gpu-accelerated-image-processing-library"], [78, "i2k-2024-clesperanto-gpu-accelerated-image-processing-library"], [90, "i2k-2024-clesperanto-gpu-accelerated-image-processing-library"]], "I2K2024 workshop material - Lazy Parallel Processing and Visualization of Large Data with ImgLib2, BigDataViewer, the N5-API, and Spark": [[47, "i2k2024-workshop-material-lazy-parallel-processing-and-visualization-of-large-data-with-imglib2-bigdataviewer-the-n5-api-and-spark"], [48, "i2k2024-workshop-material-lazy-parallel-processing-and-visualization-of-large-data-with-imglib2-bigdataviewer-the-n5-api-and-spark"], [58, "i2k2024-workshop-material-lazy-parallel-processing-and-visualization-of-large-data-with-imglib2-bigdataviewer-the-n5-api-and-spark"], [68, "i2k2024-workshop-material-lazy-parallel-processing-and-visualization-of-large-data-with-imglib2-bigdataviewer-the-n5-api-and-spark"], [90, "i2k2024-workshop-material-lazy-parallel-processing-and-visualization-of-large-data-with-imglib2-bigdataviewer-the-n5-api-and-spark"]], "I2K2024(virtual) - Bio-Image Analysis Code Generation": [[29, "i2k2024-virtual-bio-image-analysis-code-generation"], [47, "i2k2024-virtual-bio-image-analysis-code-generation"], [48, "i2k2024-virtual-bio-image-analysis-code-generation"], [55, "i2k2024-virtual-bio-image-analysis-code-generation"], [68, "i2k2024-virtual-bio-image-analysis-code-generation"], [78, "i2k2024-virtual-bio-image-analysis-code-generation"], [90, "i2k2024-virtual-bio-image-analysis-code-generation"]], "I3D bio \u2013 Information Infrastructure for BioImage Data - Bioimage Metadata": [[3, "i3d-bio-information-infrastructure-for-bioimage-data-bioimage-metadata"], [42, "i3d-bio-information-infrastructure-for-bioimage-data-bioimage-metadata"], [86, "i3d-bio-information-infrastructure-for-bioimage-data-bioimage-metadata"], [96, "i3d-bio-information-infrastructure-for-bioimage-data-bioimage-metadata"], [105, "i3d-bio-information-infrastructure-for-bioimage-data-bioimage-metadata"]], "I3D:bio list of online training material": [[42, "i3d-bio-list-of-online-training-material"], [86, "i3d-bio-list-of-online-training-material"], [105, "i3d-bio-list-of-online-training-material"]], "I3D:bio\u2019s OMERO training material: Re-usable, adjustable, multi-purpose slides for local user training": [[3, "i3d-bio-s-omero-training-material-re-usable-adjustable-multi-purpose-slides-for-local-user-training"], [8, "i3d-bio-s-omero-training-material-re-usable-adjustable-multi-purpose-slides-for-local-user-training"], [23, "i3d-bio-s-omero-training-material-re-usable-adjustable-multi-purpose-slides-for-local-user-training"], [33, "i3d-bio-s-omero-training-material-re-usable-adjustable-multi-purpose-slides-for-local-user-training"], [34, "i3d-bio-s-omero-training-material-re-usable-adjustable-multi-purpose-slides-for-local-user-training"], [35, "i3d-bio-s-omero-training-material-re-usable-adjustable-multi-purpose-slides-for-local-user-training"], [37, "i3d-bio-s-omero-training-material-re-usable-adjustable-multi-purpose-slides-for-local-user-training"], [54, "i3d-bio-s-omero-training-material-re-usable-adjustable-multi-purpose-slides-for-local-user-training"], [56, "i3d-bio-s-omero-training-material-re-usable-adjustable-multi-purpose-slides-for-local-user-training"], [64, "i3d-bio-s-omero-training-material-re-usable-adjustable-multi-purpose-slides-for-local-user-training"], [72, "i3d-bio-s-omero-training-material-re-usable-adjustable-multi-purpose-slides-for-local-user-training"], [73, "i3d-bio-s-omero-training-material-re-usable-adjustable-multi-purpose-slides-for-local-user-training"], [79, "i3d-bio-s-omero-training-material-re-usable-adjustable-multi-purpose-slides-for-local-user-training"], [99, "i3d-bio-s-omero-training-material-re-usable-adjustable-multi-purpose-slides-for-local-user-training"], [100, "i3d-bio-s-omero-training-material-re-usable-adjustable-multi-purpose-slides-for-local-user-training"], [105, "i3d-bio-s-omero-training-material-re-usable-adjustable-multi-purpose-slides-for-local-user-training"]], "IAFIG-RMS Python for Bioimage Analysis Course": [[48, "iafig-rms-python-for-bioimage-analysis-course"], [68, "iafig-rms-python-for-bioimage-analysis-course"], [83, "iafig-rms-python-for-bioimage-analysis-course"], [90, "iafig-rms-python-for-bioimage-analysis-course"]], "ICS/IDS stitched file": [[64, "ics-ids-stitched-file"], [73, "ics-ids-stitched-file"], [79, "ics-ids-stitched-file"]], "ITKElastix Examples": [[68, "itkelastix-examples"], [90, "itkelastix-examples"]], "Ibiology. Bioimage Analysis Course. The Life Cycle of an Image Data Set": [[42, "ibiology-bioimage-analysis-course-the-life-cycle-of-an-image-data-set"], [56, "ibiology-bioimage-analysis-course-the-life-cycle-of-an-image-data-set"], [90, "ibiology-bioimage-analysis-course-the-life-cycle-of-an-image-data-set"]], "If you license it, it\u2019ll be harder to steal it. Why we should license our work": [[29, "if-you-license-it-itll-be-harder-to-steal-it-why-we-should-license-our-work"], [39, "if-you-license-it-itll-be-harder-to-steal-it-why-we-should-license-our-work"], [66, "if-you-license-it-itll-be-harder-to-steal-it-why-we-should-license-our-work"], [79, "if-you-license-it-itll-be-harder-to-steal-it-why-we-should-license-our-work"], [95, "if-you-license-it-itll-be-harder-to-steal-it-why-we-should-license-our-work"], [105, "if-you-license-it-itll-be-harder-to-steal-it-why-we-should-license-our-work"]], "Image Analysis Training Resources": [[40, "image-analysis-training-resources"], [79, "image-analysis-training-resources"], [90, "image-analysis-training-resources"], [98, "image-analysis-training-resources"]], "Image Data Resources": [[42, "image-data-resources"], [43, "image-data-resources"], [52, "image-data-resources"], [71, "image-data-resources"]], "Image Data Services at Euro-BioImaging: Community efforts towards FAIR Image Data and Analysis Services": [[54, "image-data-services-at-euro-bioimaging-community-efforts-towards-fair-image-data-and-analysis-services"], [63, "image-data-services-at-euro-bioimaging-community-efforts-towards-fair-image-data-and-analysis-services"], [86, "image-data-services-at-euro-bioimaging-community-efforts-towards-fair-image-data-and-analysis-services"]], "Image Processing with Python": [[7, "image-processing-with-python"], [11, "image-processing-with-python"], [36, "image-processing-with-python"], [42, "image-processing-with-python"], [55, "image-processing-with-python"], [68, "image-processing-with-python"], [79, "image-processing-with-python"], [90, "image-processing-with-python"], [103, "image-processing-with-python"]], "Image Repository Decision Tree - Where do I deposit my imaging data": [[14, "image-repository-decision-tree-where-do-i-deposit-my-imaging-data"], [64, "image-repository-decision-tree-where-do-i-deposit-my-imaging-data"], [73, "image-repository-decision-tree-where-do-i-deposit-my-imaging-data"], [79, "image-repository-decision-tree-where-do-i-deposit-my-imaging-data"]], "Image analysis and visualization in Python with scikit-image, napari, and friends": [[48, "image-analysis-and-visualization-in-python-with-scikit-image-napari-and-friends"], [68, "image-analysis-and-visualization-in-python-with-scikit-image-napari-and-friends"], [78, "image-analysis-and-visualization-in-python-with-scikit-image-napari-and-friends"], [90, "image-analysis-and-visualization-in-python-with-scikit-image-napari-and-friends"], [103, "image-analysis-and-visualization-in-python-with-scikit-image-napari-and-friends"]], "Image analysis course material": [[4, "image-analysis-course-material"], [68, "image-analysis-course-material"], [84, "image-analysis-course-material"]], "Image analysis in Galaxy": [[1, "image-analysis-in-galaxy"], [54, "image-analysis-in-galaxy"], [63, "image-analysis-in-galaxy"], [86, "image-analysis-in-galaxy"], [90, "image-analysis-in-galaxy"]], "Image processing for beginners": [[13, "image-processing-for-beginners"], [48, "image-processing-for-beginners"], [68, "image-processing-for-beginners"], [78, "image-processing-for-beginners"], [90, "image-processing-for-beginners"], [103, "image-processing-for-beginners"]], "Image processing with Python": [[13, "image-processing-with-python"], [48, "image-processing-with-python"], [68, "id1"], [84, "image-processing-with-python"], [103, "id1"]], "Image-based Profiling Handbook": [[40, "image-based-profiling-handbook"], [68, "image-based-profiling-handbook"], [81, "image-based-profiling-handbook"], [90, "image-based-profiling-handbook"]], "ImageJ Macro Introduction": [[41, "imagej-macro-introduction"], [54, "imagej-macro-introduction"], [68, "imagej-macro-introduction"], [86, "imagej-macro-introduction"], [90, "imagej-macro-introduction"], [98, "imagej-macro-introduction"]], "ImageJ tool for percentage estimation of pneumonia in lungs": [[20, "imagej-tool-for-percentage-estimation-of-pneumonia-in-lungs"], [64, "imagej-tool-for-percentage-estimation-of-pneumonia-in-lungs"], [73, "imagej-tool-for-percentage-estimation-of-pneumonia-in-lungs"], [79, "imagej-tool-for-percentage-estimation-of-pneumonia-in-lungs"]], "ImageJ2 API-beating": [[29, "imagej2-api-beating"], [54, "imagej2-api-beating"], [67, "imagej2-api-beating"], [78, "imagej2-api-beating"], [90, "imagej2-api-beating"], [94, "imagej2-api-beating"], [98, "imagej2-api-beating"]], "Imagej (19)": [[94, null]], "Imaris Tutorials": [[42, "imaris-tutorials"], [56, "imaris-tutorials"], [76, "imaris-tutorials"], [90, "imaris-tutorials"]], "Implantation of abdominal imaging windows on the mouse kidney": [[22, "implantation-of-abdominal-imaging-windows-on-the-mouse-kidney"], [64, "implantation-of-abdominal-imaging-windows-on-the-mouse-kidney"], [73, "implantation-of-abdominal-imaging-windows-on-the-mouse-kidney"]], "Implantation of abdominal imaging windows on the mouse kidney - short version": [[22, "implantation-of-abdominal-imaging-windows-on-the-mouse-kidney-short-version"], [64, "implantation-of-abdominal-imaging-windows-on-the-mouse-kidney-short-version"], [73, "implantation-of-abdominal-imaging-windows-on-the-mouse-kidney-short-version"]], "Implantation of abdominal imaging windows on the mouse liver": [[22, "implantation-of-abdominal-imaging-windows-on-the-mouse-liver"], [64, "implantation-of-abdominal-imaging-windows-on-the-mouse-liver"], [73, "implantation-of-abdominal-imaging-windows-on-the-mouse-liver"]], "Implantation of abdominal imaging windows on the mouse liver - short version": [[22, "implantation-of-abdominal-imaging-windows-on-the-mouse-liver-short-version"], [64, "implantation-of-abdominal-imaging-windows-on-the-mouse-liver-short-version"], [73, "implantation-of-abdominal-imaging-windows-on-the-mouse-liver-short-version"]], "Impressum": [[75, "impressum"]], "Imprint": [[75, null]], "Inclusion criteria": [[60, "inclusion-criteria"]], "Ink in a dish": [[64, "ink-in-a-dish"], [73, "ink-in-a-dish"], [81, "ink-in-a-dish"]], "Insights and Impact From Five Cycles of Essential Open Source Software for Science": [[52, "insights-and-impact-from-five-cycles-of-essential-open-source-software-for-science"], [73, "insights-and-impact-from-five-cycles-of-essential-open-source-software-for-science"], [79, "insights-and-impact-from-five-cycles-of-essential-open-source-software-for-science"], [102, "insights-and-impact-from-five-cycles-of-essential-open-source-software-for-science"]], "Insights from Acquiring Open Medical Imaging Datasets for Foundation Model Development": [[64, "insights-from-acquiring-open-medical-imaging-datasets-for-foundation-model-development"], [73, "insights-from-acquiring-open-medical-imaging-datasets-for-foundation-model-development"], [79, "insights-from-acquiring-open-medical-imaging-datasets-for-foundation-model-development"]], "Insights from Acquiring Open Medical Imaging Datasets for Foundation Model Development": [[64, "id1"], [73, "id1"], [79, "id1"]], "Institutionalization and Collaboration as a Way of Addressing the Challenges Open Science Presents to Libraries: The University of Konstanz as a National Pioneer": [[64, "institutionalization-and-collaboration-as-a-way-of-addressing-the-challenges-open-science-presents-to-libraries-the-university-of-konstanz-as-a-national-pioneer"], [73, "institutionalization-and-collaboration-as-a-way-of-addressing-the-challenges-open-science-presents-to-libraries-the-university-of-konstanz-as-a-national-pioneer"], [79, "institutionalization-and-collaboration-as-a-way-of-addressing-the-challenges-open-science-presents-to-libraries-the-university-of-konstanz-as-a-national-pioneer"]], "Interactive Image Data Flow Graphs": [[20, "interactive-image-data-flow-graphs"], [64, "interactive-image-data-flow-graphs"], [73, "interactive-image-data-flow-graphs"], [79, "interactive-image-data-flow-graphs"]], "Intravital microscopy contrasting agents for application - Database": [[22, "intravital-microscopy-contrasting-agents-for-application-database"], [64, "intravital-microscopy-contrasting-agents-for-application-database"], [73, "intravital-microscopy-contrasting-agents-for-application-database"], [79, "intravital-microscopy-contrasting-agents-for-application-database"]], "Intro napari slides": [[54, "intro-napari-slides"], [84, "intro-napari-slides"], [97, "intro-napari-slides"]], "Introduction to Bioimage Analysis": [[40, "introduction-to-bioimage-analysis"], [48, "introduction-to-bioimage-analysis"], [79, "introduction-to-bioimage-analysis"], [90, "introduction-to-bioimage-analysis"], [94, "introduction-to-bioimage-analysis"], [103, "introduction-to-bioimage-analysis"]], "Introduction to Deep Learning for Microscopy": [[48, "introduction-to-deep-learning-for-microscopy"], [68, "introduction-to-deep-learning-for-microscopy"], [84, "introduction-to-deep-learning-for-microscopy"], [89, "introduction-to-deep-learning-for-microscopy"], [103, "introduction-to-deep-learning-for-microscopy"]], "Introduction to Image Analysis with Fiji": [[29, "introduction-to-image-analysis-with-fiji"], [68, "introduction-to-image-analysis-with-fiji"], [90, "introduction-to-image-analysis-with-fiji"], [94, "introduction-to-image-analysis-with-fiji"]], "Introduction to ImageJ macro programming, Scientific Computing Facility, MPI CBG Dresden": [[29, "introduction-to-imagej-macro-programming-scientific-computing-facility-mpi-cbg-dresden"], [54, "introduction-to-imagej-macro-programming-scientific-computing-facility-mpi-cbg-dresden"], [67, "introduction-to-imagej-macro-programming-scientific-computing-facility-mpi-cbg-dresden"], [86, "introduction-to-imagej-macro-programming-scientific-computing-facility-mpi-cbg-dresden"], [90, "introduction-to-imagej-macro-programming-scientific-computing-facility-mpi-cbg-dresden"], [94, "introduction-to-imagej-macro-programming-scientific-computing-facility-mpi-cbg-dresden"]], "Introduction to Research Data Management and Open Research": [[54, "introduction-to-research-data-management-and-open-research"], [73, "introduction-to-research-data-management-and-open-research"], [79, "introduction-to-research-data-management-and-open-research"], [101, "introduction-to-research-data-management-and-open-research"], [105, "introduction-to-research-data-management-and-open-research"]], "Introduction to light-microscopy / Widefield microscopy": [[64, "introduction-to-light-microscopy-widefield-microscopy"], [73, "introduction-to-light-microscopy-widefield-microscopy"]], "Isabel kemmer (4)": [[14, null]], "JIPipe: visual batch processing for ImageJ": [[45, "jipipe-visual-batch-processing-for-imagej"], [52, "jipipe-visual-batch-processing-for-imagej"], [71, "jipipe-visual-batch-processing-for-imagej"], [94, "jipipe-visual-batch-processing-for-imagej"], [108, "jipipe-visual-batch-processing-for-imagej"]], "Jean-marie burel (6)": [[15, null]], "Jens wendt (7)": [[16, null]], "Josh moore (27)": [[17, null]], "Jupyter for interactive cloud computing": [[13, "jupyter-for-interactive-cloud-computing"], [54, "jupyter-for-interactive-cloud-computing"], [63, "jupyter-for-interactive-cloud-computing"], [86, "jupyter-for-interactive-cloud-computing"], [90, "jupyter-for-interactive-cloud-computing"], [98, "jupyter-for-interactive-cloud-computing"]], "KNIME Image Processing": [[45, "knime-image-processing"], [49, "knime-image-processing"], [55, "knime-image-processing"], [83, "knime-image-processing"], [94, "knime-image-processing"], [100, "knime-image-processing"], [107, "knime-image-processing"]], "Key-Value pair template for annotation in OMERO for light microscopy data acquired with AxioScan7 - Core Facility Cellular Imaging (CFCI)": [[32, "key-value-pair-template-for-annotation-in-omero-for-light-microscopy-data-acquired-with-axioscan7-core-facility-cellular-imaging-cfci"], [37, "key-value-pair-template-for-annotation-in-omero-for-light-microscopy-data-acquired-with-axioscan7-core-facility-cellular-imaging-cfci"], [64, "key-value-pair-template-for-annotation-in-omero-for-light-microscopy-data-acquired-with-axioscan7-core-facility-cellular-imaging-cfci"], [73, "key-value-pair-template-for-annotation-in-omero-for-light-microscopy-data-acquired-with-axioscan7-core-facility-cellular-imaging-cfci"], [79, "key-value-pair-template-for-annotation-in-omero-for-light-microscopy-data-acquired-with-axioscan7-core-facility-cellular-imaging-cfci"], [99, "key-value-pair-template-for-annotation-in-omero-for-light-microscopy-data-acquired-with-axioscan7-core-facility-cellular-imaging-cfci"], [105, "key-value-pair-template-for-annotation-in-omero-for-light-microscopy-data-acquired-with-axioscan7-core-facility-cellular-imaging-cfci"]], "Key-Value pair template for annotation of datasets in OMERO (PERIKLES study)": [[3, "key-value-pair-template-for-annotation-of-datasets-in-omero-perikles-study"], [23, "key-value-pair-template-for-annotation-of-datasets-in-omero-perikles-study"], [32, "key-value-pair-template-for-annotation-of-datasets-in-omero-perikles-study"], [37, "key-value-pair-template-for-annotation-of-datasets-in-omero-perikles-study"], [64, "key-value-pair-template-for-annotation-of-datasets-in-omero-perikles-study"], [73, "key-value-pair-template-for-annotation-of-datasets-in-omero-perikles-study"], [79, "key-value-pair-template-for-annotation-of-datasets-in-omero-perikles-study"], [99, "key-value-pair-template-for-annotation-of-datasets-in-omero-perikles-study"], [105, "key-value-pair-template-for-annotation-of-datasets-in-omero-perikles-study"]], "Key-Value pair template for annotation of datasets in OMERO for light- and electron microscopy data within the research group of Prof. M\u00fcller-Reichert": [[3, "key-value-pair-template-for-annotation-of-datasets-in-omero-for-light-and-electron-microscopy-data-within-the-research-group-of-prof-muller-reichert"], [22, "key-value-pair-template-for-annotation-of-datasets-in-omero-for-light-and-electron-microscopy-data-within-the-research-group-of-prof-muller-reichert"], [23, "key-value-pair-template-for-annotation-of-datasets-in-omero-for-light-and-electron-microscopy-data-within-the-research-group-of-prof-muller-reichert"], [32, "key-value-pair-template-for-annotation-of-datasets-in-omero-for-light-and-electron-microscopy-data-within-the-research-group-of-prof-muller-reichert"], [37, "key-value-pair-template-for-annotation-of-datasets-in-omero-for-light-and-electron-microscopy-data-within-the-research-group-of-prof-muller-reichert"], [64, "key-value-pair-template-for-annotation-of-datasets-in-omero-for-light-and-electron-microscopy-data-within-the-research-group-of-prof-muller-reichert"], [73, "key-value-pair-template-for-annotation-of-datasets-in-omero-for-light-and-electron-microscopy-data-within-the-research-group-of-prof-muller-reichert"], [79, "key-value-pair-template-for-annotation-of-datasets-in-omero-for-light-and-electron-microscopy-data-within-the-research-group-of-prof-muller-reichert"]], "Key-Value pairs scripts": [[42, "key-value-pairs-scripts"], [45, "key-value-pairs-scripts"], [86, "key-value-pairs-scripts"], [100, "key-value-pairs-scripts"]], "Kollaboratives Arbeiten und Versionskontrolle mit Git": [[29, "kollaboratives-arbeiten-und-versionskontrolle-mit-git"], [54, "kollaboratives-arbeiten-und-versionskontrolle-mit-git"], [64, "kollaboratives-arbeiten-und-versionskontrolle-mit-git"], [73, "kollaboratives-arbeiten-und-versionskontrolle-mit-git"], [79, "kollaboratives-arbeiten-und-versionskontrolle-mit-git"], [93, "kollaboratives-arbeiten-und-versionskontrolle-mit-git"], [105, "kollaboratives-arbeiten-und-versionskontrolle-mit-git"]], "Kontakt": [[75, "kontakt"]], "Kota miura (11)": [[18, null]], "Kreshuk Lab\u2019s EMBL EIPP predoc course teaching material": [[55, "kreshuk-lab-s-embl-eipp-predoc-course-teaching-material"], [68, "kreshuk-lab-s-embl-eipp-predoc-course-teaching-material"], [86, "kreshuk-lab-s-embl-eipp-predoc-course-teaching-material"], [89, "kreshuk-lab-s-embl-eipp-predoc-course-teaching-material"]], "LEO: Linking ELN with OMERO": [[64, "leo-linking-eln-with-omero"], [73, "leo-linking-eln-with-omero"], [79, "leo-linking-eln-with-omero"]], "LSM example J. Dubrulle": [[64, "lsm-example-j-dubrulle"], [73, "lsm-example-j-dubrulle"], [79, "lsm-example-j-dubrulle"], [109, "lsm-example-j-dubrulle"]], "LZ4-compressed Imaris ims example datasets.": [[64, "lz4-compressed-imaris-ims-example-datasets"], [73, "lz4-compressed-imaris-ims-example-datasets"], [79, "lz4-compressed-imaris-ims-example-datasets"]], "Large Language Models: An Introduction for Life Scientists": [[29, "large-language-models-an-introduction-for-life-scientists"], [64, "large-language-models-an-introduction-for-life-scientists"], [73, "large-language-models-an-introduction-for-life-scientists"], [79, "large-language-models-an-introduction-for-life-scientists"], [89, "large-language-models-an-introduction-for-life-scientists"]], "Large tiling confocal acquisition (rat brain)": [[64, "large-tiling-confocal-acquisition-rat-brain"], [73, "large-tiling-confocal-acquisition-rat-brain"], [79, "large-tiling-confocal-acquisition-rat-brain"]], "LauLauThom/MaskFromRois-Fiji: Masks from ROIs plugins for Fiji - initial release": [[64, "laulauthom-maskfromrois-fiji-masks-from-rois-plugins-for-fiji-initial-release"], [73, "laulauthom-maskfromrois-fiji-masks-from-rois-plugins-for-fiji-initial-release"], [84, "laulauthom-maskfromrois-fiji-masks-from-rois-plugins-for-fiji-initial-release"]], "Lecture Applied Bioimage Analysis 2020": [[29, "lecture-applied-bioimage-analysis-2020"], [54, "lecture-applied-bioimage-analysis-2020"], [67, "lecture-applied-bioimage-analysis-2020"], [90, "lecture-applied-bioimage-analysis-2020"], [94, "lecture-applied-bioimage-analysis-2020"]], "Lecture-materials of the DeepLife course": [[20, "lecture-materials-of-the-deeplife-course"], [47, "lecture-materials-of-the-deeplife-course"], [48, "lecture-materials-of-the-deeplife-course"], [54, "lecture-materials-of-the-deeplife-course"], [68, "lecture-materials-of-the-deeplife-course"], [86, "lecture-materials-of-the-deeplife-course"], [91, "lecture-materials-of-the-deeplife-course"]], "Leitfaden zur digitalen Datensparsamkeit (mit Praxisbeispielen)": [[64, "leitfaden-zur-digitalen-datensparsamkeit-mit-praxisbeispielen"], [73, "leitfaden-zur-digitalen-datensparsamkeit-mit-praxisbeispielen"], [79, "leitfaden-zur-digitalen-datensparsamkeit-mit-praxisbeispielen"]], "Leitlinie? Grunds\u00e4tze? Policy? Richtlinie? \u2013 Forschungsdaten-Policies an deutschen Universit\u00e4ten": [[52, "leitlinie-grundsatze-policy-richtlinie-forschungsdaten-policies-an-deutschen-universitaten"], [79, "leitlinie-grundsatze-policy-richtlinie-forschungsdaten-policies-an-deutschen-universitaten"], [93, "leitlinie-grundsatze-policy-richtlinie-forschungsdaten-policies-an-deutschen-universitaten"], [105, "leitlinie-grundsatze-policy-richtlinie-forschungsdaten-policies-an-deutschen-universitaten"]], "License": [[87, "license"]], "Licensing (6)": [[95, null]], "LimeSeg Test Datasets": [[24, "limeseg-test-datasets"], [64, "limeseg-test-datasets"], [73, "limeseg-test-datasets"], [79, "limeseg-test-datasets"]], "Linked (Open) Data for Microbial Population Biology": [[64, "linked-open-data-for-microbial-population-biology"], [73, "linked-open-data-for-microbial-population-biology"], [79, "linked-open-data-for-microbial-population-biology"]], "Liver Micrometastases area quantification using QuPath and pixel classifier": [[25, "liver-micrometastases-area-quantification-using-qupath-and-pixel-classifier"], [31, "liver-micrometastases-area-quantification-using-qupath-and-pixel-classifier"], [64, "liver-micrometastases-area-quantification-using-qupath-and-pixel-classifier"], [73, "liver-micrometastases-area-quantification-using-qupath-and-pixel-classifier"], [79, "liver-micrometastases-area-quantification-using-qupath-and-pixel-classifier"]], "Lund Declaration on Maximising the Benefits of Research Data": [[44, "lund-declaration-on-maximising-the-benefits-of-research-data"], [105, "lund-declaration-on-maximising-the-benefits-of-research-data"]], "MDEmic: a metadata annotation tool to facilitate management of FAIR image data in the bioimaging community": [[3, "mdemic-a-metadata-annotation-tool-to-facilitate-management-of-fair-image-data-in-the-bioimaging-community"], [33, "mdemic-a-metadata-annotation-tool-to-facilitate-management-of-fair-image-data-in-the-bioimaging-community"], [34, "mdemic-a-metadata-annotation-tool-to-facilitate-management-of-fair-image-data-in-the-bioimaging-community"], [52, "mdemic-a-metadata-annotation-tool-to-facilitate-management-of-fair-image-data-in-the-bioimaging-community"], [71, "mdemic-a-metadata-annotation-tool-to-facilitate-management-of-fair-image-data-in-the-bioimaging-community"], [76, "mdemic-a-metadata-annotation-tool-to-facilitate-management-of-fair-image-data-in-the-bioimaging-community"], [96, "mdemic-a-metadata-annotation-tool-to-facilitate-management-of-fair-image-data-in-the-bioimaging-community"], [105, "mdemic-a-metadata-annotation-tool-to-facilitate-management-of-fair-image-data-in-the-bioimaging-community"]], "Machine Learning - Deep Learning. Applications to Bioimage Analysis": [[10, "machine-learning-deep-learning-applications-to-bioimage-analysis"], [54, "machine-learning-deep-learning-applications-to-bioimage-analysis"], [86, "machine-learning-deep-learning-applications-to-bioimage-analysis"], [89, "machine-learning-deep-learning-applications-to-bioimage-analysis"], [90, "machine-learning-deep-learning-applications-to-bioimage-analysis"]], "Machine Learning Workflow for Imaging Flow Cytometry (IFC)": [[55, "machine-learning-workflow-for-imaging-flow-cytometry-ifc"], [68, "machine-learning-workflow-for-imaging-flow-cytometry-ifc"], [86, "machine-learning-workflow-for-imaging-flow-cytometry-ifc"], [90, "machine-learning-workflow-for-imaging-flow-cytometry-ifc"]], "Machine and Deep Learning on the cloud: Segmentation": [[54, "machine-and-deep-learning-on-the-cloud-segmentation"], [63, "machine-and-deep-learning-on-the-cloud-segmentation"], [86, "machine-and-deep-learning-on-the-cloud-segmentation"], [89, "machine-and-deep-learning-on-the-cloud-segmentation"], [90, "machine-and-deep-learning-on-the-cloud-segmentation"], [98, "machine-and-deep-learning-on-the-cloud-segmentation"]], "Maintenance of contributions": [[60, "maintenance-of-contributions"]], "Making the most of bioimaging data through interdisciplinary interactions": [[17, "making-the-most-of-bioimaging-data-through-interdisciplinary-interactions"], [52, "making-the-most-of-bioimaging-data-through-interdisciplinary-interactions"], [79, "making-the-most-of-bioimaging-data-through-interdisciplinary-interactions"], [90, "making-the-most-of-bioimaging-data-through-interdisciplinary-interactions"], [101, "making-the-most-of-bioimaging-data-through-interdisciplinary-interactions"]], "Making your package available on conda-forge": [[45, "making-your-package-available-on-conda-forge"], [79, "making-your-package-available-on-conda-forge"], [103, "making-your-package-available-on-conda-forge"]], "Making your project citable": [[55, "making-your-project-citable"], [105, "making-your-project-citable"], [106, "making-your-project-citable"]], "Managing Scientific Python environments using Conda, Mamba and friends": [[29, "managing-scientific-python-environments-using-conda-mamba-and-friends"], [39, "managing-scientific-python-environments-using-conda-mamba-and-friends"], [66, "managing-scientific-python-environments-using-conda-mamba-and-friends"], [79, "managing-scientific-python-environments-using-conda-mamba-and-friends"], [103, "managing-scientific-python-environments-using-conda-mamba-and-friends"]], "Mara lampert (8)": [[19, null]], "Martin sch\u00e4tz (6)": [[20, null]], "Martin weigert (8)": [[21, null]], "Materials for EMBL Coding Club Mini-Tutorials": [[11, "materials-for-embl-coding-club-mini-tutorials"], [41, "materials-for-embl-coding-club-mini-tutorials"], [48, "materials-for-embl-coding-club-mini-tutorials"], [68, "materials-for-embl-coding-club-mini-tutorials"], [86, "materials-for-embl-coding-club-mini-tutorials"], [103, "materials-for-embl-coding-club-mini-tutorials"]], "Measuring reporter activity domain in EPI aggregates and Gastruloids.ijm": [[25, "measuring-reporter-activity-domain-in-epi-aggregates-and-gastruloids-ijm"], [31, "measuring-reporter-activity-domain-in-epi-aggregates-and-gastruloids-ijm"], [64, "measuring-reporter-activity-domain-in-epi-aggregates-and-gastruloids-ijm"], [73, "measuring-reporter-activity-domain-in-epi-aggregates-and-gastruloids-ijm"], [79, "measuring-reporter-activity-domain-in-epi-aggregates-and-gastruloids-ijm"]], "Meeting in the Middle: Towards Successful Multidisciplinary Bioimage Analysis Collaboration": [[29, "meeting-in-the-middle-towards-successful-multidisciplinary-bioimage-analysis-collaboration"], [52, "meeting-in-the-middle-towards-successful-multidisciplinary-bioimage-analysis-collaboration"], [79, "meeting-in-the-middle-towards-successful-multidisciplinary-bioimage-analysis-collaboration"], [90, "meeting-in-the-middle-towards-successful-multidisciplinary-bioimage-analysis-collaboration"]], "Melbourne Advanced Microscopy Facility": [[42, "melbourne-advanced-microscopy-facility"], [56, "melbourne-advanced-microscopy-facility"], [72, "melbourne-advanced-microscopy-facility"], [90, "melbourne-advanced-microscopy-facility"]], "Metabolic networks modelling with COBRApy": [[48, "metabolic-networks-modelling-with-cobrapy"], [68, "metabolic-networks-modelling-with-cobrapy"], [86, "metabolic-networks-modelling-with-cobrapy"], [103, "metabolic-networks-modelling-with-cobrapy"]], "Metadata (15)": [[96, null]], "Metadata Annotation Workflow for OMERO with Tabbles": [[64, "metadata-annotation-workflow-for-omero-with-tabbles"], [73, "metadata-annotation-workflow-for-omero-with-tabbles"], [79, "metadata-annotation-workflow-for-omero-with-tabbles"]], "Metadata matters: access to image data in the real world": [[11, "metadata-matters-access-to-image-data-in-the-real-world"], [52, "metadata-matters-access-to-image-data-in-the-real-world"], [96, "metadata-matters-access-to-image-data-in-the-real-world"]], "Methods in bioimage analysis": [[4, "methods-in-bioimage-analysis"], [49, "methods-in-bioimage-analysis"], [54, "methods-in-bioimage-analysis"], [56, "methods-in-bioimage-analysis"], [64, "methods-in-bioimage-analysis"], [70, "methods-in-bioimage-analysis"], [79, "methods-in-bioimage-analysis"], [90, "methods-in-bioimage-analysis"]], "MethodsJ2: a software tool to capture metadata and generate comprehensive microscopy methods text": [[52, "methodsj2-a-software-tool-to-capture-metadata-and-generate-comprehensive-microscopy-methods-text"], [71, "methodsj2-a-software-tool-to-capture-metadata-and-generate-comprehensive-microscopy-methods-text"], [96, "methodsj2-a-software-tool-to-capture-metadata-and-generate-comprehensive-microscopy-methods-text"]], "Metrics Reloaded - A framework for trustworthy image analysis validation": [[42, "metrics-reloaded-a-framework-for-trustworthy-image-analysis-validation"], [57, "metrics-reloaded-a-framework-for-trustworthy-image-analysis-validation"], [86, "metrics-reloaded-a-framework-for-trustworthy-image-analysis-validation"], [90, "metrics-reloaded-a-framework-for-trustworthy-image-analysis-validation"]], "MiToBo - A Toolbox for Image Processing and Analysis": [[45, "mitobo-a-toolbox-for-image-processing-and-analysis"], [52, "mitobo-a-toolbox-for-image-processing-and-analysis"], [94, "mitobo-a-toolbox-for-image-processing-and-analysis"], [108, "mitobo-a-toolbox-for-image-processing-and-analysis"]], "Michael gerlach (12)": [[22, null]], "Michele bortolomeazzi (9)": [[23, null]], "Micro-Meta App: an interactive tool for collecting microscopy metadata based on community specifications": [[11, "micro-meta-app-an-interactive-tool-for-collecting-microscopy-metadata-based-on-community-specifications"], [52, "micro-meta-app-an-interactive-tool-for-collecting-microscopy-metadata-based-on-community-specifications"], [64, "micro-meta-app-an-interactive-tool-for-collecting-microscopy-metadata-based-on-community-specifications"], [96, "micro-meta-app-an-interactive-tool-for-collecting-microscopy-metadata-based-on-community-specifications"]], "MicroSam-Talks": [[5, "microsam-talks"], [54, "microsam-talks"], [64, "microsam-talks"], [73, "microsam-talks"], [79, "microsam-talks"], [89, "microsam-talks"], [90, "microsam-talks"]], "Microscopy data analysis: machine learning and the BioImage Archive": [[7, "microscopy-data-analysis-machine-learning-and-the-bioimage-archive"], [21, "microscopy-data-analysis-machine-learning-and-the-bioimage-archive"], [38, "microscopy-data-analysis-machine-learning-and-the-bioimage-archive"], [54, "microscopy-data-analysis-machine-learning-and-the-bioimage-archive"], [56, "microscopy-data-analysis-machine-learning-and-the-bioimage-archive"], [70, "microscopy-data-analysis-machine-learning-and-the-bioimage-archive"], [79, "microscopy-data-analysis-machine-learning-and-the-bioimage-archive"], [89, "microscopy-data-analysis-machine-learning-and-the-bioimage-archive"], [90, "microscopy-data-analysis-machine-learning-and-the-bioimage-archive"], [103, "microscopy-data-analysis-machine-learning-and-the-bioimage-archive"]], "Microscopy-BIDS - An Extension to the Brain Imaging Data Structure for Microscopy Data": [[11, "microscopy-bids-an-extension-to-the-brain-imaging-data-structure-for-microscopy-data"], [52, "microscopy-bids-an-extension-to-the-brain-imaging-data-structure-for-microscopy-data"], [79, "microscopy-bids-an-extension-to-the-brain-imaging-data-structure-for-microscopy-data"], [105, "microscopy-bids-an-extension-to-the-brain-imaging-data-structure-for-microscopy-data"]], "MicroscopyDB": [[42, "microscopydb"], [76, "microscopydb"]], "Mit (24)": [[84, null]], "Modeling community standards for metadata as templates makes data FAIR": [[52, "modeling-community-standards-for-metadata-as-templates-makes-data-fair"], [71, "modeling-community-standards-for-metadata-as-templates-makes-data-fair"], [79, "modeling-community-standards-for-metadata-as-templates-makes-data-fair"], [92, "modeling-community-standards-for-metadata-as-templates-makes-data-fair"], [93, "modeling-community-standards-for-metadata-as-templates-makes-data-fair"], [96, "modeling-community-standards-for-metadata-as-templates-makes-data-fair"]], "Models and Applications for BioImage.IO": [[11, "models-and-applications-for-bioimage-io"], [68, "models-and-applications-for-bioimage-io"], [86, "models-and-applications-for-bioimage-io"]], "Modular training resources for bioimage analysis": [[4, "modular-training-resources-for-bioimage-analysis"], [7, "modular-training-resources-for-bioimage-analysis"], [9, "modular-training-resources-for-bioimage-analysis"], [36, "modular-training-resources-for-bioimage-analysis"], [64, "modular-training-resources-for-bioimage-analysis"], [73, "modular-training-resources-for-bioimage-analysis"], [79, "modular-training-resources-for-bioimage-analysis"], [90, "modular-training-resources-for-bioimage-analysis"], [98, "modular-training-resources-for-bioimage-analysis"], [109, "modular-training-resources-for-bioimage-analysis"]], "ModularImageAnalysis (MIA): Assembly of modularisedimage and object analysis workflows in ImageJ": [[45, "modularimageanalysis-mia-assembly-of-modularisedimage-and-object-analysis-workflows-in-imagej"], [52, "modularimageanalysis-mia-assembly-of-modularisedimage-and-object-analysis-workflows-in-imagej"], [64, "modularimageanalysis-mia-assembly-of-modularisedimage-and-object-analysis-workflows-in-imagej"], [94, "modularimageanalysis-mia-assembly-of-modularisedimage-and-object-analysis-workflows-in-imagej"], [108, "modularimageanalysis-mia-assembly-of-modularisedimage-and-object-analysis-workflows-in-imagej"]], "MorphoLibJ documentation": [[44, "morpholibj-documentation"], [90, "morpholibj-documentation"]], "Morphological analysis of neural cells with WEKA and SNT Fiji plugins": [[64, "morphological-analysis-of-neural-cells-with-weka-and-snt-fiji-plugins"], [73, "morphological-analysis-of-neural-cells-with-weka-and-snt-fiji-plugins"], [79, "morphological-analysis-of-neural-cells-with-weka-and-snt-fiji-plugins"]], "Most downloaded training materials in the last week": [[87, "most-downloaded-training-materials-in-the-last-week"]], "Multi-Template-Matching for object-detection (slides)": [[64, "multi-template-matching-for-object-detection-slides"], [73, "multi-template-matching-for-object-detection-slides"], [79, "multi-template-matching-for-object-detection-slides"]], "Multi-view fusion": [[29, "multi-view-fusion"], [54, "multi-view-fusion"], [67, "multi-view-fusion"], [78, "multi-view-fusion"], [90, "multi-view-fusion"], [94, "multi-view-fusion"], [98, "multi-view-fusion"]], "Multimodal large language models for bioimage analysis": [[52, "multimodal-large-language-models-for-bioimage-analysis"], [71, "multimodal-large-language-models-for-bioimage-analysis"], [90, "multimodal-large-language-models-for-bioimage-analysis"], [93, "multimodal-large-language-models-for-bioimage-analysis"], [107, "multimodal-large-language-models-for-bioimage-analysis"]], "Multiplexed histology of COVID-19 post-mortem lung samples - CONTROL CASE 1 FOV1": [[64, "multiplexed-histology-of-covid-19-post-mortem-lung-samples-control-case-1-fov1"], [73, "multiplexed-histology-of-covid-19-post-mortem-lung-samples-control-case-1-fov1"], [79, "multiplexed-histology-of-covid-19-post-mortem-lung-samples-control-case-1-fov1"]], "Multiplexed tissue imaging - tools and approaches": [[47, "multiplexed-tissue-imaging-tools-and-approaches"], [54, "multiplexed-tissue-imaging-tools-and-approaches"], [58, "multiplexed-tissue-imaging-tools-and-approaches"], [63, "multiplexed-tissue-imaging-tools-and-approaches"], [68, "multiplexed-tissue-imaging-tools-and-approaches"], [79, "multiplexed-tissue-imaging-tools-and-approaches"], [90, "multiplexed-tissue-imaging-tools-and-approaches"]], "My Journey Through Bioimage Analysis Teaching Methods From Classroom to Cloud": [[9, "my-journey-through-bioimage-analysis-teaching-methods-from-classroom-to-cloud"], [54, "my-journey-through-bioimage-analysis-teaching-methods-from-classroom-to-cloud"], [64, "my-journey-through-bioimage-analysis-teaching-methods-from-classroom-to-cloud"], [73, "my-journey-through-bioimage-analysis-teaching-methods-from-classroom-to-cloud"], [79, "my-journey-through-bioimage-analysis-teaching-methods-from-classroom-to-cloud"]], "NEUBIAS Academy @HOME: Interactive Bioimage Analysis with Python and Jupyter": [[48, "neubias-academy-home-interactive-bioimage-analysis-with-python-and-jupyter"], [68, "neubias-academy-home-interactive-bioimage-analysis-with-python-and-jupyter"], [78, "neubias-academy-home-interactive-bioimage-analysis-with-python-and-jupyter"], [90, "neubias-academy-home-interactive-bioimage-analysis-with-python-and-jupyter"], [98, "neubias-academy-home-interactive-bioimage-analysis-with-python-and-jupyter"], [103, "neubias-academy-home-interactive-bioimage-analysis-with-python-and-jupyter"]], "NEUBIAS Analyst School 2018": [[41, "neubias-analyst-school-2018"], [48, "neubias-analyst-school-2018"], [54, "neubias-analyst-school-2018"], [68, "neubias-analyst-school-2018"], [86, "neubias-analyst-school-2018"], [90, "neubias-analyst-school-2018"], [98, "neubias-analyst-school-2018"]], "NEUBIAS Bioimage Analyst Course 2017": [[11, "neubias-bioimage-analyst-course-2017"], [18, "neubias-bioimage-analyst-course-2017"], [54, "neubias-bioimage-analyst-course-2017"], [55, "neubias-bioimage-analyst-course-2017"], [68, "neubias-bioimage-analyst-course-2017"], [86, "neubias-bioimage-analyst-course-2017"], [90, "neubias-bioimage-analyst-course-2017"], [98, "neubias-bioimage-analyst-course-2017"]], "NEUBIAS Bioimage Analyst School 2019": [[11, "neubias-bioimage-analyst-school-2019"], [18, "neubias-bioimage-analyst-school-2019"], [29, "neubias-bioimage-analyst-school-2019"], [41, "neubias-bioimage-analyst-school-2019"], [48, "neubias-bioimage-analyst-school-2019"], [54, "neubias-bioimage-analyst-school-2019"], [68, "neubias-bioimage-analyst-school-2019"], [86, "neubias-bioimage-analyst-school-2019"], [90, "neubias-bioimage-analyst-school-2019"], [98, "neubias-bioimage-analyst-school-2019"]], "NEUBIAS Bioimage Analyst School 2020": [[10, "neubias-bioimage-analyst-school-2020"], [11, "neubias-bioimage-analyst-school-2020"], [18, "neubias-bioimage-analyst-school-2020"], [41, "neubias-bioimage-analyst-school-2020"], [48, "neubias-bioimage-analyst-school-2020"], [54, "neubias-bioimage-analyst-school-2020"], [68, "neubias-bioimage-analyst-school-2020"], [86, "neubias-bioimage-analyst-school-2020"], [90, "neubias-bioimage-analyst-school-2020"], [98, "neubias-bioimage-analyst-school-2020"]], "NEUBIAS YouTube Channel": [[42, "neubias-youtube-channel"], [56, "neubias-youtube-channel"], [72, "neubias-youtube-channel"], [98, "neubias-youtube-channel"]], "NFDI - Daten als gemeinsames Gut f\u00fcr exzellente Forschung, organisiert durch die Wissenschaft in Deutschland.": [[57, "nfdi-daten-als-gemeinsames-gut-fur-exzellente-forschung-organisiert-durch-die-wissenschaft-in-deutschland"], [86, "nfdi-daten-als-gemeinsames-gut-fur-exzellente-forschung-organisiert-durch-die-wissenschaft-in-deutschland"], [99, "nfdi-daten-als-gemeinsames-gut-fur-exzellente-forschung-organisiert-durch-die-wissenschaft-in-deutschland"], [105, "nfdi-daten-als-gemeinsames-gut-fur-exzellente-forschung-organisiert-durch-die-wissenschaft-in-deutschland"]], "NFDI4BIOIMAGE": [[54, "nfdi4bioimage"], [64, "nfdi4bioimage"], [73, "nfdi4bioimage"], [79, "nfdi4bioimage"], [90, "nfdi4bioimage"], [93, "nfdi4bioimage"], [99, "nfdi4bioimage"], [105, "nfdi4bioimage"]], "NFDI4BIOIMAGE - An Initiative for a National Research Data Infrastructure for Microscopy Data": [[3, "nfdi4bioimage-an-initiative-for-a-national-research-data-infrastructure-for-microscopy-data"], [8, "nfdi4bioimage-an-initiative-for-a-national-research-data-infrastructure-for-microscopy-data"], [50, "nfdi4bioimage-an-initiative-for-a-national-research-data-infrastructure-for-microscopy-data"], [52, "nfdi4bioimage-an-initiative-for-a-national-research-data-infrastructure-for-microscopy-data"], [54, "nfdi4bioimage-an-initiative-for-a-national-research-data-infrastructure-for-microscopy-data"], [64, "nfdi4bioimage-an-initiative-for-a-national-research-data-infrastructure-for-microscopy-data"], [80, "nfdi4bioimage-an-initiative-for-a-national-research-data-infrastructure-for-microscopy-data"], [99, "nfdi4bioimage-an-initiative-for-a-national-research-data-infrastructure-for-microscopy-data"], [105, "nfdi4bioimage-an-initiative-for-a-national-research-data-infrastructure-for-microscopy-data"]], "NFDI4BIOIMAGE - National Research Data Infrastructure for Microscopy and BioImage Analysis - Online Kick-Off 2023": [[33, "nfdi4bioimage-national-research-data-infrastructure-for-microscopy-and-bioimage-analysis-online-kick-off-2023"], [54, "nfdi4bioimage-national-research-data-infrastructure-for-microscopy-and-bioimage-analysis-online-kick-off-2023"], [64, "nfdi4bioimage-national-research-data-infrastructure-for-microscopy-and-bioimage-analysis-online-kick-off-2023"], [79, "nfdi4bioimage-national-research-data-infrastructure-for-microscopy-and-bioimage-analysis-online-kick-off-2023"], [90, "nfdi4bioimage-national-research-data-infrastructure-for-microscopy-and-bioimage-analysis-online-kick-off-2023"], [93, "nfdi4bioimage-national-research-data-infrastructure-for-microscopy-and-bioimage-analysis-online-kick-off-2023"], [99, "nfdi4bioimage-national-research-data-infrastructure-for-microscopy-and-bioimage-analysis-online-kick-off-2023"], [105, "nfdi4bioimage-national-research-data-infrastructure-for-microscopy-and-bioimage-analysis-online-kick-off-2023"]], "NFDI4BIOIMAGE - National Research Data Infrastructure for Microscopy and BioImage Analysis [conference talk: The Pelagic Imaging Consortium meets Helmholtz Imaging, 5.10.2023, Hamburg]": [[28, "nfdi4bioimage-national-research-data-infrastructure-for-microscopy-and-bioimage-analysis-conference-talk-the-pelagic-imaging-consortium-meets-helmholtz-imaging-5-10-2023-hamburg"], [54, "nfdi4bioimage-national-research-data-infrastructure-for-microscopy-and-bioimage-analysis-conference-talk-the-pelagic-imaging-consortium-meets-helmholtz-imaging-5-10-2023-hamburg"], [73, "nfdi4bioimage-national-research-data-infrastructure-for-microscopy-and-bioimage-analysis-conference-talk-the-pelagic-imaging-consortium-meets-helmholtz-imaging-5-10-2023-hamburg"], [79, "nfdi4bioimage-national-research-data-infrastructure-for-microscopy-and-bioimage-analysis-conference-talk-the-pelagic-imaging-consortium-meets-helmholtz-imaging-5-10-2023-hamburg"], [90, "nfdi4bioimage-national-research-data-infrastructure-for-microscopy-and-bioimage-analysis-conference-talk-the-pelagic-imaging-consortium-meets-helmholtz-imaging-5-10-2023-hamburg"], [99, "nfdi4bioimage-national-research-data-infrastructure-for-microscopy-and-bioimage-analysis-conference-talk-the-pelagic-imaging-consortium-meets-helmholtz-imaging-5-10-2023-hamburg"], [105, "nfdi4bioimage-national-research-data-infrastructure-for-microscopy-and-bioimage-analysis-conference-talk-the-pelagic-imaging-consortium-meets-helmholtz-imaging-5-10-2023-hamburg"]], "NFDI4BIOIMAGE - National Research Data Infrastructure for Microscopy and Bioimage Analysis": [[64, "nfdi4bioimage-national-research-data-infrastructure-for-microscopy-and-bioimage-analysis"], [73, "nfdi4bioimage-national-research-data-infrastructure-for-microscopy-and-bioimage-analysis"], [79, "nfdi4bioimage-national-research-data-infrastructure-for-microscopy-and-bioimage-analysis"]], "NFDI4BIOIMAGE data management illustrations by Henning Falk": [[64, "nfdi4bioimage-data-management-illustrations-by-henning-falk"], [73, "nfdi4bioimage-data-management-illustrations-by-henning-falk"], [79, "nfdi4bioimage-data-management-illustrations-by-henning-falk"], [99, "nfdi4bioimage-data-management-illustrations-by-henning-falk"], [105, "nfdi4bioimage-data-management-illustrations-by-henning-falk"]], "NFDI4BIOIMAGE: Perspective for a national bioimaging standard": [[17, "nfdi4bioimage-perspective-for-a-national-bioimaging-standard"], [34, "nfdi4bioimage-perspective-for-a-national-bioimaging-standard"], [52, "nfdi4bioimage-perspective-for-a-national-bioimaging-standard"], [79, "nfdi4bioimage-perspective-for-a-national-bioimaging-standard"], [99, "nfdi4bioimage-perspective-for-a-national-bioimaging-standard"]], "NFDI4BioImage Training Materials": [[87, null]], "NFDI4Bioimage - TA3-Hackathon - UoC-2023 (Cologne Hackathon)": [[0, "nfdi4bioimage-ta3-hackathon-uoc-2023-cologne-hackathon"], [23, "nfdi4bioimage-ta3-hackathon-uoc-2023-cologne-hackathon"], [37, "nfdi4bioimage-ta3-hackathon-uoc-2023-cologne-hackathon"], [45, "nfdi4bioimage-ta3-hackathon-uoc-2023-cologne-hackathon"], [46, "nfdi4bioimage-ta3-hackathon-uoc-2023-cologne-hackathon"], [52, "nfdi4bioimage-ta3-hackathon-uoc-2023-cologne-hackathon"], [64, "nfdi4bioimage-ta3-hackathon-uoc-2023-cologne-hackathon"], [68, "nfdi4bioimage-ta3-hackathon-uoc-2023-cologne-hackathon"], [79, "nfdi4bioimage-ta3-hackathon-uoc-2023-cologne-hackathon"], [99, "nfdi4bioimage-ta3-hackathon-uoc-2023-cologne-hackathon"], [100, "nfdi4bioimage-ta3-hackathon-uoc-2023-cologne-hackathon"], [103, "nfdi4bioimage-ta3-hackathon-uoc-2023-cologne-hackathon"], [105, "nfdi4bioimage-ta3-hackathon-uoc-2023-cologne-hackathon"]], "NFDI4Bioimage - TA3-Hackathon - UoC-2023 (Cologne-Hackathon-2023, GitHub repository)": [[0, "nfdi4bioimage-ta3-hackathon-uoc-2023-cologne-hackathon-2023-github-repository"], [16, "nfdi4bioimage-ta3-hackathon-uoc-2023-cologne-hackathon-2023-github-repository"], [23, "nfdi4bioimage-ta3-hackathon-uoc-2023-cologne-hackathon-2023-github-repository"], [37, "nfdi4bioimage-ta3-hackathon-uoc-2023-cologne-hackathon-2023-github-repository"], [47, "nfdi4bioimage-ta3-hackathon-uoc-2023-cologne-hackathon-2023-github-repository"], [73, "nfdi4bioimage-ta3-hackathon-uoc-2023-cologne-hackathon-2023-github-repository"], [79, "nfdi4bioimage-ta3-hackathon-uoc-2023-cologne-hackathon-2023-github-repository"], [90, "nfdi4bioimage-ta3-hackathon-uoc-2023-cologne-hackathon-2023-github-repository"], [93, "nfdi4bioimage-ta3-hackathon-uoc-2023-cologne-hackathon-2023-github-repository"], [99, "nfdi4bioimage-ta3-hackathon-uoc-2023-cologne-hackathon-2023-github-repository"], [105, "nfdi4bioimage-ta3-hackathon-uoc-2023-cologne-hackathon-2023-github-repository"]], "NFDI4Bioimage Calendar 2024 October; original image": [[64, "nfdi4bioimage-calendar-2024-october-original-image"], [73, "nfdi4bioimage-calendar-2024-october-original-image"], [79, "nfdi4bioimage-calendar-2024-october-original-image"], [99, "nfdi4bioimage-calendar-2024-october-original-image"], [105, "nfdi4bioimage-calendar-2024-october-original-image"]], "NGFF Converter": [[82, "ngff-converter"], [102, "ngff-converter"]], "NLP Course": [[47, "nlp-course"], [68, "nlp-course"], [84, "nlp-course"]], "Napari (13)": [[97, null]], "Neubias (27)": [[98, null]], "Neubias Academy 2020: Introduction to Nuclei Segmentation with StarDist": [[21, "neubias-academy-2020-introduction-to-nuclei-segmentation-with-stardist"], [25, "neubias-academy-2020-introduction-to-nuclei-segmentation-with-stardist"], [48, "neubias-academy-2020-introduction-to-nuclei-segmentation-with-stardist"], [54, "neubias-academy-2020-introduction-to-nuclei-segmentation-with-stardist"], [68, "neubias-academy-2020-introduction-to-nuclei-segmentation-with-stardist"], [86, "neubias-academy-2020-introduction-to-nuclei-segmentation-with-stardist"], [89, "neubias-academy-2020-introduction-to-nuclei-segmentation-with-stardist"], [90, "neubias-academy-2020-introduction-to-nuclei-segmentation-with-stardist"], [98, "neubias-academy-2020-introduction-to-nuclei-segmentation-with-stardist"], [103, "neubias-academy-2020-introduction-to-nuclei-segmentation-with-stardist"]], "NeubiasPasteur2023_AdvancedCellPose": [[47, "neubiaspasteur2023-advancedcellpose"], [68, "neubiaspasteur2023-advancedcellpose"], [78, "neubiaspasteur2023-advancedcellpose"], [89, "neubiaspasteur2023-advancedcellpose"], [90, "neubiaspasteur2023-advancedcellpose"]], "New Kid on the (NFDI) Block: NFDI4BIOIMAGE - A National Initiative for FAIR Data Management in Bioimaging and Bioimage Analysis": [[6, "new-kid-on-the-nfdi-block-nfdi4bioimage-a-national-initiative-for-fair-data-management-in-bioimaging-and-bioimage-analysis"], [64, "new-kid-on-the-nfdi-block-nfdi4bioimage-a-national-initiative-for-fair-data-management-in-bioimaging-and-bioimage-analysis"], [73, "new-kid-on-the-nfdi-block-nfdi4bioimage-a-national-initiative-for-fair-data-management-in-bioimaging-and-bioimage-analysis"], [79, "new-kid-on-the-nfdi-block-nfdi4bioimage-a-national-initiative-for-fair-data-management-in-bioimaging-and-bioimage-analysis"]], "New report highlights the scientific impact of open source software": [[39, "new-report-highlights-the-scientific-impact-of-open-source-software"], [86, "new-report-highlights-the-scientific-impact-of-open-source-software"]], "NextFlow core": [[42, "nextflow-core"], [103, "nextflow-core"]], "NextFlow documentation": [[45, "nextflow-documentation"], [108, "nextflow-documentation"]], "Nextflow Demo Pipelines for Image Processing": [[41, "nextflow-demo-pipelines-for-image-processing"], [68, "nextflow-demo-pipelines-for-image-processing"], [86, "nextflow-demo-pipelines-for-image-processing"]], "Nextflow: Scalable and reproducible scientific workflows": [[54, "nextflow-scalable-and-reproducible-scientific-workflows"], [64, "nextflow-scalable-and-reproducible-scientific-workflows"], [73, "nextflow-scalable-and-reproducible-scientific-workflows"], [79, "nextflow-scalable-and-reproducible-scientific-workflows"], [108, "nextflow-scalable-and-reproducible-scientific-workflows"]], "Nfdi4bioimage (44)": [[99, null]], "Nicolas chiaruttini (7)": [[24, null]], "Notebook (55)": [[48, null]], "OME Documentation": [[45, "ome-documentation"], [79, "ome-documentation"], [100, "ome-documentation"]], "OME Event Database": [[42, "ome-event-database"], [46, "ome-event-database"], [100, "ome-event-database"], [105, "ome-event-database"]], "OME-NGFF: a next-generation file format for expanding bioimaging data-access strategies": [[2, "ome-ngff-a-next-generation-file-format-for-expanding-bioimaging-data-access-strategies"], [4, "ome-ngff-a-next-generation-file-format-for-expanding-bioimaging-data-access-strategies"], [5, "ome-ngff-a-next-generation-file-format-for-expanding-bioimaging-data-access-strategies"], [15, "ome-ngff-a-next-generation-file-format-for-expanding-bioimaging-data-access-strategies"], [17, "ome-ngff-a-next-generation-file-format-for-expanding-bioimaging-data-access-strategies"], [52, "ome-ngff-a-next-generation-file-format-for-expanding-bioimaging-data-access-strategies"], [71, "ome-ngff-a-next-generation-file-format-for-expanding-bioimaging-data-access-strategies"], [79, "ome-ngff-a-next-generation-file-format-for-expanding-bioimaging-data-access-strategies"], [99, "ome-ngff-a-next-generation-file-format-for-expanding-bioimaging-data-access-strategies"], [105, "ome-ngff-a-next-generation-file-format-for-expanding-bioimaging-data-access-strategies"]], "OME-Zarr course": [[4, "ome-zarr-course"], [55, "ome-zarr-course"], [86, "ome-zarr-course"]], "OME2024 NGFF Challenge Results": [[17, "ome2024-ngff-challenge-results"], [64, "ome2024-ngff-challenge-results"], [73, "ome2024-ngff-challenge-results"], [79, "ome2024-ngff-challenge-results"], [99, "ome2024-ngff-challenge-results"], [105, "ome2024-ngff-challenge-results"]], "OMERO - HCS analysis pipeline using Jupyter Notebooks": [[28, "omero-hcs-analysis-pipeline-using-jupyter-notebooks"], [47, "omero-hcs-analysis-pipeline-using-jupyter-notebooks"], [68, "omero-hcs-analysis-pipeline-using-jupyter-notebooks"], [84, "omero-hcs-analysis-pipeline-using-jupyter-notebooks"], [90, "omero-hcs-analysis-pipeline-using-jupyter-notebooks"], [100, "omero-hcs-analysis-pipeline-using-jupyter-notebooks"], [103, "omero-hcs-analysis-pipeline-using-jupyter-notebooks"]], "OMERO - QuPath": [[49, "omero-qupath"], [90, "omero-qupath"], [100, "omero-qupath"]], "OMERO Guide": [[42, "omero-guide"], [77, "omero-guide"], [100, "omero-guide"]], "OMERO documentation": [[45, "omero-documentation"], [86, "omero-documentation"], [100, "omero-documentation"]], "OMERO for microscopy research data management": [[16, "omero-for-microscopy-research-data-management"], [35, "omero-for-microscopy-research-data-management"], [52, "omero-for-microscopy-research-data-management"], [76, "omero-for-microscopy-research-data-management"], [99, "omero-for-microscopy-research-data-management"], [100, "omero-for-microscopy-research-data-management"], [105, "omero-for-microscopy-research-data-management"]], "OMERO walkthrough example": [[44, "omero-walkthrough-example"], [77, "omero-walkthrough-example"], [100, "omero-walkthrough-example"]], "OMERO walkthrough for facility managers": [[44, "omero-walkthrough-for-facility-managers"], [77, "omero-walkthrough-for-facility-managers"], [100, "omero-walkthrough-for-facility-managers"]], "ONBI Image Analysis Course": [[48, "onbi-image-analysis-course"], [68, "onbi-image-analysis-course"], [82, "onbi-image-analysis-course"], [90, "onbi-image-analysis-course"], [103, "onbi-image-analysis-course"]], "Object Tracking and Track Analysis using TrackMate and CellTracksColab": [[47, "object-tracking-and-track-analysis-using-trackmate-and-celltrackscolab"], [54, "object-tracking-and-track-analysis-using-trackmate-and-celltrackscolab"], [55, "object-tracking-and-track-analysis-using-trackmate-and-celltrackscolab"], [58, "object-tracking-and-track-analysis-using-trackmate-and-celltrackscolab"], [68, "object-tracking-and-track-analysis-using-trackmate-and-celltrackscolab"], [83, "object-tracking-and-track-analysis-using-trackmate-and-celltrackscolab"], [90, "object-tracking-and-track-analysis-using-trackmate-and-celltrackscolab"]], "Olivier burri (6)": [[25, null]], "Omero (31)": [[100, null]], "Omero Deployment examples": [[42, "omero-deployment-examples"], [68, "omero-deployment-examples"], [77, "omero-deployment-examples"], [100, "omero-deployment-examples"]], "Omero-tools": [[47, "omero-tools"], [62, "omero-tools"], [79, "omero-tools"], [90, "omero-tools"], [100, "omero-tools"]], "Online tutorial (10)": [[49, null]], "Online_R_learning": [[47, "online-r-learning"], [68, "online-r-learning"], [81, "online-r-learning"]], "Open Image Data Handbook": [[40, "open-image-data-handbook"], [48, "open-image-data-handbook"], [79, "open-image-data-handbook"], [90, "open-image-data-handbook"], [97, "open-image-data-handbook"], [98, "open-image-data-handbook"], [103, "open-image-data-handbook"], [105, "open-image-data-handbook"]], "Open Micoscropy Environment (OME) Youtube Channel": [[42, "open-micoscropy-environment-ome-youtube-channel"], [56, "open-micoscropy-environment-ome-youtube-channel"], [72, "open-micoscropy-environment-ome-youtube-channel"], [79, "open-micoscropy-environment-ome-youtube-channel"], [102, "open-micoscropy-environment-ome-youtube-channel"]], "Open Microscopy Environment YouTube channel": [[42, "open-microscopy-environment-youtube-channel"], [56, "open-microscopy-environment-youtube-channel"], [72, "open-microscopy-environment-youtube-channel"], [100, "open-microscopy-environment-youtube-channel"]], "Open Science, Sharing & Licensing": [[29, "open-science-sharing-licensing"], [54, "open-science-sharing-licensing"], [64, "open-science-sharing-licensing"], [73, "open-science-sharing-licensing"], [79, "open-science-sharing-licensing"], [93, "open-science-sharing-licensing"], [95, "open-science-sharing-licensing"], [105, "open-science-sharing-licensing"]], "Open data": [[74, null]], "Open microscopy in the life sciences: quo vadis?": [[29, "open-microscopy-in-the-life-sciences-quo-vadis"], [52, "open-microscopy-in-the-life-sciences-quo-vadis"], [64, "open-microscopy-in-the-life-sciences-quo-vadis"], [76, "open-microscopy-in-the-life-sciences-quo-vadis"]], "Open science (9)": [[101, null]], "Open source software (7)": [[102, null]], "Optimisation and Validation of a Swarm Intelligence based Segmentation Algorithm for low Contrast Positron Emission Tomography": [[29, "optimisation-and-validation-of-a-swarm-intelligence-based-segmentation-algorithm-for-low-contrast-positron-emission-tomography"], [64, "optimisation-and-validation-of-a-swarm-intelligence-based-segmentation-algorithm-for-low-contrast-positron-emission-tomography"], [73, "optimisation-and-validation-of-a-swarm-intelligence-based-segmentation-algorithm-for-low-contrast-positron-emission-tomography"], [79, "optimisation-and-validation-of-a-swarm-intelligence-based-segmentation-algorithm-for-low-contrast-positron-emission-tomography"]], "Optimized cranial window implantation for subcellular and functional imaging in vivo": [[64, "optimized-cranial-window-implantation-for-subcellular-and-functional-imaging-in-vivo"], [73, "optimized-cranial-window-implantation-for-subcellular-and-functional-imaging-in-vivo"], [79, "optimized-cranial-window-implantation-for-subcellular-and-functional-imaging-in-vivo"], [109, "optimized-cranial-window-implantation-for-subcellular-and-functional-imaging-in-vivo"]], "Overview of the Galaxy OMERO-suite - Upload images and metadata in OMERO using Galaxy": [[28, "overview-of-the-galaxy-omero-suite-upload-images-and-metadata-in-omero-using-galaxy"], [55, "overview-of-the-galaxy-omero-suite-upload-images-and-metadata-in-omero-using-galaxy"], [79, "overview-of-the-galaxy-omero-suite-upload-images-and-metadata-in-omero-using-galaxy"], [96, "overview-of-the-galaxy-omero-suite-upload-images-and-metadata-in-omero-using-galaxy"], [99, "overview-of-the-galaxy-omero-suite-upload-images-and-metadata-in-omero-using-galaxy"], [100, "overview-of-the-galaxy-omero-suite-upload-images-and-metadata-in-omero-using-galaxy"]], "Parallelization and heterogeneous computing: from pure CPU to GPU-accelerated image processing": [[29, "parallelization-and-heterogeneous-computing-from-pure-cpu-to-gpu-accelerated-image-processing"], [54, "parallelization-and-heterogeneous-computing-from-pure-cpu-to-gpu-accelerated-image-processing"], [64, "parallelization-and-heterogeneous-computing-from-pure-cpu-to-gpu-accelerated-image-processing"], [65, "parallelization-and-heterogeneous-computing-from-pure-cpu-to-gpu-accelerated-image-processing"], [79, "parallelization-and-heterogeneous-computing-from-pure-cpu-to-gpu-accelerated-image-processing"]], "Photonic data analysis in 2050": [[52, "photonic-data-analysis-in-2050"], [64, "photonic-data-analysis-in-2050"], [79, "photonic-data-analysis-in-2050"], [93, "photonic-data-analysis-in-2050"], [105, "photonic-data-analysis-in-2050"]], "Pia voigt (6)": [[26, null]], "Plants & Python - A series of lessons in coding, plant biology, computation, and bioinformatics": [[52, "plants-python-a-series-of-lessons-in-coding-plant-biology-computation-and-bioinformatics"]], "Plugin \u201comero-batch-plugin\u201d": [[47, "plugin-omero-batch-plugin"], [68, "plugin-omero-batch-plugin"], [82, "plugin-omero-batch-plugin"], [94, "plugin-omero-batch-plugin"], [100, "plugin-omero-batch-plugin"]], "Plugin \u201comero-cli-transfer\u201d": [[11, "plugin-omero-cli-transfer"], [15, "plugin-omero-cli-transfer"], [47, "plugin-omero-cli-transfer"], [68, "plugin-omero-cli-transfer"], [82, "plugin-omero-cli-transfer"], [100, "plugin-omero-cli-transfer"]], "Plugin \u201csimple-omero-client\u201d": [[15, "plugin-simple-omero-client"], [47, "plugin-simple-omero-client"], [68, "plugin-simple-omero-client"], [82, "plugin-simple-omero-client"], [100, "plugin-simple-omero-client"]], "PoL Bio-Image Analysis Early Career Bio-image Analysis Training School": [[46, "pol-bio-image-analysis-early-career-bio-image-analysis-training-school"], [48, "pol-bio-image-analysis-early-career-bio-image-analysis-training-school"], [62, "pol-bio-image-analysis-early-career-bio-image-analysis-training-school"], [90, "pol-bio-image-analysis-early-career-bio-image-analysis-training-school"], [103, "pol-bio-image-analysis-early-career-bio-image-analysis-training-school"]], "PoL Bio-Image Analysis GPU Accelerated Image Analysis Training School": [[46, "pol-bio-image-analysis-gpu-accelerated-image-analysis-training-school"], [48, "pol-bio-image-analysis-gpu-accelerated-image-analysis-training-school"], [62, "pol-bio-image-analysis-gpu-accelerated-image-analysis-training-school"], [90, "pol-bio-image-analysis-gpu-accelerated-image-analysis-training-school"], [103, "pol-bio-image-analysis-gpu-accelerated-image-analysis-training-school"]], "PoL Bio-Image Analysis Training School on GPU-Accelerated Image Analysis": [[29, "pol-bio-image-analysis-training-school-on-gpu-accelerated-image-analysis"], [48, "pol-bio-image-analysis-training-school-on-gpu-accelerated-image-analysis"], [68, "pol-bio-image-analysis-training-school-on-gpu-accelerated-image-analysis"], [79, "pol-bio-image-analysis-training-school-on-gpu-accelerated-image-analysis"], [103, "pol-bio-image-analysis-training-school-on-gpu-accelerated-image-analysis"]], "Poster (10)": [[50, null]], "Practical Guide to the International Alignment of Research Data Management - Extended Edition": [[40, "practical-guide-to-the-international-alignment-of-research-data-management-extended-edition"], [64, "practical-guide-to-the-international-alignment-of-research-data-management-extended-edition"], [79, "practical-guide-to-the-international-alignment-of-research-data-management-extended-edition"]], "Preprint (5)": [[51, null]], "Preprint: \u201cBe Sustainable\u201d, Recommendations for FAIR Resources in Life Sciences research: EOSC-Life\u2019s Lessons": [[1, "preprint-be-sustainable-recommendations-for-fair-resources-in-life-sciences-research-eosc-life-s-lessons"], [15, "preprint-be-sustainable-recommendations-for-fair-resources-in-life-sciences-research-eosc-life-s-lessons"], [38, "preprint-be-sustainable-recommendations-for-fair-resources-in-life-sciences-research-eosc-life-s-lessons"], [64, "preprint-be-sustainable-recommendations-for-fair-resources-in-life-sciences-research-eosc-life-s-lessons"], [73, "preprint-be-sustainable-recommendations-for-fair-resources-in-life-sciences-research-eosc-life-s-lessons"], [79, "preprint-be-sustainable-recommendations-for-fair-resources-in-life-sciences-research-eosc-life-s-lessons"]], "Prerequisites": [[61, "prerequisites"]], "Promoting Data Management at the Nikon Imaging Center and Cell Biology Microscopy Facility": [[39, "promoting-data-management-at-the-nikon-imaging-center-and-cell-biology-microscopy-facility"], [105, "promoting-data-management-at-the-nikon-imaging-center-and-cell-biology-microscopy-facility"]], "Prompt Engineering in Bio-image Analysis": [[19, "prompt-engineering-in-bio-image-analysis"], [39, "prompt-engineering-in-bio-image-analysis"], [66, "prompt-engineering-in-bio-image-analysis"], [90, "prompt-engineering-in-bio-image-analysis"], [103, "prompt-engineering-in-bio-image-analysis"]], "Prompt-Engineering-LLMs-Course": [[47, "prompt-engineering-llms-course"], [55, "prompt-engineering-llms-course"], [68, "prompt-engineering-llms-course"], [84, "prompt-engineering-llms-course"]], "Publication (61)": [[52, null]], "Python (70)": [[103, null]], "Python BioImage Analysis Tutorial": [[68, "python-bioimage-analysis-tutorial"], [84, "python-bioimage-analysis-tutorial"], [90, "python-bioimage-analysis-tutorial"], [103, "python-bioimage-analysis-tutorial"]], "Python Workshop - Image Processing": [[41, "python-workshop-image-processing"], [48, "python-workshop-image-processing"], [68, "python-workshop-image-processing"], [86, "python-workshop-image-processing"], [103, "python-workshop-image-processing"]], "Python for Microscopists": [[42, "python-for-microscopists"], [48, "python-for-microscopists"], [68, "python-for-microscopists"], [84, "python-for-microscopists"], [90, "python-for-microscopists"], [103, "python-for-microscopists"]], "QI 2024 Analysis Lab Manual": [[12, "qi-2024-analysis-lab-manual"], [48, "qi-2024-analysis-lab-manual"], [79, "qi-2024-analysis-lab-manual"], [103, "qi-2024-analysis-lab-manual"]], "QM Course Lectures on Bio-Image Analysis with napari 2024": [[48, "qm-course-lectures-on-bio-image-analysis-with-napari-2024"], [79, "qm-course-lectures-on-bio-image-analysis-with-napari-2024"], [97, "qm-course-lectures-on-bio-image-analysis-with-napari-2024"], [103, "qm-course-lectures-on-bio-image-analysis-with-napari-2024"]], "QUAREP-LiMi: A community-driven initiative to establish guidelines for quality assessment and reproducibility for instruments and images in light microscopy": [[11, "quarep-limi-a-community-driven-initiative-to-establish-guidelines-for-quality-assessment-and-reproducibility-for-instruments-and-images-in-light-microscopy"], [52, "quarep-limi-a-community-driven-initiative-to-establish-guidelines-for-quality-assessment-and-reproducibility-for-instruments-and-images-in-light-microscopy"], [79, "quarep-limi-a-community-driven-initiative-to-establish-guidelines-for-quality-assessment-and-reproducibility-for-instruments-and-images-in-light-microscopy"]], "QuPath for Python programmers": [[42, "qupath-for-python-programmers"], [47, "qupath-for-python-programmers"], [48, "qupath-for-python-programmers"], [68, "qupath-for-python-programmers"], [86, "qupath-for-python-programmers"], [90, "qupath-for-python-programmers"], [102, "qupath-for-python-programmers"], [103, "qupath-for-python-programmers"]], "QuPath: Open source software for analysing (awkward) images": [[54, "qupath-open-source-software-for-analysing-awkward-images"], [64, "qupath-open-source-software-for-analysing-awkward-images"], [73, "qupath-open-source-software-for-analysing-awkward-images"], [79, "qupath-open-source-software-for-analysing-awkward-images"], [90, "qupath-open-source-software-for-analysing-awkward-images"]], "Quality assurance of segmentation results": [[19, "quality-assurance-of-segmentation-results"], [39, "quality-assurance-of-segmentation-results"], [66, "quality-assurance-of-segmentation-results"], [90, "quality-assurance-of-segmentation-results"], [97, "quality-assurance-of-segmentation-results"], [103, "quality-assurance-of-segmentation-results"]], "Quick contributing short-cut:": [[60, "quick-contributing-short-cut"]], "RDF as a bridge to domain-platforms like OMERO, or There and back again.": [[17, "rdf-as-a-bridge-to-domain-platforms-like-omero-or-there-and-back-again"], [34, "rdf-as-a-bridge-to-domain-platforms-like-omero-or-there-and-back-again"], [54, "rdf-as-a-bridge-to-domain-platforms-like-omero-or-there-and-back-again"], [73, "rdf-as-a-bridge-to-domain-platforms-like-omero-or-there-and-back-again"], [79, "rdf-as-a-bridge-to-domain-platforms-like-omero-or-there-and-back-again"], [90, "rdf-as-a-bridge-to-domain-platforms-like-omero-or-there-and-back-again"], [93, "rdf-as-a-bridge-to-domain-platforms-like-omero-or-there-and-back-again"], [105, "rdf-as-a-bridge-to-domain-platforms-like-omero-or-there-and-back-again"]], "RDM Starter Kit": [[57, "rdm-starter-kit"], [79, "rdm-starter-kit"], [105, "rdm-starter-kit"]], "RDM4Mic Presentations": [[42, "rdm4mic-presentations"], [68, "rdm4mic-presentations"], [79, "rdm4mic-presentations"], [105, "rdm4mic-presentations"]], "RDM4mic": [[42, "rdm4mic"], [56, "rdm4mic"], [72, "rdm4mic"], [86, "rdm4mic"], [100, "rdm4mic"], [105, "rdm4mic"]], "RDMBites BioImage metadata": [[42, "rdmbites-bioimage-metadata"], [56, "rdmbites-bioimage-metadata"], [72, "rdmbites-bioimage-metadata"]], "RDMKit Training Resources": [[42, "rdmkit-training-resources"], [79, "rdmkit-training-resources"], [105, "rdmkit-training-resources"]], "RDMO - Research Data Management Organiser": [[49, "rdmo-research-data-management-organiser"], [57, "rdmo-research-data-management-organiser"], [86, "rdmo-research-data-management-organiser"], [102, "rdmo-research-data-management-organiser"], [105, "rdmo-research-data-management-organiser"]], "RDM_system_connector": [[47, "rdm-system-connector"], [68, "rdm-system-connector"], [86, "rdm-system-connector"], [105, "rdm-system-connector"]], "REMBI - Recommended Metadata for Biological Images\u2014enabling reuse of microscopy data in biology": [[11, "rembi-recommended-metadata-for-biological-imagesenabling-reuse-of-microscopy-data-in-biology"], [38, "rembi-recommended-metadata-for-biological-imagesenabling-reuse-of-microscopy-data-in-biology"], [52, "rembi-recommended-metadata-for-biological-imagesenabling-reuse-of-microscopy-data-in-biology"], [64, "rembi-recommended-metadata-for-biological-imagesenabling-reuse-of-microscopy-data-in-biology"], [71, "rembi-recommended-metadata-for-biological-imagesenabling-reuse-of-microscopy-data-in-biology"], [86, "rembi-recommended-metadata-for-biological-imagesenabling-reuse-of-microscopy-data-in-biology"], [96, "rembi-recommended-metadata-for-biological-imagesenabling-reuse-of-microscopy-data-in-biology"], [105, "rembi-recommended-metadata-for-biological-imagesenabling-reuse-of-microscopy-data-in-biology"]], "REMBI Overview": [[42, "rembi-overview"], [70, "rembi-overview"], [81, "rembi-overview"], [93, "rembi-overview"], [96, "rembi-overview"], [105, "rembi-overview"]], "RESEARCH DATA MANAGEMENT on Campus and in NFDI4BIOIMAGE": [[6, "research-data-management-on-campus-and-in-nfdi4bioimage"], [64, "research-data-management-on-campus-and-in-nfdi4bioimage"], [73, "research-data-management-on-campus-and-in-nfdi4bioimage"], [79, "research-data-management-on-campus-and-in-nfdi4bioimage"]], "Recently added (10)": [[109, null]], "Rechtsfragen bei Open Science - Ein Leitfaden": [[40, "rechtsfragen-bei-open-science-ein-leitfaden"], [79, "rechtsfragen-bei-open-science-ein-leitfaden"], [101, "rechtsfragen-bei-open-science-ein-leitfaden"]], "Reference Collection to push back against \u201cCommon Statistical Myths\u201d": [[86, "reference-collection-to-push-back-against-common-statistical-myths"]], "Report on a pilot study: Implementation of OMERO for microscopy data management": [[32, "report-on-a-pilot-study-implementation-of-omero-for-microscopy-data-management"], [64, "report-on-a-pilot-study-implementation-of-omero-for-microscopy-data-management"], [73, "report-on-a-pilot-study-implementation-of-omero-for-microscopy-data-management"], [79, "report-on-a-pilot-study-implementation-of-omero-for-microscopy-data-management"]], "Reporting and reproducibility in microscopy": [[42, "reporting-and-reproducibility-in-microscopy"], [71, "reporting-and-reproducibility-in-microscopy"], [86, "reporting-and-reproducibility-in-microscopy"], [96, "reporting-and-reproducibility-in-microscopy"]], "Rescaling images and pixel (an)isotropy": [[19, "rescaling-images-and-pixel-an-isotropy"], [39, "rescaling-images-and-pixel-an-isotropy"], [66, "rescaling-images-and-pixel-an-isotropy"], [90, "rescaling-images-and-pixel-an-isotropy"], [97, "rescaling-images-and-pixel-an-isotropy"], [103, "rescaling-images-and-pixel-an-isotropy"]], "Research Data Management Seminar - Slides": [[54, "research-data-management-seminar-slides"], [64, "research-data-management-seminar-slides"], [73, "research-data-management-seminar-slides"], [79, "research-data-management-seminar-slides"], [105, "research-data-management-seminar-slides"]], "Research Data Managemet and how not to get overwhelmed with data": [[20, "research-data-managemet-and-how-not-to-get-overwhelmed-with-data"], [64, "research-data-managemet-and-how-not-to-get-overwhelmed-with-data"], [73, "research-data-managemet-and-how-not-to-get-overwhelmed-with-data"], [79, "research-data-managemet-and-how-not-to-get-overwhelmed-with-data"]], "Research Data Reusability - Conceptual Foundations, Barriers and Enabling Technologies": [[52, "research-data-reusability-conceptual-foundations-barriers-and-enabling-technologies"], [79, "research-data-reusability-conceptual-foundations-barriers-and-enabling-technologies"], [101, "research-data-reusability-conceptual-foundations-barriers-and-enabling-technologies"], [105, "research-data-reusability-conceptual-foundations-barriers-and-enabling-technologies"]], "Research data - what are the key issues to consider when publishing this kind of material?": [[55, "research-data-what-are-the-key-issues-to-consider-when-publishing-this-kind-of-material"], [86, "research-data-what-are-the-key-issues-to-consider-when-publishing-this-kind-of-material"], [93, "research-data-what-are-the-key-issues-to-consider-when-publishing-this-kind-of-material"], [95, "research-data-what-are-the-key-issues-to-consider-when-publishing-this-kind-of-material"], [105, "research-data-what-are-the-key-issues-to-consider-when-publishing-this-kind-of-material"]], "Research data management (128)": [[105, null]], "Research data management for bioimaging - the 2021 NFDI4BIOIMAGE community survey": [[3, "research-data-management-for-bioimaging-the-2021-nfdi4bioimage-community-survey"], [8, "research-data-management-for-bioimaging-the-2021-nfdi4bioimage-community-survey"], [11, "research-data-management-for-bioimaging-the-2021-nfdi4bioimage-community-survey"], [17, "research-data-management-for-bioimaging-the-2021-nfdi4bioimage-community-survey"], [52, "research-data-management-for-bioimaging-the-2021-nfdi4bioimage-community-survey"], [65, "research-data-management-for-bioimaging-the-2021-nfdi4bioimage-community-survey"], [79, "research-data-management-for-bioimaging-the-2021-nfdi4bioimage-community-survey"], [105, "research-data-management-for-bioimaging-the-2021-nfdi4bioimage-community-survey"]], "Research data management for bioimaging: the 2021 NFDI4BIOIMAGE community survey": [[3, "id1"], [8, "id1"], [17, "id1"], [33, "research-data-management-for-bioimaging-the-2021-nfdi4bioimage-community-survey"], [52, "id1"], [65, "id1"], [79, "id2"], [99, "research-data-management-for-bioimaging-the-2021-nfdi4bioimage-community-survey"], [105, "id1"]], "Riccardo massei (12)": [[28, null]], "Robert haase (56)": [[29, null]], "Roland nitschke (5)": [[30, null]], "Romain guiet (10)": [[31, null]], "Round Table Workshop 1 - Sample Stabilization in intravital Imaging": [[22, "round-table-workshop-1-sample-stabilization-in-intravital-imaging"], [64, "round-table-workshop-1-sample-stabilization-in-intravital-imaging"], [73, "round-table-workshop-1-sample-stabilization-in-intravital-imaging"], [79, "round-table-workshop-1-sample-stabilization-in-intravital-imaging"]], "Round Table Workshop 2 - Correction of Drift and Movement": [[64, "round-table-workshop-2-correction-of-drift-and-movement"], [73, "round-table-workshop-2-correction-of-drift-and-movement"], [79, "round-table-workshop-2-correction-of-drift-and-movement"]], "Running Deep-Learning Scripts in the BiA-PoL Omero Server": [[39, "running-deep-learning-scripts-in-the-bia-pol-omero-server"], [62, "running-deep-learning-scripts-in-the-bia-pol-omero-server"], [79, "running-deep-learning-scripts-in-the-bia-pol-omero-server"], [89, "running-deep-learning-scripts-in-the-bia-pol-omero-server"], [90, "running-deep-learning-scripts-in-the-bia-pol-omero-server"], [103, "running-deep-learning-scripts-in-the-bia-pol-omero-server"]], "Running the Training Materials Submission App": [[61, "running-the-training-materials-submission-app"]], "SWC/GCNU Software Skills": [[42, "swc-gcnu-software-skills"], [55, "swc-gcnu-software-skills"], [56, "swc-gcnu-software-skills"], [79, "swc-gcnu-software-skills"]], "SciAugment": [[20, "sciaugment"], [64, "sciaugment"], [73, "sciaugment"]], "Scientific Visualization: Python + Matplotlib": [[40, "scientific-visualization-python-matplotlib"], [68, "scientific-visualization-python-matplotlib"], [103, "scientific-visualization-python-matplotlib"]], "Scipy Cookbook": [[47, "scipy-cookbook"], [68, "scipy-cookbook"], [78, "scipy-cookbook"], [90, "scipy-cookbook"]], "Scripts_FilopodyanR - a case study for the NEUBIAS TS7 in Szeged": [[41, "scripts-filopodyanr-a-case-study-for-the-neubias-ts7-in-szeged"], [68, "scripts-filopodyanr-a-case-study-for-the-neubias-ts7-in-szeged"], [86, "scripts-filopodyanr-a-case-study-for-the-neubias-ts7-in-szeged"], [90, "scripts-filopodyanr-a-case-study-for-the-neubias-ts7-in-szeged"], [98, "scripts-filopodyanr-a-case-study-for-the-neubias-ts7-in-szeged"]], "Setting up a data management infrastructure for bioimaging": [[34, "setting-up-a-data-management-infrastructure-for-bioimaging"], [52, "setting-up-a-data-management-infrastructure-for-bioimaging"], [64, "setting-up-a-data-management-infrastructure-for-bioimaging"], [86, "setting-up-a-data-management-infrastructure-for-bioimaging"], [99, "setting-up-a-data-management-infrastructure-for-bioimaging"], [105, "setting-up-a-data-management-infrastructure-for-bioimaging"]], "Setting up a remote desktop to use Napari in a browser": [[55, "setting-up-a-remote-desktop-to-use-napari-in-a-browser"], [68, "setting-up-a-remote-desktop-to-use-napari-in-a-browser"], [86, "setting-up-a-remote-desktop-to-use-napari-in-a-browser"]], "Sharing (12)": [[106, null]], "Sharing Your Poster on Figshare: A Community Guide to How-To and Why": [[39, "sharing-your-poster-on-figshare-a-community-guide-to-how-to-and-why"], [66, "sharing-your-poster-on-figshare-a-community-guide-to-how-to-and-why"], [105, "sharing-your-poster-on-figshare-a-community-guide-to-how-to-and-why"], [106, "sharing-your-poster-on-figshare-a-community-guide-to-how-to-and-why"]], "Sharing and licensing material": [[29, "sharing-and-licensing-material"], [54, "sharing-and-licensing-material"], [65, "sharing-and-licensing-material"], [79, "sharing-and-licensing-material"], [105, "sharing-and-licensing-material"], [106, "sharing-and-licensing-material"]], "Sharing research data with Zenodo": [[29, "sharing-research-data-with-zenodo"], [39, "sharing-research-data-with-zenodo"], [66, "sharing-research-data-with-zenodo"], [79, "sharing-research-data-with-zenodo"], [105, "sharing-research-data-with-zenodo"], [106, "sharing-research-data-with-zenodo"]], "Silke tulok (6)": [[32, null]], "SimpleITK-Notebooks": [[47, "simpleitk-notebooks"], [68, "simpleitk-notebooks"], [90, "simpleitk-notebooks"]], "Slides (82)": [[54, null]], "Slides about FLUTE: a Python GUI for interactive phasor analysis of FLIM data": [[64, "slides-about-flute-a-python-gui-for-interactive-phasor-analysis-of-flim-data"], [73, "slides-about-flute-a-python-gui-for-interactive-phasor-analysis-of-flim-data"], [79, "slides-about-flute-a-python-gui-for-interactive-phasor-analysis-of-flim-data"]], "Snakemake Documentation": [[45, "snakemake-documentation"], [103, "snakemake-documentation"], [108, "snakemake-documentation"]], "So geschlossen wie n\u00f6tig, so offen wie m\u00f6glich - Datenschutz beim Umgang mit Forschungsdaten": [[26, "so-geschlossen-wie-notig-so-offen-wie-moglich-datenschutz-beim-umgang-mit-forschungsdaten"], [54, "so-geschlossen-wie-notig-so-offen-wie-moglich-datenschutz-beim-umgang-mit-forschungsdaten"], [64, "so-geschlossen-wie-notig-so-offen-wie-moglich-datenschutz-beim-umgang-mit-forschungsdaten"], [73, "so-geschlossen-wie-notig-so-offen-wie-moglich-datenschutz-beim-umgang-mit-forschungsdaten"], [79, "so-geschlossen-wie-notig-so-offen-wie-moglich-datenschutz-beim-umgang-mit-forschungsdaten"], [93, "so-geschlossen-wie-notig-so-offen-wie-moglich-datenschutz-beim-umgang-mit-forschungsdaten"], [105, "so-geschlossen-wie-notig-so-offen-wie-moglich-datenschutz-beim-umgang-mit-forschungsdaten"]], "Software Citation with CITATION.cff": [[55, "software-citation-with-citation-cff"], [105, "software-citation-with-citation-cff"], [106, "software-citation-with-citation-cff"]], "Source Control Using Git and GitHub": [[55, "source-control-using-git-and-github"], [68, "source-control-using-git-and-github"], [86, "source-control-using-git-and-github"]], "SpatialData: an open and universal data framework for spatial omics": [[17, "spatialdata-an-open-and-universal-data-framework-for-spatial-omics"], [51, "spatialdata-an-open-and-universal-data-framework-for-spatial-omics"], [52, "spatialdata-an-open-and-universal-data-framework-for-spatial-omics"], [69, "spatialdata-an-open-and-universal-data-framework-for-spatial-omics"], [79, "spatialdata-an-open-and-universal-data-framework-for-spatial-omics"], [103, "spatialdata-an-open-and-universal-data-framework-for-spatial-omics"]], "Stackview sliceplot example data": [[29, "stackview-sliceplot-example-data"], [64, "stackview-sliceplot-example-data"], [73, "stackview-sliceplot-example-data"], [79, "stackview-sliceplot-example-data"]], "Standard and Super-Resolution Bioimaging Data Analysis: A Primer": [[40, "standard-and-super-resolution-bioimaging-data-analysis-a-primer"]], "Statistical Rethinking": [[47, "statistical-rethinking"], [56, "statistical-rethinking"], [68, "statistical-rethinking"], [72, "statistical-rethinking"], [81, "statistical-rethinking"], [86, "statistical-rethinking"]], "Statistics": [[88, null]], "Stefanie weidtkamp-peters (11)": [[33, null]], "Step-by-step tutorial": [[60, "step-by-step-tutorial"]], "Structuring of Data and Metadata in Bioimaging: Concepts and technical Solutions in the Context of Linked Data": [[16, "structuring-of-data-and-metadata-in-bioimaging-concepts-and-technical-solutions-in-the-context-of-linked-data"], [34, "structuring-of-data-and-metadata-in-bioimaging-concepts-and-technical-solutions-in-the-context-of-linked-data"], [35, "structuring-of-data-and-metadata-in-bioimaging-concepts-and-technical-solutions-in-the-context-of-linked-data"], [56, "structuring-of-data-and-metadata-in-bioimaging-concepts-and-technical-solutions-in-the-context-of-linked-data"], [64, "structuring-of-data-and-metadata-in-bioimaging-concepts-and-technical-solutions-in-the-context-of-linked-data"], [73, "structuring-of-data-and-metadata-in-bioimaging-concepts-and-technical-solutions-in-the-context-of-linked-data"], [79, "structuring-of-data-and-metadata-in-bioimaging-concepts-and-technical-solutions-in-the-context-of-linked-data"], [99, "structuring-of-data-and-metadata-in-bioimaging-concepts-and-technical-solutions-in-the-context-of-linked-data"], [105, "structuring-of-data-and-metadata-in-bioimaging-concepts-and-technical-solutions-in-the-context-of-linked-data"]], "Studentsourcing - aggregating and re-using data from a practical cell biology course": [[51, "studentsourcing-aggregating-and-re-using-data-from-a-practical-cell-biology-course"], [69, "studentsourcing-aggregating-and-re-using-data-from-a-practical-cell-biology-course"], [106, "studentsourcing-aggregating-and-re-using-data-from-a-practical-cell-biology-course"]], "Submitting data to the BioImage Archive": [[55, "submitting-data-to-the-bioimage-archive"], [56, "submitting-data-to-the-bioimage-archive"], [70, "submitting-data-to-the-bioimage-archive"], [81, "submitting-data-to-the-bioimage-archive"], [105, "submitting-data-to-the-bioimage-archive"]], "Susanne kunis (16)": [[34, null]], "Sustainable Data Stewardship": [[54, "sustainable-data-stewardship"], [64, "sustainable-data-stewardship"], [73, "sustainable-data-stewardship"], [79, "sustainable-data-stewardship"], [92, "sustainable-data-stewardship"], [105, "sustainable-data-stewardship"]], "TESS Event database": [[42, "tess-event-database"], [46, "tess-event-database"], [91, "tess-event-database"]], "TESS training materials": [[42, "tess-training-materials"], [86, "tess-training-materials"], [91, "tess-training-materials"]], "Teaching Bioimage Analysis with Python": [[55, "teaching-bioimage-analysis-with-python"], [68, "teaching-bioimage-analysis-with-python"], [84, "teaching-bioimage-analysis-with-python"], [90, "teaching-bioimage-analysis-with-python"], [103, "teaching-bioimage-analysis-with-python"]], "Teaching ImageJ FIJI": [[55, "teaching-imagej-fiji"], [68, "teaching-imagej-fiji"], [84, "teaching-imagej-fiji"], [90, "teaching-imagej-fiji"]], "Ten simple rules for making training materials FAIR": [[11, "ten-simple-rules-for-making-training-materials-fair"], [52, "ten-simple-rules-for-making-training-materials-fair"], [79, "ten-simple-rules-for-making-training-materials-fair"], [91, "ten-simple-rules-for-making-training-materials-fair"], [93, "ten-simple-rules-for-making-training-materials-fair"], [96, "ten-simple-rules-for-making-training-materials-fair"]], "Terminology service for research data management and knowledge discovery in low-temperature plasma physics": [[64, "terminology-service-for-research-data-management-and-knowledge-discovery-in-low-temperature-plasma-physics"], [73, "terminology-service-for-research-data-management-and-knowledge-discovery-in-low-temperature-plasma-physics"], [79, "terminology-service-for-research-data-management-and-knowledge-discovery-in-low-temperature-plasma-physics"]], "Tess Search for Data Life Cycle": [[42, "tess-search-for-data-life-cycle"], [86, "tess-search-for-data-life-cycle"], [105, "tess-search-for-data-life-cycle"]], "Test Dataset for Whole Slide Image Registration": [[24, "test-dataset-for-whole-slide-image-registration"], [31, "test-dataset-for-whole-slide-image-registration"], [64, "test-dataset-for-whole-slide-image-registration"], [73, "test-dataset-for-whole-slide-image-registration"], [79, "test-dataset-for-whole-slide-image-registration"]], "The BioImage Archive \u2013 Building a Home for Life-Sciences Microscopy Data": [[38, "the-bioimage-archive-building-a-home-for-life-sciences-microscopy-data"], [52, "the-bioimage-archive-building-a-home-for-life-sciences-microscopy-data"], [64, "the-bioimage-archive-building-a-home-for-life-sciences-microscopy-data"], [86, "the-bioimage-archive-building-a-home-for-life-sciences-microscopy-data"], [105, "the-bioimage-archive-building-a-home-for-life-sciences-microscopy-data"]], "The Digital Cell: Cell Biology as a Data Science": [[40, "the-digital-cell-cell-biology-as-a-data-science"], [90, "the-digital-cell-cell-biology-as-a-data-science"]], "The FAIR Guiding Principles for scientific data management and stewardship": [[52, "the-fair-guiding-principles-for-scientific-data-management-and-stewardship"], [64, "the-fair-guiding-principles-for-scientific-data-management-and-stewardship"], [71, "the-fair-guiding-principles-for-scientific-data-management-and-stewardship"], [79, "the-fair-guiding-principles-for-scientific-data-management-and-stewardship"], [93, "the-fair-guiding-principles-for-scientific-data-management-and-stewardship"], [105, "the-fair-guiding-principles-for-scientific-data-management-and-stewardship"]], "The FAIR guiding principles for data stewardship - fair enough?": [[52, "the-fair-guiding-principles-for-data-stewardship-fair-enough"], [71, "the-fair-guiding-principles-for-data-stewardship-fair-enough"], [79, "the-fair-guiding-principles-for-data-stewardship-fair-enough"], [92, "the-fair-guiding-principles-for-data-stewardship-fair-enough"], [93, "the-fair-guiding-principles-for-data-stewardship-fair-enough"], [106, "the-fair-guiding-principles-for-data-stewardship-fair-enough"]], "The Fiji Updater": [[29, "the-fiji-updater"], [52, "the-fiji-updater"], [76, "the-fiji-updater"], [94, "the-fiji-updater"]], "The Information Infrastructure for BioImage Data (I3D:bio) project to advance FAIR microscopy data management for the community": [[3, "the-information-infrastructure-for-bioimage-data-i3d-bio-project-to-advance-fair-microscopy-data-management-for-the-community"], [8, "the-information-infrastructure-for-bioimage-data-i3d-bio-project-to-advance-fair-microscopy-data-management-for-the-community"], [23, "the-information-infrastructure-for-bioimage-data-i3d-bio-project-to-advance-fair-microscopy-data-management-for-the-community"], [30, "the-information-infrastructure-for-bioimage-data-i3d-bio-project-to-advance-fair-microscopy-data-management-for-the-community"], [33, "the-information-infrastructure-for-bioimage-data-i3d-bio-project-to-advance-fair-microscopy-data-management-for-the-community"], [34, "the-information-infrastructure-for-bioimage-data-i3d-bio-project-to-advance-fair-microscopy-data-management-for-the-community"], [37, "the-information-infrastructure-for-bioimage-data-i3d-bio-project-to-advance-fair-microscopy-data-management-for-the-community"], [64, "the-information-infrastructure-for-bioimage-data-i3d-bio-project-to-advance-fair-microscopy-data-management-for-the-community"], [73, "the-information-infrastructure-for-bioimage-data-i3d-bio-project-to-advance-fair-microscopy-data-management-for-the-community"], [79, "the-information-infrastructure-for-bioimage-data-i3d-bio-project-to-advance-fair-microscopy-data-management-for-the-community"], [99, "the-information-infrastructure-for-bioimage-data-i3d-bio-project-to-advance-fair-microscopy-data-management-for-the-community"], [105, "the-information-infrastructure-for-bioimage-data-i3d-bio-project-to-advance-fair-microscopy-data-management-for-the-community"]], "The Open Microscopy Environment (OME) Data Model and XML file - open tools for informatics and quantitative analysis in biological imaging": [[2, "the-open-microscopy-environment-ome-data-model-and-xml-file-open-tools-for-informatics-and-quantitative-analysis-in-biological-imaging"], [15, "the-open-microscopy-environment-ome-data-model-and-xml-file-open-tools-for-informatics-and-quantitative-analysis-in-biological-imaging"], [52, "the-open-microscopy-environment-ome-data-model-and-xml-file-open-tools-for-informatics-and-quantitative-analysis-in-biological-imaging"], [64, "the-open-microscopy-environment-ome-data-model-and-xml-file-open-tools-for-informatics-and-quantitative-analysis-in-biological-imaging"], [79, "the-open-microscopy-environment-ome-data-model-and-xml-file-open-tools-for-informatics-and-quantitative-analysis-in-biological-imaging"], [90, "the-open-microscopy-environment-ome-data-model-and-xml-file-open-tools-for-informatics-and-quantitative-analysis-in-biological-imaging"]], "The Turing Way: Guide for reproducible research": [[40, "the-turing-way-guide-for-reproducible-research"], [79, "the-turing-way-guide-for-reproducible-research"], [84, "the-turing-way-guide-for-reproducible-research"]], "The role of Helmholtz Centers in NFDI4BIOIMAGE - A national consortium enhancing FAIR data management for microscopy and bioimage analysis": [[3, "the-role-of-helmholtz-centers-in-nfdi4bioimage-a-national-consortium-enhancing-fair-data-management-for-microscopy-and-bioimage-analysis"], [8, "the-role-of-helmholtz-centers-in-nfdi4bioimage-a-national-consortium-enhancing-fair-data-management-for-microscopy-and-bioimage-analysis"], [23, "the-role-of-helmholtz-centers-in-nfdi4bioimage-a-national-consortium-enhancing-fair-data-management-for-microscopy-and-bioimage-analysis"], [28, "the-role-of-helmholtz-centers-in-nfdi4bioimage-a-national-consortium-enhancing-fair-data-management-for-microscopy-and-bioimage-analysis"], [64, "the-role-of-helmholtz-centers-in-nfdi4bioimage-a-national-consortium-enhancing-fair-data-management-for-microscopy-and-bioimage-analysis"], [73, "the-role-of-helmholtz-centers-in-nfdi4bioimage-a-national-consortium-enhancing-fair-data-management-for-microscopy-and-bioimage-analysis"], [79, "the-role-of-helmholtz-centers-in-nfdi4bioimage-a-national-consortium-enhancing-fair-data-management-for-microscopy-and-bioimage-analysis"], [99, "the-role-of-helmholtz-centers-in-nfdi4bioimage-a-national-consortium-enhancing-fair-data-management-for-microscopy-and-bioimage-analysis"], [105, "the-role-of-helmholtz-centers-in-nfdi4bioimage-a-national-consortium-enhancing-fair-data-management-for-microscopy-and-bioimage-analysis"]], "Thinking data management on different scales": [[34, "thinking-data-management-on-different-scales"], [54, "thinking-data-management-on-different-scales"], [64, "thinking-data-management-on-different-scales"], [73, "thinking-data-management-on-different-scales"], [79, "thinking-data-management-on-different-scales"], [99, "thinking-data-management-on-different-scales"], [105, "thinking-data-management-on-different-scales"]], "Thomas zobel (7)": [[35, null]], "Toby hodges (4)": [[36, null]], "Tom boissonnet (13)": [[37, null]], "Towards Preservation of Life Science Data with NFDI4BIOIMAGE": [[29, "towards-preservation-of-life-science-data-with-nfdi4bioimage"], [64, "towards-preservation-of-life-science-data-with-nfdi4bioimage"], [73, "towards-preservation-of-life-science-data-with-nfdi4bioimage"], [79, "towards-preservation-of-life-science-data-with-nfdi4bioimage"], [99, "towards-preservation-of-life-science-data-with-nfdi4bioimage"], [105, "towards-preservation-of-life-science-data-with-nfdi4bioimage"]], "Towards Transparency and Knowledge Exchange in AI-assisted Data Analysis Code Generation": [[29, "towards-transparency-and-knowledge-exchange-in-ai-assisted-data-analysis-code-generation"], [64, "towards-transparency-and-knowledge-exchange-in-ai-assisted-data-analysis-code-generation"], [73, "towards-transparency-and-knowledge-exchange-in-ai-assisted-data-analysis-code-generation"], [79, "towards-transparency-and-knowledge-exchange-in-ai-assisted-data-analysis-code-generation"]], "Towards community-driven metadata standards for light microscopy - tiered specifications extending the OME model": [[11, "towards-community-driven-metadata-standards-for-light-microscopy-tiered-specifications-extending-the-ome-model"], [52, "towards-community-driven-metadata-standards-for-light-microscopy-tiered-specifications-extending-the-ome-model"], [86, "towards-community-driven-metadata-standards-for-light-microscopy-tiered-specifications-extending-the-ome-model"], [90, "towards-community-driven-metadata-standards-for-light-microscopy-tiered-specifications-extending-the-ome-model"], [96, "towards-community-driven-metadata-standards-for-light-microscopy-tiered-specifications-extending-the-ome-model"]], "Tracking Theory, TrackMate, and Mastodon": [[29, "tracking-theory-trackmate-and-mastodon"], [54, "tracking-theory-trackmate-and-mastodon"], [67, "tracking-theory-trackmate-and-mastodon"], [78, "tracking-theory-trackmate-and-mastodon"], [90, "tracking-theory-trackmate-and-mastodon"], [94, "tracking-theory-trackmate-and-mastodon"], [98, "tracking-theory-trackmate-and-mastodon"]], "Tracking in napari": [[19, "tracking-in-napari"], [39, "tracking-in-napari"], [66, "tracking-in-napari"], [90, "tracking-in-napari"], [97, "tracking-in-napari"], [103, "tracking-in-napari"]], "Tracking of mitochondria and capturing mitoflashes": [[55, "tracking-of-mitochondria-and-capturing-mitoflashes"], [79, "tracking-of-mitochondria-and-capturing-mitoflashes"], [90, "tracking-of-mitochondria-and-capturing-mitoflashes"], [91, "tracking-of-mitochondria-and-capturing-mitoflashes"]], "Train-the-Trainer Concept on Research Data Management": [[40, "train-the-trainer-concept-on-research-data-management"], [64, "train-the-trainer-concept-on-research-data-management"], [73, "train-the-trainer-concept-on-research-data-management"], [79, "train-the-trainer-concept-on-research-data-management"], [105, "train-the-trainer-concept-on-research-data-management"]], "Training Deep Learning Models for Vision - Compact Course": [[5, "training-deep-learning-models-for-vision-compact-course"], [68, "training-deep-learning-models-for-vision-compact-course"], [86, "training-deep-learning-models-for-vision-compact-course"], [89, "training-deep-learning-models-for-vision-compact-course"], [90, "training-deep-learning-models-for-vision-compact-course"]], "Tutorial (42)": [[55, null]], "Ugis sarkans (5)": [[38, null]], "Ultrack I2K 2024 Workshop Materials": [[47, "ultrack-i2k-2024-workshop-materials"], [55, "ultrack-i2k-2024-workshop-materials"], [58, "ultrack-i2k-2024-workshop-materials"], [68, "ultrack-i2k-2024-workshop-materials"], [90, "ultrack-i2k-2024-workshop-materials"]], "Understanding metric-related pitfalls in image analysis validation": [[11, "understanding-metric-related-pitfalls-in-image-analysis-validation"], [51, "understanding-metric-related-pitfalls-in-image-analysis-validation"], [52, "understanding-metric-related-pitfalls-in-image-analysis-validation"], [90, "understanding-metric-related-pitfalls-in-image-analysis-validation"]], "Unknown (89)": [[86, null]], "Upcoming Image Analysis Events": [[11, "upcoming-image-analysis-events"], [25, "upcoming-image-analysis-events"], [31, "upcoming-image-analysis-events"], [42, "upcoming-image-analysis-events"], [46, "upcoming-image-analysis-events"], [58, "upcoming-image-analysis-events"], [86, "upcoming-image-analysis-events"], [90, "upcoming-image-analysis-events"]], "Urheberrecht": [[75, "urheberrecht"]], "User friendly Image metadata annotation tool/workflow for OMERO": [[16, "user-friendly-image-metadata-annotation-tool-workflow-for-omero"], [96, "user-friendly-image-metadata-annotation-tool-workflow-for-omero"], [100, "user-friendly-image-metadata-annotation-tool-workflow-for-omero"], [107, "user-friendly-image-metadata-annotation-tool-workflow-for-omero"]], "Using Glittr.org to find, compare and re-use online training materials": [[51, "using-glittr-org-to-find-compare-and-re-use-online-training-materials"], [52, "using-glittr-org-to-find-compare-and-re-use-online-training-materials"], [69, "using-glittr-org-to-find-compare-and-re-use-online-training-materials"], [79, "using-glittr-org-to-find-compare-and-re-use-online-training-materials"], [90, "using-glittr-org-to-find-compare-and-re-use-online-training-materials"], [105, "using-glittr-org-to-find-compare-and-re-use-online-training-materials"]], "Using the Training Materials Submission App": [[61, null]], "V4SDB_Winter_School_2025": [[42, "v4sdb-winter-school-2025"], [47, "v4sdb-winter-school-2025"], [68, "v4sdb-winter-school-2025"], [90, "v4sdb-winter-school-2025"], [109, "v4sdb-winter-school-2025"]], "Verantwortlich f\u00fcr den Inhalt nach \u00a7 55 Abs. 2 RStV:": [[75, "verantwortlich-fur-den-inhalt-nach-55-abs-2-rstv"]], "Video (32)": [[56, null]], "Virtual-I2K-2024-multiview-stitcher": [[47, "virtual-i2k-2024-multiview-stitcher"], [55, "virtual-i2k-2024-multiview-stitcher"], [68, "virtual-i2k-2024-multiview-stitcher"], [78, "virtual-i2k-2024-multiview-stitcher"]], "Website (9)": [[57, null]], "Welcome to BioImage Town": [[17, "welcome-to-bioimage-town"], [54, "welcome-to-bioimage-town"], [73, "welcome-to-bioimage-town"], [79, "welcome-to-bioimage-town"], [90, "welcome-to-bioimage-town"], [99, "welcome-to-bioimage-town"], [100, "welcome-to-bioimage-town"]], "What is Bioimage Analysis? An Introduction": [[18, "what-is-bioimage-analysis-an-introduction"], [54, "what-is-bioimage-analysis-an-introduction"], [86, "what-is-bioimage-analysis-an-introduction"], [90, "what-is-bioimage-analysis-an-introduction"], [98, "what-is-bioimage-analysis-an-introduction"]], "What is Open Data?": [[11, "what-is-open-data"], [42, "what-is-open-data"], [79, "what-is-open-data"], [101, "what-is-open-data"]], "What to contribute": [[60, "what-to-contribute"]], "Who you gonna call? - Data Stewards to the rescue": [[6, "who-you-gonna-call-data-stewards-to-the-rescue"], [16, "who-you-gonna-call-data-stewards-to-the-rescue"], [28, "who-you-gonna-call-data-stewards-to-the-rescue"], [50, "who-you-gonna-call-data-stewards-to-the-rescue"], [64, "who-you-gonna-call-data-stewards-to-the-rescue"], [73, "who-you-gonna-call-data-stewards-to-the-rescue"], [79, "who-you-gonna-call-data-stewards-to-the-rescue"], [90, "who-you-gonna-call-data-stewards-to-the-rescue"], [92, "who-you-gonna-call-data-stewards-to-the-rescue"], [99, "who-you-gonna-call-data-stewards-to-the-rescue"], [105, "who-you-gonna-call-data-stewards-to-the-rescue"]], "Workflow (9)": [[107, null]], "Workflow engine (13)": [[108, null]], "WorkflowHub": [[11, "workflowhub"], [42, "workflowhub"], [78, "workflowhub"], [91, "workflowhub"], [103, "workflowhub"], [107, "workflowhub"], [108, "workflowhub"]], "Working Group Charter. RDM Helpdesk Network": [[64, "working-group-charter-rdm-helpdesk-network"], [73, "working-group-charter-rdm-helpdesk-network"], [79, "working-group-charter-rdm-helpdesk-network"]], "Working with objects in 2D and 3D": [[29, "working-with-objects-in-2d-and-3d"], [54, "working-with-objects-in-2d-and-3d"], [67, "working-with-objects-in-2d-and-3d"], [78, "working-with-objects-in-2d-and-3d"], [90, "working-with-objects-in-2d-and-3d"], [94, "working-with-objects-in-2d-and-3d"], [98, "working-with-objects-in-2d-and-3d"]], "Working with pixels": [[29, "working-with-pixels"], [54, "working-with-pixels"], [67, "working-with-pixels"], [78, "working-with-pixels"], [90, "working-with-pixels"], [94, "working-with-pixels"], [98, "working-with-pixels"]], "Workshop (12)": [[58, null]], "Workshop-June2024-Madrid": [[42, "workshop-june2024-madrid"], [58, "workshop-june2024-madrid"], [68, "workshop-june2024-madrid"], [84, "workshop-june2024-madrid"], [90, "workshop-june2024-madrid"]], "Www.biorxiv.org (5)": [[69, null]], "Www.ebi.ac.uk (10)": [[70, null]], "Www.nature.com (16)": [[71, null]], "Www.youtube.com (22)": [[72, null]], "YMIA - Python-Based Event Series Training Material": [[28, "ymia-python-based-event-series-training-material"], [29, "ymia-python-based-event-series-training-material"], [47, "ymia-python-based-event-series-training-material"], [54, "ymia-python-based-event-series-training-material"], [68, "ymia-python-based-event-series-training-material"], [84, "ymia-python-based-event-series-training-material"], [90, "ymia-python-based-event-series-training-material"], [103, "ymia-python-based-event-series-training-material"]], "YML format": [[59, null]], "ZEN & Python workshop": [[42, "zen-python-workshop"], [48, "zen-python-workshop"], [68, "zen-python-workshop"], [83, "zen-python-workshop"], [90, "zen-python-workshop"], [97, "zen-python-workshop"], [103, "zen-python-workshop"]], "ZIDAS 2020 Introduction to Deep Learning": [[10, "zidas-2020-introduction-to-deep-learning"], [54, "zidas-2020-introduction-to-deep-learning"], [68, "zidas-2020-introduction-to-deep-learning"], [86, "zidas-2020-introduction-to-deep-learning"], [89, "zidas-2020-introduction-to-deep-learning"], [90, "zidas-2020-introduction-to-deep-learning"]], "Zeiss AxioZoom Stage Adapter": [[22, "zeiss-axiozoom-stage-adapter"], [64, "zeiss-axiozoom-stage-adapter"], [73, "zeiss-axiozoom-stage-adapter"], [79, "zeiss-axiozoom-stage-adapter"]], "Zeiss AxioZoom Stage Adapter - 12/6Well Plate": [[22, "zeiss-axiozoom-stage-adapter-12-6well-plate"], [64, "zeiss-axiozoom-stage-adapter-12-6well-plate"], [73, "zeiss-axiozoom-stage-adapter-12-6well-plate"], [79, "zeiss-axiozoom-stage-adapter-12-6well-plate"]], "Zeiss AxioZoom Stage Adapter - EM block holder": [[22, "zeiss-axiozoom-stage-adapter-em-block-holder"], [64, "zeiss-axiozoom-stage-adapter-em-block-holder"], [73, "zeiss-axiozoom-stage-adapter-em-block-holder"], [79, "zeiss-axiozoom-stage-adapter-em-block-holder"]], "Zeiss AxioZoom Stage Adapter - Microscope slides": [[22, "zeiss-axiozoom-stage-adapter-microscope-slides"], [64, "zeiss-axiozoom-stage-adapter-microscope-slides"], [73, "zeiss-axiozoom-stage-adapter-microscope-slides"], [79, "zeiss-axiozoom-stage-adapter-microscope-slides"]], "Zenodo.org (172)": [[73, null]], "ZeroCostDL4Mic: exploiting Google Colab to develop a free and open-source toolbox for Deep-Learning in microscopy": [[12, "zerocostdl4mic-exploiting-google-colab-to-develop-a-free-and-open-source-toolbox-for-deep-learning-in-microscopy"], [42, "zerocostdl4mic-exploiting-google-colab-to-develop-a-free-and-open-source-toolbox-for-deep-learning-in-microscopy"], [48, "zerocostdl4mic-exploiting-google-colab-to-develop-a-free-and-open-source-toolbox-for-deep-learning-in-microscopy"], [64, "zerocostdl4mic-exploiting-google-colab-to-develop-a-free-and-open-source-toolbox-for-deep-learning-in-microscopy"], [68, "zerocostdl4mic-exploiting-google-colab-to-develop-a-free-and-open-source-toolbox-for-deep-learning-in-microscopy"], [71, "zerocostdl4mic-exploiting-google-colab-to-develop-a-free-and-open-source-toolbox-for-deep-learning-in-microscopy"], [84, "zerocostdl4mic-exploiting-google-colab-to-develop-a-free-and-open-source-toolbox-for-deep-learning-in-microscopy"], [90, "zerocostdl4mic-exploiting-google-colab-to-develop-a-free-and-open-source-toolbox-for-deep-learning-in-microscopy"]], "[BINA CC] Scalable strategies for a next-generation of FAIR bioimaging": [[17, "bina-cc-scalable-strategies-for-a-next-generation-of-fair-bioimaging"], [64, "bina-cc-scalable-strategies-for-a-next-generation-of-fair-bioimaging"], [73, "bina-cc-scalable-strategies-for-a-next-generation-of-fair-bioimaging"], [79, "bina-cc-scalable-strategies-for-a-next-generation-of-fair-bioimaging"]], "[CIDAS] Scalable strategies for a next-generation of FAIR bioimaging": [[17, "cidas-scalable-strategies-for-a-next-generation-of-fair-bioimaging"], [64, "cidas-scalable-strategies-for-a-next-generation-of-fair-bioimaging"], [73, "cidas-scalable-strategies-for-a-next-generation-of-fair-bioimaging"], [79, "cidas-scalable-strategies-for-a-next-generation-of-fair-bioimaging"], [99, "cidas-scalable-strategies-for-a-next-generation-of-fair-bioimaging"], [109, "cidas-scalable-strategies-for-a-next-generation-of-fair-bioimaging"]], "[CMCB] Scalable strategies for a next-generation of FAIR bioimaging": [[17, "cmcb-scalable-strategies-for-a-next-generation-of-fair-bioimaging"], [64, "cmcb-scalable-strategies-for-a-next-generation-of-fair-bioimaging"], [73, "cmcb-scalable-strategies-for-a-next-generation-of-fair-bioimaging"], [79, "cmcb-scalable-strategies-for-a-next-generation-of-fair-bioimaging"], [99, "cmcb-scalable-strategies-for-a-next-generation-of-fair-bioimaging"], [109, "cmcb-scalable-strategies-for-a-next-generation-of-fair-bioimaging"]], "[CORDI 2023] Zarr: A Cloud-Optimized Storage for Interactive Access of Large Arrays": [[17, "cordi-2023-zarr-a-cloud-optimized-storage-for-interactive-access-of-large-arrays"], [50, "cordi-2023-zarr-a-cloud-optimized-storage-for-interactive-access-of-large-arrays"], [73, "cordi-2023-zarr-a-cloud-optimized-storage-for-interactive-access-of-large-arrays"], [79, "cordi-2023-zarr-a-cloud-optimized-storage-for-interactive-access-of-large-arrays"], [90, "cordi-2023-zarr-a-cloud-optimized-storage-for-interactive-access-of-large-arrays"], [105, "cordi-2023-zarr-a-cloud-optimized-storage-for-interactive-access-of-large-arrays"]], "[Community Meeting 2024] Overview Team Image Data Analysis and Management": [[34, "community-meeting-2024-overview-team-image-data-analysis-and-management"], [35, "community-meeting-2024-overview-team-image-data-analysis-and-management"], [64, "community-meeting-2024-overview-team-image-data-analysis-and-management"], [73, "community-meeting-2024-overview-team-image-data-analysis-and-management"], [79, "community-meeting-2024-overview-team-image-data-analysis-and-management"], [99, "community-meeting-2024-overview-team-image-data-analysis-and-management"], [105, "community-meeting-2024-overview-team-image-data-analysis-and-management"]], "[Community Meeting 2024] Supporting and financing RDM projects within GerBI": [[3, "community-meeting-2024-supporting-and-financing-rdm-projects-within-gerbi"], [17, "community-meeting-2024-supporting-and-financing-rdm-projects-within-gerbi"], [30, "community-meeting-2024-supporting-and-financing-rdm-projects-within-gerbi"], [33, "community-meeting-2024-supporting-and-financing-rdm-projects-within-gerbi"], [34, "community-meeting-2024-supporting-and-financing-rdm-projects-within-gerbi"], [35, "community-meeting-2024-supporting-and-financing-rdm-projects-within-gerbi"], [64, "community-meeting-2024-supporting-and-financing-rdm-projects-within-gerbi"], [73, "community-meeting-2024-supporting-and-financing-rdm-projects-within-gerbi"], [79, "community-meeting-2024-supporting-and-financing-rdm-projects-within-gerbi"]], "[ELMI 2024] AI\u2019s Dirty Little Secret: Without": [[17, "elmi-2024-ai-s-dirty-little-secret-without"], [34, "elmi-2024-ai-s-dirty-little-secret-without"], [64, "elmi-2024-ai-s-dirty-little-secret-without"], [73, "elmi-2024-ai-s-dirty-little-secret-without"], [79, "elmi-2024-ai-s-dirty-little-secret-without"], [99, "elmi-2024-ai-s-dirty-little-secret-without"], [105, "elmi-2024-ai-s-dirty-little-secret-without"]], "[ELMI 2024] AI\u2019s Dirty Little Secret: Without FAIR Data, It\u2019s Just Fancy Math": [[17, "elmi-2024-ai-s-dirty-little-secret-without-fair-data-it-s-just-fancy-math"], [34, "elmi-2024-ai-s-dirty-little-secret-without-fair-data-it-s-just-fancy-math"], [50, "elmi-2024-ai-s-dirty-little-secret-without-fair-data-it-s-just-fancy-math"], [73, "elmi-2024-ai-s-dirty-little-secret-without-fair-data-it-s-just-fancy-math"], [79, "elmi-2024-ai-s-dirty-little-secret-without-fair-data-it-s-just-fancy-math"], [90, "elmi-2024-ai-s-dirty-little-secret-without-fair-data-it-s-just-fancy-math"], [93, "elmi-2024-ai-s-dirty-little-secret-without-fair-data-it-s-just-fancy-math"], [99, "elmi-2024-ai-s-dirty-little-secret-without-fair-data-it-s-just-fancy-math"], [105, "elmi-2024-ai-s-dirty-little-secret-without-fair-data-it-s-just-fancy-math"]], "[GBI EOE VII] Five (or ten) must-have items for making IT infrastructure for managing bioimage data": [[17, "gbi-eoe-vii-five-or-ten-must-have-items-for-making-it-infrastructure-for-managing-bioimage-data"], [64, "gbi-eoe-vii-five-or-ten-must-have-items-for-making-it-infrastructure-for-managing-bioimage-data"], [73, "gbi-eoe-vii-five-or-ten-must-have-items-for-making-it-infrastructure-for-managing-bioimage-data"], [79, "gbi-eoe-vii-five-or-ten-must-have-items-for-making-it-infrastructure-for-managing-bioimage-data"]], "[GBI EoE IX] NFDI4BIOIMAGE": [[17, "gbi-eoe-ix-nfdi4bioimage"], [64, "gbi-eoe-ix-nfdi4bioimage"], [73, "gbi-eoe-ix-nfdi4bioimage"], [79, "gbi-eoe-ix-nfdi4bioimage"]], "[I2K] Scalable strategies for a next-generation of FAIR bioimaging": [[17, "i2k-scalable-strategies-for-a-next-generation-of-fair-bioimaging"], [64, "i2k-scalable-strategies-for-a-next-generation-of-fair-bioimaging"], [73, "i2k-scalable-strategies-for-a-next-generation-of-fair-bioimaging"], [79, "i2k-scalable-strategies-for-a-next-generation-of-fair-bioimaging"]], "[N4BI AHM] Welcome to BioImage Town": [[17, "n4bi-ahm-welcome-to-bioimage-town"], [54, "n4bi-ahm-welcome-to-bioimage-town"], [64, "n4bi-ahm-welcome-to-bioimage-town"], [73, "n4bi-ahm-welcome-to-bioimage-town"], [79, "n4bi-ahm-welcome-to-bioimage-town"], [105, "n4bi-ahm-welcome-to-bioimage-town"]], "[SWAT4HCLS 2023] NFDI4BIOIMAGE: Perspective for a national bioimage standard": [[17, "swat4hcls-2023-nfdi4bioimage-perspective-for-a-national-bioimage-standard"], [34, "swat4hcls-2023-nfdi4bioimage-perspective-for-a-national-bioimage-standard"], [50, "swat4hcls-2023-nfdi4bioimage-perspective-for-a-national-bioimage-standard"], [73, "swat4hcls-2023-nfdi4bioimage-perspective-for-a-national-bioimage-standard"], [79, "swat4hcls-2023-nfdi4bioimage-perspective-for-a-national-bioimage-standard"], [93, "swat4hcls-2023-nfdi4bioimage-perspective-for-a-national-bioimage-standard"], [99, "swat4hcls-2023-nfdi4bioimage-perspective-for-a-national-bioimage-standard"], [105, "swat4hcls-2023-nfdi4bioimage-perspective-for-a-national-bioimage-standard"]], "[Short Talk] NFDI4BIOIMAGE - A consortium in the National Research Data Infrastructure": [[3, "short-talk-nfdi4bioimage-a-consortium-in-the-national-research-data-infrastructure"], [54, "short-talk-nfdi4bioimage-a-consortium-in-the-national-research-data-infrastructure"], [64, "short-talk-nfdi4bioimage-a-consortium-in-the-national-research-data-infrastructure"], [73, "short-talk-nfdi4bioimage-a-consortium-in-the-national-research-data-infrastructure"], [79, "short-talk-nfdi4bioimage-a-consortium-in-the-national-research-data-infrastructure"], [90, "short-talk-nfdi4bioimage-a-consortium-in-the-national-research-data-infrastructure"], [99, "short-talk-nfdi4bioimage-a-consortium-in-the-national-research-data-infrastructure"], [105, "short-talk-nfdi4bioimage-a-consortium-in-the-national-research-data-infrastructure"]], "[Workshop Material] Fit for OMERO - How imaging facilities and IT departments work together to enable RDM for bioimaging, October 16-17, 2024, Heidelberg": [[3, "workshop-material-fit-for-omero-how-imaging-facilities-and-it-departments-work-together-to-enable-rdm-for-bioimaging-october-16-17-2024-heidelberg"], [33, "workshop-material-fit-for-omero-how-imaging-facilities-and-it-departments-work-together-to-enable-rdm-for-bioimaging-october-16-17-2024-heidelberg"], [34, "workshop-material-fit-for-omero-how-imaging-facilities-and-it-departments-work-together-to-enable-rdm-for-bioimaging-october-16-17-2024-heidelberg"], [37, "workshop-material-fit-for-omero-how-imaging-facilities-and-it-departments-work-together-to-enable-rdm-for-bioimaging-october-16-17-2024-heidelberg"], [64, "workshop-material-fit-for-omero-how-imaging-facilities-and-it-departments-work-together-to-enable-rdm-for-bioimaging-october-16-17-2024-heidelberg"], [73, "workshop-material-fit-for-omero-how-imaging-facilities-and-it-departments-work-together-to-enable-rdm-for-bioimaging-october-16-17-2024-heidelberg"], [79, "workshop-material-fit-for-omero-how-imaging-facilities-and-it-departments-work-together-to-enable-rdm-for-bioimaging-october-16-17-2024-heidelberg"], [99, "workshop-material-fit-for-omero-how-imaging-facilities-and-it-departments-work-together-to-enable-rdm-for-bioimaging-october-16-17-2024-heidelberg"], [105, "workshop-material-fit-for-omero-how-imaging-facilities-and-it-departments-work-together-to-enable-rdm-for-bioimaging-october-16-17-2024-heidelberg"]], "[Workshop] Bioimage data management and analysis with OMERO": [[3, "workshop-bioimage-data-management-and-analysis-with-omero"], [23, "workshop-bioimage-data-management-and-analysis-with-omero"], [28, "workshop-bioimage-data-management-and-analysis-with-omero"], [64, "workshop-bioimage-data-management-and-analysis-with-omero"], [73, "workshop-bioimage-data-management-and-analysis-with-omero"], [79, "workshop-bioimage-data-management-and-analysis-with-omero"], [99, "workshop-bioimage-data-management-and-analysis-with-omero"], [105, "workshop-bioimage-data-management-and-analysis-with-omero"]], "[Workshop] FAIR data handling for microscopy: Structured metadata annotation in OMERO": [[3, "workshop-fair-data-handling-for-microscopy-structured-metadata-annotation-in-omero"], [37, "workshop-fair-data-handling-for-microscopy-structured-metadata-annotation-in-omero"], [64, "workshop-fair-data-handling-for-microscopy-structured-metadata-annotation-in-omero"], [73, "workshop-fair-data-handling-for-microscopy-structured-metadata-annotation-in-omero"], [79, "workshop-fair-data-handling-for-microscopy-structured-metadata-annotation-in-omero"]], "[Workshop] Research Data Management for Microscopy and BioImage Analysis": [[3, "workshop-research-data-management-for-microscopy-and-bioimage-analysis"], [23, "workshop-research-data-management-for-microscopy-and-bioimage-analysis"], [37, "workshop-research-data-management-for-microscopy-and-bioimage-analysis"], [64, "workshop-research-data-management-for-microscopy-and-bioimage-analysis"], [73, "workshop-research-data-management-for-microscopy-and-bioimage-analysis"], [79, "workshop-research-data-management-for-microscopy-and-bioimage-analysis"], [99, "workshop-research-data-management-for-microscopy-and-bioimage-analysis"], [105, "workshop-research-data-management-for-microscopy-and-bioimage-analysis"], [109, "workshop-research-data-management-for-microscopy-and-bioimage-analysis"]], "arivis Vision4D Tutorials": [[42, "arivis-vision4d-tutorials"], [56, "arivis-vision4d-tutorials"], [72, "arivis-vision4d-tutorials"], [76, "arivis-vision4d-tutorials"], [90, "arivis-vision4d-tutorials"]], "bioformats2raw Converter": [[2, "bioformats2raw-converter"], [11, "bioformats2raw-converter"], [17, "bioformats2raw-converter"], [47, "bioformats2raw-converter"], [68, "bioformats2raw-converter"], [82, "bioformats2raw-converter"], [102, "bioformats2raw-converter"]], "cba-support-template": [[4, "cba-support-template"], [7, "cba-support-template"], [11, "cba-support-template"], [55, "cba-support-template"], [84, "cba-support-template"], [105, "cba-support-template"], [107, "cba-support-template"]], "i2k-2020-s3-zarr-workshop": [[4, "i2k-2020-s3-zarr-workshop"], [5, "i2k-2020-s3-zarr-workshop"], [47, "i2k-2020-s3-zarr-workshop"], [68, "i2k-2020-s3-zarr-workshop"], [86, "i2k-2020-s3-zarr-workshop"], [103, "i2k-2020-s3-zarr-workshop"]], "ilastik: interactive machine learning for (bio)image analysis": [[7, "ilastik-interactive-machine-learning-for-bio-image-analysis"], [54, "ilastik-interactive-machine-learning-for-bio-image-analysis"], [73, "ilastik-interactive-machine-learning-for-bio-image-analysis"], [79, "ilastik-interactive-machine-learning-for-bio-image-analysis"], [89, "ilastik-interactive-machine-learning-for-bio-image-analysis"], [90, "ilastik-interactive-machine-learning-for-bio-image-analysis"]], "introduction-to-generative-ai": [[47, "introduction-to-generative-ai"], [55, "introduction-to-generative-ai"], [68, "introduction-to-generative-ai"], [79, "introduction-to-generative-ai"], [89, "introduction-to-generative-ai"]], "introduction-to-image-analysis": [[47, "introduction-to-image-analysis"], [68, "introduction-to-image-analysis"], [80, "introduction-to-image-analysis"], [90, "introduction-to-image-analysis"]], "martinschatz-cz/SciCount: v1.0.0 with reusable example notebooks": [[20, "martinschatz-cz-scicount-v1-0-0-with-reusable-example-notebooks"], [64, "martinschatz-cz-scicount-v1-0-0-with-reusable-example-notebooks"], [73, "martinschatz-cz-scicount-v1-0-0-with-reusable-example-notebooks"]], "microlist": [[42, "microlist"], [76, "microlist"]], "nextflow-workshop": [[47, "nextflow-workshop"], [55, "nextflow-workshop"], [68, "nextflow-workshop"], [79, "nextflow-workshop"], [107, "nextflow-workshop"]], "numpy pandas course": [[13, "numpy-pandas-course"], [48, "numpy-pandas-course"], [68, "numpy-pandas-course"], [78, "numpy-pandas-course"], [103, "numpy-pandas-course"]], "ome-ngff-validator": [[17, "ome-ngff-validator"], [47, "ome-ngff-validator"], [68, "ome-ngff-validator"], [77, "ome-ngff-validator"]], "ome2024-ngff-challenge": [[15, "ome2024-ngff-challenge"], [17, "ome2024-ngff-challenge"], [37, "ome2024-ngff-challenge"], [47, "ome2024-ngff-challenge"], [68, "ome2024-ngff-challenge"], [78, "ome2024-ngff-challenge"], [99, "ome2024-ngff-challenge"], [105, "ome2024-ngff-challenge"], [106, "ome2024-ngff-challenge"]], "patho_prompt_injection": [[47, "patho-prompt-injection"], [48, "patho-prompt-injection"], [68, "patho-prompt-injection"], [83, "patho-prompt-injection"], [90, "patho-prompt-injection"]], "quantixed/TheDigitalCell: First complete code set": [[41, "quantixed-thedigitalcell-first-complete-code-set"], [64, "quantixed-thedigitalcell-first-complete-code-set"], [68, "quantixed-thedigitalcell-first-complete-code-set"], [73, "quantixed-thedigitalcell-first-complete-code-set"], [83, "quantixed-thedigitalcell-first-complete-code-set"], [90, "quantixed-thedigitalcell-first-complete-code-set"]], "raw2ometiff Converter": [[2, "raw2ometiff-converter"], [17, "raw2ometiff-converter"], [47, "raw2ometiff-converter"], [68, "raw2ometiff-converter"], [82, "raw2ometiff-converter"], [102, "raw2ometiff-converter"]], "re3data.org - registry of Research Data Repositories": [[57, "re3data-org-registry-of-research-data-repositories"], [79, "re3data-org-registry-of-research-data-repositories"], [105, "re3data-org-registry-of-research-data-repositories"]], "rse-skills-workshop": [[47, "rse-skills-workshop"], [54, "rse-skills-workshop"], [68, "rse-skills-workshop"], [83, "rse-skills-workshop"]], "scanpy-tutorials": [[47, "scanpy-tutorials"], [68, "scanpy-tutorials"], [78, "scanpy-tutorials"], [90, "scanpy-tutorials"]], "scikit-learn MOOC": [[47, "scikit-learn-mooc"], [68, "scikit-learn-mooc"], [79, "scikit-learn-mooc"], [90, "scikit-learn-mooc"]], "skimage-tutorials": [[47, "skimage-tutorials"], [68, "skimage-tutorials"], [90, "skimage-tutorials"]], "training-resources": [[4, "training-resources"], [5, "training-resources"], [7, "training-resources"], [9, "training-resources"], [36, "training-resources"], [47, "training-resources"], [68, "training-resources"], [79, "training-resources"]], "\u201cZENODO und Co.\u201d Was bringt und wer braucht ein Repositorium?": [[54, "zenodo-und-co-was-bringt-und-wer-braucht-ein-repositorium"], [64, "zenodo-und-co-was-bringt-und-wer-braucht-ein-repositorium"], [73, "zenodo-und-co-was-bringt-und-wer-braucht-ein-repositorium"], [79, "zenodo-und-co-was-bringt-und-wer-braucht-ein-repositorium"], [105, "zenodo-und-co-was-bringt-und-wer-braucht-ein-repositorium"]]}, "docnames": ["authors/astrid_schauss", "authors/beatriz_serrano-solano", "authors/chris_allan", "authors/christian_schmidt", "authors/christian_tischer", "authors/constantin_pape", "authors/cornelia_wetzker", "authors/dominik_kutra", "authors/elisa_ferrando-may", "authors/elnaz_fazeli", "authors/estibaliz_g\u00f3mez-de-mariscal", "authors/et_al.", "authors/florian_jug", "authors/guillaume_witz", "authors/isabel_kemmer", "authors/jean-marie_burel", "authors/jens_wendt", "authors/josh_moore", "authors/kota_miura", "authors/mara_lampert", "authors/martin_sch\u00e4tz", "authors/martin_weigert", "authors/michael_gerlach", "authors/michele_bortolomeazzi", "authors/nicolas_chiaruttini", "authors/olivier_burri", "authors/pia_voigt", "authors/readme", "authors/riccardo_massei", "authors/robert_haase", "authors/roland_nitschke", "authors/romain_guiet", "authors/silke_tulok", "authors/stefanie_weidtkamp-peters", "authors/susanne_kunis", "authors/thomas_zobel", "authors/toby_hodges", "authors/tom_boissonnet", "authors/ugis_sarkans", "content_types/blog post", "content_types/book", "content_types/code", "content_types/collection", "content_types/data", "content_types/document", "content_types/documentation", "content_types/event", "content_types/github repository", "content_types/notebook", "content_types/online tutorial", "content_types/poster", "content_types/preprint", "content_types/publication", "content_types/readme", "content_types/slides", "content_types/tutorial", "content_types/video", "content_types/website", "content_types/workshop", "contributing/format", "contributing/index", "contributing/submit_app", "domain/biapol.github.io", "domain/docs.google.com", "domain/doi.org", "domain/f1000research.com", "domain/focalplane.biologists.com", "domain/git.mpi-cbg.de", "domain/github.com", "domain/www.biorxiv.org", "domain/www.ebi.ac.uk", "domain/www.nature.com", "domain/www.youtube.com", "domain/zenodo.org", "export/readme", "imprint", "licenses/all_rights_reserved", "licenses/bsd-2-clause", "licenses/bsd-3-clause", "licenses/cc-by-4.0", "licenses/cc-by-sa-4.0", "licenses/cc0-1.0", "licenses/gpl-2.0", "licenses/gpl-3.0", "licenses/mit", "licenses/readme", "licenses/unknown", "readme", "statistics/readme", "tags/artificial_intelligence", "tags/bioimage_analysis", "tags/bioinformatics", "tags/data_stewardship", "tags/fair-principles", "tags/imagej", "tags/licensing", "tags/metadata", "tags/napari", "tags/neubias", "tags/nfdi4bioimage", "tags/omero", "tags/open_science", "tags/open_source_software", "tags/python", "tags/readme", "tags/research_data_management", "tags/sharing", "tags/workflow", "tags/workflow_engine", "whats_new"], "envversion": {"sphinx": 62, "sphinx.domains.c": 3, "sphinx.domains.changeset": 1, "sphinx.domains.citation": 1, "sphinx.domains.cpp": 9, "sphinx.domains.index": 1, "sphinx.domains.javascript": 3, "sphinx.domains.math": 2, "sphinx.domains.python": 4, "sphinx.domains.rst": 2, "sphinx.domains.std": 2, "sphinx.ext.intersphinx": 1}, "filenames": ["authors/astrid_schauss.md", "authors/beatriz_serrano-solano.md", "authors/chris_allan.md", "authors/christian_schmidt.md", "authors/christian_tischer.md", "authors/constantin_pape.md", "authors/cornelia_wetzker.md", "authors/dominik_kutra.md", "authors/elisa_ferrando-may.md", "authors/elnaz_fazeli.md", "authors/estibaliz_g\u00f3mez-de-mariscal.md", "authors/et_al..md", "authors/florian_jug.md", "authors/guillaume_witz.md", "authors/isabel_kemmer.md", "authors/jean-marie_burel.md", "authors/jens_wendt.md", "authors/josh_moore.md", "authors/kota_miura.md", "authors/mara_lampert.md", "authors/martin_sch\u00e4tz.md", "authors/martin_weigert.md", "authors/michael_gerlach.md", "authors/michele_bortolomeazzi.md", "authors/nicolas_chiaruttini.md", "authors/olivier_burri.md", "authors/pia_voigt.md", "authors/readme.md", "authors/riccardo_massei.md", "authors/robert_haase.md", "authors/roland_nitschke.md", "authors/romain_guiet.md", "authors/silke_tulok.md", "authors/stefanie_weidtkamp-peters.md", "authors/susanne_kunis.md", "authors/thomas_zobel.md", "authors/toby_hodges.md", "authors/tom_boissonnet.md", "authors/ugis_sarkans.md", "content_types/blog post.md", "content_types/book.md", "content_types/code.md", "content_types/collection.md", "content_types/data.md", "content_types/document.md", "content_types/documentation.md", "content_types/event.md", "content_types/github repository.md", "content_types/notebook.md", "content_types/online tutorial.md", "content_types/poster.md", "content_types/preprint.md", "content_types/publication.md", "content_types/readme.md", "content_types/slides.md", "content_types/tutorial.md", "content_types/video.md", "content_types/website.md", "content_types/workshop.md", "contributing/format.md", "contributing/index.md", "contributing/submit_app.md", "domain/biapol.github.io.md", "domain/docs.google.com.md", "domain/doi.org.md", "domain/f1000research.com.md", "domain/focalplane.biologists.com.md", "domain/git.mpi-cbg.de.md", "domain/github.com.md", "domain/www.biorxiv.org.md", "domain/www.ebi.ac.uk.md", "domain/www.nature.com.md", "domain/www.youtube.com.md", "domain/zenodo.org.md", "export/readme.md", "imprint.md", "licenses/all_rights_reserved.md", "licenses/bsd-2-clause.md", "licenses/bsd-3-clause.md", "licenses/cc-by-4.0.md", "licenses/cc-by-sa-4.0.md", "licenses/cc0-1.0.md", "licenses/gpl-2.0.md", "licenses/gpl-3.0.md", "licenses/mit.md", "licenses/readme.md", "licenses/unknown.md", "readme.md", "statistics/readme.md", "tags/artificial_intelligence.md", "tags/bioimage_analysis.md", "tags/bioinformatics.md", "tags/data_stewardship.md", "tags/fair-principles.md", "tags/imagej.md", "tags/licensing.md", "tags/metadata.md", "tags/napari.md", "tags/neubias.md", "tags/nfdi4bioimage.md", "tags/omero.md", "tags/open_science.md", "tags/open_source_software.md", "tags/python.md", "tags/readme.md", "tags/research_data_management.md", "tags/sharing.md", "tags/workflow.md", "tags/workflow_engine.md", "whats_new.md"], "indexentries": {}, "objects": {}, "objnames": {}, "objtypes": {}, "terms": {"": [0, 10, 11, 12, 16, 18, 28, 30, 45, 46, 47, 61, 65, 71, 74, 76, 87, 91, 96, 98, 101, 102, 103, 106], "0": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 54, 55, 56, 57, 58, 62, 63, 65, 66, 68, 69, 70, 71, 72, 75, 78, 84, 86, 87, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 105, 106, 107, 108, 109], "00": [3, 4, 5, 7, 9, 15, 17, 36, 37, 40, 42, 47, 48, 54, 55, 58, 64, 68, 73, 78, 79, 80, 81, 83, 84, 86, 89, 90, 91, 99, 100, 105, 106, 107, 109], "000": [40, 64, 79, 105], "00001": [4, 49, 54, 56, 64, 70, 79, 90], "00029489": [3, 8, 54, 64, 99, 105], "0004000112": [29, 52, 76, 94], "0004000267": [16, 35, 52, 76, 99, 100, 105], "00050003": [18, 40, 76, 90, 98], "001": [20, 64, 73, 79], "002": [29, 64, 73, 79], "00_opening_pag": 52, "01": [3, 4, 6, 7, 11, 16, 17, 19, 28, 29, 30, 33, 39, 42, 46, 47, 52, 54, 55, 56, 64, 66, 67, 68, 70, 72, 73, 79, 81, 84, 86, 87, 90, 94, 97, 99, 101, 103, 105, 106, 107, 109], "01166": [11, 14, 38, 52, 54, 55, 64, 71, 73, 79, 86, 90, 93, 96, 105], "01288": [3, 33, 34, 52, 71, 76, 96, 105], "01290": [52, 71, 96], "01315": [11, 52, 64, 96], "01326": [2, 4, 5, 15, 17, 52, 71, 79, 99, 105], "0160": [52, 71, 79, 92, 93, 106], "01602": [29, 52, 64, 76], "017": [25, 31, 64, 73, 79], "01744": [45, 52, 71, 94, 108], "01790v3": [11, 51, 52, 90], "018": [38, 52, 71, 79, 92, 93, 105, 106], "01815": [52, 71, 79, 92, 93, 96], "0182672": [3, 23, 32, 37, 64, 73, 79, 99, 105], "019": [11, 29, 52, 64, 76, 90, 94], "0195": [38, 52, 71, 105], "01987": [4, 12, 18, 30, 52, 71, 76], "01_example_pipelin": [45, 47, 55, 68, 78, 90, 97], "01_marcello_uliverpool_cci_zeiss_axioobserz1_lsm710": [30, 33, 64, 73, 79], "02": [0, 9, 19, 20, 25, 28, 29, 31, 32, 39, 54, 55, 56, 64, 66, 72, 73, 79, 86, 87, 90, 96, 97, 99, 100, 103, 105, 106], "021": [2, 3, 4, 5, 11, 12, 14, 15, 17, 33, 34, 38, 42, 48, 52, 54, 55, 64, 68, 71, 73, 76, 79, 84, 86, 90, 93, 96, 99, 105], "022": [29, 45, 52, 64, 71, 76, 79, 92, 93, 94, 96, 108], "02203": [52, 105], "02267597": [50, 79, 90], "023": [4, 12, 18, 30, 52, 71, 76, 79, 93, 96, 105], "02334": [52, 71, 90, 93, 107], "02367": [52, 71, 79, 93, 96, 105], "024": [52, 71, 90, 93, 107], "03": [1, 2, 3, 4, 6, 7, 8, 9, 14, 15, 16, 17, 19, 23, 26, 28, 29, 30, 33, 34, 35, 36, 37, 39, 42, 44, 45, 47, 48, 52, 54, 55, 64, 66, 68, 71, 73, 79, 81, 83, 84, 86, 89, 90, 92, 93, 95, 96, 97, 98, 99, 101, 103, 105, 106, 107, 109], "0304": [34, 52, 64, 86, 99, 105], "03427242": [40, 68, 103], "04": [3, 4, 5, 7, 8, 9, 11, 14, 19, 22, 23, 24, 26, 29, 30, 31, 33, 34, 36, 37, 39, 40, 41, 42, 47, 52, 54, 55, 64, 66, 68, 73, 79, 83, 86, 90, 91, 93, 95, 97, 99, 101, 103, 105, 106, 107], "04105": 75, "04t12": [42, 47, 68, 91, 109], "05": [2, 3, 5, 11, 15, 17, 19, 20, 23, 25, 26, 28, 29, 31, 33, 34, 37, 38, 39, 40, 47, 51, 52, 54, 64, 66, 68, 69, 71, 73, 78, 79, 80, 86, 89, 90, 91, 92, 93, 95, 96, 97, 99, 101, 103, 105, 106], "06": [1, 3, 6, 8, 11, 14, 19, 20, 22, 23, 25, 28, 29, 31, 32, 37, 38, 39, 42, 47, 48, 51, 52, 54, 55, 64, 66, 68, 69, 73, 78, 79, 80, 86, 89, 90, 91, 93, 95, 97, 99, 101, 103, 105, 106], "0650": [11, 29, 52, 64, 76, 90, 94], "07": [1, 3, 11, 16, 19, 20, 24, 25, 28, 29, 31, 35, 39, 47, 52, 54, 55, 64, 65, 66, 68, 71, 73, 79, 80, 81, 84, 86, 90, 93, 96, 99, 100, 101, 103, 105, 106], "08": [1, 6, 9, 12, 15, 17, 20, 24, 28, 29, 34, 35, 37, 39, 47, 51, 52, 54, 56, 64, 66, 68, 69, 72, 73, 78, 79, 86, 89, 90, 99, 100, 103, 105, 106], "08t08": [47, 48, 68, 83, 90], "09": [0, 1, 3, 8, 10, 11, 15, 17, 20, 22, 23, 24, 25, 28, 29, 31, 32, 37, 38, 39, 42, 47, 51, 52, 55, 64, 65, 66, 68, 69, 73, 77, 79, 81, 82, 84, 89, 90, 91, 99, 100, 101, 105, 106, 109], "09t06": [47, 68, 81], "0x1000000": [64, 73, 79], "0x7": [64, 73, 79], "1": [3, 4, 6, 11, 15, 16, 17, 20, 23, 24, 25, 28, 29, 30, 31, 33, 34, 37, 40, 42, 43, 47, 48, 49, 52, 55, 56, 65, 68, 70, 71, 75, 76, 78, 84, 87, 89, 90, 92, 93, 94, 96, 99, 100, 101, 103, 106, 107, 109], "10": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 17, 18, 20, 22, 23, 24, 25, 26, 29, 30, 32, 33, 34, 35, 36, 37, 38, 40, 42, 44, 45, 46, 47, 48, 51, 52, 55, 56, 65, 68, 69, 71, 72, 75, 76, 78, 80, 81, 83, 84, 86, 89, 92, 93, 94, 95, 96, 97, 98, 100, 101, 102, 103, 106, 107, 108], "100": [24, 31, 64, 73, 79], "10008464": [17, 54, 73, 79, 90, 99, 100], "10008465": [17, 54, 64, 73, 79, 105], "1002": [4, 9, 16, 18, 29, 35, 40, 52, 76, 79, 90, 94, 98, 99, 100, 105], "10054": [30, 33, 64, 73, 79], "10069368": [33, 64, 73, 79], "1007": [52, 105], "1007854": [11, 52, 79, 91, 93, 96], "10083555": [6, 54, 64, 73, 79, 99, 105], "101024": [52, 64, 79, 90, 100, 107], "10103316": [32, 64, 73, 79], "1016": [38, 52, 64, 79, 86, 90, 93, 100, 105, 107], "1017": [52, 64, 68, 73, 78, 79, 90], "1022": [58, 90, 100, 105], "1024x1024": [64, 73, 79], "1024x1024x62": [31, 64, 73, 79], "103": [45, 52, 87, 94, 108], "103685": [52, 64, 79, 93, 105], "1038": [11, 12, 14, 25, 29, 31, 38, 42, 48, 52, 54, 55, 64, 68, 71, 73, 76, 79, 84, 86, 90, 93, 94, 96, 105], "10460434": [29, 64, 73, 79], "1054": [29, 54, 65, 79, 90, 99, 105], "10609770": [0, 16, 23, 37, 45, 46, 47, 52, 64, 68, 73, 79, 90, 93, 99, 100, 103, 105], "1063": [3, 23, 32, 37, 64, 73, 79, 99, 105], "10679054": [9, 54, 64, 73, 79], "10679318": [64, 73, 79], "10687658": [17, 34, 54, 73, 79, 90, 93, 105], "10730423": [50, 90, 92, 99, 105], "10730424": [6, 16, 28, 64, 73, 79], "10793699": [54, 105], "10793700": [28, 64, 73, 79], "10796364": [34, 35, 64, 73, 79, 99, 105], "108": [64, 73, 79], "10805204": [3, 8, 23, 30, 33, 34, 37, 64, 73, 79, 99, 105], "10808486": [64, 73, 79], "10815329": [29, 54, 64, 73, 79, 89, 103], "10816895": [29, 54, 64, 73, 79, 90, 93, 95, 101, 105], "10839310": [64, 73, 79], "10886749": [54, 90], "10886750": [6, 64, 73, 79], "10889694": [3, 17, 30, 33, 34, 35, 64, 73, 79], "109020": [20, 64, 73, 79], "10925217": [64, 73, 79], "1093": [4, 24, 52, 64, 73, 79, 90, 105], "10939519": [54, 90, 99, 105], "10939520": [3, 64, 73, 79], "10942559": [54, 64, 73, 79, 92, 105], "10970869": [29, 54, 64, 73, 79, 105], "10972692": [29, 54, 64, 73, 79, 93, 105], "10990107": [29, 54, 64, 73, 79, 93, 95, 105], "10k": [54, 64, 73, 79, 108], "10t15": [47, 68, 84], "10th": [3, 54, 64, 73, 79, 90, 99, 105], "10x": [29, 64, 73, 79], "11": [0, 1, 3, 6, 8, 11, 12, 17, 22, 23, 26, 28, 29, 30, 32, 34, 35, 36, 37, 39, 40, 47, 48, 52, 54, 55, 56, 64, 66, 68, 71, 72, 73, 78, 79, 83, 86, 89, 90, 91, 92, 93, 96, 99, 100, 105, 109], "1101": [17, 42, 48, 51, 52, 68, 69, 78, 79, 84, 90, 103, 105, 106], "11031746": [54, 90, 93, 99, 105], "11031747": [64, 73, 79], "110385": [3, 37, 64, 73, 79], "11065": [64, 73, 79], "11066250": [29, 54, 64, 73, 79, 93, 95, 105], "11107798": [26, 54, 64, 73, 79, 105], "11109616": [3, 37, 64, 73, 79], "1111": [3, 8, 11, 29, 30, 33, 34, 37, 45, 52, 64, 79, 90, 94, 105, 108], "11146807": [64, 73, 79], "1119026": [29, 54, 64, 65, 79, 90], "1119154": [29, 54, 64, 65, 79], "11201216": [52, 73, 79, 102], "11234863": [64, 73, 79], "11235512": [17, 34, 50, 73, 79, 90, 93, 99, 105], "11235513": [17, 34, 64, 73, 79, 99, 105], "11261115": [6, 28, 37, 54, 64, 73, 79, 105], "11265038": [5, 54, 64, 73, 79, 89, 90], "11318151": [17, 64, 73, 79], "11350689": [3, 23, 28, 64, 73, 79, 99, 105], "11382341": [6, 28, 37, 54, 64, 73, 79, 105], "11396199": [26, 54, 64, 73, 79, 93, 105], "11445843": [64, 73, 79], "11472148": [54, 64, 73, 79, 95, 105], "11473803": [14, 42, 55, 64, 73, 79, 90, 93, 105], "11474407": [14, 54, 55, 64, 73, 79, 90, 93, 105], "11501662": [3, 8, 23, 28, 64, 73, 79, 99, 105], "11503289": [64, 73, 79], "11521029": [64, 73, 80], "11548617": [6, 28, 37, 54, 64, 73, 79, 105], "11588": [3, 8, 54, 64, 99, 105], "11637422": [64, 73, 79], "1171": [29, 54, 64, 65, 79], "1186": [2, 15, 52, 64, 79, 90], "1192": [29, 64, 73, 79], "1195": [29, 64, 73, 79], "11t07": [47, 55, 68, 84], "12": [1, 3, 4, 7, 9, 11, 15, 16, 20, 23, 24, 25, 29, 30, 31, 33, 35, 36, 37, 38, 39, 40, 42, 47, 48, 52, 54, 55, 65, 66, 68, 71, 78, 83, 84, 86, 89, 90, 91, 92, 93, 96, 98, 99, 100, 103, 105, 107, 108, 109], "12166710": [22, 64, 73, 79], "12252": [30, 33, 64, 73, 79], "12344": [11, 52, 64, 86, 91, 101, 105, 106], "1242": [29, 64, 73, 79], "12533989": [64, 73], "12546808": [3, 22, 23, 32, 37, 64, 73, 79, 99, 105], "12547566": [3, 22, 23, 32, 37, 64, 73, 79, 99, 105], "12578084": [3, 22, 23, 32, 37, 64, 73, 79, 99, 105], "12623730": [29, 54, 73, 79, 89, 90, 103], "12688": [3, 11, 20, 37, 52, 64, 73, 79, 86, 91, 101, 105, 106], "12699637": [64, 73, 79], "12744715": [40, 73, 79, 105], "12773657": [24, 64, 73, 79], "1282": [40, 90], "128x128x62": [31, 64, 73, 79], "13": [1, 3, 8, 15, 17, 19, 23, 28, 29, 30, 33, 34, 35, 38, 39, 50, 52, 54, 56, 64, 65, 66, 72, 73, 79, 89, 90, 93, 99, 100, 103, 105, 109], "13041": [11, 52, 79], "13168693": [64, 73, 79], "13192": [29, 52, 79, 90, 105], "13227": [45, 52, 64, 94, 108], "13317": [3, 8, 30, 33, 34, 37, 52, 105], "13325226": [64, 73, 79], "13331351": [9, 64, 73, 79], "13371196": [29, 64, 73, 79], "13380289": [64, 73, 79], "13640979": [29, 64, 73, 79, 99, 105], "13642395": [64, 73, 81], "13680725": [24, 64, 73, 79], "13682928": [22, 64, 73], "13683167": [22, 64, 73], "13684187": [6, 64, 73, 79], "13692297": [64, 73, 79], "137": [17, 52, 79, 90, 101], "1371": [11, 42, 52, 64, 78, 79, 91, 93, 96], "13736218": [22, 64, 73], "13736240": [22, 64, 73], "13739137": [10, 29, 64, 73, 79], "13752220": [64, 73, 79], "13807114": [0, 32, 64, 73, 79], "13810879": [0, 32, 64, 73, 79], "13831274": [17, 64, 73, 79], "13837146": [64, 73, 79, 99, 105], "13861026": [3, 23, 37, 64, 73, 79, 99, 105, 109], "13908108": [29, 64, 73, 79, 89, 90], "13908480": [28, 64, 73, 79], "13928832": [29, 64, 73, 79], "13945179": [14, 64, 73, 79], "13991322": [17, 64, 73, 79], "13t08": [42, 47, 68, 90, 109], "13th": [3, 23, 28, 47, 64, 68, 73, 79, 84, 90, 99, 100, 103, 105], "14": [11, 15, 22, 24, 29, 30, 31, 33, 42, 47, 54, 56, 64, 68, 72, 73, 79, 81, 82, 86, 89, 90, 93, 95, 100, 101, 105], "140": [40, 45, 49, 55, 64, 79, 83, 94, 100, 105, 107], "14001044": [29, 64, 73, 79], "14001388": [17, 64, 73, 79], "14006558": [6, 64, 73, 79], "14014252": [16, 35, 64, 73, 79], "14030307": [29, 64, 73, 79], "14032908": [64, 73, 79], "14035822": [64, 73, 79], "14044640": [1, 28, 64, 73, 79], "141": [64, 73, 79], "14113714": [64, 73, 79], "14161289": [22, 64, 73, 79], "14161633": [64, 73, 79], "14168603": [22, 64, 73, 79], "14178789": [3, 33, 34, 37, 64, 73, 79, 99, 105], "1418": [14, 54, 55, 64, 73, 79, 90, 93, 105], "14186100": [64, 73, 79, 99, 105], "14186101": [64, 73, 79, 99, 105], "14197622": [64, 73, 79], "1422": [14, 54, 55, 64, 73, 79, 90, 93, 105], "14234608": [17, 64, 73, 79, 99, 105], "14235726": [64, 73, 79], "14264885": [4, 7, 9, 36, 64, 73, 79, 90, 98, 109], "14268554": [64, 73, 79], "14278058": [29, 64, 73, 79, 99, 105], "14281237": [64, 73, 79], "14287640": [64, 73, 79], "14324": [40, 64, 79, 105], "14381522": [64, 73, 79], "14418209": [29, 64, 73, 79, 89], "14451": [4, 9, 29, 52, 79, 90], "14510432": [64, 73, 79, 109], "146": [64, 73, 79], "14602075": [1, 15, 38, 64, 73, 79], "14626054": [29, 64, 73, 79, 99, 105, 109], "14641777": [64, 73, 79, 109], "14650434": [17, 64, 73, 79, 99, 109], "14675120": [64, 73, 81, 109], "147": [54, 65, 79, 89, 90, 93], "14716546": [17, 64, 73, 79, 99, 109], "1472859": [24, 64, 73, 79], "1480": [64, 73, 79], "1481": [64, 73, 79], "15": [3, 22, 23, 28, 29, 30, 31, 33, 37, 39, 44, 52, 54, 64, 66, 71, 73, 79, 93, 99, 105, 106, 109], "1515": [34, 52, 64, 86, 99, 105], "152": [64, 73, 79], "15252": [1, 15, 38, 64, 73, 79], "1546": [64, 73, 79], "1546596": [40, 64, 79, 105], "1552": [3, 22, 23, 32, 37, 64, 73, 79], "1555": [3, 22, 23, 32, 37, 64, 73, 79], "1557": [3, 23, 32, 37, 64, 73, 79, 99, 105], "156": [64, 73, 79], "15pct": [24, 64, 73, 79], "16": [9, 17, 20, 30, 31, 50, 54, 58, 81, 90, 93, 100, 109], "161": [24, 31, 64, 73, 79], "162": [17, 18, 29, 50, 52, 65, 79, 96], "163": [64, 73, 79], "167505": [38, 52, 64, 86, 105], "16koa013a": [64, 73, 79], "16mcm": [20, 64, 73, 79], "16t03": [47, 68, 78, 90], "16th": [20, 64, 73, 79], "17": [20, 24, 41, 52, 54, 58, 68, 71, 83, 90, 92, 93, 96, 100, 101, 106, 108, 109], "1700309488232283050": [40, 90], "17192": [52, 64, 79, 105], "17204": [25, 31, 64, 73, 79], "1725": [24, 31, 64, 73, 79], "1787": 86, "17th": [24, 64, 73, 79], "18": [3, 10, 14, 19, 24, 29, 33, 34, 37, 39, 52, 54, 55, 64, 66, 71, 73, 79, 86, 90, 93, 95, 99, 103, 105, 106], "1873": [4, 9, 29, 52, 79, 90], "1883436974": [64, 73, 79], "189": [11, 52, 96], "18f": [29, 64, 73, 79], "18th": [54, 64, 73, 79, 90, 93, 99, 105], "19": [9, 10, 11, 20, 22, 26, 29, 52, 54, 56, 72, 90, 100, 103, 105, 108], "195": [29, 64, 73, 79], "19778": [52, 71, 90, 93, 107], "1henpidtpht3bc1y26altitahj2c5xcol": [54, 63, 86], "1m": [54, 64, 73, 79, 108], "1mhuqfkhzcyu3bchwmqogiybkjamu5msg": [4, 49, 54, 56, 64, 70, 79, 90], "1ojoy9ghmuusmuwckps_injf_wzazmllunvk1fueb4pa": [54, 63, 86, 89, 90, 98], "1ppvuumi5w2ojw_sabzsqvaxuuiktq7ma": [86, 90], "1q8q1xe": [13, 54, 63, 86, 90, 98], "1r9": [47, 54, 58, 63, 68, 79, 90], "1st": [3, 22, 23, 32, 37, 64, 73, 79], "1th": [3, 23, 32, 37, 64, 73, 79, 99, 105], "1wg_4307xmksgfwt3taxmvx2rzig1k0sm1e7saenjqki": [1, 54, 63, 86, 90], "1x": [24, 31, 64, 73, 79], "1z": [31, 64, 73, 79], "2": [2, 3, 11, 15, 17, 20, 23, 24, 25, 28, 29, 30, 31, 33, 37, 42, 44, 47, 48, 49, 52, 58, 68, 71, 84, 86, 90, 91, 92, 93, 94, 96, 98, 99, 100, 101, 102, 103, 106, 107], "20": [3, 8, 10, 11, 17, 22, 24, 29, 42, 48, 51, 52, 55, 64, 65, 68, 69, 73, 79, 84, 90, 91, 101, 105], "200": [24, 31, 64, 73, 79], "2005": [2, 15, 17, 34, 52, 54, 64, 73, 79, 90, 93, 105], "200m": [30, 33, 64, 73, 79], "2010": [11, 31, 52, 64, 73, 79, 96], "2011": [64, 73, 79], "2012": [30, 33, 64, 73, 79], "2014": [4, 18, 29, 30, 33, 54, 64, 68, 73, 79, 86, 90, 108], "2015": [64, 73, 79], "2016": [3, 22, 23, 32, 37, 42, 64, 71, 73, 92, 93, 99, 105], "2017": [25, 30, 31, 33, 40, 52, 64, 73, 79, 101, 105], "2018": [11, 24, 29, 40, 47, 52, 64, 71, 73, 78, 79, 91, 92, 93, 101, 105, 106], "2018h2s1": [52, 79, 93, 105], "2019": [30, 33, 39, 44, 64, 73, 79, 83, 105], "2020": [7, 9, 26, 30, 33, 36, 40, 44, 45, 55, 73, 76, 91, 93, 101, 105, 108], "2020aprilgitcours": [55, 68, 86], "2021": [4, 7, 14, 18, 24, 26, 29, 30, 31, 38, 39, 40, 42, 49, 55, 57, 64, 66, 71, 73, 78, 84, 93, 96, 98, 101, 106, 107], "2021_jamali_biologicalimag": [52, 64, 68, 78, 90], "2022": [3, 4, 5, 8, 11, 16, 17, 20, 25, 29, 30, 31, 33, 34, 35, 37, 38, 39, 42, 47, 49, 52, 54, 56, 65, 66, 70, 71, 76, 77, 92, 93, 96, 99, 105, 106], "20220923_defragmentationt": [18, 54, 86, 90, 98], "2023": [1, 3, 6, 7, 8, 11, 14, 15, 19, 20, 22, 24, 29, 32, 35, 38, 39, 51, 55, 56, 66, 69, 70, 71, 72, 78, 80, 81, 83, 84, 91, 95, 96, 97, 101, 106, 107], "2023115008": [1, 15, 38, 64, 73, 79], "2023_imageanalysissurvei": [51, 52, 68, 69, 78, 90], "2024": [0, 1, 4, 5, 6, 7, 8, 9, 10, 11, 14, 15, 16, 19, 22, 23, 24, 26, 28, 32, 36, 39, 40, 42, 45, 51, 52, 54, 63, 66, 69, 70, 80, 81, 82, 83, 84, 86, 91, 92, 95, 96, 98, 101, 102, 106, 107, 109], "2025": [17, 42, 47, 64, 68, 73, 79, 81, 87, 90, 99, 109], "205": [40, 79, 101], "2080": [25, 31, 64, 73, 79], "2083": [42, 43, 71], "20club": [11, 41, 48, 68, 86, 103], "20materi": [42, 70, 91], "20tutori": [11, 41, 48, 68, 86, 103], "20x": [25, 29, 31], "21": [11, 17, 22, 31, 34, 38, 52, 54, 64, 71, 73, 79, 86, 91, 93, 96, 99, 105], "210": [64, 73, 79], "22": [3, 14, 23, 24, 29, 37, 38, 42, 47, 52, 54, 64, 65, 73, 79, 84, 90, 99, 105, 109], "22518": [12, 42, 48, 64, 68, 71, 84, 90], "22t17": [47, 54, 68, 83], "23": [5, 6, 17, 20, 26, 44, 45, 47, 48, 52, 54, 55, 64, 73, 79, 86, 89, 99, 101, 102, 103, 105, 109], "2302": [11, 51, 52, 90], "2304": [52, 79, 101, 105], "2314": [64, 73, 79], "23t07": [4, 5, 7, 9, 36, 47, 68, 79], "23t14": [47, 68, 80, 90], "24": [17, 34, 45, 47, 54, 56, 64, 72, 73, 79, 86, 89, 91, 100, 109], "2407": [52, 71, 90, 93, 107], "2409": [64, 73, 79], "240t": [31, 64, 73, 79], "244": [64, 73, 79], "25": [1, 6, 17, 25, 29, 31, 40, 54, 64, 73, 75, 79, 92, 93, 95, 99, 101, 105], "2520": [45, 103, 108], "25g": [22, 64, 73, 79], "26": [3, 6, 17, 22, 23, 28, 32, 37, 39, 54, 64, 66, 73, 79, 86, 90, 99, 105, 106, 109], "261950": [29, 64, 73, 79], "26275": [29, 64, 73, 79], "2643411": [41, 64, 68, 73, 83, 90], "265": [29, 64, 73, 79], "267": [64, 73, 79], "26_learn_guidance_137": [40, 64, 79, 105], "27": [1, 6, 15, 24, 34, 38, 40, 47, 52, 64, 68, 73, 79, 81, 99, 105], "273": [29, 64, 73, 79], "27t14": [47, 55, 68, 79, 89], "28": [3, 17, 29, 30, 32, 33, 34, 35, 37, 45, 47, 55, 64, 68, 73, 78, 79, 90, 97, 99, 103, 105, 108], "281": [64, 73, 79], "285": [17, 34, 52, 64, 79, 99, 105], "28th": [3, 22, 23, 29, 32, 37, 42, 47, 48, 55, 64, 68, 73, 78, 79, 90, 99, 105, 109], "29": [3, 6, 8, 17, 20, 39, 42, 47, 54, 64, 66, 68, 73, 77, 79, 86, 90, 99, 105, 109], "290322": [45, 103, 108], "29489": [50, 52, 80], "299": [31, 64, 73, 79], "29t10": [47, 55, 68, 79, 107], "29th": [3, 37, 64, 73, 79], "2_t": [64, 73, 79], "2d": [25, 31], "2d_enteric_ganglia_model_qa": [29, 64, 73, 79], "2d_enteric_neuron_model_qa": [29, 64, 73, 79], "2d_enteric_neuron_subtype_model_qa": [29, 64, 73, 79], "2d_enteric_neuron_subtype_v4": [29, 64, 73, 79], "2d_enteric_neuron_v4_1": [29, 64, 73, 79], "2d_ganglia_rgb_v2": [29, 64, 73, 79], "2dbkuwkk4": [42, 56, 72, 90], "2nd": [0, 16, 23, 37, 47, 54, 64, 73, 79, 90, 93, 99, 105], "3": [3, 11, 13, 15, 16, 17, 22, 23, 24, 25, 26, 28, 29, 30, 31, 32, 33, 35, 37, 40, 41, 42, 44, 45, 47, 48, 49, 51, 52, 54, 55, 56, 58, 64, 67, 68, 69, 71, 73, 76, 79, 84, 89, 90, 91, 92, 93, 94, 96, 97, 98, 99, 100, 103, 105, 106, 107, 108], "30": [3, 16, 19, 23, 26, 35, 37, 39, 47, 54, 55, 64, 66, 68, 73, 79, 84, 90, 93, 97, 99, 103, 105, 109], "300": [24, 31, 64, 73, 79], "3002167": [42, 52, 64, 78], "302": [29, 52, 65, 79, 98], "30t07": [47, 55, 68, 78], "30t12": [15, 17, 37, 47, 68, 78, 99, 105, 106], "30th": [3, 29, 37, 47, 48, 55, 64, 68, 73, 78, 79, 90], "31": [1, 14, 64, 73, 79], "316": [29, 64, 73, 79], "31st": [42, 47, 68, 90, 109], "32": [24, 47, 48, 64, 68, 73, 79, 83, 90], "320": [24, 31, 64, 73, 79], "325": [64, 73, 79], "326": [64, 73, 79], "32nd": [3, 33, 64, 73, 79, 99, 105], "33": [29, 64, 73, 79], "33248": [32, 37, 64, 73, 79, 99, 105], "334": [18, 29, 31, 52, 65, 79, 90, 98], "337": [64, 73, 79], "3389": [11, 29, 52, 79, 90, 105], "34": [47, 52, 68, 84], "3415": [17, 34, 52, 79, 99], "345": [25, 31, 64, 73, 79], "3468": [4, 9, 29, 52, 79, 90], "3490058": [44, 64, 73, 79, 105], "35": [64, 73, 79], "35828": [11, 52, 96], "362478": [17, 52, 79, 90, 101], "36371407": [52, 71, 79, 92, 93, 96], "37": [24, 31, 64, 73, 79], "3778431": [26, 54, 64, 73, 79, 105], "38": [4, 5, 7, 9, 36, 47, 55, 68, 78, 79, 89], "39": [30, 33, 47, 54, 64, 68, 73, 79, 83], "3_20200323": [44, 45, 55, 105], "3_ch": [64, 73, 79], "3_z": [64, 73, 79], "3a": 40, "3d": [22, 24, 25, 30, 33], "3dihub": [38, 52, 64, 86, 105], "3r": [22, 64, 73], "3x": [30, 33, 64, 73, 79], "4": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 28, 29, 30, 31, 32, 33, 34, 35, 37, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 54, 55, 56, 57, 58, 62, 63, 65, 66, 68, 69, 70, 71, 72, 75, 78, 84, 87, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 105, 106, 107, 108, 109], "40": [29, 47, 55, 64, 68, 73, 79, 107], "405": [24, 64, 73, 79], "4071471": [40, 64, 73, 79, 105], "40x": [24, 31, 64, 73, 79], "42": [47, 68, 78, 90], "420": [24, 64, 73, 79], "4249": [64, 73, 79], "4317149": [36, 54, 64, 73, 79], "4326": [42, 43, 52, 71], "4328911": [54, 64, 73, 79, 90], "4330625": [7, 54, 73, 79, 89, 90], "4334697": [54, 64, 73, 79, 108], "4461261": [54, 64, 73, 79, 105], "45": [3, 23, 28, 29, 37, 55, 64, 68, 73, 79, 84, 90, 99, 100, 103, 105], "46": [6, 28, 37, 47, 54, 64, 68, 73, 78, 79, 87, 90, 105], "4630788": [26, 54, 64, 73, 79, 105], "4748510": [26, 54, 64, 73, 79, 105], "4748534": [26, 54, 64, 73, 79, 105], "4778265": [54, 73, 79, 101, 105], "48": [47, 54, 68, 83], "48550": [64, 73, 79], "488": [31, 64, 73, 79], "4915861": [40, 64, 79], "496963457": [64, 73, 79], "498": [64, 73, 79], "4d": [30, 33, 64, 73, 79], "4dn": [11, 30, 33, 52, 64, 73, 79, 86, 90, 96], "4lxamtyuyfzptmdr85sjnlspzhvz8": [47, 54, 58, 63, 68, 79, 90], "4th": 22, "4v8nsap1z24cappupxg8jzyqegz6mvw_dh": [55, 56, 86], "4x8": [24, 64, 73, 79], "5": [3, 11, 15, 16, 17, 22, 23, 24, 25, 31, 32, 33, 34, 35, 37, 50, 52, 58, 64, 71, 93, 96, 100, 101, 109], "50": [24, 64, 73, 79], "501864659": [6, 28, 37, 54, 64, 73, 79, 87, 105], "51": [4, 5, 7, 9, 36, 47, 52, 55, 68, 73, 79, 89, 102], "5101351": [31, 64, 73, 79], "5121890": [64, 73, 84], "519": [29, 54, 65, 79, 105, 106], "5281": [0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 14, 15, 16, 17, 20, 22, 23, 24, 25, 26, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 40, 41, 42, 44, 45, 46, 47, 50, 52, 54, 55, 56, 64, 68, 72, 73, 79, 80, 81, 83, 84, 89, 90, 92, 93, 95, 98, 99, 100, 101, 103, 105, 108, 109], "5282": [64, 73, 79], "52825": [17, 34, 52, 64, 79, 99, 105], "53": [15, 17, 37, 47, 68, 78, 99, 105, 106], "5334": [45, 52, 94, 108], "539647v1": [17, 51, 52, 69, 79, 103], "53j": [64, 73, 84], "540x540": [31, 64, 73, 79], "543701v1": [51, 52, 68, 69, 78, 90], "546": 87, "55": [47, 68, 80, 90], "555": [24, 31, 64, 73, 79], "55e7d8fbf6df4a54ac56942b98d94e4f": [44, 105], "561": [24, 64, 73, 79], "561479v1": [51, 69, 106], "563252v2": [42, 48, 68, 69, 84], "5675686": [24, 31, 64, 73, 79], "568": [29, 64, 73, 79], "5805939": [20, 64, 73, 79], "5847477": [30, 33, 64, 73, 79], "58h": [29, 64, 73, 79], "5996883": [25, 31, 64, 73, 79], "5_ch": [64, 73, 79], "5abw3cvxrhpobg4": [18, 54, 86, 90, 98], "5c": [31, 64, 73, 79], "6": [2, 3, 10, 22, 23, 24, 28, 29, 30, 31, 33, 37, 52, 64, 90, 99], "60": [24, 31, 64, 73, 79, 87], "60018": [11, 25, 31, 42, 46, 58, 86, 90], "6019": [4, 49, 54, 56, 64, 70, 79, 90], "6023317": [25, 31, 64, 73, 79], "6036": [64, 73, 79], "608021v1": [51, 52, 69, 79, 90, 105], "6139958": [25, 31, 64, 73, 79], "6140064": [25, 31, 64, 73, 79], "6140111": [25, 31, 64, 73, 79], "62": [64, 73, 79], "6267": [29, 64, 73, 79], "630": [24, 64, 73, 79], "634": [64, 73, 79], "638": [3, 8, 11, 17, 33, 52, 65, 79, 99, 105], "647": [24, 64, 73, 79], "64x64x62": [31, 64, 73, 79], "6523649": [25, 31, 64, 73, 79], "653203": [17, 64, 73, 79, 99, 109], "6535296": [64, 73], "6554166": [64, 73, 79], "6602101": [54, 64, 73, 79, 105], "6628764": 52, "6645978": [31, 64, 73, 79], "6646128": [31, 64, 73, 79], "67": [11, 25, 31, 42, 46, 58, 86, 90], "6775": [52, 79, 101, 105], "6821815": [34, 56], "6834214": [64, 73, 79], "6905931": [64, 73, 99], "6953610": [20, 64, 73], "6991106": [20, 64, 73], "6th": [3, 33, 64, 73, 79, 99, 105], "7": [3, 10, 29, 30, 32, 33, 37, 52, 75, 79, 99, 105], "70": [22, 64, 73, 79], "701": [29, 64, 73, 79], "7015307": [64, 73, 79], "7018750": [16, 35, 64, 73, 79, 99, 105], "7018928": [16, 35, 64, 73, 79, 99, 105], "7018929": [34, 56], "703": [64, 73, 79], "710": [30, 33, 64, 73, 79], "7209862": [29, 64, 73, 79], "7215114": [20, 64, 73, 79], "7257808": [40, 73], "7409423": [25, 31, 64, 73, 79], "7409525": [12, 29, 50, 52, 94], "7447491": [64, 73, 79], "746": [29, 54, 64, 65, 79, 90], "7490": [29, 54, 64, 65, 79, 90], "75": [24, 25, 31, 64, 73, 79], "750": [64, 73, 79], "77": [64, 73, 79], "777": [11, 52, 96], "7788899": [1, 14, 64, 73, 79], "78": [64, 73, 79], "7885379": [20, 64, 73, 79], "7890311": [3, 33, 64, 73, 79, 99, 105], "7919117": [64, 73, 79], "7928332": [17, 34, 50, 73, 79, 93, 99, 105], "7944877": [22, 64, 73, 79], "7945": [52, 64, 79, 105], "7945018": [22, 64, 73, 79], "7963006": [22, 64, 73, 79], "7963020": [22, 64, 73, 79], "7th": [1, 17, 64, 73, 79], "8": [3, 11, 14, 16, 20, 22, 23, 30, 31, 32, 33, 35, 37, 38, 52, 54, 55, 64, 71, 73, 75, 79, 86, 90, 93, 96, 99, 102, 105], "8070038": [33, 54, 64, 79, 90, 93, 99, 105], "8139353": [16, 35, 42, 73, 79, 90, 100], "8146412": [64, 73, 79], "8153907": [24, 64, 73, 79], "8182154": [1, 64, 73, 79], "8305531": [24, 64, 73, 79], "8314968": [64, 73, 79], "8323588": [3, 8, 23, 33, 34, 35, 37, 54, 56, 64, 72, 73, 79, 99, 100, 105], "8329305": [34, 54, 73, 79, 99, 105], "8329306": 64, "8338931": [1, 15, 38, 64, 73, 79], "8340247": [17, 50, 73, 79, 90, 105], "8349562": [0, 50, 73, 79, 93, 105], "8372703": [20, 64, 73, 79], "83784": [24, 64, 73, 79], "8414318": [28, 54, 73, 79, 90, 99, 105], "8434325": [28, 50, 64, 79, 90], "86304": [81, 90], "871228": [11, 52, 79, 105], "87925": [16, 96, 100, 107], "88": [29, 64, 73, 79], "880": [29, 64, 73, 79], "889755": [29, 52, 79, 90], "8_tf1": [31, 64, 73, 79], "8zd4kty": [42, 56, 72, 79, 90, 105], "9": [3, 4, 12, 17, 18, 28, 29, 30, 33, 50, 52, 58, 64, 65, 71, 73, 76, 79, 90, 96, 99, 100, 105], "91b": [0, 32, 64, 73, 79], "924": [29, 64, 73, 79], "93": [64, 73, 79], "96": [31, 64, 73, 79], "971": [29, 54, 65, 79, 89, 90, 103], "9781119096900": 40, "98": 87, "99": [64, 73, 79], "A": [0, 10, 15, 16, 18, 22, 25, 31, 32, 35, 46, 47, 49, 56, 59, 65, 72, 76, 82, 84, 87, 89, 91, 92, 93, 96, 98, 103], "AT": [64, 73], "And": [3, 47, 55, 68, 81, 86, 88, 105], "As": [3, 8, 11, 17, 24, 29, 32, 52, 64, 65, 73, 79, 93, 105], "BUT": [42, 70, 91], "BY": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 54, 55, 56, 57, 58, 62, 63, 64, 65, 66, 68, 69, 70, 71, 72, 73, 75, 78, 79, 80, 84, 87, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 105, 106, 107, 108, 109], "Being": [64, 73, 79], "By": [1, 3, 9, 22, 23, 28, 29, 32, 37, 42, 48, 51, 52, 64, 69, 73, 78, 79, 90, 99, 105], "For": [3, 8, 17, 20, 23, 29, 30, 33, 34, 37, 50, 64, 73, 74, 79, 88, 90, 99, 105], "IN": [1, 15, 38, 64, 73, 79], "IT": [6, 28, 54], "If": [22, 25, 30, 31, 33, 60, 61, 64, 73], "In": [1, 3, 8, 9, 11, 16, 17, 23, 28, 29, 33, 34, 35, 36, 37, 39, 40, 42, 47, 48, 49, 54, 55, 56, 57, 59, 60, 61, 64, 66, 68, 72, 73, 78, 79, 80, 81, 84, 86, 89, 90, 92, 93, 95, 97, 99, 100, 101, 103, 105, 106, 107], "It": [3, 6, 9, 13, 20, 22, 23, 29, 32, 33, 37, 48, 54, 57, 60, 64, 68, 80, 84, 89, 103, 108], "Its": [57, 86, 105], "No": [20, 64, 73, 79], "ON": [42, 70, 91], "On": [3, 8, 23, 33, 34, 35, 37, 54, 56, 61, 64, 72, 73, 79, 99, 100, 105], "One": [20, 29, 64, 73, 79, 81, 90], "Such": [47, 62, 64, 73, 79, 80, 90, 100], "That": [3, 22, 23, 32, 37, 64, 73, 79], "The": [1, 6, 7, 11, 16, 20, 21, 22, 24, 25, 31, 32, 35, 39, 41, 44, 45, 47, 48, 49, 55, 57, 58, 60, 61, 68, 70, 72, 78, 80, 81, 83, 89, 91, 95, 97, 100, 101, 102, 103, 107, 108], "There": [45, 64, 99], "These": [1, 3, 24, 28, 29, 31, 33, 42, 47, 48, 54, 55, 56, 59, 60, 64, 68, 70, 73, 79, 81, 86, 87, 89, 90, 92, 93, 95, 99, 102, 103, 105], "To": [0, 3, 14, 29, 31, 33, 47, 48, 50, 55, 56, 64, 68, 70, 73, 78, 79, 81, 90, 93, 99], "Will": [2, 4, 5, 11, 15, 17, 37, 47, 52, 68, 71, 77, 78, 79, 82, 99, 100, 105, 106], "With": [3, 22, 23, 32, 37, 44, 45, 55, 61, 64, 73, 79, 99, 105], "_a": [30, 33, 64, 73, 79], "_bf": [25, 31, 64, 73, 79], "_cxbfab": [42, 56, 72, 90], "_cyto": [25, 31, 64, 73, 79], "_dpc": [25, 31, 64, 73, 79], "_flow": [25, 31, 64, 73, 79], "_nuclei": [25, 31, 64, 73, 79], "_sqrdpc": [25, 31, 64, 73, 79], "a1": [3, 22, 23, 32, 37, 64, 73, 79, 99, 105], "a2": [3, 22, 23, 32, 37, 64, 73, 79, 99, 105], "a4po9z61tm": [42, 56, 72, 103], "aalbersberg": [52, 64, 71, 79, 93, 105], "aastha": [1, 4, 12, 14, 18, 30, 52, 54, 63, 64, 71, 73, 76, 79, 86, 105], "aayush": [3, 22, 23, 32, 37, 64, 73, 79], "ab": [11, 42, 43, 47, 51, 52, 68, 70, 71, 78, 90, 93, 107], "abbasi": [40, 68, 81, 90], "abdelfattah": [30, 33, 64, 73, 79], "abdelmoneim": [64, 73, 80], "abdrabb": [0, 16, 23, 37, 45, 46, 47, 52, 64, 68, 73, 79, 90, 93, 99, 100, 103, 105], "abif": [30, 33, 64, 73, 79], "abil": [47, 54, 68, 83], "abl": [3, 22, 23, 30, 32, 33, 37, 64, 73, 79, 99, 105], "abolfazl": [47, 68, 78, 90], "about": [3, 4, 5, 9, 11, 14, 29, 30, 33, 34, 37, 39, 42, 46, 47, 48, 51, 52, 54, 56, 65, 66, 68, 71, 72, 76, 78, 81, 84, 89, 90, 92, 93, 94, 95, 99, 100, 101, 105, 106], "abov": [29, 64, 73, 79], "abstract": [3, 8, 17, 33, 34, 51, 52, 54, 64, 69, 73, 99, 103], "abstractdmp4nfdi": [44, 79, 105], "ac": [4, 7, 21, 30, 33, 38, 40, 42, 43, 48, 49, 52, 54, 55, 56, 64, 73, 79, 81, 89, 90, 91, 93, 94, 96, 101, 103, 105, 106], "academ": [45, 52, 57, 64, 73, 79, 103, 105, 108], "academi": [64, 73, 79], "academia": [54, 64, 73, 79, 108], "acardona": [48, 81, 90, 94], "acceler": [3, 20, 33, 73, 99, 105], "accept": [3, 37, 64, 73, 79], "access": [1, 3, 8, 14, 20, 23, 28, 29, 30, 33, 37, 40, 42, 44, 45, 47, 49, 54, 55, 56, 57, 60, 61, 68, 70, 74, 84, 87, 89, 92, 93, 94, 95, 100, 101, 102, 103, 106, 108], "accomod": [22, 64, 73, 79], "accompani": [47, 55, 68, 78], "accord": [14, 24, 29, 31, 42, 49, 54, 55, 57, 64, 73, 79, 90, 93, 95, 101, 105], "accordingli": [14, 42, 55, 64, 73, 79, 90, 93, 105], "account": [29, 55, 56, 61, 64, 70, 73, 79, 81, 105], "accumul": [64, 73, 79], "acess": [29, 54, 64, 73, 79, 90, 93, 95, 101, 105], "achiev": [3, 8, 23, 28, 33, 64, 73, 79, 99, 105], "acid": [64, 73, 79], "acommod": [22, 64, 73, 79], "acquif": [64, 73, 79], "acquir": [3, 22, 23, 24, 25, 29, 30, 31, 33], "acquisit": [2, 3, 15, 23, 24, 25, 28, 29, 30, 31, 32, 33, 37, 52, 90, 99, 105], "across": [1, 3, 17, 28, 29, 33, 34, 37, 50, 54, 64, 73, 79, 90, 99, 105, 108], "action": [29, 31, 64, 73, 79, 99, 105], "activ": [3, 8, 23, 28, 33, 34, 35, 54, 84, 92, 99, 105], "actor": [1, 14, 64, 73, 79], "acut": [22, 64, 73], "ad": [1, 10, 20, 28, 29, 42, 45, 49, 55, 60, 61, 81, 86, 87, 100, 105, 106, 107], "ada": [29, 64, 73, 79], "adam": [4, 10, 12, 18, 29, 30, 47, 52, 64, 68, 71, 73, 76, 78, 79, 90], "adapet": [3, 22, 23, 32, 37, 64, 73, 79], "adapt": [11, 25, 29, 31, 44, 52, 91, 93, 96, 105], "add": [59, 60, 61], "addit": [3, 8, 23, 24, 25, 29, 31, 33, 34, 35, 37, 40, 42, 48, 54, 56, 60, 64, 72, 73, 78, 79, 80, 90, 99, 100, 105], "addition": [3, 37, 52, 64, 73, 79, 102], "address": [0, 1, 3, 8, 11, 14, 23, 28, 29, 33, 38, 44, 50, 52, 54, 55, 57, 71, 80, 86, 93, 96, 99, 105], "adequ": [44, 64, 73, 79, 99, 105], "adher": [0, 50, 51, 52, 69, 71, 73, 79, 90, 92, 93, 96, 105], "adjust": 87, "admin": [3, 33, 34, 37, 64, 73, 79, 99, 105], "administr": [3, 33, 34, 37, 64, 73, 79, 99, 105], "administrativen": [0, 32, 64, 73, 79], "adopt": [3, 8, 17, 23, 28, 30, 33, 34, 35, 37, 50, 52, 54, 56, 64, 72, 73, 79, 90, 99, 100, 102, 105], "adressen": 75, "adrian": [5, 55, 68, 86, 89, 90], "advanc": [1, 13, 20, 28, 47, 48, 52, 68, 78, 84, 86, 89, 102, 103], "advantag": [64, 73, 99], "adventur": [17, 34, 54, 73, 79, 90, 93, 105], "advertis": 60, "advic": [44, 55, 64, 73, 79, 80, 86, 93, 95, 105], "aenderungen": [29, 54, 64, 73, 79, 93, 105], "afford": [64, 73, 79], "afmboornr5grqog": [55, 56, 86], "after": [17, 24, 29, 30, 31, 33, 34, 40, 54, 60, 61, 64, 73, 79, 90, 93, 105], "afterward": [29, 64, 73, 79], "ag": [3, 30, 33, 34, 37, 40, 64, 73, 79, 99, 105], "again": 60, "agenc": [3, 32, 55, 64, 73, 79, 86, 105], "aggreg": [52, 90, 105], "aghajanian": [30, 33, 64, 73, 79], "agpl": [44, 45, 48, 55, 68, 89, 103, 105], "agre": [3, 22, 23, 32, 37, 64, 73, 79], "agust\u00edn": [47, 54, 58, 63, 68, 79, 90], "ahead": [29, 54, 64, 73, 79, 89, 103], "ahklemm": [41, 54, 68, 86, 90, 98], "ahmadi": [6, 16, 28, 33, 50, 64, 73, 79, 90, 92, 99, 105], "ahmet": [12, 42, 48, 64, 68, 71, 84, 90], "ai": [3, 6, 8, 23, 28, 37, 45, 48, 75, 78, 84, 87, 108], "ai4lif": [1, 52], "aic": [30, 33, 64, 73, 79], "aim": [3, 8, 9, 20, 23, 28, 29, 36, 44, 54, 64, 73, 79, 80, 87, 99, 105], "airyscan": [64, 73, 79], "ajinkya": [12, 47, 68, 90], "aktiven": [0, 32, 64, 73, 79], "aktualisiert": [26, 54, 64, 73, 79, 105], "aktualit\u00e4t": 75, "aktuel": [26, 54, 64, 73, 79, 105], "al": [2, 3, 4, 7, 8, 10, 14, 15, 17, 18, 25, 29, 30, 31, 33, 36, 37, 38, 40, 41, 42, 45, 46, 47, 48, 50, 51, 52, 54, 55, 58, 64, 65, 68, 71, 73, 75, 76, 78, 79, 82, 84, 90, 91, 93, 94, 96, 98, 100, 101, 102, 103, 106, 107, 108], "alain": [12, 42, 48, 64, 68, 71, 84, 90], "alan": [11, 20, 42, 47, 48, 64, 68, 73, 78, 79, 86, 90, 91, 102, 103, 107, 108], "alban": [11, 17, 18, 25, 29, 31, 42, 46, 50, 52, 58, 65, 79, 86, 90, 96], "albert": [21, 29, 30, 33, 42, 47, 48, 55, 64, 68, 73, 78, 79, 81, 86, 89, 90, 94, 103], "alberto": [11, 52, 79, 105], "aleksej": [48, 68, 86, 103], "alessandro": [11, 30, 33, 52, 64, 73, 79, 86, 87, 90, 96], "alex": [1, 4, 12, 15, 18, 30, 33, 38, 47, 52, 64, 68, 71, 73, 76, 78, 79, 87, 90, 96], "alexa": [24, 31, 64, 73, 79], "alexand": [12, 42, 47, 48, 55, 64, 68, 71, 73, 79, 84, 89, 90, 107], "alexandr": [11, 17, 18, 29, 50, 52, 54, 55, 65, 68, 79, 86, 90, 96, 98], "alexandra": [48, 68, 83, 90], "algorithm": [11, 20, 45, 51, 52, 90, 94, 108], "ali": [4, 12, 18, 30, 52, 71, 76], "aliaksandr": [4, 5, 7, 9, 18, 36, 47, 64, 68, 73, 79, 86, 90, 98, 109], "align": [1, 3, 8, 9, 23, 24, 28, 33, 54, 57, 73, 86, 99, 105], "alison": [4, 12, 18, 30, 52, 71, 76], "all": [3, 4, 11, 12, 16, 17, 18, 20, 22, 23, 24, 25, 26, 28, 29, 30, 31, 32, 33, 34, 35, 37, 40, 42, 44, 49, 52, 56, 60, 68, 71, 72, 80, 87, 90, 94, 96, 98, 99, 100, 101], "all_training_resourc": [42, 79, 105], "allan": [4, 5, 11, 15, 17, 42, 47, 52, 55, 56, 64, 68, 71, 79, 82, 86, 90, 99, 100, 102, 105], "allen": [57, 64, 73, 79, 86, 99, 105], "aller": [49, 57, 86, 102, 105], "allgemeinen": 75, "allianc": [64, 73, 79, 80], "allianz": [64, 73, 79], "allow": [3, 8, 14, 22, 23, 28, 29, 33, 34, 35, 37, 42, 45, 47, 49, 54, 55, 56, 64, 68, 70, 72, 73, 79, 83, 87, 89, 90, 93, 94, 99, 100, 101, 105, 106, 107], "alltag": [26, 54, 64, 73, 79, 105], "allyson": [1, 15, 38, 64, 73, 79], "alon": [64, 73, 79], "along": [64, 73, 79], "alphafold": [39, 86], "alreadi": [6, 28, 37, 54, 64, 73, 79, 105], "also": [1, 3, 9, 11, 14, 16, 20, 23, 28, 29, 34, 35, 37, 42, 44, 45, 48, 52, 54, 55, 56, 60, 64, 71, 73, 78, 79, 80, 88, 89, 90, 93, 97, 99, 103, 105, 107, 108], "altern": [48, 79, 97, 103], "although": [52, 73, 79, 102], "althous": 86, "alvi": [38, 52, 64, 71, 86, 105], "alwai": [3, 22, 23, 32, 37, 64, 73, 79, 99, 105], "alyona": [21, 48, 55, 68, 86, 89, 103], "am": [3, 23, 28, 54, 58, 90, 99, 100, 105], "amer": [64, 73, 80], "america": [30, 33, 64, 73, 79], "amherst": [30, 33, 64, 73, 79], "amin": [30, 33, 64, 73, 79], "among": [3, 29, 33, 34, 37, 64, 73, 79, 88, 99, 105], "amount": [22, 64, 73, 79], "an": [1, 2, 4, 6, 9, 12, 14, 15, 16, 20, 22, 23, 24, 25, 26, 28, 30, 31, 32, 33, 34, 35, 36, 37, 38, 45, 47, 48, 49, 55, 58, 61, 65, 68, 70, 71, 72, 75, 78, 81, 82, 84, 91, 92, 94, 95, 100, 101, 102, 106, 107, 108], "analy": [29, 64, 73, 79], "analys": [1, 6, 15, 25, 28, 31, 38, 45, 49, 55, 81, 88, 103, 107, 108], "analysi": [0, 13, 14, 17, 20, 22, 24, 30, 32, 41, 43, 44, 60, 77, 87, 91, 92, 96, 101, 102], "analyst": [56, 72, 89], "analyt": [5, 45, 48, 54, 64, 68, 73, 79, 84, 89, 90, 99, 103, 108], "analyticalsci": [16, 18, 29, 35, 40, 52, 76, 90, 94, 98, 99, 100, 105], "analyz": [20, 25, 29, 31, 45, 48, 51, 52, 54, 64, 69, 73, 79, 89, 90, 94, 97, 103, 105, 108], "anatol": [17, 18, 29, 50, 52, 65, 79, 96], "anatomi": [30, 33, 64, 73, 79], "anbiet": [54, 64, 73, 75, 79, 105], "anca": [6, 54, 64, 73, 79, 90], "anchor": [22, 64, 73, 79], "anderem": [40, 79, 101], "andi": [3, 22, 23, 32, 37, 64, 73, 79], "andor": [30, 31, 33, 79], "andra": [17, 34, 54, 73, 79, 90, 93, 105], "andrea": [0, 1, 2, 15, 16, 23, 37, 38, 45, 46, 47, 50, 52, 64, 68, 73, 79, 90, 93, 99, 100, 103, 105], "andrew": [42, 52, 64, 78, 86], "andrii": [7, 21, 38, 54, 56, 70, 79, 89, 90, 103], "andr\u00e9": [47, 54, 58, 63, 68, 79, 90], "anett": [3, 22, 23, 32, 37, 64, 73, 79, 99, 105], "anforderungen": [26, 54, 64, 73, 79, 105], "angab": 75, "angaben": [26, 54, 64, 73, 79, 105], "angebot": 75, "angeboten": [26, 54, 64, 73, 79, 105], "angefordert": 75, "angewendet": [26, 54, 64, 73, 79, 93, 105], "angl": [24, 64, 73, 79], "angrenzend": [29, 64, 73, 79, 99, 105], "anhaltspunkt": 75, "anhand": [26, 54, 64, 73, 79, 105], "ani": [29, 32, 45, 47, 55, 60, 61, 64, 68, 73, 78, 79, 87, 90, 97, 99], "anim": [22, 29, 64, 73, 79], "anirban": [86, 90], "anisotropi": [19, 31, 39, 64, 66, 73, 79, 90, 97, 103], "anja": [3, 22, 23, 32, 37, 64, 73, 79, 99, 105], "anjali": [29, 52, 79, 90], "ankerhold": [64, 73, 79], "ann": [29, 40, 42, 43, 52, 64, 65, 68, 71, 78, 79, 90, 98], "anna": [4, 7, 9, 18, 21, 29, 31, 38, 41, 42, 48, 52, 54, 55, 56, 64, 65, 68, 70, 73, 79, 86, 89, 90, 97, 98, 101, 103, 106], "annavonbachmann": [20, 47, 48, 54, 68, 86, 91], "annelien": [52, 71, 79, 92, 93, 106], "anniek": [4, 7, 9, 36, 64, 73, 79, 90, 98, 109], "anniekstok": [4, 5, 7, 9, 36, 47, 68, 79], "annika": [11, 51, 52, 90], "annoat": [64, 73, 79, 99, 105], "annot": [0, 1, 2, 8, 15, 20, 25, 30, 35, 45, 49, 54, 55, 56, 72, 81, 86, 93], "annotations_biop": [31, 64, 73, 79], "announc": [1, 52, 86, 89, 108], "anoth": [29, 64, 73, 79, 99, 105], "anschlie\u00dfend": [54, 64, 73, 79, 105], "anschrift": 75, "answer": [0, 29, 50, 64, 73, 79, 93, 99, 105, 109], "anti": [24, 31, 64, 73, 79], "antibodi": [29, 64, 73, 79], "antj": [1, 14, 15, 38, 52, 64, 73, 79, 105], "antonio": [1, 4, 5, 7, 9, 15, 36, 38, 47, 64, 68, 73, 79, 90, 98, 109], "antragsbewilligung": [0, 32, 64, 73, 79], "antragststellung": [0, 32, 64, 73, 79], "antragsvorbereitung": [0, 32, 64, 73, 79], "antti": [11, 42, 79, 101], "antuono": [18, 29, 31, 52, 65, 79, 90, 98], "antwortfoli": [26, 54, 64, 73, 79, 105], "anymor": [64, 73, 79], "anyth": [5, 54, 64, 73, 79, 89, 90], "anythign": [5, 54, 64, 73, 79, 89, 90], "apach": [47, 48, 58, 68, 90, 100], "aphaia": [3, 6, 16, 22, 23, 28, 32, 37, 50, 64, 73, 79, 90, 92, 99, 105], "api": [16, 35, 42, 45, 61, 73, 79, 84, 89, 100, 103, 108], "apismichel": [3, 23, 37, 64, 73, 79, 99, 105, 109], "apo": [29, 64, 73, 79], "apoc": [48, 79, 97, 103], "apotom": [30, 33, 64, 73, 79], "app": [29, 40, 55, 84, 87, 99, 105, 106, 109], "appear": [29, 64, 73, 79], "appleton": [52, 64, 71, 79, 93, 105], "appli": [6, 16, 22, 28, 30, 33, 35, 37, 42, 44, 45, 47, 49, 55, 64, 68, 73, 79, 81, 83, 89, 100, 103, 105, 107], "applic": [2, 3, 8, 17, 23, 29, 33, 34, 35, 37, 47, 48, 50, 52, 56, 71, 72, 77, 80, 82, 84, 93, 99, 100, 102, 103, 105, 107], "apprais": [52, 71, 79, 92, 93, 106], "approach": [1, 7, 9, 14, 17, 21, 22, 28, 29, 34, 36, 38, 56, 70, 71, 73, 89, 92, 93, 96, 103], "appropri": [1, 9, 14, 28, 42, 57, 64, 73, 79, 86, 90], "approv": [22, 64, 73], "approxim": [29, 31, 64, 73, 79], "appsubmitt": 61, "april": [3, 5, 29, 37, 47, 54, 64, 68, 73, 79, 89, 90, 93, 99, 103, 105], "apurv": [29, 48, 68, 79, 103], "ar": [0, 1, 3, 11, 14, 15, 20, 22, 23, 24, 25, 28, 29, 31, 32, 33, 34, 37, 38, 39, 42, 44, 45, 47, 48, 49, 50, 52, 54, 56, 57, 59, 60, 64, 65, 68, 70, 73, 79, 81, 84, 87, 88, 89, 90, 96, 99, 100, 101, 102, 103], "arbeit": [29, 54, 64, 73, 79, 93, 95, 105], "arbeitspapi": [64, 73, 79], "arc": [23, 37, 45, 46, 52, 64, 68, 99, 100, 103], "architect": [3, 33, 34, 37, 64, 73, 79, 99, 105], "archiv": [3, 6, 11, 23, 28, 37, 50, 73, 80, 93, 96, 99, 109], "archivieren": [29, 54, 64, 73, 79, 93, 105], "archivierung": [26, 54, 64, 73, 79, 105], "ardan": [38, 52, 64, 71, 86, 105], "area": [29, 33, 44, 54, 90, 93, 99, 105], "arganda": [10, 11, 18, 29, 41, 48, 54, 63, 68, 86, 89, 90, 98], "argu": [32, 64, 73, 79], "arhnhk07t3q": [42, 56, 72], "ariann": [10, 11, 18, 41, 48, 54, 68, 86, 90, 98], "arif": [4, 7, 9, 11, 36, 55, 64, 73, 79, 84, 90, 98, 105, 107, 109], "arina": [1, 14, 15, 38, 52, 64, 73, 79, 105], "aris": [29, 52, 64, 73, 79, 90, 105], "arn": [4, 12, 18, 30, 52, 71, 76], "arndt": [64, 73, 80], "arrai": [3, 8, 23, 30, 33, 37], "arrat": [10, 17, 18, 29, 50, 52, 64, 65, 73, 79, 96], "arrest": [54, 56, 72, 86, 89, 90], "arriv": [3, 37, 64, 73, 79], "art": [0, 3, 8, 11, 17, 29, 32, 52, 64, 65, 73, 75, 76, 79, 105], "articl": [2, 3, 4, 5, 8, 9, 11, 12, 15, 17, 18, 22, 23, 29, 30, 31, 32, 33, 34, 37, 38, 42, 43, 45, 48, 50, 51, 52, 64, 65, 68, 70, 71, 73, 76, 79, 84, 86, 90, 91, 92, 93, 94, 96, 98, 99, 101, 103, 105, 106, 107, 108], "artif": [28, 29, 39, 47, 54, 66, 68, 84, 86, 90, 103], "artifici": [1, 5, 7, 10, 13, 21, 25, 38, 39, 40, 42, 43, 45, 47, 48, 52, 55, 62, 63, 66, 68, 70, 71, 78, 81, 84, 93, 98, 99, 105, 107, 108, 109], "artikel": [64, 73, 79], "arxiv": [11, 51, 52, 64, 71, 73, 79, 90, 93, 107], "asadipour": [64, 73, 79], "ashi": [4, 24, 52, 64, 79, 90, 105], "ask": [29, 32, 54, 64, 73, 79, 89, 103], "aslihan": [4, 12, 18, 30, 52, 71, 76], "aspect": [4, 6, 9, 28, 29, 37, 40, 42, 52, 54, 64, 73, 79, 90, 101, 105], "aspekt": [26, 29, 40, 54, 64, 73, 79, 93, 95, 101, 105], "aspekten": [26, 54, 64, 73, 79, 105], "assaf": [17, 41, 48, 52, 54, 68, 79, 86, 90, 98, 101], "assembl": [45, 52, 64, 94, 108], "assembli": [30, 33, 49, 55, 73, 79, 81, 107], "assess": [3, 8, 17, 36, 54, 64, 65, 73, 105], "asset": [3, 8, 10, 23, 33, 34, 35, 37, 42, 54, 56, 64, 72, 73, 86, 89, 92, 93, 99, 100, 105], "assist": [3, 8, 23, 30, 33, 34, 37, 48, 52, 71, 92, 93, 96, 97, 99, 103, 105], "associ": [24, 29, 31, 42, 47, 48, 64, 68, 73, 79, 86, 90, 102, 103], "assunta": [1, 15, 38, 64, 73, 79], "assur": [29, 64, 73, 79, 89], "astrid": [16, 23, 32, 37, 45, 46, 47, 50, 52, 64, 68, 73, 79, 90, 93, 99, 100, 103, 105], "athar": [7, 21, 38, 54, 56, 70, 79, 89, 90, 103], "atkinson": [47, 54, 68, 83], "attach": [16, 35, 64, 73, 79], "attende": [36, 54, 64, 73, 79], "attribut": [1, 28, 64, 73, 79, 80, 99, 105], "attributionpleas": [64, 73, 79, 99, 105], "au": [0, 32, 39, 40, 49, 55, 64, 73, 79, 81, 86, 101, 105, 106, 107], "auch": [26, 54, 64, 73, 75, 79, 105], "audergon": [1, 15, 38, 64, 73, 79], "audienc": [3, 6, 9, 28, 29, 33, 34, 37, 54, 64, 73, 79, 89, 99, 105], "audrei": [1, 15, 38, 64, 73, 79], "auf": [26, 29, 54, 64, 73, 75, 79, 93, 105], "aufbewahren": [26, 54, 64, 73, 79, 93, 105], "aufgaben": [0, 26, 32, 49, 54, 57, 64, 73, 79, 86, 102, 105], "aufloesen": [29, 54, 64, 73, 79, 93, 105], "aufl\u00f6sung": [26, 54, 64, 73, 79, 105], "aufmerksam": 75, "aufteilen": [0, 32, 64, 73, 79], "aufweisen": 75, "aufzeigen": [26, 54, 64, 73, 79, 105], "aug": [3, 22, 23, 32, 37, 64, 73, 79, 99, 105], "augment": [20, 29, 64, 73, 79, 89], "august": [17, 64, 73, 79, 99, 109], "auml": [30, 33, 64, 73, 79], "aurelien": [48, 68, 83, 90], "ausdr\u00fccklich": 75, "ausgab": [64, 73, 79], "ausgesprochen": [64, 73, 79], "austausch": [0, 32, 64, 73, 79], "author": [3, 8, 11, 17, 22, 29, 38, 39, 42, 52, 56, 59, 60, 61, 64, 65, 66, 71, 72, 73, 79, 86, 89, 90, 91, 92, 93, 96, 98, 100, 101, 105, 106, 107], "auto": [25, 31, 64, 73, 79], "autom": [1, 24, 28, 29, 31, 50, 64, 99], "automat": [1, 25, 29, 31, 55, 60, 73, 74, 87, 99, 105, 106], "automatisiert": [29, 54, 64, 73, 79, 93, 105], "autonoma": [30, 33, 64, 73, 79], "autostain": [24, 31, 64, 73, 79], "au\u00dferhalb": 75, "av": [64, 73, 79], "avail": [1, 3, 6, 8, 14, 15, 20, 22, 23, 28, 29, 32, 33, 34, 35, 37, 38, 39, 51, 52, 54, 59, 64, 69, 72, 73, 86, 90, 93, 99, 100, 105], "avanzada": [30, 33, 64, 73, 79], "averag": [29, 64, 73, 79], "avilov": [4, 12, 18, 30, 52, 71, 76], "avoid": [3, 22, 23, 32, 37, 64, 73, 79, 99, 105], "awai": [7, 21, 38, 54, 56, 70, 79, 89, 90, 103], "await": [44, 64, 73, 79, 105], "axial": [64, 73, 79], "axio": [29, 30, 33, 64, 73, 79], "axioscan": [32, 37, 99, 105], "axioscan7": [3, 22, 23], "axiovert": [30, 33, 64, 73, 79], "axton": [52, 64, 71, 79, 93, 105], "ayala": [30, 33, 64, 73, 79], "ayam": [10, 29, 64, 73, 79], "aybukeki": [15, 17, 37, 47, 68, 78, 99, 105, 106], "ayoub": [1, 14, 52, 64, 73, 79, 105], "ays": [4, 12, 18, 30, 52, 71, 76], "azid": [24, 31, 64, 73, 79], "b": [11, 18, 29, 30, 33, 52, 54, 55, 64, 68, 73, 75, 79, 86, 90, 98], "b1": [3, 23, 32, 37, 64, 73, 79, 99, 105], "babaki": [0, 16, 23, 37, 45, 46, 47, 52, 64, 68, 73, 79, 90, 93, 99, 100, 103, 105], "bacal": [11, 42, 78, 91, 103, 107, 108], "back": [64, 108], "background": [29, 30, 33, 48, 52, 64, 68, 73, 76, 79, 84, 89, 94, 99, 103], "backward": [36, 54, 64, 73, 79], "bacteri": [30, 33, 49, 55, 64, 73, 79, 81, 107], "bad": [47, 68, 84], "baden": [64, 73, 79], "baecker": [54, 68, 77, 90, 98], "baker": [24, 31, 64, 73, 79], "bakker": [4, 12, 18, 30, 52, 71, 76], "balanc": [24, 31, 64, 73, 79], "bankhead": [25, 29, 31, 40, 42, 48, 52, 54, 64, 65, 73, 78, 79, 90, 94, 98, 103], "bar": [64, 73, 79, 87], "barbara": [26, 42, 52, 54, 64, 73, 78, 79, 105], "barbora": [29, 52, 64, 76], "barbotin": [48, 68, 83, 90], "barcel\u00f3": [45, 86, 89, 90, 108], "barlow": [38, 52, 71, 105], "barranco": [11, 45, 103, 108], "barri": [47, 51, 52, 68, 69, 78, 80, 90], "barrier": [1, 15, 38, 64, 73], "barrutia": [10, 17, 18, 29, 50, 52, 64, 65, 73, 79, 96], "barwil": [20, 47, 48, 54, 68, 86, 91], "base": [0, 1, 3, 8, 13, 15, 20, 22, 23, 30, 32, 33, 34, 35, 37, 38, 45, 49, 50, 51, 56, 69, 72, 77, 78, 80, 86, 91, 93, 94, 99, 100, 101, 106, 108], "base4nfdi": [44, 64, 73, 79, 105], "basel": [17, 34, 50, 73, 79, 93, 99, 105], "basi": [3, 8, 23, 33, 34, 35, 37, 54, 56, 64, 72, 73, 75, 79, 99, 100, 105], "basic": [3, 6, 7, 11, 13, 20, 23, 28, 29, 36, 37, 42, 48, 54, 55, 64, 73, 78, 84, 88, 89, 99, 103], "bastien": [54, 68, 77, 90, 98], "batch": [10, 29, 55, 64, 73, 78, 79, 90, 97], "batchconvert_cli": [64, 73, 79], "batut": [11, 52, 79, 91, 93, 96], "baum": [64, 73, 79], "baustein": [52, 64, 79, 105], "bayesian": [47, 68, 81], "bbbc": [42, 43, 71], "bdv": [24, 31, 64, 73, 79], "bea": [52, 79, 93, 105], "beachten": [26, 54, 64, 73, 79, 105], "beachtet": 75, "beam": [24, 64, 73, 79], "beantragung": [0, 32, 64, 73, 79], "beantwortet": [40, 79, 101], "bearbeitung": 75, "beatriz": [14, 15, 28, 38, 52, 54, 63, 64, 73, 79, 86, 89, 90, 105, 108], "becaus": [3, 6, 28, 29, 33, 34, 37, 54, 64, 73, 79, 99, 105], "becker": [7, 11, 33, 36, 40, 55, 64, 68, 73, 79, 81, 90, 103], "becom": [3, 8, 23, 33, 34, 35, 37, 52, 54, 56, 64, 71, 72, 73, 79, 92, 93, 99, 100, 105, 106], "bedarfsanmeldung": [0, 32, 64, 73, 79], "bedeutung": [26, 54, 64, 73, 79, 105], "bedingungen": [26, 54, 64, 73, 79, 93, 105], "been": [3, 6, 8, 17, 22, 23, 28, 29, 50, 52, 61, 64, 71, 73, 79, 87, 90, 93, 99, 102, 105, 107], "befor": [24, 29, 31, 64, 73, 79], "begin": [3, 11, 17, 29, 33, 34, 37, 38, 44, 50, 52, 64, 71, 73, 79, 86, 93, 96, 99, 105], "beginn": [0, 3, 8, 23, 32, 33, 34, 35, 37, 54, 56, 64, 72, 73, 79, 99, 100, 105], "begriff": [29, 54, 64, 73, 79, 93, 95, 105], "begr\u00fc\u00dfenswert": [64, 73, 79], "begun": [3, 8, 23, 30, 33, 34, 37, 64, 73, 79, 99, 105], "begutachtungsverfahren": [40, 79, 101], "behalten": [26, 54, 64, 73, 75, 79, 105], "behandelt": [0, 26, 32, 54, 64, 73, 79, 105], "behavior": [30, 33, 64, 73, 79], "behind": [52, 60, 64, 71, 79, 93, 105], "bei": [49, 54, 57, 64, 73, 75, 86, 95, 102, 105], "beiden": [26, 54, 64, 73, 79, 105], "beigetragen": [29, 54, 64, 73, 79, 93, 105], "being": [1, 6, 14, 28, 37, 54, 64, 73, 79, 105], "beispiel": [26, 54, 64, 73, 79, 105], "beispielsweis": 75, "beitrag": [0, 32, 64, 73, 79], "beitr\u00e4gen": [0, 32, 64, 73, 79], "bekanntwerden": 75, "bekemei": [6, 28, 37, 54, 64, 73, 79, 105], "bekeschu": [33, 64, 73, 79], "beleuchtet": [29, 54, 64, 73, 79, 105], "belevich": [4, 9, 29, 52, 79, 90], "belgium": [22, 64, 73, 79], "believ": [42, 48, 78, 90, 105], "below": [3, 30, 33, 37, 64, 73, 79], "ben": [64, 73, 79, 109], "benchmark": [42, 43, 71], "benedict": [1, 15, 29, 38, 52, 64, 73, 76, 79], "benefit": [3, 6, 17, 28, 33, 34, 37, 39, 51, 52, 54, 64, 69, 73, 79, 86, 90, 93, 99, 106], "benjamin": [21, 48, 54, 68, 77, 86, 89, 90, 98, 103], "benoit": [29, 54, 67, 68, 86, 90, 94], "benthamiana": [30, 33, 64, 73, 79], "beratungsstammtisch": [54, 64, 73, 79, 92, 105], "bercowski": [10, 11, 18, 41, 48, 54, 68, 86, 90, 98], "bereich": [26, 54], "bereit": [29, 54, 64, 73, 79, 93, 105], "berlin": [44, 64, 73, 79, 105], "berndt": [1, 28, 64, 73, 79], "bernhardt": [3, 8, 23, 30, 33, 34, 37, 52, 64, 73, 79, 86, 99, 105], "bert": [11, 42, 78, 91, 103, 107, 108], "bertinetti": [6, 28, 37, 54, 64, 73, 79, 105], "bertocchi": [4, 12, 18, 30, 52, 71, 76], "bertolini": [52, 64, 79, 90, 100, 107], "ber\u00fccksichtigen": [54, 64, 73, 79, 95, 105], "beschaffungsprozess": [0, 32, 64, 73, 79], "besch\u00e4ftigt": [0, 32, 64, 73, 79], "besond": [54, 64, 73, 79, 95, 105], "besprechen": [26, 54, 64, 73, 79, 105], "besson": [2, 4, 5, 11, 15, 17, 47, 52, 68, 71, 77, 79, 82, 99, 102, 105], "best": [1, 3, 8, 15, 20, 23, 28, 29, 33, 34, 35, 37, 38, 40, 44, 54, 56, 72, 73, 79, 84, 92, 99, 100], "beteiligt": [54, 64, 73, 79, 95, 105], "beth": [4, 7, 12, 18, 21, 30, 38, 40, 42, 48, 51, 52, 54, 56, 64, 68, 69, 70, 71, 76, 78, 79, 81, 89, 90, 103, 105], "bethac07": [12, 48, 79, 103], "betreib": 75, "betroffenen": [40, 79, 101], "better": [1, 15, 38, 55, 56, 64, 73, 79, 86], "bettina": [3, 33, 34, 37, 64, 73, 79, 99, 105], "between": [3, 9, 11, 15, 23, 28, 29, 37, 47, 54, 62, 64, 68, 73, 79, 82, 89, 90, 99, 100, 103, 105], "beyond": [3, 8, 23, 28, 44, 54, 57, 64, 73, 79, 86, 99, 105], "bfdm": [52, 64, 79, 105], "bhattiprolu": [42, 48, 56, 68, 72, 84, 90, 103], "bi": 75, "bia23": [46, 90], "biabob": [19, 29, 39, 47, 48, 55, 66, 68, 78, 90, 103], "biapol": [19, 29, 39, 46, 47, 48, 68, 79, 89, 90, 97, 100, 103], "bias_nucleus_segment": [18, 40, 55, 68, 86, 90, 98], "bib": [52, 64, 73, 79, 93, 105], "bibliotheken": [40, 79, 101], "bid": 48, "bielefeld": [0, 16, 23, 37, 47, 73, 79, 90, 93, 99, 105], "biernacka": [40, 44, 64, 73, 79, 105], "bierwirth": [40, 64, 73, 79, 105], "bietet": [0, 26, 32, 54, 64, 73, 79, 105], "big": [1, 3, 4, 5, 13, 14, 33, 47, 48, 55, 64, 68, 73, 78, 79, 84, 86, 90, 99, 103, 105], "bigstich": [31, 64, 73, 79], "biifsweden": [47, 54, 58, 63, 68, 79, 90], "biii": [42, 90], "bildanalys": [0, 32, 64, 73, 79], "bildern": [54, 64, 73, 79, 95, 105], "bina": [11, 30, 33, 52, 86, 90, 96], "binari": [17, 34, 50, 54, 64, 73, 79, 84, 90, 93, 99, 105], "bind": [16, 35, 42, 73, 79, 90, 100], "bio": [11, 20, 22, 24, 28, 31, 32, 41, 45, 60, 87, 98, 101], "biochristian": [3, 23, 37, 64, 73, 79, 99, 105, 109], "biocompat": [3, 23, 32, 37, 64, 73, 79, 99, 105], "biocur": [42, 55, 56, 70, 79, 101, 106], "bioformat": [24, 64, 73], "biohackathon": [0, 16, 23, 37, 47, 73, 79, 90, 93, 99, 105], "bioimag": [0, 12, 19, 20, 22, 24, 25, 32, 39, 44, 47, 51, 57, 62, 66, 69, 77, 82, 87, 91, 92, 95, 102], "bioimage_analysis_icob_2023": [42, 58, 68, 90], "bioimage_team": [29, 54, 67, 86, 90, 94], "bioimageanalysi": [4, 5, 7, 9, 36, 47, 55, 68, 78, 79], "bioimageanalysis22": [4, 49, 54, 56, 64, 70, 79, 90], "bioimageanalysisnotebook": [40, 48, 78, 79, 90, 103], "bioimagebook": [40, 48, 79, 90, 94, 103], "bioimagedataanalysi": [18, 40, 76, 90, 98], "bioimagelist": [42, 86, 105], "bioimaging_and_optics_platform_biop": [24, 31, 64, 73, 79], "bioimagingguid": [42, 52, 64, 78], "bioimagingnorthamerica": [17, 64, 73, 79], "bioinformat": [4, 11, 20, 24, 38, 42, 45, 46, 47, 48, 54, 55, 64, 68, 70, 78, 79, 86, 90, 93, 96, 101, 103, 105, 106, 107, 108, 109], "bioinformatician": [3, 23, 28, 64, 73, 79, 99, 105], "biolog": [1, 3, 9, 14, 17, 23, 28, 29, 33, 34, 37, 42, 50, 54, 55, 56, 70, 72, 73, 81, 93, 99, 102], "biologi": [3, 14, 30, 33, 34, 37, 41, 42, 54, 55, 56, 84, 93, 99, 107], "biologist": [17, 19, 29, 47, 54, 56, 72, 81, 90, 95, 97, 101, 105, 106], "biomateri": [3, 23, 32, 37, 64, 73, 79, 99, 105], "biomed": [6, 29, 30, 33, 42, 45, 52, 54, 57, 64, 73, 79, 86, 102, 103, 108], "biomedcentr": [2, 15, 52, 64, 79, 90], "biomolecular": [42, 47, 68, 91, 109], "biop": [49, 90, 100], "bioport": [64, 73, 79], "biorxiv": [17, 30, 33, 42, 48, 51, 52, 64, 68, 73, 78, 79, 84, 90, 103, 105, 106], "biotec": [29, 48, 68, 79, 90, 97, 103], "biotechnologi": [30, 33, 64, 73, 79], "biotin": [24, 31, 64, 73, 79], "birgit": [45, 52, 94, 108], "birgitta": [29, 64, 73, 79, 99, 105], "birnei": [38, 52, 71, 105], "birt": [64, 73, 80], "bischof": [1, 4, 12, 14, 18, 30, 52, 64, 71, 73, 76, 79, 105], "bit": [3, 20, 22, 23, 31, 32, 37, 64, 73, 79, 99, 105], "bitten": 75, "bjorn": [1, 15, 38, 64, 73, 79], "bj\u00f6rn": [1, 28, 54, 55, 63, 79, 86, 90, 96, 99, 100], "black": [24, 31, 64, 73, 79], "bleiben": 75, "blick": [64, 73, 79], "blog": [19, 29, 59, 60, 62, 66, 79, 86, 89, 90, 95, 97, 100, 103, 105, 106], "blomberg": [1, 15, 38, 64, 73, 79], "blood": [20, 64, 73], "bmbf": [26, 54, 64, 73, 79, 105], "bnsreenu": [42, 48, 68, 84, 90, 103], "bob": [52, 71, 93, 96, 105], "bocklitz": [52, 64, 79, 93, 105], "bodi": [29, 30, 33, 57, 64, 73, 79, 105], "boeckhout": [52, 71, 79, 92, 93, 106], "boehm": [4, 11, 12, 18, 30, 33, 52, 64, 71, 73, 76, 79, 86, 87, 90, 96], "bogov": [17, 18, 29, 50, 52, 65, 79, 96], "bohr": [1, 28, 50, 64, 73, 79, 90], "boissonnet": [0, 3, 6, 8, 15, 16, 17, 22, 23, 28, 30, 32, 33, 34, 35, 45, 46, 47, 52, 54, 56, 64, 68, 72, 73, 78, 79, 87, 90, 93, 99, 100, 103, 105, 106, 109], "boiten": [1, 15, 38, 64, 73, 79], "bombrun": [4, 55, 68, 86, 90, 98], "book": [12, 18, 41, 42, 48, 55, 64, 68, 73, 76, 78, 79, 81, 83, 84, 86, 87, 89, 90, 94, 97, 98, 101, 103, 105], "bornholdt": [40, 68, 86, 89, 90], "bortolomeazzi": [0, 3, 8, 16, 22, 28, 30, 32, 33, 34, 35, 37, 45, 46, 47, 52, 54, 56, 64, 68, 72, 73, 79, 87, 90, 93, 99, 100, 103, 105, 109], "bot": [15, 17, 37, 47, 68, 78, 90, 99, 105, 106], "both": [0, 3, 22, 23, 29, 32, 37, 50, 52, 55, 57, 64, 71, 73, 79, 80, 92, 93, 96, 99, 105], "bottleneck": [64, 73, 79], "bottom": 60, "botzki": [47, 55, 68, 79, 89], "boundari": [64, 73, 79], "bourget": [11, 52, 79, 105], "box": [10, 29, 64, 73, 79], "bo\u00dfe": [64, 73, 80], "bq8zccqc": [30, 33, 64, 73, 79], "bragantini": [47, 55, 58, 68, 90], "brain": [0, 24, 50, 93], "branch": 60, "brazma": [38, 52, 64, 71, 86, 105], "brdu": [24, 31, 64, 73, 79], "break": [3, 16, 30, 33, 35, 37, 64, 73, 79], "bredenoord": [52, 71, 79, 92, 93, 106], "bret": [55, 56, 86], "brian": [29, 48, 68, 79, 103], "brick": [54, 64, 73, 79, 105], "brideau": [42, 52, 64, 78], "bridg": [0, 16, 23, 37, 47, 99, 100, 107], "brief": [52, 71, 90, 93, 107], "brighfield": [64, 73, 79], "bright": [64, 73], "brightfield": [30, 33], "bring": [1, 3, 8, 15, 23, 28, 30, 33, 34, 37, 38, 42, 55, 56, 64, 70, 73, 79, 99, 101, 106], "brisanz": [64, 73, 79], "britni": [42, 47, 68, 91, 109], "britta": [4, 12, 18, 20, 30, 47, 48, 52, 54, 68, 71, 76, 86, 91], "broad": [3, 6, 8, 23, 28, 33, 34, 35, 37, 42, 43, 54, 56, 64, 71, 72, 73, 79, 99, 100, 105], "broaddu": [29, 64, 73, 79], "broader": [1, 28, 51, 52, 64, 69, 73, 79, 90, 105], "broadinstitut": [42, 43, 71], "brocher": [4, 12, 18, 30, 52, 71, 76], "brook": [10, 29, 64, 73, 79], "brought": [1, 14, 33, 64, 73, 79], "brown": [30, 33, 52, 64, 71, 73, 79, 87, 96], "brows": [14, 54, 55, 64, 73, 90, 93, 105], "browser": [34, 56], "browsing_idr": [29, 39, 62, 79, 100, 103], "bruna": [47, 55, 68, 79, 89, 107], "bsd": [11, 13, 15, 17, 29, 37, 40, 42, 44, 45, 47, 48, 49, 51, 52, 54, 55, 58, 64, 67, 68, 69, 79, 89, 90, 91, 94, 97, 98, 99, 100, 103, 105, 106, 107, 108], "bsd3": [47, 55, 58, 68, 90], "bsg": [24, 64, 73, 79], "btab106": [4, 24, 52, 64, 79, 90, 105], "bu1": [24, 31, 64, 73, 79], "buchholz": [4, 5, 7, 9, 12, 36, 40, 42, 47, 48, 64, 68, 71, 73, 79, 84, 90, 98, 105, 109], "budapest": [42, 47, 68, 90, 109], "bug": [10, 29, 64, 73, 79], "buglakova": [21, 48, 55, 68, 86, 89, 103], "bugra": [1, 4, 7, 9, 14, 36, 55, 64, 73, 79, 86, 90, 98, 109], "bugraoezdemir": [4, 5, 7, 9, 36, 47, 64, 68, 73, 79], "build": [11, 30, 33, 45, 49, 55, 56, 71, 81, 93, 102, 103, 107, 108], "built": [42, 55, 56, 70, 79, 101, 103, 106], "bumberg": [1, 3, 8, 23, 28, 50, 64, 73, 79, 90, 99, 105], "bunch": [10, 29, 47, 64, 68, 73, 78, 79, 90], "bundl": [44, 64, 73, 79, 105], "burden": [17, 50, 73, 79, 90, 105], "burdett": [64, 73, 79], "burel": [1, 2, 4, 5, 11, 17, 37, 38, 47, 52, 64, 68, 71, 73, 78, 79, 82, 90, 99, 100, 105, 106], "burk": [11, 52, 79, 91, 93, 96], "burri": [11, 21, 24, 31, 42, 46, 48, 54, 58, 64, 68, 73, 79, 86, 89, 90, 98, 103], "burst": [30, 33, 64, 73, 79], "busch": [1, 28, 50, 64, 73, 79, 90], "busi": 60, "butler": [41, 48, 54, 68, 86, 90, 98], "button": 60, "bu\u011fra": [52, 105], "bwfdm": [64, 73, 79], "byod": [58, 90, 105], "b\u00e9r\u00e9nice": [11, 52, 79, 91, 93, 96], "c": [4, 11, 12, 18, 24, 29, 30, 31, 33, 38, 42, 47, 52, 56, 64, 68, 71, 72, 73, 76, 79, 81, 84, 86, 90, 91, 96, 101, 105, 106], "c35tvcsrxzay98s2uywwxpp0cfcljbmmfpco": [13, 54, 63, 86, 90, 98], "c4scienc": [24, 31, 64, 73, 79], "ca": [30, 33, 64, 73, 79], "cai": [30, 33, 64, 73, 79], "caicedo": [40, 68, 86, 89, 90], "calbindin": [29, 64, 73, 79], "calcul": [22, 64, 73, 79], "calf": [31, 64, 73, 79], "call": [1, 11, 15, 45, 47, 68, 82, 100, 103, 108], "callaghan": [42, 47, 48, 68, 86, 90, 102, 103], "callum": [40, 68, 81, 90], "calretinin": [29, 64, 73, 79], "cam": [48, 81, 90, 94], "camacho": [55, 68, 84, 90, 103], "camachodejai": [55, 68, 84, 90, 103], "camera": [20, 24, 25, 31], "cameron": [52, 71, 96], "camp": [64, 73, 79], "campu": [17, 28, 30, 33, 37, 54, 99, 105, 109], "can": [1, 3, 8, 12, 14, 15, 17, 20, 22, 23, 24, 25, 28, 29, 30, 31, 32, 33, 34, 37, 39, 42, 45, 47, 48, 49, 52, 54, 55, 56, 59, 60, 61, 62, 64, 66, 68, 70, 71, 72, 73, 74, 79, 80, 81, 82, 84, 86, 87, 88, 89, 90, 91, 92, 93, 96, 99, 100, 101, 103, 105, 106, 107], "cancer": [3, 23, 25, 28, 31, 33, 34, 37, 64, 73, 79, 99, 105, 109], "candid": [20, 64, 73, 79], "cannot": [17, 34, 54, 64, 73, 79, 90, 93, 105], "canva": [49, 55, 81, 107], "capabl": [1, 28, 64, 73, 79], "capolupo": [25, 31, 64, 73, 79], "captur": [0, 50, 64, 73, 93, 99, 105], "carbon": [10, 29, 64, 73, 79], "cardiac": [3, 23, 32, 37, 64, 73, 79, 99, 105], "cardiovascular": [3, 23, 32, 37, 64, 73, 79, 99, 105], "cardona": [48, 81, 90, 94], "care": [17, 34, 50, 60, 73, 79, 93, 99, 105], "career": [3, 9, 23, 28, 37, 39, 64, 73, 79, 86, 99, 105, 106], "carl": [3, 20, 23, 24, 29, 30, 32, 33, 37, 47, 48, 51, 52, 54, 68, 69, 78, 86, 90, 91, 99, 105], "carla": [1, 15, 38, 64, 73, 79], "carli": [52, 73, 79, 102], "carol": [1, 15, 38, 64, 73, 79], "carolin": [26, 40, 54, 64, 73, 79, 105], "carolina": [4, 30, 33, 55, 64, 68, 73, 79, 86, 90, 98], "carpent": [29, 42, 43, 52, 64, 65, 68, 71, 78, 79, 90, 98], "carpenti": [42, 47, 68, 91, 109], "carpentri": [36, 54, 64, 73], "carrera": [10, 11, 18, 29, 41, 48, 54, 63, 68, 86, 89, 90, 98], "carsten": [3, 8, 23, 33, 34, 35, 37, 54, 56, 64, 72, 73, 79, 87, 90, 93, 99, 100, 105], "cart": [40, 90], "caru": [3, 23, 29, 32, 37, 64, 73, 79, 99, 105], "carvalho": [4, 12, 18, 30, 52, 71, 76], "case": [0, 1, 3, 6, 8, 16, 23, 24, 28, 29, 33, 34, 35, 37, 47, 52, 54, 56, 60, 72, 76, 93, 99, 100, 105], "castillia": [3, 4, 12, 18, 30, 33, 34, 52, 64, 71, 73, 76, 79, 87, 96, 105], "catalin": [4, 12, 18, 30, 52, 71, 76], "catalog": [40, 79, 101], "catalogu": [54, 73, 79, 101, 105], "categor": [9, 64, 73, 79], "caterina": [3, 4, 12, 18, 30, 33, 34, 45, 52, 64, 71, 73, 76, 79, 86, 87, 89, 90, 96, 105, 108], "caus": [47, 68, 81], "causal": [47, 68, 81], "cavanagh": [64, 73, 81], "cbg": [78, 98], "cc": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 25, 26, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 54, 55, 56, 57, 58, 62, 63, 65, 66, 68, 69, 70, 71, 72, 75, 78, 84, 87, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 105, 106, 107, 108, 109], "cc0": [40, 42, 43, 47, 48, 49, 55, 56, 64, 68, 70, 73, 89, 90, 91, 93, 94, 96, 105, 107, 109], "cc1": [24, 31, 64, 73, 79], "cci": [30, 33, 64, 73, 79], "cck": [64, 73, 79], "ccy": [3, 8, 54, 64, 99, 105], "cd": 61, "cedar": [52, 71, 79, 92, 93, 96], "cell": [1, 3, 5, 22, 23, 24, 25, 28, 29, 30, 32, 33, 37, 41, 47, 48, 52, 67, 68, 71, 78, 83, 84, 93, 94, 96, 98, 99, 103], "cellcarri": [31, 64, 73, 79], "cellmigrationlab": [42, 47, 54, 55, 58, 68, 83, 90, 109], "cellpos": [47, 68, 78, 89, 90], "cellprofil": [1, 28, 64], "cellprofiler_introduct": [54, 68, 86, 90, 98], "celltrackingchalleng": [42, 43, 86, 90], "celltrackscolab": [42, 48, 69, 84], "cellulair": [30, 33, 64, 73, 79], "cellular": [3, 22, 23], "cenalmor": [39, 66, 86], "center": [6, 30, 33, 34, 37, 54, 75, 109], "centr": [28, 30, 33, 42, 54, 55, 56, 64, 73, 79, 90, 99, 105], "central": [3, 6, 33, 34, 37, 64, 73, 79, 99, 105], "cerebr": [30, 33, 64, 73, 79], "certain": [14, 29, 42, 47, 52, 55, 64, 68, 73, 79, 84, 90, 93, 105], "ceur": [17, 34, 52, 64, 73, 79, 99], "cfci": [3, 22, 23], "cgrp": [29, 64, 73, 79], "ch": [24, 31, 49, 64, 73, 79, 90, 100], "cha": [48, 68, 83, 90], "chaeroni": [64, 73, 79], "chain": [49, 55, 81, 107], "challeng": [0, 1, 3, 6, 8, 9, 14, 20, 23, 28, 30, 33, 34, 36, 38, 50, 89, 93, 95, 101, 103], "cham": [29, 64, 73, 79], "chamber": [11, 24, 30, 33, 52, 64, 73, 79, 86, 87, 90, 96], "chamier": [12, 29, 42, 48, 64, 68, 71, 73, 79, 84, 90], "champion": [64, 73, 79], "chan": [25, 31, 52, 64, 73, 79, 102], "chan2": [25, 31, 64, 73, 79], "chanc": [3, 33, 34, 37, 64, 73, 79, 99, 105], "chang": [10, 16, 20, 29, 35, 52, 54, 60, 64, 73, 79, 89, 102, 103], "changelog": [10, 29, 64, 73, 79], "channel": [20, 24, 29, 31, 54, 93, 95, 101, 105], "charact": [3, 22, 23, 32, 37, 64, 73, 79, 99, 105], "characterist": [14, 54, 55, 64, 73, 79, 90, 93, 105], "charg": [64, 73, 79], "charl": [38, 52, 71, 105], "charlott": [17, 18, 29, 50, 52, 65, 79, 96], "chat": 29, "chatgpt": 54, "check": [22, 60, 64, 73], "checkalina": [48, 68, 84, 89, 103], "checklist": [3, 37, 64, 73, 79], "chen": [52, 71, 90, 93, 107], "chessel": [17, 18, 29, 50, 52, 65, 79, 96], "chiang": [55, 79, 90, 91], "chiara": [6, 54, 64, 73, 79, 90], "chiaruttini": [4, 31, 52, 64, 73, 79, 90, 105], "chiesa": [54, 64, 73, 79, 92, 105], "chines": [30, 33, 64, 73, 79], "chiritescu": [4, 12, 18, 30, 52, 71, 76], "chitwood": 52, "chiu": [11, 14, 38, 52, 54, 55, 64, 71, 73, 79, 86, 90, 93, 96, 105], "chong": [11, 17, 18, 29, 41, 48, 50, 52, 54, 65, 68, 79, 86, 90, 96, 98], "choos": [4, 9, 29, 36, 39, 52, 54, 61, 64, 66, 73, 79, 90, 95], "chosen": [6, 28, 37, 54, 64, 73, 79, 105], "chri": [4, 5, 11, 15, 17, 47, 52, 55, 64, 68, 71, 79, 82, 99, 100, 102, 105], "chrishavlin": [42, 56, 72, 86, 90, 97, 103], "christi": [10, 29, 64, 73, 79], "christian": [1, 2, 5, 7, 8, 9, 11, 12, 15, 17, 18, 22, 23, 24, 28, 29, 30, 32, 33, 34, 35, 36, 37, 38, 42, 47, 49, 50, 52, 54, 55, 56, 64, 65, 68, 70, 71, 72, 73, 76, 79, 80, 84, 86, 87, 90, 96, 98, 99, 100, 103, 105, 107, 109], "christoph": [0, 4, 11, 12, 15, 16, 18, 23, 30, 37, 42, 45, 46, 47, 48, 52, 54, 64, 68, 71, 73, 76, 79, 81, 82, 84, 86, 90, 93, 99, 100, 103, 105], "chromogen": [24, 31, 64, 73, 79], "chronic": [22, 64, 73, 79], "chukwuebuka": [3, 22, 23, 32, 37, 64, 73, 79, 99, 105], "chum": [30, 33, 64, 73, 79], "chumontr": [30, 33, 64, 73, 79], "chungyoun": [42, 47, 68, 91, 109], "chunk": [17, 50, 73, 79, 90, 105], "ci": [47, 68, 78, 90], "cimini": [4, 7, 12, 18, 21, 30, 38, 40, 42, 48, 51, 52, 54, 56, 64, 68, 69, 70, 71, 76, 78, 79, 81, 89, 90, 103, 105], "ciminilab": [52, 64, 68, 78, 90], "circul": [29, 54, 56, 64, 72, 73, 79, 86, 89, 90, 93, 95, 101, 105], "circumst": [22, 64, 73], "cisa": [30, 33, 64, 73, 79], "cite": [29, 55, 64, 73, 79, 105, 106], "clair": [30, 33, 52, 64, 71, 73, 79, 87, 96], "clariti": [10, 29, 64, 73, 79], "class": [14, 20, 22, 64, 73, 79], "classic": 61, "classif": [45, 48, 49, 55, 68, 78, 83, 84, 89, 90, 94, 100, 103, 107], "claudia": [1, 15, 38, 64, 73, 79], "claus": [11, 13, 15, 17, 29, 37, 40, 42, 44, 45, 47, 48, 49, 51, 52, 54, 55, 58, 64, 67, 68, 69, 79, 89, 90, 91, 94, 97, 98, 99, 100, 103, 105, 106, 107, 108], "clausthal": [44, 45, 55, 105], "clear": [24, 39, 64, 73, 79, 80, 86], "clearli": [9, 64, 73, 79], "clemen": [4, 18, 26, 54, 64, 68, 73, 79, 86, 90, 105], "clermont": [15, 47, 68, 82, 94, 100], "clesperanto": [79, 103], "clesperanto_workshop_i2k24": [29, 47, 48, 55, 58, 68, 78, 90], "click": [60, 61], "clij2": [20, 64, 73, 79], "clinic": [29, 64, 73, 79], "clone": [24, 31, 64, 73, 79], "close": [3, 8, 23, 28, 64, 73, 79, 80, 99, 105], "closer": [40, 73, 79, 105], "cloud": [1, 15, 28, 29, 38, 108], "cluster": [25, 31, 47, 54, 64, 68, 73, 79, 87, 100, 108], "cmcb": [6, 28, 29, 37, 54, 67, 90, 94, 105], "cmd": 61, "cnr": [30, 33, 64, 73, 79], "co": [30, 33, 42, 103], "coars": [24, 64, 73, 79], "coba": [51, 52, 68, 69, 78], "cobacenterforopenbioimagea1864": [42, 56, 72, 90], "cochin": [30, 33, 64, 73, 79], "code": [3, 6, 10, 12, 18, 20, 22, 23, 25, 28, 31, 32, 37, 54, 84, 98, 99, 105], "coderefineri": [55, 105, 106], "codingclub": [11, 41, 48, 68, 86, 103], "coffe": [3, 37, 64, 73, 79], "cohort": [1, 64, 73, 79], "coia": [40, 68, 81, 90], "colab": [20, 29, 47, 73, 79, 91, 109], "colarusso": [42, 52, 64, 78], "coleman": [30, 33, 64, 73, 79, 87], "collabor": [0, 1, 3, 8, 15, 16, 17, 23, 28, 33, 34, 37, 38, 44, 47, 49, 50, 51, 57, 69, 92, 93, 102], "collect": [2, 3, 12, 14, 16, 20, 25, 29, 31, 35, 43, 44, 45, 46, 47, 48, 49, 55, 56, 57, 58, 59, 60, 62, 69, 71, 72, 76, 77, 78, 79, 81, 83, 87, 88, 92, 93, 94, 97, 98, 99, 100, 101, 102, 103, 105, 106, 107, 108, 109], "colleg": [30, 33, 64, 73, 79], "collinson": [11, 14, 38, 52, 54, 55, 64, 71, 73, 79, 86, 90, 93, 96, 105], "coloc": [30, 33, 64, 73, 79], "cologn": [3, 33, 34], "colombelli": [17, 18, 29, 31, 50, 52, 65, 79, 90, 96, 98], "colon": [29, 64, 73, 79], "coloni": [20, 64, 73], "color": [24, 25, 31, 64, 73, 79], "colorect": [25, 31, 64, 73, 79], "column": [3, 23, 32, 37, 64, 73, 79, 80, 99, 105], "com": [1, 2, 3, 4, 5, 8, 9, 10, 11, 12, 13, 15, 16, 17, 18, 19, 23, 29, 30, 31, 33, 34, 35, 37, 38, 39, 40, 42, 43, 45, 47, 48, 49, 50, 52, 54, 55, 56, 58, 64, 70, 73, 76, 79, 82, 83, 84, 86, 89, 90, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 105, 106, 107, 108], "combin": [3, 17, 23, 28, 29, 33, 34, 39, 52, 54, 64, 66, 89, 90, 99], "come": [3, 23, 28, 37, 64, 73, 79, 99, 105], "comlinetool": [45, 79], "comma": [3, 22, 23, 32, 37, 64, 73, 79, 99, 105], "command": [25, 31, 47, 55, 61, 62, 64, 73, 90, 100, 105, 106], "comment": [29, 52, 64, 71, 73, 76, 79, 80, 93, 105], "commerci": [52, 60, 64, 71, 73, 79, 90, 93, 107], "commit": [47, 60, 68, 78, 90], "common": [1, 3, 14, 17, 20, 29, 33, 34, 44, 52, 54, 65, 75, 90, 93, 98, 99, 105], "commonli": [4, 9, 16, 29, 35, 42, 52, 56, 64, 72, 73, 79, 90, 98, 100], "commun": [0, 9, 15, 19, 28, 29, 38, 44, 45, 49, 50, 55, 80, 83, 89, 94, 100, 102, 103, 107, 108], "compani": [42, 86], "companion": [42, 48, 78, 90, 105], "compar": [9, 10, 29, 31, 54, 64, 73, 80, 88, 89, 93, 103], "compat": [2, 11, 16, 17, 29, 34, 35, 47, 50, 64, 68, 73, 79, 82, 93, 99, 102, 105], "compens": [64, 73, 79], "compet": [44, 54, 64, 73, 79, 80, 105], "complement": [3, 8, 23, 31, 33, 34, 35, 37, 40, 54, 56, 64, 72, 73, 79, 99, 100, 105], "complet": 79, "complex": [0, 1, 3, 6, 8, 9, 11, 14, 23, 28, 30, 33, 34, 37, 50, 52, 54, 64, 73, 79, 86, 90, 93, 96, 99, 105], "compli": [3, 8, 23, 30, 33, 34, 37, 64, 73, 79, 99, 105], "complianc": [54, 64, 73, 79, 105], "compliant": [44, 45, 55, 105], "complic": [20, 64, 73, 79], "compon": [54, 56, 64, 72, 73, 79, 86, 89, 90], "compos": [55, 56, 86], "compound": [64, 73, 79], "comprehens": [11, 42, 48, 57, 64, 73, 78, 79, 86, 90, 105], "comput": [1, 5, 11, 15, 20, 25, 30, 31, 33, 38, 42, 47, 51, 55, 56, 69, 78, 81, 84, 91, 93, 100, 101, 105, 106, 107, 108], "concentr": [22, 64, 73, 79], "concept": [1, 6, 15, 28, 29, 37, 38, 54, 72, 86, 89, 90], "conceptu": [47, 68, 81], "concern": [81, 90], "conclus": [64, 73, 79], "concret": [64, 73, 79], "cond": [4, 12, 18, 30, 52, 71, 76], "conda": [19, 31, 62], "conda_forge_packag": [45, 79, 103], "condit": [24, 31, 54, 56, 64, 72, 73, 79, 86, 89, 90], "conduct": [3, 8, 11, 17, 23, 32, 37, 52, 64, 65, 73, 79, 99, 105], "confer": [3, 6, 8, 17, 29, 34, 46, 47, 48, 52, 55, 68, 78], "configur": [64, 73, 99], "conflict": [29, 52, 79, 90, 105], "confoc": [24, 29, 30, 31, 33], "confront": [44, 64, 73, 79, 105], "confus": [25, 31, 39, 64, 73, 79, 86], "congress": [17, 20, 64, 73, 79], "connect": [1, 3, 23, 28, 47, 52, 64, 68, 73, 79, 81, 84, 90, 99, 100, 103, 105, 107], "conni": [6, 54, 64, 73, 79, 90], "connor": [52, 71, 79, 92, 93, 96], "conrad": [7, 21, 38, 54, 56, 70, 79, 89, 90, 103], "consequ": [64, 73, 79], "conserv": [64, 73], "consid": [3, 4, 9, 29, 33, 34, 37, 52, 60, 64, 73, 79, 87, 90, 99], "consider": [17, 34, 54, 55, 73, 79, 86, 90, 93, 95, 105], "consist": [29, 48, 64, 68, 73, 79, 84, 89, 99, 103], "consorti": [64, 73, 79], "consortia": [17, 44, 50, 64, 73, 79, 90, 105], "consortium": [1, 6, 15, 16, 17, 29, 33, 34, 35, 37, 38, 50, 56, 72, 92, 93, 100], "constanc": [17, 64, 73, 79, 99, 109], "constantin": [2, 4, 7, 9, 15, 17, 21, 36, 47, 48, 52, 54, 64, 68, 71, 73, 79, 84, 86, 89, 90, 99, 103, 105], "constantinpap": [5, 68, 84, 86, 89, 90], "constantli": [64, 73, 79], "construct": [1, 28, 64, 73, 79], "construct_cli": [64, 73, 79], "consult": [30, 33, 47, 55, 64, 68, 73, 79, 80, 84], "contact": [22, 64, 73, 79], "contain": [3, 5, 12, 16, 20, 22, 23, 24, 25, 29, 30, 31, 32, 33, 35, 37, 40, 45, 47, 48, 54, 55, 59, 60, 62, 64, 68, 73, 78, 79, 82, 87, 89, 90, 99, 100, 103, 105, 108], "content": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 23, 24, 25, 26, 29, 30, 31, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 51, 54, 55, 56, 57, 58, 59, 60, 62, 63, 65, 66, 67, 68, 69, 70, 72, 76, 77, 78, 80, 81, 82, 83, 84, 86, 87, 89, 91, 92, 94, 95, 97, 98, 99, 100, 101, 102, 103, 106, 107, 108, 109], "contentasset": [44, 105], "context": [0, 29, 50, 52, 54, 60, 65, 76, 87, 90, 93, 101], "contextu": [0, 50, 73, 79, 93, 105], "continu": [14, 42, 55, 64, 73, 79, 90, 93, 105], "contour": [29, 64, 73, 79], "contract": [64, 73, 79, 99, 105], "contribut": [0, 1, 3, 6, 8, 10, 16, 20, 23, 28, 29, 33, 34, 35, 37, 47, 52, 54, 56, 61, 64, 72, 73, 79, 80, 90, 93, 99, 100, 102, 105], "contributor": [10, 29, 64, 73, 79], "control": [1, 11, 25, 28, 30, 31, 33, 39, 42, 48, 52, 57, 66, 78, 90, 96, 106], "convei": [64, 73, 80], "conveni": [48, 74, 79, 97, 103], "convenin": [31, 64, 73, 79], "convent": [6, 20, 28, 37, 54, 64, 73, 79, 105], "convers": [2, 17, 47, 68, 78, 82, 90, 102], "convert": [24, 31, 64, 73, 79], "convex": [29, 64, 73, 79], "cook": [38, 52, 71, 105], "cooper": [64, 73, 79], "coordin": [3, 23, 28, 64, 73, 79, 99, 105], "cope": [47, 68, 81], "copi": [3, 22, 23, 32, 37, 64, 73, 79, 99, 105], "coppen": [1, 15, 38, 64, 73, 79], "copyright": [29, 40, 54, 64, 73, 79, 93, 95, 101, 105], "corbat": [47, 54, 58, 63, 68, 79, 90], "cord": [40, 64, 73, 79, 105], "cordeli\u00e8r": [18, 29, 31, 52, 65, 79, 90, 98], "cordi": [34, 52, 64, 99], "core": [0, 16, 20, 22, 23, 24, 31, 35, 56, 72, 80, 87, 93, 100], "core_facil": [30, 33, 64, 73, 79], "cornelia": [16, 28, 37, 50, 54, 64, 73, 79, 90, 92, 99, 105], "corner": 61, "cornerston": [52, 71, 79, 92, 93, 106], "correct": [10, 14, 24, 29, 31], "correspond": [3, 22, 23, 25, 31, 32, 37, 64, 73, 79, 87, 99, 105], "cortez": [44, 64, 73, 79, 105], "corvellec": [42, 68], "costantin": [48, 68, 84, 89, 103], "costantino": [52, 79, 101, 105], "could": [22, 29, 52, 59, 64, 71, 73, 79, 87, 90, 93, 107], "count": [20, 54, 64, 73, 79, 108], "counterstain": [24, 31, 64, 73, 79], "countri": [64, 73, 79], "couponcod": [55, 56, 86], "cours": [7, 9, 12, 28, 29, 38, 58, 64, 70, 72, 73, 81, 100, 101, 107], "course_script": [4, 55, 86], "courseintrotoia": [29, 68, 90, 94], "coursematerialimageanalysi": [29, 54, 67, 86, 90, 94], "courtesi": [24, 64, 73, 79], "courtnei": [42, 47, 68, 91, 109], "cover": [6, 28, 37, 40, 42, 47, 49, 54, 55, 56, 57, 64, 68, 70, 73, 79, 81, 84, 91, 101, 105, 106, 107, 109], "covid": 20, "cr": [30, 33, 64, 73, 79], "craig": [42, 45, 52, 64, 78, 84, 89, 103, 108], "crbm": [30, 33, 64, 73, 79], "crchum": [30, 33, 64, 73, 79], "creager": [2, 15, 52, 64, 79, 90], "creat": [1, 3, 8, 11, 14, 15, 20, 22, 23, 25, 28, 30, 31, 32, 33, 34, 35, 37, 45, 47, 52, 56, 60, 61, 68, 72, 74, 80, 81, 82, 86, 90, 93, 96, 99, 100, 103, 106, 108], "creation": [22, 64, 73, 79, 84], "creativ": [20, 64, 73, 75, 79], "cristina": [4, 12, 18, 30, 52, 71, 76], "critera": [14, 64, 73, 79], "critic": [52, 54, 56, 64, 71, 72, 73, 79, 86, 89, 90, 92, 93, 102, 105, 106], "crockett": [1, 15, 38, 64, 73, 79], "crop": [17, 31, 50, 64, 73, 79, 90, 99, 105], "crop_ds441": [31, 64, 73, 79], "crops_biop_v1": [31, 64, 73, 79], "cross": [1, 3, 8, 15, 22, 23, 28, 29, 32, 37, 38, 45, 48, 52, 64, 68, 73, 79, 83, 90, 94, 99, 105, 108], "crtd": [17, 64, 73, 79, 99, 109], "crucial": [0, 1, 14, 28, 29, 50, 54, 56, 64, 72, 73, 79, 86, 89, 90, 93, 99, 105], "cryosect": [30, 33, 64, 73, 79], "csaba": [41, 48, 54, 68, 86, 90, 98], "cseresny\u00e9": [45, 52, 71, 94, 108], "cshl": [12, 48, 79, 103], "cshlpress": [40, 90], "csv": [3, 16, 20, 22, 23, 25, 31, 32, 35, 37, 64, 73, 74, 79, 80, 99, 105], "csv_axioscan": [32, 37, 64, 73, 79, 99, 105], "csv_tem": [3, 22, 23, 32, 37, 64, 73, 79], "csv_tomo": [3, 22, 23, 32, 37, 64, 73, 79], "cullei": [4, 9, 11, 18, 21, 25, 29, 41, 48, 52, 54, 68, 79, 86, 89, 90, 98, 103], "culmin": [0, 16, 23, 37, 47, 73, 79, 90, 93, 99, 105], "curat": [3, 8, 11, 20, 23, 33, 34, 35, 37, 41, 42, 48, 54, 56, 64, 68, 72, 73, 79, 86, 99, 100, 103, 105], "current": [1, 5, 6, 14, 29, 32, 52, 54, 64, 73, 76, 79, 89, 90, 93, 95, 99, 101, 105], "curti": [11, 12, 18, 25, 29, 31, 42, 46, 50, 52, 54, 55, 58, 68, 86, 90, 94, 98], "custom": [29, 30, 33, 64, 73, 79], "cut": [24, 29, 31, 64, 73, 79], "cxli233": [47, 68, 81, 84], "cycl": [0, 50, 93], "cylindr": [22, 64, 73, 79], "cyto": [25, 31, 64, 73, 79], "cyto2": [25, 31, 64, 73, 79], "cyto_from_bf": [25, 31, 64, 73, 79], "cyto_from_dpc": [25, 31, 64, 73, 79], "cyto_from_sqrdpc": [25, 31, 64, 73, 79], "cytomin": [40, 68, 81, 86, 89, 90], "cytoskeleton": [30, 33, 64, 73, 79], "cz": 79, "czi": [31, 54, 90], "c\u00b3rdm": [3, 54, 64, 73, 79, 90, 99, 105], "d": [1, 4, 13, 18, 29, 30, 31, 33, 45, 47, 49, 52, 54, 56, 57, 58, 63, 64, 65, 68, 70, 71, 73, 79, 86, 89, 90, 92, 93, 94, 98, 105, 108], "da": [0, 29, 30, 32, 33, 40, 57, 86, 93, 99, 101], "dab": [24, 31], "dabei": [26, 29, 54, 64, 73, 79, 93, 95, 105], "dabmap": [24, 31, 64, 73, 79], "dachorganis": [57, 86, 99, 105], "dafour": [11, 18, 54, 55, 68, 86, 90, 98], "daf\u00fcr": [29, 54, 64, 73, 79, 105], "dahm": [0, 16, 23, 37, 45, 46, 47, 52, 64, 68, 73, 79, 90, 93, 99, 100, 103, 105], "dai": [3, 22, 31, 33, 34, 37, 52, 71, 90, 93, 99, 105, 107], "daili": [3, 8, 23, 33, 34, 35, 37, 54, 56, 64, 72, 73, 79, 99, 100, 105], "daim": [47, 48, 68, 80, 83, 90], "damag": [20, 64, 73, 79], "damit": [26, 54, 64, 73, 79, 105], "dan": 52, "daniel": [10, 11, 12, 17, 18, 29, 42, 45, 48, 49, 50, 52, 54, 55, 57, 64, 65, 68, 71, 73, 79, 80, 84, 86, 90, 96, 98, 99, 100, 101, 103, 105, 108], "danni": [45, 52, 94, 108], "danta": [47, 55, 68, 79, 107], "dapi": [24, 31, 64, 73, 79], "dapi_mcherry_22lambda": [24, 64, 73, 79], "dapi_mcherry_4t_5lambda": [24, 64, 73, 79], "dapi_mcherry_4z_5lambda": [24, 64, 73, 79], "darauf": 75, "dario": [1, 15, 20, 38, 47, 48, 52, 54, 64, 68, 73, 79, 86, 91, 102], "darm": [64, 73, 79], "darrow": [11, 38, 52, 64, 71, 86, 96, 105], "dar\u00fcber": [0, 32, 64, 73, 79], "dask": [29, 79], "daskcours": [13, 48, 68, 86, 90, 103], "dass": 75, "data": [13, 25, 26, 31, 45, 46, 59, 60, 67, 75, 78, 82, 87, 94, 107, 108], "data_life_cycl": [42, 49, 57, 79, 105], "databas": [11, 29, 38, 44, 45, 52, 55, 71, 76, 86, 87, 96], "datacarpentri": [7, 11, 36, 41, 42, 55, 68, 79, 84, 90, 103], "dataksenia": [3, 23, 37, 64, 73, 79, 99, 105, 109], "datamanag": [39, 57, 86, 105], "datamethod": 86, "datapl": [0, 16, 23, 37, 45, 46, 47, 50, 52, 64, 68, 73, 90, 93, 99, 100, 103], "dataset": [0, 1, 9, 11, 14, 17, 20, 25, 28, 29, 30, 33, 47, 50, 52, 54, 55, 62, 91, 93, 100, 101, 106, 109], "date": [3, 23, 37, 64, 73, 79, 99, 105, 109], "datei": [29, 54, 64, 73, 79, 93, 105], "daten": [26, 29, 54, 64, 73, 75, 79, 93, 95], "datenmanag": [93, 95], "datenmanagementplan": [26, 29, 54, 64, 73, 79, 105], "datenproblem": [26, 54, 64, 73, 79, 105], "datenschutzrechtlich": [26, 54, 64, 73, 79, 93, 105], "daten\u00fcbertragung": 75, "dave": [47, 68, 80, 90], "davi": [47, 55, 68, 79, 107], "david": [1, 2, 4, 5, 7, 9, 11, 12, 15, 17, 18, 20, 29, 30, 36, 38, 41, 42, 47, 48, 50, 51, 52, 54, 55, 64, 65, 68, 69, 71, 73, 76, 78, 79, 82, 86, 89, 90, 91, 96, 98, 99, 102, 103, 105], "dcimg": [64, 73, 79], "de": [0, 3, 4, 7, 11, 12, 15, 16, 17, 18, 21, 22, 23, 25, 26, 29, 30, 31, 32, 33, 34, 37, 38, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 52, 54, 55, 56, 57, 58, 59, 64, 65, 68, 70, 71, 73, 75, 76, 78, 79, 80, 84, 86, 87, 89, 90, 93, 94, 95, 96, 98, 99, 100, 101, 102, 103, 105, 106, 107, 108, 109], "deal": [44, 64, 73, 79, 86, 99, 105], "dean": [17, 18, 29, 50, 52, 65, 79, 96], "deborah": [12, 29, 50, 52, 94], "debri": [64, 73, 79], "decad": [17, 50, 64, 73, 79, 90, 105], "decemb": [54, 64, 73, 79, 90], "decid": [64, 73, 79], "decim": [3, 22, 23, 32, 37, 64, 73, 79, 99, 105], "decis": [29, 44, 54, 65, 90, 105], "deck": [3, 8, 23, 29, 33, 34, 35, 37, 54, 56, 64, 72, 73, 79, 89, 99, 100, 105, 109], "decod": [3, 22, 23, 32, 37, 64, 73, 79, 99, 105], "decontextu": [22, 64, 73], "dedic": [17, 30, 32, 33, 34, 50, 64, 73, 79, 90, 93, 99, 105], "deed": [42, 56, 90], "deep": [29, 47, 73, 91, 109], "deep_vision_and_graph": [48, 68, 84, 89, 103], "deepak": [29, 48, 68, 79, 103], "deepbac": [54, 65, 79, 89, 90, 93], "deeper": [29, 64, 73, 79, 89], "deepimagej": [29, 54, 64, 65, 73, 79, 89, 90, 93], "deeplearningbasedsegmentationforbiologist": [48, 68, 89, 103], "deeplife4eu": [20, 47, 48, 54, 68, 86, 91], "default": [25, 31, 40, 64, 73, 79, 90], "defin": [1, 2, 9, 15, 28, 52, 64, 73, 79, 90], "definieren": [29, 54, 64, 73, 79, 93, 95, 105], "definit": [17, 34, 50, 64, 73, 79, 80, 93, 99, 105], "deform": [64, 73, 79], "defragmentation_trainingschool_eosc": [54, 68, 77, 90, 98], "degre": [29, 64, 73, 79, 99], "deinert": [54, 64, 73, 79, 105], "delet": [25, 31, 64, 73, 79], "delimit": [3, 22, 23, 32, 37, 64, 73, 79, 99, 105], "delin": [29, 64, 73, 79], "delisl": [1, 28, 64, 73, 79], "deliv": [29, 42, 55, 56, 64, 70, 73, 79, 91], "deliver": [17, 34, 50, 73, 79, 93, 99, 105], "della": [54, 64, 73, 79, 92, 105], "delmic": [39, 105], "delv": [54, 64, 73, 79, 92, 105], "dem": [0, 26, 32, 54, 64, 73, 75, 79, 105], "demand": [3, 20, 33, 34, 37, 42, 55, 64, 70, 73, 79, 86, 91, 99, 105], "demo": [24, 29, 47, 48, 55, 58, 64, 73, 78, 79, 90], "democratis": [29, 64, 73, 79], "demograph": [52, 73, 79, 102], "demonstr": [17, 29, 39, 52, 64, 66, 71, 73, 79, 89, 90, 93, 99, 105, 107], "den": [26, 40, 54, 64, 73, 79, 101, 105], "denis": [6, 28, 37, 54, 64, 73, 79, 105], "depart": [20, 23, 30, 32], "depend": [3, 37, 42, 64, 70, 73, 79, 91, 99], "dependabot": [15, 17, 37, 47, 68, 78, 99, 105, 106], "depict": [64, 73, 79], "deploi": [54, 65, 79, 89, 90, 93], "deploy": [45, 79, 84, 89, 103, 108], "deppen": [42, 68], "der": [0, 26, 32, 40, 49, 52, 54, 57, 75, 86, 93, 95, 101, 102], "derartig": 75, "deren": [26, 52, 54, 64, 73, 75, 79, 95, 105], "deriv": [30, 33, 64, 73, 79], "deschamp": [12, 47, 68, 90], "describ": [1, 11, 15, 22, 30, 33, 38, 42, 45, 64, 73, 78, 79, 86, 91, 100, 103, 107, 108], "descript": [3, 23, 25, 28, 29, 30, 31, 33, 34, 37, 39, 59, 61, 64, 66, 73, 79, 80, 89, 99, 105], "deshalb": 75, "design": [3, 20, 23, 29, 32, 36, 37, 40, 42, 45, 47, 54, 57, 64, 68, 70, 73, 79, 84, 89, 91, 99, 103, 105, 108, 109], "desir": [64, 73, 79], "desktop": [29, 64, 73, 79, 99, 105, 109], "desouki": [64, 73, 80], "despit": [54, 56, 72, 86, 89, 90], "detail": [3, 11, 25, 29, 31, 33, 37, 51, 52, 55, 60, 64, 73, 79, 86, 90, 93, 95, 105], "detailed_worflow": [25, 31, 64, 73, 79], "detect": [20, 22, 24, 29, 31, 55, 90, 91], "determin": [29, 42, 48, 64, 73, 78, 79, 90, 105], "deutsch": [0, 6, 32, 64, 73, 79, 87], "deutschen": 75, "dev": [42, 55, 56, 79], "develop": [0, 1, 3, 6, 8, 14, 17, 20, 22, 23, 32, 33, 34, 36, 37, 44, 45, 47, 54, 55, 56, 61, 72, 80, 89, 92, 93, 94, 96, 99, 102, 103, 106, 108], "devic": [52, 64, 79, 93, 105], "dewax": [24, 31, 64, 73, 79], "dfc": [24, 31, 64, 73, 79], "dfc350fxr2": [24, 31, 64, 73, 79], "dfg": [0, 6, 16, 23, 26, 28, 37, 47, 54, 64, 73, 79, 87, 90, 93, 99, 105], "di": [54, 64, 73, 75, 79, 108], "diagnost": [24, 31, 64, 73, 79], "dialog": [10, 29, 64, 73, 79], "dialogu": [29, 64, 73, 79], "diamond": [64, 73, 79], "diana": [55, 79, 90, 91], "diataxi": [55, 57, 80], "diaz": [42, 52, 64, 73, 78, 79], "dickscheid": [0, 3, 8, 23, 28, 50, 64, 73, 79, 93, 99, 105], "didact": [40, 64, 73, 79, 105], "didier": [25, 31, 64, 73, 79], "die": [0, 26, 32, 40, 52, 54, 75, 93, 95, 101], "diederich": [29, 52, 64, 76], "diel": [2, 4, 5, 15, 17, 52, 71, 79, 99, 105], "dienst": [29, 64, 73, 79, 99, 105], "diensteanbiet": 75, "dies": [0, 26, 29, 32, 40, 54, 64, 73, 75, 79, 93, 95, 101, 105], "diesbez\u00fcglich": 75, "diesem": [26, 29, 40, 54, 64, 73, 79, 93, 99, 101, 105], "diesen": 75, "dieser": [0, 26, 29, 32, 54, 64, 73, 75, 79, 105], "dietrich": [11, 42, 79, 101], "diez": [4, 12, 18, 30, 52, 71, 76], "dif": [64, 73, 80], "differ": [1, 3, 9, 14, 24, 25, 28, 29, 31, 33, 37, 42, 44, 45, 47, 49, 55, 57, 68, 70, 83, 86, 88, 89, 91, 94, 100, 103, 107], "difficult": [3, 8, 23, 30, 33, 34, 37, 64, 73, 79, 99, 105], "difficulti": [1, 14, 64, 73, 79], "diffract": [64, 73], "diffus": [64, 73], "digit": [1, 15, 29, 38, 41, 42, 45, 52, 54, 83, 92, 93, 94, 95, 101, 105, 108], "digital": [64, 73, 79], "digitalen": [26, 54, 105], "digitalisierung": [40, 79, 101], "digitalit\u00e4t": [64, 73, 79], "dilut": [24, 31, 64, 73, 79], "dimens": [24, 31, 47, 68, 81], "dimension": [3, 8, 17, 23, 28, 29, 30, 33, 34, 37, 50, 64, 73, 79, 90, 93, 99, 105], "dinesh": [47, 68, 78, 90], "dini": [40, 64, 73, 79, 105], "dir": [25, 31, 64, 73, 79], "direct": [1, 3, 8, 14, 22, 23, 28, 33, 34, 35, 37, 54, 56, 64, 72, 73, 79, 99, 100, 105], "directli": [22, 32, 37, 47, 49, 64, 68, 73, 79, 90, 99, 100, 105], "directori": [2, 17, 47, 68, 82, 102], "direkt": [40, 79, 101], "dirk": [0, 50, 64, 73, 79, 93, 105], "disc": [24, 64, 73, 79], "disciplin": [1, 28, 29, 57, 64, 73, 79, 99, 105], "disciplinari": [1, 3, 8, 15, 23, 28, 38, 64, 73, 79, 99, 105], "discours": 86, "discov": [42, 55, 56, 70, 79, 101, 106], "discover": [64, 73, 79], "discoveri": [1, 24, 28, 31, 40, 50, 52, 90, 102, 105], "discuss": [0, 3, 16, 23, 28, 29, 37, 39, 47, 52, 54, 64, 71, 73, 79, 86, 90, 92, 93, 99, 101, 105, 106, 107], "diseas": [30, 33, 64, 73, 79], "disk": [30, 33, 64, 73, 79], "diskutieren": [29, 54, 64, 73, 79, 93, 95, 105], "distinct": [55, 57, 80], "distribut": [22, 30, 33, 64, 73, 79, 88], "disziplinen": [29, 64, 73, 79, 99, 105], "dive": [29, 47, 48, 54, 64, 68, 73, 79, 89, 90, 99, 103, 105, 109], "divers": [0, 9, 11, 38, 50, 52, 71, 73, 86, 93, 96, 102, 105], "divis": [30, 33, 64, 73, 79], "djiciihhjh": [42, 71, 86, 96], "dkfz": [3, 23, 28, 33, 34, 37, 42, 57, 58, 64, 73, 79, 86, 90, 99, 100, 105, 109], "dl": [18, 21, 65, 79, 84, 93, 98, 103], "dl4mia": [86, 90], "dl4mic": [29, 64, 73, 79], "dl4miceverywher": [54, 65, 89, 93], "dlimag": [13, 48, 68, 86, 89, 90, 103], "dm5500": [24, 30, 31, 33, 64, 73, 79], "dm5500b": [30, 33, 64, 73, 79], "dmem": [31, 64, 73, 79], "dmi6000b": [30, 33, 64, 73, 79], "dmi8": [30, 33, 64, 73, 79], "dmp": [14, 29, 39, 42, 44, 54, 55, 64, 66, 73, 79, 89, 90, 93, 105], "dmp4nfdi": [44, 79, 105], "dna": [30, 33, 64, 73, 79], "do": [7, 11, 13, 16, 18, 20, 25, 29, 31, 35, 36, 40, 42, 48, 52, 55, 60, 68, 76, 78, 84, 90, 94, 98, 99, 100, 103, 105], "dobson": [52, 64, 68, 78, 90], "doc": [1, 13, 40, 45, 47, 49, 54, 58, 68, 77, 79, 86, 89, 90, 94, 98, 100], "document": [0, 3, 11, 16, 23, 24, 25, 28, 29, 31, 32, 37, 40, 42, 46, 47, 49, 50, 52, 60, 68, 71, 77, 78, 83, 87, 93, 94, 97, 99, 105, 107], "doe": [3, 55, 64, 73, 86, 105], "doesn": [31, 64, 73, 79], "dofunct": [64, 73, 79], "dohl": [3, 8, 23, 30, 33, 34, 35, 37, 52, 54, 56, 64, 72, 73, 79, 87, 99, 100, 105], "doi": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 17, 20, 22, 23, 24, 25, 26, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 52, 54, 55, 56, 59, 65, 68, 70, 71, 72, 73, 76, 78, 79, 80, 81, 83, 84, 86, 89, 90, 91, 92, 93, 94, 95, 96, 98, 99, 100, 101, 103, 105, 106, 107, 108, 109], "dolzycka": [40, 64, 73, 79, 105], "domain": [1, 28, 42, 50], "domart": [17, 18, 29, 50, 52, 65, 79, 96], "domin": [17, 18, 29, 48, 50, 52, 65, 68, 79, 82, 83, 90, 96, 103], "dominguez": [21, 48, 68, 86, 89, 103], "dominik": [2, 4, 5, 9, 11, 15, 17, 21, 36, 38, 47, 52, 54, 55, 56, 64, 68, 70, 71, 73, 79, 84, 89, 90, 98, 99, 103, 105, 107, 109], "dominika": [40, 64, 73, 79, 105], "don": [47, 60, 61, 64, 68, 73, 79, 84], "donati": [29, 64, 73, 79], "done": [1, 15, 25, 31, 32, 38, 47, 48, 64, 68, 73, 79, 80, 81, 97, 103], "donkei": [24, 31, 64, 73, 79], "donor": [64, 73, 79], "doolin": [29, 64, 73, 79], "dor": [29, 64, 73, 79], "dornier": [49, 90, 100], "dorun": [64, 73, 79], "dosag": [22, 64, 73, 79], "dose": [1, 28, 29, 64, 73, 79], "dostat": [64, 73, 79], "doub": [4, 9, 29, 52, 64, 73, 79, 90], "doubl": [30, 33, 64, 73, 79], "doug": [2, 15, 52, 64, 79, 90], "dougla": [4, 12, 18, 30, 52, 71, 76], "down": [17, 50, 73, 79, 90, 105], "download": [30, 33, 54, 64, 73, 74, 79, 88, 108], "downsampl": [31, 64, 73, 79], "downscal": [31, 64, 73, 79], "dozuki": 42, "dpc": [31, 64, 73, 79], "dr": [3, 23, 28, 33, 34, 37, 64, 73, 75, 79, 99, 105], "draft": [11, 38, 52, 64, 71, 86, 96, 105], "dragon": [30, 33, 64, 73, 79], "draw": [64, 73, 79], "dreht": [26, 54, 64, 73, 79, 105], "drei": [0, 32, 64, 73, 79], "dresden": [3, 17, 22, 23, 28, 32, 34, 37, 48, 68, 75, 87, 97, 103, 109], "dresden16th": [17, 64, 73, 79, 99, 109], "dritt": 75, "dritter": 75, "drive": [4, 49, 54, 56, 64, 70, 73, 79, 86, 90], "driven": [1, 15, 29, 30, 33, 38, 64, 73, 93, 105], "droesbek": [11, 42, 78, 91, 103, 107, 108], "dropbox": [18, 54, 86, 90, 98], "drosophila": [24, 64, 73, 79], "drosophilaeggchamb": [24, 64, 73, 79], "drosopoulo": [30, 33, 64, 73, 79], "dtc": [48, 68, 82, 90, 103], "dtl": [58, 90, 105], "du": [30, 33, 64, 73, 79], "dublin": [64, 73, 80], "duc": [25, 31, 64, 73, 79], "due": [3, 23, 28, 37, 64, 73, 79, 99, 105], "dufour": [17, 18, 29, 50, 52, 65, 79, 96], "dumonti": [52, 64, 71, 79, 93, 105], "duodenum": [24, 31, 64, 73, 79], "durch": 75, "dure": [3, 17, 20, 22, 23, 28, 29, 32, 34, 37, 44, 50, 54, 64, 73, 79, 93, 99, 105], "dust": [64, 73, 79], "dvoretskii": [64, 73, 79], "dwaith": [48, 68, 82, 90, 103], "dwyer": [4, 12, 18, 30, 52, 71, 76], "dynam": [22, 64, 73, 99], "d\u00e9v\u00e9dec": [4, 12, 18, 30, 52, 71, 76, 79, 93, 96, 105], "d\u00f6rfel": [6, 28, 37, 54, 64, 73, 79, 105], "d\u00fcrfen": [26, 54, 64, 73, 79, 93, 105], "d\u00fcsseldorf": [3, 17, 23, 33, 34, 37, 54, 64, 73, 79, 90, 99, 100, 105, 109], "e": [0, 1, 3, 11, 14, 16, 17, 23, 28, 29, 30, 32, 33, 34, 35, 37, 38, 42, 43, 52, 54, 56, 60, 64, 65, 68, 71, 72, 73, 74, 75, 78, 79, 86, 87, 88, 90, 91, 93, 96, 98, 99, 100, 101, 105, 106, 109], "e1": 52, "each": [3, 24, 29, 30, 33, 34, 37, 42, 47, 55, 56, 64, 68, 70, 73, 79, 86, 99, 101, 105, 106], "eacut": [30, 33, 64, 73, 79], "earli": [3, 9, 33, 34, 37, 44, 64, 73, 79, 87, 99, 105], "earlier": [29, 64, 73, 79], "earliest": [64, 73, 79], "eas": [17, 31, 50, 64, 73, 79, 90, 105], "easi": [29, 44, 45, 52, 64, 71, 73, 79, 82, 89, 90, 94, 102, 105, 108], "easier": [3, 37, 51, 52, 64, 69, 73, 79, 90, 105], "easili": [39, 54, 61, 64, 65, 73, 79, 80, 86, 89, 90, 93], "easur": [30, 33, 64, 73, 79], "ebi": [4, 7, 21, 38, 43, 49, 52, 54, 55, 56, 64, 73, 79, 81, 86, 89, 90, 93, 96, 101, 103, 105, 106], "ebner": [4, 12, 18, 30, 52, 71, 76], "echo": 61, "ecker": [4, 12, 18, 30, 52, 71, 76], "eclips": [30, 33, 64, 73, 79], "ecol": [49, 76, 90], "econom": [64, 73, 79], "ecosystem": [54, 108], "edg": [24, 29, 64, 73, 79], "edit": [1, 13, 16, 35, 42, 45, 47, 54, 58, 60, 63, 68, 73, 86, 89, 90, 98, 99, 100], "editor": 40, "edu": [24, 30, 31, 33, 39, 64, 73, 79, 86, 105, 106], "eduardo": [4, 12, 18, 30, 52, 71, 76], "educ": [3, 8, 20, 23, 33, 34, 35, 37, 40, 44, 54, 56, 60, 72, 80, 99, 100, 105], "eduroam": [3, 23, 28, 64, 73, 79, 99, 105], "edward": [42, 52, 64, 78], "edwardson": [30, 33, 64, 73, 79], "edx": [49, 76, 90], "effect": [1, 9, 15, 28, 29, 33, 38, 50, 64, 73, 79, 90], "effektiv": [29, 54, 64, 73, 79, 93, 105], "effici": [1, 9, 28, 29, 45, 50, 55, 57, 86, 90], "effizi": [26, 54, 64, 73, 79, 105], "effort": [1, 3, 8, 14, 23, 28, 29, 30, 33, 34, 37, 42, 64, 73, 79, 99, 103, 105], "egfp": [30, 31, 33, 64, 73, 79], "egg": [24, 64, 73, 79], "egling": [4, 5, 7, 9, 36, 47, 68, 79], "ehrlich": [3, 23, 37, 64, 73, 79, 99, 105, 109], "eigen": [29, 54, 64, 73, 75, 79, 93, 95, 105], "eigenen": [26, 54, 64, 73, 79, 105], "eija": [4, 9, 29, 52, 79, 90], "eik": [0, 16, 23, 37, 45, 46, 47, 52, 64, 68, 73, 79, 90, 93, 99, 100, 103, 105], "ein": [26, 29, 57, 75, 86, 93, 95, 99], "einblick": [29, 93, 99], "einen": [26, 29, 52, 54, 75, 99, 105], "einer": [26, 54, 73, 75], "einfluss": 75, "einf\u00fchrung": [26, 54, 64, 73, 79, 105], "einhaltung": 75, "einstein": [30, 33, 64, 73, 79], "einsteinm": [30, 33, 64, 73, 79], "einstieg": [26, 54, 64, 73, 79, 105], "einzelnen": [26, 54, 64, 73, 79, 105], "either": [64, 73], "el": [1, 14, 52, 64, 73, 79, 105], "elabftw": [6, 28, 37, 54, 64, 73, 79], "elastix": [4, 68, 84], "elastix_tutori": [42, 48, 78], "electron": [6, 11, 20, 24, 28, 38, 42, 52, 54, 71, 76, 86, 96, 99], "element": [26, 39, 40, 54, 64, 73, 79, 80, 86, 105], "eleni": [12, 42, 48, 64, 68, 71, 84, 90], "elfi": [54, 64, 73, 79, 105], "elia": [12, 42, 48, 64, 68, 71, 84, 90], "eliceiri": [4, 12, 18, 29, 30, 42, 51, 52, 64, 65, 68, 69, 71, 76, 78, 79, 90, 98], "elimin": [29, 64, 73, 79], "elisa": [3, 11, 17, 23, 28, 30, 33, 34, 35, 37, 50, 52, 54, 56, 64, 65, 72, 73, 79, 80, 87, 99, 100, 105], "elisabeth": [29, 39, 52, 66, 79, 90, 105, 106], "elixir": [42, 46, 49, 57, 79, 86, 91, 105], "ellen": [52, 64, 68, 78, 90], "ellenberg": [11, 38, 52, 64, 71, 86, 96, 105], "elmar": [0, 32, 64, 73, 79], "elmer": [24, 31, 64, 73, 79], "elmi2024": [17, 34, 50, 64, 73, 79, 90, 93, 99, 105], "elmi23_cfd": [54, 80, 90, 101, 105], "elnaz": [4, 5, 7, 29, 36, 47, 52, 54, 64, 68, 73, 79, 90, 98, 109], "elodi": [20, 47, 48, 54, 68, 86, 91], "elt": [42, 47, 68, 90, 109], "elya": [17, 51, 52, 69, 79, 103], "elz": [26, 54, 64, 73, 79, 105], "em": 24, "email": 75, "embed": [29, 54, 64, 73, 79, 92, 99, 105], "embj": [1, 15, 38, 64, 73, 79], "embl": [4, 7, 38, 46, 52, 64, 73, 79, 84, 90, 105, 107], "embo": [1, 5, 15, 38, 54, 64, 73, 79], "embo_2022": [21, 48, 68, 86, 89, 90, 103], "embopress": [1, 15, 38, 64, 73, 79], "embrac": [1, 28, 64, 73, 79], "emil": [2, 42, 55, 56, 86, 100], "emilia": [64, 73, 79], "emiss": 24, "emma": [1, 15, 38, 64, 73, 79], "emmanuel": [29, 52, 64, 76], "empfehlung": [52, 64, 79, 105], "empfehlungen": [64, 73, 79], "emphas": [0, 16, 23, 37, 47, 54, 64, 73, 79, 90, 92, 93, 99, 105], "emphasi": [42, 71, 86, 96], "emploi": [16, 29, 35, 42, 64, 73, 79, 90, 100], "empow": [9, 64, 73, 79], "en": [11, 17, 30, 33, 40, 42, 44, 45, 49, 55, 57, 64, 73, 77, 79, 86, 90, 93, 94, 95, 99, 100, 101, 103, 105, 108, 109], "enabl": [1, 8, 9, 14, 22, 23, 28, 29, 42, 45, 54, 55, 57, 70, 81, 91, 92, 93, 108], "encod": [64, 73, 79], "encompass": [64, 73, 79], "encount": [3, 22, 23, 32, 37, 64, 73, 79, 99, 105], "encourag": [3, 14, 23, 28, 37, 42, 55, 64, 73, 79, 90, 93, 99, 105], "end": [40, 58, 60, 64, 73, 79, 90, 100, 105], "endeavor": [3, 17, 33, 34, 50, 64, 73, 79, 93, 99, 105], "endl": [0, 32, 64, 73, 79], "endotheli": [54, 56, 72, 86, 89, 90], "engag": [52, 73, 79, 102], "engel": [29, 64, 73, 79, 99, 105], "engelska": [44, 105], "engin": [1, 11, 29, 42, 45, 52, 54, 71, 78, 79, 83, 86, 89, 91, 94, 107], "englisch": [64, 73, 79], "english": [40, 64, 73, 79, 105], "enhanc": [9, 29, 37, 44, 52, 55, 57, 80, 102], "enicolai": [28, 29, 47, 54, 68, 84, 90, 103], "enorm": [1, 14, 64, 73, 79], "enrich": [1, 3, 23, 28, 37, 64, 73, 79, 99, 105], "ensur": [1, 11, 14, 28, 29, 30, 33, 42, 52, 54, 55, 57, 61, 64, 65, 73, 79, 86, 89, 90, 93, 96, 99, 105], "enter": [1, 14, 60, 61, 87], "entfernen": 75, "entfernung": 75, "enthalten": [0, 26, 32, 54, 64, 73, 79, 105], "enthaltenen": [0, 32, 64, 73, 79], "enth\u00e4lt": [26, 54, 64, 73, 75, 79, 105], "entir": [64, 73, 79, 99], "entitl": [30, 33, 64, 73, 79], "entri": [25, 29, 31, 59, 60, 64, 73, 79], "entsprechenden": 75, "entstand": [40, 79, 101], "entstehen": [29, 54, 64, 73, 79, 105], "entsteht": [57, 86, 99, 105], "entstehung": [54, 64, 73, 79, 95, 105], "entwerfen": [29, 54, 64, 73, 79, 105], "entwicklung": [64, 73, 79], "environ": [3, 8, 22, 23, 25, 30, 31, 32, 33, 34, 35, 37, 54, 61, 105, 107], "environment": [3, 23, 28, 54, 64, 73, 79, 90, 99, 105], "envisag": [64, 73, 79], "eosin": [25, 31, 64, 73, 79], "eoss": [42, 52, 54, 64, 73, 79, 90, 102], "epfl": [24, 31, 49, 64, 73, 79, 90, 100], "epi": [30, 33], "epifluoresc": [30, 33, 64, 73, 79], "eprint": [40, 64, 79, 105], "epsilon": [30, 33, 64, 73, 79], "eqskudatarq": [40, 90], "equip": [30, 33, 64, 73, 79], "equipment_710": [30, 33, 64, 73, 79], "equiti": [52, 73, 79, 102], "era": [1, 14, 64, 73, 79], "erarbeitet": [40, 79, 101], "eraslan": [47, 68, 78, 90], "erfahren": [54, 64, 73, 79, 95, 105], "erfahrungen": [54, 64, 73, 79, 105], "erfahrungsbericht": [52, 64, 79, 105], "erfolgt": 75, "erf\u00fcllt": [26, 54, 64, 73, 79, 93, 105], "erg\u00e4nzend": [26, 54, 64, 73, 79, 105], "erhalten": [26, 54, 64, 73, 79, 93, 105], "erhebung": [26, 54, 64, 73, 79, 105], "erhoben": 75, "eric": [14, 15, 17, 37, 42, 47, 52, 54, 55, 68, 78, 90, 93, 99, 100, 105, 106, 107], "erick": [11, 15, 47, 68, 82], "erik": [52, 71, 79, 92, 93, 96], "eriksson": [1, 14, 52, 64, 73, 79, 105], "erin": [2, 4, 5, 7, 11, 15, 17, 36, 52, 55, 71, 79, 90, 99, 101, 103, 105], "erkennbar": 75, "erkennen": [54, 64, 73, 79, 95, 105], "erlaubt": [29, 54, 64, 73, 79, 93, 105], "erl\u00e4utert": [54, 64, 73, 79, 105], "erl\u00e4uterungen": [26, 54, 64, 73, 79, 105], "erm\u00f6glicht": [40, 79, 101], "error": [60, 64, 73, 79], "erschienen": [64, 73, 79], "erst": 75, "erstel": [26, 54, 64, 73, 79, 105], "erstellt": [26, 54, 64, 73, 75, 79, 105], "erstellten": 75, "ersten": [26, 54, 64, 73, 79, 105], "erua": [64, 73, 79], "erwarten": [26, 54, 64, 73, 79, 105], "escobar": [64, 73, 79], "esfri": [64, 73, 79], "esgomezm": [10, 54, 68, 86, 89, 90], "especi": [1, 15, 17, 22, 28, 29, 38, 48, 64, 73, 79, 80, 97, 99, 103, 105], "essenti": [3, 28, 33, 40, 50, 64, 90, 93, 99, 105], "establish": [1, 3, 8, 14, 15, 17, 23, 28, 29, 33, 34, 37, 38, 50, 54, 64, 73, 93, 99, 105], "estibali": [7, 21, 38, 54, 56, 70, 79, 89, 90, 103], "estibaliz": [11, 17, 18, 29, 41, 48, 50, 52, 54, 64, 65, 68, 73, 79, 86, 89, 90, 93, 96, 98], "est\u00e8v": [47, 68, 79, 90], "et": [2, 3, 4, 7, 8, 10, 14, 15, 17, 18, 25, 29, 30, 31, 33, 36, 37, 38, 40, 41, 42, 45, 46, 47, 48, 50, 51, 52, 54, 55, 58, 64, 65, 68, 71, 73, 76, 78, 79, 82, 84, 86, 90, 91, 93, 94, 96, 98, 100, 101, 102, 103, 105, 106, 107, 108], "etc": [47, 59, 62, 64, 73, 79, 90, 100], "ethic": [1, 15, 38, 64, 73, 79], "ethisch": [26, 54, 64, 73, 79, 105], "etwa": [54, 64, 73, 75, 79, 95, 105], "eu": [1, 11, 14, 17, 26, 30, 33, 42, 45, 52, 54, 55, 64, 73, 78, 79, 86, 89, 90, 91, 93, 99, 103, 105, 107, 108], "eugen": [12, 29, 50, 52, 94], "euniwel": [64, 73, 79], "euro": [52, 71, 96], "eurobioimag": [1, 14, 17, 45, 52, 54, 55, 64, 73, 79, 86, 89, 90, 93, 99, 105, 108], "eurobioimagingcommun": [42, 56, 72], "europ": [42, 46, 49, 57, 64, 73, 79, 86, 91, 105], "european": [1, 15, 17, 34, 38, 44, 50, 52, 64, 73, 79, 86, 90, 93, 99, 105], "evalu": [20, 52, 54, 64, 65, 71, 73, 79, 89, 90, 92, 93, 96, 99, 105], "evan": [42, 52, 54, 64, 73, 78, 79, 108], "evanbiederstedt": [47, 68, 78, 90], "even": [1, 17, 20, 28, 29, 57, 64, 73, 79, 86, 105], "event": [0, 3, 4, 16, 17, 23, 37, 45, 48, 49, 52, 56, 60, 62, 64, 70, 73, 79, 93, 99], "ever": [1, 14, 64, 73, 79], "everi": [3, 22, 23, 31, 32, 37, 64, 73, 79], "everydai": [3, 8, 23, 28, 30, 33, 34, 37, 64, 73, 79, 99, 105], "everyth": 60, "evid": [47, 68, 81], "ewan": [38, 52, 71, 105], "ewbank": [1, 15, 38, 64, 73, 79], "exact": [3, 22, 23, 32, 37, 64, 73, 79, 99, 105], "exactli": [11, 42, 79, 101], "exampl": [2, 3, 9, 16, 22, 23, 28, 31, 32, 35, 37, 52, 54, 56, 60, 71, 76, 80, 84, 86, 87, 92, 93, 96, 99, 103, 105], "example_facility_manag": [44, 77, 100], "exc2068": 87, "excel": [3, 22, 23, 32, 37, 87, 99, 105], "except": [24, 60, 64, 73, 79], "exchang": [17, 34, 54, 90, 93, 99, 105], "excit": [24, 64, 73, 79], "execut": [25, 31, 45, 64, 73, 79, 84, 89, 103, 108], "exemplar": [52, 64, 71, 79, 93, 105], "exemplari": [6, 28, 37, 54, 64, 73, 79, 105], "exercis": [14, 29, 55, 64, 67, 73, 78, 79, 84, 93, 94, 105], "exist": [3, 6, 8, 16, 20, 23, 28, 33, 34, 35, 37, 42, 49, 52, 54, 55, 56, 64, 71, 72, 73, 79, 80, 81, 87, 90, 92, 93, 96, 99, 100, 105, 107], "expand": [64, 73], "expect": [3, 33, 34, 37, 64, 73, 79, 99, 105], "experi": [1, 2, 3, 8, 11, 15, 17, 22, 23, 28, 32, 34, 35, 37, 44, 45, 54, 55, 56, 71, 72, 86, 87, 93, 96, 99, 100, 105], "experienc": [29, 64, 73, 79], "experiment": [11, 22, 52, 64, 73, 86, 87, 90, 96], "expert": [3, 6, 28, 33, 34, 37, 42, 48, 54, 64, 70, 73, 78, 79, 90, 91, 99, 105], "expertis": [3, 8, 23, 28, 33, 34, 37, 44, 64, 73, 79, 99, 105], "explain": [29, 42, 52, 54, 55, 56, 64, 70, 72, 73, 76, 79, 81, 89, 94, 98, 103, 105, 106], "explan": [55, 57, 60, 64, 73, 80], "exploit": [3, 33, 73, 79, 99, 105], "explor": [0, 3, 16, 23, 28, 37, 42, 47, 48, 64, 73, 79, 87, 90, 93, 97, 99, 103, 105], "exploratori": [39, 86], "export": [3, 23, 28, 29, 31, 37, 61, 64, 73, 74, 79, 99, 105], "expos": [13, 48, 68, 84, 103], "express": [25, 30, 31, 33, 64, 73, 79], "extend": [0, 30, 33, 50, 73, 93, 99, 105], "extens": [3, 28, 37, 45, 49, 50, 55, 59, 60, 73, 83, 94, 99], "exter": [1, 15, 38, 64, 73, 79], "extern": [6, 22, 28, 37, 54, 64, 73, 79, 99, 105], "externen": 75, "extra": [24, 64, 73, 79], "extract": [9, 17, 45, 49, 55, 64, 73, 79, 83, 94, 99, 100, 105, 107], "extravas": [54, 56, 72, 86, 89, 90], "ezomero": [16, 35, 42, 73, 79, 90, 100], "e\u00f6tv\u00f6": [42, 47, 68, 90, 109], "f": [0, 11, 12, 16, 23, 25, 29, 31, 37, 42, 44, 45, 46, 47, 48, 52, 55, 58, 64, 68, 71, 73, 79, 84, 86, 90, 93, 99, 100, 103, 105], "f1000research": [3, 8, 11, 17, 18, 20, 29, 31, 33, 37, 50, 52, 54, 64, 73, 79, 86, 89, 90, 91, 93, 96, 98, 99, 101, 103, 105, 106], "f1000reserach": [20, 64, 73, 79], "f100research": [3, 37, 64, 73, 79], "f505": [25, 31, 64, 73, 79], "fabian": [17, 51, 52, 69, 79, 103], "fabiann": [17, 18, 29, 50, 52, 65, 79, 96], "fabig": [3, 22, 23, 32, 37, 64, 73, 79, 99, 105], "fabric": [18, 29, 31, 52, 65, 79, 90, 98], "fabrizio": [42, 90, 97, 103], "fabriziomusacchio": [42, 90, 97, 103], "face": [42, 47, 48, 64, 68, 73, 78, 79, 81, 90, 105], "facet": [42, 70, 91], "facil": [0, 16, 20, 22, 23, 24, 31, 35, 87], "facilit": [1, 8, 9, 15, 23, 28, 29, 35, 37, 38, 54, 56, 64, 72, 73, 79, 80, 90, 93, 99, 100, 107], "factor": [64, 73, 79], "faculti": [3, 20, 23, 30, 32, 33, 37, 64, 73, 79, 99, 105], "fahren": [1, 15, 38, 64, 73, 79], "fair": [0, 16, 20, 26, 29, 47, 49, 51, 57, 58, 65, 68, 69, 70, 74, 80, 81, 87, 89, 95, 100, 101, 107], "fairdata": [32, 64, 73, 79], "fairif": [3, 33, 54, 64, 73, 79, 90, 93, 99, 105], "fairwar": [52, 71, 79, 92, 93, 96], "faisal": [12, 56, 72, 86, 89], "faklari": [30, 33, 64, 73, 79, 87], "falconi": [2, 15, 52, 64, 79, 90], "fall": 75, "fallen": [64, 73, 79], "fallesen": [48, 68, 83, 90], "familiar": [45, 47, 55, 68, 78, 90, 97], "fanci": 64, "fand": [54, 64, 73, 79, 105], "faq": [55, 86, 93, 95, 105], "far": [32, 64, 73, 79, 99], "fashion": [1, 28, 64, 73, 79], "fast": [54, 64, 73, 79, 108], "faster": [1, 14, 20, 64, 73, 79], "fastq": [49, 55, 81, 107], "favorit": [60, 87], "faze": [4, 5, 7, 29, 36, 47, 52, 54, 64, 68, 73, 79, 90, 98, 109], "fbi": [54, 80, 90, 101, 105], "fbinf": [29, 52, 79, 90], "fchi": [20, 64, 73, 79], "fdm": [6, 44, 45, 52, 55, 64, 73, 79, 105], "fdmentor": [40, 44, 64, 73, 79, 105], "fdo": [16, 35, 64, 73, 79, 99, 105], "featur": [3, 22, 30, 33, 37, 42, 45, 49, 55, 64, 71, 73, 79, 83, 86, 94, 96, 100, 107], "feb": [4, 9, 17, 28, 29, 34, 50, 52, 54, 64, 73, 79, 90, 93, 99, 105], "februari": [3, 6, 33, 54, 64, 73, 79, 90, 99, 105], "feder": [44, 64, 73, 79, 105], "federal": [49, 76, 90], "fedor": [48, 68, 84, 89, 103], "feed": [64, 73, 99], "feedback": [3, 8, 17, 23, 30, 33, 34, 36, 37, 44, 50, 54, 64, 73, 79, 80, 87, 93, 99, 105], "feel": [60, 61], "felder": [64, 73, 80], "feldhau": [64, 73, 79], "feldman": [29, 64, 73, 79], "felix": [4, 5, 7, 9, 11, 36, 47, 55, 64, 68, 73, 79, 84, 90, 98, 105, 107, 109], "feriel": [14, 64, 73, 79], "fermi": [39, 105], "fernandez": [4, 12, 18, 30, 33, 52, 64, 71, 73, 76, 79], "ferrando": [3, 11, 17, 23, 28, 30, 33, 34, 35, 37, 50, 52, 54, 56, 64, 65, 72, 73, 79, 80, 87, 99, 100, 105], "fertigstellung": [0, 32, 64, 73, 79], "fertil": [64, 73, 79], "festigen": [26, 54, 64, 73, 79, 105], "fetal": [31, 64, 73, 79], "fetch": [1, 28, 64, 73, 79], "few": [64, 73], "fib": [24, 64, 73, 79], "fibroblast": [30, 33], "fibsem": [24, 64, 73, 79], "fictiv": [29, 39, 66, 79, 89, 105], "fidel": [47, 68, 78, 90], "field": [1, 3, 8, 14, 23, 28, 29, 31, 42, 52, 54, 55, 60, 64, 71, 73, 79, 80, 90, 93, 95, 99, 101, 102, 105, 107], "figg": [45, 52, 71, 94, 108], "figshar": [12, 29, 50, 52, 94], "figur": [3, 23, 28, 37, 42, 48, 64, 73, 78, 79, 90, 99, 105], "fiji": [3, 15, 16, 20, 21, 23, 28, 31, 35, 37, 45, 47, 54, 67, 78, 82, 86, 89, 98, 99, 108], "file": [0, 3, 8, 11, 16, 22, 23, 25, 28, 29, 31, 32, 34, 35, 37, 42, 45, 47, 48, 49, 50, 54, 55, 56, 59, 60, 61, 68, 70, 72, 74, 77, 82, 86, 87, 93, 100, 102, 103, 106], "fileadmin": [44, 45, 55, 105], "filenam": [64, 73, 79], "filippo": [24, 31, 64, 73, 79], "fill": [3, 22, 23, 29, 32, 37, 54, 64, 73, 79, 90, 93, 95, 99, 101, 105], "fillabl": [14, 42, 55, 64, 73, 79, 90, 93, 105], "filter": [22, 64, 73, 79], "finaci": [0, 32, 64, 73, 79], "final": [1, 3, 15, 22, 23, 25, 28, 29, 31, 37, 38, 64, 73, 79, 99, 105], "financi": [0, 1, 15, 32, 38, 64, 73, 79], "find": [1, 11, 15, 28, 38, 48, 60, 64, 73, 78, 87, 91, 93, 96], "findabl": [1, 3, 8, 14, 23, 28, 29, 30, 33, 34, 37, 42, 52, 54, 64, 71, 73, 79, 87, 92, 93, 95, 99, 105, 106], "finden": [26, 54, 64, 73, 79, 105], "findet": [26, 54, 64, 73, 79, 105], "finish": [45, 47, 55, 68, 78, 90, 97], "finn": [11, 42, 78, 91, 103, 107, 108], "fiona": [3, 6, 16, 22, 23, 28, 32, 37, 50, 64, 73, 79, 90, 92, 99, 105], "first": [0, 6, 10, 16, 17, 20, 22, 23, 28, 29, 34, 37, 44, 47, 52, 54, 71, 79, 89, 93, 99, 105], "fish": [64, 73, 79], "fisher": [24, 31, 45, 52, 55, 56, 64, 73, 79, 86, 94, 108], "fit": [8, 23, 30], "five": [20, 44], "fix": [10, 22, 24, 29, 31, 64, 73, 79], "fiz": [64, 73, 79], "flagship": [52, 71, 79, 93, 96, 105], "flexibl": [45, 52, 64, 79, 84, 89, 93, 103, 105, 108], "fli": [64, 73, 79], "flim": [48, 97, 103], "float": [64, 73, 79], "floden": [54, 64, 73, 79, 108], "florian": [4, 11, 17, 18, 29, 30, 42, 47, 48, 50, 52, 54, 55, 56, 64, 65, 68, 71, 72, 76, 79, 84, 86, 89, 90, 94, 96, 98, 103], "flow": [14, 25, 31, 42, 54, 56, 72, 89, 93, 105], "flowchart": [9, 64, 73, 79], "fluor": [24, 31, 64, 73, 79], "fluoresc": [6, 22, 24, 30, 31, 33, 49, 54, 90, 100], "fluorobrit": [31, 64, 73, 79], "fluoromisonidazol": [29, 64, 73, 79], "fluotar": [24, 31, 64, 73, 79], "fly": [17, 30, 33, 50, 64, 73, 79, 90, 105], "fmiso": [29, 64, 73, 79], "fnin": [11, 52, 79, 105], "focalplan": [19, 29, 39, 79, 86, 89, 90, 95, 97, 103, 105, 106], "focu": [3, 8, 11, 20, 23, 28, 29, 30, 33, 34, 37, 42, 52, 57, 64, 71, 73, 79, 86, 91, 96, 99, 101, 105, 106], "focus": [1, 7, 9, 21, 24, 28, 29, 38, 47, 54, 55, 56, 64, 68, 70, 73, 79, 81, 86, 89, 90, 93, 95, 101, 103, 105], "focuss": [29, 47, 48, 55, 58, 64, 68, 73, 78, 79, 90], "foix": [7, 21, 38, 54, 56, 70, 79, 89, 90, 103], "foku": [0, 32], "folder": [20, 25, 29, 31, 61, 64, 73, 79], "folgen": [26, 54, 64, 73, 79, 105], "foliensatz": [26, 54, 64, 73, 79, 93, 105], "follow": [3, 23, 28, 29, 30, 31, 33, 42, 48, 61, 64, 73, 74, 78, 79, 87, 90, 99, 105], "folter": [15, 17, 37, 47, 68, 78, 99, 105, 106], "foong": [10, 29, 64, 73, 79], "forc": [64, 73, 79], "form": [6, 36, 44, 54, 55, 56, 64, 70, 73, 79, 80, 81, 99, 105], "formal": [52, 64, 71, 79, 92, 93, 96, 105], "format": [3, 8, 9, 11, 16, 20, 22, 23, 24, 28, 30, 31, 32, 33, 34, 35, 37, 42, 45, 47, 54, 56, 60, 68, 72, 82, 86, 89, 100, 102], "formatread": [64, 73, 79], "formul": [44, 64, 73, 79, 99, 105], "forschen": 75, "forschend": [26, 54, 64, 73, 79, 93, 105], "forschungsdaten": [6, 28, 29, 37, 40, 99], "forschungsdateninfrastruktur": [6, 54, 99, 105], "forschungsdatenlebenszyklu": [64, 73, 79], "forschungsdatenmanag": [49, 52, 57, 86, 99, 102], "forschungsf\u00f6rder": [26, 54, 64, 73, 79, 105], "forschungsgemeinschaft": [6, 64, 73, 79, 87], "forschungsgro\u00dfger\u00e4ten": [0, 32, 64, 73, 79], "forschungspraxi": [26, 54, 64, 73, 79, 105], "forschungsprojekt": [26, 49, 54, 57, 64, 73, 79, 86, 102, 105], "forschungsvorhaben": [54, 64, 73, 79, 95, 105], "fortbildungen": [40, 64, 73, 79, 105], "fortmann": [3, 8, 23, 33, 34, 35, 37, 54, 56, 64, 72, 73, 79, 87, 90, 93, 99, 100, 105], "forum": [11, 16, 24, 25, 31, 42, 46, 58, 64, 73, 79, 81, 86, 90, 96, 100, 107], "forward": [32, 64, 73, 79], "foster": [1, 3, 8, 9, 14, 23, 28, 29, 30, 33, 34, 37, 52, 64, 73, 79, 99, 102, 105], "foti": [11, 52, 79, 91, 93, 96], "found": [17, 20, 29, 52, 64, 73, 79, 90, 99, 105], "foundat": [1, 5, 15, 38, 54, 87, 89, 90], "foundinggid": [17, 64, 73, 79, 99, 105], "four": [31, 44, 55, 57, 64, 73, 79, 80, 105], "fov": [64, 73, 79], "foyl": [64, 73, 81, 109], "fr": [30, 33, 54, 55, 57, 64, 73, 79, 80, 90, 101, 105], "frage": [54, 64, 73, 79, 95, 105], "fragen": [26, 29, 40, 54, 64, 73, 79, 93, 101, 105], "frame": [1, 28, 99, 105], "framework": [0, 1, 2, 9, 15, 16, 28, 29, 32, 33, 34, 35, 38, 45, 47, 54, 55, 56, 68, 72, 73, 80, 89, 94, 96, 99, 101, 105, 108], "framwork": [0, 32, 64, 73, 79], "franc": [3, 7, 21, 33, 34, 38, 52, 56, 70, 71, 76, 79, 89, 96, 103], "franco": [11, 45, 103, 108], "frank": [64, 73, 80], "frankfurt": [3, 23, 37, 44, 64, 73, 79, 99, 105, 109], "fratelli": [1, 15, 38, 64, 73, 79], "fraunhof": [64, 73, 79], "fred": [64, 73, 79, 109], "frederik": [1, 15, 38, 64, 73, 79], "free": [6, 28, 31, 37, 49, 54, 56, 60, 61, 72, 73, 77, 86, 89, 94, 99, 100], "freeli": [44, 64, 73, 79, 105], "freiburg": [28, 30, 33, 54, 64, 73, 79, 105], "freie": [44, 64, 73, 79, 105], "freien": [40, 79, 101], "freiwillig": 75, "fremd": 75, "fremden": 75, "frerick": [26, 54, 64, 73, 79, 105], "fri": [22, 64, 73, 79], "friend": [47, 84], "friendli": [20, 29, 64, 73, 79], "from": [1, 3, 14, 15, 16, 17, 20, 22, 23, 24, 30, 32, 33, 34, 35, 38, 42, 45, 47, 48, 49, 50, 57, 68, 70, 71, 74, 76, 78, 81, 82, 93, 94, 96, 97, 99, 100, 101, 103, 107], "fromlink": [40, 90], "frontiersin": [11, 29, 52, 79, 90, 105], "fr\u00fchzeitig": [54, 64, 73, 79, 95, 105], "fuch": [0, 3, 6, 16, 22, 23, 28, 32, 37, 45, 46, 47, 50, 52, 64, 68, 73, 79, 90, 92, 93, 99, 100, 103, 105], "fuer": [29, 54, 64, 73, 79, 93, 105], "fuhrman": [64, 73, 80], "fulfil": [29, 52, 79, 90, 105], "full": [0, 1, 4, 9, 10, 11, 14, 18, 22, 24, 28, 29, 30, 31, 33, 40, 50, 52, 64, 73, 76, 79, 90, 93, 98, 105], "full_model_path": [25, 31, 64, 73, 79], "fulli": [16, 24, 31, 32, 35, 42, 64, 73, 79, 90, 100], "function": [1, 6, 11, 15, 16, 28, 29, 30, 33, 35, 37, 38, 47, 52, 54, 68, 76, 78, 87, 89, 90, 94, 105], "fund": [0, 3, 6, 16, 17, 20, 23, 28, 32, 33, 34, 37, 44, 47, 50, 52, 54, 55, 57, 64, 73, 79, 86, 87, 90, 93, 99, 102, 105], "fundament": [42, 64, 73, 79, 105], "fundamentals_image_process": [13, 48, 68, 86, 90], "funder": [3, 8, 14, 23, 28, 42, 55, 64, 73, 79, 90, 93, 99, 105], "funk": [5, 48, 54, 68, 86, 89, 90], "furth": [29, 64, 73, 79], "further": [3, 14, 22, 23, 29, 32, 37, 40, 42, 55, 64, 73, 79, 90, 93, 99, 105], "furthermor": [1, 3, 22, 23, 28, 29, 32, 37, 40, 54, 64, 65, 73, 79, 89, 90, 93, 99, 105, 109], "fuse": [24, 64, 73, 79], "fuster": [45, 86, 89, 90, 108], "futur": [0, 16, 23, 32, 37, 47, 73, 79, 93, 99, 105], "f\u00f6rstner": [29, 64, 73, 79, 99, 105], "f\u00fchrt": [64, 73, 79], "f\u00fcr": [0, 26, 32], "g": [2, 3, 4, 12, 15, 16, 18, 23, 29, 30, 31, 32, 33, 35, 37, 42, 47, 52, 60, 64, 65, 68, 71, 73, 74, 76, 78, 79, 87, 88, 90, 98, 99, 100, 105], "ga456d5535c_2_53": [13, 54, 63, 86, 90, 98], "gabeau79": [42, 47, 68, 91, 109], "gabriel": [4, 12, 18, 29, 30, 31, 52, 64, 65, 71, 73, 76, 79, 90, 93, 98, 105], "gaell": [47, 68, 78, 89, 90], "gai": [11, 15, 47, 68, 82, 100], "gaignard": [17, 18, 29, 50, 52, 65, 79, 96], "galaxyproject": [1, 28, 42, 49, 54, 55, 64, 68, 73, 79, 84, 90, 91, 96, 99, 100, 107], "galluss": [21, 48, 68, 86, 89, 103], "ganglia": 10, "gaol": [52, 71, 90, 93, 107], "gap": [29, 54, 64, 73, 79, 90, 93, 95, 101, 105], "garcia": [11, 52, 64, 73, 79, 91, 93, 96], "garc\u00eda": [29, 64, 73, 79], "gase": [64, 73, 79], "gat": [29, 64, 73, 79], "gatewai": [15, 47, 64, 68, 73, 79, 82, 100], "gather": [3, 8, 16, 23, 33, 34, 35, 37, 54, 56, 64, 72, 73, 79, 99, 100, 105], "gatsbi": [42, 55, 56, 79], "gaudreault": [4, 12, 18, 30, 52, 71, 76], "gault": [2, 4, 5, 11, 15, 17, 47, 52, 68, 71, 79, 82, 99, 102, 105], "gayoso": [47, 68, 78, 90], "gb": [2, 15, 52, 64, 79, 90], "ge3a24e733b_0_54": [54, 63, 86, 89, 90, 98], "geben": [29, 52, 64, 73, 79, 99, 105], "geert": [51, 52, 69, 79, 90, 105], "geest": [51, 52, 69, 79, 90, 105], "geforc": [25, 31, 64, 73, 79], "gegebenenfal": [29, 54, 64, 73, 79, 105], "gehrig": [64, 73, 79], "geht": [0, 26, 32, 54, 64, 73, 79, 105], "geh\u00f6ren": [29, 54, 64, 73, 79, 95, 105], "gei": [26, 54, 64, 73, 79, 105], "geiger": [64, 73, 80], "gekennzeichnet": 75, "gelernt": [26, 54, 64, 73, 79, 105], "gelman": [4, 12, 18, 30, 52, 71, 76], "gemacht": [26, 29, 54, 64, 73, 79, 93, 105], "gemeinsam": [26, 29, 54, 64, 73, 79, 93], "gener": [1, 3, 6, 16, 20, 22, 23, 24, 25, 28, 31, 35, 37, 38, 42, 50, 56, 61, 72, 82, 84, 100, 102, 106], "genom": [6, 28, 37, 49, 54, 55, 64, 73, 79, 81, 105, 107, 108], "genomebiologi": [2, 15, 52, 64, 79, 90], "gentzel": [6, 28, 37, 54, 64, 73, 79, 105], "geoffrei": [1, 15, 38, 64, 73, 79], "georg": [17, 64, 73, 79, 99, 109], "gerald": [52, 64, 79, 105], "gerard": [38, 52, 64, 86, 105], "gerbi": [6, 54, 55, 90, 96, 105], "geregelt": [0, 32, 64, 73, 79], "gerhard": [52, 71, 79, 92, 93, 106], "gerlach": [3, 23, 32, 37, 64, 73, 79], "german": [3, 6, 17, 23, 28, 29, 33, 34, 35, 37, 40, 42, 44, 50, 52, 54, 64, 68, 73, 79, 86, 87, 90, 93, 99, 105, 109], "germanbioimag": [0, 32, 64, 73, 79], "germani": [1, 3, 8, 11, 17, 23, 28, 33, 34, 35, 37, 52, 54, 56, 64, 65, 72, 73, 79, 87, 90, 99, 100, 105], "gerst": [45, 52, 71, 94, 108], "gerstung": [17, 51, 52, 69, 79, 103], "gert": [4, 12, 18, 30, 52, 71, 76], "gesellschaft": [0, 32, 57, 64, 73, 79, 86, 99, 105], "gesetzen": 75, "gespeichert": 75, "gestalten": [26, 54, 105], "gestaltung": [52, 64, 79, 105], "gestattet": 75, "get": [3, 22, 29, 30, 31, 33, 40, 44, 45, 47, 55, 57, 68, 74, 78, 86, 89, 90, 97, 105], "getrieben": [29, 54, 64, 73, 79, 93, 105], "getting_started_with_mambaforge_and_python": [19, 39, 62, 79, 103], "gew\u00e4hr": 75, "gfp": [24, 30, 33, 64, 73, 79], "gg": [0, 32, 64, 73, 79], "ggf": [29, 54, 64, 73, 79, 105], "ghadraoui": [1, 14, 52, 64, 73, 79, 105], "ghga": [64, 73, 79], "ghosh": [11, 52, 79, 105], "giacomo": [11, 52, 79, 105], "gian": [24, 31, 64, 73, 79], "giant": [24, 64, 73, 79], "gibco": [31, 64, 73, 79], "gide": [17, 64, 73, 79, 99, 105], "gilet": [48, 68, 86, 89, 90], "gilloteaux": [17, 18, 29, 31, 50, 52, 65, 79, 90, 96, 98], "giovanni": [17, 47, 51, 52, 68, 69, 78, 79, 90, 103], "girgin": [25, 31, 64, 73, 79], "git": [4, 6, 7, 11, 28, 37, 51, 52, 69, 78, 84, 90, 94, 98, 107], "github": [2, 4, 5, 7, 9, 10, 11, 12, 15, 17, 19, 20, 28, 29, 36, 39, 40, 42, 45, 46, 48, 49, 52, 54, 57, 58, 59, 60, 61, 63, 64, 66, 74, 77, 78, 80, 81, 82, 83, 84, 87, 89, 91, 94, 97, 98, 100, 102, 103, 106, 107, 108, 109], "github_api_kei": 61, "githubusercont": [10, 47, 54, 55, 68, 79, 86, 89, 90, 107], "give": [1, 4, 6, 9, 11, 28, 29, 32, 36, 44, 51, 52, 54, 64, 71, 73, 79, 81, 90, 93, 99, 105, 107], "given": [5, 17, 25, 31, 34, 54, 64, 73, 79, 86, 89, 90, 93, 99, 105, 109], "gkr469": [64, 73, 79], "glass": [22, 64, 73, 79], "gla\u00df": [45, 52, 94, 108], "gleichzeitig": [29, 54, 64, 73, 79, 93, 105], "glencoesoftwar": [2, 11, 17, 42, 47, 55, 56, 68, 82, 86, 100, 102], "glennan": [10, 29, 64, 73, 79], "gletort": [47, 68, 78, 89, 90], "glob": [64, 73, 79], "global": [30, 33, 57, 64, 73, 79, 105], "globalbioimag": [17, 42, 46, 56, 64, 72, 73, 79, 90], "globia": [29, 64, 73, 79, 89, 99, 105, 109], "glutamax": [31, 64, 73, 79], "glycogen": [30, 33, 64, 73, 79], "glyn": [11, 30, 33, 52, 64, 73, 79, 87], "gmb": [0, 3, 32, 33, 42, 55, 64, 73, 79, 86, 96, 99, 105], "gmhydnm1rsm": [56, 72, 90, 100], "go": [20, 42, 57, 61, 64, 73, 79, 92, 93, 105], "goal": [1, 3, 6, 15, 24, 28, 33, 34, 37, 38, 42, 45, 47, 48, 54, 55, 64, 68, 73, 78, 79, 84, 90, 97, 99, 100, 103, 105], "gobl": [1, 15, 38, 64, 73, 79], "goedhart": [39, 40, 51, 69, 73, 86, 106], "goettingen": [17, 64, 73, 79, 99, 109], "gogoberidz": [7, 21, 38, 54, 56, 70, 79, 89, 90, 103], "golani": [17, 18, 29, 31, 50, 52, 65, 79, 90, 96, 98], "gold": [47, 68, 78, 90], "goldberg": [2, 15, 52, 64, 79, 90], "gomez": [7, 21, 38, 54, 56, 70, 79, 89, 90, 103], "gonzal": [42, 52, 64, 78], "gonzalez": [4, 5, 7, 9, 11, 36, 47, 55, 64, 68, 73, 79, 84, 90, 98, 105, 107, 109], "googl": [1, 4, 13, 20, 29, 47, 49, 54, 56, 58, 70, 73, 79, 86, 89, 98], "gormann": [1, 15, 38, 64, 73, 79], "got": 60, "gottlieb": [64, 73, 79], "gottschal": [0, 50, 73, 79, 93, 105], "gould": [29, 64, 73, 79], "gov": [3, 11, 22, 23, 32, 37, 38, 52, 64, 71, 73, 79, 86, 90, 92, 93, 96, 99, 105], "govern": [54, 64, 73, 79, 105, 108], "gpl": [2, 11, 15, 17, 41, 42, 45, 47, 48, 49, 54, 55, 58, 64, 68, 73, 90, 94, 97, 100, 102, 103, 107], "gpu": [20, 25, 31, 73], "grade": [31, 64, 73, 79], "graduat": [1, 64, 73, 79], "graham": [29, 64, 73, 79], "grai": [11, 42, 47, 68, 79, 91, 101, 109], "grain": [24, 64, 73, 79], "grammar": [10, 29, 64, 73, 79], "grant": [6, 20, 28, 37, 54, 64, 73, 79, 105], "grantner": [1, 15, 38, 64, 73, 79], "graph": [16, 35, 80, 99, 105], "graphic": [11, 29, 39, 52, 64, 76, 86, 90, 94], "graybeal": [52, 71, 79, 92, 93, 96], "graylab": [42, 47, 68, 91, 109], "grayscal": [64, 73, 79], "great": [64, 73, 79], "gred": [15, 47, 68, 82, 94, 100], "gregor": [47, 68, 78, 90], "gregori": [40, 68, 81, 90], "greifbar": [40, 79, 101], "greiner": [22, 64, 73, 79], "grenzen": 75, "gribbon": [1, 15, 38, 64, 73, 79], "grid": [31, 64, 73, 79], "griffin": [11, 52, 64, 86, 91, 101, 105, 106], "grimm": [30, 33, 64, 73, 79], "grinic": [4, 5, 7, 9, 36, 47, 68, 79], "gritti": [4, 7, 9, 36, 64, 73, 79, 90, 98, 109], "gro": [4, 5, 7, 9, 36, 47, 68, 79], "grob": [0, 32, 64, 73, 79], "groovi": [25, 31, 64, 73, 79], "grote": [3, 8, 23, 33, 34, 35, 37, 54, 56, 64, 72, 73, 79, 87, 90, 93, 99, 100, 105], "ground": [3, 33, 64, 73, 79, 99, 105], "groundbreak": [44, 64, 73, 79, 105], "group": [8, 17, 28, 30, 33, 34, 35, 40, 54, 56, 72, 88, 99, 100, 105], "grow": [54, 64, 73, 79, 108], "growth": [52, 73, 79, 102], "gro\u00dfger\u00e4tebeschaffung": [0, 32, 64, 73, 79], "grp": [4, 7, 11, 55, 84, 105, 107], "grundlagen": [26, 54, 64, 73, 79, 105], "grundlegend": [64, 73, 79], "grundlegenden": [26, 54, 64, 73, 79, 105], "grunds\u00e4tzen": [26, 54, 64, 73, 79, 105], "grunds\u00e4tzlich": [54, 64, 73, 79, 105], "grune": [1, 15, 38, 64, 73, 79], "grunwald": [4, 11, 12, 18, 30, 38, 52, 64, 71, 76, 86, 96, 105], "gruppenarbeit": [29, 54, 64, 73, 79, 105], "gr\u00f6\u00dfere": [64, 73, 79], "gr\u00f6\u00dfter": 75, "gr\u00fcning": [1, 28, 54, 55, 63, 79, 86, 90, 96, 99, 100], "gt": [3, 22, 23, 32, 37, 64, 73, 79], "gu": [4, 12, 18, 30, 52, 71, 76], "guerrero": [64, 73, 79], "gui": [82, 102], "guid": [11, 23, 35, 36, 44, 45, 48, 49, 56, 57, 60, 68, 72, 80, 86, 94, 99, 101], "guidanc": [42, 48, 54, 73, 78, 90], "guidelin": [3, 8, 23, 30, 33, 34, 37, 38, 42, 44, 57, 64, 70, 71, 73, 81, 86, 92, 93, 96, 99, 105], "guiet": [11, 18, 24, 25, 29, 42, 46, 52, 58, 64, 65, 73, 79, 86, 90, 98], "guijacquemet": [42, 48, 68, 69, 84], "guilbert": [30, 33, 64, 73, 79, 87], "guillaum": [7, 11, 12, 15, 21, 38, 42, 47, 48, 63, 64, 68, 69, 70, 71, 78, 79, 82, 84, 98, 100, 103], "guillermo": [52, 71, 96], "guiwitz": [13, 48, 68, 78, 84, 86, 89, 90, 98, 103], "gunar": [3, 22, 23, 32, 37, 64, 73, 79, 99, 105], "guo": [52, 64, 79, 93, 105], "gurwitz": [1, 15, 38, 64, 73, 79], "gustafsson": [11, 42, 78, 91, 103, 107, 108], "gustav": [3, 23, 29, 32, 37, 64, 73, 79, 99, 105], "gutanalysistoolbox": [10, 29, 64, 73, 79], "guten": [26, 54, 64, 73, 79, 105], "guyad": [4, 12, 18, 30, 52, 71, 76], "g\u00f3mez": [11, 17, 18, 29, 41, 48, 50, 52, 54, 64, 65, 68, 73, 79, 86, 89, 90, 93, 96, 98], "g\u00f6ttingen": [6, 17, 64, 73, 79, 99, 109], "h": [0, 16, 18, 23, 29, 30, 31, 33, 37, 45, 46, 47, 52, 64, 65, 68, 73, 79, 87, 90, 93, 98, 99, 100, 103, 105], "h22": [3, 23, 37, 64, 73, 79, 99, 105, 109], "h2b": [31, 64, 73, 79], "h5": [31, 64, 73, 79], "ha": [1, 3, 6, 8, 9, 14, 15, 17, 20, 22, 23, 29, 32, 33, 34, 35, 37, 38, 42, 44, 50, 52, 54, 55, 56, 61, 64, 72, 73, 79, 87, 89, 90, 93, 99, 100, 102, 103, 105], "haas": [4, 6, 9, 10, 11, 12, 17, 18, 20, 28, 31, 37, 39, 40, 41, 47, 48, 50, 52, 54, 55, 58, 62, 64, 65, 66, 67, 68, 73, 75, 76, 78, 79, 84, 86, 87, 89, 90, 93, 94, 95, 96, 97, 98, 99, 100, 101, 103, 105, 106, 109], "haben": [29, 54, 64, 73, 75, 79, 93, 105], "habing": [64, 73, 79], "hackathon": [28, 54], "had": [52, 64, 73, 79, 102], "haeflig": [51, 52, 69, 79, 90, 105], "haesleinhuepf": [29, 40, 47, 48, 55, 64, 68, 73, 78, 79, 90, 103], "hagen": [3, 33, 34, 37, 64, 73, 79, 99, 105], "hahn": [52, 64, 79, 105], "hal": [40, 50, 68, 79, 90, 103], "halavatyi": [4, 5, 7, 9, 18, 36, 47, 64, 68, 73, 79, 86, 90, 98, 109], "halchenko": [17, 47, 68, 77], "half": [2, 17, 47, 68, 82, 102], "halfwai": [48, 68, 78, 90, 103], "halidi": [4, 12, 18, 30, 52, 71, 76], "hall": [3, 23, 37, 45, 52, 64, 73, 79, 94, 99, 105, 108, 109], "halo": [30, 33, 64, 73, 79], "hamburg": [40, 101], "hamdah": [40, 68, 81, 90], "hammer": [4, 11, 12, 18, 30, 52, 71, 76, 86, 90, 96], "hamnett": [10, 29, 64, 73, 79], "hamper": [1, 14, 64, 73, 79], "hamster": [30, 33, 64, 73, 79], "han": [1, 10, 15, 22, 29, 38, 40, 64, 68, 73, 79, 86, 89, 90], "hancock": [1, 15, 38, 64, 73, 79], "hand": [3, 14, 17, 22, 29, 32, 33, 34, 37, 47, 48, 54, 55, 58, 64, 68, 73, 78, 79, 90, 93, 99, 100, 105], "handbook": [11, 42, 101], "handhabbar": [26, 54, 64, 73, 79, 105], "handl": [1, 6, 8, 14, 16, 23, 24, 28, 30, 32, 33, 34, 35, 50, 52, 54, 90, 93, 99], "hann": [1, 3, 8, 11, 17, 23, 28, 30, 33, 34, 37, 50, 52, 64, 65, 73, 79, 90, 99, 105], "hanna": [3, 22, 23, 32, 37, 64, 73, 79], "hannib": [30, 33, 64, 73, 79], "harald": [1, 15, 17, 38, 51, 52, 64, 69, 73, 79, 103], "hardi": [52, 71, 79, 92, 93, 96], "hardwar": [29, 30, 33, 52, 64, 73, 76, 79], "harmon": [1, 3, 8, 14, 23, 28, 64, 73, 79, 99, 105], "harmoni": [31, 64, 73, 79], "harmonis": [1, 15, 38, 64, 73, 79], "harms": [1, 15, 38, 64, 73, 79], "haro": [29, 64, 73, 79], "harri": [24, 31, 64, 73, 79], "hartlei": [4, 12, 17, 18, 30, 38, 52, 64, 71, 76, 79, 86, 90, 101, 105], "hartmann": [11, 41, 48, 68, 84, 86, 90, 103], "harvard": [30, 33, 39, 64, 73, 79, 105], "hat": [29, 40, 54, 64, 73, 79, 93, 101, 105], "hau": [3, 23, 37, 64, 73, 79, 99, 105, 109], "haugwitz": [64, 73, 80], "hauser": [64, 73, 79], "have": [3, 8, 9, 11, 14, 22, 23, 28, 29, 30, 33, 34, 36, 37, 38, 42, 44, 48, 52, 54, 55, 56, 59, 60, 61, 70, 71, 78, 86, 87, 90, 92, 93, 96, 99, 101, 102, 105, 106, 107], "hc": [24, 29, 52, 71, 93, 96, 105], "hca": [30, 33, 64, 73, 79], "head": [3, 16, 30, 33, 34, 35, 37, 47, 55, 64, 68, 73, 79, 89, 99, 105], "header": [64, 73, 80], "health": [17, 34, 50, 73, 79, 93, 99, 105], "healthcar": [64, 73, 79], "hearth": [30, 33, 64, 73, 79], "heat": [24, 31, 64, 73, 79], "heavili": [13, 48, 64, 68, 73, 84, 99, 103], "heber": [64, 73, 79], "hedrich": [0, 32, 64, 73, 79], "heidari": [17, 51, 52, 69, 79, 103], "heidelberg": [23, 28, 47, 50, 52, 68, 80, 84, 90, 100, 103, 109], "heidi": [40, 73, 79, 105], "heidok": [3, 8, 54, 64, 99, 105], "heilemann": [12, 42, 48, 64, 68, 71, 84, 90], "heiligenstein": [17, 18, 29, 50, 52, 65, 79, 96], "hein": [3, 23, 30, 33, 34, 37, 64, 73, 79, 99, 105, 109], "heinrich": [3, 23, 30, 33, 34, 37, 64, 73, 79, 99, 105, 109], "hela": [24, 25, 30, 33], "helacel": [24, 64, 73, 79], "helbig": [40, 44, 52, 64, 73, 79, 105], "held": [3, 4, 6, 12, 18, 23, 28, 30, 47, 52, 54, 64, 68, 71, 73, 76, 79, 84, 90, 99, 100, 103, 105], "helen": [1, 15, 38, 64, 73, 79], "helena": [4, 12, 18, 30, 42, 52, 64, 71, 73, 76, 78, 79], "helfen": [26, 54, 64, 73, 79, 105], "helfrich": [4, 5, 7, 9, 36, 47, 68, 79], "hell": [64, 73, 79], "hellen": [64, 73, 79], "helmholtzai": [47, 55, 68, 84], "help": [3, 8, 11, 16, 23, 24, 28, 35, 37, 42, 52, 55, 56, 57, 61, 70, 81, 91, 93, 96, 99, 101, 105, 106], "helpdesk": [6, 16, 28, 50, 90, 92, 99, 105], "hematoxylin": [24, 25, 31, 64, 73, 79], "hen": [40, 101], "henni": [4, 7, 9, 36, 64, 73, 79, 90, 98, 109], "henriett": [1, 15, 38, 64, 73, 79], "henriqu": [12, 40, 42, 48, 64, 68, 71, 84, 90], "henriqueslab": [11, 12, 42, 48, 64, 68, 71, 79, 84, 90], "hensel": [34, 52, 64, 86, 99, 105], "herausforderungen": [26, 29, 54, 64, 73, 79, 93, 105], "here": [1, 3, 8, 11, 14, 15, 22, 23, 28, 29, 32, 37, 38, 45, 47, 52, 54, 55, 56, 59, 60, 61, 64, 68, 70, 71, 73, 79, 81, 86, 89, 90, 93, 96, 99, 103, 105, 107], "herearii": [48, 68, 86, 89, 90], "hereariim": [48, 68, 86, 89, 90], "herich": [1, 15, 38, 64, 73, 79], "hern\u00e1ndez": [12, 29, 42, 48, 64, 68, 71, 73, 79, 84, 90], "herrenbr\u00fcck": [64, 73, 79], "herrmann": [20, 47, 48, 54, 68, 86, 91], "herstellen": [54, 64, 73, 79, 95, 105], "hertweck": [52, 73, 79, 102], "herv\u00e9": [17, 18, 29, 50, 52, 65, 79, 96], "hess": [54, 64, 73, 79, 105], "heterogen": [3, 33, 34, 37, 73, 99, 105], "hettn": [17, 34, 54, 73, 79, 90, 93, 105], "heuckeroth": [29, 64, 73, 79], "hhu": [30, 33, 64, 73, 79], "hi": [64, 73, 79], "hidalgo": [11, 39, 42, 48, 66, 68, 79, 86, 90], "hiemenz": [52, 79, 93, 105], "hier": [0, 32, 54, 64, 73, 79, 95, 105], "hierf\u00fcr": [26, 54, 64, 73, 79, 105], "hiermit": 75, "hiervon": 75, "hierzu": [26, 54, 64, 73, 79, 105], "high": [3, 22, 23, 29, 31, 37, 45, 47, 54, 56, 72, 81, 89, 99, 103, 107, 108], "higher": [29, 32, 44, 64, 73, 79, 105], "highest": [64, 73, 79], "highli": [22, 64, 73], "highlight": [1, 3, 6, 14, 28, 33, 37, 54, 64, 73, 89, 99, 105], "hilf": [26, 29, 54, 64, 73, 79, 93, 95, 105], "hilft": [26, 54, 64, 73, 79, 105], "hilo": [30, 33, 64, 73, 79], "hilsenstein": [55, 68, 86], "hin": 75, "hinau": [0, 32, 64, 73, 79], "hinder": [29, 64, 73, 79], "hindranc": [64, 73, 79], "hinter": [26, 54, 64, 73, 79, 105], "hintergrund": [64, 73, 79], "hinwei": 75, "hinweis": [26, 54, 64, 73, 79, 105], "hinweisen": 75, "hip": [3, 8, 23, 28, 64, 73, 79, 99, 105], "histologi": [24, 31], "histopathologi": [47, 48, 68, 83, 90], "histor": [52, 71, 90, 93, 107], "histori": [49, 55, 81, 107], "hiv": [30, 33, 64, 73, 79], "hki": [45, 52, 71, 94, 108], "hm": [30, 33, 39, 64, 73, 79, 105], "hmc": [3, 8, 23, 28, 64, 73, 79, 99, 105], "hochgeladen": [29, 54, 64, 73, 79, 93, 105], "hodg": [4, 5, 7, 9, 11, 42, 47, 54, 55, 64, 68, 73, 79, 90, 98, 103, 109], "hoeb": [52, 64, 79, 90, 100, 107], "hoevel": [0, 16, 23, 37, 45, 46, 47, 52, 64, 68, 73, 79, 90, 93, 99, 100, 103, 105], "hoffmann": [26, 54, 64, 73, 79, 105], "hohlbein": [29, 52, 64, 76], "hoksza": [20, 47, 48, 54, 68, 86, 91], "hoku": [64, 73, 81, 109], "hold": [64, 73, 79], "holden": [12, 29, 42, 48, 52, 64, 68, 71, 73, 76, 79, 84, 90], "holgerhennig": [55, 68, 86, 90], "holub": [1, 15, 38, 64, 73, 79], "hope": [42, 48, 78, 90, 105], "hopp": [64, 73, 79], "horizon2020": [64, 73, 79], "hospit": [3, 20, 23, 29, 37, 64, 73, 79, 99, 105, 109], "hospitali": [30, 33, 64, 73, 79], "hossein": [11, 18, 29, 41, 48, 54, 68, 86, 90, 98], "hosseini": [52, 71, 79, 93, 96, 105], "host": [3, 29, 30, 33, 37, 48, 64, 68, 73, 79, 88, 103], "hot": [29, 64, 73, 79], "hoth": [0, 32, 64, 73, 79], "hour": [24, 31, 64, 73, 79], "hover": [14, 42, 55, 64, 73, 79, 90, 93, 105], "how": [1, 7, 11, 12, 13, 14, 15, 17, 22, 23, 25, 28, 29, 30, 31, 32, 35, 36, 38, 47, 48, 52, 54, 55, 57, 61, 65, 68, 71, 76, 78, 80, 84, 86, 88, 89, 93, 94, 98, 107], "how_to": [45, 79, 103], "howard": [29, 30, 33, 64, 73, 79], "howev": [1, 3, 17, 28, 29, 33, 34, 44, 54, 60, 64, 73, 79, 90, 93, 99, 105], "hpc": [47, 52, 54, 64, 68, 73, 79, 90, 100, 107, 108], "hrovatin": [47, 68, 78, 90], "hsz": [34, 52, 64, 86, 99, 105], "htmanalysiscours": [4, 18, 68, 86, 90], "html": [7, 11, 12, 17, 19, 28, 29, 30, 33, 36, 39, 40, 42, 43, 44, 45, 46, 48, 49, 52, 54, 55, 56, 62, 64, 68, 70, 73, 77, 78, 79, 80, 81, 86, 89, 90, 91, 94, 96, 97, 98, 99, 100, 101, 103, 105, 106, 109], "http": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 54, 55, 56, 57, 58, 59, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 76, 77, 78, 79, 80, 81, 82, 83, 84, 86, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 105, 106, 107, 108, 109], "htw": [54, 64, 73, 79, 105], "hu": [29, 64, 73, 79], "huang": [4, 5, 7, 9, 36, 47, 52, 68, 71, 79, 90, 93, 107], "huber": [17, 51, 52, 69, 79, 103], "huerta": [29, 64, 73, 79], "hugger": [55, 68, 86, 89], "hugh": [30, 33, 64, 73, 79], "huijben": [47, 55, 58, 68, 90], "huisken": [29, 52, 79, 90], "huisman": [11, 52, 86, 90, 96], "human": [5, 14, 25, 29, 30, 31, 33, 47, 48, 54, 55, 56, 58, 68, 72, 78, 86, 89, 90], "humantechnopol": [17, 64, 73, 79], "humboldstra\u00df": 75, "humboldt": [44, 64, 73, 79, 105], "humenick": [10, 29, 64, 73, 79], "hundt": [26, 54, 64, 73, 79, 105], "hungari": [42, 47, 68, 90, 109], "hup": [40, 79, 101], "hutchinson": [64, 73, 79, 109], "huygenspro": [31, 64, 73, 79], "hypox": [29, 64, 73, 79], "hypoxia": [29, 64, 73, 79], "hzdr": [54, 64, 73, 79, 105], "h\u00e4nsch": [3, 33, 34, 52, 71, 76, 96, 105], "h\u00e4ufig": [26, 54, 64, 73, 79, 105], "h\u00e9l\u00e8ne": [11, 52, 79, 105], "h\u00f6rl": [17, 18, 29, 50, 52, 65, 79, 96], "i": [1, 2, 3, 6, 8, 9, 13, 16, 17, 20, 22, 23, 24, 25, 28, 29, 30, 31, 32, 33, 34, 35, 37, 38, 39, 40, 44, 45, 47, 48, 49, 50, 51, 52, 55, 57, 58, 59, 60, 61, 66, 68, 69, 71, 76, 77, 78, 80, 81, 82, 84, 87, 89, 92, 93, 94, 95, 97, 99, 100, 102, 103, 105, 106, 107, 108], "i2k": [42, 54, 63, 83, 102], "i2k2024": [54, 63, 79], "i2k_2024": [47, 54, 55, 58, 68, 83, 90], "i2k_poster_haase_v6_imagej_repro_pdf": [12, 29, 50, 52, 94], "i3d": [87, 109], "i3dbio": [3, 8, 23, 33, 34, 35, 37, 42, 54, 55, 56, 64, 72, 73, 79, 86, 96, 99, 100, 105], "i7dl": [29, 64, 73, 79], "ial": [30, 33, 64, 73, 79], "ic": [30, 33], "icr1": [24, 31, 64, 73, 79], "icsread": [64, 73, 79], "id": [1, 11, 13, 40, 47, 52, 54, 58, 63, 68, 86, 89, 90, 91, 93, 96, 98, 105], "ideal": [42, 48, 78, 90, 105], "idealerweis": [26, 54, 64, 73, 79, 105], "ident": [29, 64, 73, 79], "identif": [3, 33, 34, 37, 64, 73, 79, 99, 105], "identifi": [1, 28, 36, 54, 64, 73, 79], "idr": [1, 28, 42, 43, 52, 56, 64, 71, 72, 73, 79, 100], "ifetim": [64, 73, 79], "iglesia": [47, 48, 68, 78, 90, 103], "ignacio": [10, 11, 18, 29, 41, 48, 54, 63, 68, 86, 89, 90, 98], "ihda": [64, 73, 79], "ihnen": [26, 29, 54, 64, 73, 79, 93, 105], "ihr": [26, 54, 64, 73, 75, 79, 95, 105], "ihren": [26, 54, 64, 73, 79, 93, 105], "ii": [64, 73, 79], "iii": [42, 52, 64, 73, 78, 79], "ij": [64, 73, 79], "ij_textbook1": [18, 40, 68, 76, 90], "ijsbrand": [52, 64, 71, 79, 93, 105], "ilan": [47, 68, 78, 90], "illumin": [24, 30, 33, 64, 73, 79], "illustr": [1, 9, 15, 38, 54], "ilya": [2, 4, 9, 15, 29, 52, 64, 79, 90], "im": [0, 30, 32, 33, 40, 57, 75, 86, 93, 101], "imag": [0, 8, 17, 23, 44, 60, 70, 77, 87, 91, 101, 102], "image_1_2023_08_18__14_32_31_964": [24, 64, 73, 79], "image_analysi": 42, "image_analysis_with_python": [29, 48, 68, 79, 90, 97, 103], "imageinlif": [64, 73, 79], "imagej": [3, 11, 23, 24, 25, 28, 31, 37, 40, 44, 47, 48, 49, 76, 77, 78, 81, 82, 83, 99, 100, 103, 105, 107], "imagejmacro_24h_2017": [29, 54, 67, 86, 90, 94], "imagejmacro_introduct": [41, 54, 68, 86, 90, 98], "imageri": [30, 33, 64, 73, 79], "images_for_classifier_train": [25, 31, 64, 73, 79], "imcf": [64, 73, 79], "img_filt": [25, 31, 64, 73, 79], "imjoi": [11, 68, 86], "immedi": [17, 34, 54, 73, 79, 90, 93, 105], "immer": [26, 54, 64, 73, 79, 105], "immun": [54, 56, 72, 86, 89, 90], "immunofluoresc": [30, 33, 64, 73, 79], "immunolabel": [29, 64, 73, 79], "immunoresearch": [24, 31, 64, 73, 79], "immunostain": [64, 73, 79], "impact": [29, 54, 64, 89, 103], "imperfect": [47, 68, 81], "implement": [2, 3, 6, 8, 12, 15, 22, 23, 28, 29, 33, 34, 37, 42, 47, 50, 52, 54, 68, 71, 90, 92, 93, 96, 99, 105], "import": [3, 22, 23, 29, 30, 32, 33, 37, 39, 54, 56, 64, 66, 72, 73, 79, 86, 89, 90, 95, 99, 105], "importprocess": [64, 73, 79], "impressumspflicht": 75, "improv": [1, 3, 8, 22, 23, 28, 29, 30, 33, 34, 37, 42, 47, 52, 54, 64, 68, 71, 73, 79, 83, 86, 92, 93, 96, 99, 102, 105], "includ": [1, 2, 3, 6, 9, 11, 14, 15, 16, 17, 20, 24, 25, 28, 29, 30, 31, 33, 34, 37, 38, 39, 47, 50, 52, 54, 55, 57, 60, 64, 68, 71, 73, 79, 80, 82, 86, 90, 92, 93, 99, 101, 102, 105], "inclus": [52, 73, 79, 102], "incomplet": [54, 56, 72, 86, 89, 90], "inconveni": [3, 8, 23, 30, 33, 34, 37, 64, 73, 79, 99, 105], "incorpor": [29, 33, 64, 73, 79, 80], "increas": [1, 14, 15, 28, 29, 38, 52, 64, 73, 79, 102], "increasingli": [1, 14, 44, 64, 73, 79, 105], "incub": [24, 31, 64, 73, 79], "independ": [6, 28, 29, 37, 54, 64, 73, 79, 99, 105], "index": [40, 45, 48, 49, 51, 52, 55, 56, 69, 77, 79, 87, 94, 100, 103, 105], "indic": [30, 33, 64, 73, 79], "indico": [58, 90, 100, 105], "indispens": [64, 73, 79], "individu": [24, 31, 60, 87], "individualis": [29, 64, 73, 79], "individuel": [26, 54, 64, 73, 79, 105], "industri": [1, 3, 15, 33, 38, 54, 64, 73, 79, 99, 105, 108], "infect": [30, 33, 64, 73, 79], "infiltr": [24, 31, 64, 73, 79], "influenc": [29, 33, 54, 64, 65, 73, 79, 90], "info": [46, 64, 73, 79, 90], "inform": [1, 9, 14, 17, 24, 28, 29, 35, 48, 49, 52, 54, 55, 56, 57, 68, 71, 72, 90, 93, 100, 103], "informatik": [45, 52, 94, 108], "informationen": 75, "informationsmaterialien": 75, "infrasstructur": 87, "infrastructur": [1, 11, 14, 15, 29, 35, 38, 56, 65, 72, 89, 100, 108], "infrastruktur": [29, 64, 73, 79, 99, 105], "infuerung": [29, 54, 64, 73, 79, 93, 105], "inhalt": [29, 54, 64, 73, 79, 93, 105], "inhalten": [29, 54, 64, 73, 79, 93, 105], "inhaltlich": 75, "inhibit": [22, 64, 73], "initfil": [64, 73, 79], "initi": [0, 16, 17, 23, 28, 29, 30, 33, 34, 37, 47, 65, 86, 90, 93, 96, 102], "initializefil": [64, 73, 79], "initialvortrag": [0, 32, 64, 73, 79], "inject": 87, "innat": [54, 56, 72, 86, 89, 90], "innerhalb": [0, 32, 64, 73, 79], "innov": [1, 3, 28, 29, 33, 64, 73, 79, 99, 105], "inp": [64, 73, 79], "input": [3, 37, 49, 55, 64, 73, 79, 81, 107], "inria": [40, 47, 68, 79, 90, 103], "insbesonder": [40, 75, 79, 101], "inserm": [30, 33, 64, 73, 79], "insert": [3, 23, 32, 37, 64, 73, 79, 99, 105], "insid": [64, 73, 79], "insight": [22, 29, 51, 54, 69, 90, 92, 105], "insightsoftwareconsortium": [47, 68, 90], "inspect": [31, 73], "instal": [3, 20, 30, 33, 34, 37, 61, 64, 73, 79, 84, 99, 105], "instanc": [3, 8, 12, 23, 28, 33, 34, 35, 37, 47, 54, 56, 64, 68, 72, 73, 79, 84, 90, 99, 100, 103, 105], "institut": [0, 3, 8, 17, 22, 23, 28, 30, 32, 33, 34, 35, 37, 38, 40, 52, 54, 56, 57, 72, 86, 92, 99, 100, 109], "institutcochin": [30, 33, 64, 73, 79], "instruct": [3, 8, 14, 23, 30, 33, 34, 35, 37, 42, 54, 55, 56, 64, 72, 73, 79, 81, 87, 90, 93, 99, 100, 105], "instrument": [30, 33, 64, 73], "integr": [0, 1, 3, 16, 17, 28, 29, 30, 33, 34, 35, 37, 42, 50, 54, 55, 56, 64, 72, 73, 79, 80, 86, 89, 90, 92, 93, 99, 100, 105, 106, 109], "integrieren": [29, 54, 64, 73, 79, 93, 95, 105], "intellig": [1, 5, 7, 10, 13, 21, 25, 28, 38, 39, 40, 42, 43, 45, 47, 48, 52, 55, 62, 63, 66, 68, 70, 71, 78, 81, 84, 93, 98, 99, 105, 107, 108, 109], "intend": [3, 29, 33, 34, 37, 42, 45, 47, 55, 64, 68, 73, 78, 79, 86, 90, 92, 93, 97, 99, 105], "intens": [1, 3, 8, 23, 24, 25, 28, 31, 33, 34, 35, 37, 45, 54, 56, 64, 72, 73, 79, 99, 100, 105, 108], "intent": [11, 52, 86, 90, 96], "inter": [29, 64, 73, 79], "interact": [1, 14, 29, 36, 45, 55, 93, 97, 108], "interaktiv": [26, 54, 64, 73, 79, 105], "interconnect": [64, 73, 79], "interdisciplinari": [0, 1, 28, 29, 50, 64, 73, 93, 105], "interessierten": [26, 54, 64, 73, 79, 105], "interest": [1, 17, 28, 29, 32, 39, 50, 52, 64, 73, 79, 86, 90, 105, 106], "interfac": [16, 35, 42, 45, 47, 61, 62, 73, 79, 86, 90, 100], "interfer": [29, 64, 73, 79], "intermedi": [2, 11, 17, 47, 68, 82, 102], "intern": [3, 6, 8, 23, 28, 29, 30, 33, 34, 37, 42, 44, 46, 52, 54, 73, 90, 93, 99, 105], "internation": [3, 33, 34, 37, 64, 73, 79, 99, 105], "internet": [3, 23, 28, 29, 54, 59, 64, 73, 75, 79, 93, 99, 105], "interoper": [0, 1, 3, 8, 14, 15, 16, 23, 28, 29, 30, 33, 34, 37, 38, 42, 47, 52, 54, 64, 71, 73, 79, 80, 87, 90, 92, 93, 95, 99, 105, 106], "interpret": [28, 42, 48, 50, 64, 73, 78, 79, 90, 105], "interrog": [3, 33, 34, 37, 64, 73, 79, 99, 105], "intersect": [64, 73, 79], "intervent": [29, 64, 73, 79], "intracellular": [64, 73, 79], "intravit": 109, "intro": [12, 29, 40, 42, 46, 48, 62, 78, 90, 98, 103, 105], "introduc": [3, 6, 7, 21, 28, 29, 33, 37, 38, 52, 54, 56, 64, 65, 70, 73, 79, 81, 89, 90, 93, 95, 99, 100, 101, 103, 105, 107, 109], "introducemi": [9, 54, 64, 73, 79], "introduct": [3, 23, 37, 39, 44, 65, 66, 93, 97, 99, 106, 109], "intuit": [1, 28, 64, 73, 79], "invert": [64, 73, 79], "investig": [3, 22, 23, 28, 30, 33, 37, 52, 64, 73, 79, 88, 93, 99, 102, 105], "invit": [17, 34, 50, 73, 79, 93, 99, 105], "invok": [29, 64, 73, 79], "involv": [1, 28, 32, 44, 64, 73, 79, 105], "io": [10, 12, 17, 19, 29, 39, 40, 42, 44, 45, 46, 47, 48, 49, 52, 54, 55, 57, 58, 64, 73, 76, 77, 78, 79, 81, 84, 89, 90, 93, 94, 97, 98, 99, 100, 102, 103, 105, 106, 107, 108], "ioer": [54, 64, 73, 79, 92, 105], "ion": [24, 64, 73, 79], "ioniz": [64, 73, 79], "iota": [30, 33, 64, 73, 79], "ipynb": [31, 64, 73, 79], "ipython": [47, 68, 78, 90], "irizarri": [39, 86], "irradi": [29, 64, 73, 79], "isa": [16, 35, 64, 73, 79, 99, 105], "isaac": [17, 47, 51, 52, 68, 69, 78, 79, 90, 103], "isabel": [1, 15, 38, 42, 52, 54, 55, 64, 73, 79, 90, 93, 105], "ishikawa": [64, 73, 79], "ison": [17, 18, 29, 50, 52, 65, 79, 96], "isotrop": [24, 64, 73, 79], "issu": [3, 8, 17, 22, 23, 24, 28, 29, 30, 32, 33, 37, 42, 44, 50, 59, 60, 64, 71, 73, 79, 87, 90, 96, 99, 109], "ist": [0, 26, 29, 32, 54, 64, 73, 75, 79, 105], "isyntax": [2, 17, 47, 68, 82, 102], "itali": [29, 47, 48, 55, 58, 68, 78, 90], "itano": [30, 33, 52, 64, 71, 73, 79, 87, 96], "iter": [3, 8, 23, 30, 33, 34, 37, 64, 73, 79, 99, 105], "itk": [31, 42, 48, 64, 73, 78, 79], "itll": [29, 39, 66, 79, 95, 105], "itn": [64, 73, 79], "its": [3, 6, 8, 23, 28, 29, 33, 44, 54, 64, 73, 79, 99, 105], "itself": [64, 73, 79], "iudin": [7, 21, 38, 54, 56, 70, 79, 89, 90, 103], "ivana": [17, 18, 29, 50, 52, 65, 79, 96], "iv\u00e1n": [11, 39, 42, 48, 66, 68, 79, 86, 90], "ix71": [29, 30, 33, 64, 73, 79], "j": [11, 17, 18, 24, 29, 30, 31, 33, 38, 45, 51, 52, 54, 55, 68, 69, 71, 78, 86, 87, 90, 92, 93, 94, 96, 98, 100, 103, 105, 107, 108], "jablonski": [64, 73], "jack": [47, 54, 68, 83], "jackson": [24, 31, 64, 73, 79], "jacob": [42, 68], "jacquemet": [7, 12, 21, 38, 42, 48, 64, 68, 69, 70, 71, 79, 84, 103], "jagusch": [52, 64, 79, 105], "jahr": [29, 52, 64, 76], "jaim": [10, 29, 30, 33, 64, 73, 79, 87], "jakob": [12, 56, 64, 72, 73, 79, 80, 86, 89], "jamali": [42, 52, 64, 68, 78, 90], "jambor": [4, 12, 18, 30, 42, 52, 64, 71, 76, 78], "jame": [11, 30, 33, 52, 64, 73, 79, 86, 87, 90, 96], "jan": [1, 3, 4, 5, 7, 8, 9, 11, 12, 15, 18, 20, 23, 28, 29, 30, 36, 38, 47, 48, 50, 52, 54, 64, 68, 71, 73, 76, 78, 79, 80, 86, 89, 90, 93, 96, 99, 105], "jana": [0, 4, 12, 18, 30, 32, 52, 64, 71, 73, 76, 79], "janabraung": [20, 47, 48, 54, 68, 86, 91], "janclusmann": [47, 48, 68, 83, 90], "janelia": [30, 33, 64, 73, 79], "janeliascicomp": [41, 55, 68, 86], "janina": [3, 8, 11, 17, 23, 30, 33, 34, 37, 52, 64, 65, 73, 79, 99, 105], "janna": [40, 64, 73, 79, 105], "jannasch": [3, 22, 23, 32, 37, 64, 73, 79, 99, 105], "januari": [17, 42, 47, 64, 68, 73, 79, 90, 99, 109], "japan": [17, 64, 73, 79, 99, 105], "jason": [2, 4, 5, 15, 17, 38, 42, 52, 64, 71, 78, 79, 86, 99, 105], "jatkinson1000": [47, 54, 68, 83], "java": [2, 11, 15, 17, 45, 47, 64, 68, 73, 79, 82, 100, 102], "jax": [54, 84, 97], "jc": [17, 29, 52, 64, 73, 79, 90, 101], "jcb": [11, 52, 96], "jcs262139": [17, 52, 79, 90, 101], "je": [0, 32, 64, 73, 79], "jean": [1, 2, 4, 5, 11, 17, 18, 29, 31, 37, 38, 41, 47, 48, 49, 52, 54, 55, 64, 65, 68, 71, 73, 78, 79, 82, 86, 90, 98, 99, 100, 105, 106], "jede": 75, "jedoch": 75, "jeffrei": [42, 47, 68, 91, 109], "jen": [0, 6, 23, 28, 35, 37, 42, 45, 46, 47, 48, 50, 52, 64, 68, 73, 76, 79, 82, 90, 92, 93, 96, 99, 100, 103, 105, 107], "jena": [45, 52, 71, 94, 108], "jenkinson": [48, 68, 82, 90, 103], "jennif": [39, 105], "jenswendt": [15, 17, 37, 47, 68, 78, 99, 105, 106], "jepson": [45, 52, 64, 94, 108], "jeremi": [1, 45, 52, 84, 86, 89, 103, 108], "jessica": [1, 15, 38, 52, 64, 73, 79, 105], "jeweil": [26, 54, 64, 73, 79, 105], "jeweilig": 75, "jh": [10, 29, 64, 73, 79], "jhenni": [4, 5, 7, 9, 36, 47, 68, 79], "jianxu": [52, 71, 90, 93, 107], "jing": [1, 15, 38, 64, 73, 79], "jlrumberg": [5, 48, 54, 68, 86, 89, 90], "jmb": [38, 52, 64, 86, 105], "jmi": [3, 8, 11, 29, 30, 33, 34, 37, 45, 52, 64, 79, 90, 94, 105, 108], "jn": [28, 47, 68, 84, 90, 100, 103], "jnguyenvan": [20, 47, 48, 54, 68, 86, 91], "jni": [48, 68, 78, 90, 103], "jo": [1, 28, 64, 73, 79], "joachim": [39, 40, 51, 69, 73, 86, 106], "joachimgoedhart": [40, 73], "joakim": [17, 18, 29, 50, 52, 65, 79, 96], "joanna": [42, 47, 54, 55, 58, 68, 83, 90, 109], "job": [39, 105], "jochen": [64, 73, 79], "joel": [4, 5, 7, 9, 30, 33, 36, 47, 52, 64, 68, 71, 73, 79, 87, 96], "johan": [11, 42, 78, 91, 103, 107, 108], "johann": [29, 47, 48, 52, 55, 62, 64, 68, 76, 79, 86, 89, 90, 97, 100, 103, 105], "johanna": [1, 4, 12, 14, 18, 30, 42, 48, 52, 64, 68, 71, 73, 76, 79, 84, 90, 105], "john": [1, 14, 15, 17, 18, 29, 38, 50, 52, 64, 65, 71, 73, 79, 92, 93, 96, 105], "join": [64, 73, 79], "joint": [64, 73, 79], "jointli": [44, 64, 73, 79, 105], "jokitalo": [4, 9, 29, 52, 79, 90], "jon": [17, 18, 29, 50, 52, 65, 79, 96], "jona": [11, 41, 47, 48, 54, 58, 63, 68, 79, 84, 86, 90, 103], "jonathan": [1, 11, 15, 38, 42, 64, 73, 79, 80, 101], "jone": [12, 17, 18, 29, 42, 47, 48, 50, 51, 52, 64, 65, 68, 69, 71, 78, 79, 80, 84, 90, 96], "joost": [15, 17, 37, 47, 52, 68, 71, 78, 79, 93, 96, 99, 105, 106], "jor": [45, 52, 94, 108], "joran": [12, 47, 68, 90], "jordan": [36, 45, 52, 54, 64, 73, 79, 94, 108], "jord\u00e3o": [47, 55, 58, 68, 90], "josef": [52, 71, 79, 92, 93, 96], "josh": [2, 3, 4, 5, 8, 11, 15, 18, 29, 30, 33, 34, 35, 37, 47, 50, 51, 52, 54, 64, 65, 68, 69, 71, 73, 77, 78, 79, 82, 90, 93, 96, 99, 100, 101, 102, 103, 105, 106, 109], "joshua": [0, 16, 23, 37, 45, 46, 47, 52, 64, 68, 73, 79, 90, 93, 99, 100, 103, 105], "journal": [1, 3, 11, 14, 15, 17, 37, 38, 42, 52, 54, 55, 64, 73, 78, 79, 90, 91, 93, 96, 101, 105], "json": [16, 35, 87, 99, 105], "juan": [40, 47, 48, 68, 78, 86, 89, 90, 103], "jubrail": [30, 33, 64, 73, 79], "judith": [4, 12, 18, 29, 30, 33, 52, 64, 71, 73, 76, 79, 87, 99, 105], "jug": [4, 18, 29, 30, 42, 47, 48, 50, 52, 56, 64, 68, 71, 72, 76, 79, 84, 86, 89, 90, 94, 103], "juglab": [12, 47, 68, 90], "jukkala": [12, 29, 42, 48, 64, 68, 71, 73, 79, 84, 90], "juli": [24, 29, 47, 54, 64, 68, 73, 79, 89, 90, 103], "julia": [0, 3, 4, 8, 10, 12, 16, 18, 23, 28, 29, 30, 32, 33, 34, 35, 37, 45, 46, 47, 52, 54, 56, 64, 68, 71, 72, 73, 76, 79, 87, 90, 93, 99, 100, 103, 105], "julian": [0, 4, 7, 9, 32, 36, 64, 73, 79, 80, 90, 98, 109], "julien": [17, 18, 25, 29, 31, 50, 52, 64, 65, 73, 79, 90, 96, 98], "juliu": [11, 18, 29, 41, 48, 54, 68, 86, 90, 98], "jupp": [64, 73, 79], "jupyt": [3, 19, 23, 29, 31, 39, 55, 64, 66, 73, 79, 87, 89, 97, 99, 105], "jurado": [55, 79, 90, 91], "jurenait": [29, 48, 68, 79, 103], "just": [54, 59, 60, 64], "juti": [1, 15, 38, 64, 73, 79], "jyoti": [11, 52, 64, 86, 91, 101, 105, 106], "j\u00fcngst": [64, 73, 79, 99, 105], "k": [10, 29, 64, 73, 79], "k40": [25, 31, 64, 73, 79], "kala": [50, 79, 90], "kala\u0161": [17, 18, 29, 50, 52, 65, 79, 96], "kaltschmidt": [10, 29, 64, 73, 79], "kamentski": [11, 52, 79, 105], "kamvar": [36, 54, 64, 73, 79], "kandpal": [0, 3, 8, 16, 23, 33, 34, 35, 37, 45, 46, 47, 50, 52, 54, 56, 64, 68, 72, 73, 79, 87, 90, 93, 99, 100, 103, 105], "kann": [26, 29, 54, 64, 73, 75, 79, 93, 105], "kapitel": [64, 73, 79], "kapoor": [4, 12, 18, 30, 52, 71, 76], "karaica": [17, 18, 29, 50, 52, 65, 79, 96], "karen": [3, 8, 23, 30, 33, 34, 37, 52, 64, 73, 79, 86, 99, 105], "kari": [36, 54, 64, 73, 79], "karim": [1, 15, 38, 64, 73, 79], "karin": [12, 29, 41, 42, 47, 48, 64, 68, 71, 73, 78, 79, 84, 86, 90, 103], "karinsasaki": [41, 48, 68, 86, 103], "karlsruh": [64, 73, 79], "karnbach": [1, 15, 38, 64, 73, 79], "karolin": [64, 73, 80], "karolina": [20, 64, 73], "karoun": [1, 15, 38, 64, 73, 79], "karreman": [17, 18, 29, 50, 52, 65, 79, 96], "katarzyna": [40, 44, 64, 73, 79, 105], "kate": [11, 52, 64, 73, 79, 86, 91, 101, 102, 105, 106], "kather": [12, 56, 72, 86, 89], "katherin": [17, 34, 42, 43, 52, 54, 71, 73, 79, 90, 93, 96, 105], "katherlab": [47, 48, 68, 83, 90], "katrin": [44, 64, 73, 79, 105], "katrina": [1, 15, 38, 64, 73, 79], "kedar": [7, 21, 38, 54, 56, 70, 79, 89, 90, 103], "keep": [11, 16, 35, 52, 64, 73, 79, 86, 90, 96], "kei": [1, 6, 7, 11, 15, 17, 28, 33, 34, 36, 38, 50, 54, 61, 103], "kein": 75, "keinen": 75, "keith": [10, 29, 64, 73, 79], "kekunn": [15, 47, 68, 82, 100], "kemmer": [1, 42, 52, 54, 55, 64, 73, 79, 90, 93, 105], "kenntni": 75, "keppler": [1, 14, 15, 38, 52, 64, 73, 79, 105], "kerstin": [40, 44, 52, 64, 73, 79, 105], "kevin": [2, 4, 5, 12, 15, 17, 18, 29, 30, 40, 42, 45, 48, 51, 52, 64, 65, 68, 69, 71, 76, 78, 79, 90, 97, 98, 99, 103, 105], "kevinyamauchi": [40, 45, 48, 79, 90, 97, 98, 103, 105], "keynot": [17, 54, 64, 73, 79, 105], "keyval_from_csv": [16, 35, 64, 73, 79], "keyword": [64, 73, 79], "khadak": [11, 52, 64, 86, 91, 101, 105, 106], "khan": [4, 7, 9, 11, 36, 55, 64, 73, 79, 84, 90, 98, 105, 107, 109], "kheop": [24, 31, 64, 73, 79], "kidnei": [30, 33, 79], "kiepa": [30, 33, 64, 73, 79], "kim": [1, 15, 38, 64, 73, 79], "kimberli": [42, 68], "kind": [29, 45, 47, 49, 59, 64, 68, 73, 79, 81, 83, 87, 89, 94, 100, 107], "king": [10, 29, 64, 73, 79], "kirk": [42, 52, 64, 78], "kirschmann": [17, 18, 29, 50, 52, 65, 79, 96], "kirti": [29, 52, 64, 76], "kit": [24, 31, 42, 49, 64, 73], "kitchen": [29, 54, 64, 73, 79, 90, 93, 95, 101, 105], "klara": [4, 12, 18, 30, 52, 71, 76], "klau": [4, 5, 7, 9, 36, 47, 68, 79], "klemeier": [1, 15, 38, 64, 73, 79], "klemm": [4, 9, 18, 29, 31, 41, 52, 54, 65, 68, 79, 86, 90, 98], "kleywegt": [38, 52, 64, 86, 105], "kneip": [64, 73, 79], "know": [59, 60, 64, 73], "knowledg": [16, 35, 44, 47, 48, 54, 55, 56, 68, 70, 78, 80, 90, 99, 101, 106], "knowledgebas": [42, 79, 105], "known": [3, 8, 23, 29, 33, 34, 35, 37, 45, 49, 54, 55, 56, 64, 72, 73, 79, 83, 94, 99, 100, 105, 107], "koala": [64, 73, 79], "kobe": [47, 55, 68, 79, 107], "koeckert": [3, 22, 23, 32, 37, 64, 73, 79], "koksoi": [4, 12, 18, 30, 52, 71, 76], "kommen": [40, 79, 101], "kommunik": [29, 54, 64, 73, 75, 79, 93, 95, 105], "komplex": [0, 32, 64, 73, 79], "kondata": [64, 73, 79], "konflikten": [29, 54, 64, 73, 79, 93, 105], "konkret": 75, "konkreten": [64, 73, 75, 79], "konrad": [29, 64, 73, 79, 99, 105], "kontaktdaten": 75, "kontaktstel": [6, 28, 37, 54, 64, 73, 79, 105], "kontext": [40, 79, 101], "kontrol": 75, "kop": [64, 73, 79], "korten": [29, 48, 68, 79, 103], "kostina": [64, 73, 79], "kostrykin": [55, 79, 90, 91], "kota": [4, 9, 10, 11, 12, 17, 29, 30, 31, 40, 41, 48, 50, 52, 54, 55, 64, 65, 68, 71, 73, 76, 79, 86, 90, 96, 98], "koushouu": [42, 58, 68, 90], "kozlowski": [2, 4, 5, 15, 17, 52, 71, 79, 99, 105], "kozubek": [11, 18, 54, 55, 68, 86, 90, 98], "kralj": [64, 73, 79], "krawczyk": [52, 64, 79, 90, 100, 107], "krempp": [54, 56, 72, 86, 89, 90], "krentzel": [12, 29, 42, 48, 64, 68, 71, 73, 79, 84, 90], "kreshuk": [7, 21, 29, 38, 41, 48, 52, 54, 56, 65, 70, 73, 79, 90, 98, 103], "kreshuklab": [5, 21, 48, 55, 68, 86, 89, 103], "kreutzer": [40, 79, 101], "kri": [47, 55, 68, 79, 107], "kristina": [17, 34, 54, 73, 79, 90, 93, 105], "krist\u00edna": [47, 54, 58, 63, 68, 79, 90], "kriterien": [64, 73, 79], "kroo\u00df": [3, 23, 37, 64, 73, 79, 99, 105, 109], "krull": [12, 42, 48, 64, 68, 71, 84, 90], "krzysztof": [47, 68, 78, 90], "kr\u00e1lovsk\u00e9": [20, 64, 73, 79], "ksenia": [3, 23, 37, 64, 73, 79, 99, 105, 109], "ktdzbgscyjq": [54, 56, 72, 86, 89, 90], "kuberek": [52, 79, 93, 105], "kugel": [32, 64, 73, 79], "kugler": [29, 39, 52, 66, 79, 90, 105, 106], "kulkarni": [12, 29, 47, 48, 68, 79, 90, 103], "kulturwandel": [40, 79, 101], "kundegorski": [48, 68, 83, 90], "kuni": [3, 8, 17, 23, 30, 33, 35, 37, 50, 52, 54, 56, 64, 71, 72, 73, 76, 79, 86, 87, 90, 93, 96, 99, 100, 105], "kur": [26, 54, 64, 73, 79, 105], "kutra": [4, 5, 9, 11, 21, 36, 38, 47, 54, 55, 56, 64, 68, 70, 73, 79, 84, 89, 90, 98, 103, 105, 107, 109], "kuzak": [11, 52, 79, 91, 93, 96], "kvpair": [42, 45, 86, 100], "kxhm4xht7uuvgwwvsu": [56, 72, 86], "kxp": [64, 73, 79], "kyoto": 25, "k\u00f6nig": [29, 64, 73, 79, 99, 105], "k\u00f6nnen": [26, 29, 54, 64, 73, 75, 79, 93, 95, 105], "k\u00f6nnten": [29, 64, 73, 79, 99, 105], "k\u00fcchengespr\u00e4ch": [54, 64, 73, 79, 105], "k\u00fchne": [26, 54, 64, 73, 79, 105], "k\u00fchnel": [64, 73, 79], "k\u00fcrzere": [64, 73, 79], "l": [1, 3, 8, 11, 12, 14, 15, 23, 29, 33, 34, 35, 37, 38, 42, 43, 48, 51, 52, 54, 55, 56, 64, 68, 69, 71, 72, 73, 78, 79, 84, 90, 91, 92, 93, 96, 99, 100, 105, 106], "la": [11, 24, 25, 31, 42, 46, 58, 64, 73, 79, 86, 90], "lab": [3, 6, 23, 24, 28, 29, 37, 44, 45, 54, 64, 90, 99, 105, 109], "label": [54, 56, 72, 86, 89, 90], "laboratori": [20, 24, 30, 31, 33, 64, 73, 79, 99], "laboratorio": [30, 33, 64, 73, 79], "laboratoryopticsbiosci": [64, 73, 79], "lack": [64, 73, 79, 99], "lacost": [4, 12, 18, 30, 33, 52, 64, 71, 73, 76, 79, 87], "lahmann": [40, 79, 101], "laia": [25, 31, 64, 73, 79], "laid": [1, 15, 38, 64, 73, 79], "lain": [12, 20, 29, 42, 47, 48, 54, 64, 68, 71, 73, 79, 84, 86, 90, 91], "lakner": [4, 18, 68, 86, 90], "lalit": [12, 47, 68, 90], "lampert": [39, 62, 66, 79, 90, 97, 103], "lancelotti": [1, 15, 38, 64, 73, 79], "landesdirekt": [22, 64, 73], "landmark": [64, 73, 79], "landscap": [64, 73, 79, 80], "landwehr": [64, 73, 79], "lang": [64, 73, 79, 80], "languag": [16, 35, 42, 45, 47, 68, 84, 94, 100, 108], "laps": [30, 31, 33, 64, 73, 79], "laptop": [3, 23, 28, 64, 73, 79, 99, 105], "larg": [0, 1, 3, 6, 8, 15, 23, 28, 30, 31, 33, 37, 38], "larger": [29, 31, 64, 73, 79], "largest": [64, 73, 99], "larva": [1, 28, 64, 73, 79], "laser": [24, 64, 73, 79], "lassi": [17, 18, 29, 50, 52, 65, 79, 96], "last": [3, 20, 23, 28, 64, 73, 79, 99, 105], "later": [32, 37, 64, 73, 79, 99, 105], "latest": [42, 44, 45, 49, 64, 73, 77, 84, 86, 94, 100], "lattic": [3, 22, 23, 32, 37, 64, 73, 79], "laud": [30, 33, 64, 73, 79, 87], "laufzeit": [40, 79, 101], "laur": [4, 12, 17, 18, 29, 30, 50, 52, 65, 71, 76, 79, 96], "laura": [17, 18, 25, 29, 31, 50, 52, 64, 65, 73, 79, 96], "laurent": [4, 12, 18, 30, 52, 64, 71, 73, 76, 79, 84], "laus": [47, 68, 78, 90], "lausann": [49, 76, 90], "lavaert": [47, 55, 68, 79, 107], "layer": [54, 56, 72, 86, 89, 90], "lazari": [11, 52, 79, 105], "ld": [16, 35, 64, 73, 79, 99, 105], "le": [4, 12, 18, 30, 52, 71, 76, 79, 93, 96, 105], "lead": [1, 14, 15, 29, 38, 39, 49, 57, 64, 73, 79, 86, 92, 93, 105], "leandro": [17, 18, 29, 50, 52, 65, 79, 96], "learn": [1, 3, 15, 20, 23, 25, 28, 30, 31, 33, 34, 36, 37, 40, 49, 52, 76, 80, 81, 91, 99, 101, 105, 106, 107, 109], "least": [64, 73, 79], "leav": [30, 33, 36, 54, 64, 73, 79], "lebenszyklu": [29, 54, 64, 73, 79, 105], "lectur": [3, 8, 23, 33, 34, 35, 37, 64, 78, 84, 87, 98, 99, 100, 105, 109], "lecture_applied_bioimage_analysi": [29, 54, 67, 90, 94], "lecture_imagej2_dev": [29, 54, 67, 78, 90, 94, 98], "lecture_multiview_registr": [29, 54, 67, 78, 90, 94, 98], "lecture_tracking_trackm": [29, 54, 67, 78, 90, 94, 98], "lecture_working_with_objects_in_2d_and_3d": [29, 54, 67, 78, 90, 94, 98], "lecture_working_with_pixel": [29, 54, 67, 78, 90, 94, 98], "led": [17, 34, 52, 54, 73, 79, 90, 93, 102, 105], "lee": [11, 52, 79, 105], "leenderts": [64, 73, 79], "left": [61, 64, 73, 79, 87], "legal": [0, 1, 15, 32, 38, 44, 52, 55, 64, 73, 79, 86, 93, 95, 101, 105], "legend": [64, 73, 80], "legland": [4, 9, 29, 52, 64, 73, 79, 90], "legra": [1, 15, 38, 64, 73, 79], "leibniz": [54, 64, 73, 79, 92, 105], "leica": [24, 29, 30, 31, 33, 64, 73, 79], "leicht": [26, 54, 64, 73, 79, 105], "leiden": [52, 71, 79, 93, 96, 105], "leipzig": [3, 6, 23, 26, 28, 37, 47, 48, 64, 68, 75, 87, 99, 105], "leistungsschutzrecht": [54, 64, 73, 79, 95, 105], "lemai": [11, 52, 64, 86, 91, 101, 105, 106], "lemair": [64, 73, 80], "lemk": [29, 64, 73, 79, 80], "len": [52, 71, 90, 93, 107], "lenz": [47, 48, 68, 83, 90], "leomil": [29, 48, 68, 79, 90, 97, 103], "leonid": [55, 79, 90, 91], "leopoldina": [64, 73, 79], "leplat": [42, 47, 48, 68, 86, 90, 102, 103], "lerch": [12, 29, 42, 48, 64, 68, 71, 73, 79, 84, 90], "lernen": [26, 29, 54, 64, 73, 79, 93, 95, 105], "leroi": [64, 73, 79], "less": [64, 73, 80], "lesson": [7, 11, 36, 54, 55, 90, 103], "let": [47, 59, 60, 64, 68, 73, 84], "leterri": [12, 42, 48, 64, 68, 71, 84, 90], "letort": [47, 68, 78, 89, 90], "leukocyt": [54, 56, 72, 86, 89, 90], "leuven": [22, 64, 73, 79], "level": [3, 23, 28, 33, 37, 44, 64, 73, 79, 99, 105], "leverag": [3, 33, 34, 37, 64, 73, 79, 99, 105], "levet": [11, 17, 18, 29, 50, 52, 54, 55, 65, 68, 79, 86, 90, 96, 98], "lewi": [11, 52, 64, 86, 91, 101, 105, 106], "leyla": [11, 52, 79, 91, 93, 96], "li": [47, 68, 81, 84], "liascript": [47, 55, 68, 79, 89, 107], "librari": [3, 8, 11, 15, 23, 24, 30, 33, 34, 37, 39, 42, 45, 61, 70, 82, 86, 91, 97, 99, 100, 103, 105, 106, 108], "librarian": [64, 73, 79], "lic": [30, 33, 64, 73, 79], "licens": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 28, 30, 31, 32, 33, 34, 35, 36, 37, 38, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 55, 56, 57, 58, 59, 61, 62, 63, 67, 68, 69, 70, 71, 72, 76, 77, 78, 80, 81, 82, 83, 84, 86, 89, 90, 91, 92, 94, 96, 97, 98, 99, 100, 101, 102, 103, 107, 108, 109], "lidayov\u00e1": [47, 54, 58, 63, 68, 79, 90], "lienhard": [44, 45, 55, 105], "lif": [30, 33, 64, 73, 79], "life": [6, 17, 28, 30, 33, 34, 37, 45, 47, 50, 51, 54, 55, 68, 69, 70, 71, 72, 78, 87, 92, 93, 109], "life_2022": [54, 68, 77, 90, 98], "lifecycl": [3, 8, 54, 64, 73, 79, 99, 105], "lifetim": [6, 54, 64, 73, 79, 90], "light": [17, 25, 30, 31, 33, 34, 38, 42, 50, 71, 76, 92, 93, 106], "lighter": [64, 73], "lightn": [29, 64, 73, 79], "lightsheet": [24, 64, 73, 79], "like": [0, 1, 16, 23, 24, 28, 29, 37, 45, 47, 49, 55, 59, 60, 64, 80, 83, 86, 87, 94, 95, 99, 100, 107], "likewis": [44, 64, 73, 79, 105], "lim": [30, 33, 64, 73, 79], "limi": [30, 33, 42, 48, 64, 73, 78, 90, 105], "limit": [29, 52, 60, 64, 73, 79, 93, 102, 105], "lindblad": [17, 18, 29, 50, 52, 65, 79, 96], "lindner": [2, 4, 5, 15, 17, 52, 71, 79, 99, 105], "lindst\u00e4dt": [64, 73, 80], "line": [1, 3, 25, 28, 31, 33, 34, 37, 47, 55, 62, 64, 73, 90, 99, 100, 105, 106], "link": [1, 24, 26, 28, 47, 52, 54, 59, 60, 68, 87], "linkact": [40, 90], "linkert": [2, 4, 5, 11, 15, 17, 47, 52, 68, 71, 79, 82, 96, 99, 102, 105], "linksortbi": [40, 90], "lipid": [24, 64, 73, 79], "lisa": [52, 71, 96], "lish": [24, 64, 73, 79], "list": [3, 6, 8, 22, 23, 29, 30, 33, 34, 35, 37, 45, 54, 55, 56, 57, 59, 60, 61, 64, 70, 72, 73, 76, 79, 81, 87, 90, 97, 99, 100, 103], "lister": [1, 15, 38, 64, 73, 79], "literaci": [64, 73, 80], "literaturhinweis": [26, 54, 64, 73, 79, 105], "liu": [4, 12, 18, 24, 30, 52, 64, 71, 73, 76, 79], "liv": [30, 33, 64, 73, 79], "live": [30, 33, 49, 57, 64, 73, 79, 92, 93, 105], "liverpool": [17, 30, 33, 34, 50, 64, 73, 79, 90, 93, 99, 105], "livio": [1, 15, 38, 64, 73, 79], "lizenz": 75, "lizenzbedingungen": 75, "lizenzierung": [26, 29, 54, 64, 73, 79, 93, 95, 105], "ljosa": [42, 43, 71], "ll": [55, 56, 70, 81], "llm": [29, 54, 64, 73, 79, 89, 103], "llopi": [4, 12, 18, 30, 33, 42, 52, 64, 71, 73, 76, 78, 79, 87, 96], "lmb": [48, 81, 90, 94], "lmu": [6, 54, 64, 73, 79, 90], "lnma": [30, 33, 64, 73, 79], "load": [3, 37, 64, 73, 79], "local": [1, 22, 28, 87, 108], "locat": [3, 29, 33, 34, 37, 60, 64, 73, 79, 99, 105], "loci": [64, 73, 79], "lociimport": [64, 73, 79], "loesung": [29, 54, 64, 73, 79, 93, 105], "loic": [11, 12, 29, 42, 48, 52, 64, 68, 71, 76, 84, 90, 94], "lokal": [54, 64, 73, 79, 105], "lombardot": [29, 54, 67, 68, 86, 90, 94], "long": [64, 73, 79, 99, 105], "longitudin": [22, 64, 73], "longo": [1, 15, 38, 64, 73, 79], "look": [29, 30, 33, 52, 64, 71, 73, 79, 87, 92, 93, 99, 105, 106], "lookup": [64, 73, 79], "lopez": [1, 28, 29, 64, 73, 79], "lorenzo": [24, 64, 73, 79], "lor\u00e1nd": [42, 47, 68, 90, 109], "lot": [29, 54, 64, 73, 79, 90, 93, 95, 101, 105], "louveaux": [10, 11, 18, 29, 31, 41, 48, 52, 54, 65, 68, 79, 86, 90, 98], "low": 31, "lower": [64, 73, 79], "lox": [30, 33, 64, 73, 79], "lo\u00efc": [47, 68, 79, 90], "lsen": [24, 64, 73, 79], "lsm": [30, 33], "lsm700": [64, 73, 79], "lsm710": [31, 64, 73, 79], "ltain19007": [20, 64, 73, 79], "ltima": [64, 73, 79], "ltp": [64, 73, 79], "luc": [1, 15, 38, 64, 73, 79], "luca": [4, 6, 12, 17, 18, 28, 30, 37, 42, 48, 51, 52, 54, 64, 68, 69, 71, 73, 76, 79, 84, 90, 103, 105], "luci": [11, 38, 42, 52, 64, 71, 78, 86, 96, 105], "lucil": [1, 28, 64, 73, 79], "ludwig": [1, 15, 38, 64, 73, 79], "luik": [47, 52, 64, 68, 79, 90, 100, 107], "luis": [1, 28, 64, 73, 79], "luka": [0, 16, 23, 37, 45, 46, 47, 52, 64, 68, 73, 79, 90, 93, 99, 100, 103, 105], "luke": [10, 29, 64, 73, 79], "luk\u00e1\u0161": [20, 64, 73], "lunch": [3, 37, 64, 73, 79], "lundberg": [29, 64, 73, 79], "luoresc": [64, 73, 79], "lutolf": [25, 31, 64, 73, 79], "l\u00e4sst": [0, 32, 64, 73, 79], "l\u00e9o": [42, 47, 48, 68, 86, 90, 102, 103], "l\u00f3pez": [29, 64, 73, 79], "l\u00f6schen": [54, 64, 73, 79, 105], "l\u00f6sungen": [26, 54, 64, 73, 79, 105], "l\u00fcckenlos": 75, "l\u00fcthi": [4, 5, 7, 9, 36, 47, 68, 79], "m": [0, 3, 10, 22, 23, 25, 29, 30, 31, 32, 33, 37, 42, 45, 46, 47, 48, 51, 52, 55, 64, 68, 69, 71, 73, 78, 79, 87, 90, 96, 99, 100, 103, 105, 107, 109], "m2": [29, 64, 73, 79], "macdonald": [29, 52, 79, 90], "machado": [24, 64, 73, 79], "machen": [40, 79, 101], "machin": [1, 3, 28, 33, 34, 37, 42, 47, 52, 64, 72, 93, 99, 105], "machinelearning_spaomworkshop_publ": [10, 54, 86, 89, 90], "maco": 61, "macro": [3, 16, 20, 25, 31, 35, 37, 47, 64, 73, 79, 82, 100], "macrophag": [30, 33, 64, 73, 79], "macrorunn": [64, 73, 79], "maddalena": [1, 15, 38, 64, 73, 79], "made": [10, 17, 20, 24, 29, 44, 52, 54, 64, 71, 73, 74, 79, 92, 93, 96, 105, 108], "madon": [1, 15, 38, 64, 73, 79], "maduni\u0107": [17, 18, 29, 50, 52, 65, 79, 96], "magnif": [29, 64, 73, 79], "mahdavian": [10, 29, 64, 73, 79], "mahmood": [12, 56, 72, 86, 89], "mai": [3, 5, 7, 11, 17, 21, 23, 28, 29, 30, 33, 34, 35, 37, 38, 42, 47, 48, 50, 52, 54, 55, 56, 64, 65, 68, 70, 72, 73, 78, 79, 80, 84, 86, 87, 89, 90, 99, 100, 103, 105], "maik": [40, 64, 73, 79, 105], "mail": 75, "main": [1, 15, 28, 29, 38, 47, 54, 55, 64, 68, 73, 79, 80, 84, 89, 90, 99, 100, 101, 103, 105, 107], "maintain": [1, 28, 42, 55, 64, 73, 79, 90], "mainten": [52, 73, 79, 102], "major": [16, 35, 54, 64, 73, 79, 108], "make": [1, 3, 8, 23, 25, 28, 29, 30, 31, 33, 34, 37, 39, 40, 42, 47, 51, 54, 56, 61, 65, 68, 69, 70, 80, 84, 86, 87, 99], "malchiodi": [20, 47, 48, 54, 68, 86, 91], "mallm": [3, 8, 23, 28, 64, 73, 79, 99, 105], "mamba": [19, 62], "man": [3, 26, 29, 33, 54, 64, 73, 79, 93, 99, 105], "manag": [0, 1, 2, 4, 5, 7, 15, 20, 24, 26, 38, 45, 46, 47, 51, 58, 60, 69, 70, 78, 81, 84, 87, 91, 92, 95, 97, 98, 106, 107, 108], "managementtom": [3, 23, 37, 64, 73, 79, 99, 105, 109], "managementv": [64, 73, 99], "manan": [12, 47, 68, 90], "mancini": [24, 31, 64, 73, 79], "mandatori": 59, "mani": [40, 44, 52, 64, 71, 73, 79, 80, 90, 93, 105, 107], "manifold": [29, 44, 64, 73, 79, 105], "manipul": [42, 56, 72, 79, 102], "manner": [54, 65, 79, 89, 90, 93], "mant": [21, 48, 68, 86, 89, 103], "manual": [3, 8, 23, 25, 29, 31, 33, 34, 35, 37, 42, 54, 56, 64, 72, 73, 99, 100, 105], "manual_elab_v0": [44, 45, 55, 105], "manufactur": [22, 30, 33, 64, 73, 79], "manuscript": [3, 30, 33, 37, 64, 73, 79], "manz": [2, 4, 5, 15, 17, 52, 71, 79, 99, 105], "maohan": [64, 73, 79], "map": [0, 16, 23, 37, 47, 64, 73, 79, 90, 93, 99, 105], "maplotlib": [13, 48, 68, 84, 103], "mar": [28, 54, 64, 73, 79, 105], "mara": [39, 62, 66, 79, 90, 97, 103], "mara_lampert": [19, 39, 62, 79, 103], "marc": [6, 28, 37, 45, 52, 54, 64, 71, 73, 79, 80, 94, 105, 108], "marcel": [47, 55, 68, 79, 107], "marcella": [17, 51, 52, 69, 79, 103], "marcello": [30, 33, 64, 73, 79, 87], "marcelo": [6, 28, 29, 37, 39, 48, 54, 62, 64, 68, 73, 79, 89, 90, 97, 103, 105], "marcelo_zoccol": [39, 62, 79, 89, 90, 103], "march": [47, 55, 68, 79, 107], "marco": [30, 33, 52, 64, 71, 73, 79, 87, 92, 93, 96], "marconato": [17, 51, 52, 69, 79, 103], "marcotti": [4, 5, 7, 9, 36, 47, 51, 52, 64, 68, 69, 73, 78, 79, 80, 90, 98, 109], "marcu": [64, 73, 79], "mare\u0161": [20, 64, 73, 79], "margareta": [3, 22, 23, 32, 37, 64, 73, 79], "margineanu": [6, 54, 64, 73, 79, 90], "mari": [1, 2, 4, 5, 11, 12, 17, 18, 29, 30, 37, 38, 47, 50, 52, 64, 65, 68, 71, 73, 76, 78, 79, 82, 90, 96, 99, 100, 105, 106], "maria": [1, 3, 15, 22, 23, 32, 37, 38, 64, 73, 79], "mariana": [4, 12, 18, 30, 52, 71, 76], "mariann": [42, 68], "marin": [64, 73, 79], "marina": [64, 73, 80], "marinalemair": [64, 73, 79], "mario": [64, 73, 79], "marion": [10, 11, 18, 29, 31, 41, 48, 52, 54, 65, 68, 79, 86, 90, 98], "marionlouveaux": [41, 68, 86, 90, 98], "marisc": [7, 11, 17, 18, 21, 29, 38, 41, 48, 50, 52, 54, 56, 64, 65, 68, 70, 73, 79, 86, 89, 90, 93, 96, 98, 103], "mark": [7, 11, 36, 45, 48, 52, 55, 64, 68, 71, 79, 82, 90, 92, 93, 94, 96, 103, 105, 108], "marker": [29, 64, 73, 79], "marku": [33, 45, 52, 64, 73, 79, 94, 108], "marqu\u00e9": [52, 71, 96], "marsikova": [29, 52, 64, 76], "martha": [64, 73, 79], "martin": [1, 3, 4, 5, 7, 9, 11, 12, 15, 17, 18, 22, 23, 25, 29, 30, 31, 32, 36, 37, 38, 42, 47, 48, 50, 51, 52, 54, 64, 65, 68, 69, 70, 71, 73, 76, 78, 79, 80, 82, 84, 86, 89, 91, 92, 93, 96, 98, 103, 106, 109], "martina": [12, 42, 48, 64, 68, 71, 84, 90], "mart\u00ednez": [52, 71, 79, 92, 93, 96], "marvin": [29, 42, 48, 68, 78, 79, 103], "marzia": [1, 15, 38, 64, 73, 79], "mask": [31, 79], "mask_filt": [25, 31, 64, 73, 79], "mass": [33, 64, 73, 79], "massachusett": [30, 33, 64, 73, 79], "massei": [1, 3, 6, 8, 16, 23, 29, 37, 47, 50, 54, 55, 64, 68, 73, 79, 84, 90, 92, 96, 99, 100, 103, 105], "massimi": [1, 15, 38, 64, 73, 79], "massiv": [64, 73, 79], "master": [10, 29, 54, 67, 86, 89, 90, 94], "mat": [42, 86, 105], "match": [14, 54, 55, 90, 93, 105], "materi": [6, 7, 9, 15, 17, 22, 38, 40, 44, 59, 60, 63, 67, 78, 80, 82, 83, 94, 101, 107, 109], "materialien": [40, 79, 101], "mateusz": [11, 52, 79, 91, 93, 96], "math": 64, "mathemat": [30, 33, 64, 73, 79], "mathia": [4, 11, 12, 18, 30, 52, 71, 76, 86, 90, 96], "mathur": [1, 4, 12, 14, 18, 30, 52, 54, 63, 64, 71, 73, 76, 79, 86, 105], "matser": [1, 15, 38, 64, 73, 79], "matt": [4, 5, 7, 9, 36, 47, 68, 79], "matteoni": [1, 15, 38, 64, 73, 79], "matter": [64, 73, 79, 99, 105], "mattern": [33, 64, 73, 79], "matthew": [4, 12, 17, 18, 30, 38, 52, 64, 71, 76, 79, 86, 90, 101, 105], "matthia": [1, 17, 18, 25, 28, 29, 31, 50, 52, 64, 65, 73, 79, 96], "matthij": [52, 71, 79, 93, 96, 105], "mattila": [12, 29, 42, 48, 64, 68, 71, 73, 79, 84, 90], "mattyrowei": [10, 29, 64, 73, 79], "matu": [50, 79, 90], "mat\u00fa\u0161": [17, 18, 29, 50, 52, 65, 79, 96], "maulakhan": [4, 5, 7, 9, 36, 47, 68, 79], "maven": [15, 47, 68, 82, 100], "maweigert": [21, 25, 48, 54, 68, 86, 89, 90, 98, 103], "max": [3, 33, 34, 37, 64, 73, 79, 99, 105], "maxim": [4, 55, 68, 86, 90, 98], "maximiliaan": [11, 52, 86, 90, 96], "maximilian": [6, 16, 28, 50, 64, 73, 79, 90, 92, 99, 105], "mayrhof": [1, 15, 38, 64, 73, 79], "mazzamuto": [11, 52, 79, 105], "mccormick": [4, 5, 7, 9, 36, 47, 68, 79], "mcelreath": [47, 56, 68, 72, 81, 86], "mcgill": [30, 33, 64, 73, 79], "mcherri": [24, 31, 64, 73, 79], "mcherry_3t_4z_5lambda": [24, 64, 73, 79], "mcnamara": [11, 42, 79, 101], "mcquad": [10, 29, 64, 73, 79], "md": [47, 55, 68, 79, 89, 107], "mdemic": [64, 73, 99], "mdpi": [52, 79, 101, 105], "me": [64, 73], "mea": [41, 48, 54, 68, 86, 90, 98], "mean": [1, 15, 29, 38, 64, 73, 79, 81, 99], "meaning": [9, 60, 64, 73, 79], "measur": [1, 3, 15, 22, 33, 38, 99, 105], "mechan": [17, 34, 36, 54, 56, 64, 72, 73, 79, 86, 89, 90, 93, 105], "med": [3, 22, 23, 30, 32, 33, 37, 54, 64, 73, 79, 90, 99, 105], "media": [2, 31, 42, 55, 56, 64, 73, 79, 86, 100], "medic": [1, 15, 29, 30, 33, 38, 75], "medicin": [3, 23, 30, 32, 37, 54, 90, 99, 105], "meechan": [42, 68], "meester": [3, 8, 11, 17, 33, 52, 65, 79, 99, 105], "meet": [5, 14, 50, 89, 93, 100], "mehmet": [25, 31, 64, 73, 79], "mehrer": [29, 54, 64, 73, 79, 93, 95, 105], "mehrnaz": [0, 16, 23, 37, 45, 46, 47, 52, 64, 68, 73, 79, 90, 93, 99, 100, 103, 105], "mehrten": [64, 73, 79], "mein": 26, "melbourneadvancedmicroscop2617": [42, 56, 72, 90], "melissa": [2, 4, 5, 11, 15, 17, 47, 52, 68, 71, 79, 82, 91, 93, 96, 99, 102, 105], "member": [3, 6, 8, 17, 22, 23, 32, 33, 36, 37, 40, 42, 48, 52, 54, 55, 56, 64, 65, 73, 78, 79, 90, 99, 105], "membran": [24, 64, 73, 79], "mengen": [64, 73, 79], "mention": 87, "mentor": [1, 64, 73, 79], "mercier": [24, 64, 73, 79], "merg": [29, 54, 60, 64, 73, 79, 93, 105], "messag": 60, "meta": [54, 59, 87, 90, 93, 99, 105], "metadata": [0, 1, 6, 8, 14, 17, 18, 20, 22, 23, 24, 29, 31, 32, 54, 65, 70, 80, 81, 87, 91], "metadaten": [64, 73, 79], "metajnl": [45, 52, 94, 108], "metastas": [25, 31, 64, 73, 79], "method": [3, 12, 14, 22, 29, 30, 33, 34, 37, 40, 42, 45, 47, 48, 55, 68, 72, 78, 83, 86, 89, 93, 94, 99, 100, 105, 107], "methoden": [29, 54, 64, 73, 79, 93, 95, 105], "methodolog": [52, 79, 93, 105], "methodologi": [64, 73, 79], "metric": [25, 31, 64, 69, 73, 79, 105], "metuarea": [48, 68, 86, 89, 90], "metz": [1, 45, 52, 84, 86, 89, 103, 108], "mexico": [30, 33, 64, 73, 79], "meysenburg": [7, 11, 36, 55, 79, 90, 103], "meystr": [64, 73, 79], "miacellavi": [30, 33, 64, 73, 79], "mianalysi": [45, 52, 64, 94, 108], "miao": [47, 68, 78, 90], "miap": [30, 33, 64, 73, 79], "mice": [22, 64, 73, 79], "michael": [1, 3, 4, 6, 9, 12, 15, 18, 23, 29, 30, 32, 34, 37, 38, 40, 42, 47, 52, 64, 68, 71, 73, 76, 79, 80, 86, 89, 90, 91, 99, 105, 109], "michaela": [1, 15, 38, 64, 73, 79], "michal": [11, 18, 54, 55, 68, 86, 90, 98], "michel": [0, 3, 8, 11, 16, 22, 28, 30, 32, 33, 34, 35, 37, 38, 45, 46, 47, 52, 54, 56, 64, 68, 71, 72, 73, 79, 86, 87, 90, 93, 96, 99, 100, 103, 105, 109], "micpy_workshop_2022": [13, 48, 68, 86, 90, 103], "micro": [5, 48, 54, 68, 84, 87, 89, 90, 103], "microcopi": [3, 22, 23, 32, 37, 64, 73, 79], "microfluid": [54, 56, 72, 86, 89, 90], "micron": [24, 25, 30, 31, 33, 64, 73, 79], "microscop": [0, 3, 24, 25, 29, 30, 31, 33, 45, 50, 52, 93, 94, 99, 105, 108], "microscopi": [5, 6, 17, 20, 24, 25, 31, 45, 47, 55, 78, 87, 97, 101, 102], "microscopia": [30, 33, 64, 73, 79], "microscopist": 76, "microsoft": [3, 22, 23, 32, 37, 64, 73, 79, 99, 105], "microsystem": [30, 33, 64, 73, 79], "might": [3, 16, 22, 23, 25, 31, 32, 35, 37, 64, 73, 79, 99, 105], "mihcsm": [52, 71, 79, 93, 96, 105], "mikautsch": [64, 73, 79], "mike": [12, 42, 48, 64, 68, 71, 84, 90], "mikolaj": [48, 68, 83, 90], "mikroskopi": [0, 32, 64, 73, 79], "milan": [29, 47, 48, 55, 58, 68, 78, 90], "mild": [24, 31, 64, 73, 79], "mileston": [52, 71, 90, 93, 107], "million": [52, 73, 79, 102], "mimic": [64, 73, 79], "mind": [16, 35, 64, 73, 79], "minim": [42, 48, 78, 90, 105], "minimis": [64, 73, 99], "minimum": [52, 71, 79, 93, 96, 105], "ministri": [20, 44, 64, 73, 79, 105], "minut": [31, 64, 73, 79], "miroscopi": [22, 64, 73, 79], "mirror": [64, 73, 79], "misiak": [45, 52, 94, 108], "misinterpret": [39, 86], "mission": [33, 42, 54, 57, 64, 79, 86, 90, 93, 99, 105], "misus": [22, 64, 73], "mit": [0, 4, 5, 7, 11, 12, 13, 28, 32, 40, 41, 42, 45, 47, 48, 55, 58, 68, 69, 71, 75, 89, 90, 95, 97, 100, 103, 107, 108], "mitarbeit": [29, 54, 64, 73, 79, 93, 105], "mitglied": [0, 32, 64, 73, 79], "mithilf": [26, 54, 64, 73, 79, 105], "mitot": [30, 33, 64, 73, 79], "miura": [4, 9, 10, 11, 12, 17, 29, 30, 31, 40, 41, 48, 50, 52, 54, 55, 64, 65, 68, 71, 73, 76, 79, 86, 90, 96, 98], "mm": [22, 64, 73, 79], "mmunofluoresc": [30, 33, 64, 73, 79], "modal": [29, 33, 64, 73, 79], "model": [1, 3, 8, 16, 22, 23, 28, 30, 33, 34, 35, 37, 38, 40, 45, 47, 56, 65, 81, 84, 99, 105, 108], "modern": [44, 45, 55, 105], "modif": [60, 64, 73, 79, 99, 105], "modifi": [29, 60, 64, 73, 79], "modul": [3, 23, 28, 47, 49, 54, 55, 64, 68, 73, 79, 81, 84, 90, 99, 100, 103, 105, 107], "modular": [45, 52, 94, 108], "moehl": [11, 15, 47, 68, 82, 100], "moham": [0, 16, 23, 37, 45, 46, 47, 52, 64, 68, 73, 79, 90, 93, 99, 100, 103, 105], "mohsen": [6, 16, 28, 33, 50, 64, 73, 79, 90, 92, 99, 105], "molecul": [1, 3, 28, 33, 64, 73, 79, 99, 105], "molecular": [30, 33, 64, 73, 79], "molnar": [41, 48, 54, 68, 86, 90, 98], "moman": [3, 33, 64, 73, 79, 99, 105], "moment": [26, 29, 54, 64, 73, 79, 105], "mondai": [3, 33, 64, 73, 79, 99, 105], "monica": [0, 16, 23, 37, 45, 46, 47, 50, 52, 64, 68, 73, 79, 90, 93, 99, 100, 103, 105], "monika": [52, 79, 93, 105], "moniqu": [51, 52, 69, 79, 90, 105], "monko": [45, 47, 55, 68, 78, 90, 97], "montero": [4, 12, 18, 30, 33, 42, 52, 64, 71, 73, 76, 78, 79, 87, 96], "monthli": [64, 73, 79], "montpelli": [30, 33, 64, 73, 79], "montr": [30, 33, 64, 73, 79], "montreal": [30, 33, 64, 73, 79], "moor": [0, 2, 3, 4, 5, 8, 11, 15, 16, 18, 23, 29, 30, 33, 34, 35, 37, 45, 46, 47, 50, 51, 52, 54, 64, 65, 68, 69, 71, 73, 77, 78, 79, 82, 90, 93, 96, 99, 100, 101, 102, 103, 105, 106, 109], "mor": [29, 64, 73, 79], "more": [1, 3, 6, 9, 17, 28, 29, 30, 33, 34, 37, 39, 42, 45, 48, 49, 54, 55, 59, 64, 73, 78, 79, 83, 86, 90, 93, 94, 99, 100, 105, 106, 107], "moreov": [3, 29, 37, 64, 73, 79], "morgan": [5, 48, 54, 68, 86, 89, 90], "moritz": [0, 16, 17, 18, 23, 29, 37, 45, 46, 47, 50, 51, 52, 64, 65, 68, 69, 73, 79, 90, 93, 96, 99, 100, 103, 105], "moshkov": [40, 68, 86, 89, 90], "most": [42, 54, 55, 56, 64, 70, 73, 105, 106], "mostli": [13, 15, 42, 47, 48, 60, 64, 68, 70, 73, 79, 82, 84, 91, 97, 99, 100, 103], "moth": [64, 73, 79], "motion": [64, 73, 79], "motivationen": [64, 73, 79], "mous": [24, 25, 29, 30, 31, 33, 79], "move": [3, 23, 28, 37, 64, 73, 79, 99, 105], "mpg": [54, 64, 73, 79, 90, 93, 99, 105], "mpi": [78, 98], "mpicbg": [29, 68, 90, 94], "mraz\u00edk": [20, 64, 73], "mrc": [48, 81, 90, 94], "mri": [30, 33, 64, 73, 79], "mrx": [2, 11, 17, 47, 68, 82, 102], "mtiworkshop": [47, 54, 58, 63, 68, 79, 90], "mu": [30, 33, 64, 73, 79], "much": [17, 47, 64, 68, 73, 79, 81, 99, 105], "mueller": [3, 22, 23, 32, 37, 64, 73, 79, 99, 105], "multi": [24, 28, 31, 87], "multidisciplinari": [54, 64, 73, 105], "multimod": 99, "multin": [20, 64, 73, 79], "multipl": [17, 30, 33, 50, 52, 59, 64, 71, 73, 79, 90, 93, 96, 105], "multiresolut": [24, 31, 64, 73, 79], "multiview": [29, 54, 67, 90, 94, 98], "munck": [17, 18, 29, 50, 52, 65, 79, 96], "munich": [47, 55, 64, 68, 73, 79, 84], "musacchio": [42, 90, 97, 103], "musen": [52, 64, 71, 73, 79, 92, 93, 96], "must": [3, 23, 28, 37, 44, 59, 99, 105], "mutli": [24, 64, 73, 79], "mutual": [9, 64, 73, 79], "mutunduw": [10, 29, 64, 73, 79], "muylderman": [47, 55, 68, 79, 107], "mu\u00f1oz": [10, 17, 18, 29, 50, 52, 64, 65, 73, 79, 96], "mx": [30, 33, 64, 73, 79], "myat": [10, 29, 64, 73, 79], "myer": [12, 29, 50, 52, 64, 73, 79, 94], "myle": [52, 64, 71, 79, 93, 105], "myocardium": [30, 33, 64, 73, 79], "m\u00e1hlerova": [20, 64, 73], "m\u00e9nager": [17, 18, 29, 50, 52, 65, 79, 96], "m\u00f6chten": [26, 54, 64, 73, 79, 105], "m\u00f6glich": 75, "m\u00f6glichkeit": [0, 32, 64, 73, 79], "m\u00f6glichkeiten": [52, 64, 79, 105], "m\u00f6hl": [0, 16, 23, 37, 45, 46, 47, 52, 64, 68, 73, 79, 90, 93, 99, 100, 103, 105], "m\u00f6ller": [45, 52, 94, 108], "m\u00fchlhau": [0, 50, 73, 79, 93, 105], "m\u00fcller": [6, 16, 24, 28, 29, 31, 48, 50, 54, 68, 90, 92, 97, 99, 103, 105], "m\u00fcnchen": [6, 54, 64, 73, 79, 90], "m\u00fcnster": [16, 35, 52, 64, 73, 76, 79, 99, 100, 105], "m\u00fcssen": [26, 54, 64, 73, 79, 93, 105], "n": [3, 8, 17, 23, 24, 25, 30, 31, 33, 34, 37, 50, 64, 73, 79, 90, 93, 99, 105], "na": [29, 64, 73, 79], "nach": [0, 29, 32, 54, 64, 73, 79, 93, 105], "nachfolgenden": [0, 32, 64, 73, 79], "nachhaltigkeit": [64, 73, 79], "nacion": [30, 33, 64, 73, 79], "nadia": [4, 12, 18, 30, 52, 71, 76], "nadin": [64, 73, 79], "nagi": [0, 32, 64, 73, 79], "naji": [29, 64, 73, 79], "name": [3, 6, 20, 22, 23, 24, 25, 28, 30, 31, 32, 33, 37, 54, 59, 61, 64, 73, 75, 79, 99, 105], "nanguag": [45, 84, 89, 103, 108], "nanoparticl": [30, 33, 64, 73, 79], "nanosecond": [64, 73, 79], "napari": [29, 40, 45, 47, 56, 72, 83, 98, 105], "nar": [64, 73, 79], "narayan": [7, 21, 38, 54, 56, 70, 79, 89, 90, 103], "narg": [10, 29, 64, 73, 79], "nasim": [42, 52, 64, 68, 78, 90], "nat": [14, 54, 55, 64, 73, 79, 90, 93, 105], "nata\u0161a": [17, 18, 29, 50, 52, 65, 79, 96], "nathali": [4, 12, 18, 24, 30, 31, 52, 64, 71, 73, 76, 79], "nation": [1, 11, 65, 87], "national": [6, 54, 64, 73, 79, 99, 105], "nationalen": [29, 99, 105], "nativ": [17, 34, 50, 54, 73, 79, 90, 93, 105], "natur": [2, 3, 4, 5, 11, 12, 15, 17, 18, 23, 28, 29, 30, 33, 34, 37, 38, 42, 43, 45, 47, 48, 52, 64, 68, 73, 76, 78, 79, 84, 86, 90, 92, 93, 94, 96, 99, 105, 106, 107, 108], "navaroli": [30, 33, 64, 73, 79], "navig": [9, 61], "nbi": [0, 16, 23, 37, 47, 73, 79, 90, 93, 99, 105], "nbsp": [1, 15, 24, 30, 33, 38, 64, 73, 79], "nc": [12, 29, 40, 42, 47, 49, 52, 56, 68, 71, 73, 90, 93, 94, 100, 107], "ncbi": [3, 11, 22, 23, 32, 37, 38, 52, 64, 71, 73, 79, 86, 90, 92, 93, 96, 99, 105], "ncl": [30, 33, 64, 73, 79], "nd": [22, 40, 42, 48, 56, 64, 68, 73, 79, 90, 97, 103], "ndev": [45, 47, 55, 68, 78, 90, 97], "nearli": [24, 64, 73, 79], "necessari": [3, 8, 16, 23, 28, 29, 33, 35, 61, 64, 73, 79, 80, 99, 105], "necessarili": [64, 73, 79], "neckel": [10, 29, 64, 73, 79], "need": [3, 8, 11, 17, 23, 28, 29, 30, 31, 33, 34, 37, 38, 42, 48, 55, 56, 57, 65, 70, 71, 73, 74, 79, 80, 81, 86, 87, 96, 99, 105], "nehm": [12, 29, 42, 48, 64, 68, 71, 73, 79, 84, 90], "nelson": [4, 11, 12, 18, 30, 33, 48, 51, 52, 64, 68, 69, 71, 73, 76, 78, 79, 83, 87, 90], "neringa": [29, 48, 68, 79, 103], "nervou": [29, 64, 73, 79], "nestor": [40, 64, 73, 79, 105], "net": [24, 31, 42, 43, 44, 45, 55, 64, 73, 79, 86, 90, 105], "netherland": [52, 71, 79, 93, 96, 105], "netlifi": [40, 55, 79, 84, 105, 106], "network": [3, 6, 16, 23, 28, 33, 34, 35, 37, 52, 54, 71, 76, 93, 96, 99, 100, 105], "netzwerk": [0, 32, 64, 73, 79], "neubia": [5, 7, 9, 13, 36, 40, 47, 63, 64, 67, 73, 76, 77, 94, 97, 105, 109], "neubias2018_ts7": [41, 68, 86, 90, 98], "neubias_academy_biapi": [48, 68, 78, 90, 98, 103], "neubias_academy_stardist": [21, 25, 48, 54, 68, 86, 89, 90, 98, 103], "neubias_analystschool2018": [41, 48, 54, 68, 86, 90, 98], "neubias_analystschool2019": [11, 18, 29, 41, 48, 54, 68, 86, 90, 98], "neubias_analystschool2020": [10, 11, 18, 41, 48, 54, 68, 86, 90, 98], "neubias_bioimage_analyst_course2017": [11, 18, 54, 55, 68, 86, 90, 98], "neubias_chapter_dl_2020": [10, 54, 68, 86, 89, 90], "neue": [29, 54, 64, 73, 79, 93, 105], "neumann": [40, 64, 73, 79, 105], "neurit": [64, 73, 79], "neuro": [30, 33, 64, 73, 79], "neurobia": [4, 5, 7, 9, 36, 47, 68, 79], "neurofila": [29, 64, 73, 79], "neuroglanc": [0, 16, 23, 37, 47, 73, 79, 90, 93, 99, 105], "neuroimag": [0, 11, 50, 52, 73, 79, 93, 105], "neuroinformat": [42, 55, 56, 79], "neurosci": [11, 30, 33, 42, 52, 55, 56, 64, 73, 79, 105], "nevertheless": [3, 33, 34, 37, 64, 73, 79, 99, 105], "new": [1, 3, 10, 14, 17, 22, 23, 25, 28, 29, 31, 32, 37, 38, 52, 54, 55, 60, 61, 90, 93, 99, 105, 109], "newbi": [64, 73, 79], "newcastl": [30, 33, 64, 73, 79], "newli": [3, 6, 17, 23, 32, 34, 37, 40, 50, 54, 64, 73, 79, 93, 99, 105], "next": [24, 31, 42, 56, 60, 72, 82, 100, 102], "nf": [41, 42, 68, 86, 103], "nfdi": [3, 8, 17, 23, 28, 33, 37, 50, 54, 80, 87, 90, 93], "nfdi46": [64, 73, 79], "nfdi4bioimag": [1, 2, 4, 5, 15, 30, 32, 35, 55, 56, 57, 59, 71, 72, 76, 78, 86, 92, 96, 106, 109], "nfdi4biomag": [64, 73, 79], "nfdi4plant": [42, 79, 105], "ngff": [11, 42, 56, 72, 100], "nichola": [10, 29, 64, 73, 79], "nicht": [26, 29, 54, 64, 73, 75, 79, 93, 105], "nick": [1, 15, 33, 38, 64, 73, 79], "nicola": [4, 7, 9, 31, 36, 40, 47, 52, 55, 64, 68, 73, 79, 90, 98, 103, 105, 107, 109], "nicol\u00e1": [17, 18, 29, 50, 52, 65, 79, 96], "night": [20, 64, 73], "nih": [3, 11, 22, 23, 32, 37, 38, 51, 52, 64, 68, 69, 71, 73, 78, 79, 86, 90, 92, 93, 96, 99, 105], "nikita": [40, 68, 86, 89, 90], "nikla": [1, 15, 38, 64, 73, 79], "nikola": [12, 56, 72, 86, 89], "nikon": [30, 33, 64, 73, 79], "nil": [4, 24, 52, 64, 79, 90, 105], "nima": [4, 5, 7, 9, 36, 47, 64, 68, 73, 79, 90, 98, 109], "nina": [0, 32, 64, 73, 79], "niraj": [0, 3, 8, 16, 23, 33, 34, 35, 37, 45, 46, 47, 50, 52, 54, 56, 64, 68, 72, 73, 79, 87, 90, 93, 99, 100, 103, 105], "nitschk": [3, 4, 8, 12, 17, 18, 23, 33, 34, 35, 37, 52, 64, 71, 73, 76, 79, 87, 99, 105], "nl": [47, 58, 68, 90, 100, 105], "nlm": [3, 11, 22, 23, 32, 37, 38, 52, 64, 71, 73, 79, 86, 90, 92, 93, 96, 99, 105], "nlp_cours": [47, 68, 84], "nm": [24, 31, 64, 73, 79], "nmeth": [42, 43, 52, 71], "nno": [29, 64, 73, 79], "no_npi": [25, 31, 64, 73, 79], "nobi": [64, 73, 79], "nobst": [3, 22, 23, 32, 37, 64, 73, 79, 99, 105], "nodar": [7, 21, 38, 54, 56, 70, 79, 89, 90, 103], "node": [52, 71, 79, 93, 96, 105], "noe": [10, 29, 64, 73, 79], "noi": [64, 73, 79], "nois": [24, 64, 73, 79], "non": [42, 48, 64, 73, 78, 79, 90, 105], "none": [28, 29, 42, 45, 47, 48, 49, 52, 54, 55, 56, 58, 64, 68, 72, 79, 83, 84, 90, 94, 100, 102, 103, 107], "norlin": [4, 24, 52, 64, 79, 90, 105], "normal": [64, 73, 79], "norman": [15, 17, 37, 47, 68, 78, 99, 105, 106], "normox": [29, 64, 73, 79], "north": [4, 12, 18, 30, 33, 52, 64, 71, 73, 76, 79], "northan": [29, 48, 68, 79, 103], "note": [3, 17, 22, 23, 25, 29, 31, 32, 37, 64, 73, 79, 99, 105], "notebook": [3, 5, 6, 10, 11, 12, 13, 18, 21, 23, 25, 29, 31, 37, 41, 46, 52, 54, 55, 58, 62, 69, 71, 80, 81, 82, 83, 86, 89, 94, 97, 98, 99, 102, 105], "noth": [47, 68, 81], "novel": [1, 3, 8, 28, 54, 56, 64, 72, 73, 79, 86, 89, 90, 99, 105], "novo": [3, 33, 34, 37, 64, 73, 79, 99, 105], "now": [1, 14, 16, 17, 35, 50, 60, 64, 73, 79, 90, 105], "nowadai": [3, 33, 64, 73, 79, 99, 105], "nr": [30, 33, 64, 73, 79], "nrg": [24, 64, 73, 79], "nsch": [30, 33, 64, 73, 79], "nsg": [25, 31, 64, 73, 79], "nuclei": [24, 28, 47, 84, 100], "nuclei_from_bf": [25, 31, 64, 73, 79], "nuclei_from_dpc": [25, 31, 64, 73, 79], "nuclei_from_sqrdpc": [25, 31, 64, 73, 79], "nucleic": [64, 73, 79], "nucleom": [30, 33, 64, 73, 79], "nucleu": [30, 33, 64, 73, 79], "null": [24, 64, 73, 79], "nullpointerexcept": [64, 73, 79], "number": [6, 20, 22, 28, 37, 39, 54, 64, 73, 79, 86, 105], "numer": [28, 50, 64, 79, 90], "numpi": 84, "numpypandas_cours": [13, 48, 68, 78, 103], "nun": [40, 79, 101], "nunez": [30, 33, 47, 48, 64, 68, 73, 78, 79, 90, 103], "nur": [29, 54, 64, 73, 79, 93, 105], "nutzer": [54, 64, 73, 79, 105], "nutzt": [0, 32, 64, 73, 79], "nutzung": [0, 32, 64, 73, 75, 79], "nutzungsm\u00f6glichkeiten": [57, 86, 99, 105], "nutzungsrechten": [26, 54, 64, 73, 79, 105], "nvidia": [25, 31, 64, 73, 79], "nyffel": [1, 28, 64, 73, 79], "nyula": [64, 73, 79], "n\u00e4her": [29, 54, 64, 73, 79, 105], "n\u00f8rrelykk": [11, 25, 31, 42, 46, 58, 86, 90], "n\u00fcst": [29, 64, 73, 79, 99, 105], "n\u00fctzlichen": [26, 54, 64, 73, 79, 105], "o": [42, 47, 48, 52, 64, 68, 71, 73, 79, 86, 90, 92, 93, 96, 102, 103, 105], "oa": [40, 64, 73, 79, 101], "oan": [4, 5, 7, 9, 36, 47, 68, 79], "object": [1, 3, 6, 8, 9, 17, 20, 23, 24, 25, 28, 31, 33, 34, 35, 36, 37, 50, 56, 72, 93, 99, 100, 105], "observ": [9, 29, 30, 33, 52, 64, 73, 79, 87, 102], "observatori": [52, 71, 79, 93, 96, 105], "obtain": [1, 22, 25, 28, 31, 64, 73, 79], "oct": [58, 90, 100, 105], "octob": [17, 29, 47, 48, 54, 55, 68, 78, 90, 100], "od": [64, 73, 80], "odc": [42, 90], "odebrecht": [40, 64, 73, 79, 105], "oder": [26, 40, 44, 75, 101], "oer": [64, 73, 79], "oezdemir": [4, 7, 9, 36, 55, 64, 73, 79, 86, 90, 98, 109], "offen": [40, 101], "offer": [1, 6, 11, 16, 28, 29, 42, 44, 47, 50, 52, 54, 55, 64, 68, 71, 73, 79, 84, 86, 90, 91, 92, 93, 95, 96, 99, 103, 105], "offic": [3, 20, 22, 23, 32, 37, 46, 64, 73, 79, 90, 99, 105], "ofra": [17, 18, 29, 31, 50, 52, 65, 79, 90, 96, 98], "oft": [26, 54, 64, 73, 79, 93, 105], "often": [3, 8, 23, 28, 29, 33, 34, 35, 37, 44, 54, 56, 64, 72, 73, 79, 87, 99, 100, 105], "oh3vki7era": [56, 72], "ohmann": [1, 15, 38, 64, 73, 79], "ohn": 75, "oil": [24, 31, 64, 73, 79], "okafornta": [3, 22, 23, 32, 37, 64, 73, 79, 99, 105], "okazaki": [17, 64, 73, 79, 99, 105], "ola": [48, 68, 83, 90], "oladazimi": [64, 73, 79], "olaf": [4, 12, 18, 30, 52, 71, 76], "old": [3, 22, 23, 32, 37, 64, 73, 79, 99, 105], "older": [16, 35, 64, 73, 79], "oleg": [52, 64, 79, 93, 105], "olga": [20, 64, 73, 79], "oliv": [4, 5, 7, 9, 12, 17, 36, 42, 47, 48, 51, 52, 64, 68, 69, 71, 73, 79, 84, 90, 98, 103, 109], "olivi": [11, 21, 24, 31, 42, 46, 48, 54, 58, 64, 68, 73, 79, 86, 89, 90, 98, 103], "olympu": [25, 29, 30, 31, 33, 64, 73, 79], "om": [1, 3, 8, 22, 23, 24, 28, 30, 31, 32, 33, 34, 35, 37, 50, 54, 73, 78, 82, 93, 106], "om9tl4dh2yw": [12, 56, 72, 86, 89], "omero": [0, 1, 2, 6, 29, 30, 46, 80, 83, 87, 101, 109], "omero_batch": [47, 68, 82, 94, 100], "omero_script": [39, 62, 79, 89, 90, 103], "omfreder": [47, 54, 58, 63, 68, 79, 90], "omic": [11, 64, 73, 86, 91, 101, 105, 106], "omp2info": [24, 64, 73, 79], "onbi_image_analysi": [48, 68, 82, 90, 103], "onc": 61, "one": [3, 8, 14, 23, 28, 29, 31, 33, 34, 35, 37, 49, 54, 55, 56, 64, 72, 73, 79, 81, 86, 90, 99, 100, 105, 107], "ones": [64, 73, 79], "ongo": [3, 8, 23, 28, 52, 64, 71, 73, 79, 92, 93, 99, 105, 106], "onli": [1, 22, 24, 28, 52, 60, 64, 71, 73, 79, 80, 88, 90, 93, 99, 107], "onlin": [3, 4, 5, 9, 14, 29, 45, 47, 55, 56, 57, 65, 68, 70, 73, 76, 77, 80, 81, 83, 87, 89, 91, 92, 94, 100, 101, 102, 106, 107], "onlinelibrari": [3, 4, 8, 9, 11, 29, 30, 33, 34, 37, 52, 79, 90, 105], "onlineplatform": [29, 54, 64, 73, 79, 93, 105], "ontologi": [3, 8, 14, 16, 23, 28, 35, 37, 54, 55, 64, 73, 93, 99, 105], "ontologieentwicklung": [64, 73, 79], "oop_titl": [40, 90], "open": [1, 3, 8, 14, 20, 22, 23, 25, 28, 30, 31, 32, 33, 34, 35, 37, 38, 44, 45, 47, 49, 55, 57, 61, 70, 77, 80, 82, 89, 91, 92, 94, 99, 106, 108], "openai": [29, 64, 73, 79], "opendatahandbook": [11, 42, 79, 101], "openli": [3, 8, 23, 29, 33, 34, 35, 37, 39, 54, 56, 60, 64, 72, 73, 79, 86, 90, 93, 95, 99, 100, 101, 105, 106], "openlink": [64, 73, 99], "openmicroscopi": [24, 42, 43, 45, 46, 52, 64, 71, 73, 79, 100, 105], "openmicroscopyenviron": [42, 56, 72, 79, 100, 102], "openresearchsoftwar": [45, 52, 94, 108], "opensci": [57, 86, 105], "oper": [3, 8, 23, 28, 31, 64, 73, 99, 105], "operetta": 31, "operetta_import": [31, 64, 73, 79], "opportun": [3, 33, 34, 37, 64, 73], "opt": [64, 73, 79], "optic": [22, 30, 33], "optim": [3, 37, 54, 92, 93], "option": [3, 6, 10, 20, 23, 28, 29, 37, 54, 59, 64, 73, 89, 99, 105], "oral": [3, 33, 64, 73, 79, 99, 105], "orchard": [11, 52, 64, 86, 91, 101, 105, 106], "order": [22, 25, 31, 33, 44, 52, 64, 71, 73, 79, 92, 93, 99, 105, 106], "oresti": [30, 33, 64, 73, 79, 87], "org": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 17, 20, 22, 23, 24, 25, 26, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 43, 44, 45, 46, 47, 48, 49, 50, 54, 55, 56, 65, 66, 68, 70, 71, 72, 76, 78, 80, 81, 83, 84, 86, 87, 89, 91, 92, 93, 94, 95, 96, 98, 99, 100, 101, 102, 103, 106, 107, 108, 109], "organ": [1, 3, 6, 8, 11, 20, 22, 23, 28, 30, 33, 34, 35, 37, 52, 54, 55, 56, 57, 64, 72, 80, 86, 90, 92, 99, 100], "organigram_team": [30, 33, 64, 73, 79], "organis": [1, 15, 38, 42, 55, 56, 70, 81], "organisatorisch": [64, 73, 79], "organisatorischen": [64, 73, 79], "organoid": [30, 33, 64, 73, 79], "orient": [3, 37, 64, 73, 79, 80], "origin": [3, 20, 55, 86], "orign": [22, 64, 73, 79], "orkg": [64, 73, 79], "osman": [7, 21, 38, 54, 56, 70, 79, 89, 90, 103], "osnabr\u00fcck": [3, 33, 34, 37, 64, 73, 79, 99, 105], "oss": [52, 73, 79, 102], "other": [1, 3, 8, 11, 17, 20, 23, 28, 29, 33, 34, 37, 42, 44, 47, 50, 52, 54, 55, 56, 60, 64, 67, 68, 70, 73, 74, 79, 81, 86, 87, 90, 91, 93, 94, 96, 99, 101, 105, 106, 109], "otherwis": [3, 22, 23, 32, 37, 64, 73, 79, 87, 99, 105], "oup": [45, 52, 103, 108], "our": [1, 3, 9, 28, 30, 32, 33, 40, 55, 56, 60, 61, 64, 70, 73, 74, 81, 87, 88, 99], "out": [3, 23, 29, 32, 37, 64, 73, 79, 99, 105], "outcom": [44, 64, 73, 79, 105], "outlin": [1, 6, 14, 28, 29, 42, 52, 54, 55, 64, 65, 73, 76, 79, 89, 90, 93, 98, 99, 103, 105], "outlook": [3, 37, 64, 73, 79], "output": [25, 31, 52, 64, 73, 79, 102], "outreach": [64, 73, 79, 99, 105], "ouyang": [1, 11, 29, 45, 52, 64, 68, 73, 79, 84, 86, 89, 103, 108], "ovari": [30, 33, 64, 73, 79], "over": [14, 42, 52, 54, 55, 64, 73, 79, 90, 93, 102, 105, 108], "overcom": [3, 8, 23, 28, 64, 73, 79, 99, 105], "overnight": [24, 31, 64, 73, 79], "overview": [3, 4, 5, 6, 9, 11, 14, 16, 17, 20, 29, 30, 33, 37, 51, 52, 54, 56, 65, 71, 86, 89, 90, 91, 92, 101, 106, 107], "own": [3, 8, 23, 33, 34, 35, 37, 54, 56, 64, 72, 73, 79, 99, 100], "oxford": [48, 64, 68, 73, 82, 90, 103], "oxinst": [42, 56, 76, 90], "oxncaxrqu4xqdujzwfhvwlf06pvdvm": [42, 56, 72, 79, 90, 105], "oyi": [42, 56, 72, 79, 90, 105], "p": [1, 3, 4, 10, 12, 18, 23, 25, 29, 30, 31, 37, 40, 52, 54, 63, 64, 65, 68, 71, 73, 76, 79, 86, 90, 98, 99, 103, 105, 109], "p1": [47, 54, 58, 63, 68, 79, 86, 90], "pa": [44, 105], "paavolainen": [17, 18, 29, 50, 52, 65, 79, 96], "packag": [13, 47, 48, 49, 68, 77, 84, 89, 90, 94, 100, 108], "packet": [11, 15, 47, 64, 68, 73, 82, 100], "page": [17, 29, 47, 57, 60, 61, 64, 68, 73, 77, 79, 87, 99, 105, 109], "pair": [1, 28], "palagi": [51, 52, 69, 79, 90, 105], "paland": 52, "palett": [39, 86], "palla": [17, 47, 51, 52, 68, 69, 78, 79, 90, 103], "palli": [47, 68, 78, 90], "palmquist": [7, 11, 36, 42, 55, 68, 79, 90, 103], "pan": [29, 64, 73, 79], "panagiotopoul": [1, 15, 38, 64, 73, 79], "pancreat": [54, 56, 72, 86, 89, 90], "panda": 61, "panel": [64, 73], "paolo": [1, 15, 38, 54, 64, 73, 79, 108], "pape": [2, 4, 7, 9, 15, 17, 21, 36, 47, 48, 52, 54, 64, 68, 71, 73, 79, 84, 86, 89, 90, 99, 103, 105], "paper": [9, 17, 20, 29, 34, 42, 48, 52, 71, 78, 81, 90, 96, 99], "paper_29": [64, 73, 79], "paraffin": [24, 31, 64, 73, 79], "paramet": [2, 15, 20, 29, 32, 33, 37, 52, 64, 73, 79, 90, 99, 105], "pari": [30, 33, 64, 73, 79], "parker": [6, 28, 37, 54, 64, 73, 79, 105], "parkinson": [1, 15, 38, 64, 73, 79], "parse_s3_filenam": [64, 73, 79], "parslow": [4, 12, 18, 30, 52, 71, 76], "part": [3, 5, 6, 13, 20, 23, 24, 28, 29, 30, 33, 40, 48, 54, 64, 68, 73, 79, 84, 89, 90, 99, 103, 105], "partial": [64, 73, 79], "particip": [3, 23, 28, 33, 34, 37, 64, 73, 79, 80, 99, 105], "particl": [30, 33], "particlar": 60, "particular": [1, 3, 6, 11, 24, 28, 37, 42, 52, 54, 60, 64, 73, 79, 86, 91, 101, 105, 106], "particularli": [1, 6, 9, 28, 37, 52, 54, 64, 73, 79, 102, 105], "partner": [3, 23, 28, 33, 37, 44, 64, 73, 79, 99, 105], "pascual": [64, 73, 79], "pass": [60, 64, 73, 79], "passiert": [29, 54, 64, 73, 79, 105], "past": [3, 22, 23, 32, 37, 60, 64, 73, 79, 99, 105], "patch": [31, 64, 73, 79], "patent": [64, 73, 79], "patents4sci": [64, 73, 79], "path": [1, 3, 14, 23, 25, 28, 31, 37, 64, 73, 79, 99, 105], "patholog": [22, 64, 73], "pathologi": [25, 31, 64, 73, 79], "patient": [6, 28, 29, 30, 33, 37, 54, 64, 73, 79, 105], "patricia": [51, 52, 69, 79, 90, 105], "patrick": [64, 73, 79], "patter": [52, 64, 79, 90, 100, 107], "pattern": [51, 52, 69, 79, 90, 105], "patwardhan": [38, 52, 64, 71, 86, 105], "paul": [3, 17, 18, 23, 29, 31, 37, 50, 52, 64, 65, 73, 79, 90, 96, 98, 99, 105, 109], "paula": [4, 12, 17, 18, 29, 30, 33, 42, 50, 52, 64, 65, 71, 73, 76, 78, 79, 87, 96], "pauli": [47, 68, 78, 90], "paulin": [1, 15, 38, 64, 73, 79], "pave": [3, 33, 64, 73, 79, 99, 105], "pavel": [12, 29, 50, 52, 94], "pavi": [54, 68, 77, 90, 98], "paxillin": [30, 33, 64, 73, 79], "payn": [4, 12, 18, 30, 52, 71, 76], "paywal": 60, "pb": [15, 17, 24, 31, 37, 47, 64, 68, 73, 78, 79, 99, 105, 106], "pbio": [42, 52, 64, 78], "pcbi": [11, 52, 79, 91, 93, 96], "pcna": [30, 33, 64, 73, 79], "pdac": [54, 56, 72, 86, 89, 90], "pdf": [10, 14, 17, 18, 25, 31, 34, 40, 42, 44, 45, 52, 54, 55, 64, 73, 76, 79, 80, 86, 89, 90, 93, 98, 99, 105], "peer": [40, 79, 101], "pejamn": [11, 18, 29, 41, 48, 54, 68, 86, 90, 98], "pejman": [21, 48, 68, 86, 89, 90, 103], "penghuan": [4, 12, 18, 30, 52, 71, 76], "pengo": [42, 52, 64, 71, 78, 96], "peopl": 88, "pepperkok": [4, 24, 52, 64, 79, 90, 105], "per": [25, 31, 64, 73, 75, 79], "perfectli": [64, 73, 79], "perform": [1, 3, 17, 24, 28, 29, 31, 34, 47, 48, 54, 57, 68, 84, 86, 87, 89, 90, 93, 97, 99, 100, 103, 105, 107], "pericardi": [3, 23, 32, 37, 64, 73, 79, 99, 105], "perikl": 22, "period": [17, 34, 50, 73, 79, 93, 99, 105], "perkin": [24, 31, 64, 73, 79], "perkinelm": [31, 64, 73, 79], "perlman": [15, 17, 37, 47, 68, 78, 99, 105, 106], "perman": [57, 79, 105], "permanent": 75, "permeabil": [24, 31, 64, 73, 79], "permiss": [64, 73, 79], "perrin": [17, 18, 29, 31, 50, 52, 64, 65, 73, 79, 90, 96, 98], "perseil": [1, 15, 38, 64, 73, 79], "person": [3, 9, 33, 34, 37, 45, 47, 54, 55, 61, 64, 68, 73, 78, 79, 90, 97, 99, 105], "personen": [54, 64, 73, 79, 95, 105], "personenbezogen": [26, 54, 64, 73, 75, 79, 93, 105], "personenbezogenen": [26, 54, 64, 73, 79, 93, 105], "personnel": [32, 64, 73, 79], "perspect": [3, 22, 23, 30, 32, 33, 37, 64], "pet": [29, 64, 73, 79], "pete": [40, 48, 79, 90, 94, 103], "peter": [0, 3, 8, 10, 16, 17, 23, 29, 30, 34, 35, 37, 42, 45, 46, 47, 48, 50, 52, 54, 56, 64, 65, 68, 71, 72, 73, 76, 78, 79, 84, 87, 90, 93, 96, 97, 98, 99, 100, 103, 105], "petersen": [24, 64, 73, 79], "petr": [1, 7, 15, 21, 38, 54, 56, 64, 70, 73, 79, 89, 90, 103], "petra": [40, 64, 73, 79, 80, 105], "peuckert": [22, 64, 73, 79], "pfa": [24, 31, 64, 73, 79], "pfander": [1, 15, 38, 64, 73, 79], "ph488": [31, 64, 73, 79], "phalloidin": [31, 64, 73, 79], "phase": [3, 33, 34, 37, 99, 105], "phasen": [64, 73, 79], "phasor": [48, 97, 103], "phd": [64, 73, 79], "phenol": [31, 64, 73, 79], "philip": [1, 15, 38, 64, 73, 79], "philipp": [1, 3, 8, 15, 23, 28, 33, 38, 47, 64, 68, 73, 78, 79, 90, 99, 105], "philippa": [11, 52, 64, 86, 91, 101, 105, 106], "photo": 61, "photon": [30, 33, 73], "physic": 87, "physiolog": [22, 54, 56, 64, 72, 73, 86, 89, 90], "pia": [54, 64, 73, 79, 93, 105], "piereck": [47, 55, 68, 79, 89, 107], "pierr": [15, 47, 64, 68, 73, 82, 84, 100], "pieruschka": [1, 15, 38, 64, 73, 79], "pieta": [12, 42, 48, 64, 68, 71, 84, 90], "pietzsch": [47, 48, 58, 68, 90], "pii": [38, 42, 43, 52, 64, 70, 86, 105], "pike": [25, 31, 64, 73, 79], "pilliod": [30, 33, 64, 73, 79], "pilot": [40, 52, 71, 93, 96, 105], "pimentel": [30, 33, 64, 73, 79, 87], "pina": [42, 52, 64, 78], "pip": [20, 61, 64, 73], "pipelin": [29, 42, 48, 52, 58, 71, 94], "pitfal": [42, 57, 86], "pivot": [64, 73, 79], "pixel": [24, 48, 49, 68, 100], "pixel2": [29, 64, 73, 79], "pl": [24, 29, 31, 64, 73, 79], "pl2k": [3, 8, 23, 33, 34, 35, 37, 54, 56, 64, 72, 73, 79, 99, 100, 105], "pl5edc1v41fyclfzbbclo41zfo": [42, 56, 72, 90], "pl5esqnfm5lc7samsteu082ivw4bdmvd0u": [42, 56, 72, 90], "pla": [1, 15, 17, 18, 29, 38, 50, 52, 64, 65, 73, 79, 96], "place": [3, 22, 33, 34, 37, 64, 73, 79, 99, 105], "plai": [1, 28, 64, 73, 79], "plan": [3, 15, 17, 20, 23, 28, 31, 33, 34, 36, 37, 40, 47, 49, 54, 57, 68, 92, 99, 106], "planck": [3, 33, 34, 37, 64, 73, 79, 99, 105], "plane": [24, 64, 73, 79], "plant": [42, 64, 73, 79, 105], "plantard": [4, 12, 17, 18, 29, 30, 50, 52, 65, 71, 76, 79, 96], "plantsandpython": 52, "planung": [49, 57, 86, 102, 105], "plasmatechnologi": [64, 73, 79], "plate": 31, "plateform": [30, 33, 64, 73, 79], "platform": [1, 3, 8, 23, 28, 29, 30, 33, 37, 42, 45, 47, 49, 51, 52, 56, 57, 68, 69, 72, 80, 86, 88, 92, 99, 100, 103, 107, 108], "plath": [33, 64, 73, 79], "playlist": [3, 8, 23, 33, 34, 35, 37, 42, 54, 56, 64, 72, 73, 76, 79, 86, 90, 97, 99, 100, 103, 105], "plcell": 52, "pldcum9us4xdpz": [56, 72, 86], "pleas": [16, 22, 29, 30, 33, 35, 60, 64, 73, 79], "plenti": [3, 33, 64, 73, 79, 99, 105], "plo": [11, 52, 79, 91, 93, 96], "ploscompbiol": [11, 52, 79, 91, 93, 96], "plotter": [48, 97, 103], "plqbhamyzu5kxuacnnbixybkivueikswq1": [42, 56, 72, 86, 90, 97, 103], "plrc9dt9lezh_svrc4g5moovghuvjpwmv0": [42, 56, 72, 76, 90], "plugin": [3, 20, 31, 37, 42, 44, 45, 48, 56, 72, 97, 103, 108], "plw": [42, 56, 72, 79, 90, 105], "plycntv": [42, 56, 72], "pm": [3, 23, 28, 47, 58, 64, 68, 73, 79, 84, 90, 99, 100, 103, 105], "pmc": [3, 11, 22, 23, 32, 37, 38, 52, 64, 71, 73, 79, 86, 90, 96, 99, 105], "pmc8606015": [3, 11, 22, 23, 32, 37, 38, 52, 64, 71, 73, 79, 86, 96, 99, 105], "pmc9271325": [11, 52, 86, 90, 96], "pml": [30, 33, 64, 73, 79], "png": [29, 64, 73, 79], "png_umap": [29, 64, 73, 79], "poddar": [3, 22, 23, 32, 37, 64, 73, 79], "poetsch": [29, 48, 68, 79, 90, 97, 103], "poikola": [11, 42, 79, 101], "point": [7, 11, 24, 31, 36, 44, 55, 60, 64, 73, 79, 90, 103, 105], "pointer": [24, 64, 73, 79], "pol": [3, 6, 22, 23, 28, 32, 34, 37, 54, 64, 73, 99, 105], "polanski": [47, 68, 78, 90], "polici": [14, 44, 54, 55, 73, 90, 101], "policlin": [29, 64, 73, 79], "policymak": [3, 8, 23, 28, 64, 73, 79, 99, 105], "politi": [4, 5, 7, 9, 36, 47, 64, 68, 73, 79, 90, 98, 109], "pollock": [11, 42, 79, 101], "polygon": [29, 64, 73, 79], "polym": [30, 33, 64, 73, 79], "polytechniqu": [49, 76, 90], "pool": [10, 29, 64, 73, 79], "poon": [10, 29, 64, 73, 79], "popular": [1, 28, 49, 64, 73, 77, 79, 94, 100], "popz": [30, 33, 64, 73, 79], "portabl": [54, 64, 73, 79, 108], "portal": [42, 105], "portfolio": [3, 8, 23, 33, 34, 35, 37, 54, 56, 64, 72, 73, 79, 99, 100, 105], "posch": [45, 52, 94, 108], "pose": [17, 50, 73, 79, 90, 105], "posit": [24, 29, 64, 73, 79], "possibl": [1, 3, 11, 17, 22, 23, 28, 32, 34, 37, 44, 52, 54, 64, 73, 79, 86, 90, 93, 96, 99, 105], "post": [11, 16, 19, 20, 24, 25, 29, 31, 42, 46, 58, 59, 60, 62, 66, 81, 86, 89, 90, 95, 96, 97, 100, 103, 105, 106, 107], "poster": [0, 6, 12, 17, 18, 28, 29, 34, 52, 56, 60, 64, 65, 73, 79, 80, 90, 92, 93, 94, 96, 99], "potenti": [1, 3, 11, 14, 17, 28, 29, 33, 38, 47, 50, 52, 54, 59, 64, 68, 71, 73, 79, 84, 86, 87, 90, 93, 95, 96, 99, 100, 101, 103, 105, 107], "potsdam": [44, 64, 73, 79, 105], "pouchin": [3, 15, 37, 47, 64, 68, 73, 79, 82, 100], "power": [39, 44, 45, 47, 55, 68, 81, 86, 105], "pp": [10, 29, 64, 73, 79], "ppt": [64, 73], "pptx": [29, 54, 73, 79, 89, 90, 103], "pr4deepr": [10, 29, 64, 73, 79], "practic": [1, 15, 20, 23, 28, 29, 35, 36, 38, 42, 56, 65, 72, 84, 92, 99, 100, 102], "pradeep": [10, 29, 64, 73, 79], "prakash": [29, 52, 64, 76], "praktisch": [29, 54, 64, 73, 79, 93, 95, 105], "praxi": [26, 54, 64, 73, 79, 105], "praxisorientiert": [26, 54, 64, 73, 79, 105], "pre": [47, 64, 68, 73, 78, 90, 99], "precis": [47, 68, 81], "preclin": [14, 64, 73, 79], "predict": [42, 47, 68, 91, 109], "preliminari": 87, "premis": [3, 23, 28, 64, 73, 79, 99, 105], "prepar": [1, 3, 4, 5, 7, 8, 9, 17, 20, 23, 24, 28, 29, 31, 33, 34, 35, 36, 37, 45, 47, 50, 54, 55, 56, 64, 68, 70, 72, 73, 79, 81, 90, 98, 99, 100, 103, 105, 108, 109], "preparatori": [0, 16, 23, 37, 47, 64, 73, 79, 90, 93, 99, 105], "preprint": [11, 17, 52, 68, 69, 78, 90, 103, 105, 106], "preprocess": [24, 45, 49, 55, 64, 73, 79, 83, 94, 100, 107], "prerequisit": [3, 23, 28, 64, 73, 79, 99, 105], "presenc": [20, 64, 73, 79], "present": [1, 3, 4, 8, 13, 15, 16, 17, 20, 23, 29, 33, 34, 35, 38, 47, 48, 50, 55, 56, 58, 63, 65, 72, 78, 80, 86, 89, 90, 92, 93, 98, 99, 100, 101], "press": [1, 15, 38, 61, 64, 73, 79], "pretrain": [25, 31, 64, 73, 79], "pretrained_model": [25, 31, 64, 73, 79], "pretreat": [24, 31, 64, 73, 79], "pretti": [64, 73, 79], "preview": [64, 73], "previou": [25, 31, 64, 73, 79, 80], "previous": [1, 17, 28, 50, 64, 73, 79, 90, 105], "primari": [24, 31], "primarili": [54, 73, 79, 101, 105], "primer": [54, 73, 79, 101, 105], "princip": [52, 73, 79, 102], "principl": [0, 1, 3, 8, 11, 14, 16, 17, 20, 23, 26, 28, 29, 30, 32, 33, 34, 37, 42, 47, 49, 50, 51, 54, 55, 57, 65, 69, 70, 73, 74, 81, 86, 87, 89, 90, 91, 95, 96, 99, 101, 103, 107], "printabl": [22, 64, 73, 79], "prinzipien": [29, 54, 64, 73, 79, 93, 95, 105], "priorit": [47, 54, 64, 68, 73, 79, 81, 108], "prioriti": [44, 64, 73, 79, 105], "privat": [29, 54, 64, 73, 79, 90, 93, 95, 101, 105], "probe": [64, 73], "problem": [42, 44, 48, 57, 64, 68, 73, 79, 84, 86, 89, 90, 103, 105], "procedur": [1, 3, 15, 20, 23, 24, 25, 28, 31, 33, 37, 38, 64, 73, 79, 99, 105], "process": [1, 3, 9, 14, 20, 22, 23, 25, 28, 31, 33, 34, 37, 56, 57, 60, 61, 72, 73, 77, 80, 91, 92, 97, 99, 101, 106], "procida": [55, 57, 80], "produc": [29, 31, 39, 66, 87, 89, 105], "product": [60, 82, 102], "prof": [33, 34, 99, 105], "profession": [64, 73, 79], "profil": [61, 64, 73, 80], "prognosi": [29, 64, 73, 79], "program": [20, 30, 33, 45, 52, 71, 102, 108], "programm": [3, 20, 28, 37, 40, 54, 64, 73, 79, 99, 101, 105], "programmat": [7, 21, 38, 54, 56, 70, 79, 89, 90, 103], "project": [0, 1, 4, 6, 9, 14, 15, 16, 20, 22, 24, 25, 28, 29, 31, 32, 38, 39, 40, 42, 44, 47, 52, 54, 56, 57, 58, 66, 72, 78, 80, 82, 87, 93, 100, 102], "project_for_classifier_train": [25, 31, 64, 73, 79], "projektbezogenen": [26, 54, 64, 73, 79, 105], "promin": [64, 73, 79], "promis": [29, 64, 73, 79], "promot": [29, 64, 73, 79], "prompt": [10, 29, 54, 61, 64, 73, 79, 89], "proper": [24, 64, 73, 79], "properli": [17, 50, 57, 73, 79, 86, 90, 105], "properti": [29, 59, 64, 73, 79], "propos": [1, 9, 11, 28, 29, 38, 42, 48, 52, 54, 60, 64, 71, 73, 78, 79, 86, 90, 93, 95, 96, 101, 105], "proprietari": [3, 8, 23, 28, 30, 33, 34, 37, 64, 73, 79, 99, 105], "protect": [6, 26, 28, 37, 52, 54, 64, 73, 79, 93, 101, 105], "protein": [24, 64, 73, 79], "protocol": [1, 17, 28, 31, 34, 54, 64, 79, 90, 93, 105], "protoplast": [30, 33, 64, 73, 79], "prototyp": [22, 87], "proven": [30, 33, 44, 64, 73, 79, 105], "provid": [0, 1, 3, 6, 8, 9, 11, 14, 15, 16, 20, 23, 25, 28, 29, 31, 34, 35, 37, 38, 40, 42, 44, 45, 50, 51, 52, 54, 55, 56, 57, 64, 69, 70, 73, 79, 80, 81, 86, 87, 90, 91, 92, 93, 94, 96, 99, 101, 102, 105, 106, 108], "prozess": [0, 32, 64, 73, 79], "przemek": [52, 64, 79, 90, 100, 107], "pr\u00e4sentat": [26, 54, 64, 73, 79, 105], "psf": [31, 64, 73, 79], "psfgen_wf_wbpsf": [31, 64, 73, 79], "psfgen_wfsquare_wbpsf": [31, 64, 73, 79], "psfhuygens_confocal_theopsf": [31, 64, 73, 79], "psomopoulo": [11, 52, 79, 91, 93, 96], "ptbiop": [24, 64, 73, 79], "pub": [40, 79, 101], "public": [0, 1, 2, 3, 4, 5, 8, 9, 11, 12, 15, 16, 17, 18, 22, 23, 24, 28, 29, 30, 31, 32, 33, 34, 35, 37, 40, 42, 43, 45, 46, 48, 50, 51, 55, 56, 60, 64, 65, 68, 69, 70, 73, 76, 78, 79, 80, 81, 86, 89, 90, 91, 92, 93, 94, 96, 98, 99, 100, 101, 102, 103, 106, 107, 108, 109], "publication_d": 59, "publicli": [29, 54, 64, 73, 90, 93, 95, 105], "publik": [26, 54, 64, 73, 79, 105], "publish": [0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 14, 15, 16, 17, 20, 22, 23, 24, 25, 26, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 44, 45, 47, 51, 54, 56, 57, 58, 64, 65, 66, 68, 69, 70, 72, 73, 77, 79, 80, 82, 83, 84, 89, 91, 92, 94, 96, 97, 98, 99, 100, 101, 102, 103, 106, 107, 108, 109], "publisso": [55, 86, 93, 95, 105], "publizieren": [29, 54, 64, 73, 79, 93, 95, 105], "pubm": [52, 71, 79, 92, 93, 96], "pull": [29, 54, 60, 61, 64, 73, 79, 93, 105], "purpos": 87, "pursu": [64, 73, 79], "push": [32, 64, 73, 79], "puv": [42, 47, 68, 91, 109], "py": [16, 35, 61, 64, 73, 79], "pyclesperanto": [48, 79, 97, 103], "pygithub": 61, "pyimagecourse_beginn": [13, 48, 68, 78, 90, 103], "pylv\u00e4n\u00e4inen": [42, 47, 54, 55, 58, 68, 83, 90, 109], "pyramid": [2, 17, 47, 68, 82, 102], "python": [0, 4, 5, 12, 16, 17, 21, 23, 25, 31, 35, 37, 38, 46, 51, 56, 61, 65, 69, 70, 72, 82, 89, 91, 94, 99, 105, 107], "python_for_microscopist": [42, 48, 68, 84, 90, 103], "python_image_process": [13, 48, 68, 84, 103], "pythoncod": [25, 31, 64, 73, 79], "pyyaml": 61, "p\u00e9rez": [12, 29, 42, 48, 64, 68, 71, 73, 79, 84, 90], "q": [29, 42, 64, 73, 79, 86, 105], "qc": [30, 33, 64, 73, 79], "qc_notebook": [31, 64, 73, 79], "qi_2024_analysis_lab_manu": [12, 48, 79, 103], "qian": [30, 33, 64, 73, 79], "qin": [55, 68, 86, 89], "qm_course_bio_image_analysis_with_napari_2024": [48, 79, 97, 103], "qptdat": [64, 73, 79], "quad": [30, 33, 64, 73, 79], "quadrant": [31, 64, 73, 79], "qualifi": [44, 64, 73, 79, 105], "qualiti": [1, 14, 25, 28, 29, 30, 31, 33, 42, 48, 57, 64, 68, 73, 78, 86, 89, 96], "qualit\u00e4tssicherung": [64, 73, 79], "quantit": [3, 12, 23, 28, 29, 37, 48, 73, 99, 103, 105], "quareo": [11, 52, 79], "quarep": [30, 33, 42, 48, 64, 73, 78, 90, 105], "quelloffen": [40, 79, 101], "queri": [17, 34, 42, 54, 64, 70, 73, 79, 90, 91, 93, 99, 105], "question": [0, 3, 14, 29, 36, 42, 47, 50, 54, 55, 64, 68, 73, 79, 81, 86, 90, 93, 105], "quick": [64, 73, 79], "quota": [64, 73, 79], "qupath": [24, 29], "r": [2, 4, 5, 11, 15, 17, 29, 38, 40, 42, 45, 47, 48, 52, 61, 64, 68, 71, 73, 78, 79, 81, 86, 87, 89, 91, 94, 99, 103, 105, 107, 108], "r2": [24, 31, 64, 73, 79], "r47": [2, 15, 52, 64, 79, 90], "rachel": [10, 29, 64, 73, 79], "radbruch": [64, 73, 79], "radiat": [29, 64, 73, 79], "radiochemotherapi": [29, 64, 73, 79], "radiolog": [20, 64, 73, 79], "radiooncolog": [29, 64, 73, 79], "radiooncologi": [29, 64, 73, 79], "radiorestist": [29, 64, 73, 79], "radiotherapi": [29, 64, 73, 79], "raess": [1, 15, 38, 64, 73, 79], "raf": [17, 18, 29, 50, 52, 65, 79, 96], "rafael": [1, 15, 38, 39, 55, 64, 68, 73, 79, 84, 86, 90, 103], "rahmen": [54, 64, 73, 75, 79, 105], "rahul": [17, 51, 52, 69, 79, 103], "rai": [86, 90], "rainer": [4, 24, 52, 64, 79, 90, 105], "rajasekhar": [10, 29, 64, 73, 79], "rama": [10, 11, 18, 41, 48, 54, 68, 86, 90, 98], "ramirez": [47, 68, 78, 90], "ran": [7, 21, 25, 31, 38, 54, 56, 64, 70, 73, 79, 89, 90, 103], "random": [31, 64, 73, 79], "rang": [1, 3, 14, 22, 23, 28, 32, 37, 40, 64, 73, 79, 99, 105], "raphaelbuzzi": [47, 68, 78, 90], "rare": [29, 54, 64, 73, 79, 90, 93, 95, 101, 105], "rasband": [12, 29, 50, 52, 94], "rasti": [11, 18, 21, 29, 41, 48, 54, 68, 86, 89, 90, 98, 103], "rat": [24, 29], "ratamero": [11, 15, 47, 68, 82], "rather": [29, 64, 73, 79], "rational": [52, 64, 71, 79, 93, 105], "ratnikov": [48, 68, 84, 89, 103], "rauber": [1, 15, 38, 64, 73, 79], "ravindran": [4, 24, 52, 64, 79, 90, 105], "raw": [1, 6, 10, 28, 31, 47, 49, 54, 55, 64, 68, 73, 79, 84, 86, 89, 90, 99, 100, 103, 105, 107], "raw_larg": [31, 64, 73, 79], "rawdata": [64, 73, 79], "rdf": [16, 35, 50, 64, 99], "rdm": [6, 8, 11, 23, 28, 42, 44, 52, 54, 56, 65, 72, 80, 92, 96, 101], "rdm4mic": [3, 8, 23, 33, 34, 35, 37, 54, 64, 73, 99], "rdmkit": [49, 57], "rdmorganis": [49, 57, 86, 102, 105], "rdornier": [15, 47, 68, 82, 100], "re": [11, 42, 60, 71, 86, 87, 91, 93, 96, 103], "reach": [29, 64, 73, 79], "read": [6, 39, 42, 45, 49, 54, 55, 81, 83, 86, 90, 94, 100, 105, 106, 107], "readabl": [1, 28, 64, 73, 79], "reader": [24, 64, 73, 79], "readi": [3, 23, 28, 64, 73, 79, 99, 105], "readm": [19, 20, 29, 39, 47, 55, 62, 64, 68, 73, 79, 84, 89, 90, 100, 103, 107], "readthedoc": [11, 42, 44, 45, 49, 77, 79, 86, 94, 100, 103, 108], "real": [14, 54, 55, 64, 73, 79, 90, 93, 99, 105], "realiz": [1, 14, 28, 50, 64, 73, 79, 90], "reason": [20, 29, 52, 64, 73, 79, 90], "rebecca": [1, 15, 38, 40, 42, 52, 64, 68, 73, 78, 79, 86, 89, 90], "receiv": [36, 54, 64, 73, 79], "recent": [30, 33, 64, 73, 79, 87], "recherch": [30, 33, 64, 73, 79], "rechtlich": [26, 40, 54, 64, 73, 75, 79, 93, 101, 105], "rechtsbereich": [26, 54, 64, 73, 79, 105], "rechtsgebiet": [40, 79, 101], "rechtssicherheit": [54, 64, 73, 79, 95, 105], "rechtsverletzung": 75, "rechtsverletzungen": 75, "rechtsverst\u00f6\u00df": 75, "rechtswidrig": 75, "recogn": [52, 64, 71, 73, 79, 90, 93, 107], "recommend": [3, 8, 14, 22, 23, 29, 30, 32, 33, 34, 37, 42, 48, 54, 55, 59, 70, 78, 81, 90, 93, 99], "record": [0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 14, 15, 16, 17, 20, 22, 23, 24, 25, 26, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 40, 41, 42, 44, 52, 54, 55, 56, 60, 64, 68, 72, 73, 79, 80, 81, 83, 84, 86, 87, 88, 89, 90, 92, 93, 95, 96, 98, 99, 100, 101, 102, 103, 105, 108, 109], "rector": [64, 73, 79], "red": [31, 64, 73, 79], "rediscov": [17, 34, 54, 73, 79, 90, 93, 105], "reduc": [29, 64, 73, 79], "reduct": [22, 64, 73], "ref": [47, 55, 68, 79, 89], "refer": [3, 22, 23, 29, 30, 32, 33, 37, 40, 55, 57, 64, 73, 79, 80, 99, 105], "reflect": [64, 73, 99], "reform": [64, 73, 79], "regard": [6, 22, 28, 37, 54, 64, 73, 79, 105], "regardless": [14, 42, 55, 64, 73, 79, 90, 93, 105], "regel": 75, "regelm\u00e4\u00dfig": [0, 32, 64, 73, 79], "reger": [1, 28, 64, 73, 79], "regeringen": [44, 105], "region": [1, 28, 64, 73, 79], "regist": [24, 31, 55, 56, 64, 70, 73, 79, 81, 105], "registr": [4, 42, 48, 68, 78, 84], "registri": [11, 42, 78, 91, 103, 107, 108], "reguant": [64, 73, 79], "rehabilit": [20, 64, 73, 79], "rehydr": [24, 31, 64, 73, 79], "reichert": [99, 105], "reih": [0, 26, 32, 54, 64, 73, 79, 105], "reink": [11, 51, 52, 90], "reit": [52, 64, 79, 90, 100, 107], "reither": [4, 24, 52, 64, 73, 79, 90, 105], "relat": [1, 10, 15, 29, 38, 40, 42, 57, 60, 64, 73, 79, 84, 86, 87, 99, 105, 109], "relationship": [64, 73, 79], "releas": [17, 34, 54, 79, 87, 90, 93, 105], "relev": [9, 29, 54, 60, 64, 73, 79, 99, 105], "releva": [32, 64, 73, 79], "relevant": [26, 54, 64, 73, 79, 105], "relevanten": [26, 54, 64, 73, 79, 105], "reli": [64, 73, 79], "reliabl": [29, 64, 73, 79], "remain": [3, 8, 9, 16, 23, 28, 29, 35, 54, 56, 64, 72, 73, 79, 86, 89, 90, 99, 105], "rembi": [0, 3, 14, 16, 22, 23, 32, 37, 47, 54, 55, 73, 79, 90, 99], "remix": [22, 64, 73], "remot": [1, 3, 8, 23, 28, 33, 34, 35, 37, 47, 54, 56, 64, 72, 73, 79, 99, 100, 105], "remov": [42, 48, 60, 78, 90, 105], "renku": [25, 31, 64, 73, 79], "renown": [52, 64, 79, 90, 100, 107], "repeat": [20, 29, 64, 73, 79], "replac": [64, 73, 79], "report": [0, 16, 23, 29, 30, 33, 37, 47, 52, 90, 92, 93, 99, 105], "repositori": [1, 2, 4, 5, 6, 7, 8, 9, 11, 15, 17, 20, 24, 25, 28, 29, 31, 36, 42, 45, 48, 51, 52, 54, 58, 59, 60, 61, 62, 63, 69, 74, 77, 78, 80, 81, 82, 83, 84, 87, 89, 91, 94, 95, 97, 100, 101, 102, 103, 106, 107, 109], "repositorien": [54, 64, 73, 79, 105], "repres": [1, 15, 38, 64, 73, 79, 99], "represent": [2, 15, 17, 34, 50, 52, 64, 73, 79, 90, 93, 99, 105], "reproduc": [1, 3, 22, 23, 28, 30, 33, 37, 45, 47, 48, 55, 65, 68, 78, 89, 90, 91, 93, 97, 99, 100, 101, 103, 106, 109], "request": [29, 54, 60, 61, 64, 73, 79, 93, 105], "requir": [0, 1, 3, 6, 8, 11, 14, 17, 23, 28, 30, 33, 34, 35, 37, 38, 42, 48, 50, 52, 54, 55, 56, 64, 71, 72, 73, 78, 79, 80, 86, 90, 93, 96, 99, 100, 105], "rer": 75, "resav": [31, 64, 73, 79], "rescal": [29, 64, 73, 79], "research": [2, 4, 5, 7, 9, 24, 26, 45, 46, 47, 48, 51, 56, 58, 60, 68, 69, 70, 71, 72, 78, 81, 83, 87, 91, 92, 96, 97, 98, 103, 107, 108], "reserv": [3, 4, 11, 12, 16, 18, 29, 30, 33, 34, 35, 40, 42, 49, 52, 56, 60, 64, 68, 71, 72, 90, 94, 96, 98, 99, 100, 105], "reshuffl": [20, 64, 73, 79], "resin": [22, 64, 73, 79], "resolut": [3, 23, 28, 29, 30, 33, 37, 64, 73, 79, 99, 105], "resourc": [3, 8, 11, 17, 23, 28, 30, 33, 34, 35, 37, 48, 50, 51, 54, 56, 57, 59, 60, 65, 69, 70, 72, 74, 76, 80, 84, 88, 91, 96, 99, 106], "respect": [29, 64, 73, 79], "respons": [1, 14, 28, 42, 54, 55, 56, 64, 72, 73, 79, 86, 89, 90, 93, 105], "responsibli": [29, 57, 64, 73, 79, 86, 105], "restrict": [42, 55, 56, 70, 79, 101, 106], "result": [2, 3, 15, 20, 23, 25, 30, 31, 33, 34, 37, 44, 93], "results_csv": [20, 64, 73, 79], "resuspend": [31, 64, 73, 79], "retriev": [1, 3, 17, 23, 28, 29, 34, 37, 54, 64, 73, 79, 89, 90, 93, 99, 105], "reuasabl": [29, 54, 64, 73, 79, 93, 95, 105], "reus": [1, 14, 15, 42, 54, 55, 73, 79, 90, 92, 93], "reusabl": [1, 3, 8, 11, 14, 23, 28, 29, 30, 33, 34, 35, 37, 54, 56, 71, 72, 87, 90, 92, 93, 95, 99, 100, 106], "revel": [24, 31, 64, 73, 79], "reversibli": [22, 64, 73, 79], "review": [20, 22, 40, 54, 56, 64, 72, 73, 79, 86, 89, 90, 101], "revolution": [64, 73, 79], "revolutionari": [52, 71, 90, 93, 107], "rex": [52, 64, 79, 105], "rey": [11, 42, 78, 91, 103, 107, 108], "reynaud": [29, 52, 64, 76], "rfc": [54, 90, 93, 99, 105], "rgb": [49, 64, 73, 79, 90, 100], "rhaas": [29, 54, 67, 78, 90, 94, 98], "rhinoviru": [30, 33, 64, 73, 79], "rhode": [24, 64, 73, 79], "ri": [1, 15, 29, 38, 64, 73, 79, 99, 105], "ribeiro": [47, 55, 68, 79, 107], "ricardo": [12, 40, 42, 48, 64, 68, 71, 84, 90], "riccardo": [1, 3, 6, 8, 16, 23, 29, 37, 47, 50, 54, 55, 64, 68, 73, 79, 84, 90, 92, 96, 99, 100, 103, 105], "rich": [3, 23, 28, 37, 64, 73, 79, 99, 105], "richard": [1, 15, 29, 38, 41, 47, 48, 52, 54, 56, 64, 68, 72, 73, 79, 81, 86, 90, 97, 98, 103, 105], "richtigkeit": 75, "rigano": [11, 30, 33, 52, 64, 71, 73, 79, 86, 87, 90, 96], "rigaud": [29, 47, 48, 55, 58, 68, 78, 79, 90, 103], "right": [3, 4, 11, 12, 16, 18, 29, 30, 33, 34, 35, 40, 42, 49, 52, 54, 56, 60, 61, 64, 68, 71, 72, 73, 79, 89, 90, 94, 96, 98, 99, 100, 103, 105], "rigor": [11, 52, 86, 90, 96], "rise": [64, 73, 79], "rittscher": [48, 68, 82, 90, 103], "riudalba": [25, 31, 64, 73, 79], "rizwan": [4, 12, 18, 30, 52, 71, 76], "rm": [47, 80], "rmassei": [28, 29, 47, 54, 68, 84, 90, 100, 103], "rmcelreath": [47, 68, 81], "roadmap": [44, 64, 73, 79, 105], "robert": [4, 6, 9, 10, 11, 12, 17, 18, 20, 28, 30, 31, 33, 37, 39, 40, 41, 42, 47, 48, 50, 52, 54, 55, 58, 62, 64, 65, 66, 67, 68, 73, 75, 76, 78, 79, 84, 86, 87, 89, 90, 93, 94, 95, 96, 97, 98, 99, 100, 101, 103, 105, 106, 109], "robert_haas": [29, 39, 62, 79, 100, 103], "robust": [29, 64, 73, 79], "rocco": [18, 29, 31, 52, 65, 79, 90, 98], "roccod": [54, 68, 77, 90, 98], "roch": [24, 31, 64, 73, 79], "rodent": [22, 64, 73, 79], "rodrigo": [52, 64, 73, 79, 90, 100, 107], "rodriguez": [4, 12, 18, 30, 52, 71, 76], "rohola": [52, 71, 79, 93, 96, 105], "rohrer": [42, 52, 64, 78], "roimanag": [64, 73, 84], "roland": [1, 3, 4, 8, 12, 15, 17, 18, 23, 33, 34, 35, 37, 38, 52, 64, 71, 73, 76, 79, 87, 99, 105], "role": 1, "roll": [29, 54, 64, 73, 79, 93, 95, 105], "romain": [1, 11, 12, 15, 18, 24, 25, 29, 38, 42, 46, 48, 52, 58, 64, 65, 68, 71, 73, 79, 84, 86, 90, 98], "roman": [64, 73, 79], "romano": [1, 15, 38, 64, 73, 79], "romdhan": [14, 64, 73, 79], "romero": [7, 21, 38, 52, 54, 56, 70, 71, 79, 89, 90, 92, 93, 96, 103], "romi": [26, 54, 64, 73, 79, 105], "ron": [52, 64, 79, 90, 100, 107], "ronja": [64, 73, 79], "ronni": [26, 54, 64, 73, 79, 105], "root": [64, 73, 79], "rosa": [52, 64, 79, 90, 100, 107], "rosato": [1, 15, 38, 64, 73, 79], "rot": [26, 54, 64, 73, 79, 105], "rotkreuz": [24, 31, 64, 73, 79], "rougier": [40, 68, 103], "round": [24, 31, 80], "rousseau": [11, 18, 29, 41, 48, 54, 68, 86, 89, 90, 98], "rout": [22, 64, 73, 79], "routin": [3, 8, 23, 33, 34, 35, 37, 54, 56, 64, 72, 73, 79, 99, 100, 105], "royer": [11, 12, 29, 42, 48, 52, 64, 68, 71, 76, 84, 90, 94], "royerlab": [47, 55, 58, 68, 90], "royl": [40, 41, 64, 68, 73, 83, 90], "rozbicki": [2, 42, 55, 56, 86, 100], "rpc": [45, 84, 89, 103, 108], "rtx": [25, 31, 64, 73, 79], "rube\u0161ov\u00e1": [20, 64, 73, 79], "rudolf": [1, 15, 38, 64, 73, 79], "rueden": [11, 12, 18, 25, 29, 31, 42, 46, 50, 52, 54, 55, 58, 68, 86, 90, 94, 98], "rufu": [11, 42, 79, 101], "ruman": [45, 52, 71, 94, 108], "run": [1, 25, 29, 31, 32, 47, 52, 54, 60, 64, 68, 73, 78, 82, 84, 86, 94, 97, 100, 108], "run_convers": [64, 73, 79], "runcommand": [64, 73, 79], "rund": [0, 32, 64, 73, 79], "runplugin": [64, 73, 79], "runuserplugin": [64, 73, 79], "rupert": [4, 12, 18, 30, 52, 71, 76], "rupress": [11, 52, 96], "russel": [45, 84, 89, 103, 108], "ruth": [64, 73, 79], "ryabchykov": [52, 64, 79, 93, 105], "ryan": [4, 7, 10, 12, 18, 21, 29, 30, 33, 38, 52, 54, 56, 64, 70, 71, 73, 76, 79, 87, 89, 90, 96, 103], "rybakov": [47, 68, 78, 90], "rybina": [1, 14, 15, 38, 52, 64, 73, 79, 105], "rzepka": [15, 17, 37, 47, 68, 78, 99, 105, 106], "r\u00e9my": [49, 90, 100], "r\u00f6der": [64, 73, 80], "r\u00f6ssler": [17, 18, 29, 50, 52, 65, 79, 96], "s0022283622000791": [38, 42, 43, 52, 64, 70, 86, 105], "s00418": [52, 105], "s10nm": [24, 64, 73, 79], "s2633903x21000039": [52, 64, 68, 78, 90], "s2633903x23000211": [64, 73, 79], "s3": [64, 73, 79], "s41431": [52, 71, 79, 92, 93, 106], "s41467": [12, 42, 48, 64, 68, 71, 84, 90], "s41592": [2, 3, 4, 5, 11, 12, 14, 15, 17, 18, 29, 30, 33, 34, 38, 45, 52, 54, 55, 64, 71, 73, 76, 79, 86, 90, 93, 94, 96, 99, 105, 107, 108], "s41597": [52, 71, 79, 92, 93, 96, 105], "s41598": [25, 31, 64, 73, 79], "s50nm": [24, 64, 73, 79], "sa": [3, 8, 40, 42, 47, 49, 50, 52, 54, 55, 57, 64, 68, 71, 73, 90, 93, 97, 99, 100, 101, 103, 105, 107], "saalfeld": [47, 48, 58, 68, 90], "saalfeldlab": [47, 48, 58, 68, 90], "sabin": [4, 24, 52, 64, 73, 79, 90, 105], "sabrina": [10, 29, 64, 73, 79], "sachsen": [22, 64, 73], "sack": [64, 73, 79], "sage": [11, 17, 18, 29, 50, 52, 54, 55, 64, 65, 68, 73, 79, 86, 90, 96, 98], "sai": [47, 68, 81], "saibotmagd": [47, 68, 86, 105], "sainsburi": [42, 55, 56, 79], "saito": [10, 29, 64, 73, 79], "salama": [64, 73, 79, 109], "salih": [7, 21, 38, 54, 56, 70, 79, 89, 90, 103], "sam": [5, 54, 64, 73, 79, 89, 90], "same": [3, 31, 33, 34, 37, 64, 73, 79, 99, 105], "sampaio": [17, 18, 29, 50, 52, 65, 79, 96], "sampl": [3, 14, 17, 25, 28, 29, 31, 37, 42, 50, 55, 90, 93, 105], "sanchez": [1, 15, 38, 64, 73, 79], "sander": [33, 52, 64, 71, 73, 79, 96], "sandra": [11, 52, 64, 86, 91, 101, 105, 106], "sanson": [1, 15, 38, 64, 73, 79], "sara": [1, 12, 15, 38, 42, 48, 64, 68, 71, 73, 79, 84, 90], "sarah": [16, 24, 35, 42, 52, 64, 73, 76, 79, 90, 99, 100, 105], "sarkan": [1, 7, 11, 14, 15, 21, 52, 54, 55, 56, 64, 70, 71, 73, 79, 86, 89, 90, 93, 96, 103, 105], "sasaki": [41, 48, 68, 86, 103], "sass": [64, 73, 79], "sathya": [4, 12, 18, 30, 52, 71, 76], "satisfi": [14, 42, 55, 64, 73, 79, 90, 93, 105], "satrajit": [11, 52, 79, 105], "save": [3, 22, 23, 31, 32, 37, 60, 64, 73, 79, 99, 105], "save_tif": [25, 31, 64, 73, 79], "saxfdm": [29, 54, 64, 73, 79, 90, 92, 93, 95, 101, 105], "sc": [11, 16, 24, 25, 31, 42, 46, 58, 64, 73, 79, 81, 86, 90, 96, 100, 107], "scad": [6, 28, 37, 47, 48, 64, 68, 75, 87, 105], "scalabl": [45, 103], "scale": [1, 11, 15, 17, 32, 38, 45, 50, 52, 86, 90, 96, 103, 108], "scan": [30, 32, 33, 37, 99, 105], "scanner": [25, 30, 31, 33, 64, 73, 79], "scatter": [64, 73], "scene": [64, 73, 79], "schauss": [16, 23, 32, 37, 45, 46, 47, 50, 52, 64, 68, 73, 79, 90, 93, 99, 100, 103, 105], "schemat": [64, 73, 80], "scheme": [14, 22, 54, 55, 64, 73, 79, 90, 93, 105], "schiklenk": [4, 18, 68, 86, 90], "schiller": [42, 68], "schlaeppi": [29, 52, 79, 90], "schlierf": [6, 64, 73, 79], "schlussbericht": [64, 73, 79], "schmi": [4, 12, 18, 30, 52, 71, 76, 81, 90], "schmidt": [8, 11, 12, 17, 21, 22, 23, 25, 28, 29, 30, 32, 33, 34, 35, 37, 42, 48, 50, 52, 54, 55, 56, 64, 65, 68, 71, 72, 73, 76, 79, 80, 86, 87, 89, 90, 94, 96, 98, 99, 100, 103, 105, 109], "schmitz": [31, 64, 73, 79], "schneider": [0, 4, 7, 9, 11, 29, 36, 50, 55, 64, 73, 79, 84, 90, 93, 98, 105, 107, 109], "schnike": [28, 50, 64, 79, 90], "scholarli": [57, 79, 105], "scholten": [64, 73, 79], "scholz": [1, 17, 18, 28, 29, 50, 52, 64, 65, 73, 79, 90, 96], "school": [30, 33, 42, 47, 64, 73, 84, 109], "schorb": [4, 5, 7, 9, 29, 36, 47, 52, 64, 68, 73, 79, 90, 98, 109], "schrader": [0, 16, 23, 37, 45, 46, 47, 50, 52, 64, 68, 73, 79, 90, 93, 99, 100, 103, 105], "schritt": [57, 75, 86, 99, 105], "schroth": [4, 12, 18, 30, 52, 71, 76], "schult": [52, 71, 79, 92, 93, 96], "schulungen": [40, 64, 73, 79, 105], "schulz": [0, 32, 64, 73, 79], "schutz": 75, "schwartz": [5, 48, 54, 68, 86, 89, 90], "schwarzbach": [3, 22, 23, 32, 37, 64, 73, 79], "schwerpunkt": [64, 73, 79], "sch\u00e4tz": [47, 48, 54, 64, 68, 73, 79, 86, 91], "sch\u00fctz": [4, 12, 18, 30, 52, 71, 76], "sci": [29, 64, 73, 79], "scicomp": [29, 54, 67, 68, 86, 90, 94], "scienc": [3, 6, 17, 19, 23, 28, 30, 33, 34, 37, 39, 43, 45, 47, 50, 51, 55, 56, 57, 66, 69, 70, 71, 75, 78, 80, 87, 92, 109], "science23rd": [17, 64, 73, 79, 99, 109], "sciencedirect": [38, 42, 43, 52, 64, 70, 86, 105], "scienceeurop": [40, 64, 79], "scientif": [3, 8, 11, 14, 20, 23, 25, 28, 30, 31, 33, 34, 37, 42, 45, 47, 50, 70, 78, 80, 81, 91, 92, 96, 99, 101, 102, 107], "scientist": [3, 6, 9, 11, 23, 28, 33, 34, 37, 47, 54, 56, 68, 72, 81, 86, 96, 99, 105], "scikit": [7, 11, 13, 36, 55, 84, 97], "scipi": [48, 103], "scope": [14, 25, 99], "scott": [4, 12, 18, 30, 52, 71, 76], "script": [3, 15, 16, 22, 23, 25, 31, 32, 35, 37, 47, 54, 61, 64, 67, 68, 73, 82, 99], "scvers": [47, 68, 78, 90], "sd": [29, 30, 33, 64, 73, 79], "sdata": [52, 64, 71, 79, 93, 105], "sdata201618": [52, 64, 71, 79, 93, 105], "se": [44, 54, 68, 77, 90, 98, 105], "seamless": [1, 28, 29, 64, 73, 79, 99, 105], "seamlessli": [54, 64, 73, 79, 99, 108], "search": [14, 54, 55, 64, 73, 79, 80, 87, 90, 93], "searchabl": [42, 76], "sebastian": [3, 4, 5, 7, 9, 10, 11, 17, 18, 24, 26, 29, 33, 34, 36, 47, 48, 50, 52, 54, 55, 64, 65, 68, 71, 73, 76, 79, 84, 90, 96, 98, 103, 105, 107, 109], "sebastien": [11, 18, 54, 55, 68, 86, 90, 98], "second": [2, 17, 22, 24, 31, 47, 60, 64, 68, 73, 78, 79, 82, 90, 99, 102], "section": [17, 24, 29, 31, 42, 49, 50, 54, 57, 64, 73, 79, 84, 90, 97, 105], "section_a01_g001_": [24, 64, 73, 79], "secur": [3, 33, 34, 37, 44, 45, 55, 64, 73, 79, 99, 105], "see": [3, 22, 23, 32, 37, 61, 64, 73, 79, 84, 99, 105], "seed": [1, 31, 64, 73, 79], "seek": [29, 45, 47, 55, 64, 68, 73, 78, 79, 90, 97, 99, 105], "seem": [64, 73, 79], "seen": [3, 22, 23, 32, 37, 54, 64, 73, 79, 99, 101, 105], "segment": [5, 10, 12, 24, 28, 31, 45, 47, 49, 55, 78, 83, 84, 94, 100, 107], "seibold": [40, 73, 79, 105], "sein": [26, 29, 54, 64, 73, 79, 93, 99, 105], "seit": [26, 54, 64, 73, 75, 79, 105], "seiten": 75, "seitenbetreib": 75, "seitz": [4, 12, 18, 30, 52, 71, 76], "selbst": [26, 54, 64, 73, 79, 105], "selchow": [4, 12, 18, 30, 52, 71, 76], "select": [1, 3, 14, 15, 20, 22, 23, 32, 37, 38, 42, 55, 57, 61, 64, 73, 79, 80, 86, 90, 93, 95, 99, 105], "self": [3, 8, 23, 33, 34, 35, 37, 54, 56, 64, 72, 73, 79, 99, 100, 105], "sem": [24, 64, 73, 79], "semant": [1, 15, 17, 34, 38, 50, 64, 73, 79, 84, 93, 99, 105], "semest": [41, 55, 79, 84], "semi": [1, 73], "semin": [64, 73, 79], "seminar": [3, 17, 33, 86, 90, 99, 109], "seminarstechnisch": [17, 64, 73, 79, 99, 109], "semisupervis": [20, 64, 73], "send": [60, 61], "senft": [40, 42, 52, 64, 68, 78, 86, 89, 90], "sengupta": [29, 64, 73, 79], "sens": [64, 73, 79], "sensit": [1, 15, 38, 64, 73, 79], "sensor": [20, 64, 73], "separ": [3, 22, 23, 32, 37, 52, 64, 71, 73, 79, 92, 93, 96, 99, 105], "seper": [3, 14, 23, 32, 37, 64, 73, 79, 99, 105], "sept": [3, 22, 23, 32, 37, 64, 73, 79, 99, 105, 109], "septemb": [6, 29, 64, 73, 79], "sequenc": [6, 28, 37, 49, 54, 55, 64, 73, 79, 81, 105, 107], "sequenti": [64, 73, 79], "sergei": [47, 68, 78, 90], "sergii": [4, 12, 18, 30, 52, 71, 76], "seri": [13, 40, 42, 48, 64, 71, 73, 79, 86, 96, 105], "serotec": [24, 31, 64, 73, 79], "serrano": [14, 15, 28, 38, 52, 54, 63, 64, 73, 79, 86, 89, 90, 105, 108], "serum": [31, 64, 73, 79], "serv": [2, 3, 8, 15, 23, 33, 34, 35, 37, 45, 47, 52, 54, 55, 56, 57, 60, 64, 68, 72, 73, 78, 79, 90, 97, 99, 100, 105], "server": [1, 3, 11, 15, 17, 28, 33, 34, 37, 47, 50, 64, 68, 73, 82, 99, 100, 105], "servic": [1, 3, 6, 8, 16, 28, 29, 30, 33, 34, 37, 42, 50, 52, 90, 92, 99], "session": [0, 3, 16, 23, 29, 36, 37, 47, 54, 64, 67, 73, 78, 79, 90, 93, 94, 98, 99, 105], "set": [3, 8, 20, 22, 23, 24, 25, 29, 30, 31, 32, 33, 35, 37, 44, 48, 54, 57, 61, 72, 78, 81, 92, 93, 100, 103, 106], "setid": [64, 73, 79], "setup": [3, 32, 33, 34, 37, 64, 73, 79, 99, 105], "setx": 61, "sever": [1, 3, 14, 22, 23, 29, 32, 37, 45, 64, 73, 79, 80, 99, 105], "severina": [4, 5, 7, 9, 36, 47, 68, 79], "sf4af": [55, 56, 86], "sh": [64, 73, 79], "shade": [24, 31, 64, 73, 79], "shah": [64, 73, 79], "shall": [64, 73, 79], "shanghang": [52, 71, 90, 93, 107], "shanmugasundaram": [54, 73, 79, 101, 105], "shantanu": [40, 68, 81, 90], "shape": [1, 9, 14, 64, 73, 79], "share": [0, 1, 3, 6, 8, 11, 14, 15, 17, 23, 28, 30, 33, 34, 37, 38, 42, 47, 50, 51, 52, 55, 56, 57, 68, 69, 70, 71, 78, 81, 89, 90, 91, 92, 96, 99, 101, 103, 107, 108], "sharma": [4, 12, 18, 30, 52, 71, 76], "shechtman": [12, 42, 48, 64, 68, 71, 84, 90], "sheer": [1, 28, 64, 73, 79], "sheet": [3, 22, 23, 32, 37, 64, 73, 79, 99, 105], "shenoi": [29, 64, 73, 79], "sherwoodf": [15, 17, 37, 47, 68, 78, 99, 105, 106], "short": [6, 40], "shorten": [22, 64, 73], "shortli": [29, 64, 73, 79], "should": [3, 22, 23, 28, 32, 37, 55, 64, 73, 86, 99, 106], "show": [1, 3, 7, 11, 15, 22, 23, 28, 32, 36, 37, 38, 47, 48, 52, 55, 64, 68, 71, 73, 79, 84, 89, 90, 92, 93, 96, 99, 100, 103, 105], "showcas": [16, 35, 54, 64, 73, 79, 92, 105], "showdialog": [64, 73, 79], "shown": [29, 64, 73, 79], "shrestha": [17, 51, 52, 69, 79, 103], "shuxia": [52, 64, 79, 93, 105], "sibarta": [11, 18, 54, 55, 68, 86, 90, 98], "sich": [0, 26, 32, 54, 64, 73, 75, 79, 95, 105], "sicher": [26, 54, 64, 73, 79, 93, 105], "sicherheitsl\u00fccken": 75, "sicht": [0, 32, 64, 73, 79], "sidebar": 61, "sie": [26, 29, 54, 64, 73, 75, 79, 93, 95, 105], "siffa": [64, 73, 79], "signal": [30, 33, 64, 73, 79], "signific": [1, 3, 8, 11, 17, 23, 28, 38, 50, 52, 64, 71, 73, 79, 86, 90, 96, 99, 105], "significantli": [29, 52, 64, 73, 79, 102], "silk": [0, 3, 22, 23, 37, 64, 73, 79, 99, 105], "silva": [30, 33, 64, 73, 79], "similar": [20, 29, 64, 73, 79, 99, 105], "simon": [6, 7, 10, 11, 21, 25, 28, 29, 31, 37, 38, 42, 46, 54, 56, 58, 64, 70, 73, 79, 86, 89, 90, 103, 105], "simona": [10, 29, 64, 73, 79], "simpl": [16, 22, 28, 35, 59, 64, 73, 84, 90, 99, 103], "simplifi": [9, 64, 73, 79, 99], "simplystatist": [39, 86], "simul": [24, 64, 73, 79], "sim\u00f3": [25, 31, 64, 73, 79], "sinc": [3, 8, 23, 33, 34, 35, 37, 54, 56, 64, 72, 73, 79, 99, 100, 105, 108], "sind": [54, 64, 73, 75, 79, 95, 105], "singer": [30, 33, 64, 73, 79], "singh": [40, 68, 81, 90], "singl": [1, 3, 23, 28, 30, 33, 47, 64, 68, 73, 78, 79, 90, 99, 105], "site": [30, 33, 64, 73, 79, 84], "sivagurunathan": [7, 21, 38, 51, 52, 54, 56, 68, 69, 70, 78, 79, 89, 90, 103], "size": [1, 3, 14, 23, 25, 28, 31, 33, 34, 37, 73, 99], "si\u00e2n": [4, 9, 11, 18, 21, 25, 29, 41, 48, 52, 54, 68, 79, 86, 89, 90, 98, 103], "skill": [36, 40, 64, 73, 80, 84], "skimag": [48, 78, 103], "skin": [30, 33, 64, 73, 79], "sladoj": [17, 18, 29, 50, 52, 65, 79, 96], "slater": [51, 52, 68, 69, 78, 90], "slice": [24, 25, 31, 64, 73, 79], "slide": [1, 4, 5, 6, 7, 9, 10, 11, 13, 14, 17, 18, 20, 21, 25, 26, 28, 29, 32, 36, 38, 41, 47, 48, 49, 55, 58, 60, 63, 65, 67, 68, 70, 77, 78, 80, 83, 86, 87, 89, 90, 91, 92, 93, 94, 95, 98, 101, 103, 106, 108, 109], "slidedeck": [29, 64, 73, 79, 99, 105], "slightli": [16, 35, 64, 73, 79], "small": [32, 64, 73, 79, 99], "smaller": [3, 23, 28, 37, 64, 73, 79, 99, 105], "smallest": [64, 73, 79], "smart": [16, 35, 64, 73, 79, 99, 105], "smear": [20, 64, 73], "smooth": [64, 73, 80], "snap": [31, 64, 73, 79], "so": [3, 32, 55, 86, 87, 99], "sobolewski": [54, 84, 97], "social": [47, 68, 81], "societ": [64, 73, 79], "societi": [17, 64, 73, 79, 99, 109], "softli": [29, 54, 64, 73, 79, 90, 93, 95, 101, 105], "softwar": [1, 3, 8, 11, 15, 17, 20, 23, 24, 25, 28, 30, 31, 32, 33, 34, 35, 37, 38, 45, 47, 48, 49, 57, 60, 68, 72, 82, 83, 92, 93, 97, 103, 108, 109], "softwarecontext": [29, 54, 64, 73, 79, 93, 105], "softwareentwicklern": [29, 54, 64, 73, 79, 93, 105], "soiland": [11, 42, 78, 91, 103, 107, 108], "sokolnicki": [42, 43, 71], "solak": [12, 42, 48, 64, 68, 71, 84, 90], "solano": [14, 15, 28, 38, 52, 54, 63, 64, 73, 79, 86, 89, 90, 105, 108], "solar": [64, 73, 79], "solch": [29, 54, 64, 73, 75, 79, 93, 105], "solchen": [26, 54, 64, 73, 79, 105], "soll": [26, 40, 54, 64, 73, 79, 101, 105], "sollt": [26, 54, 64, 73, 79, 105], "sollten": 75, "soltwedel": [29, 47, 48, 52, 62, 68, 79, 90, 100, 103, 105], "solut": [1, 3, 8, 14, 15, 23, 24, 29, 30, 31, 33, 37, 38, 42, 44, 47, 54, 68, 84, 93, 95, 100, 101, 103], "soma": [64, 73, 79], "somasundharam": [7, 21, 38, 54, 56, 70, 79, 89, 90, 103], "some": [11, 13, 16, 24, 35, 39, 42, 44, 48, 52, 54, 60, 64, 68, 71, 73, 74, 78, 79, 84, 86, 87, 88, 90, 91, 92, 93, 96, 97, 103, 105, 106], "sondern": [64, 73, 79], "sonja": [64, 73, 79, 80], "soon": [1, 15, 16, 17, 34, 35, 38, 50, 64, 73, 79, 93, 99, 105], "sophi": [29, 64, 73, 79, 80, 99, 105], "sophist": [33, 64, 73, 79], "sorensen": [10, 29, 64, 73, 79], "sorgfalt": 75, "sort": [6, 11, 28, 37, 42, 54, 64, 73, 79, 101, 105], "sourabh": 52, "sourc": [1, 2, 3, 8, 11, 17, 22, 23, 25, 28, 31, 33, 34, 35, 36, 37, 40, 45, 47, 49, 56, 57, 72, 77, 82, 89, 92, 93, 94, 95, 97, 99, 100, 101, 103, 108], "sousa": [4, 12, 18, 30, 52, 71, 76], "soweit": 75, "sowi": [26, 52, 54, 64, 73, 79, 105], "sp8": [29, 30, 33, 64, 73, 79], "space": [1, 11, 15, 38, 52, 64, 73, 79, 86, 91, 101, 105, 106], "spahn": [12, 29, 42, 48, 64, 68, 71, 73, 79, 84, 90], "spam": 75, "spaom2018": [10, 54, 86, 89, 90], "sparc": [29, 64, 73, 79], "spark": [20, 64, 73, 79], "sparql": [16, 35, 64, 73, 79, 99, 105], "spatial": [3, 23, 28, 37, 64, 73, 99, 105], "spatiotempor": [64, 73, 79], "speak": [64, 73, 79], "speci": [29, 64, 73, 79], "special": [64, 73, 80, 99], "specialis": [64, 73, 79], "specialist": [3, 23, 28, 33, 34, 37, 64, 73, 79, 99, 105], "specif": [1, 2, 4, 9, 17, 20, 22, 28, 29, 30, 33, 39, 47, 60, 66, 68, 73, 74, 79, 82, 84, 89, 100, 102, 103, 105], "spectral": [64, 73, 79], "spectrometri": [33, 64, 73, 79], "spectroscopi": [52, 64, 79, 93, 105], "speed": [54, 56, 72, 86, 89, 90], "speichern": [26, 54, 64, 73, 79, 93, 105], "speichert": [26, 54, 64, 73, 79, 105], "speicherung": [26, 54, 64, 73, 79, 105], "spermatocyt": [30, 33, 64, 73, 79], "sperrung": 75, "spielen": [29, 54, 64, 73, 79, 93, 95, 105], "spin": [24, 30, 33, 64, 73, 79], "spital": [4, 12, 18, 30, 52, 71, 76], "sponsor": [39, 86], "sport": [20, 64, 73, 79], "sprecherin": [54, 64, 73, 79, 105], "spring": [54, 84, 97], "springer": [29, 52, 64, 73, 79, 105], "sql": [17, 34, 54, 73, 79, 90, 93, 105], "squar": [31, 64, 73, 79], "sreeni": [42, 56, 72, 103], "sreeniva": [42, 48, 68, 84, 90, 103], "sreev": [42, 47, 68, 91, 109], "srinivasan": [4, 12, 18, 30, 52, 71, 76], "sriram": [7, 21, 38, 54, 56, 70, 79, 89, 90, 103], "srsltid": [55, 56, 86], "ss": [26, 54, 64, 73, 79, 105], "sseldorf": [30, 33, 64, 73, 79], "ssh": [47, 68, 100], "sst": [29, 64, 73, 79], "st": [15, 17, 37, 47, 68, 78, 99, 105, 106], "st2mt110724bnew": [55, 56, 86], "stabil": [30, 33], "stabl": [29, 45, 64, 73, 79, 86, 100, 103, 108], "stack": 84, "staff": [3, 8, 22, 23, 28, 32, 33, 34, 35, 37, 44, 54, 56, 64, 72, 73, 79, 99, 100, 105], "stage": [36, 42, 49, 54, 57, 105], "stain": [24, 25, 31], "stakehold": [3, 33, 34, 37, 64, 73, 79, 99, 105], "stand": [0, 32, 64, 73, 79], "standalon": [45, 79], "standard": [0, 3, 13, 22, 23, 24, 28, 29, 31, 32, 37, 42, 48, 56, 64, 68, 72, 78, 80, 84, 100, 102, 103], "standardis": [64, 73, 99], "star": [29, 64, 73, 79], "stardist": 29, "stardist0": [31, 64, 73, 79], "stardist3d": [31, 64, 73, 79], "stark": [29, 48, 68, 79, 103], "start": [3, 23, 24, 29, 30, 33, 37, 45, 47, 55, 57, 58, 68, 78, 89, 90, 97, 99, 100], "stat_rethinking_2024": [47, 68, 81], "state": [3, 8, 11, 17, 29, 52, 64, 65, 73, 76, 79, 105], "statist": 87, "statnew": [39, 86], "statt": [26, 54, 64, 73, 79, 105], "stattfindend": [0, 32, 64, 73, 79], "steckt": [26, 54, 64, 73, 79, 105], "sted": [30, 33, 64, 73, 79], "stefan": [1, 4, 5, 7, 9, 26, 28, 36, 45, 47, 50, 52, 54, 64, 68, 73, 79, 90, 94, 105, 108], "stefani": [3, 8, 17, 23, 30, 34, 35, 37, 52, 54, 56, 64, 65, 71, 72, 73, 76, 79, 87, 90, 93, 96, 99, 100, 105], "stefania": [4, 5, 7, 9, 36, 47, 51, 52, 64, 68, 69, 73, 78, 79, 80, 90, 98, 109], "stefano": [54, 64, 73, 79, 92, 105], "steff": [47, 55, 68, 79, 107], "stegl": [17, 51, 52, 69, 79, 103], "steigen": [26, 54, 64, 73, 79, 105], "steinbach": [29, 48, 68, 79, 103], "steiner": [64, 73, 80], "stellmach": [64, 73, 79], "stellt": [26, 29, 54, 64, 73, 79, 93, 95, 105], "stem": [64, 73, 79], "step": [3, 14, 24, 30, 31, 33, 34, 37, 49, 54, 55, 56, 61, 64, 70, 72, 81, 86, 89, 90, 99, 107], "stephan": [26, 29, 47, 48, 54, 55, 58, 64, 68, 73, 78, 79, 90, 95, 103, 105], "stephen": [40, 41, 45, 48, 52, 64, 68, 73, 83, 90, 94, 108], "stereomicroscop": [22, 64, 73, 79], "stet": 75, "stetig": [26, 54, 64, 73, 79, 105], "steuerungsgremium": [64, 73, 79], "steward": [3, 8, 23, 32, 37], "stewardship": [6, 42, 50, 90, 96, 99], "stian": [11, 42, 78, 91, 103, 107, 108], "stich": [24, 64, 73, 79], "still": [3, 29, 33, 54, 64, 73, 79, 90, 93, 95, 99, 101, 105], "stitch_a01_g001": [24, 64, 73, 79], "stitch_a01_g001_00001": [24, 64, 73, 79], "stokkerman": [4, 7, 9, 36, 64, 73, 79, 90, 98, 109], "stoma": [21, 48, 68, 86, 89, 103], "storag": [0, 3, 6, 28, 30, 33, 37, 42, 54, 56, 57, 72, 93, 102], "store": [1, 3, 8, 17, 23, 24, 28, 33, 34, 35, 37, 54, 56, 57, 59, 64, 72, 73, 79, 86, 90, 93, 99, 100, 105], "strambio": [3, 4, 12, 18, 30, 33, 34, 52, 64, 71, 73, 76, 79, 87, 96, 105], "strand": [30, 33, 64, 73, 79], "strasser": [52, 73, 79, 102], "strateg": [47, 54, 55, 64, 68, 73, 79, 89, 92, 105], "strategi": [3, 33, 34, 37, 44, 87], "strategien": [26, 54, 64, 73, 79, 93, 105], "strau\u00df": [44, 45, 55, 105], "streamlin": [1, 3, 28, 37, 61, 64, 73, 79], "streamlit": 61, "stress": [42, 48, 78, 90, 105], "stretch": [29, 64, 73, 79], "strigaud": [29, 47, 48, 55, 58, 68, 78, 90], "stringari": [6, 54, 64, 73, 79, 90], "strongli": [14, 64, 73, 79], "structur": [2, 8, 9, 17, 22, 23, 28, 30, 33, 42, 47, 59, 68, 80, 82, 91, 102, 109], "strukturiert": [26, 54, 64, 73, 79, 105], "stucchi": [64, 73, 79], "student": [29, 42, 47, 48, 54, 68, 73, 79, 89, 90, 103, 105, 109], "studi": [1, 22, 28], "stufen": [0, 32, 64, 73, 79], "sturm": [47, 68, 78, 90], "st\u00e9phane": [10, 11, 18, 41, 48, 54, 68, 86, 90, 98], "st\u00f6ter": [0, 16, 23, 33, 37, 45, 46, 47, 50, 52, 64, 68, 73, 79, 90, 93, 99, 100, 103, 105], "su": [64, 73, 79], "sub": [40, 64, 73, 79, 101, 105], "subject": [64, 73, 79], "submiss": [55, 56, 64, 70, 73, 79, 81, 105], "submit": [14, 42, 60, 61, 64, 73, 79, 90, 93], "subscript": [64, 73, 79], "subsequ": [22, 29, 64, 73, 79, 80], "subset": [64, 73, 99], "substanc": [22, 64, 73, 79], "substanti": [52, 60, 61, 73, 79, 102], "substitut": [3, 23, 32, 37, 64, 73, 79, 99, 105], "success": [64, 73, 99], "suchodoletz": [0, 50, 64, 73, 79, 93, 105], "suffix": [30, 33, 64, 73, 79], "suganya": [7, 21, 38, 51, 52, 54, 56, 68, 69, 70, 78, 79, 89, 90, 103], "suit": [1, 64, 73], "suitabl": [3, 23, 28, 37, 39, 64, 73, 79, 86, 99, 105], "sum": [20, 64, 73, 79], "summer": [3, 8, 11, 17, 52, 65, 79, 105], "sun": [4, 5, 7, 9, 36, 47, 64, 68, 73, 79, 90, 98, 109], "sundar": [7, 21, 38, 54, 56, 70, 79, 89, 90, 103], "super": [30, 33, 64, 73, 79], "superhydrophob": [30, 33, 64, 73, 79], "supervis": [64, 73, 79], "supplement": [40, 64, 73, 79, 105], "support": [1, 6, 8, 15, 20, 23, 25, 28, 29, 31, 37, 38, 42, 44, 49, 51, 52, 54, 69, 87, 90, 99, 100, 102], "suppos": [57, 79, 105], "sure": [1, 15, 25, 31, 38, 61, 64, 73, 79, 87], "surfac": [64, 73, 79], "surgeri": [3, 20, 23, 32, 37, 64, 73, 79, 99, 105], "surgic": [22, 64, 73], "survei": 73, "susann": [3, 8, 17, 23, 30, 33, 35, 37, 50, 52, 54, 56, 64, 71, 72, 73, 76, 79, 80, 86, 87, 90, 93, 96, 99, 100, 105], "susanna": [1, 15, 38, 64, 73, 79], "sustain": [3, 8, 23, 28, 29, 33, 34, 37, 52, 99, 102], "suzanna": [11, 52, 64, 86, 91, 101, 105, 106], "sven": [1, 15, 38, 64, 73, 79], "swan": [42, 55, 56, 70, 79, 101, 106], "swedlow": [2, 4, 5, 15, 17, 38, 52, 64, 71, 79, 86, 99, 105], "swift": [42, 52, 64, 78], "swim": [30, 33, 64, 73, 79], "swissbia": [5, 54, 64, 73, 79, 89, 90], "switzerland": [17, 24, 31, 34, 50, 64, 73, 79, 93, 99, 105], "sylvi": [4, 12, 18, 30, 52, 71, 76], "sylvia": [4, 12, 18, 30, 52, 71, 76, 79, 93, 96, 105], "symposium": [3, 6, 22, 23, 28, 29, 32, 34, 37, 48, 54, 64, 68, 73, 79, 99, 103, 105], "syn": [48, 81, 90, 94], "synergi": [1, 14, 64, 73, 79], "syntact": [64, 73, 99], "synthet": [24, 64, 73, 79], "system": [0, 1, 3, 8, 22, 23, 28, 29, 32, 33, 34, 35, 37, 44, 45, 50, 52, 54, 55, 56, 64, 71, 72, 73, 79, 92, 93, 96, 99, 100, 103, 105, 108], "systemat": [9, 11, 38, 52, 64, 71, 73, 79, 86, 96, 105], "systematisch": [40, 79, 101], "szymon": [21, 48, 68, 86, 89, 103], "s\u00e1enz": [17, 18, 29, 50, 52, 65, 79, 96], "s\u00e9amu": [12, 29, 42, 48, 52, 64, 68, 71, 76, 84, 90], "s\u00e9bastien": [2, 4, 5, 11, 15, 17, 47, 52, 68, 71, 77, 79, 82, 99, 102, 105], "t": [3, 4, 11, 12, 16, 18, 22, 23, 24, 25, 29, 30, 31, 32, 33, 37, 40, 42, 46, 47, 48, 52, 58, 60, 61, 62, 64, 68, 71, 73, 76, 78, 79, 81, 84, 86, 90, 96, 100, 103, 107], "ta5": [54, 90, 93, 99, 105], "taatj": [4, 12, 18, 30, 52, 71, 76], "tabl": [30, 33, 80], "tabular": [64, 73, 80], "tach": [29, 64, 73, 79], "tackl": [29, 64, 73, 79], "taelman": [47, 55, 68, 79, 107], "tag": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 23, 24, 25, 26, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 54, 55, 56, 57, 58, 59, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 76, 77, 78, 79, 80, 81, 82, 83, 84, 86, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 105, 106, 107, 108, 109], "tailor": [3, 8, 14, 16, 22, 23, 32, 33, 34, 35, 37, 42, 54, 55, 56, 64, 72, 73, 79, 90, 93, 99, 100, 105], "take": [1, 28, 29, 30, 33, 60, 64, 73, 79, 99, 105], "taken": [24, 31, 64, 73, 79], "talk": [6, 11, 17, 29, 37, 42, 56, 72, 86, 101, 103, 109], "tal\u00e0": [24, 64, 73, 79], "tamara": [1, 15, 38, 64, 73, 79], "tang": [1, 15, 38, 64, 73, 79], "tanoli": [1, 15, 38, 64, 73, 79], "taraborelli": [52, 73, 79, 102], "target": [3, 22, 23, 28, 29, 33, 34, 37, 64, 73, 79, 99, 105], "tarkowska": [48, 68, 83, 90], "task": [3, 8, 23, 29, 30, 33, 34, 37, 89, 99, 103], "tau": [30, 33, 64, 73, 79], "tauchen": [29, 54, 64, 73, 79, 93, 95, 105], "tc": [29, 30, 33, 64, 73, 79], "teach": [4, 7, 21, 24, 28, 29, 31, 36, 47, 48, 56, 70, 78, 80, 81, 83, 97, 98, 100, 101, 105, 106, 109], "teaching_bioimage_analysi": [42, 90, 97, 103], "team": [0, 3, 6, 8, 11, 16, 23, 28, 30, 32, 33, 36, 37, 50, 54, 56, 68, 72, 92, 100], "technic": [1, 3, 15, 37, 38, 52, 102], "techniqu": [16, 20, 29, 34, 35, 52, 56, 64, 73, 79, 89, 93, 99, 105], "technisch": [3, 23, 26, 32, 37, 44, 54, 64, 73, 79, 99, 105], "technischen": [64, 73, 79], "technolog": [52, 73, 101, 105], "technologi": [1, 14, 54, 64, 65, 73, 89, 90, 93], "technologien": [40, 79, 101], "technopol": [5, 29, 47, 48, 54, 55, 58, 64, 68, 73, 78, 79, 89, 90], "tedd": [1, 15, 38, 64, 73, 79], "teil": [40, 101], "teilen": [26, 54, 64, 73, 79, 93, 105], "teilten": [54, 64, 73, 79, 105], "teilvorhaben": [64, 73, 79], "templat": [25, 31, 54, 80], "tempor": [3, 23, 28, 37, 64, 73, 79, 99, 105], "terabyt": [17, 50, 73, 79, 90, 105], "term": [1, 14, 22, 64, 73, 79, 87, 99, 105], "termin": [17, 61, 64, 73, 79, 99, 109], "terminologi": [3, 29, 37, 89, 99, 105, 109], "tesm": [30, 33, 64, 73, 79], "test": [20, 25, 29, 32, 55, 56, 60, 81, 84, 86], "test_dir": [25, 31, 64, 73, 79], "test_grai": [24, 64, 73, 79], "teun": [47, 55, 58, 68, 90], "text": [29, 60, 64, 73, 79], "textdaten": [54, 64, 73, 79, 95, 105], "th": [1, 15, 38, 64, 73, 79], "than": [1, 14, 29, 45, 49, 55, 64, 73, 79, 83, 94, 100, 107], "thank": [64, 73, 79, 80], "thano": [52, 79, 101, 105], "thecolon": [29, 64, 73, 79], "thei": [1, 3, 17, 22, 23, 24, 28, 29, 32, 36, 37, 42, 47, 48, 51, 52, 54, 60, 64, 68, 69, 71, 73, 78, 79, 84, 87, 89, 90, 92, 93, 96, 103, 105], "thejacksonlaboratori": [54, 84, 97], "them": [3, 9, 11, 30, 33, 37, 48, 52, 54, 60, 61, 64, 65, 71, 73, 79, 89, 90, 91, 93, 96, 97, 99, 103, 105], "thema": [0, 26, 32, 54, 64, 73, 79, 105], "themat": [14, 64, 73, 79], "themen": [26, 54, 64, 73, 79, 105], "themselv": [6, 16, 28, 50, 64, 73, 79, 90, 92, 99, 105], "thenod": [39, 86], "theoret": [31, 64, 73, 79], "theoretischen": [26, 54, 64, 73, 79, 105], "therapeut": [29, 64, 73, 79], "therapi": [29, 64, 73, 79], "therebi": [9, 64, 73, 79], "therefor": [44, 64, 73, 79, 105], "thereof": [6, 28, 37, 54, 64, 73, 79, 105], "thermo": [24, 31, 64, 73, 79], "thermofish": [64, 73], "thesi": [29, 64, 73, 79, 99], "thi": [0, 1, 2, 3, 4, 6, 7, 8, 9, 11, 12, 14, 16, 17, 20, 22, 23, 24, 25, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 42, 45, 47, 48, 49, 50, 51, 52, 54, 56, 57, 59, 60, 62, 64, 65, 66, 68, 70, 71, 72, 73, 74, 76, 78, 79, 80, 81, 82, 84, 88, 89, 90, 96, 97, 98, 99, 100, 101, 102, 103, 106, 107, 109], "thilo": [45, 52, 71, 94, 108], "thing": [64, 73, 79], "think": [60, 61], "thoennissen": [3, 8, 23, 28, 64, 73, 79, 99, 105], "thoma": [3, 8, 16, 17, 22, 23, 28, 30, 32, 33, 34, 37, 42, 47, 50, 51, 52, 54, 56, 64, 68, 69, 71, 72, 73, 76, 78, 79, 84, 87, 90, 91, 93, 96, 99, 100, 105, 109], "thorough": [30, 33, 64, 73, 79], "thoroughli": [42, 45, 86, 100], "those": [1, 6, 14, 28, 37, 47, 54, 64, 68, 73, 79, 81, 105], "thread": [64, 73, 79], "three": [3, 6, 8, 14, 23, 28, 31, 36, 37, 54, 64, 73, 79, 87, 99, 105], "thrive": [64, 73, 80], "through": [0, 3, 8, 14, 16, 20, 23, 36, 47, 68, 71, 92, 93, 99, 100, 107], "throughout": [3, 8, 14, 23, 28, 37, 42, 52, 54, 55, 57, 64, 71, 73, 79, 86, 90, 93, 96, 99, 105], "thu": [29, 64, 73, 79], "thursdai": [3, 23, 37, 64, 73, 79, 99, 105, 109], "th\u00f6nni\u00dfen": [0, 16, 23, 37, 45, 46, 47, 52, 64, 68, 73, 79, 90, 93, 99, 100, 103, 105], "ti": [30, 33, 64, 73, 79], "ti2": [30, 33, 64, 73, 79], "tiejun": [52, 71, 90, 93, 107], "tier": [30, 33, 64, 73, 79], "tif": [24, 31, 64, 73, 79], "tiff": [2, 3, 8, 17, 23, 24, 30, 31, 33, 34, 37, 47, 64, 68, 73, 79, 82, 99, 102, 105], "tile": [2, 17, 31, 47, 68, 82, 102], "till": [29, 40, 48, 68, 79, 101, 103], "tim": [4, 5, 7, 9, 11, 12, 17, 36, 40, 42, 45, 47, 48, 51, 52, 55, 64, 68, 69, 71, 73, 78, 79, 81, 83, 84, 90, 97, 98, 101, 103, 109], "time": [3, 20, 31, 33, 34, 37, 60, 64, 73, 79, 87, 99, 105], "timepoint": [24, 64, 73, 79], "timmonko": [45, 47, 55, 68, 78, 90, 97], "timo": [0, 3, 8, 23, 28, 50, 64, 73, 79, 93, 99, 105], "tinevez": [11, 18, 29, 31, 41, 48, 52, 54, 55, 65, 68, 79, 86, 90, 98], "tingt": [4, 12, 18, 30, 52, 71, 76], "tip": [22, 64, 73, 79], "tirado": [4, 5, 7, 9, 36, 47, 64, 68, 73, 79, 90, 98, 109], "tirf": [30, 33, 64, 73, 79], "tischer": [2, 5, 7, 9, 11, 12, 15, 17, 18, 24, 29, 30, 36, 47, 49, 52, 54, 55, 56, 64, 68, 70, 71, 73, 76, 79, 84, 86, 90, 98, 99, 103, 105, 107, 109], "tischi": [4, 5, 47, 54, 55, 68, 84, 86, 90, 98, 103], "tissu": [3, 23, 29, 32, 37, 64, 73, 99, 105], "titanium": [22, 64, 73, 79], "titl": [54, 59, 80, 90, 101, 105], "tjxnmiwtdac": [54, 56, 72, 86, 89, 90], "tobi": [4, 5, 7, 9, 11, 42, 47, 54, 55, 64, 68, 73, 79, 90, 98, 103, 109], "tobia": [0, 1, 3, 8, 15, 23, 30, 33, 34, 37, 38, 47, 48, 50, 52, 58, 64, 68, 73, 79, 90, 93, 99, 105], "todai": [64, 73, 79], "todd": [48, 68, 83, 90], "togeth": [1, 14, 15, 32, 38, 42, 44, 55, 56, 70, 101, 106], "token": 61, "tol": [4, 49, 54, 56, 64, 70, 79, 90], "toler": [22, 64, 73], "tom": [0, 3, 6, 8, 15, 16, 17, 22, 23, 28, 30, 32, 33, 34, 35, 45, 46, 47, 52, 54, 56, 64, 68, 72, 73, 78, 79, 87, 90, 93, 99, 100, 103, 105, 106, 109], "tomancak": [12, 29, 50, 52, 94], "tommaso": [54, 64, 73, 79, 108], "tomograph": [29, 64, 73, 79], "tomographi": 20, "too": [3, 55, 60, 64, 73, 84, 86, 105], "tool": [1, 8, 17, 23, 26, 28, 29, 30, 35, 36, 37, 38, 39, 40, 49, 50, 55, 56, 65, 72, 81, 84, 93, 98, 99, 102, 103, 108, 109], "toolkit": [40, 64, 79, 105], "top": [3, 8, 23, 33, 34, 35, 37, 54, 56, 64, 72, 73, 79, 99, 100, 105], "topic": [0, 3, 6, 16, 20, 23, 28, 33, 34, 37, 40, 42, 44, 47, 54, 55, 56, 60, 64, 70, 73, 84, 86, 88, 89, 91, 93, 99, 100, 101, 105, 106], "torec": [47, 52, 64, 68, 79, 90, 100, 107], "torsten": [0, 15, 16, 17, 23, 33, 37, 45, 46, 47, 50, 52, 64, 68, 73, 78, 79, 90, 93, 99, 100, 103, 105, 106], "tosi": [11, 18, 54, 55, 68, 77, 86, 90, 98], "toth": [17, 51, 52, 69, 79, 103], "touch": 74, "toward": [1, 3, 14, 33, 37], "toxicolog": [1, 28, 64, 73, 79], "tpecot": [48, 68, 89, 103], "tpl": [40, 90], "trace": 87, "traceabl": [14, 42, 55, 64, 73, 79, 90, 93, 105], "tracer": [29, 64, 73, 79], "track": [30, 33, 44, 45, 46, 48, 49, 62, 64, 71, 73, 88, 96, 100, 105, 107, 109], "trackem2": [24, 64, 73, 79], "tradit": [17, 34, 54, 73, 79, 90, 93, 105], "train": [1, 15, 21, 25, 30, 32, 38, 44, 49, 59, 60, 70, 78, 81, 106, 107], "trainer": [3, 11, 23, 28, 33, 34, 37, 52, 91, 93, 96, 99], "training_materi": 74, "training_notebook": [31, 64, 73, 79], "transcript": [64, 73, 79], "transfer": [25, 31, 62, 64, 73, 79, 80, 90, 99], "transform": [3, 9, 22, 23, 25, 28, 31, 32, 37, 64, 73, 79, 99, 105], "translat": [3, 8, 23, 30, 33, 34, 37, 40, 64, 73, 79, 99, 105], "transloc": [48, 68, 78, 90], "transmit": [30, 33, 64, 73, 79], "transpar": 80, "transparent": [40, 79, 101], "transpos": [3, 22, 23, 32, 37, 64, 73, 79, 99, 105], "trap": [20, 64, 73], "treat": [33, 64, 73, 79], "treatment": [29, 33, 64, 73, 79], "tree": [29, 54, 67, 86, 90, 94], "treffen": [0, 32, 64, 73, 79], "trehin": [64, 73, 84], "trei": [17, 51, 52, 69, 79, 103], "trello": [30, 33, 64, 73, 79], "trevor": [2, 4, 5, 15, 17, 52, 71, 79, 99, 105], "tri": [64, 73, 79], "trial": [29, 64, 73, 79], "trick": [22, 64, 73, 79], "tripl": [17, 34, 54, 73, 79, 90, 93, 105], "triton": [24, 31, 64, 73, 79], "troman": [40, 68, 81, 90], "trono": [25, 31, 64, 73, 79], "trotzdem": 75, "troubleshoot": [64, 73], "troublshoot": [64, 73, 81], "truth": [47, 68, 81], "try": [29, 64, 73, 79], "ts4nfdi": [64, 73, 79], "tu": [3, 17, 22, 23, 28, 29, 32, 37, 44, 45, 55, 67, 86, 87, 90, 94, 109], "tubulin": [31, 64, 73, 79], "tudorach": [64, 73, 79], "tulok": [0, 3, 22, 23, 37, 64, 73, 79, 99, 105], "tumour": [29, 54, 56, 64, 72, 73, 79, 86, 89, 90], "ture": [55, 105, 106], "turner": [42, 68], "tutori": [2, 3, 4, 7, 8, 14, 18, 20, 23, 28, 29, 30, 33, 34, 35, 36, 37, 40, 44, 54, 57, 58, 64, 70, 73, 77, 79, 80, 83, 91, 92, 93, 95, 96, 98, 99, 100, 101, 102, 105, 106, 107], "tuur": [47, 55, 68, 79, 107], "two": [3, 5, 6, 20, 22, 23, 28, 29, 30, 32, 33, 34, 37, 54, 64, 73, 79, 81, 89, 90, 99, 105], "txt": [25, 31, 64, 73, 79], "type": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 76, 77, 78, 79, 80, 81, 82, 83, 84, 86, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 105, 106, 107, 108, 109], "t\u00e4tigkeit": 75, "u": [3, 14, 23, 26, 28, 29, 37, 40, 42, 54, 55, 56, 59, 60, 64, 70, 73, 79, 87, 89, 90, 93, 99, 101, 105, 106], "uag": [40, 64, 73, 79, 105], "ub": [50, 52, 80], "ubvujf7wausq5q7v7pi1vepi1": [42, 56, 72], "uc": [3, 22, 23, 32, 37, 64, 73, 79, 99, 105], "ucl": [40, 42, 55, 56, 64, 79, 105], "udemi": [55, 56, 86], "ufz": [1, 3, 23, 28, 54, 64, 73, 79, 90, 99, 105], "ugent": [57, 86, 105], "ugi": [1, 7, 11, 15, 21, 52, 54, 56, 64, 70, 71, 73, 79, 86, 89, 90, 96, 103, 105], "uhlmann": [17, 42, 52, 64, 78, 79, 90, 101], "uk": [4, 7, 21, 30, 33, 38, 40, 42, 43, 48, 49, 52, 54, 55, 56, 64, 73, 79, 81, 89, 90, 91, 93, 94, 96, 101, 103, 105, 106], "ulf": [42, 68], "uli": [52, 64, 79, 105], "ulm": [3, 33, 64, 73, 79, 99, 105], "ulrich": [22, 64, 73, 79], "ulrik": [4, 11, 12, 18, 30, 33, 40, 52, 64, 71, 73, 76, 79, 86, 87, 90, 96, 105], "ultim": [1, 28, 64, 73, 79], "ultra": [31, 64, 73, 79], "um": [0, 26, 32, 54, 64, 73, 75, 79, 93, 95, 105], "umap": [29, 64, 73, 79], "umass": [30, 33, 64, 73, 79], "umfassend": [29, 54, 64, 73, 79, 93, 105], "umfeld": [40, 79, 101], "umgehend": 75, "umsetzung": [49, 57, 86, 102, 105], "umsetzungsvorschl\u00e4g": [64, 73, 79], "umst\u00e4nden": 75, "umzusetzen": [26, 54, 64, 73, 79, 105], "un": [29, 54, 64, 73, 79, 93, 105], "unam": [30, 33, 64, 73, 79], "unambigu": [14, 42, 55, 64, 73, 79, 90, 93, 105], "unannot": [64, 73, 79], "unbalanc": [52, 64, 79, 93, 105], "unber\u00fchrt": 75, "unc": [30, 33, 64, 73, 79], "uncertain": [64, 73, 79], "unchang": [16, 35, 64, 73, 79], "und": [0, 32, 40, 49, 57, 75, 86, 95, 99, 101, 102], "under": [22, 24, 25, 44, 54, 56, 61, 72, 86, 87, 89, 90, 99, 105], "undergo": [16, 35, 64, 73, 79], "underli": [15, 20, 47, 64, 68, 73, 79, 82, 99, 100], "underlin": [64, 73, 79], "understand": [1, 9, 15, 33, 38, 39, 55, 57, 64, 73, 79, 80, 86], "understood": [54, 56, 72, 86, 89, 90], "unet": [29, 64, 73, 79], "unfortun": [47, 68, 81], "ung": [64, 73, 79], "uni": [6, 16, 17, 28, 35, 37, 40, 45, 47, 48, 50, 52, 64, 68, 75, 80, 87, 94, 99, 101, 105, 108, 109], "uniform": [29, 64, 73, 79], "unilamellar": [24, 64, 73, 79], "uniqu": [64, 73, 99], "unit": [11, 29, 30, 33, 40, 42, 52, 55, 56, 64, 73, 76, 79, 90, 94, 105], "univer": [30, 33, 64, 73, 79], "univers": [3, 6, 8, 20, 23, 30, 33, 34, 35, 37, 42, 44, 47, 48, 56, 68, 72, 82, 93, 99, 100, 109], "universidad": [30, 33, 64, 73, 79], "universit": [30, 33, 64, 73, 79], "universit\u00e4t": [3, 17, 23, 26, 32, 37, 44, 54, 64, 73, 79, 99, 105, 109], "universit\u00e4ten": [40, 101], "unknown": [1, 2, 3, 4, 5, 10, 11, 12, 13, 18, 20, 21, 25, 29, 31, 34, 38, 39, 40, 41, 42, 43, 45, 46, 47, 48, 49, 52, 54, 55, 56, 57, 58, 63, 64, 66, 67, 68, 71, 72, 89, 90, 91, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 105, 106, 108], "unkown": [49, 57, 92, 93, 105], "unless": 87, "unlock": [11, 38, 52, 64, 71, 73, 79, 86, 96, 105], "unmount": [24, 31, 64, 73, 79], "unseen": [25, 31, 64, 73, 79], "unser": [0, 29, 32, 54, 57, 64, 73, 75, 79, 86, 93, 95, 99, 105], "unseren": 75, "unter": [26, 40, 54, 64, 73, 75, 79, 93, 101, 105], "untereinand": [0, 32, 64, 73, 79], "unterliegen": 75, "unterschiedlich": [0, 26, 32, 54, 64, 73, 79, 105], "unterst\u00fctzt": [40, 49, 57, 79, 86, 101, 102, 105], "until": [47, 60, 68, 81], "unverlangten": 75, "un\u00fcbersichtlich": [26, 54, 64, 73, 79, 105], "up": [3, 20, 22, 23, 24, 32, 33, 37, 54], "updat": [10, 40, 64, 73, 74, 84, 87, 105], "upload": [1, 22, 29, 32, 37, 47, 54, 56, 61, 64, 68, 70, 73, 81, 84, 90, 93, 95, 101, 103, 105], "uplsapo": [25, 31, 64, 73, 79], "upper": 61, "upright": [64, 73], "uptak": [3, 8, 23, 33, 34, 35, 37, 54, 56, 64, 72, 73, 79, 99, 100, 105], "upton": [30, 33, 64, 73, 79], "uq": [39, 86, 105, 106], "urbanc": [41, 48, 54, 68, 86, 90, 98], "urheb": [54, 64, 73, 79, 95, 105], "urheberrecht": 26, "urheberrechtsverletzung": 75, "url": [30, 33, 59, 61, 64, 73, 79], "uruguai": [17, 64, 73, 79], "us": [0, 3, 4, 6, 7, 8, 9, 11, 12, 14, 15, 16, 20, 21, 22, 23, 24, 32, 34, 35, 36, 37, 38, 45, 59, 60, 71, 72, 76, 78, 80, 81, 82, 88, 91, 93, 94, 95, 97, 98, 102, 108], "usabl": [52, 71, 87, 92, 93, 96, 102], "usag": [2, 3, 22, 23, 32, 37, 42, 55, 56, 64, 73, 79, 86, 99, 100, 105], "use_gpu": [25, 31, 64, 73, 79], "usegalaxi": [45, 108], "user": [1, 10, 14, 20, 22, 24, 28, 29, 32, 42, 45, 47, 48, 55, 57, 58, 74, 80, 87, 93, 97, 108], "usual": [64, 73, 79], "utf": [3, 22, 23, 32, 37, 64, 73, 79, 99, 105], "util": [30, 33, 45, 47, 52, 55, 64, 68, 71, 73, 78, 79, 90, 93, 96, 97, 105], "utz": [64, 73, 79], "uuml": [30, 33, 64, 73, 79], "uw": [21, 25, 48, 54, 68, 86, 89, 90, 98, 103], "v": [0, 1, 3, 12, 15, 17, 32, 33, 34, 35, 38, 42, 54, 56, 64, 72, 73, 79, 86, 89, 90, 99, 100, 103, 105, 109], "v0": [10, 20, 29], "v1": [18, 29, 31, 52, 65, 79, 90, 98], "v1i": [17, 34, 52, 64, 79, 99, 105], "v2": [3, 8, 11, 17, 52, 64, 65, 73, 79, 105], "v3": [29, 64, 73, 79], "v4sdb": [42, 47, 68, 90, 109], "v8": [45, 79], "vakili": [4, 5, 7, 9, 36, 47, 64, 68, 73, 79, 90, 98, 109], "val_rdm42": [31, 64, 73, 79], "valencia": [0, 16, 23, 37, 45, 46, 47, 50, 52, 64, 68, 73, 79, 90, 93, 99, 100, 103, 105], "valentin": [48, 68, 86, 89, 90], "valentyna": [21, 48, 68, 86, 89, 103], "valid": 31, "valu": [1, 11, 28, 30, 33, 52, 90, 96], "valuabl": [1, 28, 54, 60, 64, 73, 79, 80, 92, 105], "van": [17, 18, 29, 50, 51, 52, 65, 69, 71, 79, 90, 93, 96, 105], "vanessa": [0, 3, 6, 16, 22, 23, 28, 32, 37, 45, 46, 47, 50, 52, 64, 68, 73, 79, 90, 92, 93, 99, 100, 103, 105], "vannari": [41, 48, 54, 68, 86, 90, 98], "vannieuwkerk": [47, 55, 68, 79, 107], "vari": [29, 64, 73, 79], "variabl": [3, 29, 33, 34, 37, 61, 64, 73, 79, 99, 105], "variat": [29, 52, 64, 73, 79, 93, 105], "varieti": [3, 23, 28, 37, 42, 55, 56, 64, 70, 73, 79, 99, 101, 105, 106], "variou": [0, 1, 3, 8, 16, 22, 23, 28, 30, 33, 34, 37, 47, 51, 52, 60, 69, 90, 93, 99, 101, 102, 105], "varun": [4, 12, 18, 30, 52, 71, 76], "vasja": [41, 48, 54, 68, 86, 90, 98], "vebjorn": [42, 43, 71], "ved": [4, 12, 18, 30, 52, 71, 76], "veldhui": [10, 29, 64, 73, 79], "velten": [20, 47, 48, 54, 68, 86, 91], "vem": [42, 43, 70, 81, 90], "vendor": [29, 54, 64, 73, 79, 89, 103], "ventana": [24, 31, 64, 73, 79], "venu": [3, 23, 37, 64, 73, 79, 99, 105, 109], "vera": [1, 15, 38, 64, 73, 79], "veranstaltung": [26, 54, 64, 73, 79, 105], "veranstaltungsreih": [26, 54, 64, 73, 79, 105], "verantwortlich": [29, 54, 64, 73, 79, 105], "verarbeiten": [26, 54, 64, 73, 79, 93, 105], "verarbeitet": [26, 29, 54, 64, 73, 79, 93, 105], "verbessern": [57, 86, 99, 105], "verbos": [25, 31, 64, 73, 79], "verbreitung": 75, "verbunden": [26, 54, 64, 73, 79, 105], "verbundprojekt": [64, 73, 79], "verein": [57, 86, 99, 105], "vereinbarungen": [54, 64, 73, 79, 95, 105], "verfuegung": [29, 54, 64, 73, 79, 93, 105], "verger": [10, 11, 18, 41, 48, 54, 68, 86, 90, 98], "veri": [16, 35, 44, 60, 64, 73, 79, 87, 99, 105], "verifi": 61, "verlauf": [64, 73, 79], "verlinkten": 75, "verlinkung": 75, "vermaerck": [64, 73, 79, 109], "vernetzung": [64, 73, 79], "verpflichtet": 75, "verpflichtungen": 75, "versatil": [44, 45, 55, 105], "verschiedenen": [26, 52, 54, 64, 73, 79, 105], "version": [3, 16, 17, 20, 23, 25, 26, 31, 32, 34, 35, 37, 39, 40, 54, 66, 80, 90, 93, 106], "versionskontrollwerkzeug": [29, 54, 64, 73, 79, 93, 105], "vervielf\u00e4ltigung": 75, "verwaltung": [49, 57, 86, 102, 105], "verwenden": [26, 54, 64, 73, 79, 105], "verwertung": 75, "ver\u00e4ndern": [29, 54, 64, 73, 79, 93, 95, 105], "ver\u00f6ffentlichen": 29, "ver\u00f6ffentlichten": 75, "ver\u00f6ffentlichung": [26, 54, 64, 73, 79, 105], "ver\u00f6ffentlichungen": [40, 79, 101], "vesicl": [24, 64, 73, 79], "vesicular": [30, 33, 64, 73, 79], "via": [7, 21, 25, 31, 38, 42, 45, 48, 52, 54, 56, 64, 70, 73, 79, 84, 86, 89, 90, 97, 100, 103, 105, 108], "viabl": [1, 28, 64, 73, 79], "viadrina": [44, 64, 73, 79, 105], "vibbit": [47, 55, 68, 79, 89, 107], "vibrant": [64, 73, 79], "vibspec": [52, 64, 79, 93, 105], "victor": [48, 68, 84, 89, 103], "video": [2, 3, 4, 7, 8, 12, 16, 21, 22, 23, 30, 33, 34, 35, 37, 38, 42, 49, 54, 55, 59, 64, 70, 72, 73, 76, 79, 81, 86, 89, 90, 95, 97, 98, 99, 100, 101, 102, 103, 105, 106], "viel": [26, 40, 54, 64, 73, 79, 101, 105], "view": [4, 30, 31, 33, 45, 47, 49, 52, 55, 56, 64, 68, 70, 73, 79, 86, 88, 93, 97, 105], "viewer": [48, 64, 73, 79, 97, 103], "viktoriia": [48, 68, 84, 89, 103], "vilardel": [64, 73, 79], "villalobo": [30, 33, 64, 73, 79], "villegeorg": [11, 25, 31, 42, 46, 58, 86, 90], "vincent": [24, 64, 73, 79], "vinohradi": [20, 64, 73, 79], "virgini": [17, 42, 52, 64, 78, 79, 90, 101], "virshup": [17, 47, 51, 52, 68, 69, 78, 79, 90, 103], "virtanen": [47, 68, 78, 90], "virtual": [3, 33, 34, 37, 64, 73, 79, 99, 105], "vision": [30, 33, 54, 64, 73, 79], "visit": [29, 64, 73, 79], "vison": [5, 68, 86, 89, 90], "visual": [6, 9, 16, 24, 29, 30, 31, 33, 35, 49, 54, 55, 64, 79, 84, 97, 99, 105, 107], "visualis": [42, 43, 70, 81, 90], "vital": [29, 30, 33, 52, 64, 73, 79, 102], "vlasveld": [52, 71, 79, 93, 96, 105], "vocabulari": [64, 73, 79], "vogel": [3, 22, 23, 32, 37, 64, 73, 79], "vogelsang": [3, 22, 23, 32, 37, 64, 73, 79], "voic": [64, 73, 79], "voigt": [54, 64, 73, 79, 93, 105], "vol": [17, 34, 52, 64, 73, 79, 99], "volker": [54, 55, 68, 77, 86, 90, 98], "volkerh": [55, 68, 86], "vollst\u00e4ndigkeit": 75, "volltextserv": [50, 52, 80], "volum": [1, 28, 29, 64, 73, 79, 99], "volumetr": [29, 64, 73, 79], "vom": 75, "von": [0, 12, 26, 29, 32, 42, 48, 50, 54, 57, 64, 68, 71, 73, 75, 79, 84, 86, 90, 93, 95, 99, 105], "vor": [26, 29, 54, 64, 73, 75, 79, 93, 105], "voraussetzungen": [26, 54, 64, 73, 79, 93, 105], "vorfeld": [64, 73, 79], "vorgaben": [26, 54, 64, 73, 79, 105], "vorliegend": [40, 52, 64, 73, 79, 101, 105], "vorliegenden": [26, 54, 64, 73, 79, 105], "vorteil": [26, 54, 64, 73, 79, 105], "voxel": [24, 64, 73, 79], "vo\u00df": [64, 73, 80], "vpn": [3, 23, 28, 64, 73, 79, 99, 105], "vre": [64, 73, 79], "vrhovac": [17, 18, 29, 50, 52, 65, 79, 96], "vs120": [25, 31, 64, 73, 79], "vsi": [25, 31, 64, 73, 79], "v\u00f6hringer": [17, 51, 52, 69, 79, 103], "w": [2, 4, 5, 12, 14, 15, 17, 24, 29, 31, 34, 42, 49, 50, 51, 52, 54, 55, 56, 64, 65, 68, 69, 70, 71, 73, 78, 79, 90, 93, 94, 96, 98, 99, 105], "w10nm": [24, 64, 73, 79], "w541": [64, 73, 79], "w545": [64, 73, 79], "wa": [1, 3, 6, 8, 16, 17, 18, 20, 23, 24, 25, 26, 28, 29, 30, 31, 32, 33, 34, 35, 37, 40, 52, 76, 80, 90, 93, 94, 98, 99, 100], "waagmeest": [17, 34, 54, 73, 79, 90, 93, 105], "wachsenden": [64, 73, 79], "wagen": [1, 15, 38, 64, 73, 79], "wagner": [33, 64, 73, 79], "wah": [11, 38, 52, 64, 71, 86, 96, 105], "wai": [3, 11, 14, 29, 33, 39, 42, 48, 52, 54, 55, 57, 68, 81, 86, 89, 90, 93, 97, 99, 103, 105, 106], "waiger": [64, 73, 79], "wait": 60, "waith": [17, 18, 29, 48, 50, 52, 65, 68, 79, 82, 83, 90, 96, 103], "walczysko": [7, 21, 38, 54, 56, 70, 79, 89, 90, 103], "walkthrough": [16, 34, 35, 56, 64, 73, 79, 99, 105], "wang": [29, 64, 73, 79], "wann": [29, 54, 64, 73, 79, 93, 105], "want": [1, 22, 28, 29, 30, 33, 47, 48, 54, 55, 58, 60, 61, 64, 68, 73, 78, 79, 80, 89, 90, 103, 105], "waren": [54, 64, 73, 75, 79, 95, 105], "warp": [4, 68, 84], "warpi": [24, 31, 64, 73, 79], "watch": [12, 34, 42, 54, 56, 72, 79, 86, 89, 90, 100, 103, 105], "water": [39, 52, 71, 79, 93, 96, 105], "watson": [30, 33, 64, 73, 79], "wayn": [12, 29, 50, 52, 94], "we": [1, 3, 6, 8, 11, 14, 15, 16, 17, 22, 23, 25, 28, 31, 32, 33, 34, 35, 37, 38, 42, 47, 48, 49, 50, 52, 54, 55, 56, 59, 60, 64, 68, 71, 72, 73, 74, 78, 80, 81, 84, 87, 88, 89, 90, 92, 93, 97, 99, 100, 101, 103, 107, 109], "weaken": [64, 73, 99], "web": [1, 16, 17, 28, 30, 33, 34, 35, 39, 42, 45, 47, 50, 55, 56, 64, 68, 70, 73, 77, 79, 81, 86, 93, 99, 100, 105, 106, 108], "webclient": [3, 22, 23, 32, 37, 64, 73, 79, 99, 105], "webinar": [70, 91, 98], "webseit": 75, "webseiten": [29, 54, 64, 73, 75, 79, 93, 105], "websit": [29, 30, 33, 34, 42, 49, 55, 56, 59, 64, 73, 74, 79, 80, 86, 90, 92, 93, 95, 99, 102, 105], "week": [26, 54, 64, 73, 79, 105], "wege": [52, 64, 79, 105], "wegewitz": [44, 45, 55, 105], "wei": [1, 11, 45, 52, 68, 84, 86, 89, 103, 108], "weichenchu": [42, 58, 68, 90], "weidtkamp": [3, 8, 17, 23, 30, 34, 35, 37, 52, 54, 56, 64, 65, 71, 72, 73, 76, 79, 87, 90, 93, 96, 99, 100, 105], "weigert": [5, 7, 25, 29, 38, 48, 54, 56, 64, 68, 70, 73, 79, 86, 89, 90, 98, 103], "weil": [0, 16, 23, 37, 45, 46, 47, 52, 64, 68, 73, 79, 90, 93, 99, 100, 103, 105], "weimer": [64, 73, 79], "weiner": [26, 54, 64, 73, 79, 105], "weis": [1, 15, 38, 64, 73, 79], "weisbart": [17, 52, 79, 90, 101], "weischer": [16, 35, 42, 64, 73, 79, 90, 99, 100, 105], "weischner": [16, 35, 52, 76, 99, 100, 105], "weisen": 75, "weiteren": [0, 32, 64, 73, 79], "weiterf\u00fchrend": [26, 54, 64, 73, 79, 105], "weitergeben": [54, 64, 73, 79, 95, 105], "weitergegeben": 75, "welch": [0, 26, 32, 54, 64, 73, 79, 93, 105], "welchen": [26, 54, 64, 73, 79, 93, 105], "welcom": [3, 37, 40, 60, 68, 86, 87, 89], "well": [1, 3, 16, 22, 24, 28, 29, 31, 33, 34, 35, 37, 40, 42, 45, 49, 54, 55, 56, 57, 64, 72, 73, 79, 83, 86, 89, 90, 94, 99, 100, 105, 107], "wellcom": [42, 55, 56, 79], "welt": [29, 54, 64, 73, 79, 93, 95, 105], "weltweit": [29, 54, 64, 73, 79, 93, 105], "wem": [29, 54, 64, 73, 79, 95, 105], "wendt": [0, 6, 23, 28, 35, 37, 42, 45, 46, 47, 50, 52, 64, 68, 73, 76, 79, 90, 92, 93, 96, 99, 100, 103, 105, 107], "wenn": [29, 54, 64, 73, 79, 93, 105], "wer": [29, 93], "werbeinformationen": 75, "werbung": 75, "werden": [0, 26, 29, 32, 40, 54, 64, 73, 75, 79, 93, 101, 105], "were": [0, 3, 6, 16, 20, 22, 23, 24, 25, 28, 29, 31, 32, 37, 42, 47, 52, 54, 64, 73, 79, 87, 90, 92, 93, 99, 102, 105], "werk": 75, "werner": [0, 50, 73, 79, 93, 105], "wernet": [3, 8, 23, 30, 33, 34, 37, 52, 64, 73, 79, 99, 105], "wessen": [54, 64, 73, 79, 95, 105], "west": [64, 73, 81, 109], "westerhoff": [1, 15, 38, 64, 73, 79], "wetzker": [16, 28, 37, 50, 54, 64, 73, 79, 90, 92, 99, 105], "weyend": [7, 21, 38, 54, 56, 70, 79, 89, 90, 103], "wg12_checklists_for_image_publish": [42, 48, 78, 90, 105], "what": [10, 29, 36, 39, 47, 48, 56, 64, 66, 68, 70, 73, 78, 81, 84, 87, 89, 106], "wheeler": 40, "when": [4, 9, 11, 25, 29, 31, 39, 51, 52, 64, 65, 66, 73, 79, 90, 98, 99], "where": [0, 1, 3, 8, 16, 23, 28, 33, 34, 35, 37, 42, 47, 54, 55, 56, 61, 72, 86, 90, 93, 98, 99, 100, 105], "wherev": [64, 73, 79], "whetzel": [64, 73, 79], "which": [1, 3, 4, 7, 9, 14, 15, 20, 21, 22, 23, 24, 25, 29, 31, 32, 34, 37, 38, 40, 45, 47, 52, 54, 56, 59, 60, 64, 68, 70, 73, 79, 80, 82, 84, 87, 89, 90, 99, 100, 102, 103, 105], "while": [0, 3, 8, 23, 28, 29, 50, 54, 56, 60, 64, 72, 73, 79, 86, 89, 90, 93, 99, 105], "white": [24, 31, 64, 73, 79], "who": [3, 23, 29, 33, 34, 37, 47, 48, 54, 55, 58, 68, 78, 80, 89, 103], "whoisjack": [11, 41, 48, 68, 84, 86, 90, 103], "whole": [1, 25, 28], "whole_ds441": [31, 64, 73, 79], "wholemount": [29, 64, 73, 79], "why": [3, 17, 30, 33, 34, 35, 47, 60, 64, 68, 73, 84, 86], "wibk": [1, 28, 50, 64, 73, 79, 90], "wichtig": [29, 54, 64, 73, 79, 93, 95, 105], "wide": [1, 14, 64, 73, 79], "widersprochen": 75, "widget": [45, 47, 55, 64, 68, 73, 78, 79, 90, 97], "wie": [29, 95], "wiebk": [29, 52, 64, 76], "wiederzuverwenden": [29, 54, 64, 73, 79, 105], "wifi": [3, 23, 28, 64, 73, 79, 99, 105], "wiki": [42, 49, 79, 86, 90, 100, 105], "wildlif": [20, 64, 73], "wilei": [3, 4, 8, 9, 11, 16, 18, 29, 30, 33, 34, 35, 37, 40, 52, 76, 79, 90, 94, 98, 99, 100, 105], "wilj": [40, 64, 73, 79, 105], "wilkin": [29, 64, 73, 79], "wilkinson": [52, 64, 71, 79, 93, 105], "willem": [1, 15, 38, 64, 73, 79], "willems": [52, 71, 79, 93, 96, 105], "william": [11, 42, 78, 91, 103, 107, 108], "wilson": [29, 52, 79, 90], "win": 61, "windhag": [47, 54, 58, 63, 68, 79, 90], "window": [24, 61], "winter": [42, 47, 68, 90, 109], "wir": [26, 29, 54, 57, 64, 73, 75, 79, 86, 93, 95, 99, 105], "wird": [26, 29, 54, 64, 73, 75, 79, 105], "wirkt": [26, 54, 64, 73, 79, 105], "wise": [20, 64, 73], "wissenschaft": [40, 64, 73, 79, 101], "wissenschaftlich": [26, 54, 64, 73, 79, 105], "wissenschaftlichen": [26, 29, 40, 54, 64, 73, 79, 93, 95, 101, 105], "wissenschaftszweigen": [57, 86, 99, 105], "wissensgraph": [64, 73, 79], "within": [1, 8, 11, 14, 15, 28, 29, 38, 40, 52, 54, 65, 71, 80, 86, 90, 92, 96, 99, 102, 105], "without": [20, 24, 29, 32, 39, 55, 80, 86, 106], "wittner": [1, 15, 38, 64, 73, 79], "witz": [48, 54, 63, 68, 78, 84, 86, 89, 90, 98, 103], "wizard": [24, 31, 64, 73, 79], "wo": [26, 54, 64, 73, 79, 105], "wolf": [47, 64, 68, 73, 78, 79, 90], "wolfgang": [17, 51, 52, 69, 79, 103], "wolni": [5, 55, 68, 86, 89, 90], "wolstencroft": [17, 34, 52, 54, 71, 73, 79, 90, 93, 96, 105], "wong": [3, 7, 21, 33, 34, 38, 52, 54, 56, 70, 71, 76, 79, 89, 90, 96, 103, 105], "work": [0, 1, 6, 8, 14, 15, 16, 17, 20, 23, 28, 30, 38, 40, 44, 45, 47, 52, 55, 60, 62, 68, 76, 84, 86, 93, 97], "workbook": [16, 35, 64, 73, 79], "workflow": [0, 1, 2, 3, 4, 7, 11, 15, 23, 29, 33, 36, 37, 38, 47, 56, 57, 71, 78, 80, 83, 84, 91, 93, 97, 99, 103, 109], "workload": [64, 73, 99], "workshop": [11, 14, 16, 20, 25, 26, 29, 31, 35, 40, 44, 46, 63, 78, 93, 100, 102], "workshopthi": [3, 23, 28, 64, 73, 79, 99, 105], "world": [14, 16, 34, 35, 54, 55, 56, 64, 73, 79, 90, 93, 99, 105], "worldwid": [42, 54, 55, 64, 73, 79, 90, 108], "wors": [29, 64, 73, 79], "worth": [44, 64, 73, 79, 105], "would": [20, 59, 60, 64, 73, 79, 80], "wp": [30, 33, 64, 73, 79], "wrap": [3, 15, 37, 47, 64, 68, 73, 79, 82, 100], "wrapper": [15, 47, 68, 82, 100], "wright": [29, 64, 73, 79], "write": [29, 45, 47, 54, 60, 64, 68, 73, 79, 83], "wurd": [26, 54, 64, 73, 79, 105], "wurden": 75, "wuttk": [40, 64, 73, 79, 105], "www": [1, 2, 3, 4, 5, 7, 8, 11, 12, 14, 15, 17, 18, 20, 21, 22, 23, 24, 29, 30, 32, 33, 34, 35, 37, 38, 39, 40, 42, 43, 44, 45, 46, 48, 49, 50, 51, 52, 54, 55, 56, 57, 58, 64, 68, 73, 76, 78, 79, 80, 81, 82, 83, 84, 86, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 105, 106, 107, 108, 109], "w\u00e4hlby": [4, 55, 68, 86, 90, 98], "w\u00fcnsche": [26, 54, 64, 73, 79, 95, 105], "w\u00fcrttemberg": [64, 73, 79], "x": [24, 29, 31, 64, 73, 79], "xavier": [17, 18, 29, 50, 52, 65, 79, 96], "xe7en": [47, 68, 78, 90], "xf6k": [47, 68, 78, 90], "xf6ter": [15, 17, 37, 47, 68, 78, 99, 105, 106], "xiaoyan": [5, 48, 54, 68, 86, 89, 90], "xlsx": [20, 64, 73, 79], "xperiment": [30, 33, 64, 73, 79], "xplorer": [64, 73, 79], "xt": [24, 31, 64, 73, 79], "xyz": [31, 64, 73, 79], "xz": [24, 64, 73, 79], "xzt": [24, 64, 73, 79], "y": [29, 64, 73, 79], "yamauchi": [17, 40, 45, 48, 51, 52, 69, 79, 90, 97, 98, 103, 105], "yaml": 61, "yandex": [47, 68, 84], "yandexdataschool": [47, 48, 68, 84, 89, 103], "yaniv": [47, 68, 90], "yann": [51, 52, 69, 79, 90, 105], "yannick": [54, 56, 72, 86, 89, 90], "yanqi": [24, 64, 73, 79], "yaroslav": [17, 47, 68, 77], "year": [17, 34, 50, 52, 54, 64, 73, 79, 90, 93, 99, 102, 105], "yedid": [41, 48, 54, 68, 86, 90, 98], "yet": [9, 52, 64, 73, 79, 102], "yi": [4, 5, 7, 9, 36, 47, 64, 68, 73, 79, 90, 98, 109], "yield": [25, 31, 64, 73, 79], "yimin": [47, 68, 78, 90], "yishen": [47, 68, 78, 90], "ymia": [64, 73, 79], "ymia_python_event_series_materi": [28, 29, 47, 54, 68, 84, 90, 103], "yml": [29, 31, 60, 64, 73, 79], "yoav": [12, 42, 48, 64, 68, 71, 84, 90], "yolo_v5": [20, 64, 73], "yolov5": [20, 64, 73], "you": [14, 22, 25, 30, 31, 33, 34, 40, 42, 44, 45, 47, 49, 55, 56, 59, 60, 61, 68, 70, 74, 81, 83, 86, 87, 94, 100, 101, 106, 107], "your": [3, 14, 32, 33, 34, 40, 44, 56, 60, 61, 70, 81, 87, 99], "your_github_api_kei": 61, "yourself": [60, 61], "youth": [20, 64, 73, 79], "youtub": [3, 8, 12, 23, 33, 34, 35, 37, 54, 64, 73, 76, 89, 99, 105], "ysda": [47, 68, 84], "yu": [40, 55, 68, 86, 89, 90], "yuan": [29, 64, 73, 79], "yurchenko": [48, 68, 84, 89, 103], "yve": [11, 18, 29, 31, 41, 48, 52, 54, 55, 65, 68, 79, 86, 90, 98], "z": [3, 11, 33, 34, 52, 64, 71, 73, 75, 76, 79, 96, 105], "z1": [24, 29, 30, 33, 64, 73, 79], "z2": [30, 33, 64, 73, 79], "zabal": [51, 52, 69, 79, 90, 105], "zach": [64, 73, 79], "zahlreich": [26, 54, 64, 73, 79, 105], "zahn": [51, 52, 69, 79, 90, 105], "zaritski": [17, 41, 48, 52, 54, 68, 79, 86, 90, 98, 101], "zarr": [0, 2, 11, 15, 16, 23, 34, 37, 54, 64, 78, 82, 93, 99, 102, 106], "zb": [3, 54, 64, 73, 79, 90, 99, 105], "zebrafish": [1, 28, 64, 73, 79], "zeiss": [24, 29, 30, 31, 32, 33, 37, 99, 105], "zeissmicroscopi": [42, 48, 68, 83, 90, 97, 103], "zeitpunkt": 75, "zeitschrift": [64, 73, 79], "zelezniak": [48, 68, 86, 103], "zen": [64, 73], "zen_python_oad_workshop": [42, 48, 68, 83, 90, 97, 103], "zenodo": [0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 14, 15, 16, 17, 20, 22, 23, 24, 25, 26, 28, 30, 31, 32, 33, 34, 35, 36, 37, 38, 40, 41, 42, 44, 45, 46, 47, 50, 52, 55, 56, 60, 68, 72, 80, 81, 83, 84, 87, 88, 89, 90, 92, 93, 95, 98, 99, 100, 101, 102, 103, 108, 109], "zenti": [0, 3, 8, 16, 23, 33, 34, 35, 37, 45, 46, 47, 50, 52, 54, 56, 64, 68, 72, 73, 79, 87, 90, 93, 99, 100, 103, 105], "zentral": [64, 73, 79], "zerocost": [29, 64, 73, 79], "zerocostdl4m": [25, 29, 31, 54, 65, 73, 79, 89, 93], "zhang": [11, 17, 18, 29, 41, 48, 50, 52, 54, 65, 68, 71, 79, 86, 90, 93, 96, 98, 107], "zheng": [47, 68, 78, 90], "zhian": [36, 54, 64, 73, 79], "ziaurrehman": [1, 15, 38, 64, 73, 79], "zidas2020_intro_dl": [10, 54, 68, 86, 89, 90], "zidas_2020_dl_intro_part_2": [21, 48, 68, 86, 89, 90], "zielhui": [52, 71, 79, 92, 93, 106], "zih": [54, 64, 73, 79, 105], "zimmermann": [64, 73, 79], "zinchenko": [21, 48, 68, 86, 89, 103], "zink": [64, 73, 79], "zip": [25, 29, 31, 64, 73, 79], "ziqiang": [4, 5, 7, 9, 36, 47, 68, 79], "ziv": [47, 68, 90], "zmb": 42, "zobel": [3, 8, 16, 17, 23, 30, 33, 34, 37, 42, 52, 54, 56, 64, 72, 73, 76, 79, 87, 90, 99, 100, 105], "zoccol": [6, 28, 29, 37, 39, 48, 54, 62, 64, 68, 73, 79, 89, 90, 97, 103, 105], "zolt\u00e1n": [45, 52, 71, 94, 108], "zoo": [1, 52, 54, 65, 79, 93, 108], "zu": [26, 29, 40, 44, 52, 54, 64, 73, 75, 79, 93, 95, 101, 105], "zuckerberg": [52, 73, 79, 102], "zudem": [26, 54, 64, 73, 79, 105], "zugang": [40, 79, 101], "zuge": [26, 54, 64, 73, 79, 105], "zugeh\u00f6rigen": [52, 64, 79, 105], "zugriff": 75, "zuletzt": [26, 54, 64, 73, 79, 105], "zum": [26, 29, 54, 75, 93], "zumutbar": 75, "zun\u00e4chst": [40, 54, 64, 73, 79, 101, 105], "zur": [26, 29, 54, 75, 93, 105], "zusammen": [29, 54, 64, 73, 79, 93, 105], "zusammenfuehren": [29, 54, 64, 73, 79, 93, 105], "zusammenwirken": [57, 86, 99, 105], "zuschratt": [0, 50, 73, 79, 93, 105], "zusendung": 75, "zustimmung": 75, "zus\u00e4tzlich": [64, 73, 79], "zweiten": [0, 26, 32, 40, 54, 64, 73, 79, 101, 105], "zwpor7shjg1hhdiwlzj0mb_stlu": [3, 8, 23, 33, 34, 35, 37, 54, 56, 64, 72, 73, 79, 99, 100, 105], "zyla": [31, 64, 73, 79], "\u00b5": [3, 22, 23, 32, 37, 64, 73, 79, 99, 105], "\u00b5m": [29, 64, 73, 79], "\u00f6zdemir": [1, 14, 52, 64, 73, 79, 105], "\u00fcber": [0, 26, 32, 52, 54, 64, 73, 79, 105], "\u00fcberarbeitet": [26, 54, 64, 73, 79, 105], "\u00fcberblick": [26, 52, 54, 64, 73, 79, 105], "\u00fcberfordernd": [26, 54, 64, 73, 79, 105], "\u00fcbermittelt": 75, "\u00fcbernehmen": 75, "\u00fcberpr\u00fcft": 75, "\u00fcbersendung": 75, "\u00fcbersetzt": [64, 73, 79], "\u00fcberwachen": 75, "\u00fcbungsaufgaben": [26, 54, 64, 73, 79, 105], "\u03b1": [31, 64, 73, 79]}, "titles": ["Astrid schauss (5)", "Beatriz serrano-solano (6)", "Chris allan (5)", "Christian schmidt (19)", "Christian tischer (14)", "Constantin pape (8)", "Cornelia wetzker (6)", "Dominik kutra (6)", "Elisa ferrando-may (7)", "Elnaz fazeli (5)", "Estibaliz g\u00f3mez-de-mariscal (5)", "Et al. (25)", "Florian jug (6)", "Guillaume witz (8)", "Isabel kemmer (4)", "Jean-marie burel (6)", "Jens wendt (7)", "Josh moore (27)", "Kota miura (11)", "Mara lampert (8)", "Martin sch\u00e4tz (6)", "Martin weigert (8)", "Michael gerlach (12)", "Michele bortolomeazzi (9)", "Nicolas chiaruttini (7)", "Olivier burri (6)", "Pia voigt (6)", "<no title>", "Riccardo massei (12)", "Robert haase (56)", "Roland nitschke (5)", "Romain guiet (10)", "Silke tulok (6)", "Stefanie weidtkamp-peters (11)", "Susanne kunis (16)", "Thomas zobel (7)", "Toby hodges (4)", "Tom boissonnet (13)", "Ugis sarkans (5)", "Blog post (23)", "Book (19)", "Code (10)", "Collection (81)", "Data (8)", "Document (7)", "Documentation (19)", "Event (8)", "Github repository (45)", "Notebook (55)", "Online tutorial (10)", "Poster (10)", "Preprint (5)", "Publication (61)", "<no title>", "Slides (82)", "Tutorial (42)", "Video (32)", "Website (9)", "Workshop (12)", "YML format", "How to contribute", "Using the Training Materials Submission App", "Biapol.github.io (6)", "Docs.google.com (5)", "Doi.org (183)", "F1000research.com (11)", "Focalplane.biologists.com (14)", "Git.mpi-cbg.de (7)", "Github.com (123)", "Www.biorxiv.org (5)", "Www.ebi.ac.uk (10)", "Www.nature.com (16)", "Www.youtube.com (22)", "Zenodo.org (172)", "Open data", "Imprint", "All rights reserved (13)", "Bsd-2-clause (7)", "Bsd-3-clause (26)", "Cc-by-4.0 (249)", "Cc-by-sa-4.0 (5)", "Cc0-1.0 (13)", "Gpl-2.0 (7)", "Gpl-3.0 (7)", "Mit (24)", "<no title>", "Unknown (89)", "NFDI4BioImage Training Materials", "Statistics", "Artificial intelligence (44)", "Bioimage analysis (183)", "Bioinformatics (10)", "Data stewardship (6)", "Fair-principles (27)", "Imagej (19)", "Licensing (6)", "Metadata (15)", "Napari (13)", "Neubias (27)", "Nfdi4bioimage (44)", "Omero (31)", "Open science (9)", "Open source software (7)", "Python (70)", "<no title>", "Research data management (128)", "Sharing (12)", "Workflow (9)", "Workflow engine (13)", "Recently added (10)"], "titleterms": {"": [1, 3, 4, 8, 9, 14, 15, 17, 23, 29, 31, 33, 34, 35, 37, 38, 42, 50, 52, 54, 55, 56, 64, 68, 72, 73, 78, 79, 86, 89, 90, 93, 99, 100, 105], "0": [20, 64, 73, 79, 80, 81, 82, 83], "1": [0, 22, 26, 32, 54, 64, 73, 79, 81, 105], "10": [28, 31, 41, 49, 50, 54, 64, 70, 73, 79, 90, 91, 99, 105, 109], "11": [18, 33, 65], "12": [22, 28, 58, 64, 73, 79, 106], "123": 68, "128": 105, "13": [37, 76, 81, 97, 108], "14": [4, 66], "15": 96, "16": [3, 33, 34, 37, 64, 71, 73, 79, 99, 105], "17": [3, 33, 34, 37, 64, 73, 79, 99, 105], "172": 73, "183": [64, 90], "19": [3, 40, 45, 64, 73, 79, 94], "2": [0, 26, 32, 54, 64, 73, 75, 77, 79, 82, 105], "2013": [18, 40, 55, 68, 86, 90, 98], "2016": [18, 29, 31, 52, 65, 79, 90, 98], "2017": [4, 11, 18, 54, 55, 68, 86, 90, 98], "2018": [41, 48, 54, 68, 86, 90, 98], "2019": [11, 18, 21, 29, 41, 48, 54, 68, 86, 89, 90, 98, 103], "2020": [4, 5, 10, 11, 17, 18, 21, 25, 29, 31, 41, 42, 47, 48, 50, 52, 54, 56, 64, 65, 67, 68, 72, 78, 79, 86, 89, 90, 94, 96, 98, 103], "2021": [3, 5, 8, 11, 17, 21, 33, 48, 52, 54, 65, 68, 79, 86, 89, 90, 99, 103, 105], "2022": [13, 21, 48, 64, 68, 73, 79, 86, 89, 90, 103], "2023": [0, 16, 17, 21, 23, 28, 33, 34, 37, 42, 45, 46, 47, 48, 50, 52, 54, 58, 64, 68, 73, 79, 86, 89, 90, 93, 99, 100, 103, 105], "2024": [3, 12, 17, 29, 30, 33, 34, 35, 37, 47, 48, 50, 55, 56, 58, 64, 68, 72, 73, 78, 79, 89, 90, 93, 97, 99, 100, 103, 105], "2050": [52, 64, 79, 93, 105], "20x": [64, 73, 79], "22": [5, 21, 48, 68, 72, 86, 89, 103], "23": [39, 42, 58, 68, 90], "24": [42, 58, 68, 84, 90], "249": 79, "25": 11, "26": 78, "27": [17, 93, 98], "2d": [29, 42, 43, 54, 64, 67, 73, 78, 79, 86, 90, 94, 98], "3": [78, 83], "31": 100, "32": 56, "3d": [19, 29, 31, 39, 42, 43, 54, 64, 66, 67, 73, 78, 79, 86, 90, 94, 97, 98, 103], "4": [14, 36, 64, 73, 79, 80], "4000": [24, 64, 73, 79], "42": 55, "44": [89, 99], "45": 47, "4pi": [64, 73, 79], "4th": [64, 73, 79], "5": [0, 2, 9, 10, 28, 30, 38, 51, 54, 63, 69, 73, 75, 79, 80, 90, 99, 105], "55": [48, 75], "56": 29, "6": [1, 6, 7, 12, 15, 20, 25, 26, 32, 40, 62, 73, 79, 92, 95, 105], "61": 52, "6well": [22, 64, 73, 79], "7": [8, 16, 24, 35, 44, 64, 67, 73, 77, 82, 83, 102], "70": 103, "8": [5, 13, 19, 21, 43, 46], "81": 42, "82": 54, "89": 86, "9": [23, 57, 101, 107], "A": [3, 4, 6, 8, 9, 11, 12, 17, 23, 24, 28, 29, 30, 33, 34, 37, 38, 39, 40, 42, 45, 48, 50, 51, 52, 54, 55, 57, 64, 66, 68, 69, 71, 73, 78, 79, 80, 81, 86, 90, 94, 99, 100, 105, 106, 107, 108], "Be": [1, 15, 38, 64, 73, 79], "For": [48, 68, 89, 103], "IT": [3, 17, 33, 34, 37, 58, 64, 73, 79, 90, 99, 100, 105], "If": [29, 39, 66, 79, 95, 105], "It": [17, 34, 50, 73, 79, 90, 93, 99, 105], "The": [2, 3, 8, 15, 17, 18, 23, 28, 29, 30, 33, 34, 37, 38, 40, 42, 50, 52, 54, 56, 64, 65, 71, 73, 76, 79, 84, 86, 90, 92, 93, 94, 96, 99, 105, 106], "There": [17, 34, 54, 73, 79, 90, 93, 105], "To": [39, 66, 105, 106], "ab": 75, "abdomin": [22, 64, 73, 79], "aberr": [64, 73, 79], "about": [64, 73, 79], "abstract": [44, 79, 105], "ac": 70, "academi": [21, 25, 48, 54, 68, 78, 86, 89, 90, 98, 103], "acceler": [11, 29, 46, 47, 48, 52, 54, 55, 58, 62, 64, 65, 68, 76, 78, 79, 90, 94, 103], "access": [2, 4, 5, 11, 15, 17, 34, 50, 52, 64, 71, 73, 79, 90, 96, 99, 105], "acknowledg": 87, "acquir": [32, 37, 64, 73, 79, 99, 105], "acquisit": [64, 73, 79], "actin": [64, 73, 81, 109], "activ": [25, 31, 64, 73, 79], "ad": [16, 35, 54, 64, 68, 73, 77, 79, 90, 98, 109], "adapt": [22, 64, 73, 79], "address": [64, 73, 79], "adjust": [3, 8, 23, 33, 34, 35, 37, 54, 56, 64, 72, 73, 79, 99, 100, 105], "advanc": [3, 8, 23, 29, 30, 33, 34, 37, 42, 46, 49, 54, 55, 56, 64, 72, 73, 79, 90, 93, 95, 99, 105, 107], "again": [17, 34, 54, 73, 79, 90, 93, 105], "against": 86, "agent": [22, 64, 73, 79], "aggreg": [25, 31, 51, 64, 69, 73, 79, 106], "ahm": [17, 54, 64, 73, 79, 105], "ai": [17, 29, 34, 42, 43, 47, 50, 54, 55, 56, 64, 68, 70, 72, 73, 79, 81, 86, 89, 90, 93, 99, 103, 105], "ai4lif": [45, 86, 89, 90, 108], "aiw": [22, 64, 73, 79], "al": [11, 57, 86, 99, 105], "algorithm": [29, 64, 73, 79], "align": [40, 64, 79], "all": [54, 64, 73, 76, 79, 95, 105], "allan": 2, "am": [0, 32, 64, 73, 79], "ambassador": [1, 64, 73, 79], "an": [3, 8, 11, 17, 18, 19, 29, 39, 40, 42, 50, 51, 52, 54, 56, 64, 66, 69, 73, 79, 80, 86, 89, 90, 93, 96, 97, 98, 99, 103, 105], "analys": [4, 12, 18, 30, 42, 48, 52, 54, 64, 71, 73, 76, 78, 79, 90, 105], "analysi": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 15, 16, 18, 19, 21, 23, 25, 28, 29, 31, 33, 34, 35, 36, 37, 38, 39, 40, 42, 45, 46, 47, 48, 49, 50, 51, 52, 54, 55, 56, 57, 58, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 76, 78, 79, 80, 81, 82, 83, 84, 86, 89, 90, 93, 94, 95, 97, 98, 99, 100, 103, 105, 106, 107, 108, 109], "analyst": [9, 10, 11, 18, 29, 31, 41, 48, 52, 54, 55, 64, 65, 68, 73, 79, 86, 90, 98], "andor": [64, 73, 81, 109], "angaben": 75, "angebot": [29, 64, 73, 79, 99, 105], "anleitung": [44, 45, 55, 105], "annot": [3, 16, 19, 22, 23, 28, 31, 32, 33, 34, 37, 39, 42, 43, 50, 52, 64, 66, 71, 73, 76, 79, 90, 96, 97, 99, 100, 103, 105, 107], "annual": [64, 73, 79], "api": [29, 47, 48, 54, 58, 67, 68, 78, 90, 94, 98], "app": [11, 30, 33, 52, 61, 64, 73, 79, 96], "appli": [29, 54, 67, 90, 94], "applic": [10, 11, 22, 54, 64, 68, 73, 79, 86, 89, 90], "approach": [11, 47, 52, 54, 55, 57, 58, 63, 64, 68, 79, 80, 86, 90, 91, 101, 105, 106], "ar": [55, 86, 93, 95, 105], "arbeiten": [29, 54, 64, 73, 79, 93, 105], "arc": [0, 50, 73, 79, 93, 105], "archiv": [7, 21, 38, 42, 43, 52, 54, 55, 56, 64, 70, 71, 79, 81, 86, 89, 90, 103, 105], "area": [25, 31, 64, 73, 79], "arivi": [42, 56, 72, 76, 90], "arrai": [17, 34, 50, 52, 64, 73, 79, 90, 99, 105], "artifici": [12, 29, 54, 56, 64, 65, 72, 73, 79, 86, 89, 90, 103], "assembli": [45, 52, 64, 94, 108], "assess": [11, 52, 79], "asset": [18, 29, 31, 52, 65, 79, 90, 98], "assist": [29, 64, 73, 79], "assur": [19, 39, 66, 90, 97, 103], "astigmat": [64, 73, 79], "astrid": 0, "author": [55, 57, 80], "autom": [16, 35, 42, 73, 79, 90, 100], "automat": [28, 50, 64, 79, 90], "avail": [42, 45, 55, 56, 70, 79, 101, 103, 106], "awkward": [54, 64, 73, 79, 90], "axioscan": [64, 73, 79], "axioscan7": [32, 37, 64, 73, 79, 99, 105], "axiozoom": [22, 64, 73, 79], "back": [17, 34, 54, 73, 79, 86, 90, 93, 105], "barrier": [52, 79, 101, 105], "base": [11, 28, 29, 40, 42, 47, 48, 52, 54, 64, 68, 73, 79, 81, 84, 89, 90, 96, 103, 105], "basic": [18, 40, 44, 68, 76, 79, 90, 105], "batch": [45, 47, 52, 68, 71, 82, 94, 100, 108], "batchconvert": [64, 73, 79], "bead": [64, 73, 79], "beat": [29, 54, 67, 78, 90, 94, 98], "beatriz": 1, "bedarf": [0, 32, 64, 73, 79], "beginn": [13, 48, 68, 78, 90, 103], "bei": [40, 79, 101], "beim": [26, 54, 64, 73, 79, 93, 105], "benefit": [44, 105], "bereich": [29, 64, 73, 79, 99, 105], "best": [11, 52, 64, 86, 91, 101, 105, 106], "besten": [0, 32, 64, 73, 79], "bf": [64, 73, 79], "bi": [0, 32, 64, 73, 79], "bia": [29, 39, 62, 64, 73, 79, 89, 90, 103], "biaflow": [54, 68, 77, 90, 98], "biapi": [11, 45, 103, 108], "biapol": 62, "bid": [0, 11, 29, 47, 50, 52, 68, 73, 79, 89, 90, 93, 103, 105], "bigdataprocessor2": [4, 24, 52, 64, 79, 90, 105], "bigdataview": [47, 48, 58, 68, 90], "bina": [17, 64, 73, 79], "bio": [3, 4, 6, 7, 8, 9, 19, 23, 29, 30, 33, 34, 35, 37, 39, 42, 46, 47, 48, 52, 54, 55, 56, 58, 62, 64, 65, 66, 68, 72, 73, 78, 79, 86, 89, 90, 91, 93, 95, 96, 97, 99, 100, 103, 105, 106], "bioengin": [1, 45, 52, 84, 86, 89, 103, 108], "bioformat": [45, 79], "bioformats2raw": [2, 11, 17, 47, 68, 82, 102], "bioimag": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 21, 23, 28, 29, 30, 31, 33, 34, 35, 36, 37, 38, 40, 41, 42, 43, 45, 46, 48, 49, 50, 52, 54, 55, 56, 58, 63, 64, 65, 67, 68, 70, 71, 72, 73, 76, 78, 79, 80, 81, 83, 84, 86, 89, 90, 93, 94, 96, 97, 98, 99, 100, 101, 103, 105, 107, 108, 109], "bioinformat": [52, 91], "biolog": [2, 11, 15, 38, 52, 64, 71, 79, 86, 90, 96, 105], "biologi": [11, 21, 29, 38, 39, 40, 48, 51, 52, 64, 68, 69, 71, 73, 79, 86, 89, 90, 96, 97, 103, 105, 106], "biologist": [9, 39, 41, 42, 48, 52, 55, 64, 66, 68, 73, 78, 79, 84, 86, 89, 103], "biomed": [5, 68, 84, 89, 90], "biomero": [47, 52, 64, 68, 79, 90, 100, 107], "biorxiv": 69, "biostatist": [29, 48, 68, 79, 90, 97, 103], "blob": [64, 73, 79], "block": [6, 22, 64, 73, 79], "blog": 39, "bob": [29, 64, 73, 79, 89, 90], "boissonnet": 37, "book": 40, "bortolomeazzi": 23, "bpae": [64, 73, 81, 109], "brain": [11, 31, 52, 64, 73, 79, 105], "braucht": [54, 64, 73, 79, 105], "bridg": [9, 17, 34, 51, 52, 54, 64, 68, 69, 73, 78, 79, 90, 93, 105], "brightfield": [25, 31, 64, 73, 79], "bring": [58, 90, 105], "bringt": [54, 64, 73, 79, 105], "brows": [29, 39, 62, 79, 100, 103], "browser": [55, 68, 86], "bsd": [77, 78], "bug": [19, 39, 66, 103], "build": [1, 10, 14, 28, 38, 52, 54, 64, 68, 73, 79, 86, 89, 90, 105], "burel": 15, "burri": 25, "calendar": [64, 73, 79, 99, 105], "call": [6, 16, 28, 38, 50, 52, 64, 71, 73, 79, 90, 92, 99, 105], "camera": [64, 73, 79], "campu": [6, 64, 73, 79], "cancer": [54, 56, 72, 86, 89, 90], "captain": [55, 56, 86], "captur": [52, 55, 71, 79, 90, 91, 96], "care": [21, 48, 68, 86, 89, 90, 103], "career": [46, 48, 62, 90, 103], "carl": [64, 73, 79], "carpentri": [41, 55, 79, 84], "case": [41, 64, 68, 73, 79, 86, 90, 98], "cba": [4, 7, 11, 55, 84, 105, 107], "cbg": [29, 54, 67, 86, 90, 94], "cc": [17, 64, 73, 79, 80], "cc0": 81, "cell": [9, 31, 39, 40, 42, 43, 51, 54, 56, 64, 69, 72, 73, 79, 81, 86, 89, 90, 105, 106, 109], "cellpos": [25, 31, 64, 73, 79], "cellprofil": [4, 16, 35, 42, 48, 54, 55, 68, 73, 78, 79, 86, 90, 98, 100], "celltrackcolab": [42, 48, 68, 69, 84], "celltrackscolab": [47, 54, 55, 58, 68, 83, 90], "cellular": [32, 37, 64, 73, 79, 99, 105], "center": [3, 8, 23, 28, 39, 42, 56, 64, 72, 73, 79, 90, 99, 105], "cfci": [32, 37, 64, 73, 79, 99, 105], "cff": [55, 105, 106], "challeng": [15, 17, 29, 37, 39, 42, 43, 47, 52, 54, 64, 65, 66, 68, 73, 78, 79, 86, 90, 98, 99, 105, 106], "channel": [42, 56, 64, 72, 73, 79, 86, 90, 97, 98, 100, 102, 103], "charter": [64, 73, 79], "chat": [0, 32, 64, 73, 79], "chatgpt": [29, 39, 64, 66, 73, 79, 89, 105], "checklist": [4, 12, 18, 30, 42, 48, 52, 71, 76, 78, 81, 90, 105], "chiaruttini": 24, "choos": [3, 55, 86, 105], "chri": [2, 42, 56, 72, 86, 90, 97, 103], "christian": [3, 4], "cida": [17, 64, 73, 79, 99, 109], "citabl": [55, 105, 106], "citat": [55, 105, 106], "classifi": [25, 31, 64, 73, 79], "classroom": [9, 54, 64, 73, 79], "claus": [77, 78], "clesperanto": [29, 47, 48, 55, 58, 68, 78, 90], "cli": [11, 15, 45, 47, 68, 79, 82, 100], "client": [15, 47, 68, 82, 100], "clij": [11, 29, 52, 64, 76, 90, 94], "cloud": [9, 13, 17, 34, 50, 52, 54, 63, 64, 73, 79, 86, 89, 90, 98, 99, 105], "club": [11, 41, 48, 68, 86, 103], "cmcb": [17, 64, 73, 79, 99, 109], "co": [54, 64, 73, 79, 105], "coba": [42, 56, 72, 90], "cobrapi": [48, 68, 86, 103], "code": [11, 29, 41, 47, 48, 52, 55, 64, 68, 73, 78, 79, 83, 86, 89, 90, 103], "colab": [12, 42, 48, 56, 64, 68, 71, 72, 84, 90], "collabor": [29, 39, 52, 64, 66, 73, 79, 90, 99, 105, 106, 109], "collect": [5, 11, 42, 52, 64, 68, 70, 73, 84, 86, 89, 90, 91, 96], "cologn": [0, 16, 23, 37, 45, 46, 47, 52, 64, 68, 73, 79, 90, 93, 99, 100, 103, 105], "color": [39, 86], "com": [63, 65, 66, 68, 71, 72], "combin": [0, 50, 73, 79, 93, 105], "command": [45, 79], "common": [9, 64, 73, 79, 86], "commun": [1, 3, 4, 8, 11, 12, 14, 17, 18, 23, 30, 33, 34, 35, 37, 39, 42, 48, 51, 52, 54, 56, 63, 64, 65, 66, 68, 69, 71, 72, 73, 76, 78, 79, 86, 90, 92, 93, 96, 99, 105, 106], "compact": [5, 68, 86, 89, 90], "compar": [51, 52, 69, 79, 90, 105], "complet": [41, 64, 68, 73, 83, 90], "comprehens": [52, 71, 96], "compress": [64, 73, 79], "comput": [13, 21, 29, 36, 48, 52, 54, 63, 64, 65, 67, 68, 73, 79, 86, 89, 90, 94, 97, 98, 103], "concept": [16, 34, 35, 40, 56, 64, 73, 79, 99, 105], "conceptu": [52, 79, 101, 105], "conda": [28, 29, 39, 45, 64, 66, 73, 79, 103], "confer": [28, 54, 64, 73, 79, 90, 99, 105], "confoc": [64, 73, 79, 81, 109], "consid": [55, 86, 93, 95, 105], "consortium": [3, 8, 23, 28, 54, 64, 73, 79, 90, 99, 105], "constantin": 5, "content": [1, 28, 50, 52, 64, 71, 73, 79, 90, 93, 96, 105], "context": [16, 34, 35, 56, 64, 73, 79, 99, 105], "contrast": [22, 25, 29, 31, 64, 73, 79], "contribut": [60, 87], "control": [29, 55, 64, 68, 73, 79, 86, 99, 105, 109], "convers": [28, 54, 64, 73, 79, 105], "convert": [2, 11, 17, 47, 68, 82, 102], "cookbook": [47, 68, 78, 90], "cordi": [17, 50, 73, 79, 90, 105], "core": [3, 6, 8, 29, 30, 32, 33, 34, 37, 42, 52, 54, 64, 65, 73, 79, 90, 99, 103, 105], "cornelia": 6, "correct": [64, 73, 79], "cours": [4, 5, 11, 13, 18, 20, 21, 40, 42, 46, 47, 48, 51, 54, 55, 56, 68, 69, 78, 79, 82, 83, 84, 86, 89, 90, 91, 97, 98, 103, 106], "covid": [64, 73, 79], "cpu": [29, 54, 64, 65, 79], "cranial": [64, 73, 79, 109], "crashkur": [26, 54, 64, 73, 79, 105], "creat": [29, 36, 39, 49, 54, 55, 64, 66, 73, 79, 89, 105, 107], "criteria": 60, "csbdeep": [21, 48, 68, 86, 89, 90, 103], "cultiv": [29, 54, 64, 73, 79, 90, 93, 95, 101, 105], "curricula": [36, 54, 64, 73, 79], "cut": 60, "cycl": [11, 42, 49, 52, 56, 57, 64, 73, 79, 86, 90, 91, 101, 102, 105, 106], "cytometri": [55, 68, 86, 90], "cz": [20, 64, 73], "czi": [24, 42, 64, 73, 79], "da": [26, 54, 64, 73, 79, 105], "dab": [64, 73, 79], "dai": [64, 73, 79], "dalia": [64, 73, 80], "darf": [26, 54, 64, 73, 79, 105], "dask": [13, 48, 68, 86, 90, 103], "data": [0, 1, 2, 3, 4, 5, 6, 7, 8, 11, 14, 15, 16, 17, 18, 20, 21, 22, 23, 24, 28, 29, 30, 32, 33, 34, 35, 37, 38, 39, 40, 41, 42, 43, 44, 47, 48, 49, 50, 51, 52, 54, 55, 56, 57, 58, 62, 63, 64, 65, 66, 68, 69, 70, 71, 72, 73, 74, 76, 79, 80, 81, 84, 86, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 102, 103, 105, 106, 109], "databas": [22, 42, 46, 64, 73, 79, 90, 91, 100, 105], "datapl": [42, 79, 105], "dataset": [3, 22, 23, 24, 31, 32, 37, 42, 43, 64, 73, 79, 86, 90, 99, 105], "dataviz": [40, 73], "daten": [57, 86, 99, 105], "datenmanag": [26, 29, 54, 64, 73, 79, 105], "datenmanagementpl\u00e4n": [26, 54, 64, 73, 79, 105], "datenschutz": [26, 54, 64, 73, 75, 79, 93, 105], "datensparsamkeit": [64, 73, 79], "de": [10, 67], "decis": [14, 64, 73, 79], "declar": [44, 105], "deconvolut": [31, 64, 73, 79], "deep": [5, 10, 12, 13, 21, 39, 42, 48, 54, 56, 62, 63, 64, 65, 66, 68, 71, 72, 79, 84, 86, 89, 90, 93, 98, 103], "deeplif": [20, 47, 48, 54, 68, 86, 91], "deepprofil": [40, 68, 86, 89, 90], "demo": [41, 68, 86], "den": 75, "depart": [3, 33, 34, 37, 58, 64, 73, 79, 90, 99, 100, 105], "deploy": [42, 68, 77, 100], "deposit": [14, 64, 73, 79], "der": [29, 64, 73, 79, 99, 105], "dermal": [64, 73, 79], "desktop": [55, 68, 86], "detect": [64, 73, 79], "deutschen": [52, 79, 93, 105], "deutschland": [57, 86, 99, 105], "develop": [4, 12, 18, 28, 29, 30, 40, 42, 48, 50, 52, 64, 65, 68, 71, 73, 76, 78, 79, 84, 90, 98, 105], "die": [29, 57, 64, 73, 79, 86, 99, 105], "differ": [34, 54, 64, 73, 79, 99, 105], "diffus": [86, 90], "digit": [12, 13, 25, 31, 40, 48, 56, 64, 68, 72, 73, 79, 86, 89, 90], "digitalen": [64, 73, 79], "digitalsreeni": [42, 56, 72, 103], "dimens": [64, 73, 79], "directori": [0, 50, 73, 79, 93, 105], "dirti": [17, 34, 50, 64, 73, 79, 90, 93, 99, 105], "discoveri": [64, 73, 79], "dish": [64, 73, 81], "displai": [64, 73], "divers": [29, 54, 64, 65, 79, 90], "di\u00e1taxi": [55, 57, 80], "dl": [5, 48, 54, 56, 68, 72, 86, 89, 90], "dl4miceverywher": [11, 39, 42, 48, 66, 68, 79, 86, 90], "dl4protein": [42, 47, 68, 91, 109], "do": [14, 64, 73, 79], "doc": 63, "docker": [55, 56, 86], "document": [30, 33, 44, 45, 55, 57, 64, 73, 79, 80, 84, 86, 89, 90, 100, 103, 108], "doi": 64, "dokument": [44, 45, 55, 105], "domain": [17, 25, 31, 34, 54, 64, 73, 79, 90, 93, 105], "dominik": 7, "download": 87, "dr": [54, 56, 72, 86, 89, 90], "dragonfli": [64, 73, 81, 109], "dresden": [6, 29, 54, 64, 67, 73, 79, 86, 90, 94, 99, 105], "drift": [64, 73, 79], "driven": [11, 52, 79, 86, 90, 96], "dubrul": [64, 73, 79, 109], "durch": [57, 86, 99, 105], "dynam": [18, 40, 55, 68, 86, 90, 98], "earli": [46, 48, 62, 90, 103], "ebi": [42, 70, 91], "ecosystem": [1, 14, 52, 56, 64, 72, 73, 79, 90, 100, 105], "edam": [17, 18, 29, 50, 52, 65, 79, 90, 96], "edit": [29, 39, 40, 64, 66, 79, 105, 106], "educ": [64, 73, 79], "effici": [44, 64, 73, 79, 105], "effort": [54, 63, 86], "ein": [40, 54, 64, 73, 79, 101, 105], "einblick": [26, 54, 64, 73, 79, 105], "einen": [0, 32, 64, 73, 79], "einer": [52, 64, 79, 105], "eipp": [55, 68, 86, 89], "elabftw": [44, 45, 55, 105], "elastix": [42, 48, 78], "electron": [3, 22, 23, 32, 37, 39, 64, 73, 79, 105], "elektronischen": [44, 45, 55, 105], "elisa": 8, "elmi": [17, 34, 50, 64, 73, 79, 90, 93, 99, 105], "eln": [64, 73, 79], "elnaz": 9, "em": [22, 42, 43, 64, 70, 73, 79, 81, 90], "embedseg": [12, 47, 68, 90], "embl": [5, 11, 21, 41, 42, 48, 55, 68, 70, 86, 89, 91, 103], "embo": [21, 46, 48, 68, 86, 89, 90, 103], "emiss": [29, 64, 73, 79], "enabl": [3, 11, 33, 34, 37, 38, 52, 58, 64, 71, 73, 79, 86, 90, 96, 99, 100, 101, 105], "engin": [19, 39, 47, 55, 64, 66, 68, 73, 84, 90, 99, 103, 108], "enhanc": [3, 8, 23, 28, 45, 64, 73, 79, 86, 89, 90, 99, 105, 108], "enough": [52, 71, 79, 92, 93, 106], "enter": [29, 64, 73, 79], "environ": [2, 15, 28, 29, 39, 42, 52, 56, 64, 66, 72, 73, 79, 90, 99, 100, 102, 103], "eoe": [17, 64, 73, 79], "eosc": [1, 15, 38, 64, 73, 79], "epflx": [49, 76, 90], "epi": [25, 31, 64, 73, 79], "era": [54, 56, 72, 86, 89, 90], "eric": [64, 73, 79], "erick": [56, 72, 90, 100], "erstellen": [26, 54, 64, 73, 79, 105], "erstellung": [52, 64, 79, 105], "essenti": [52, 73, 79, 102], "establish": [11, 52, 79], "estibaliz": 10, "estim": [20, 64, 73, 79], "et": 11, "eubia": [18, 40, 55, 68, 86, 90, 98], "euro": [1, 14, 42, 54, 55, 56, 63, 64, 72, 73, 79, 86, 90, 93, 105], "event": [11, 25, 28, 29, 31, 42, 46, 47, 54, 58, 68, 84, 86, 90, 91, 100, 103, 105], "everyon": [11, 29, 52, 64, 76, 90, 94], "evid": [24, 64, 73, 79], "exampl": [20, 24, 29, 30, 33, 42, 44, 45, 47, 55, 64, 68, 73, 77, 78, 79, 90, 97, 100, 109], "excel": [16, 35, 64, 73, 79], "exchang": [29, 64, 73, 79], "exclus": 60, "exercis": [5, 21, 48, 54, 68, 86, 89, 90, 103], "expand": [2, 4, 5, 15, 17, 52, 56, 71, 72, 79, 90, 99, 100, 105], "experi": [30, 33, 42, 52, 64, 68, 73, 78, 79, 90], "exploit": [12, 42, 48, 64, 68, 71, 84, 90], "extend": [11, 40, 52, 64, 79, 86, 90, 96], "extens": [11, 52, 64, 79, 90, 100, 105, 107], "extract": [19, 39, 66, 90, 97, 103], "exzellent": [57, 86, 99, 105], "f1000research": 65, "facil": [3, 6, 8, 29, 30, 32, 33, 34, 37, 39, 42, 44, 52, 54, 56, 58, 64, 65, 67, 72, 73, 77, 79, 86, 90, 94, 99, 100, 105], "facilit": [3, 33, 34, 52, 71, 76, 96, 105], "fair": [1, 3, 6, 8, 11, 14, 15, 17, 23, 28, 30, 33, 34, 37, 38, 42, 50, 52, 54, 55, 56, 63, 64, 71, 72, 73, 76, 79, 86, 90, 91, 92, 93, 96, 99, 105, 106, 109], "fairi": [54, 65, 79, 89, 90, 93], "falk": [64, 73, 79, 99, 105], "fanci": [17, 34, 50, 73, 79, 90, 93, 99, 105], "faze": 9, "featur": [19, 39, 66, 90, 97, 103], "ferrando": 8, "fibroblast": [64, 73, 79], "figshar": [39, 66, 105, 106], "fiji": [4, 12, 24, 29, 42, 48, 49, 50, 52, 55, 56, 64, 68, 72, 73, 76, 77, 79, 81, 84, 90, 94, 100, 105], "file": [2, 4, 5, 15, 17, 24, 30, 33, 52, 64, 71, 73, 79, 81, 90, 99, 105, 109], "financ": [3, 17, 30, 33, 34, 35, 64, 73, 79], "find": [3, 42, 51, 52, 55, 56, 69, 70, 79, 86, 90, 101, 105, 106], "first": [41, 64, 68, 73, 83, 90], "fit": [3, 33, 34, 37, 58, 64, 73, 79, 90, 99, 100, 105], "five": [17, 39, 52, 64, 73, 79, 86, 102, 105, 106], "flim": [6, 54, 64, 73, 79, 90], "flimfit": [6, 54, 64, 73, 79, 90], "florian": 12, "flow": [20, 55, 64, 68, 73, 79, 86, 90], "fluoresc": [64, 73, 79], "flute": [6, 54, 64, 73, 79, 90], "fly": [39, 86], "focalplan": 66, "foku": [26, 54, 64, 73, 79, 105], "forg": [45, 79, 103], "format": [2, 4, 5, 15, 17, 18, 29, 50, 52, 59, 64, 65, 71, 73, 79, 80, 81, 90, 96, 99, 105, 109], "forschung": [29, 57, 64, 73, 79, 86, 99, 105], "forschungsdaten": [26, 42, 52, 54, 64, 73, 79, 93, 95, 105], "forschungsdateninfrastruktur": [64, 73, 79], "forschungsdatenmanag": [26, 54, 64, 73, 79, 105], "foundat": [52, 64, 73, 79, 101, 105], "fov1": [64, 73, 79], "fractal": [45, 103, 108], "frame": [64, 73, 79], "framework": [17, 42, 51, 52, 57, 64, 69, 79, 86, 90, 100, 103, 107], "franc": [54, 80, 90, 101, 105], "free": [4, 12, 24, 42, 48, 52, 64, 68, 71, 79, 84, 90, 105], "friend": [29, 39, 48, 66, 68, 78, 79, 90, 103], "friendli": [16, 96, 100, 107], "friendsdontletfriend": [47, 68, 84], "from": [6, 9, 18, 25, 28, 29, 31, 37, 51, 52, 54, 55, 56, 64, 65, 69, 73, 79, 84, 86, 90, 98, 102, 105, 106], "function": [64, 73, 79, 109], "fundament": [13, 48, 68, 78, 86, 90, 103], "fusion": [29, 54, 67, 78, 90, 94, 98], "futur": [52, 64, 68, 78, 90], "fv": [24, 64, 73, 79], "f\u00fcr": [29, 54, 57, 64, 73, 75, 79, 86, 95, 99, 105], "galaxi": [1, 28, 42, 45, 49, 54, 55, 63, 64, 68, 73, 79, 81, 84, 86, 89, 90, 96, 99, 100, 105, 107, 108], "galleri": [42, 43, 70, 81, 89, 90], "ganglia": [29, 64, 73, 79], "gastruloid": [25, 31, 64, 73, 79], "gbi": [17, 64, 73, 79], "gcnu": [42, 55, 56, 79], "gemeinsam": [57, 86, 99, 105], "gem\u00e4\u00df": 75, "gener": [2, 4, 5, 15, 17, 29, 47, 48, 52, 54, 55, 64, 65, 68, 71, 73, 78, 79, 89, 90, 96, 99, 103, 105, 109], "gerbi": [0, 3, 17, 30, 32, 33, 34, 35, 42, 64, 73, 79, 86], "gerlach": 22, "geschlossen": [26, 54, 64, 73, 79, 93, 105], "gestalten": [64, 73, 79], "get": [19, 20, 28, 39, 56, 62, 64, 72, 73, 79, 103], "ghent": [57, 86, 105], "git": [29, 39, 54, 55, 64, 66, 67, 68, 73, 79, 86, 93, 99, 105, 106, 109], "github": [0, 16, 23, 37, 47, 55, 62, 68, 73, 79, 86, 90, 93, 99, 105], "glenco": [2, 42, 55, 56, 86, 100], "glimps": [6, 54, 64, 73, 79, 90], "glittr": [51, 52, 69, 79, 90, 105], "global": [42, 46, 56, 72, 90], "gonna": [6, 16, 28, 50, 64, 73, 79, 90, 92, 99, 105], "googl": [12, 42, 48, 63, 64, 68, 71, 84, 90], "gpl": [82, 83], "gpu": [11, 29, 46, 47, 48, 52, 54, 55, 58, 62, 64, 65, 68, 76, 78, 79, 90, 94, 103], "graph": [20, 64, 73, 79], "graphic": [48, 68, 84, 89, 103], "great": [39, 86, 105, 106], "group": [3, 22, 23, 32, 37, 64, 73, 79], "gro\u00dfegr\u00e4teantrag": [0, 32, 64, 73, 79], "gro\u00dfger\u00e4teantrag": [0, 32, 64, 73, 79], "grunds\u00e4tz": [52, 79, 93, 105], "gtn": [45, 86, 89, 90, 108], "gui": [64, 73, 79], "guid": [3, 4, 8, 9, 14, 29, 30, 33, 34, 37, 39, 40, 42, 52, 54, 55, 64, 66, 71, 73, 77, 78, 79, 84, 90, 92, 93, 100, 105, 106], "guidanc": [40, 64, 79, 105], "guidelin": [11, 52, 79], "guiet": 31, "guillaum": [13, 54, 56, 72, 86, 89, 90], "gut": [10, 29, 57, 64, 73, 79, 86, 99, 105], "g\u00f3mez": 10, "haas": 29, "hackathon": [0, 16, 23, 37, 45, 46, 47, 52, 64, 68, 73, 79, 90, 93, 99, 100, 103, 105], "hackaton": [28, 54, 64, 73, 79, 105], "haftung": 75, "haftungsausschluss": 75, "halvin": [42, 56, 72, 86, 90, 97, 103], "hamburg": [28, 54, 73, 79, 90, 99, 105], "handbook": [40, 48, 68, 79, 81, 86, 89, 90, 97, 98, 103, 105], "handl": [3, 37, 39, 64, 73, 79, 105], "harder": [29, 39, 66, 79, 95, 105], "have": [17, 64, 73, 79], "hc": [1, 28, 47, 50, 64, 68, 73, 79, 84, 90, 100, 103], "heidelberg": [3, 33, 34, 37, 64, 73, 79, 99, 105], "hela": [31, 64, 73, 79], "helmholtz": [3, 8, 23, 28, 54, 64, 73, 79, 90, 99, 105], "help": [29, 54, 64, 73, 79, 89, 103], "helpdesk": [64, 73, 79], "hen": [64, 73, 79, 99, 105], "heterogen": [29, 54, 64, 65, 79], "high": [1, 4, 16, 18, 28, 35, 42, 43, 50, 52, 64, 68, 71, 73, 79, 86, 90, 93, 96, 100, 105], "highlight": [18, 29, 31, 39, 52, 65, 79, 86, 90, 98], "histologi": [64, 73, 79], "hitchhik": [4, 9, 29, 52, 54, 64, 65, 79, 90], "hodg": 36, "holder": [22, 64, 73, 79], "home": [38, 48, 52, 64, 68, 78, 86, 90, 98, 103, 105], "how": [3, 19, 20, 33, 34, 37, 39, 42, 56, 58, 60, 64, 66, 72, 73, 79, 87, 90, 99, 100, 103, 105, 106], "hub": [29, 64, 73, 79, 99, 105, 109], "human": [64, 73, 79], "i": [11, 14, 18, 42, 54, 56, 64, 72, 73, 79, 86, 90, 98, 101], "i2k": [4, 5, 17, 21, 29, 47, 48, 55, 58, 64, 68, 73, 78, 79, 86, 89, 90, 103], "i2k2024": [29, 47, 48, 55, 58, 68, 78, 90], "i3d": [3, 8, 23, 30, 33, 34, 35, 37, 42, 54, 56, 64, 72, 73, 79, 86, 96, 99, 100, 105], "iafig": [48, 68, 83, 90], "ibiologi": [42, 56, 90], "ic": [64, 73, 79], "ich": [0, 26, 32, 54, 64, 73, 79, 105], "icob": [42, 58, 68, 90], "id": [64, 73, 79], "ifc": [55, 68, 86, 90], "ijm": [25, 31, 64, 73, 79], "ilastik": [7, 54, 73, 79, 89, 90], "illustr": [64, 73, 79, 99, 105], "im": [26, 29, 54, 64, 73, 79, 81, 99, 105, 109], "imag": [1, 2, 3, 4, 5, 6, 7, 9, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 24, 25, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 45, 46, 47, 48, 49, 50, 51, 52, 54, 55, 56, 57, 58, 62, 63, 64, 65, 66, 68, 69, 71, 72, 73, 76, 78, 79, 80, 81, 82, 83, 84, 86, 89, 90, 93, 94, 95, 96, 97, 98, 99, 100, 103, 105, 106, 107, 108, 109], "imagej": [12, 20, 29, 41, 42, 45, 50, 52, 54, 55, 56, 64, 67, 68, 71, 72, 73, 79, 84, 86, 90, 94, 98, 108], "imagej2": [29, 54, 67, 78, 90, 94, 98], "imari": [42, 56, 64, 73, 76, 79, 90], "imglib2": [47, 48, 58, 68, 90], "impact": [39, 52, 73, 79, 86, 102], "implant": [22, 64, 73, 79, 109], "implement": [32, 64, 73, 79], "impressum": 75, "imprint": 75, "impuls": [64, 73, 79], "inclus": 60, "index": [42, 90], "individu": [30, 33, 64, 73, 79], "info": [42, 105], "inform": [3, 8, 23, 30, 33, 34, 37, 42, 64, 73, 79, 86, 96, 99, 105], "informat": [2, 15, 17, 18, 29, 42, 50, 52, 64, 65, 79, 90, 96], "infrastructur": [3, 8, 17, 23, 28, 30, 33, 34, 37, 42, 50, 52, 54, 64, 73, 79, 80, 86, 90, 93, 96, 99, 105], "inhalt": 75, "initi": [3, 6, 8, 11, 50, 52, 54, 64, 73, 79, 80, 84, 99, 105], "ink": [64, 73, 81], "ins": [26, 54, 64, 73, 79, 105], "insight": [52, 64, 73, 79, 102], "inspect": [4, 24, 52, 64, 79, 90, 105], "institut": [44, 64, 73, 79, 105], "institution": [64, 73, 79], "institutionellen": [52, 64, 79, 105], "instrument": [11, 52, 79], "intellig": [12, 29, 54, 56, 64, 65, 72, 73, 79, 86, 89, 90, 103], "intens": [18, 40, 55, 68, 86, 90, 98], "interact": [7, 11, 13, 17, 20, 34, 48, 50, 52, 54, 63, 64, 68, 73, 78, 79, 86, 89, 90, 96, 98, 99, 101, 103, 105], "interchang": [64, 73, 80], "interdisciplinari": [17, 52, 79, 90, 101], "intern": [40, 64, 79], "intravit": [22, 64, 73, 79], "intro": [28, 54, 64, 73, 79, 84, 97], "introduct": [10, 18, 21, 25, 29, 40, 41, 47, 48, 54, 55, 64, 67, 68, 73, 79, 80, 84, 86, 89, 90, 94, 98, 101, 103, 105], "io": [11, 62, 68, 86], "isabel": 14, "isotropi": [19, 39, 66, 90, 97, 103], "issu": [55, 86, 93, 95, 105], "item": [17, 64, 73, 79], "itkelastix": [68, 90], "ix": [17, 64, 73, 79], "j": [64, 73, 79, 109], "jacquemet": [54, 56, 72, 86, 89, 90], "jean": 15, "jen": 16, "jipip": [45, 52, 71, 94, 108], "josh": 17, "journei": [3, 9, 33, 54, 64, 73, 79, 99, 105], "json": [30, 33, 64, 73, 79], "jug": 12, "june2024": [42, 58, 68, 84, 90], "jupyt": [13, 28, 47, 48, 54, 56, 63, 68, 72, 78, 84, 86, 90, 98, 100, 103], "just": [17, 34, 42, 50, 56, 72, 73, 79, 90, 93, 99, 105], "kei": [3, 16, 18, 22, 23, 29, 31, 32, 35, 37, 42, 45, 52, 55, 64, 65, 73, 79, 86, 90, 93, 95, 98, 99, 100, 105], "kemmer": 14, "kick": [33, 54, 64, 79, 90, 93, 99, 105], "kid": [6, 64, 73, 79], "kidnei": [22, 64, 73], "kind": [55, 86, 93, 95, 105], "kioto": [42, 58, 68, 90], "kit": [57, 79, 105], "kl\u00e4ren": [54, 64, 73, 79, 95, 105], "knime": [28, 45, 49, 54, 55, 64, 73, 79, 83, 94, 100, 105, 107], "knowledg": [29, 42, 64, 73, 79, 105], "kollabor": [29, 54, 64, 73, 79, 93, 105], "konstanz": [64, 73, 79], "kontakt": 75, "kota": 18, "kreshuk": [55, 68, 86, 89], "kubernet": [55, 56, 86], "kuni": 34, "kutra": 7, "kyoto": [31, 64, 73, 79], "lab": [12, 40, 48, 55, 68, 73, 79, 86, 89, 103], "label": [25, 31, 64, 73, 79], "laborbuch": [44, 45, 55, 105], "lambda": [24, 64, 73, 79], "lampert": 19, "languag": [9, 29, 52, 54, 64, 71, 73, 79, 89, 90, 93, 103, 107], "larg": [17, 29, 34, 39, 47, 48, 50, 52, 54, 58, 64, 68, 71, 73, 79, 89, 90, 93, 99, 103, 105, 107], "last": 87, "laulauthom": [64, 73, 84], "lazi": [47, 48, 58, 68, 90], "learn": [5, 7, 10, 12, 13, 21, 29, 38, 39, 42, 47, 48, 54, 55, 56, 62, 63, 64, 65, 66, 68, 70, 71, 72, 73, 79, 84, 86, 89, 90, 93, 97, 98, 103], "lectur": [20, 29, 42, 47, 48, 54, 56, 67, 68, 72, 73, 79, 86, 89, 90, 91, 94, 97, 103], "leipzig": [29, 54, 73, 79, 89, 90, 103], "leitfaden": [40, 64, 73, 79, 101], "leitlini": [52, 79, 93, 105], "leo": [64, 73, 79], "lesson": [1, 15, 38, 52, 64, 73, 79], "librari": [29, 47, 48, 55, 58, 64, 68, 73, 78, 79, 90], "licens": [29, 39, 54, 64, 65, 66, 73, 79, 87, 93, 95, 105, 106], "life": [1, 11, 15, 18, 29, 31, 38, 42, 49, 52, 56, 57, 64, 65, 73, 76, 79, 86, 89, 90, 91, 98, 99, 101, 105, 106], "light": [3, 11, 22, 23, 32, 37, 52, 64, 73, 79, 86, 90, 96, 99, 105], "like": [17, 34, 54, 73, 79, 90, 93, 105], "limeseg": [24, 64, 73, 79], "limi": [11, 52, 79], "line": [45, 79], "link": [16, 33, 34, 35, 42, 56, 64, 73, 75, 79, 86, 99, 105], "lisbon": [4, 55, 68, 86, 90, 98], "list": [42, 86, 105], "littl": [17, 34, 50, 64, 73, 79, 90, 93, 99, 105], "liver": [22, 25, 31, 64, 73, 79], "ll": [29, 39, 66, 79, 95, 105], "llm": [47, 55, 68, 84], "local": [3, 8, 23, 33, 34, 35, 37, 54, 56, 64, 72, 73, 79, 99, 100, 105], "long": [12, 29, 50, 52, 94], "low": [29, 64, 73, 79], "lsm": [64, 73, 79, 109], "lund": [44, 105], "lung": [20, 64, 73, 79], "lz4": [64, 73, 79], "machin": [7, 10, 21, 29, 38, 48, 54, 55, 56, 63, 68, 70, 73, 79, 86, 89, 90, 97, 98, 103], "macro": [29, 41, 54, 67, 68, 86, 90, 94, 98], "madrid": [42, 58, 68, 84, 90], "mai": 8, "mainten": 60, "make": [11, 17, 45, 52, 55, 64, 71, 73, 79, 90, 91, 92, 93, 96, 101, 103, 105, 106], "mamba": [29, 39, 66, 79, 103], "mambaforg": [19, 39, 62, 79, 103], "manag": [3, 6, 8, 11, 14, 16, 17, 23, 28, 29, 30, 32, 33, 34, 35, 37, 39, 40, 42, 44, 48, 49, 50, 52, 54, 55, 56, 57, 64, 65, 66, 68, 71, 72, 73, 76, 77, 79, 80, 86, 89, 90, 93, 96, 99, 100, 101, 102, 103, 105, 109], "managemet": [20, 64, 73, 79], "manual": [12, 48, 79, 103], "mara": 19, "mari": 15, "marisc": 10, "martin": [20, 21, 56, 72, 90, 100], "martinschatz": [20, 64, 73], "mask": [64, 73, 84], "maskfromroi": [64, 73, 84], "massei": 28, "masteri": [55, 56, 86], "mastodon": [29, 54, 67, 78, 90, 94, 98], "match": [64, 73, 79], "materi": [3, 4, 5, 8, 11, 20, 21, 23, 28, 29, 33, 34, 35, 37, 41, 42, 47, 48, 51, 52, 54, 55, 56, 58, 61, 64, 65, 68, 69, 70, 72, 73, 79, 84, 86, 87, 89, 90, 91, 93, 95, 96, 99, 100, 103, 105, 106], "math": [17, 34, 50, 73, 79, 90, 93, 99, 105], "matplotlib": [40, 68, 103], "matter": [11, 52, 96], "maximis": [44, 105], "mbl": [5, 48, 54, 68, 86, 89, 90], "mdemic": [3, 33, 34, 52, 71, 76, 96, 105], "measur": [25, 31, 64, 73, 79], "medic": [64, 73, 79], "medicin": [33, 64, 73, 79], "meet": [3, 17, 28, 29, 30, 33, 34, 35, 52, 54, 64, 73, 79, 90, 99, 105], "mein": [54, 64, 73, 79, 95, 105], "melbourn": [42, 56, 72, 90], "meta": [11, 30, 33, 52, 64, 73, 79, 96], "metabol": [48, 68, 86, 103], "metadata": [3, 11, 16, 28, 30, 33, 34, 35, 37, 38, 42, 50, 52, 55, 56, 64, 71, 72, 73, 76, 79, 86, 90, 92, 93, 96, 99, 100, 105, 107], "metastasi": [54, 56, 72, 86, 89, 90], "method": [4, 9, 46, 49, 52, 54, 56, 64, 70, 71, 73, 79, 90, 96], "methodsj2": [52, 71, 96], "metric": [11, 42, 51, 52, 57, 86, 90], "mia": [45, 52, 64, 94, 108], "mic": [13, 48, 68, 86, 90, 103], "michael": 22, "michel": 23, "micoscropi": [42, 56, 72, 79, 102], "micro": [11, 30, 33, 52, 64, 73, 79, 96], "microbi": [64, 73, 79], "microlist": [42, 76], "micrometastas": [25, 31, 64, 73, 79], "microsam": [5, 54, 64, 73, 79, 89, 90], "microscop": [22, 64, 73, 79], "microscopi": [1, 2, 3, 4, 7, 8, 11, 12, 14, 15, 16, 18, 21, 22, 23, 28, 29, 30, 32, 33, 34, 35, 37, 38, 39, 42, 43, 48, 50, 52, 54, 56, 62, 64, 66, 68, 70, 71, 72, 73, 76, 79, 80, 84, 86, 89, 90, 93, 96, 99, 100, 103, 105, 109], "microscopist": [42, 48, 68, 84, 90, 103], "microscopydb": [42, 76], "middl": [29, 52, 79, 90], "mini": [11, 41, 48, 68, 86, 103], "mit": [26, 29, 54, 64, 73, 79, 84, 93, 105], "mitobo": [45, 52, 94, 108], "mitochondria": [55, 79, 90, 91], "mitoflash": [55, 79, 90, 91], "miura": 18, "ml": [54, 56, 72, 86, 89, 90], "model": [2, 5, 11, 15, 25, 29, 31, 42, 48, 52, 54, 64, 68, 71, 73, 79, 86, 89, 90, 92, 93, 96, 103, 107], "modular": [4, 7, 9, 36, 64, 73, 79, 90, 98, 109], "modularimageanalysi": [45, 52, 64, 94, 108], "modularisedimag": [45, 52, 64, 94, 108], "mooc": [47, 68, 79, 90], "moor": 17, "morpholibj": [44, 90], "morpholog": [64, 73, 79], "mortem": [64, 73, 79], "most": [17, 52, 79, 87, 90, 101], "mous": [22, 64, 73], "movement": [64, 73, 79], "mpi": [29, 54, 67, 86, 90, 94], "multi": [3, 8, 23, 29, 33, 34, 35, 37, 54, 56, 64, 67, 72, 73, 78, 79, 90, 94, 98, 99, 100, 105], "multidisciplinari": [29, 52, 79, 90], "multimod": [0, 33, 50, 52, 64, 71, 73, 79, 90, 93, 105, 107], "multiplex": [47, 54, 58, 63, 64, 68, 73, 79, 90], "multiview": [47, 55, 68, 78], "must": [17, 64, 73, 79], "my": [9, 14, 54, 64, 73, 79], "myth": 86, "m\u00f6glich": [26, 54, 64, 73, 79, 93, 105], "m\u00fcller": [3, 22, 23, 32, 37, 64, 73, 79], "n4bi": [17, 54, 64, 73, 79, 105], "n5": [47, 48, 58, 68, 90], "nach": 75, "napari": [6, 19, 39, 42, 48, 54, 55, 64, 66, 68, 73, 78, 79, 84, 86, 89, 90, 97, 103], "nation": [3, 6, 8, 17, 23, 28, 33, 34, 50, 52, 54, 64, 73, 79, 80, 90, 93, 99, 105], "nationalen": [64, 73, 79], "natur": 71, "navig": [6, 28, 37, 54, 64, 73, 79, 105], "need": [52, 64, 68, 78, 90], "network": [45, 48, 64, 68, 73, 79, 86, 89, 90, 103, 108], "neubia": [4, 10, 11, 18, 21, 25, 29, 31, 41, 42, 48, 52, 54, 55, 56, 65, 68, 72, 78, 79, 86, 89, 90, 98, 103], "neubiaspasteur2023_advancedcellpos": [47, 68, 78, 89, 90], "neural": [64, 73, 79], "neuron": [29, 64, 73, 79], "new": [6, 39, 64, 73, 79, 86], "next": [2, 4, 5, 15, 17, 52, 64, 71, 73, 79, 99, 105, 109], "nextflow": [41, 42, 45, 47, 54, 55, 64, 68, 73, 79, 86, 103, 107, 108], "nfdi": [6, 29, 44, 57, 64, 73, 79, 86, 99, 105], "nfdi4bioimag": [0, 3, 6, 8, 11, 16, 17, 23, 28, 29, 33, 34, 37, 45, 46, 47, 50, 52, 54, 64, 65, 68, 73, 79, 80, 87, 90, 93, 99, 100, 103, 105], "ngff": [2, 4, 5, 15, 17, 37, 47, 52, 64, 68, 71, 73, 77, 78, 79, 82, 99, 102, 105, 106], "nicola": 24, "nikon": [39, 105], "nitschk": 30, "nlp": [47, 68, 84], "notebook": [20, 28, 40, 42, 47, 48, 64, 68, 73, 78, 79, 84, 90, 91, 100, 103, 109], "nuclei": [21, 25, 31, 48, 54, 64, 68, 73, 79, 86, 89, 90, 98, 103], "nucleu": [18, 40, 55, 68, 86, 90, 98], "numpi": [13, 48, 68, 78, 103], "n\u00f6tig": [26, 54, 64, 73, 79, 93, 105], "object": [29, 45, 47, 52, 54, 55, 58, 64, 67, 68, 73, 78, 79, 83, 90, 94, 98, 108], "octob": [3, 33, 34, 37, 64, 73, 79, 99, 105], "oder": [54, 64, 73, 79, 95, 105], "off": [33, 54, 64, 79, 90, 93, 99, 105], "offen": [26, 54, 64, 73, 79, 93, 105], "oir": [24, 64, 73, 79], "olivi": 25, "om": [2, 4, 5, 11, 15, 17, 42, 45, 46, 47, 52, 55, 56, 64, 68, 71, 72, 77, 79, 86, 90, 96, 99, 100, 102, 105], "ome2024": [15, 17, 37, 47, 64, 68, 73, 78, 79, 99, 105, 106], "omero": [3, 8, 11, 15, 16, 17, 22, 23, 28, 32, 33, 34, 35, 37, 39, 42, 44, 45, 47, 49, 52, 54, 55, 56, 58, 62, 64, 68, 72, 73, 76, 77, 79, 82, 84, 86, 89, 90, 93, 94, 96, 99, 100, 103, 105, 107], "omic": [17, 51, 52, 69, 79, 103], "onbi": [48, 68, 82, 90, 103], "onlin": [33, 42, 49, 51, 52, 54, 64, 69, 79, 86, 90, 93, 99, 105], "online_r_learn": [47, 68, 81], "ontologi": [17, 18, 29, 50, 52, 65, 79, 90, 96], "open": [2, 4, 6, 11, 12, 15, 17, 24, 29, 36, 39, 40, 42, 48, 51, 52, 54, 56, 62, 64, 65, 68, 69, 71, 72, 73, 74, 76, 79, 84, 86, 90, 93, 95, 97, 98, 100, 101, 102, 103, 105], "oper": [17, 18, 29, 50, 52, 65, 79, 90, 96], "operetta": [24, 64, 73, 79], "opportun": [29, 52, 54, 65, 79, 90, 98, 99, 105], "optic": [21, 48, 64, 68, 73, 79, 86, 89, 90, 103], "optim": [17, 34, 50, 52, 64, 73, 79, 90, 99, 105, 109], "optimis": [29, 64, 73, 79], "option": [49, 55, 79, 107], "org": [42, 51, 52, 57, 64, 69, 73, 79, 90, 105], "organ": [0, 50, 73, 79, 93, 105], "organis": [26, 49, 54, 57, 64, 73, 79, 86, 102, 105], "organisiert": [57, 86, 99, 105], "origin": [64, 73, 79, 99, 105], "our": [29, 39, 66, 79, 95, 105], "overcom": [39, 66, 86], "overview": [28, 34, 35, 42, 55, 64, 70, 73, 79, 81, 93, 96, 99, 100, 105], "overwhelm": [20, 64, 73, 79], "own": [58, 90, 105], "packag": [45, 79, 103], "pair": [3, 16, 22, 23, 32, 35, 37, 42, 45, 64, 73, 79, 86, 99, 100, 105], "panda": [13, 48, 68, 78, 103], "pape": 5, "paper": [6, 28, 37, 39, 54, 64, 73, 79, 86, 105], "parallel": [29, 47, 48, 54, 58, 64, 65, 68, 79, 90], "particl": [64, 73, 79], "patho_prompt_inject": [47, 48, 68, 83, 90], "pathologi": [12, 56, 72, 86, 89], "pelag": [28, 54, 73, 79, 90, 99, 105], "percentag": [20, 64, 73, 79], "perform": [30, 33, 42, 52, 64, 73, 78, 79], "perikl": [3, 23, 32, 37, 64, 73, 79, 99, 105], "peripheri": [18, 40, 55, 68, 86, 90, 98], "perspect": [17, 34, 50, 52, 73, 79, 93, 99, 105], "peter": 33, "phase": [25, 31, 64, 73, 79], "phasor": [6, 54, 64, 73, 79, 90], "photon": [52, 64, 79, 93, 105], "physic": [64, 73, 79], "pia": 26, "pilot": [32, 64, 73, 79], "pioneer": [64, 73, 79], "pipelin": [1, 11, 28, 41, 45, 47, 50, 55, 64, 68, 73, 78, 79, 84, 86, 90, 97, 100, 103, 108], "pitfal": [11, 51, 52, 90], "pixel": [6, 9, 19, 25, 28, 29, 31, 37, 39, 54, 64, 66, 67, 73, 78, 79, 90, 94, 97, 98, 103, 105], "plan": [14, 29, 39, 42, 44, 52, 55, 64, 66, 73, 78, 79, 89, 90, 93, 105], "plant": 52, "plasma": [33, 64, 73, 79], "plate": [22, 64, 73, 79], "platform": [17, 34, 54, 64, 73, 79, 90, 93, 105], "plot": [39, 86], "plotter": [6, 54, 64, 73, 79, 90], "plugin": [4, 11, 15, 24, 47, 52, 64, 68, 73, 79, 82, 84, 90, 94, 100, 105], "pneumonia": [20, 64, 73, 79], "pol": [29, 39, 46, 48, 62, 68, 79, 89, 90, 103], "polici": [40, 52, 57, 64, 79, 86, 93, 105], "popul": [64, 73, 79], "positron": [29, 64, 73, 79], "post": [39, 64, 73, 79], "poster": [39, 50, 66, 105, 106], "practic": [3, 4, 8, 11, 14, 21, 30, 33, 34, 37, 40, 46, 48, 51, 52, 54, 55, 64, 68, 69, 73, 79, 86, 89, 90, 91, 93, 98, 101, 103, 105, 106], "praxisbeispielen": [64, 73, 79], "predict": [25, 31, 64, 73, 79], "predoc": [55, 68, 86, 89], "preprint": [1, 15, 38, 51, 64, 73, 79], "prerequisit": 61, "present": [6, 28, 37, 42, 54, 64, 68, 73, 79, 105], "preserv": [29, 64, 73, 79, 99, 105], "pricipl": [42, 79, 92, 93, 105], "primari": [64, 73, 79], "primer": 40, "principl": [52, 64, 71, 79, 92, 93, 105, 106], "process": [4, 7, 11, 13, 18, 24, 29, 36, 40, 41, 42, 45, 47, 48, 49, 52, 54, 55, 58, 64, 65, 68, 71, 76, 78, 79, 83, 84, 86, 90, 94, 100, 103, 105, 107, 108], "produc": [30, 33, 64, 73, 79], "prof": [3, 22, 23, 32, 37, 64, 73, 79], "profil": [40, 68, 81, 90], "program": [1, 29, 42, 48, 54, 64, 67, 68, 73, 79, 86, 90, 94, 97, 103], "programm": [42, 47, 48, 68, 86, 90, 102, 103], "project": [3, 8, 17, 23, 30, 33, 34, 35, 37, 48, 55, 64, 68, 73, 79, 86, 89, 90, 99, 105, 106], "promot": [39, 105], "prompt": [19, 39, 47, 55, 66, 68, 84, 90, 103], "protocol": [40, 73], "prototyp": [64, 73, 79], "public": [38, 52, 71, 105], "publicli": [42, 55, 56, 70, 79, 101, 106], "publish": [4, 12, 18, 30, 42, 48, 52, 55, 71, 76, 78, 81, 86, 90, 93, 95, 105], "pure": [29, 54, 64, 65, 79], "purpos": [3, 8, 23, 33, 34, 35, 37, 54, 56, 64, 72, 73, 79, 99, 100, 105], "push": 86, "put": [39, 86], "python": [7, 11, 13, 19, 28, 29, 36, 39, 40, 41, 42, 45, 47, 48, 52, 54, 55, 62, 64, 66, 68, 73, 78, 79, 83, 84, 86, 90, 97, 98, 100, 102, 103, 108], "pytorch": [13, 48, 68, 86, 89, 90, 103], "qi": [12, 48, 79, 103], "qm": [48, 79, 97, 103], "qualiti": [11, 19, 39, 52, 66, 79, 90, 97, 103], "quantif": [25, 31, 64, 73, 79], "quantit": [2, 15, 42, 52, 64, 78, 79, 90], "quantix": [41, 64, 68, 73, 83, 90], "quarep": [11, 52, 79], "quick": 60, "quo": [29, 52, 64, 76], "qupath": [25, 31, 42, 47, 48, 49, 54, 64, 68, 73, 79, 86, 90, 100, 102, 103], "rat": [31, 64, 73, 79], "ratamero": [56, 72, 90, 100], "raw2ometiff": [2, 17, 47, 68, 82, 102], "rdf": [17, 34, 54, 73, 79, 90, 93, 105], "rdm": [3, 17, 30, 33, 34, 35, 37, 40, 57, 58, 64, 73, 79, 86, 90, 99, 100, 105], "rdm4mic": [42, 56, 68, 72, 79, 86, 100, 105], "rdm_system_connector": [47, 68, 86, 105], "rdmbite": [42, 56, 72], "rdmkit": [42, 79, 105], "rdmo": [49, 57, 86, 102, 105], "re": [3, 8, 23, 33, 34, 35, 37, 51, 52, 54, 56, 64, 69, 72, 73, 79, 90, 99, 100, 105, 106], "re3data": [57, 79, 105], "read": [64, 73, 79], "real": [11, 52, 96], "realisierung": [52, 64, 79, 105], "reason": [39, 86, 105, 106], "recent": 109, "rechtsfragen": [26, 40, 54, 64, 73, 79, 101, 105], "recommend": [1, 11, 15, 38, 52, 64, 71, 73, 79, 86, 96, 105], "refer": 86, "registr": [24, 31, 64, 73, 79], "registri": [57, 79, 105], "reichert": [3, 22, 23, 32, 37, 64, 73, 79], "relat": [11, 51, 52, 90], "releas": [64, 73, 84], "reload": [42, 57, 86, 90], "rembi": [11, 38, 42, 52, 64, 70, 71, 81, 86, 93, 96, 105], "remot": [55, 68, 86], "report": [19, 25, 31, 32, 39, 42, 64, 66, 71, 73, 79, 86, 96, 103], "repositori": [0, 3, 12, 14, 16, 23, 37, 47, 55, 57, 64, 68, 73, 79, 86, 90, 93, 99, 105], "repositorium": [54, 64, 73, 79, 105], "reproduc": [11, 12, 29, 39, 40, 42, 50, 52, 54, 64, 66, 71, 73, 79, 84, 86, 94, 96, 105, 108], "rescal": [19, 39, 66, 90, 97, 103], "rescu": [6, 16, 28, 50, 64, 73, 79, 90, 92, 99, 105], "research": [0, 1, 3, 6, 8, 11, 14, 15, 16, 17, 20, 22, 23, 28, 29, 30, 32, 33, 34, 35, 37, 38, 39, 40, 42, 44, 49, 50, 52, 54, 55, 57, 64, 65, 66, 73, 76, 79, 80, 84, 86, 89, 90, 93, 95, 99, 100, 101, 102, 105, 106, 109], "reserv": 76, "resolut": 40, "resourc": [1, 4, 5, 7, 9, 15, 29, 36, 38, 39, 40, 42, 43, 45, 47, 52, 62, 64, 68, 71, 73, 79, 86, 87, 89, 90, 98, 100, 103, 105, 108, 109], "restor": [86, 90], "result": [12, 17, 19, 28, 29, 39, 50, 52, 54, 64, 66, 73, 79, 90, 94, 97, 99, 103, 105], "rethink": [47, 56, 68, 72, 81, 86], "reus": [11, 38, 52, 64, 71, 86, 96, 105], "reusabl": [20, 52, 64, 73, 79, 101, 105], "riccardo": 28, "richtlini": [52, 79, 93, 105], "right": 76, "rm": [48, 68, 83, 90], "robert": 29, "roi": [64, 73, 84], "roland": 30, "role": [3, 8, 23, 28, 64, 73, 79, 99, 105], "romain": 31, "round": [22, 64, 73, 79], "rse": [47, 54, 68, 83], "rstv": 75, "rule": [11, 52, 79, 91, 93, 96], "run": [39, 61, 62, 79, 89, 90, 103], "s3": [4, 5, 47, 68, 86, 103], "sa": 80, "sampl": [22, 24, 64, 73, 79], "sarkan": 38, "scad": [29, 54, 73, 79, 89, 90, 103], "scalabl": [17, 52, 54, 64, 73, 79, 90, 99, 100, 107, 108, 109], "scale": [34, 39, 54, 64, 73, 79, 99, 105], "scan": [24, 64, 73, 79], "scanpi": [47, 68, 78, 90], "schauss": 0, "schmidt": 3, "school": [10, 11, 18, 29, 31, 41, 46, 48, 52, 54, 62, 65, 68, 79, 86, 90, 98, 103], "schreib": [0, 32, 64, 73, 79], "schreiben": [0, 32, 64, 73, 79], "sch\u00e4tz": 20, "sciaugment": [20, 64, 73], "scicount": [20, 64, 73], "scienc": [1, 11, 15, 29, 38, 40, 42, 48, 52, 54, 64, 68, 73, 76, 79, 86, 89, 90, 91, 93, 95, 99, 101, 102, 103, 105, 106], "scientif": [1, 29, 39, 40, 52, 54, 64, 66, 67, 68, 71, 73, 79, 86, 90, 93, 94, 103, 105, 108], "scientist": [18, 29, 31, 40, 49, 52, 64, 65, 73, 76, 79, 89, 90, 98], "scikit": [47, 48, 68, 78, 79, 90, 103], "scipi": [47, 56, 68, 72, 78, 90, 100], "scope": [31, 64, 73, 79], "screen": [1, 28, 50, 52, 64, 71, 73, 79, 90, 93, 96, 105], "script": [29, 39, 42, 45, 48, 62, 66, 79, 81, 86, 89, 90, 94, 100, 103, 105, 106], "scripts_filopodyanr": [41, 68, 86, 90, 98], "search": [42, 86, 105], "secret": [17, 34, 50, 64, 73, 79, 90, 93, 99, 105], "segment": [19, 21, 25, 29, 39, 48, 54, 63, 64, 66, 68, 73, 79, 86, 89, 90, 97, 98, 103], "semi": [28, 50, 64, 79, 90], "seminar": [54, 64, 73, 79, 105], "seri": [28, 29, 47, 52, 54, 68, 84, 90, 103], "serrano": 1, "server": [39, 62, 79, 89, 90, 103], "servic": [44, 54, 63, 64, 73, 79, 86, 105], "set": [28, 34, 41, 42, 43, 52, 55, 56, 64, 68, 71, 73, 79, 83, 86, 90, 99, 105], "share": [29, 39, 54, 64, 65, 66, 73, 79, 86, 93, 95, 105, 106], "short": [3, 22, 54, 60, 64, 73, 79, 90, 99, 105], "should": [29, 39, 66, 79, 95, 105], "silk": 32, "simpl": [11, 15, 47, 52, 68, 79, 82, 91, 93, 96, 100], "simpleitk": [47, 68, 90], "size": [4, 24, 52, 64, 79, 90, 105], "skill": [42, 47, 54, 55, 56, 68, 79, 83], "skimag": [47, 68, 90], "sliceplot": [29, 64, 73, 79], "slide": [3, 8, 22, 23, 24, 31, 33, 34, 35, 37, 54, 56, 64, 72, 73, 79, 84, 97, 99, 100, 105], "snakemak": [45, 103, 108], "snt": [64, 73, 79], "so": [26, 54, 64, 73, 79, 93, 105], "softwar": [2, 4, 6, 9, 29, 39, 42, 52, 54, 55, 56, 64, 65, 71, 73, 79, 86, 90, 96, 98, 99, 100, 102, 105, 106], "solano": 1, "solut": [16, 28, 34, 35, 50, 56, 64, 73, 79, 90, 99, 105], "sourc": [4, 6, 12, 24, 29, 39, 42, 48, 52, 54, 55, 64, 65, 68, 71, 73, 79, 84, 86, 90, 98, 102, 105], "spark": [47, 48, 58, 68, 90], "spatial": [17, 51, 52, 69, 79, 103], "spatialdata": [17, 51, 52, 69, 79, 103], "speaker": [6, 28, 37, 54, 64, 73, 79, 105], "specif": [11, 52, 64, 86, 90, 96], "speicherstrategien": [26, 54, 64, 73, 79, 105], "stabil": [22, 64, 73, 79], "stack": [64, 73, 79], "stackview": [29, 64, 73, 79], "stage": [22, 64, 73, 79], "stain": [64, 73, 79, 81, 109], "standard": [11, 17, 34, 40, 50, 52, 71, 73, 79, 86, 90, 92, 93, 96, 99, 105], "stardist": [21, 25, 31, 48, 54, 64, 68, 73, 79, 86, 89, 90, 98, 103], "start": [19, 28, 39, 44, 56, 62, 64, 72, 73, 79, 103, 105], "starter": [57, 79, 105], "statist": [47, 56, 68, 72, 81, 86, 88], "steal": [29, 39, 66, 79, 95, 105], "stefani": 33, "step": [40, 60, 73, 79, 105], "steward": [6, 16, 28, 50, 64, 73, 79, 90, 92, 99, 105], "stewardship": [33, 49, 52, 54, 57, 64, 71, 73, 79, 92, 93, 105, 106], "stitch": [24, 64, 73, 79], "stitcher": [47, 55, 68, 78], "stop": [39, 86], "storag": [17, 34, 50, 52, 64, 73, 79, 90, 99, 105], "stori": [18, 29, 31, 52, 65, 79, 90, 98], "strategi": [2, 4, 5, 15, 17, 52, 64, 71, 73, 79, 99, 105, 109], "structur": [0, 3, 11, 16, 34, 35, 37, 50, 52, 56, 64, 73, 79, 93, 99, 105], "strudel": [6, 54, 64, 73, 79, 99, 105], "strukturevalu": [64, 73, 79], "studentsourc": [51, 69, 106], "studi": [3, 12, 23, 29, 32, 37, 41, 50, 52, 54, 56, 64, 68, 72, 73, 79, 86, 89, 90, 94, 98, 99, 105], "subcellular": [64, 73, 79, 109], "submiss": 61, "submit": [55, 56, 70, 81, 105], "subtyp": [29, 64, 73, 79], "success": [18, 29, 31, 52, 65, 79, 90, 98], "suit": [28, 55, 79, 96, 99, 100], "super": 40, "support": [3, 4, 7, 11, 17, 30, 33, 34, 35, 55, 57, 64, 73, 79, 84, 86, 105, 107], "survei": [3, 8, 11, 17, 33, 51, 52, 64, 65, 68, 69, 78, 79, 90, 99, 105], "susann": 34, "sustain": [1, 15, 38, 54, 64, 73, 79, 92, 105], "swarm": [29, 55, 56, 64, 73, 79, 86], "swat4hcl": [17, 34, 50, 73, 79, 93, 99, 105], "swc": [42, 55, 56, 79], "systemat": [55, 57, 80], "szege": [41, 68, 86, 90, 98], "ta3": [0, 16, 23, 37, 45, 46, 47, 52, 64, 68, 73, 79, 90, 93, 99, 100, 103, 105], "tabbl": [64, 73, 79], "tabl": [22, 64, 73, 79], "taipei": [42, 58, 68, 90], "talk": [3, 5, 28, 54, 64, 73, 79, 89, 90, 99, 105], "task": [14, 54, 55, 64, 73, 79, 90, 93, 105], "tb": [4, 24, 52, 64, 79, 90, 105], "teach": [5, 9, 42, 54, 55, 64, 68, 73, 79, 84, 86, 89, 90, 103], "team": [34, 35, 45, 64, 73, 79, 86, 89, 90, 99, 105, 108], "technic": [16, 34, 35, 55, 56, 57, 64, 73, 79, 80, 99, 105], "technolog": [28, 50, 64, 79, 90], "technologi": [52, 79, 101, 105], "teil": [0, 26, 32, 54, 64, 73, 79, 105], "temperatur": [64, 73, 79], "templat": [3, 4, 7, 11, 14, 16, 22, 23, 32, 35, 37, 42, 52, 55, 64, 71, 73, 79, 84, 90, 92, 93, 96, 99, 105, 107], "ten": [11, 17, 52, 64, 73, 79, 91, 93, 96], "term": [12, 29, 50, 52, 94], "terminologi": [64, 73, 79], "tess": [42, 46, 86, 91, 105], "test": [24, 31, 64, 73, 79], "text": [52, 71, 96], "thedigitalcel": [41, 64, 68, 73, 83, 90], "theori": [29, 54, 67, 78, 90, 94, 98], "thi": [55, 86, 87, 93, 95, 105], "think": [34, 54, 64, 73, 79, 99, 105], "thoma": 35, "through": [4, 6, 9, 17, 28, 29, 37, 52, 54, 64, 65, 73, 79, 90, 101, 105], "throughput": [4, 16, 18, 35, 42, 43, 68, 71, 73, 79, 86, 90, 100], "tier": [11, 52, 86, 90, 96], "tile": [24, 64, 73, 79], "tischer": 4, "tissu": [47, 54, 58, 63, 68, 79, 90], "tmg": 75, "tobi": 36, "togeth": [3, 33, 34, 37, 58, 64, 73, 79, 90, 99, 100, 105], "tom": 37, "tomographi": [29, 64, 73, 79], "tool": [2, 3, 6, 11, 15, 16, 20, 33, 34, 42, 45, 47, 48, 52, 54, 58, 62, 63, 64, 68, 71, 73, 76, 79, 86, 89, 90, 91, 96, 100, 105, 107], "toolbox": [10, 12, 29, 42, 45, 48, 52, 64, 68, 71, 73, 79, 84, 90, 94, 108], "topic": [17, 18, 29, 50, 52, 65, 79, 90, 96], "toward": [11, 29, 40, 52, 54, 63, 64, 73, 79, 86, 90, 96, 99, 105], "town": [17, 54, 64, 73, 79, 90, 99, 100, 105], "track": [19, 29, 39, 42, 43, 47, 54, 55, 58, 66, 67, 68, 78, 79, 83, 86, 90, 91, 94, 97, 98, 103], "trackmat": [29, 47, 54, 55, 58, 67, 68, 78, 83, 90, 94, 98], "train": [3, 4, 5, 7, 8, 9, 11, 18, 23, 28, 29, 31, 33, 34, 35, 36, 37, 40, 42, 45, 46, 47, 48, 51, 52, 54, 55, 56, 61, 62, 64, 65, 68, 69, 72, 73, 79, 84, 86, 87, 89, 90, 91, 93, 95, 96, 98, 99, 100, 101, 103, 105, 108, 109], "trainer": [40, 64, 73, 79, 105], "transfer": [11, 15, 47, 68, 82, 100], "transpar": [29, 64, 73, 79], "tree": [14, 64, 73, 79], "trustworthi": [42, 57, 86, 90], "ts7": [41, 68, 86, 90, 98], "tu": [6, 54, 64, 73, 79, 99, 105], "tulok": 32, "ture": [40, 79, 84], "tutori": [11, 21, 41, 42, 45, 47, 48, 49, 55, 56, 60, 68, 72, 76, 78, 81, 84, 86, 89, 90, 94, 97, 103], "ugi": 38, "uk": 70, "ultrack": [47, 55, 58, 68, 90], "umap": [39, 86], "umgang": [26, 54, 64, 73, 79, 93, 105], "und": [26, 29, 44, 45, 52, 54, 55, 64, 73, 79, 93, 105], "under": [31, 64, 73, 79], "understand": [11, 51, 52, 90], "uni": [29, 54, 73, 79, 89, 90, 103], "univers": [4, 9, 17, 29, 51, 52, 54, 57, 64, 65, 69, 73, 79, 86, 90, 103, 105], "universit\u00e4ten": [52, 79, 93, 105], "unknown": 86, "uoc": [0, 16, 23, 37, 45, 46, 47, 52, 64, 68, 73, 79, 90, 93, 99, 100, 103, 105], "up": [28, 34, 45, 52, 55, 64, 68, 73, 79, 86, 89, 90, 99, 105, 108], "upcom": [11, 25, 31, 42, 46, 58, 86, 90], "updat": [17, 18, 29, 50, 52, 65, 76, 79, 94, 96], "upload": [28, 55, 79, 96, 99, 100], "urheberrecht": [54, 64, 73, 75, 79, 95, 105], "us": [1, 10, 13, 25, 28, 29, 30, 31, 33, 39, 42, 47, 48, 51, 52, 54, 55, 56, 58, 61, 64, 66, 68, 69, 70, 73, 79, 83, 84, 86, 87, 89, 90, 96, 99, 100, 101, 103, 105, 106], "usabl": [3, 8, 23, 33, 34, 35, 37, 54, 56, 64, 72, 73, 79, 99, 100, 105], "user": [3, 8, 16, 23, 33, 34, 35, 37, 51, 52, 54, 56, 64, 68, 69, 72, 73, 78, 79, 90, 96, 99, 100, 105, 107], "v0": [64, 73, 79], "v1": [20, 64, 73], "v4sdb_winter_school_2025": [42, 47, 68, 90, 109], "vadi": [29, 52, 64, 76], "valid": [11, 17, 29, 42, 43, 47, 51, 52, 57, 64, 68, 71, 73, 77, 79, 86, 90], "valu": [3, 16, 22, 23, 32, 35, 37, 42, 45, 64, 73, 79, 86, 99, 100, 105], "variou": [64, 73, 79], "verantwortlich": 75, "version": [22, 29, 64, 73, 79, 99, 105, 109], "versionskontrol": [29, 54, 64, 73, 79, 93, 105], "ver\u00f6ffentlichen": [26, 54, 64, 73, 79, 105], "video": 56, "view": [29, 54, 67, 78, 90, 94, 98], "vii": [17, 64, 73, 79], "virtual": [29, 47, 48, 55, 68, 78, 90], "vision": [5, 48, 68, 84, 86, 89, 90, 103], "vision4d": [42, 56, 72, 76, 90], "visual": [39, 40, 42, 43, 45, 47, 48, 52, 58, 68, 70, 71, 73, 78, 81, 86, 90, 94, 103, 108], "vivo": [64, 73, 79, 109], "voigt": 26, "volum": [42, 43, 70, 81, 90], "vom": [0, 32, 64, 73, 79], "wa": [54, 64, 73, 79, 105], "wai": [40, 64, 73, 79, 84], "walkthrough": [44, 77, 100], "we": [29, 39, 66, 79, 95, 105], "webinar": [2, 42, 54, 55, 56, 72, 86, 89, 90, 100], "websit": 57, "week": 87, "weidtkamp": 33, "weigert": 21, "weka": [64, 73, 79], "welcom": [17, 54, 64, 73, 79, 90, 99, 100, 105], "wendt": 16, "wer": [54, 64, 73, 79, 105], "wet": [40, 73], "wetzker": 6, "what": [11, 18, 42, 54, 55, 60, 79, 86, 90, 93, 95, 98, 101, 105], "when": [55, 86, 93, 95, 105], "where": [14, 64, 73, 79], "who": [6, 16, 28, 50, 64, 73, 79, 90, 92, 99, 105], "whole": [24, 31, 64, 73, 79], "why": [29, 39, 66, 79, 95, 105, 106], "widefield": [64, 73], "wie": [0, 26, 32, 54, 64, 73, 79, 93, 105], "window": [22, 64, 73, 79, 109], "wissenschaft": [57, 86, 99, 105], "within": [3, 17, 22, 23, 30, 32, 33, 34, 35, 37, 64, 73, 79], "without": [17, 34, 50, 64, 73, 79, 90, 93, 99, 105], "witz": 13, "wizard": [49, 57, 92, 93, 105], "work": [3, 29, 33, 34, 37, 39, 54, 58, 64, 66, 67, 73, 78, 79, 90, 94, 95, 98, 99, 100, 105, 109], "workflow": [10, 16, 28, 35, 42, 45, 49, 52, 54, 55, 64, 68, 73, 77, 79, 81, 86, 89, 90, 94, 96, 98, 100, 105, 107, 108], "workflowhub": [11, 42, 78, 91, 103, 107, 108], "workshop": [3, 4, 5, 6, 13, 22, 23, 28, 33, 34, 37, 41, 42, 47, 48, 54, 55, 58, 64, 68, 73, 79, 83, 84, 86, 90, 97, 99, 103, 105, 107, 109], "world": [11, 52, 96], "write": [19, 39, 66, 103], "www": [69, 70, 71, 72], "xml": [2, 15, 52, 64, 79, 90], "ymia": [28, 29, 47, 54, 68, 84, 90, 103], "yml": 59, "you": [6, 16, 28, 29, 39, 50, 64, 66, 73, 79, 90, 92, 95, 99, 105], "your": [6, 28, 37, 39, 45, 54, 55, 58, 64, 66, 73, 79, 86, 90, 103, 105, 106], "youtub": [42, 56, 72, 79, 86, 90, 97, 98, 100, 102, 103], "zarr": [4, 5, 17, 47, 50, 55, 68, 73, 79, 86, 90, 103, 105], "zeiss": [22, 64, 73, 79], "zen": [42, 48, 68, 83, 90, 97, 103], "zenodo": [29, 39, 54, 64, 66, 73, 79, 105, 106], "zerocostdl4m": [12, 42, 48, 64, 68, 71, 84, 90], "zida": [10, 54, 68, 86, 89, 90], "zobel": 35, "zoo": [42, 86, 89, 90], "zoologi": [29, 64, 73, 79, 99, 105], "zukunftsfest": [64, 73, 79], "zum": [0, 32, 44, 45, 55, 64, 73, 79, 105], "zur": [64, 73, 79]}}) \ No newline at end of file diff --git a/statistics/readme.html b/statistics/readme.html index e1aaa731..874422c8 100644 --- a/statistics/readme.html +++ b/statistics/readme.html @@ -182,36 +182,29 @@

    By tag

    By content type

    @@ -236,11 +228,12 @@ diff --git a/tags/artificial_intelligence.html b/tags/artificial_intelligence.html index d91c9aa7..12fddf1b 100644 --- a/tags/artificial_intelligence.html +++ b/tags/artificial_intelligence.html @@ -8,7 +8,7 @@ - Artificial intelligence (32) — NFDI4BioImage Training Materials + Artificial intelligence (44) — NFDI4BioImage Training Materials @@ -63,7 +63,7 @@ - + @@ -182,36 +182,29 @@

    By tag

    By content type

    @@ -236,11 +228,12 @@ @@ -447,7 +440,7 @@
    -

    Artificial intelligence (32)

    +

    Artificial intelligence (44)

    @@ -460,8 +453,14 @@

    Contents

  • AI ML DL in Bioimage Analysis - Webinar
  • AI4Life teams up with Galaxy Training Network (GTN) to enhance training resources
  • Artificial Intelligence for Digital Pathology
    • +
  • BIDS-lecture-2024
  • Bio-image Analysis Code Generation using bia-bob
    • +
  • Bio-image Analysis with the Help of Large Language Models
    • +
  • Bio-image Data Science
    • +
  • Bio-image Data Science Lectures @ Uni Leipzig / ScaDS.AI
  • BioEngine
    • +
  • BioEngine Documentation
    • +
  • BioImage Archive AI Gallery
  • Bioimage Model Zoo
  • Building a Bioimage Analysis Workflow using Deep Learning
  • CARE/Stardist tutorials for EMBO Practical Course — Computational optical biology 2022
    • @@ -475,15 +474,21 @@

    Contents

  • Deep Learning for image analysis - Exercises
  • Deep Vision and Graphics
  • DeepProfiler Handbook
    • +
  • Dr Guillaume Jacquemet on studying cancer cell metastasis in the era of deep learning for microscopy
  • EMBL Deep Learning course 2019 exercises and materials
  • EMBL Deep Learning course 2021/22 exercises and materials
  • EMBL Deep Learning course 2023 exercises and materials
    • +
  • FAIRy deep-learning for bioImage analysis
  • Generative artificial intelligence for bio-image analysis
    • +
  • Introduction to Deep Learning for Microscopy
  • Kreshuk Lab’s EMBL EIPP predoc course teaching material
  • Large Language Models: An Introduction for Life Scientists
  • Machine Learning - Deep Learning. Applications to Bioimage Analysis
  • Machine and Deep Learning on the cloud: Segmentation
    • +
  • MicroSam-Talks
    • +
  • Microscopy data analysis: machine learning and the BioImage Archive
  • Neubias Academy 2020: Introduction to Nuclei Segmentation with StarDist
    • +
  • NeubiasPasteur2023_AdvancedCellPose
  • Running Deep-Learning Scripts in the BiA-PoL Omero Server
  • Training Deep Learning Models for Vision - Compact Course
  • ZIDAS 2020 Introduction to Deep Learning
    • @@ -500,16 +505,16 @@

    Contents

    -
    -

    Artificial intelligence (32)#

    +
    +

    Artificial intelligence (44)#

    AI ML DL in Bioimage Analysis - Webinar#

    Yannick KREMPP

    Published 2024-11-14

    Licensed UNKNOWN

    A review of the tools, methods and concepts useful for biologists and life scientists as well as bioimage analysts.

    -

    Tags: Deep Learning, Machine Learning, Artificial Intelligence, Bioimage Analysis, Large Language Models

    -

    Content type: Youtube Video, Slides, Webinar

    +

    Tags: Artificial Intelligence, Bioimage Analysis

    +

    Content type: Video, Slides, Webinar

    https://www.youtube.com/watch?v=TJXNMIWtdac

    @@ -529,10 +534,20 @@

    Artificial Intelligence for Digital Pathologyhttps://www.youtube.com/watch?v=Om9tl4Dh2yw

    +
    +

    BIDS-lecture-2024#

    +

    Robert Haase

    +

    Licensed CC-BY-4.0

    +

    Training resources for Students at Uni Leipzig who want to dive into bio-image data science with Python. The material developed here between April and July 2024.

    +

    Tags: Bioimage Analysis, Artificial Intelligence, Python

    +

    Content type: Github Repository

    +

    ScaDS/BIDS-lecture-2024

    +
    +

    Bio-image Analysis Code Generation using bia-bob#

    Robert Haase

    @@ -544,6 +559,38 @@

    Bio-image Analysis Code Generation using bia-bobhttps://doi.org/10.5281/zenodo.13908108

    +
    +

    Bio-image Analysis with the Help of Large Language Models#

    +

    Robert Haase

    +

    Published 2024-03-13

    +

    Licensed CC-BY-4.0

    +

    Large Language Models (LLMs) change the way how we use computers. This also has impact on the bio-image analysis community. We can generate code that analyzes biomedical image data if we ask the right prompts. This talk outlines introduces basic principles, explains prompt engineering and how to apply it to bio-image analysis. We also compare how different LLM vendors perform on code generation tasks and which challenges are ahead for the bio-image analysis community.

    +

    Tags: Artificial Intelligence, Python

    +

    Content type: Slides

    +

    https://zenodo.org/records/10815329

    +

    https://doi.org/10.5281/zenodo.10815329

    +
    +
    +
    +

    Bio-image Data Science#

    +

    Robert Haase

    +

    Licensed CC-BY-4.0

    +

    This repository contains training resources for Students at Uni Leipzig who want to dive into bio-image data science with Python.

    +

    Tags: Research Data Management, Artificial Intelligence, Bioimage Analysis, Python

    +

    Content type: Notebook

    +

    ScaDS/BIDS-lecture-2024

    +
    +
    +
    +

    Bio-image Data Science Lectures @ Uni Leipzig / ScaDS.AI#

    +

    Robert Haase

    +

    Licensed CC-BY-4.0

    +

    These are the PPTx training resources for Students at Uni Leipzig who want to dive into bio-image data science with Python. The material developed here between April and July 2024.

    +

    Tags: Bioimage Analysis, Artificial Intelligence, Python

    +

    Content type: Slides

    +

    https://zenodo.org/records/12623730

    +
    +

    BioEngine#

    Jeremy Metz, Beatriz Serrano-Solano, Wei Ouyang

    @@ -554,6 +601,24 @@

    BioEnginehttps://ai4life.eurobioimaging.eu/announcing-bioengine/

    +
    +

    BioEngine Documentation#

    +

    Wei Ouyang, Nanguage, Jeremy Metz, Craig Russell

    +

    Licensed MIT

    +

    BioEngine, a Python package designed for flexible deployment and execution of bioimage analysis models and workflows using AI, accessible via HTTP API and RPC.

    +

    Tags: Workflow Engine, Artificial Intelligence, Python

    +

    Content type: Documentation

    +

    https://bioimage-io.github.io/bioengine/#/

    +
    +
    + +

    Bioimage Model Zoo#

    Licensed UNKNOWN

    @@ -567,7 +632,7 @@

    Building a Bioimage Analysis Workflow using Deep Learningesgomezm/NEUBIAS_chapter_DL_2020

    @@ -612,8 +677,8 @@

    Creating a Research Data Management Plan using chatGPTPublished 2023-11-06

    Licensed CC-BY-4.0

    In this blog post the author demonstrates how chatGPT can be used to combine a fictive project description with a DMP specification to produce a project-specific DMP.

    -

    Tags: Research Data Management, Large Language Models, Artificial Intelligence

    -

    Content type: Blog

    +

    Tags: Research Data Management, Artificial Intelligence

    +

    Content type: Blog Post

    https://focalplane.biologists.com/2023/11/06/creating-a-research-data-management-plan-using-chatgpt/

    @@ -631,7 +696,7 @@

    DL@MBL 2021 ExercisesJan Funke, Constantin Pape, Morgan Schwartz, Xiaoyan

    Licensed UNKNOWN

    Tags: Artificial Intelligence, Bioimage Analysis

    -

    Content type: Slide, Notebook

    +

    Content type: Slides, Notebook

    JLrumberger/DL-MBL-2021

    @@ -671,6 +736,17 @@

    DeepProfiler Handbookhttps://cytomining.github.io/DeepProfiler-handbook/docs/00-welcome.html

    +
    +

    Dr Guillaume Jacquemet on studying cancer cell metastasis in the era of deep learning for microscopy#

    +

    Guillaume Jacquemet

    +

    Published 2024-10-24

    +

    Licensed UNKNOWN

    +

    Leukocyte extravasation is a critical component of the innate immune response, while circulating tumour cell extravasation is a crucial step in metastasis formation. Despite their importance, these extravasation mechanisms remain incompletely understood. In this talk, Guillaume Jacquemet presents a novel imaging framework that integrates microfluidics with high-speed, label-free imaging to study the arrest of pancreatic cancer cells (PDAC) on human endothelial layers under physiological flow conditions.

    +

    Tags: Artificial Intelligence, Bioimage Analysis

    +

    Content type: Video, Slides

    +

    https://www.youtube.com/watch?v=KTdZBgSCYJQ

    +
    +

    EMBL Deep Learning course 2019 exercises and materials#

    Valentyna Zinchenko, Pejman Rasti, Martin Weigert, Szymon Stoma

    @@ -698,15 +774,35 @@

    EMBL Deep Learning course 2023 exercises and materialskreshuklab/teaching-dl-course-2023

    +
    +

    FAIRy deep-learning for bioImage analysis#

    +

    Estibaliz Gómez de Mariscal

    +

    Licensed CC-BY-4.0

    +

    Introduction to FAIR deep learning. Furthermore, tools to deploy trained DL models (deepImageJ), easily train and evaluate them (ZeroCostDL4Mic and DeepBacs) ensure reproducibility (DL4MicEverywhere), and share this technology in an open-source and reproducible manner (BioImage Model Zoo) are introduced.

    +

    Tags: Artificial Intelligence, FAIR-Principles, Bioimage Analysis

    +

    Content type: Slides

    +

    https://f1000research.com/slides/13-147

    +
    +

    Generative artificial intelligence for bio-image analysis#

    Robert Haase

    Licensed CC-BY-4.0

    Tags: Python, Bioimage Analysis, Artificial Intelligence

    -

    Content type: Slide

    +

    Content type: Slides

    https://f1000research.com/slides/12-971

    +
    +

    Introduction to Deep Learning for Microscopy#

    +

    Costantin Pape

    +

    Licensed MIT

    +

    This course consists of lectures and exercises that teach the background of deep learning for image analysis and show applications to classification and segmentation analysis problems.

    +

    Tags: Artificial Intelligence, Python

    +

    Content type: Notebook

    +

    computational-cell-analytics/dl-for-micro

    +
    +

    Kreshuk Lab’s EMBL EIPP predoc course teaching material#

    Adrian Wolny, Johannes Hugger, Qin Yu, Buglakova Alyona

    @@ -719,12 +815,12 @@

    Kreshuk Lab’s EMBL EIPP predoc course teaching material

    Large Language Models: An Introduction for Life Scientists#

    Robert Haase

    -

    Published 2024-08-27

    +

    Published 2024-12-12

    Licensed CC-BY-4.0

    -

    Large Language Models (LLMs) are changing the way how humans interact with computers. This has impact on all scientific fields by enabling new ways to achieve for example data analysis goals. In this talk we will go through an introduction to LLMs with respect to applications in the life sciences, focusing on bio-image analysis. We will see how to generate text and images using LLMs and how LLMs can extract information from reproducibly images through code-generation. We will go through selected prompt engineering techniques enabling scientists to tune the output of LLMs towards their scientific goal and how to do quality assurance in this context.

    -

    Tags: Artificial Intelligence

    -

    https://zenodo.org/records/13379394

    -

    https://doi.org/10.5281/zenodo.13379394

    +

    This slide deck introduces Large Language Models to an audience of life-scientists. We first dive into terminology: Different kinds of Language Models and what they can be used for. The remaining slides are optional slides to allow us to dive deeper into topics such as tools for using LLMs in Science, Quality Assurance, Techniques such as Retrieval Augmented Generation and Prompt Engineering.

    +

    Tags: Globias, Artificial Intelligence

    +

    https://zenodo.org/records/14418209

    +

    https://doi.org/10.5281/zenodo.14418209

    @@ -732,7 +828,7 @@

    Machine Learning - Deep Learning. Applications to Bioimage AnalysisEstibaliz Gómez-de-Mariscal

    Licensed UNKNOWN

    Tags: Artificial Intelligence, Bioimage Analysis

    -

    Content type: Slide

    +

    Content type: Slides

    https://raw.githubusercontent.com/esgomezm/esgomezm.github.io/master/assets/pdf/SPAOM2018/MachineLearning_SPAOMworkshop_public.pdf

    @@ -741,25 +837,60 @@

    Machine and Deep Learning on the cloud: SegmentationIgnacio Arganda-Carreras

    Licensed UNKNOWN

    Tags: Neubias, Artificial Intelligence, Bioimage Analysis

    -

    Content type: Slide

    +

    Content type: Slides

    https://docs.google.com/presentation/d/1oJoy9gHmUuSmUwCkPs_InJf_WZAzmLlUNvK1FUEB4PA/edit#slide=id.ge3a24e733b_0_54

    +
    +

    MicroSam-Talks#

    +

    Constantin Pape

    +

    Published 2024-05-23

    +

    Licensed CC-BY-4.0

    +

    Talks about Segment Anything for Microscopy: computational-cell-analytics/micro-sam. +Currently contains slides for two talks:

    +

    Overview of Segment Anythign for Microscopy given at the SWISSBIAS online meeting in April 2024 +Talk about vision foundation models and Segment Anything for Microscopy given at Human Technopole as part of the EMBO Deep Learning Course in May 2024

    +

    Tags: Bioimage Analysis, Artificial Intelligence

    +

    Content type: Slides

    +

    https://zenodo.org/records/11265038

    +

    https://doi.org/10.5281/zenodo.11265038

    +
    +
    +
    +

    Microscopy data analysis: machine learning and the BioImage Archive#

    +

    Andrii Iudin, Anna Foix-Romero, Anna Kreshuk, Awais Athar, Beth Cimini, Dominik Kutra, Estibalis Gomez de Mariscal, Frances Wong, Guillaume Jacquemet, Kedar Narayan, Martin Weigert, Nodar Gogoberidze, Osman Salih, Petr Walczysko, Ryan Conrad, Simone Weyend, Sriram Sundar Somasundharam, Suganya Sivagurunathan, Ugis Sarkans

    +

    Licensed CC-BY-4.0

    +

    The Microscopy data analysis: machine learning and the BioImage Archive course, which focused on introducing programmatic approaches used in the analysis of bioimage data via the BioImage Archive, ran in May 2023.

    +

    Tags: Bioimage Analysis, Python, Artificial Intelligence

    +

    Content type: Video, Slides

    +

    https://www.ebi.ac.uk/training/materials/microscopy-data-analysis-machine-learning-and-the-bioimage-archive-materials/

    +
    +

    Neubias Academy 2020: Introduction to Nuclei Segmentation with StarDist#

    Martin Weigert, Olivier Burri, Siân Culley, Uwe Schmidt

    Licensed UNKNOWN

    Tags: Python, Neubias, Artificial Intelligence, Bioimage Analysis

    -

    Content type: Slide, Notebook

    +

    Content type: Slides, Notebook

    maweigert/neubias_academy_stardist

    +
    +

    NeubiasPasteur2023_AdvancedCellPose#

    +

    Gaelle Letort

    +

    Licensed BSD-3-CLAUSE

    +

    Tutorial for running CellPose advanced functions

    +

    Tags: Bioimage Analysis, Artificial Intelligence

    +

    Content type: Github Repository

    +

    gletort/NeubiasPasteur2023_AdvancedCellPose

    +
    +

    Running Deep-Learning Scripts in the BiA-PoL Omero Server#

    Marcelo Zoccoler

    Licensed CC-BY-4.0

    Tags: Python, Artificial Intelligence, Bioimage Analysis

    -

    Content type: Blog

    +

    Content type: Blog Post

    https://biapol.github.io/blog/marcelo_zoccoler/omero_scripts/readme.html

    @@ -776,7 +907,7 @@

    ZIDAS 2020 Introduction to Deep Learningesgomezm/zidas2020_intro_DL

    @@ -785,7 +916,7 @@

    ilastik: interactive machine learning for (bio)image analysishttps://zenodo.org/doi/10.5281/zenodo.4330625

    @@ -847,7 +978,7 @@

    introduction-to-generative-ai

    next

    -

    Bioimage analysis (174)

    +

    Bioimage analysis (183)

     @@ -871,8 +1002,14 @@

    introduction-to-generative-aiAI ML DL in Bioimage Analysis - Webinar
  • AI4Life teams up with Galaxy Training Network (GTN) to enhance training resources
  • Artificial Intelligence for Digital Pathology
    • +
  • BIDS-lecture-2024
  • Bio-image Analysis Code Generation using bia-bob
    • +
  • Bio-image Analysis with the Help of Large Language Models
    • +
  • Bio-image Data Science
    • +
  • Bio-image Data Science Lectures @ Uni Leipzig / ScaDS.AI
  • BioEngine
    • +
  • BioEngine Documentation
    • +
  • BioImage Archive AI Gallery
  • Bioimage Model Zoo
  • Building a Bioimage Analysis Workflow using Deep Learning
  • CARE/Stardist tutorials for EMBO Practical Course — Computational optical biology 2022
    • @@ -886,15 +1023,21 @@

    introduction-to-generative-aiDeep Learning for image analysis - Exercises
  • Deep Vision and Graphics
  • DeepProfiler Handbook
    • +
  • Dr Guillaume Jacquemet on studying cancer cell metastasis in the era of deep learning for microscopy
  • EMBL Deep Learning course 2019 exercises and materials
  • EMBL Deep Learning course 2021/22 exercises and materials
  • EMBL Deep Learning course 2023 exercises and materials
    • +
  • FAIRy deep-learning for bioImage analysis
  • Generative artificial intelligence for bio-image analysis
    • +
  • Introduction to Deep Learning for Microscopy
  • Kreshuk Lab’s EMBL EIPP predoc course teaching material
  • Large Language Models: An Introduction for Life Scientists
  • Machine Learning - Deep Learning. Applications to Bioimage Analysis
  • Machine and Deep Learning on the cloud: Segmentation
    • +
  • MicroSam-Talks
    • +
  • Microscopy data analysis: machine learning and the BioImage Archive
  • Neubias Academy 2020: Introduction to Nuclei Segmentation with StarDist
    • +
  • NeubiasPasteur2023_AdvancedCellPose
  • Running Deep-Learning Scripts in the BiA-PoL Omero Server
  • Training Deep Learning Models for Vision - Compact Course
  • ZIDAS 2020 Introduction to Deep Learning
    • diff --git a/tags/bioimage_analysis.html b/tags/bioimage_analysis.html index a85f8732..f480a8fd 100644 --- a/tags/bioimage_analysis.html +++ b/tags/bioimage_analysis.html @@ -8,7 +8,7 @@ - Bioimage analysis (174) — NFDI4BioImage Training Materials + Bioimage analysis (183) — NFDI4BioImage Training Materials @@ -63,8 +63,8 @@ - - + + @@ -182,36 +182,29 @@

    By tag

    By content type

    @@ -236,11 +228,12 @@ @@ -447,7 +440,7 @@
    -

    Bioimage analysis (174)

    +

    Bioimage analysis (183)

    @@ -476,6 +469,7 @@

    Contents

  • Bio-image Analysis ICOB 2023
  • Bio-image Analysis Workshop Kioto and Taipei 23/24
  • Bio-image Analysis Workshop Taipei
    • +
  • Bio-image Data Science
  • Bio-image Data Science Lectures @ Uni Leipzig / ScaDS.AI
  • Bio-image analysis, biostatistics, programming and machine learning for computational biology
  • BioImage Analysis Notebooks
    • @@ -500,6 +494,7 @@

    Contents

  • CellProfiler tutorials
  • Challenges and opportunities for bio-image analysis core-facilities
  • Challenges and opportunities for bioimage analysis core-facilities
    • +
  • Checklists for publishing images and image analysis
  • Chris Halvin YouTube channel
  • Collection of teaching material for deep learning for (biomedical) image analysis
  • Community-developed checklists for publishing images and image analyses
    • @@ -514,6 +509,7 @@

    Contents

  • DeepProfiler Handbook
  • Developing (semi)automatic analysis pipelines and technological solutions for metadata annotation and management in high-content screening (HCS) bioimaging
  • Diffusion Models for Image Restoration - An Introduction
    • +
  • Dr Guillaume Jacquemet on studying cancer cell metastasis in the era of deep learning for microscopy
  • EDAM-bioimaging - The ontology of bioimage informatics operations, topics, data, and formats
  • EMBO Practical Course Advanced methods in bioimage analysis
  • EPFLx: Image Processing and Analysis for Life Scientists
    • @@ -524,6 +520,7 @@

    Contents

  • Euro-BioImaging’s Template for Research Data Management Plans
  • Example Pipeline Tutorial
  • FAIR BioImage Data
    • +
  • FAIRy deep-learning for bioImage analysis
  • Feature extraction in napari
  • Fiji Is Just ImageJ Tutorials
  • Fit for OMERO: How imaging facilities and IT departments work together to enable RDM for bioimaging
    • @@ -536,6 +533,7 @@

    Contents

  • Highlights from the 2016-2020 NEUBIAS training schools for Bioimage Analysts: a success story and key asset for analysts and life scientists
  • Hitchhiking through a diverse Bio-image Analysis Software Universe
  • I2K 2024: clEsperanto - GPU-Accelerated Image Processing Library
    • +
  • I2K2024 workshop material - Lazy Parallel Processing and Visualization of Large Data with ImgLib2, BigDataViewer, the N5-API, and Spark
  • I2K2024(virtual) - Bio-Image Analysis Code Generation
  • IAFIG-RMS Python for Bioimage Analysis Course
  • ITKElastix Examples
    • @@ -563,6 +561,8 @@

    Contents

  • Methods in bioimage analysis
  • Metrics Reloaded - A framework for trustworthy image analysis validation
  • MicroSam-Talks
    • +
  • Microscopy data analysis: machine learning and the BioImage Archive
    • +
  • Modular training resources for bioimage analysis
  • MorphoLibJ documentation
  • Multi-view fusion
  • Multimodal large language models for bioimage analysis
    • @@ -577,6 +577,7 @@

    Contents

  • NFDI4BIOIMAGE - National Research Data Infrastructure for Microscopy and BioImage Analysis [conference talk: The Pelagic Imaging Consortium meets Helmholtz Imaging, 5.10.2023, Hamburg]
  • NFDI4Bioimage - TA3-Hackathon - UoC-2023 (Cologne-Hackathon-2023, GitHub repository)
  • Neubias Academy 2020: Introduction to Nuclei Segmentation with StarDist
    • +
  • NeubiasPasteur2023_AdvancedCellPose
  • OMERO - HCS analysis pipeline using Jupyter Notebooks
  • OMERO - QuPath
  • ONBI Image Analysis Course
    • @@ -601,6 +602,7 @@

    Contents

  • Teaching ImageJ FIJI
  • The Digital Cell: Cell Biology as a Data Science
  • The Open Microscopy Environment (OME) Data Model and XML file - open tools for informatics and quantitative analysis in biological imaging
    • +
  • Towards community-driven metadata standards for light microscopy - tiered specifications extending the OME model
  • Tracking Theory, TrackMate, and Mastodon
  • Tracking in napari
  • Tracking of mitochondria and capturing mitoflashes
    • @@ -642,8 +644,8 @@

    Contents

    -
    -

    Bioimage analysis (174)#

    +
    +

    Bioimage analysis (183)#

    2020 BioImage Analysis Survey: Community experiences and needs for the future#

    Nasim Jamali, Ellen T. A. Dobson, Kevin W. Eliceiri, Anne E. Carpenter, Beth A. Cimini

    @@ -699,8 +701,8 @@

    AI ML DL in Bioimage Analysis - Webinarhttps://www.youtube.com/watch?v=TJXNMIWtdac

    @@ -718,7 +720,7 @@

    Adding a Workflow to BIAFLOWSRoccoDAnt/Defragmentation_TrainingSchool_EOSC-Life_2022

    @@ -735,7 +737,7 @@

    Analysis of High-Throughput Microscopy Image DataAnnotating 3D images in napari#

    Mara Lampert

    Tags: Python, Napari, Bioimage Analysis

    -

    Content type: Blog

    +

    Content type: Blog Post

    https://focalplane.biologists.com/2023/03/30/annotating-3d-images-in-napari/

    @@ -744,7 +746,7 @@

    BIDS-lecture-2024ScaDS/BIDS-lecture-2024

    @@ -755,7 +757,7 @@

    BIOMERO - A scalable and extensible image analysis frameworkhttps://doi.org/10.1016/j.patter.2024.101024

    @@ -817,12 +819,22 @@

    Bio-image Analysis Workshop TaipeiKoushouu/Bioimage-Analysis-Workshop-Taipei

    +
    +

    Bio-image Data Science#

    +

    Robert Haase

    +

    Licensed CC-BY-4.0

    +

    This repository contains training resources for Students at Uni Leipzig who want to dive into bio-image data science with Python.

    +

    Tags: Research Data Management, Artificial Intelligence, Bioimage Analysis, Python

    +

    Content type: Notebook

    +

    ScaDS/BIDS-lecture-2024

    +
    +

    Bio-image Data Science Lectures @ Uni Leipzig / ScaDS.AI#

    Robert Haase

    Licensed CC-BY-4.0

    These are the PPTx training resources for Students at Uni Leipzig who want to dive into bio-image data science with Python. The material developed here between April and July 2024.

    -

    Tags: Bioimage Analysis, Deep Learning, Microscopy Image Analysis, Python

    +

    Tags: Bioimage Analysis, Artificial Intelligence, Python

    Content type: Slides

    https://zenodo.org/records/12623730

    @@ -848,7 +860,7 @@

    BioImage Analysis Notebooks

    BioImage Archive AI Gallery#

    Licensed CC0-1.0

    -

    Tags: Bioimage Analysis, Deep Learning

    +

    Tags: Bioimage Analysis, Artificial Intelligence

    Content type: Collection, Data

    https://www.ebi.ac.uk/bioimage-archive/galleries/AI.html

    @@ -926,7 +938,7 @@

    Building a Bioimage Analysis Workflow using Deep Learningesgomezm/NEUBIAS_chapter_DL_2020

    @@ -987,7 +999,7 @@

    CellProfiler Introductionahklemm/CellProfiler_Introduction

    @@ -1014,7 +1026,7 @@

    Challenges and opportunities for bio-image analysis core-facilitiesRobert Haase

    Licensed CC-BY-4.0

    Tags: Research Data Management, Bioimage Analysis, Nfdi4Bioimage

    -

    Content type: Slide

    +

    Content type: Slides

    https://f1000research.com/slides/12-1054

    @@ -1028,6 +1040,17 @@

    Challenges and opportunities for bioimage analysis core-facilitieshttps://onlinelibrary.wiley.com/doi/full/10.1111/jmi.13192

    +
    +

    Checklists for publishing images and image analysis#

    +

    Christopher Schmied

    +

    Published 2023-09-14

    +

    Licensed CC0-1.0

    +

    In this paper we introduce two sets of checklists. One is concerned with the publication of images. The other one gives instructions for the publication of image analysis.

    +

    Tags: Bioimage Analysis

    +

    Content type: Forum Post

    +

    https://forum.image.sc/t/checklists-for-publishing-images-and-image-analysis/86304

    +
    +

    Chris Halvin YouTube channel#

    Licensed UNKNOWN

    @@ -1048,7 +1071,7 @@

    Collection of teaching material for deep learning for (biomedical) image ana

    Community-developed checklists for publishing images and image analyses#

    Beth Cimini et al.

    -

    Licensed BSD LICENSE

    +

    Licensed BSD-3-CLAUSE

    This book is a companion to the Nature Methods publication Community-developed checklists for publishing images and image analyses. In this paper, members of QUAREP-LiMi have proposed 3 sets of standards for publishing image figures and image analysis - minimal requirements, recommended additions, and ideal comprehensive goals. By following this guidance, we hope to remove some of the stress non-experts may face in determining what they need to do, and we also believe that researchers will find their science more interpretable and more reproducible.

    Tags: Bioimage Analysis, Research Data Management

    Content type: Notebook, Collection

    @@ -1099,7 +1122,7 @@

    DL@MBL 2021 ExercisesJan Funke, Constantin Pape, Morgan Schwartz, Xiaoyan

    Licensed UNKNOWN

    Tags: Artificial Intelligence, Bioimage Analysis

    -

    Content type: Slide, Notebook

    +

    Content type: Slides, Notebook

    JLrumberger/DL-MBL-2021

    @@ -1116,7 +1139,7 @@

    Dask Course#

    Published 2023-07-05

    Licensed CC-BY-SA-4.0

    -

    Tags: Research Data Management, Bioimage Analysis, Image Data Management, Open Science

    +

    Tags: Research Data Management, Bioimage Analysis, Open Science

    Content type: Slides, Presentation

    https://omero-fbi.fr/slides/elmi23_cfd/main.html#/title-slide

    @@ -1160,6 +1183,17 @@

    Diffusion Models for Image Restoration - An Introductionhttps://drive.google.com/file/d/1pPVUUMi5w2Ojw_SaBzSQVaXUuIKtQ7Ma/view

    +
    +

    Dr Guillaume Jacquemet on studying cancer cell metastasis in the era of deep learning for microscopy#

    +

    Guillaume Jacquemet

    +

    Published 2024-10-24

    +

    Licensed UNKNOWN

    +

    Leukocyte extravasation is a critical component of the innate immune response, while circulating tumour cell extravasation is a crucial step in metastasis formation. Despite their importance, these extravasation mechanisms remain incompletely understood. In this talk, Guillaume Jacquemet presents a novel imaging framework that integrates microfluidics with high-speed, label-free imaging to study the arrest of pancreatic cancer cells (PDAC) on human endothelial layers under physiological flow conditions.

    +

    Tags: Artificial Intelligence, Bioimage Analysis

    +

    Content type: Video, Slides

    +

    https://www.youtube.com/watch?v=KTdZBgSCYJQ

    +
    +

    EDAM-bioimaging - The ontology of bioimage informatics operations, topics, data, and formats#

    Matúš Kalaš et al.

    @@ -1200,7 +1234,7 @@

    Erick Martins Ratamero - Expanding the OME ecosystem for imaging data manage

    SciPy, Erick Martins Ratamero

    Published 2024-08-19

    Licensed YOUTUBE STANDARD LICENSE

    -

    Tags: Image Data Management, OMERO, Bioimage Analysis

    +

    Tags: OMERO, Bioimage Analysis

    Content type: Video, Presentation

    https://www.youtube.com/watch?v=GmhyDNm1RsM

    @@ -1246,7 +1280,7 @@

    Example Pipeline Tutorialhttps://timmonko.github.io/napari-ndev/tutorial/01_example_pipeline/

    timmonko/napari-ndev

    @@ -1260,11 +1294,21 @@

    FAIR BioImage Datahttps://www.youtube.com/watch?v=8zd4KTy-oYI&list=PLW-oxncaXRqU4XqduJzwFHvWLF06PvdVm

    +
    +

    FAIRy deep-learning for bioImage analysis#

    +

    Estibaliz Gómez de Mariscal

    +

    Licensed CC-BY-4.0

    +

    Introduction to FAIR deep learning. Furthermore, tools to deploy trained DL models (deepImageJ), easily train and evaluate them (ZeroCostDL4Mic and DeepBacs) ensure reproducibility (DL4MicEverywhere), and share this technology in an open-source and reproducible manner (BioImage Model Zoo) are introduced.

    +

    Tags: Artificial Intelligence, FAIR-Principles, Bioimage Analysis

    +

    Content type: Slides

    +

    https://f1000research.com/slides/13-147

    +
    +

    Feature extraction in napari#

    Mara Lampert

    Tags: Python, Napari, Bioimage Analysis

    -

    Content type: Blog

    +

    Content type: Blog Post

    https://focalplane.biologists.com/2023/05/03/feature-extraction-in-napari/

    @@ -1316,7 +1360,7 @@

    Generative artificial intelligence for bio-image analysishttps://f1000research.com/slides/12-971

    @@ -1354,7 +1398,7 @@

    Hitchhiking through a diverse Bio-image Analysis Software Universehttps://f1000research.com/slides/11-746

    https://doi.org/10.7490/f1000research.1119026.1

    @@ -1364,11 +1408,22 @@

    I2K 2024: clEsperanto - GPU-Accelerated Image Processing LibraryStRigaud/clesperanto_workshop_I2K24

    +
    +

    I2K2024 workshop material - Lazy Parallel Processing and Visualization of Large Data with ImgLib2, BigDataViewer, the N5-API, and Spark#

    +

    Stephan Saalfeld, Tobias Pietzsch

    +

    Published None

    +

    Licensed APACHE-2.0

    +

    Tags: Bioimage Analysis

    +

    Content type: Workshop, Notebook, Github Repository

    +

    https://saalfeldlab.github.io/i2k2024-lazy-workshop/

    +

    saalfeldlab/i2k2024-lazy-workshop

    +
    +

    I2K2024(virtual) - Bio-Image Analysis Code Generation#

    Robert Haase

    @@ -1416,7 +1471,7 @@

    Image Processing with Pythonhttps://datacarpentry.org/image-processing/key-points.html

    @@ -1435,7 +1490,7 @@

    Image analysis in Galaxyhttps://docs.google.com/presentation/d/1WG_4307XmKsGfWT3taxMvX2rZiG1k0SM1E7SAENJQkI/edit#slide=id.p

    @@ -1463,7 +1518,7 @@

    ImageJ Macro Introductionahklemm/ImageJMacro_Introduction

    @@ -1472,7 +1527,7 @@

    ImageJ2 API-beatinghttps://git.mpi-cbg.de/rhaase/lecture_imagej2_dev

    @@ -1506,7 +1561,7 @@

    Introduction to ImageJ macro programming, Scientific Computing Facility, MPI

    Robert Haase, Benoit Lombardot

    Licensed UNKNOWN

    Tags: Imagej, Bioimage Analysis

    -

    Content type: Slide

    +

    Content type: Slides

    https://git.mpi-cbg.de/scicomp/bioimage_team/coursematerialimageanalysis/tree/master/ImageJMacro_24h_2017-01

    @@ -1515,7 +1570,7 @@

    Jupyter for interactive cloud computinghttps://docs.google.com/presentation/d/1q8q1xE-c35tvCsRXZay98s2UYWwXpp0cfCljBmMFpco/edit#slide=id.ga456d5535c_2_53

    @@ -1524,7 +1579,7 @@

    Lecture Applied Bioimage Analysis 2020https://git.mpi-cbg.de/rhaase/lecture_applied_bioimage_analysis

    @@ -1533,7 +1588,7 @@

    Machine Learning - Deep Learning. Applications to Bioimage AnalysisEstibaliz Gómez-de-Mariscal

    Licensed UNKNOWN

    Tags: Artificial Intelligence, Bioimage Analysis

    -

    Content type: Slide

    +

    Content type: Slides

    https://raw.githubusercontent.com/esgomezm/esgomezm.github.io/master/assets/pdf/SPAOM2018/MachineLearning_SPAOMworkshop_public.pdf

    @@ -1550,7 +1605,7 @@

    Machine and Deep Learning on the cloud: SegmentationIgnacio Arganda-Carreras

    Licensed UNKNOWN

    Tags: Neubias, Artificial Intelligence, Bioimage Analysis

    -

    Content type: Slide

    +

    Content type: Slides

    https://docs.google.com/presentation/d/1oJoy9gHmUuSmUwCkPs_InJf_WZAzmLlUNvK1FUEB4PA/edit#slide=id.ge3a24e733b_0_54

    @@ -1559,7 +1614,7 @@

    Making the most of bioimaging data through interdisciplinary interactionsVirginie Uhlmann, Matthew Hartley, Josh Moore, Erin Weisbart, Assaf Zaritsky

    Published 2024-10-23

    Licensed CC-BY-4.0

    -

    Tags: Bioimage Analysis, Open Science, Microscopy Image Analysis, Microscopy

    +

    Tags: Bioimage Analysis, Open Science, Microscopy

    Content type: Publication

    https://journals.biologists.com/jcs/article/137/20/jcs262139/362478/Making-the-most-of-bioimaging-data-through

    @@ -1586,7 +1641,7 @@

    Methods in bioimage analysishttps://www.ebi.ac.uk/training/events/methods-bioimage-analysis/

    https://doi.org/10.6019/TOL.BioImageAnalysis22-w.2022.00001.1

    https://drive.google.com/file/d/1MhuqfKhZcYu3bchWMqogIybKjamU5Msg/view

    @@ -1596,7 +1651,7 @@

    Methods in bioimage analysis#

    Licensed UNKNOWN

    The mission of Metrics Reloaded is to guide researchers in the selection of appropriate performance metrics for biomedical image analysis problems, as well as provide a comprehensive online resource for metric-related information and pitfalls

    -

    Tags: Bioimage Analysis, Image Segmentation, Machine Learning

    +

    Tags: Bioimage Analysis, Quality Control

    Content type: Website, Collection

    https://metrics-reloaded.dkfz.de/

    @@ -1610,12 +1665,33 @@

    MicroSam-Talkshttps://zenodo.org/records/11265038

    https://doi.org/10.5281/zenodo.11265038

    +
    +

    Microscopy data analysis: machine learning and the BioImage Archive#

    +

    Andrii Iudin, Anna Foix-Romero, Anna Kreshuk, Awais Athar, Beth Cimini, Dominik Kutra, Estibalis Gomez de Mariscal, Frances Wong, Guillaume Jacquemet, Kedar Narayan, Martin Weigert, Nodar Gogoberidze, Osman Salih, Petr Walczysko, Ryan Conrad, Simone Weyend, Sriram Sundar Somasundharam, Suganya Sivagurunathan, Ugis Sarkans

    +

    Licensed CC-BY-4.0

    +

    The Microscopy data analysis: machine learning and the BioImage Archive course, which focused on introducing programmatic approaches used in the analysis of bioimage data via the BioImage Archive, ran in May 2023.

    +

    Tags: Bioimage Analysis, Python, Artificial Intelligence

    +

    Content type: Video, Slides

    +

    https://www.ebi.ac.uk/training/materials/microscopy-data-analysis-machine-learning-and-the-bioimage-archive-materials/

    +
    +
    +
    +

    Modular training resources for bioimage analysis#

    +

    Christian Tischer, Antonio Politi, Tim-Oliver Buchholz, Elnaz Fazeli, Nicola Gritti, Aliaksandr Halavatyi, Sebastian Gonzalez Tirado, Julian Hennies, Toby Hodges, Arif Khan, Dominik Kutra, Stefania Marcotti, Bugra Oezdemir, Felix Schneider, Martin Schorb, Anniek Stokkermans, Yi Sun, Nima Vakili

    +

    Published 2024-12-03

    +

    Licensed CC-BY-4.0

    +

    Resources for teaching/preparing to teach bioimage analysis

    +

    Tags: Neubias, Bioimage Analysis

    +

    https://zenodo.org/records/14264885

    +

    https://doi.org/10.5281/zenodo.14264885

    +
    +

    MorphoLibJ documentation#

    Tags: Bioimage Analysis

    @@ -1629,16 +1705,16 @@

    Multi-view fusionhttps://git.mpi-cbg.de/rhaase/lecture_multiview_registration

    Multimodal large language models for bioimage analysis#

    Shanghang Zhang, Gaole Dai, Tiejun Huang, Jianxu Chen

    -

    Licensed [‘CC-BY-NC-SA’]

    +

    Licensed CC-BY-NC-SA

    Multimodal large language models have been recognized as a historical milestone in the field of artificial intelligence and have demonstrated revolutionary potentials not only in commercial applications, but also for many scientific fields. Here we give a brief overview of multimodal large language models through the lens of bioimage analysis and discuss how we could build these models as a community to facilitate biology research

    -

    Tags: Bioimage Analysis, Large Language Models, FAIR-Principles, Workflow

    +

    Tags: Bioimage Analysis, FAIR-Principles, Workflow

    Content type: Publication

    https://www.nature.com/articles/s41592-024-02334-2

    https://arxiv.org/abs/2407.19778

    @@ -1649,7 +1725,7 @@

    Multiplexed tissue imaging - tools and approachesAgustín Andrés Corbat, OmFrederic, Jonas Windhager, Kristína Lidayová

    Licensed CC-BY-4.0

    Material for the I2K 2024 “Multiplexed tissue imaging - tools and approaches” workshop

    -

    Tags: Bioimage Analysis, Microscopy Image Analysis

    +

    Tags: Bioimage Analysis

    Content type: Github Repository, Slides, Workshop

    BIIFSweden/I2K2024-MTIWorkshop

    https://docs.google.com/presentation/d/1R9-4lXAmTYuyFZpTMDR85SjnLsPZhVZ8/edit#slide=id.p1

    @@ -1668,7 +1744,7 @@

    NEUBIAS Analyst School 2018miura/NEUBIAS_AnalystSchool2018

    @@ -1677,7 +1753,7 @@

    NEUBIAS Bioimage Analyst Course 2017miura/NEUBIAS_Bioimage_Analyst_Course2017

    @@ -1686,7 +1762,7 @@

    NEUBIAS Bioimage Analyst School 2019miura/NEUBIAS_AnalystSchool2019

    @@ -1695,7 +1771,7 @@

    NEUBIAS Bioimage Analyst School 2020miura/NEUBIAS_AnalystSchool2020

    @@ -1744,10 +1820,20 @@

    Neubias Academy 2020: Introduction to Nuclei Segmentation with StarDistMartin Weigert, Olivier Burri, Siân Culley, Uwe Schmidt

    Licensed UNKNOWN

    Tags: Python, Neubias, Artificial Intelligence, Bioimage Analysis

    -

    Content type: Slide, Notebook

    +

    Content type: Slides, Notebook

    maweigert/neubias_academy_stardist

    +
    +

    NeubiasPasteur2023_AdvancedCellPose#

    +

    Gaelle Letort

    +

    Licensed BSD-3-CLAUSE

    +

    Tutorial for running CellPose advanced functions

    +

    Tags: Bioimage Analysis, Artificial Intelligence

    +

    Content type: Github Repository

    +

    gletort/NeubiasPasteur2023_AdvancedCellPose

    +
    +

    OMERO - HCS analysis pipeline using Jupyter Notebooks#

    Riccardo Massei

    @@ -1761,7 +1847,7 @@

    OMERO - HCS analysis pipeline using Jupyter Notebooks

    OMERO - QuPath#

    Rémy Jean Daniel Dornier

    -

    Licensed [‘CC-BY-NC-SA-4.0’]

    +

    Licensed CC-BY-NC-SA-4.0

    OMERO-RAW extension for QuPath allows to directly access to the raw pixels of images. All types of images (RGB, fluorescence, …) are supported with this extension.

    Tags: Bioimage Analysis, OMERO

    Content type: Online Tutorial

    @@ -1784,7 +1870,7 @@

    Object Tracking and Track Analysis using TrackMate and CellTracksColabPublished None

    Licensed GPL-3.0

    I2K 2024 workshop materials for “Object Tracking and Track Analysis using TrackMate and CellTracksColab”

    -

    Tags: Bioimage Analysis, Training

    +

    Tags: Bioimage Analysis

    Content type: Github Repository, Tutorial, Workshop, Slides

    CellMigrationLab/I2K_2024

    @@ -1826,7 +1912,7 @@

    PoL Bio-Image Analysis GPU Accelerated Image Analysis Training SchoolPrompt Engineering in Bio-image Analysis#

    Mara Lampert

    Tags: Python, Jupyter, Bioimage Analysis, Prompt Engineering, Biabob

    -

    Content type: Blog

    +

    Content type: Blog Post

    https://focalplane.biologists.com/2024/07/18/prompt-engineering-in-bio-image-analysis/

    @@ -1865,7 +1951,7 @@

    QuPath: Open source software for analysing (awkward) imageshttps://zenodo.org/records/4328911

    https://doi.org/10.5281/zenodo.4328911

    @@ -1874,7 +1960,7 @@

    QuPath: Open source software for analysing (awkward) images#

    Mara Lampert

    Tags: Python, Napari, Bioimage Analysis

    -

    Content type: Blog

    +

    Content type: Blog Post

    https://focalplane.biologists.com/2023/04/13/quality-assurance-of-segmentation-results/

    @@ -1892,7 +1978,7 @@

    RDF as a bridge to domain-platforms like OMERO, or There and back again.Rescaling images and pixel (an)isotropy#

    Mara Lampert

    Tags: Python, Napari, Bioimage Analysis

    -

    Content type: Blog

    +

    Content type: Blog Post

    https://focalplane.biologists.com/2023/03/02/rescaling-images-and-pixel-anisotropy/

    @@ -1901,7 +1987,7 @@

    Running Deep-Learning Scripts in the BiA-PoL Omero Serverhttps://biapol.github.io/blog/marcelo_zoccoler/omero_scripts/readme.html

    @@ -1927,7 +2013,7 @@

    Scripts_FilopodyanR - a case study for the NEUBIAS TS7 in Szeged

    SimpleITK-Notebooks#

    Ziv Yaniv et al.

    -

    Licensed APACHE-2.0 LICENSE

    +

    Licensed APACHE-2.0

    Jupyter notebooks for learning how to use SimpleITK

    Tags: Bioimage Analysis, Simpleitk

    Content type: Github Repository

    @@ -1966,19 +2052,30 @@

    The Open Microscopy Environment (OME) Data Model and XML file - open tools f

    Published 2005-05-03

    Licensed CC-BY-4.0

    The Open Microscopy Environment (OME) defines a data model and a software implementation to serve as an informatics framework for imaging in biological microscopy experiments, including representation of acquisition parameters, annotations and image analysis results.

    -

    Tags: Microscopy Image Analysis, Bioimage Analysis

    +

    Tags: Bioimage Analysis

    Content type: Publication

    https://genomebiology.biomedcentral.com/articles/10.1186/gb-2005-6-5-r47

    https://doi.org/10.1186/gb-2005-6-5-r47

    +
    +

    Towards community-driven metadata standards for light microscopy - tiered specifications extending the OME model#

    +

    Mathias Hammer, Maximiliaan Huisman, Alessandro Rigano, Ulrike Boehm, James J. Chambers, et al.

    +

    Published 2022-07-10

    +

    Licensed UNKNOWN

    +

    Rigorous record-keeping and quality control are required to ensure the quality, reproducibility and value of imaging data. The 4DN Initiative and BINA here propose light Microscopy Metadata specifications that extend the OME data model, scale with experimental intent and complexity, and make it possible for scientists to create comprehensive records of imaging experiments.

    +

    Tags: Reproducibility, Bioimage Analysis, Metadata

    +

    Content type: Publication

    +

    https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9271325/

    +
    +

    Tracking Theory, TrackMate, and Mastodon#

    Robert Haase

    Licensed BSD-3-CLAUSE

    Lecture slides of a session on Cell Tracking in Fiji

    Tags: Neubias, Imagej, Bioimage Analysis

    -

    Content type: Slide

    +

    Content type: Slides

    https://git.mpi-cbg.de/rhaase/lecture_tracking_trackmate

    @@ -1986,7 +2083,7 @@

    Tracking Theory, TrackMate, and Mastodon#

    Mara Lampert

    Tags: Python, Napari, Bioimage Analysis

    -

    Content type: Blog

    +

    Content type: Blog Post

    https://focalplane.biologists.com/2023/06/01/tracking-in-napari/

    @@ -2012,7 +2109,7 @@

    Training Deep Learning Models for Vision - Compact Course#

    Jordão Bragantini, Teun Huijben

    Licensed BSD3-CLAUSE

    -

    Tags: Segmentation, Bioimage Analysis, Training

    +

    Tags: Bioimage Analysis

    Content type: Workshop, Github Repository, Tutorial

    royerlab/ultrack-i2k2024

    https://royerlab.github.io/ultrack-i2k2024/

    @@ -2032,7 +2129,7 @@

    Understanding metric-related pitfalls in image analysis validation#

    Curtis Rueden, Albane de la Villegeorges, Simon F. Nørrelykke, Romain Guiet, Olivier Burri, et al.

    Licensed UNKNOWN

    -

    Tags: Bioimage Analysis, Microscopy Image Analysis

    +

    Tags: Bioimage Analysis

    Content type: Collection, Event, Forum Post, Workshop

    https://forum.image.sc/t/upcoming-image-analysis-events/60018/67

    @@ -2042,7 +2139,7 @@

    Using Glittr.org

    Geert van Geest, Yann Haefliger, Monique Zahn-Zabal, Patricia M. Palagi

    Licensed CC-BY-4.0

    Glittr.org is a platform that aggregates and indexes training materials on computational life sciences from public git repositories, making it easier for users to find, compare, and analyze these resources based on various metrics. By providing insights into the availability of materials, collaboration patterns, and licensing practices, Glittr.org supports adherence to the FAIR principles, benefiting the broader life sciences community.

    -

    Tags: Training, Bioimage Analysis, Research Data Management

    +

    Tags: Bioimage Analysis, Research Data Management

    Content type: Publication, Preprint

    https://www.biorxiv.org/content/10.1101/2024.08.20.608021v1

    @@ -2072,7 +2169,7 @@

    What is Bioimage Analysis? An Introductionhttps://www.dropbox.com/s/5abw3cvxrhpobg4/20220923_DefragmentationTS.pdf?dl=0

    @@ -2091,7 +2188,7 @@

    Working with objects in 2D and 3Dhttps://git.mpi-cbg.de/rhaase/lecture_working_with_objects_in_2d_and_3d

    @@ -2100,7 +2197,7 @@

    Working with pixelshttps://git.mpi-cbg.de/rhaase/lecture_working_with_pixels

    @@ -2118,7 +2215,7 @@

    YMIA - Python-Based Event Series Training MaterialPublished None

    Licensed MIT

    This repository offer access to teaching material and useful resources for the YMIA - Python-Based Event Series.

    -

    Tags: Python, Large Language Models, Prompt Engineering, Biabob, Bioimage Analysis, Microscopy Image Analysis

    +

    Tags: Python, Artifical Intelligence, Bioimage Analysis

    Content type: Github Repository, Slides

    rmassei/ymia_python_event_series_material

    @@ -2136,7 +2233,7 @@

    ZIDAS 2020 Introduction to Deep Learningesgomezm/zidas2020_intro_DL

    @@ -2194,7 +2291,7 @@

    ilastik: interactive machine learning for (bio)image analysishttps://zenodo.org/doi/10.5281/zenodo.4330625

    @@ -2212,7 +2309,7 @@

    introduction-to-image-analysis#

    JanClusmann, Tim Lenz

    Published 2024-11-08T08:32:03+00:00

    -

    Licensed GNU GENERAL PUBLIC LICENSE V3.0

    +

    Licensed GPL-3.0

    Tags: Histopathology, Bioimage Analysis

    Content type: Github Repository, Notebook

    KatherLab/patho_prompt_injection

    @@ -2233,9 +2330,9 @@

    quantixed/TheDigitalCell: First complete code set

    scanpy-tutorials#

    -

    Alex Wolf, pre-commit-ci[bot], Philipp A., Isaac Virshup, Ilan Gold, Giovanni Palla, Fidel Ramirez, Gökçen Eraslan, Sergei Rybakov, Abolfazl (Abe), Adam Gayoso, Dinesh Palli, Gregor Sturm, Jan Lause, Karin Hrovatin, Krzysztof Polanski, RaphaelBuzzi, Yimin Zheng, Yishen Miao, evanbiederstedt

    +

    Alex Wolf, pre-commit-ci[bot], Philipp A., Isaac Virshup, Ilan Gold, Giovanni Palla, Fidel Ramirez, G\xF6k\xE7en Eraslan, Sergei Rybakov, Abolfazl (Abe), Adam Gayoso, Dinesh Palli, Gregor Sturm, Jan Lause, Karin Hrovatin, Krzysztof Polanski, RaphaelBuzzi, Yimin Zheng, Yishen Miao, evanbiederstedt

    Published 2018-12-16T03:42:46+00:00

    -

    Licensed BSD-3

    +

    Licensed BSD-3-CLAUSE

    Scanpy Tutorials.

    Tags: Single-Cell Analysis, Bioimage Analysis

    Content type: Github Repository

    @@ -2300,15 +2397,15 @@

    skimage-tutorials

    previous

    -

    Artificial intelligence (32)

    +

    Artificial intelligence (44)

    next

    -

    Bioimage data (20)

    +

    Bioinformatics (10)

     @@ -2348,6 +2445,7 @@

    skimage-tutorialsBio-image Analysis ICOB 2023
  • Bio-image Analysis Workshop Kioto and Taipei 23/24
  • Bio-image Analysis Workshop Taipei
    • +
  • Bio-image Data Science
  • Bio-image Data Science Lectures @ Uni Leipzig / ScaDS.AI
  • Bio-image analysis, biostatistics, programming and machine learning for computational biology
  • BioImage Analysis Notebooks
    • @@ -2372,6 +2470,7 @@

    skimage-tutorialsCellProfiler tutorials
  • Challenges and opportunities for bio-image analysis core-facilities
  • Challenges and opportunities for bioimage analysis core-facilities
    • +
  • Checklists for publishing images and image analysis
  • Chris Halvin YouTube channel
  • Collection of teaching material for deep learning for (biomedical) image analysis
  • Community-developed checklists for publishing images and image analyses
    • @@ -2386,6 +2485,7 @@

    skimage-tutorialsDeepProfiler Handbook
  • Developing (semi)automatic analysis pipelines and technological solutions for metadata annotation and management in high-content screening (HCS) bioimaging
  • Diffusion Models for Image Restoration - An Introduction
    • +
  • Dr Guillaume Jacquemet on studying cancer cell metastasis in the era of deep learning for microscopy
  • EDAM-bioimaging - The ontology of bioimage informatics operations, topics, data, and formats
  • EMBO Practical Course Advanced methods in bioimage analysis
  • EPFLx: Image Processing and Analysis for Life Scientists
    • @@ -2396,6 +2496,7 @@

    skimage-tutorialsEuro-BioImaging’s Template for Research Data Management Plans
  • Example Pipeline Tutorial
  • FAIR BioImage Data
    • +
  • FAIRy deep-learning for bioImage analysis
  • Feature extraction in napari
  • Fiji Is Just ImageJ Tutorials
  • Fit for OMERO: How imaging facilities and IT departments work together to enable RDM for bioimaging
    • @@ -2408,6 +2509,7 @@

    skimage-tutorialsHighlights from the 2016-2020 NEUBIAS training schools for Bioimage Analysts: a success story and key asset for analysts and life scientists
  • Hitchhiking through a diverse Bio-image Analysis Software Universe
  • I2K 2024: clEsperanto - GPU-Accelerated Image Processing Library
    • +
  • I2K2024 workshop material - Lazy Parallel Processing and Visualization of Large Data with ImgLib2, BigDataViewer, the N5-API, and Spark
  • I2K2024(virtual) - Bio-Image Analysis Code Generation
  • IAFIG-RMS Python for Bioimage Analysis Course
  • ITKElastix Examples
    • @@ -2435,6 +2537,8 @@

    skimage-tutorialsMethods in bioimage analysis
  • Metrics Reloaded - A framework for trustworthy image analysis validation
  • MicroSam-Talks
    • +
  • Microscopy data analysis: machine learning and the BioImage Archive
    • +
  • Modular training resources for bioimage analysis
  • MorphoLibJ documentation
  • Multi-view fusion
  • Multimodal large language models for bioimage analysis
    • @@ -2449,6 +2553,7 @@

    skimage-tutorialsNFDI4BIOIMAGE - National Research Data Infrastructure for Microscopy and BioImage Analysis [conference talk: The Pelagic Imaging Consortium meets Helmholtz Imaging, 5.10.2023, Hamburg]
  • NFDI4Bioimage - TA3-Hackathon - UoC-2023 (Cologne-Hackathon-2023, GitHub repository)
  • Neubias Academy 2020: Introduction to Nuclei Segmentation with StarDist
    • +
  • NeubiasPasteur2023_AdvancedCellPose
  • OMERO - HCS analysis pipeline using Jupyter Notebooks
  • OMERO - QuPath
  • ONBI Image Analysis Course
    • @@ -2473,6 +2578,7 @@

    skimage-tutorialsTeaching ImageJ FIJI
  • The Digital Cell: Cell Biology as a Data Science
  • The Open Microscopy Environment (OME) Data Model and XML file - open tools for informatics and quantitative analysis in biological imaging
    • +
  • Towards community-driven metadata standards for light microscopy - tiered specifications extending the OME model
  • Tracking Theory, TrackMate, and Mastodon
  • Tracking in napari
  • Tracking of mitochondria and capturing mitoflashes
    • diff --git a/tags/bioimage_data.html b/tags/bioimage_data.html deleted file mode 100644 index 4da09c39..00000000 --- a/tags/bioimage_data.html +++ /dev/null @@ -1,849 +0,0 @@ - - - - - - - - - - - Bioimage data (20) — NFDI4BioImage Training Materials - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    - - - - - - -
    - - - Ctrl+K -
    -     - -
    - -
    - - -
    -
    - - -
    -
    - - - - -
    - - - -
    - - - - -
    - -
    -
    - - - - - -
    -
    - -
    -
    -
    - - -
    -
    -
    -
    -
    - - -
    - -
    - - - -
    - -
    -
    - -
    -
    - -
    - -
    - -
    - - -
    - -
    - -
    - - - - - -
    - - -
    - - - - - - -
    - - -
    - - - - - - - - - - - - - -
    - -
    - -
    -
    - - - -
    -

    Bioimage data (20)

    - -
    - -
    -
    - - - - -
    - -
    -

    Bioimage data (20)#

    -
    -

    Bio-Image Data Strudel for Workshop on Research Data Management in TU Dresden Core Facilities#

    -

    Cornelia Wetzker

    -

    Published 2023-11-08

    -

    Licensed CC-BY-4.0

    -

    This presentation gives a short outline of the complexity of data and metadata in the bioimaging universe. It introduces NFDI4BIOIMAGE as a newly formed consortium as part of the German ‘Nationale Forschungsdateninfrastruktur’ (NFDI) and its goals and tools for data management including its current members on TU Dresden campus.  

    -

    Tags: Research Data Management, Tu Dresden, Bioimage Data, Nfdi4Bioimage

    -

    Content type: Slide

    -

    https://zenodo.org/records/10083555

    -

    https://doi.org/10.5281/zenodo.10083555

    -
    -
    -
    -

    BioFormats Command line (CLI) tools#

    -

    Published 2024-10-24

    -

    Licensed CC-BY-4.0

    -

    Bio-Formats is a standalone Java library for reading and writing life sciences image file formats. There are several scripts for using Bio-Formats on the command line, which are listed here.

    -

    Tags: Bioimage Data

    -

    Content type: Documentation

    -

    https://bio-formats.readthedocs.io/en/v8.0.0/users/comlinetools/index.html

    -
    -
    -
    -

    Checklists for publishing images and image analysis#

    -

    Christopher Schmied

    -

    Published 2023-09-14

    -

    Licensed CC0-1.0

    -

    In this paper we introduce two sets of checklists. One is concerned with the publication of images. The other one gives instructions for the publication of image analysis.

    -

    Tags: Bioimage Data, Microscopy Image Analysis

    -

    Content type: Forum Post

    -

    https://forum.image.sc/t/checklists-for-publishing-images-and-image-analysis/86304

    -
    -
    -
    -

    FAIR High Content Screening in Bioimaging#

    -

    Rohola Hosseini, Matthijs Vlasveld, Joost Willemse, Bob van de Water, Sylvia E. Le Dévédec, Katherine J. Wolstencroft

    -

    Published 2023-07-17

    -

    Licensed CC-BY-4.0

    -

    The authors show the utility of Minimum Information for High Content Screening Microscopy Experiments (MIHCSME) for High Content Screening (HCS) data using multiple examples from the Leiden FAIR Cell Observatory, a Euro-Bioimaging flagship node for high content screening and the pilot node for implementing FAIR bioimaging data throughout the Netherlands Bioimaging network.

    -

    Tags: FAIR-Principles, Metadata, Research Data Management, Image Data Management, Bioimage Data

    -

    Content type: Publication

    -

    https://www.nature.com/articles/s41597-023-02367-w

    -
    -
    -
    -

    I3D bio – Information Infrastructure for BioImage Data - Bioimage Metadata#

    -

    Christian Schmidt

    -

    Licensed UNKNOWN

    -

    A Microscopy Research Data Management Resource.

    -

    Tags: Metadata, I3Dbio, Research Data Management, Bioimage Data

    -

    Content type: Collection

    -

    https://gerbi-gmb.de/i3dbio/i3dbio-rdm/i3dbio-bioimage-metadata/

    -
    -
    -
    -

    KNIME Image Processing#

    -

    None

    -

    Licensed GPLV3

    -

    The KNIME Image Processing Extension allows you to read in more than 140 different kinds of images and to apply well known methods on images, like preprocessing. segmentation, feature extraction, tracking and classification in KNIME.

    -

    Tags: Imagej, OMERO, Bioimage Data, Workflow

    -

    Content type: Tutorial, Online Tutorial, Documentation

    -

    https://www.knime.com/community/image-processing

    -
    -
    -
    -

    Microscopy-BIDS - An Extension to the Brain Imaging Data Structure for Microscopy Data#

    -

    Marie-Hélène Bourget, Lee Kamentsky, Satrajit S. Ghosh, Giacomo Mazzamuto, Alberto Lazari, et al.

    -

    Published 2022-04-19

    -

    Licensed CC-BY-4.0

    -

    The Brain Imaging Data Structure (BIDS) is a specification for organizing, sharing, and archiving neuroimaging data and metadata in a reusable way.

    -

    Tags: Research Data Management, Image Data Management, Bioimage Data

    -

    Content type: Publication

    -

    https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2022.871228/full

    -
    -
    -
    -

    NFDI4BIOIMAGE - An Initiative for a National Research Data Infrastructure for Microscopy Data#

    -

    Christian Schmidt, Elisa Ferrando-May

    -

    Published 2021-04-29

    -

    Licensed CCY-BY-SA-4.0

    -

    Align existing and establish novel services & solutions for data management tasks throughout the bioimage data lifecycle.

    -

    Tags: Nfdi4Bioimage, Image Data Management, Bioimage Data, Research Data Management

    -

    Content type: Conference Abstract, Slide

    -

    https://doi.org/10.11588/heidok.00029489

    -
    -
    -
    -

    NGFF Converter#

    -

    Licensed GPL-2.0

    -

    An easy to use and open source converter for bioimaging data. NGFF-Converter is a GUI application for conversion of bioimage formats into OME-NGFF (Next-Generation File Format) or OME-TIFF.

    -

    Tags: Bioimage Data, Open Source Software

    -

    Content type: Application

    -

    https://www.glencoesoftware.com/products/ngff-converter/

    -
    -
    -
    -

    Open Micoscropy Environment (OME) Youtube Channel#

    -

    Published None

    -

    Licensed CC-BY-4.0

    -

    OME develops open-source software and data format standards for the storage and manipulation of biological microscopy data

    -

    Tags: Open Source Software, Microscopy Image Analysis, Bioimage Data

    -

    Content type: Video, Collection

    -

    https://www.youtube.com/@OpenMicroscopyEnvironment

    -
    -
    - -
    -
    -

    REMBI Overview#

    -

    Licensed CC0-1.0

    -

    Recommended Metadata for Biological Images (REMBI) provides guidelines for metadata for biological images to enable the FAIR sharing of scientific data.

    -

    Tags: FAIR-Principles, Metadata, Image Data Management, Research Data Management, Bioimage Data

    -

    Content type: Collection

    -

    https://www.ebi.ac.uk/bioimage-archive/rembi-help-overview/

    -
    -
    -
    -

    Reporting and reproducibility in microscopy#

    -

    Published 2021-12-03

    -

    Licensed UNKNOWN

    -

    This Focus issue features a series of papers offering guidelines and tools for improving the tracking and reporting of microscopy metadata with an emphasis on reproducibility and data re-use.

    -

    Tags: Reproducibility, Metadata, Bioimage Data

    -

    Content type: Collection

    -

    https://www.nature.com/collections/djiciihhjh

    -
    -
    -
    -

    Research data management for bioimaging - the 2021 NFDI4BIOIMAGE community survey#

    -

    Christian Schmidt, Janina Hanne, Josh Moore, Christian Meesters, Elisa Ferrando-May, et al.

    -

    Published 2022-09-20

    -

    Licensed CC-BY-4.0

    -

    As an initiative within Germany’s National Research Data Infrastructure, the authors conducted this community survey in summer 2021 to assess the state of the art of bioimaging RDM and the community needs.

    -

    Tags: Research Data Management, Image Data Management, Bioimage Data

    -

    Content type: Publication

    -

    https://f1000research.com/articles/11-638/v2

    -
    -
    -
    -

    Submitting data to the BioImage Archive#

    -

    Licensed CC0-1.0

    -

    To submit, you’ll need to register an account, organise and upload your data, prepare a file list, and then submit using our web submission form. These steps are explained here.

    -

    Tags: Research Data Management, Image Data Management, Bioimage Data

    -

    Content type: Tutorial, Video

    -

    https://www.ebi.ac.uk/bioimage-archive/submit/

    -
    -
    -
    -

    The BioImage Archive – Building a Home for Life-Sciences Microscopy Data#

    -

    Matthew Hartley, Gerard J. Kleywegt, Ardan Patwardhan, Ugis Sarkans, Jason R. Swedlow, Alvis Brazma

    -

    Published 2022-06-22

    -

    Licensed UNKNOWN

    -

    The BioImage Archive is a new archival data resource at the European Bioinformatics Institute (EMBL-EBI).

    -

    Tags: Image Data Management, Research Data Management, Bioimage Data

    -

    Content type: Publication

    -

    https://www.sciencedirect.com/science/article/pii/S0022283622000791?via%3Dihub

    -

    https://doi.org/10.1016/j.jmb.2022.167505

    -
    -
    -
    -

    Towards community-driven metadata standards for light microscopy - tiered specifications extending the OME model#

    -

    Mathias Hammer, Maximiliaan Huisman, Alessandro Rigano, Ulrike Boehm, James J. Chambers, et al.

    -

    Published 2022-07-10

    -

    Licensed UNKNOWN

    -

    Rigorous record-keeping and quality control are required to ensure the quality, reproducibility and value of imaging data. The 4DN Initiative and BINA here propose light Microscopy Metadata specifications that extend the OME data model, scale with experimental intent and complexity, and make it possible for scientists to create comprehensive records of imaging experiments.

    -

    Tags: Reproducibility, Microscopy Image Analysis, Metadata, Image Data Management, Bioimage Data

    -

    Content type: Publication

    -

    https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9271325/

    -
    -
    -
    -

    bioformats2raw Converter#

    -

    Melissa Linkert, Chris Allan, Josh Moore, Sébastien Besson, David Gault, et al.

    -

    Licensed GPL-2.0

    -

    Java application to convert image file formats, including .mrxs, to an intermediate Zarr structure compatible with the OME-NGFF specification.

    -

    Tags: Open Source Software, Bioimage Data

    -

    Content type: Application, Github Repository

    -

    glencoesoftware/bioformats2raw

    -
    -
    -
    -

    ome-ngff-validator#

    -

    Will Moore, Josh Moore, Yaroslav Halchenko, Sébastien Besson

    -

    Published 2022-09-29

    -

    Licensed BSD-2-CLAUSE

    -

    Web page for validating OME-NGFF files.

    -

    Tags: Bioimage Data

    -

    Content type: Github Repository, Application

    -

    https://ome.github.io/ome-ngff-validator/

    -

    ome/ome-ngff-validator

    -
    -
    -
    -

    raw2ometiff Converter#

    -

    Melissa Linkert, Chris Allan, Sébastien Besson, Josh Moore

    -

    Licensed GPL-2.0

    -

    Java application to convert a directory of tiles to an OME-TIFF pyramid. This is the second half of iSyntax/.mrxs => OME-TIFF conversion.

    -

    Tags: Open Source Software, Bioimage Data

    -

    Content type: Application, Github Repository

    -

    glencoesoftware/raw2ometiff

    -
    -
    -
    - - - - -
    - - - - - - - - -
    - - - - - - - -
    -
    - -
    - -
    - -

    -By Robert Haase, Clément Caporal,... and the NFDI4BioImage Initiative -

    - -
    - -
    - - -
    - -
    -

    - Last updated on 2025-02-01. -
    -

    -
    - -
    - -
    -

    -Copyright: Licensed CC-BY 4.0 unless mentioned otherwise. -Contributions and feedback are welcome. -

    - -
    -
    - -
    -
    - - -
    -
    -
    - - - - - -
    -
    - - \ No newline at end of file diff --git a/tags/bioinformatics.html b/tags/bioinformatics.html index a7e07a77..72bc88c5 100644 --- a/tags/bioinformatics.html +++ b/tags/bioinformatics.html @@ -64,7 +64,7 @@ - + @@ -182,36 +182,29 @@

    By tag

    By content type

    @@ -236,11 +228,12 @@ @@ -514,7 +507,7 @@

    EMBL-EBI material collectionhttps://www.ebi.ac.uk/training/on-demand?facets=type:Course%20materials&query=

    @@ -550,7 +543,7 @@

    Ten simple rules for making training materials FAIRPublished 2020-05-21

    Licensed CC-BY-4.0

    The authors offer trainers some simple rules, to help make their training materials FAIR, enabling others to find, (re)use, and adapt them.

    -

    Tags: Metadata, Bioinformatics, FAIR-Principles, Training

    +

    Tags: Metadata, Bioinformatics, FAIR-Principles

    Content type: Publication

    https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1007854

    @@ -608,12 +601,12 @@

    WorkflowHub

    previous

    -

    Bioimage data (20)

    +

    Bioimage analysis (183)

     DOCUMENTATION_OPTIONS.pagename = 'tags/data_stewardship'; - + @@ -182,36 +182,29 @@

    By tag

    By content type

    @@ -236,11 +228,12 @@ @@ -581,11 +574,11 @@

    Who you gonna call? - Data Stewards to the rescue

    next

    -

    Deep learning (12)

    +

    Fair-principles (27)

     diff --git a/tags/deep_learning.html b/tags/deep_learning.html deleted file mode 100644 index 5766e6aa..00000000 --- a/tags/deep_learning.html +++ /dev/null @@ -1,746 +0,0 @@ - - - - - - - - - - - Deep learning (12) — NFDI4BioImage Training Materials - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    - - - - - - -
    - - - Ctrl+K -
    -     - -
    - -
    - - -
    -
    - - -
    -
    - - - - -
    - - - -
    - - - - -
    - -
    -
    - - - - - -
    -
    - -
    -
    -
    - - -
    -
    -
    -
    -
    - - -
    - -
    - - - -
    - -
    -
    - -
    -
    - -
    - -
    - -
    - - -
    - -
    - -
    - - - - - -
    - - -
    - - - - - - -
    - - -
    - - - - - - - - - - - - - -
    - -
    - -
    -
    - - - -
    -

    Deep learning (12)

    - -
    - -
    -
    - - - - -
    - -
    -

    Deep learning (12)#

    -
    -

    AI ML DL in Bioimage Analysis - Webinar#

    -

    Yannick KREMPP

    -

    Published 2024-11-14

    -

    Licensed UNKNOWN

    -

    A review of the tools, methods and concepts useful for biologists and life scientists as well as bioimage analysts.

    -

    Tags: Deep Learning, Machine Learning, Artificial Intelligence, Bioimage Analysis, Large Language Models

    -

    Content type: Youtube Video, Slides, Webinar

    -

    https://www.youtube.com/watch?v=TJXNMIWtdac

    -
    -
    -
    -

    BIDS-lecture-2024#

    -

    Robert Haase

    -

    Licensed CC-BY-4.0

    -

    Training resources for Students at Uni Leipzig who want to dive into bio-image data science with Python. The material developed here between April and July 2024.

    -

    Tags: Bioimage Analysis, Deep Learning, Microscopy Image Analysis, Python

    -

    Content type: Github Repository

    -

    ScaDS/BIDS-lecture-2024

    -
    -
    -
    -

    Bio-image Data Science#

    -

    Robert Haase

    -

    Licensed CC-BY-4.0

    -

    This repository contains training resources for Students at Uni Leipzig who want to dive into bio-image data science with Python.

    -

    Tags: Image Data Management, Deep Learning, Microscopy Image Analysis, Python

    -

    Content type: Notebook

    -

    ScaDS/BIDS-lecture-2024

    -
    -
    -
    -

    Bio-image Data Science Lectures @ Uni Leipzig / ScaDS.AI#

    -

    Robert Haase

    -

    Licensed CC-BY-4.0

    -

    These are the PPTx training resources for Students at Uni Leipzig who want to dive into bio-image data science with Python. The material developed here between April and July 2024.

    -

    Tags: Bioimage Analysis, Deep Learning, Microscopy Image Analysis, Python

    -

    Content type: Slides

    -

    https://zenodo.org/records/12623730

    -
    -
    -
    -

    BioEngine Documentation#

    -

    Wei Ouyang, Nanguage, Jeremy Metz, Craig Russell

    -

    Licensed MIT

    -

    BioEngine, a Python package designed for flexible deployment and execution of bioimage analysis models and workflows using AI, accessible via HTTP API and RPC.

    -

    Tags: Workflow Engine, Deep Learning, Python

    -

    Content type: Documentation

    -

    https://bioimage-io.github.io/bioengine/#/

    -
    -
    - -
    -
    -

    DL4MicEverywhere – Overcoming reproducibility challenges in deep learning microscopy imaging#

    -

    Iván Hidalgo-Cenalmor

    -

    Published 2024-07-29

    -

    Licensed UNKNOWN

    -

    Tags: Deep Learning, Microscopy, Microsycopy Image Analysis, Bio Image Analysis, Artifical Intelligence

    -

    Content type: Blog Post

    -

    https://focalplane.biologists.com/2024/07/29/dl4miceverywhere-overcoming-reproducibility-challenges-in-deep-learning-microscopy-imaging/

    -
    -
    -
    -

    Dr Guillaume Jacquemet on studying cancer cell metastasis in the era of deep learning for microscopy#

    -

    Guillaume Jacquemet

    -

    Published 2024-10-24

    -

    Licensed UNKNOWN

    -

    Leukocyte extravasation is a critical component of the innate immune response, while circulating tumour cell extravasation is a crucial step in metastasis formation. Despite their importance, these extravasation mechanisms remain incompletely understood. In this talk, Guillaume Jacquemet presents a novel imaging framework that integrates microfluidics with high-speed, label-free imaging to study the arrest of pancreatic cancer cells (PDAC) on human endothelial layers under physiological flow conditions.

    -

    Tags: Deep Learning, Microscopy Image Analysis

    -

    Content type: Youtube Video, Slides

    -

    https://www.youtube.com/watch?v=KTdZBgSCYJQ

    -
    -
    -
    -

    FAIRy deep-learning for bioImage analysis#

    -

    Estibaliz Gómez de Mariscal

    -

    Licensed CC-BY-4.0

    -

    Introduction to FAIR deep learning. Furthermore, tools to deploy trained DL models (deepImageJ), easily train and evaluate them (ZeroCostDL4Mic and DeepBacs) ensure reproducibility (DL4MicEverywhere), and share this technology in an open-source and reproducible manner (BioImage Model Zoo) are introduced.

    -

    Tags: Deep Learning, FAIR-Principles, Microscopy Image Analysis

    -

    Content type: Slides

    -

    https://f1000research.com/slides/13-147

    -
    -
    -
    -

    Introduction to Deep Learning for Microscopy#

    -

    Costantin Pape

    -

    Licensed MIT

    -

    This course consists of lectures and exercises that teach the background of deep learning for image analysis and show applications to classification and segmentation analysis problems.

    -

    Tags: Deep Learning, Pytorch, Segmentation, Python

    -

    Content type: Notebook

    -

    computational-cell-analytics/dl-for-micro

    -
    -
    -
    -

    MicroSam-Talks#

    -

    Constantin Pape

    -

    Published 2024-05-23

    -

    Licensed CC-BY-4.0

    -

    Talks about Segment Anything for Microscopy: computational-cell-analytics/micro-sam. -Currently contains slides for two talks:

    -

    Overview of Segment Anythign for Microscopy given at the SWISSBIAS online meeting in April 2024 -Talk about vision foundation models and Segment Anything for Microscopy given at Human Technopole as part of the EMBO Deep Learning Course in May 2024

    -

    Tags: Image Segmentation, Bioimage Analysis, Deep Learning

    -

    Content type: Slides

    -

    https://zenodo.org/records/11265038

    -

    https://doi.org/10.5281/zenodo.11265038

    -
    -
    -
    -

    Microscopy data analysis: machine learning and the BioImage Archive#

    -

    Andrii Iudin, Anna Foix-Romero, Anna Kreshuk, Awais Athar, Beth Cimini, Dominik Kutra, Estibalis Gomez de Mariscal, Frances Wong, Guillaume Jacquemet, Kedar Narayan, Martin Weigert, Nodar Gogoberidze, Osman Salih, Petr Walczysko, Ryan Conrad, Simone Weyend, Sriram Sundar Somasundharam, Suganya Sivagurunathan, Ugis Sarkans

    -

    Licensed CC-BY-4.0

    -

    The Microscopy data analysis: machine learning and the BioImage Archive course, which focused on introducing programmatic approaches used in the analysis of bioimage data via the BioImage Archive, ran in May 2023.

    -

    Tags: Microscopy Image Analysis, Python, Deep Learning

    -

    Content type: Video, Slides

    -

    https://www.ebi.ac.uk/training/materials/microscopy-data-analysis-machine-learning-and-the-bioimage-archive-materials/

    -
    -
    -
    - - - - -
    - - - - - - - - -
    - - - - - - - -
    -
    - -
    - -
    - -

    -By Robert Haase, Clément Caporal,... and the NFDI4BioImage Initiative -

    - -
    - -
    - - -
    - -
    -

    - Last updated on 2025-02-01. -
    -

    -
    - -
    - -
    -

    -Copyright: Licensed CC-BY 4.0 unless mentioned otherwise. -Contributions and feedback are welcome. -

    - -
    -
    - -
    -
    - - -
    -
    -
    - - - - - -
    -
    - - \ No newline at end of file diff --git a/tags/fair-principles.html b/tags/fair-principles.html index 885ac9c1..9037d887 100644 --- a/tags/fair-principles.html +++ b/tags/fair-principles.html @@ -63,8 +63,8 @@ - - + + @@ -182,36 +182,29 @@

    By tag

    By content type

    @@ -236,11 +228,12 @@ @@ -571,7 +564,7 @@

    FAIR High Content Screening in Bioimaginghttps://www.nature.com/articles/s41597-023-02367-w

    @@ -590,7 +583,7 @@

    FAIRy deep-learning for bioImage analysishttps://f1000research.com/slides/13-147

    @@ -638,9 +631,9 @@

    Modeling community standards for metadata as templates makes data FAIR

    Multimodal large language models for bioimage analysis#

    Shanghang Zhang, Gaole Dai, Tiejun Huang, Jianxu Chen

    -

    Licensed [‘CC-BY-NC-SA’]

    +

    Licensed CC-BY-NC-SA

    Multimodal large language models have been recognized as a historical milestone in the field of artificial intelligence and have demonstrated revolutionary potentials not only in commercial applications, but also for many scientific fields. Here we give a brief overview of multimodal large language models through the lens of bioimage analysis and discuss how we could build these models as a community to facilitate biology research

    -

    Tags: Bioimage Analysis, Large Language Models, FAIR-Principles, Workflow

    +

    Tags: Bioimage Analysis, FAIR-Principles, Workflow

    Content type: Publication

    https://www.nature.com/articles/s41592-024-02334-2

    https://arxiv.org/abs/2407.19778

    @@ -712,7 +705,7 @@

    RDF as a bridge to domain-platforms like OMERO, or There and back again.REMBI Overview#

    Licensed CC0-1.0

    Recommended Metadata for Biological Images (REMBI) provides guidelines for metadata for biological images to enable the FAIR sharing of scientific data.

    -

    Tags: FAIR-Principles, Metadata, Image Data Management, Research Data Management, Bioimage Data

    +

    Tags: FAIR-Principles, Metadata, Research Data Management

    Content type: Collection

    https://www.ebi.ac.uk/bioimage-archive/rembi-help-overview/

    @@ -744,7 +737,7 @@

    Ten simple rules for making training materials FAIRPublished 2020-05-21

    Licensed CC-BY-4.0

    The authors offer trainers some simple rules, to help make their training materials FAIR, enabling others to find, (re)use, and adapt them.

    -

    Tags: Metadata, Bioinformatics, FAIR-Principles, Training

    +

    Tags: Metadata, Bioinformatics, FAIR-Principles

    Content type: Publication

    https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1007854

    @@ -825,20 +818,20 @@

    [SWAT4HCLS 2023] NFDI4BIOIMAGE: Perspective for a national bioimage standard

    previous

    -

    Deep learning (12)

    +

    Data stewardship (6)

    next

    -

    Image data management (14)

    +

    Imagej (19)

     diff --git a/tags/image_data_management.html b/tags/image_data_management.html deleted file mode 100644 index b800bd6f..00000000 --- a/tags/image_data_management.html +++ /dev/null @@ -1,773 +0,0 @@ - - - - - - - - - - - Image data management (14) — NFDI4BioImage Training Materials - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    - - - - - - -
    - - - Ctrl+K -
    -     - -
    - -
    - - -
    -
    - - -
    -
    - - - - -
    - - - -
    - - - - -
    - -
    -
    - - - - - -
    -
    - -
    -
    -
    - - -
    -
    -
    -
    -
    - - -
    - -
    - - - -
    - -
    -
    - -
    -
    - -
    - -
    - -
    - - -
    - -
    - -
    - - - - - -
    - - -
    - - - - - - -
    - - -
    - - - - - - - - - - - - - -
    - -
    - -
    -
    - - - -
    -

    Image data management (14)

    - -
    - -
    -
    - - - - -
    - -
    -

    Image data management (14)#

    -
    -

    BIOMERO - A scalable and extensible image analysis framework#

    -

    Torec T. Luik, Rodrigo Rosas-Bertolini, Eric A.J. Reits, Ron A. Hoebe, Przemek M. Krawczyk

    -

    Published None

    -

    Licensed CC-BY-4.0

    -

    The authors introduce BIOMERO (bioimage analysis in OMERO), a bridge connecting OMERO, a renowned bioimaging data management platform, FAIR workflows, and high-performance computing (HPC) environments.

    -

    Tags: OMERO, Workflow, Bioimage Analysis, Image Data Management

    -

    Content type: Publication

    -

    https://doi.org/10.1016/j.patter.2024.101024

    -
    -
    -
    -

    Bio-image Data Science#

    -

    Robert Haase

    -

    Licensed CC-BY-4.0

    -

    This repository contains training resources for Students at Uni Leipzig who want to dive into bio-image data science with Python.

    -

    Tags: Image Data Management, Deep Learning, Microscopy Image Analysis, Python

    -

    Content type: Notebook

    -

    ScaDS/BIDS-lecture-2024

    -
    -
    -
    -

    Data management at France BioImaging#

    -

    Published 2023-07-05

    -

    Licensed CC-BY-SA-4.0

    -

    Tags: Research Data Management, Bioimage Analysis, Image Data Management, Open Science

    -

    Content type: Slides, Presentation

    -

    https://omero-fbi.fr/slides/elmi23_cfd/main.html#/title-slide

    -
    -
    -
    -

    Erick Martins Ratamero - Expanding the OME ecosystem for imaging data management | SciPy 2024#

    -

    SciPy, Erick Martins Ratamero

    -

    Published 2024-08-19

    -

    Licensed YOUTUBE STANDARD LICENSE

    -

    Tags: Image Data Management, OMERO, Bioimage Analysis

    -

    Content type: Video, Presentation

    -

    https://www.youtube.com/watch?v=GmhyDNm1RsM

    -
    -
    -
    -

    FAIR High Content Screening in Bioimaging#

    -

    Rohola Hosseini, Matthijs Vlasveld, Joost Willemse, Bob van de Water, Sylvia E. Le Dévédec, Katherine J. Wolstencroft

    -

    Published 2023-07-17

    -

    Licensed CC-BY-4.0

    -

    The authors show the utility of Minimum Information for High Content Screening Microscopy Experiments (MIHCSME) for High Content Screening (HCS) data using multiple examples from the Leiden FAIR Cell Observatory, a Euro-Bioimaging flagship node for high content screening and the pilot node for implementing FAIR bioimaging data throughout the Netherlands Bioimaging network.

    -

    Tags: FAIR-Principles, Metadata, Research Data Management, Image Data Management, Bioimage Data

    -

    Content type: Publication

    -

    https://www.nature.com/articles/s41597-023-02367-w

    -
    -
    -
    -

    Microscopy-BIDS - An Extension to the Brain Imaging Data Structure for Microscopy Data#

    -

    Marie-Hélène Bourget, Lee Kamentsky, Satrajit S. Ghosh, Giacomo Mazzamuto, Alberto Lazari, et al.

    -

    Published 2022-04-19

    -

    Licensed CC-BY-4.0

    -

    The Brain Imaging Data Structure (BIDS) is a specification for organizing, sharing, and archiving neuroimaging data and metadata in a reusable way.

    -

    Tags: Research Data Management, Image Data Management, Bioimage Data

    -

    Content type: Publication

    -

    https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2022.871228/full

    -
    -
    -
    -

    NFDI4BIOIMAGE - An Initiative for a National Research Data Infrastructure for Microscopy Data#

    -

    Christian Schmidt, Elisa Ferrando-May

    -

    Published 2021-04-29

    -

    Licensed CCY-BY-SA-4.0

    -

    Align existing and establish novel services & solutions for data management tasks throughout the bioimage data lifecycle.

    -

    Tags: Nfdi4Bioimage, Image Data Management, Bioimage Data, Research Data Management

    -

    Content type: Conference Abstract, Slide

    -

    https://doi.org/10.11588/heidok.00029489

    -
    -
    -
    -

    RDM_system_connector#

    -

    SaibotMagd

    -

    Licensed UNKNOWN

    -

    This tool is intended to link different research data management platforms with each other.

    -

    Tags: Research Data Management, Image Data Management

    -

    Content type: Github Repository

    -

    SaibotMagd/RDM_system_connector

    -
    -
    - -
    -
    -

    REMBI Overview#

    -

    Licensed CC0-1.0

    -

    Recommended Metadata for Biological Images (REMBI) provides guidelines for metadata for biological images to enable the FAIR sharing of scientific data.

    -

    Tags: FAIR-Principles, Metadata, Image Data Management, Research Data Management, Bioimage Data

    -

    Content type: Collection

    -

    https://www.ebi.ac.uk/bioimage-archive/rembi-help-overview/

    -
    -
    -
    -

    Research data management for bioimaging - the 2021 NFDI4BIOIMAGE community survey#

    -

    Christian Schmidt, Janina Hanne, Josh Moore, Christian Meesters, Elisa Ferrando-May, et al.

    -

    Published 2022-09-20

    -

    Licensed CC-BY-4.0

    -

    As an initiative within Germany’s National Research Data Infrastructure, the authors conducted this community survey in summer 2021 to assess the state of the art of bioimaging RDM and the community needs.

    -

    Tags: Research Data Management, Image Data Management, Bioimage Data

    -

    Content type: Publication

    -

    https://f1000research.com/articles/11-638/v2

    -
    -
    -
    -

    Submitting data to the BioImage Archive#

    -

    Licensed CC0-1.0

    -

    To submit, you’ll need to register an account, organise and upload your data, prepare a file list, and then submit using our web submission form. These steps are explained here.

    -

    Tags: Research Data Management, Image Data Management, Bioimage Data

    -

    Content type: Tutorial, Video

    -

    https://www.ebi.ac.uk/bioimage-archive/submit/

    -
    -
    -
    -

    The BioImage Archive – Building a Home for Life-Sciences Microscopy Data#

    -

    Matthew Hartley, Gerard J. Kleywegt, Ardan Patwardhan, Ugis Sarkans, Jason R. Swedlow, Alvis Brazma

    -

    Published 2022-06-22

    -

    Licensed UNKNOWN

    -

    The BioImage Archive is a new archival data resource at the European Bioinformatics Institute (EMBL-EBI).

    -

    Tags: Image Data Management, Research Data Management, Bioimage Data

    -

    Content type: Publication

    -

    https://www.sciencedirect.com/science/article/pii/S0022283622000791?via%3Dihub

    -

    https://doi.org/10.1016/j.jmb.2022.167505

    -
    -
    -
    -

    Towards community-driven metadata standards for light microscopy - tiered specifications extending the OME model#

    -

    Mathias Hammer, Maximiliaan Huisman, Alessandro Rigano, Ulrike Boehm, James J. Chambers, et al.

    -

    Published 2022-07-10

    -

    Licensed UNKNOWN

    -

    Rigorous record-keeping and quality control are required to ensure the quality, reproducibility and value of imaging data. The 4DN Initiative and BINA here propose light Microscopy Metadata specifications that extend the OME data model, scale with experimental intent and complexity, and make it possible for scientists to create comprehensive records of imaging experiments.

    -

    Tags: Reproducibility, Microscopy Image Analysis, Metadata, Image Data Management, Bioimage Data

    -

    Content type: Publication

    -

    https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9271325/

    -
    -
    -
    - - - - -
    - - - - - - - - -
    - - - - - - - -
    -
    - -
    - -
    - -

    -By Robert Haase, Clément Caporal,... and the NFDI4BioImage Initiative -

    - -
    - -
    - - -
    - -
    -

    - Last updated on 2025-02-01. -
    -

    -
    - -
    - -
    -

    -Copyright: Licensed CC-BY 4.0 unless mentioned otherwise. -Contributions and feedback are welcome. -

    - -
    -
    - -
    -
    - - -
    -
    -
    - - - - - -
    -
    - - \ No newline at end of file diff --git a/tags/imagej.html b/tags/imagej.html index 1e435c56..37f8c853 100644 --- a/tags/imagej.html +++ b/tags/imagej.html @@ -63,8 +63,8 @@ - - + + @@ -182,36 +182,29 @@

    By tag

    By content type

    @@ -236,11 +228,12 @@ @@ -531,7 +524,7 @@

    ImageJ2 API-beatinghttps://git.mpi-cbg.de/rhaase/lecture_imagej2_dev

    @@ -557,7 +550,7 @@

    Introduction to ImageJ macro programming, Scientific Computing Facility, MPI

    Robert Haase, Benoit Lombardot

    Licensed UNKNOWN

    Tags: Imagej, Bioimage Analysis

    -

    Content type: Slide

    +

    Content type: Slides

    https://git.mpi-cbg.de/scicomp/bioimage_team/coursematerialimageanalysis/tree/master/ImageJMacro_24h_2017-01

    @@ -574,9 +567,9 @@

    JIPipe: visual batch processing for ImageJ

    KNIME Image Processing#

    None

    -

    Licensed GPLV3

    +

    Licensed GPL-3.0

    The KNIME Image Processing Extension allows you to read in more than 140 different kinds of images and to apply well known methods on images, like preprocessing. segmentation, feature extraction, tracking and classification in KNIME.

    -

    Tags: Imagej, OMERO, Bioimage Data, Workflow

    +

    Tags: Imagej, OMERO, Workflow

    Content type: Tutorial, Online Tutorial, Documentation

    https://www.knime.com/community/image-processing

    @@ -586,7 +579,7 @@

    Lecture Applied Bioimage Analysis 2020https://git.mpi-cbg.de/rhaase/lecture_applied_bioimage_analysis

    @@ -616,7 +609,7 @@

    Multi-view fusionhttps://git.mpi-cbg.de/rhaase/lecture_multiview_registration

    @@ -645,7 +638,7 @@

    Tracking Theory, TrackMate, and Mastodonhttps://git.mpi-cbg.de/rhaase/lecture_tracking_trackmate

    @@ -654,7 +647,7 @@

    Working with objects in 2D and 3Dhttps://git.mpi-cbg.de/rhaase/lecture_working_with_objects_in_2d_and_3d

    @@ -663,7 +656,7 @@

    Working with pixelshttps://git.mpi-cbg.de/rhaase/lecture_working_with_pixels

    @@ -700,20 +693,20 @@

    Working with pixels

    previous

    -

    Image data management (14)

    +

    Fair-principles (27)

    next

    -

    Large language models (5)

    +

    Licensing (6)

     diff --git a/tags/large_language_models.html b/tags/large_language_models.html deleted file mode 100644 index 3cc20a68..00000000 --- a/tags/large_language_models.html +++ /dev/null @@ -1,663 +0,0 @@ - - - - - - - - - - - Large language models (5) — NFDI4BioImage Training Materials - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    - - - - - - -
    - - - Ctrl+K -
    -     - -
    - -
    - - -
    -
    - - -
    -
    - - - - -
    - - - -
    - - - - -
    - -
    -
    - - - - - -
    -
    - -
    -
    -
    - - -
    -
    -
    -
    -
    - - -
    - -
    - - - -
    - -
    -
    - -
    -
    - -
    - -
    - -
    - - -
    - -
    - -
    - - - - - -
    - - -
    - - - - - - -
    - - -
    - - - - - - - - - - - - - -
    - -
    - -
    -
    - - - -
    -

    Large language models (5)

    - -
    - -
    -
    - - - - -
    - -
    -

    Large language models (5)#

    -
    -

    AI ML DL in Bioimage Analysis - Webinar#

    -

    Yannick KREMPP

    -

    Published 2024-11-14

    -

    Licensed UNKNOWN

    -

    A review of the tools, methods and concepts useful for biologists and life scientists as well as bioimage analysts.

    -

    Tags: Deep Learning, Machine Learning, Artificial Intelligence, Bioimage Analysis, Large Language Models

    -

    Content type: Youtube Video, Slides, Webinar

    -

    https://www.youtube.com/watch?v=TJXNMIWtdac

    -
    -
    -
    -

    Bio-image Analysis with the Help of Large Language Models#

    -

    Robert Haase

    -

    Published 2024-03-13

    -

    Licensed CC-BY-4.0

    -

    Large Language Models (LLMs) change the way how we use computers. This also has impact on the bio-image analysis community. We can generate code that analyzes biomedical image data if we ask the right prompts. This talk outlines introduces basic principles, explains prompt engineering and how to apply it to bio-image analysis. We also compare how different LLM vendors perform on code generation tasks and which challenges are ahead for the bio-image analysis community.

    -

    Tags: Large Language Models, Python

    -

    Content type: Slide

    -

    https://zenodo.org/records/10815329

    -

    https://doi.org/10.5281/zenodo.10815329

    -
    -
    -
    -

    Creating a Research Data Management Plan using chatGPT#

    -

    Robert Haase

    -

    Published 2023-11-06

    -

    Licensed CC-BY-4.0

    -

    In this blog post the author demonstrates how chatGPT can be used to combine a fictive project description with a DMP specification to produce a project-specific DMP.

    -

    Tags: Research Data Management, Large Language Models, Artificial Intelligence

    -

    Content type: Blog

    -

    https://focalplane.biologists.com/2023/11/06/creating-a-research-data-management-plan-using-chatgpt/

    -
    -
    -
    -

    Multimodal large language models for bioimage analysis#

    -

    Shanghang Zhang, Gaole Dai, Tiejun Huang, Jianxu Chen

    -

    Licensed [‘CC-BY-NC-SA’]

    -

    Multimodal large language models have been recognized as a historical milestone in the field of artificial intelligence and have demonstrated revolutionary potentials not only in commercial applications, but also for many scientific fields. Here we give a brief overview of multimodal large language models through the lens of bioimage analysis and discuss how we could build these models as a community to facilitate biology research

    -

    Tags: Bioimage Analysis, Large Language Models, FAIR-Principles, Workflow

    -

    Content type: Publication

    -

    https://www.nature.com/articles/s41592-024-02334-2

    -

    https://arxiv.org/abs/2407.19778

    -
    -
    -
    -

    YMIA - Python-Based Event Series Training Material#

    -

    Riccardo Massei, Robert Haase, ENicolay

    -

    Published None

    -

    Licensed MIT

    -

    This repository offer access to teaching material and useful resources for the YMIA - Python-Based Event Series.

    -

    Tags: Python, Large Language Models, Prompt Engineering, Biabob, Bioimage Analysis, Microscopy Image Analysis

    -

    Content type: Github Repository, Slides

    -

    rmassei/ymia_python_event_series_material

    -
    -
    -
    - - - - -
    - - - - - - - - -
    - - - - - - - -
    -
    - -
    - -
    - -

    -By Robert Haase, Clément Caporal,... and the NFDI4BioImage Initiative -

    - -
    - -
    - - -
    - -
    -

    - Last updated on 2025-02-01. -
    -

    -
    - -
    - -
    -

    -Copyright: Licensed CC-BY 4.0 unless mentioned otherwise. -Contributions and feedback are welcome. -

    - -
    -
    - -
    -
    - - -
    -
    -
    - - - - - -
    -
    - - \ No newline at end of file diff --git a/tags/licensing.html b/tags/licensing.html index d46d1a95..55035c3c 100644 --- a/tags/licensing.html +++ b/tags/licensing.html @@ -64,7 +64,7 @@ - + @@ -182,36 +182,29 @@

    By tag

    By content type

    @@ -236,11 +228,12 @@ @@ -520,7 +513,7 @@

    If you license it, it’ll be harder to steal it. Why we should license our

    Licensed CC-BY-4.0

    Blog post about why we should license our work and what is important when choosing a license.

    Tags: Licensing, Research Data Management

    -

    Content type: Blog

    +

    Content type: Blog Post

    https://focalplane.biologists.com/2023/05/06/if-you-license-it-itll-be-harder-to-steal-it-why-we-should-license-our-work/

    @@ -578,12 +571,12 @@

    Research data - what are the key issues to consider when publishing this kin

    previous

    -

    Large language models (5)

    +

    Imagej (19)

     DOCUMENTATION_OPTIONS.pagename = 'tags/metadata'; - + @@ -182,36 +182,29 @@

    By tag

    By content type

    @@ -236,11 +228,12 @@ @@ -500,7 +493,7 @@

    FAIR High Content Screening in Bioimaginghttps://www.nature.com/articles/s41597-023-02367-w

    @@ -510,7 +503,7 @@

    I3D bio – Information Infrastructure for BioImage Data - Bioimage Metadata

    Christian Schmidt

    Licensed UNKNOWN

    A Microscopy Research Data Management Resource.

    -

    Tags: Metadata, I3Dbio, Research Data Management, Bioimage Data

    +

    Tags: Metadata, I3Dbio, Research Data Management

    Content type: Collection

    https://gerbi-gmb.de/i3dbio/i3dbio-rdm/i3dbio-bioimage-metadata/

    @@ -566,7 +559,7 @@

    Overview of the Galaxy OMERO-suite - Upload images and metadata in OMERO usi

    Riccardo Massei, Björn Grüning

    Published 2024-12-02

    Licensed CC-BY-4.0

    -

    Tags: OMERO, Galaxy, Metadata

    +

    Tags: OMERO, Galaxy, Metadata, Nfdi4Bioimage

    Content type: Tutorial, Framework, Workflow

    https://training.galaxyproject.org/training-material/topics/imaging/tutorials/omero-suite/tutorial.html

    @@ -577,7 +570,7 @@

    REMBI - Recommended Metadata for Biological Images—enabling reuse of micro

    Published 2021-05-21

    Licensed UNKNOWN

    Bioimaging data have significant potential for reuse, but unlocking this potential requires systematic archiving of data and metadata in public databases. The authors propose draft metadata guidelines to begin addressing the needs of diverse communities within light and electron microscopy.

    -

    Tags: Metadata, Bioimage Data, Image Data Management, Research Data Management

    +

    Tags: Metadata, Research Data Management

    Content type: Publication

    https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8606015/

    https://www.nature.com/articles/s41592-021-01166-8

    @@ -588,7 +581,7 @@

    REMBI - Recommended Metadata for Biological Images—enabling reuse of micro

    REMBI Overview#

    Licensed CC0-1.0

    Recommended Metadata for Biological Images (REMBI) provides guidelines for metadata for biological images to enable the FAIR sharing of scientific data.

    -

    Tags: FAIR-Principles, Metadata, Image Data Management, Research Data Management, Bioimage Data

    +

    Tags: FAIR-Principles, Metadata, Research Data Management

    Content type: Collection

    https://www.ebi.ac.uk/bioimage-archive/rembi-help-overview/

    @@ -598,7 +591,7 @@

    Reporting and reproducibility in microscopyhttps://www.nature.com/collections/djiciihhjh

    @@ -609,7 +602,7 @@

    Ten simple rules for making training materials FAIRPublished 2020-05-21

    Licensed CC-BY-4.0

    The authors offer trainers some simple rules, to help make their training materials FAIR, enabling others to find, (re)use, and adapt them.

    -

    Tags: Metadata, Bioinformatics, FAIR-Principles, Training

    +

    Tags: Metadata, Bioinformatics, FAIR-Principles

    Content type: Publication

    https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1007854

    @@ -620,7 +613,7 @@

    Towards community-driven metadata standards for light microscopy - tiered sp

    Published 2022-07-10

    Licensed UNKNOWN

    Rigorous record-keeping and quality control are required to ensure the quality, reproducibility and value of imaging data. The 4DN Initiative and BINA here propose light Microscopy Metadata specifications that extend the OME data model, scale with experimental intent and complexity, and make it possible for scientists to create comprehensive records of imaging experiments.

    -

    Tags: Reproducibility, Microscopy Image Analysis, Metadata, Image Data Management, Bioimage Data

    +

    Tags: Reproducibility, Bioimage Analysis, Metadata

    Content type: Publication

    https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9271325/

    @@ -675,11 +668,11 @@

    User friendly Image metadata annotation tool/workflow for OMERO

    next

    -

    Microscopy image analysis (15)

    +

    Napari (13)

     diff --git a/tags/microscopy_image_analysis.html b/tags/microscopy_image_analysis.html deleted file mode 100644 index 2da0b7a6..00000000 --- a/tags/microscopy_image_analysis.html +++ /dev/null @@ -1,785 +0,0 @@ - - - - - - - - - - - Microscopy image analysis (15) — NFDI4BioImage Training Materials - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    - - - - - - -
    - - - Ctrl+K -
    -     - -
    - -
    - - -
    -
    - - -
    -
    - - - - -
    - - - -
    - - - - -
    - -
    -
    - - - - - -
    -
    - -
    -
    -
    - - -
    -
    -
    -
    -
    - - -
    - -
    - - - -
    - -
    -
    - -
    -
    - -
    - -
    - -
    - - -
    - -
    - -
    - - - - - -
    - - -
    - - - - - - -
    - - -
    - - - - - - - - - - - - - -
    - -
    - -
    -
    - - - -
    -

    Microscopy image analysis (15)

    - -
    - -
    -
    - - - - -
    - -
    -

    Microscopy image analysis (15)#

    -
    -

    BIDS-lecture-2024#

    -

    Robert Haase

    -

    Licensed CC-BY-4.0

    -

    Training resources for Students at Uni Leipzig who want to dive into bio-image data science with Python. The material developed here between April and July 2024.

    -

    Tags: Bioimage Analysis, Deep Learning, Microscopy Image Analysis, Python

    -

    Content type: Github Repository

    -

    ScaDS/BIDS-lecture-2024

    -
    -
    -
    -

    Bio-image Data Science#

    -

    Robert Haase

    -

    Licensed CC-BY-4.0

    -

    This repository contains training resources for Students at Uni Leipzig who want to dive into bio-image data science with Python.

    -

    Tags: Image Data Management, Deep Learning, Microscopy Image Analysis, Python

    -

    Content type: Notebook

    -

    ScaDS/BIDS-lecture-2024

    -
    -
    -
    -

    Bio-image Data Science Lectures @ Uni Leipzig / ScaDS.AI#

    -

    Robert Haase

    -

    Licensed CC-BY-4.0

    -

    These are the PPTx training resources for Students at Uni Leipzig who want to dive into bio-image data science with Python. The material developed here between April and July 2024.

    -

    Tags: Bioimage Analysis, Deep Learning, Microscopy Image Analysis, Python

    -

    Content type: Slides

    -

    https://zenodo.org/records/12623730

    -
    -
    -
    -

    Checklists for publishing images and image analysis#

    -

    Christopher Schmied

    -

    Published 2023-09-14

    -

    Licensed CC0-1.0

    -

    In this paper we introduce two sets of checklists. One is concerned with the publication of images. The other one gives instructions for the publication of image analysis.

    -

    Tags: Bioimage Data, Microscopy Image Analysis

    -

    Content type: Forum Post

    -

    https://forum.image.sc/t/checklists-for-publishing-images-and-image-analysis/86304

    -
    -
    -
    -

    Dr Guillaume Jacquemet on studying cancer cell metastasis in the era of deep learning for microscopy#

    -

    Guillaume Jacquemet

    -

    Published 2024-10-24

    -

    Licensed UNKNOWN

    -

    Leukocyte extravasation is a critical component of the innate immune response, while circulating tumour cell extravasation is a crucial step in metastasis formation. Despite their importance, these extravasation mechanisms remain incompletely understood. In this talk, Guillaume Jacquemet presents a novel imaging framework that integrates microfluidics with high-speed, label-free imaging to study the arrest of pancreatic cancer cells (PDAC) on human endothelial layers under physiological flow conditions.

    -

    Tags: Deep Learning, Microscopy Image Analysis

    -

    Content type: Youtube Video, Slides

    -

    https://www.youtube.com/watch?v=KTdZBgSCYJQ

    -
    -
    -
    -

    Example Pipeline Tutorial#

    -

    Tim Monko

    -

    Published 2024-10-28

    -

    Licensed BSD-3-CLAUSE

    -

    Napari-ndev is a collection of widgets intended to serve any person seeking to process microscopy images from start to finish. The goal of this example pipeline is to get the user familiar with working with napari-ndev for batch processing and reproducibility (view Image Utilities and Workflow Widget).

    -

    Tags: Napari, Microscopy Image Analysis, Bioimage Analysis

    -

    Content type: Documentation, Github Repository, Tutorial

    -

    https://timmonko.github.io/napari-ndev/tutorial/01_example_pipeline/

    -

    timmonko/napari-ndev

    -
    -
    -
    -

    FAIRy deep-learning for bioImage analysis#

    -

    Estibaliz Gómez de Mariscal

    -

    Licensed CC-BY-4.0

    -

    Introduction to FAIR deep learning. Furthermore, tools to deploy trained DL models (deepImageJ), easily train and evaluate them (ZeroCostDL4Mic and DeepBacs) ensure reproducibility (DL4MicEverywhere), and share this technology in an open-source and reproducible manner (BioImage Model Zoo) are introduced.

    -

    Tags: Deep Learning, FAIR-Principles, Microscopy Image Analysis

    -

    Content type: Slides

    -

    https://f1000research.com/slides/13-147

    -
    -
    -
    -

    Making the most of bioimaging data through interdisciplinary interactions#

    -

    Virginie Uhlmann, Matthew Hartley, Josh Moore, Erin Weisbart, Assaf Zaritsky

    -

    Published 2024-10-23

    -

    Licensed CC-BY-4.0

    -

    Tags: Bioimage Analysis, Open Science, Microscopy Image Analysis, Microscopy

    -

    Content type: Publication

    -

    https://journals.biologists.com/jcs/article/137/20/jcs262139/362478/Making-the-most-of-bioimaging-data-through

    -
    -
    -
    -

    Microscopy data analysis: machine learning and the BioImage Archive#

    -

    Andrii Iudin, Anna Foix-Romero, Anna Kreshuk, Awais Athar, Beth Cimini, Dominik Kutra, Estibalis Gomez de Mariscal, Frances Wong, Guillaume Jacquemet, Kedar Narayan, Martin Weigert, Nodar Gogoberidze, Osman Salih, Petr Walczysko, Ryan Conrad, Simone Weyend, Sriram Sundar Somasundharam, Suganya Sivagurunathan, Ugis Sarkans

    -

    Licensed CC-BY-4.0

    -

    The Microscopy data analysis: machine learning and the BioImage Archive course, which focused on introducing programmatic approaches used in the analysis of bioimage data via the BioImage Archive, ran in May 2023.

    -

    Tags: Microscopy Image Analysis, Python, Deep Learning

    -

    Content type: Video, Slides

    -

    https://www.ebi.ac.uk/training/materials/microscopy-data-analysis-machine-learning-and-the-bioimage-archive-materials/

    -
    -
    -
    -

    Multiplexed tissue imaging - tools and approaches#

    -

    Agustín Andrés Corbat, OmFrederic, Jonas Windhager, Kristína Lidayová

    -

    Licensed CC-BY-4.0

    -

    Material for the I2K 2024 “Multiplexed tissue imaging - tools and approaches” workshop

    -

    Tags: Bioimage Analysis, Microscopy Image Analysis

    -

    Content type: Github Repository, Slides, Workshop

    -

    BIIFSweden/I2K2024-MTIWorkshop

    -

    https://docs.google.com/presentation/d/1R9-4lXAmTYuyFZpTMDR85SjnLsPZhVZ8/edit#slide=id.p1

    -
    -
    -
    -

    Open Micoscropy Environment (OME) Youtube Channel#

    -

    Published None

    -

    Licensed CC-BY-4.0

    -

    OME develops open-source software and data format standards for the storage and manipulation of biological microscopy data

    -

    Tags: Open Source Software, Microscopy Image Analysis, Bioimage Data

    -

    Content type: Video, Collection

    -

    https://www.youtube.com/@OpenMicroscopyEnvironment

    -
    -
    -
    -

    The Open Microscopy Environment (OME) Data Model and XML file - open tools for informatics and quantitative analysis in biological imaging#

    -

    Ilya G. Goldberg, Chris Allan, Jean-Marie Burel, Doug Creager, Andrea Falconi, et. al

    -

    Published 2005-05-03

    -

    Licensed CC-BY-4.0

    -

    The Open Microscopy Environment (OME) defines a data model and a software implementation to serve as an informatics framework for imaging in biological microscopy experiments, including representation of acquisition parameters, annotations and image analysis results.

    -

    Tags: Microscopy Image Analysis, Bioimage Analysis

    -

    Content type: Publication

    -

    https://genomebiology.biomedcentral.com/articles/10.1186/gb-2005-6-5-r47

    -

    https://doi.org/10.1186/gb-2005-6-5-r47

    -
    -
    -
    -

    Towards community-driven metadata standards for light microscopy - tiered specifications extending the OME model#

    -

    Mathias Hammer, Maximiliaan Huisman, Alessandro Rigano, Ulrike Boehm, James J. Chambers, et al.

    -

    Published 2022-07-10

    -

    Licensed UNKNOWN

    -

    Rigorous record-keeping and quality control are required to ensure the quality, reproducibility and value of imaging data. The 4DN Initiative and BINA here propose light Microscopy Metadata specifications that extend the OME data model, scale with experimental intent and complexity, and make it possible for scientists to create comprehensive records of imaging experiments.

    -

    Tags: Reproducibility, Microscopy Image Analysis, Metadata, Image Data Management, Bioimage Data

    -

    Content type: Publication

    -

    https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9271325/

    -
    -
    -
    -

    Upcoming Image Analysis Events#

    -

    Curtis Rueden, Albane de la Villegeorges, Simon F. Nørrelykke, Romain Guiet, Olivier Burri, et al.

    -

    Licensed UNKNOWN

    -

    Tags: Bioimage Analysis, Microscopy Image Analysis

    -

    Content type: Collection, Event, Forum Post, Workshop

    -

    https://forum.image.sc/t/upcoming-image-analysis-events/60018/67

    -
    -
    -
    -

    YMIA - Python-Based Event Series Training Material#

    -

    Riccardo Massei, Robert Haase, ENicolay

    -

    Published None

    -

    Licensed MIT

    -

    This repository offer access to teaching material and useful resources for the YMIA - Python-Based Event Series.

    -

    Tags: Python, Large Language Models, Prompt Engineering, Biabob, Bioimage Analysis, Microscopy Image Analysis

    -

    Content type: Github Repository, Slides

    -

    rmassei/ymia_python_event_series_material

    -
    -
    -
    - - - - -
    - - - - - - - - -
    - - - - - - - -
    -
    - -
    - -
    - -

    -By Robert Haase, Clément Caporal,... and the NFDI4BioImage Initiative -

    - -
    - -
    - - -
    - -
    -

    - Last updated on 2025-02-01. -
    -

    -
    - -
    - -
    -

    -Copyright: Licensed CC-BY 4.0 unless mentioned otherwise. -Contributions and feedback are welcome. -

    - -
    -
    - -
    -
    - - -
    -
    -
    - - - - - -
    -
    - - \ No newline at end of file diff --git a/tags/napari.html b/tags/napari.html index 10584765..466ba83d 100644 --- a/tags/napari.html +++ b/tags/napari.html @@ -63,8 +63,8 @@ - - + + @@ -182,36 +182,29 @@

    By tag

    By content type

    @@ -236,11 +228,12 @@ @@ -487,7 +480,7 @@

    Napari (13)#

    Mara Lampert

    Tags: Python, Napari, Bioimage Analysis

    -

    Content type: Blog

    +

    Content type: Blog Post

    https://focalplane.biologists.com/2023/03/30/annotating-3d-images-in-napari/

    @@ -524,7 +517,7 @@

    Example Pipeline Tutorialhttps://timmonko.github.io/napari-ndev/tutorial/01_example_pipeline/

    timmonko/napari-ndev

    @@ -534,7 +527,7 @@

    Example Pipeline Tutorial#

    Mara Lampert

    Tags: Python, Napari, Bioimage Analysis

    -

    Content type: Blog

    +

    Content type: Blog Post

    https://focalplane.biologists.com/2023/05/03/feature-extraction-in-napari/

    @@ -571,7 +564,7 @@

    QM Course Lectures on Bio-Image Analysis with napari 2024#

    Mara Lampert

    Tags: Python, Napari, Bioimage Analysis

    -

    Content type: Blog

    +

    Content type: Blog Post

    https://focalplane.biologists.com/2023/04/13/quality-assurance-of-segmentation-results/

    @@ -579,7 +572,7 @@

    Quality assurance of segmentation results#

    Mara Lampert

    Tags: Python, Napari, Bioimage Analysis

    -

    Content type: Blog

    +

    Content type: Blog Post

    https://focalplane.biologists.com/2023/03/02/rescaling-images-and-pixel-anisotropy/

    @@ -587,7 +580,7 @@

    Rescaling images and pixel (an)isotropy#

    Mara Lampert

    Tags: Python, Napari, Bioimage Analysis

    -

    Content type: Blog

    +

    Content type: Blog Post

    https://focalplane.biologists.com/2023/06/01/tracking-in-napari/

    @@ -632,12 +625,12 @@

    ZEN & Python workshop

    previous

    -

    Microscopy image analysis (15)

    +

    Metadata (15)

     ZEN & Python workshop

    next

    -

    Neubias (26)

    +

    Neubias (27)

     diff --git a/tags/neubias.html b/tags/neubias.html index 97864e7c..3d4db985 100644 --- a/tags/neubias.html +++ b/tags/neubias.html @@ -8,7 +8,7 @@ - Neubias (26) — NFDI4BioImage Training Materials + Neubias (27) — NFDI4BioImage Training Materials @@ -63,7 +63,7 @@ - + @@ -182,36 +182,29 @@

    By tag

    By content type

    @@ -236,11 +228,12 @@ @@ -447,7 +440,7 @@
    -

    Neubias (26)

    +

    Neubias (27)

    @@ -469,6 +462,7 @@

    Contents

  • ImageJ2 API-beating
  • Jupyter for interactive cloud computing
  • Machine and Deep Learning on the cloud: Segmentation
    • +
  • Modular training resources for bioimage analysis
  • Multi-view fusion
  • NEUBIAS Academy @HOME: Interactive Bioimage Analysis with Python and Jupyter
  • NEUBIAS Analyst School 2018
    • @@ -494,14 +488,14 @@

    Contents

    -
    -

    Neubias (26)#

    +
    +

    Neubias (27)#

    Adding a Workflow to BIAFLOWS#

    Sébastien Tosi, Volker Baecker, Benjamin Pavie

    Licensed BSD-2-CLAUSE

    Tags: Neubias, Bioimage Analysis

    -

    Content type: Slide

    +

    Content type: Slides

    RoccoDAnt/Defragmentation_TrainingSchool_EOSC-Life_2022

    @@ -519,7 +513,7 @@

    CellProfiler Introductionahklemm/CellProfiler_Introduction

    @@ -575,7 +569,7 @@

    ImageJ Macro Introductionahklemm/ImageJMacro_Introduction

    @@ -584,7 +578,7 @@

    ImageJ2 API-beatinghttps://git.mpi-cbg.de/rhaase/lecture_imagej2_dev

    @@ -593,7 +587,7 @@

    Jupyter for interactive cloud computinghttps://docs.google.com/presentation/d/1q8q1xE-c35tvCsRXZay98s2UYWwXpp0cfCljBmMFpco/edit#slide=id.ga456d5535c_2_53

    @@ -602,17 +596,28 @@

    Machine and Deep Learning on the cloud: SegmentationIgnacio Arganda-Carreras

    Licensed UNKNOWN

    Tags: Neubias, Artificial Intelligence, Bioimage Analysis

    -

    Content type: Slide

    +

    Content type: Slides

    https://docs.google.com/presentation/d/1oJoy9gHmUuSmUwCkPs_InJf_WZAzmLlUNvK1FUEB4PA/edit#slide=id.ge3a24e733b_0_54

    +
    +

    Modular training resources for bioimage analysis#

    +

    Christian Tischer, Antonio Politi, Tim-Oliver Buchholz, Elnaz Fazeli, Nicola Gritti, Aliaksandr Halavatyi, Sebastian Gonzalez Tirado, Julian Hennies, Toby Hodges, Arif Khan, Dominik Kutra, Stefania Marcotti, Bugra Oezdemir, Felix Schneider, Martin Schorb, Anniek Stokkermans, Yi Sun, Nima Vakili

    +

    Published 2024-12-03

    +

    Licensed CC-BY-4.0

    +

    Resources for teaching/preparing to teach bioimage analysis

    +

    Tags: Neubias, Bioimage Analysis

    +

    https://zenodo.org/records/14264885

    +

    https://doi.org/10.5281/zenodo.14264885

    +
    +

    Multi-view fusion#

    Robert Haase

    Licensed BSD-3-CLAUSE

    Lecture slides of a session on Multiview Fusion in Fiji

    Tags: Neubias, Imagej, Bioimage Analysis

    -

    Content type: Slide

    +

    Content type: Slides

    https://git.mpi-cbg.de/rhaase/lecture_multiview_registration

    @@ -629,7 +634,7 @@

    NEUBIAS Analyst School 2018miura/NEUBIAS_AnalystSchool2018

    @@ -638,7 +643,7 @@

    NEUBIAS Bioimage Analyst Course 2017miura/NEUBIAS_Bioimage_Analyst_Course2017

    @@ -647,7 +652,7 @@

    NEUBIAS Bioimage Analyst School 2019miura/NEUBIAS_AnalystSchool2019

    @@ -656,7 +661,7 @@

    NEUBIAS Bioimage Analyst School 2020miura/NEUBIAS_AnalystSchool2020

    @@ -673,7 +678,7 @@

    Neubias Academy 2020: Introduction to Nuclei Segmentation with StarDistMartin Weigert, Olivier Burri, Siân Culley, Uwe Schmidt

    Licensed UNKNOWN

    Tags: Python, Neubias, Artificial Intelligence, Bioimage Analysis

    -

    Content type: Slide, Notebook

    +

    Content type: Slides, Notebook

    maweigert/neubias_academy_stardist

    @@ -701,7 +706,7 @@

    Tracking Theory, TrackMate, and Mastodonhttps://git.mpi-cbg.de/rhaase/lecture_tracking_trackmate

    @@ -710,7 +715,7 @@

    What is Bioimage Analysis? An Introductionhttps://www.dropbox.com/s/5abw3cvxrhpobg4/20220923_DefragmentationTS.pdf?dl=0

    @@ -719,7 +724,7 @@

    Working with objects in 2D and 3Dhttps://git.mpi-cbg.de/rhaase/lecture_working_with_objects_in_2d_and_3d

    @@ -728,7 +733,7 @@

    Working with pixelshttps://git.mpi-cbg.de/rhaase/lecture_working_with_pixels

    @@ -778,7 +783,7 @@

    Working with pixels

    next

    -

    Nfdi4bioimage (23)

    +

    Nfdi4bioimage (44)

     @@ -811,6 +816,7 @@

    Working with pixelsImageJ2 API-beating
  • Jupyter for interactive cloud computing
  • Machine and Deep Learning on the cloud: Segmentation
    • +
  • Modular training resources for bioimage analysis
  • Multi-view fusion
  • NEUBIAS Academy @HOME: Interactive Bioimage Analysis with Python and Jupyter
  • NEUBIAS Analyst School 2018
    • diff --git a/tags/nfdi4bioimage.html b/tags/nfdi4bioimage.html index fafc71f1..050e1418 100644 --- a/tags/nfdi4bioimage.html +++ b/tags/nfdi4bioimage.html @@ -8,7 +8,7 @@ - Nfdi4bioimage (23) — NFDI4BioImage Training Materials + Nfdi4bioimage (44) — NFDI4BioImage Training Materials @@ -64,7 +64,7 @@ - + @@ -182,36 +182,29 @@

    By tag

    By content type

    @@ -236,11 +228,12 @@ @@ -447,7 +440,7 @@
    -

    Nfdi4bioimage (23)

    +

    Nfdi4bioimage (44)

    @@ -458,28 +451,49 @@

    Contents

    @@ -491,8 +505,8 @@

    Contents

    -
    -

    Nfdi4bioimage (23)#

    +
    +

    Nfdi4bioimage (44)#

    A Cloud-Optimized Storage for Interactive Access of Large Arrays#

    Josh Moore, Susanne Kunis

    @@ -502,14 +516,39 @@

    A Cloud-Optimized Storage for Interactive Access of Large Arrayshttps://doi.org/10.52825/cordi.v1i.285

    +
    +

    A journey to FAIR microscopy data#

    +

    Stefanie Weidtkamp-Peters, Janina Hanne, Christian Schmidt

    +

    Published 2023-05-03

    +

    Licensed CC-BY-4.0

    +

    Oral presentation, 32nd MoMAN “From Molecules to Man” Seminar, Ulm, online. Monday February 6th, 2023

    +

    Abstract:

    +

    Research data management is essential in nowadays research, and one of the big opportunities to accelerate collaborative and innovative scientific projects. To achieve this goal, all our data needs to be FAIR (findable, accessible, interoperable, reproducible). For data acquired on microscopes, however, a common ground for FAIR data sharing is still to be established. Plenty of work on file formats, data bases, and training needs to be performed to highlight the value of data sharing and exploit its potential for bioimaging data.

    +

    In this presentation, Stefanie Weidtkamp-Peters will introduce the challenges for bioimaging data management, and the necessary steps to achieve data FAIRification. German BioImaging - GMB e.V., together with other institutions, contributes to this endeavor. Janina Hanne will present how the network of imaging core facilities, research groups and industry partners is key to the German bioimaging community’s aligned collaboration toward FAIR bioimaging data. These activities have paved the way for two data management initiatives in Germany: I3D:bio (Information Infrastructure for BioImage Data) and NFDI4BIOIMAGE, a consortium of the National Research Data Infrastructure. Christian Schmidt will introduce the goals and measures of these initiatives to the benefit of imaging scientist’s work and everyday practice.  

    +

    Tags: Nfdi4Bioimage, Research Data Management

    +

    https://zenodo.org/records/7890311

    +

    https://doi.org/10.5281/zenodo.7890311

    +
    +
    +
    +

    Angebote der NFDI für die Forschung im Bereich Zoologie#

    +

    Birgitta König-Ries, Robert Haase, Daniel Nüst, Konrad Förstner, Judith Sophie Engel

    +

    Published 2024-12-04

    +

    Licensed CC-BY-4.0

    +

    In diesem Slidedeck geben wir einen Einblick in Angebote und Dienste der Nationalen Forschungsdaten Infrastruktur (NFDI), die Relevant für die Zoologie und angrenzende Disziplinen relevant sein könnten.

    +

    Tags: Nfdi4Bioimage, Research Data Management

    +

    https://zenodo.org/records/14278058

    +

    https://doi.org/10.5281/zenodo.14278058

    +
    +

    Bio-Image Data Strudel for Workshop on Research Data Management in TU Dresden Core Facilities#

    Cornelia Wetzker

    Published 2023-11-08

    Licensed CC-BY-4.0

    This presentation gives a short outline of the complexity of data and metadata in the bioimaging universe. It introduces NFDI4BIOIMAGE as a newly formed consortium as part of the German ‘Nationale Forschungsdateninfrastruktur’ (NFDI) and its goals and tools for data management including its current members on TU Dresden campus.  

    -

    Tags: Research Data Management, Tu Dresden, Bioimage Data, Nfdi4Bioimage

    -

    Content type: Slide

    +

    Tags: Research Data Management, Nfdi4Bioimage

    +

    Content type: Slides

    https://zenodo.org/records/10083555

    https://doi.org/10.5281/zenodo.10083555

    @@ -519,10 +558,31 @@

    Challenges and opportunities for bio-image analysis core-facilitiesRobert Haase

    Licensed CC-BY-4.0

    Tags: Research Data Management, Bioimage Analysis, Nfdi4Bioimage

    -

    Content type: Slide

    +

    Content type: Slides

    https://f1000research.com/slides/12-1054

    +
    +

    Collaborative Working and Version Control with git[hub]#

    +

    Robert Haase

    +

    Published 2024-01-10

    +

    Licensed CC-BY-4.0

    +

    This slide deck introduces the version control tool git, related terminology and the Github Desktop app for managing files in Git[hub] repositories. We furthermore dive into:* Working with repositories* Collaborative with others* Github-Zenodo integration* Github pages* Artificial Intelligence answering Github Issues

    +

    Tags: Nfdi4Bioimage, Globias, Research Data Management, Research Software Management

    +

    https://zenodo.org/records/14626054

    +

    https://doi.org/10.5281/zenodo.14626054

    +
    +
    +
    +

    Engineering a Software Environment for Research Data Management of Microscopy Image Data in a Core Facility#

    +

    Kunis

    +

    Published 2022-05-30

    +

    This thesis deals with concepts and solutions in the field of data management in everyday scientific life for image data from microscopy. The focus of the formulated requirements has so far been on published data, which represent only a small subset of the data generated in the scientific process. More and more, everyday research data are moving into the focus of the principles for the management of research data that were formulated early on (FAIR-principles). The adequate management of this mostly multimodal data is a real challenge in terms of its heterogeneity and scope. There is a lack of standardised and established workflows and also the software solutions available so far do not adequately reflect the special requirements of this area. However, the success of any data management process depends heavily on the degree of integration into the daily work routine. Data management must, as far as possible, fit seamlessly into this process. Microscopy data in the scientific process is embedded in pre-processing, which consists of preparatory laboratory work and the analytical evaluation of the microscopy data. In terms of volume, the image data often form the largest part of data generated within this entire research process. In this paper, we focus on concepts and techniques related to the handling and description of this image data and address the necessary basics. The aim is to improve the embedding of the existing data management solution for image data (OMERO) into the everyday scientific work. For this purpose, two independent software extensions for OMERO were implemented within the framework of this thesis: OpenLink and MDEmic. OpenLink simplifies the access to the data stored in the integrated repository in order to feed them into established workflows for further evaluations and enables not only the internal but also the external exchange of data without weakening the advantages of the data repository. The focus of the second implemented software solution, MDEmic, is on the capturing of relevant metadata for microscopy. Through the extended metadata collection, a corresponding linking of the multimodal data by means of a unique description and the corresponding semantic background is aimed at. The configurability of MDEmic is designed to address the currently very dynamic development of underlying concepts and formats. The main goal of MDEmic is to minimise the workload and to automate processes. This provides the scientist with a tool to handle this complex and extensive task of metadata acquisition for microscopic data in a simple way. With the help of the software, semantic and syntactic standardisation can take place without the scientist having to deal with the technical concepts. The generated metadata descriptions are automatically integrated into the image repository and, at the same time, can be transferred by the scientists into formats that are needed when publishing the data.

    +

    Tags: Nfdi4Bioimage, Research Data Managementv

    +

    https://zenodo.org/records/6905931

    +

    https://doi.org/10.5281/zenodo.6905931

    +
    +

    I3D:bio’s OMERO training material: Re-usable, adjustable, multi-purpose slides for local user training#

    Christian Schmidt, Michele Bortolomeazzi, Tom Boissonnet, Carsten Fortmann-Grote, Julia Dohle, Peter Zentis, Niraj Kandpal, Susanne Kunis, Thomas Zobel, Stefanie Weidtkamp-Peters, Elisa Ferrando-May

    @@ -530,12 +590,64 @@

    I3D:bio’s OMERO training material: Re-usable, adjustable, multi-purpose sl

    Licensed CC-BY-4.0

    The open-source software OME Remote Objects (OMERO) is a data management software that allows storing, organizing, and annotating bioimaging/microscopy data. OMERO has become one of the best-known systems for bioimage data management in the bioimaging community. The Information Infrastructure for BioImage Data (I3D:bio) project facilitates the uptake of OMERO into research data management (RDM) practices at universities and research institutions in Germany. Since the adoption of OMERO into researchers’ daily routines requires intensive training, a broad portfolio of training resources for OMERO is an asset. On top of using the OMERO guides curated by the Open Microscopy Environment Consortium (OME) team, imaging core facility staff at institutions where OMERO is used often prepare additional material tailored to be applicable for their own OMERO instances. Based on experience gathered in the Research Data Management for Microscopy group (RDM4mic) in Germany, and in the use cases in the I3D:bio project, we created a set of reusable, adjustable, openly available slide decks to serve as the basis for tailored training lectures, video tutorials, and self-guided instruction manuals directed at beginners in using OMERO. The material is published as an open educational resource complementing the existing resources for OMERO contributed by the community.

    Tags: OMERO, Research Data Management, Nfdi4Bioimage, I3Dbio

    -

    Content type: Slide, Video

    +

    Content type: Slides, Video

    https://zenodo.org/records/8323588

    https://www.youtube.com/playlist?list=PL2k-L-zWPoR7SHjG1HhDIwLZj0MB_stlU

    https://doi.org/10.5281/zenodo.8323588

    +
    +

    Key-Value pair template for annotation in OMERO for light microscopy data acquired with AxioScan7 - Core Facility Cellular Imaging (CFCI)#

    +

    Silke Tulok, Anja Nobst, Anett Jannasch, Tom Boissonnet, Gunar Fabig

    +

    Published 2024-06-28

    +

    Licensed CC-BY-4.0

    +

    This Key-Value pair template is used for the data documentation during imaging experiments and the later data annotation in OMERO. It is tailored for the usage and image acquisition at the slide scanning system Zeiss AxioScan 7 in the Core Facility Cellular Imaging (CFCI). It contains important metadata of the imaging experiment, which are not saved in the corresponding imaging files. All users of the Core Facility Cellular Imaging are trained to use that file to document their imaging parameters directly during the data acquisition with the possibility for a later upload to OMERO. Furthermore, there is a corresponding public example image used in the publication “Setting up an institutional OMERO environment for bioimage data: perspectives from both facility staff and users” and is available here: +https://omero.med.tu-dresden.de/webclient/?show=image-33248 +This template was developed by the CFCI staff during the setup and usage of the AxioScan 7 and is based on the REMBI recommendations (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8606015). +With this template it is possible to create a csv-file, that can be used to annotate an image or dataset in OMERO using the annotation script (ome/omero-scripts). +How to use:

    +

    fill the template sheet  with your metadata +select and copy the data range containing the Keys and Values +open a new excel sheet and paste transpose in cell A1  +Important: cell A1 contains always the name ‘dataset’ and cell A2 contains the exact name of the image/dataset, which should be annotated in OMERO +save the new excel sheet in csv-file (comma separated values) format

    +

    An example can be seen in sheet 3 ‘csv_AxioScan’. +Important note: The code has to be 8-Bit UCS transformation format (UTF-8) otherwise several characters (for example µ, %,°) might be not able to decode by the annotation script. We encountered this issue with old Microsoft-Office versions (MS Office 2016).  +Note: By filling the values in the excel sheet, avoid the usage of comma as decimal delimiter. +See cross reference: +10.5281/zenodo.12547566 Key-Value pair template for annotation of datasets in OMERO for light- and electron microscopy data within the research group of Prof. Mueller-Reichert +10.5281/zenodo.12546808 Key-Value pair template for annotation of datasets in OMERO (PERIKLES study)

    +

    Tags: Nfdi4Bioimage, Research Data Management

    +

    https://zenodo.org/records/12578084

    +

    https://doi.org/10.5281/zenodo.12578084

    +
    +
    +
    +

    Key-Value pair template for annotation of datasets in OMERO (PERIKLES study)#

    +

    Anett Jannasch, Silke Tulok, Vanessa Aphaia Fiona Fuchs, Tom Boissonnet, Christian Schmidt, Michele Bortolomeazzi, Gunar Fabig, Chukwuebuka Okafornta

    +

    Published 2024-06-26

    +

    Licensed CC-BY-4.0

    +

    This is a Key-Value pair template used for the annotation of datasets in OMERO. It is tailored for a research study (PERIKLES project) on the biocompatibility of newly designed biomaterials out of pericardial tissue for cardiovascular substitutes (https://doi.org/10.1063/5.0182672) conducted in the research department of Cardiac Surgery at the Faculty of Medicine Carl Gustav Carus at the Technische Universität Dresden . A corresponding public example dataset is used in the publication “Setting up an institutional OMERO environment for bioimage data: perspectives from both facility staff and users” and is available here +(https://omero.med.tu-dresden.de/webclient/?show=dataset-1557). +The template is based on the REMBI recommendations (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8606015) and it was developed during the PoL-Bio-Image Analysis Symposium in Dresden Aug 28th- Sept 1th 2023.  +With this template it is possible to create a csv-file, that can be used to annotate a dataset in OMERO using the annotation script (ome/omero-scripts). +How to use: +select and copy the data range containing Keys and Values +open a new excel sheet and paste transpose in column B1 +type in A1 ‘dataset’ +insert in A2 the exact name of the dataset, which should be annotated in OMERO +save the new excel sheet in csv- (comma seperated values) file format

    +

    Example can be seen in sheet 1 ‘csv import’. Important note; the code has to be 8-Bit UCS transformation format (UTF-8) otherwise several characters (for example µ, %,°) might not be able to decode by the annotation script. We encountered this issue with old Microsoft Office versions (e.g. MS Office 2016).  +Note: By filling the values in the excel sheet, avoid the usage of decimal delimiter. +  +See cross reference: +10.5281/zenodo.12547566 Key-Value pair template for annotation of datasets in OMERO (light- and electron microscopy data within the research group of Prof. Mueller-Reichert) +10.5281/zenodo.12578084 Key-Value pair template for annotation in OMERO for light microscopy data acquired with AxioScan7 - Core Facility Cellular Imaging (CFCI)

    +

    Tags: Nfdi4Bioimage, Research Data Management

    +

    https://zenodo.org/records/12546808

    +

    https://doi.org/10.5281/zenodo.12546808

    +
    +

    NFDI - Daten als gemeinsames Gut für exzellente Forschung, organisiert durch die Wissenschaft in Deutschland.#

    Licensed UNKNOWN

    @@ -561,8 +673,8 @@

    NFDI4BIOIMAGE - An Initiative for a National Research Data Infrastructure fo

    Published 2021-04-29

    Licensed CCY-BY-SA-4.0

    Align existing and establish novel services & solutions for data management tasks throughout the bioimage data lifecycle.

    -

    Tags: Nfdi4Bioimage, Image Data Management, Bioimage Data, Research Data Management

    -

    Content type: Conference Abstract, Slide

    +

    Tags: Nfdi4Bioimage, Research Data Management

    +

    Content type: Conference Abstract, Slides

    https://doi.org/10.11588/heidok.00029489

    @@ -586,6 +698,18 @@

    NFDI4BIOIMAGE - National Research Data Infrastructure for Microscopy and Bio

    https://zenodo.org/doi/10.5281/zenodo.8414318

    +
    +

    NFDI4BIOIMAGE data management illustrations by Henning Falk#

    +

    NFDI4BIOIMAGE Consortium

    +

    Published 2024-11-29

    +

    Licensed CC-BY-4.0

    +

    These illustrations were contracted by the Heinrich Heine University Düsseldorf in the frame of the consortium NFDI4BIOIMAGE from Henning Falk for the purpose of education and public outreach. The illustrations are free to use under a CC-BY 4.0 license.AttributionPlease include an attribution similar to: “Data annoation matters”, NFDI4BIOIMAGE Consortium (2024): NFDI4BIOIMAGE data management illustrations by Henning Falk, Zenodo, https://doi.org/10.5281/zenodo.14186100, is used under a CC-BY 4.0 license. Modifications to this illustration include cropping. + 

    +

    Tags: Nfdi4Bioimage, Research Data Management

    +

    https://zenodo.org/records/14186101

    +

    https://doi.org/10.5281/zenodo.14186101

    +
    +

    NFDI4BIOIMAGE: Perspective for a national bioimaging standard#

    Josh Moore, Susanne Kunis

    @@ -615,6 +739,17 @@

    NFDI4Bioimage - TA3-Hackathon - UoC-2023 (Cologne-Hackathon-2023, GitHub rep

    https://zenodo.org/doi/10.5281/zenodo.10609770

    +
    +

    NFDI4Bioimage Calendar 2024 October; original image#

    +

    Christian Jüngst, Peter Zentis

    +

    Published 2024-09-25

    +

    Licensed CC-BY-4.0

    +

    Raw microscopy image from the NFDI4Bioimage calendar October 2024

    +

    Tags: Nfdi4Bioimage, Research Data Management

    +

    https://zenodo.org/records/13837146

    +

    https://doi.org/10.5281/zenodo.13837146

    +
    +

    OME-NGFF: a next-generation file format for expanding bioimaging data-access strategies#

    Josh Moore, Chris Allan, Sébastien Besson, Jean-Marie Burel, Erin Diel, David Gault, Kevin Kozlowski, Dominik Lindner, Melissa Linkert, Trevor Manz, Will Moore, Constantin Pape, Christian Tischer, Jason R. Swedlow

    @@ -624,6 +759,19 @@

    OME-NGFF: a next-generation file format for expanding bioimaging data-access

    https://www.nature.com/articles/s41592-021-01326-w

    +
    +

    OME2024 NGFF Challenge Results#

    +

    Josh Moore

    +

    Published 2024-11-01

    +

    Licensed CC-BY-4.0

    +

    Presented at the 2024 FoundingGIDE event in Okazaki, Japan: https://founding-gide.eurobioimaging.eu/event/foundinggide-community-event-2024/ +Note: much of the presentation was a demonstration of the OME2024-NGFF-Challenge – https://ome.github.io/ome2024-ngff-challenge/ especially of querying an extraction of the metadata (ome/ome2024-ngff-challenge-metadata) + 

    +

    Tags: Nfdi4Bioimage, Research Data Management

    +

    https://zenodo.org/records/14234608

    +

    https://doi.org/10.5281/zenodo.14234608

    +
    +

    OMERO for microscopy research data management#

    Thomas Zobel, Sarah Weischner, Jens Wendt

    @@ -634,6 +782,16 @@

    OMERO for microscopy research data managementhttps://analyticalscience.wiley.com/do/10.1002/was.0004000267/

    +
    +

    Overview of the Galaxy OMERO-suite - Upload images and metadata in OMERO using Galaxy#

    +

    Riccardo Massei, Björn Grüning

    +

    Published 2024-12-02

    +

    Licensed CC-BY-4.0

    +

    Tags: OMERO, Galaxy, Metadata, Nfdi4Bioimage

    +

    Content type: Tutorial, Framework, Workflow

    +

    https://training.galaxyproject.org/training-material/topics/imaging/tutorials/omero-suite/tutorial.html

    +
    +

    Research data management for bioimaging: the 2021 NFDI4BIOIMAGE community survey#

    Christian Schmidt, Janina Hanne, Josh Moore, Christian Meesters, Elisa Ferrando-May, Stefanie Weidtkamp-Peters, members of the NFDI4BIOIMAGE initiative

    @@ -654,16 +812,52 @@

    Setting up a data management infrastructure for bioimaging

    Structuring of Data and Metadata in Bioimaging: Concepts and technical Solutions in the Context of Linked Data#

    -

    Susanne Kunis

    -

    Published 2022-08-24

    +

    Sarah Weischer, Jens Wendt, Thomas Zobel

    +

    Published 2022-07-12

    +

    Licensed CC-BY-4.0

    +

    Provides an overview of contexts, frameworks, and models from the world of bioimage data as well as metadata. Visualizes the techniques for structuring this data as Linked Data. (Walkthrough Video: https://doi.org/10.5281/zenodo.7018928 )

    +

    Content:

    +
    Types of metadata
+Data formats
+Data Models Microscopy Data
+Tools to edit/gather metadata
+ISA Framework
+FDO Framework
+Ontology
+RDF
+JSON-LD
+SPARQL
+Knowledge Graph
+Linked Data
+Smart Data
+...
+
    +
    +

    Tags: Nfdi4Bioimage, Research Data Management

    +

    https://zenodo.org/records/7018750

    +

    https://doi.org/10.5281/zenodo.7018750

    +
    +
    +
    +

    The Information Infrastructure for BioImage Data (I3D:bio) project to advance FAIR microscopy data management for the community#

    +

    Christian Schmidt, Michele Bortolomeazzi, Tom Boissonnet, Julia Dohle, Tobias Wernet, Janina Hanne, Roland Nitschke, Susanne Kunis, Karen Bernhardt, Stefanie Weidtkamp-Peters, Elisa Ferrando-May

    +

    Published 2024-03-04

    +

    Licensed CC-BY-4.0

    +

    Research data management (RDM) in microscopy and image analysis is a challenging task. Large files in proprietary formats, complex N-dimensional array structures, and various metadata models and formats can make image data handling inconvenient and difficult. For data organization, annotation, and sharing, researchers need solutions that fit everyday practice and comply with the FAIR (Findable, Accessible, Interoperable, Reusable) principles. International community-based efforts have begun creating open data models (OME), an open file format and translation library (OME-TIFF, Bio-Formats), data management software platforms, and microscopy metadata recommendations and annotation tools. Bringing these developments into practice requires support and training. Iterative feedback and tool improvement is needed to foster practical adoption by the scientific community. The Information Infrastructure for BioImage Data (I3D:bio) project works on guidelines, training resources, and practical assistance for FAIR microscopy RDM adoption with a focus on the management platform OMERO and metadata annotations.

    +

    Tags: Nfdi4Bioimage, Research Data Management

    +

    https://zenodo.org/records/10805204

    +

    https://doi.org/10.5281/zenodo.10805204

    +
    +
    +
    +

    The role of Helmholtz Centers in NFDI4BIOIMAGE - A national consortium enhancing FAIR data management for microscopy and bioimage analysis#

    +

    Riccardo Massei, Christian Schmidt, Michele Bortolomeazzi, Julia Thoennissen, Jan Bumberger, Timo Dickscheid, Jan-Philipp Mallm, Elisa Ferrando-May

    +

    Published 2024-06-06

    Licensed CC-BY-4.0

    -

    guided walkthrough of poster at https://doi.org/10.5281/zenodo.6821815

    -

    which provides an overview of contexts, frameworks, and models from the world of bioimage data as well as metadata and the techniques for structuring this data as Linked Data.

    -

    You can also watch the video in the browser on the I3D:bio website.

    +

    Germany’s National Research Data Infrastructure (NFDI) aims to establish a sustained, cross-disciplinary research data management (RDM) infrastructure that enables researchers to handle FAIR (findable, accessible, interoperable, reusable) data. While FAIR principles have been adopted by funders, policymakers, and publishers, their practical implementation remains an ongoing effort. In the field of bio-imaging, harmonization of data formats, metadata ontologies, and open data repositories is necessary to achieve FAIR data. The NFDI4BIOIMAGE was established to address these issues and develop tools and best practices to facilitate FAIR microscopy and image analysis data in alignment with international community activities. The consortium operates through its Data Stewards team to provide expertise and direct support to help overcome RDM challenges. The three Helmholtz Centers in NFDI4BIOIMAGE aim to collaborate closely with other centers and initiatives, such as HMC, Helmholtz AI, and HIP. Here we present NFDI4BIOIMAGE’s work and its significance for research in Helmholtz and beyond

    Tags: Nfdi4Bioimage, Research Data Management

    -

    Content type: Video

    -

    https://zenodo.org/record/7018929

    -

    https://doi.org/10.5281/zenodo.7018929

    +

    https://zenodo.org/records/11501662

    +

    https://doi.org/10.5281/zenodo.11501662

    @@ -676,6 +870,17 @@

    Thinking data management on different scaleshttps://zenodo.org/doi/10.5281/zenodo.8329305

    +
    +

    Towards Preservation of Life Science Data with NFDI4BIOIMAGE#

    +

    Robert Haase

    +

    Published 2024-09-03

    +

    Licensed CC-BY-4.0

    +

    This talk will present the initiatives of the NFDI4BioImage consortium aimed at the long-term preservation of life science data. We will discuss our efforts to establish metadata standards, which are crucial for ensuring data reusability and integrity. The development of sustainable infrastructure is another key focus, enabling seamless data integration and analysis in the cloud. We will take a look at how we manage training materials and communicate with our community. Through these actions, NFDI4BioImage seeks to enable FAIR bioimage data management for German researchers, across disciplines and embedded in the international framework.

    +

    Tags: Nfdi4Bioimage, Research Data Management

    +

    https://zenodo.org/records/13640979

    +

    https://doi.org/10.5281/zenodo.13640979

    +
    +

    Welcome to BioImage Town#

    Josh Moore

    @@ -696,6 +901,55 @@

    Who you gonna call? - Data Stewards to the rescuehttps://zenodo.org/doi/10.5281/zenodo.10730423

    +
    +

    [CIDAS] Scalable strategies for a next-generation of FAIR bioimaging#

    +

    Josh Moore

    +

    Published 2025-01-23

    +

    Licensed CC-BY-4.0

    +

    Talk given at Georg-August-Universität Göttingen Campus Institute Data Science23rd January 2025 +https://www.uni-goettingen.de/en/653203.html

    +

    Tags: Nfdi4Bioimage

    +

    https://zenodo.org/records/14716546

    +

    https://doi.org/10.5281/zenodo.14716546

    +
    +
    +
    +

    [CMCB] Scalable strategies for a next-generation of FAIR bioimaging#

    +

    Josh Moore

    +

    Published 2025-01-16

    +

    Licensed CC-BY-4.0

    +

    CMCB LIFE SCIENCES SEMINARSTechnische Universität Dresden16th January 2025 +https://tu-dresden.de/cmcb/crtd/news-termine/termine/cmcb-life-sciences-seminar-josh-moore-german-bioimaging-e-v-society-for-microscopy-and-image-analysis-constance + 

    +

    Tags: Nfdi4Bioimage

    +

    https://zenodo.org/records/14650434

    +

    https://doi.org/10.5281/zenodo.14650434

    +
    +
    +
    +

    [Community Meeting 2024] Overview Team Image Data Analysis and Management#

    +

    Susanne Kunis, Thomas Zobel

    +

    Published 2024-03-08

    +

    Licensed CC-BY-4.0

    +

    Overview of Activities of the Team Image Data Analysis and Management of German BioImaging e.V. + 

    +

    Tags: Nfdi4Bioimage, Research Data Management

    +

    https://zenodo.org/records/10796364

    +

    https://doi.org/10.5281/zenodo.10796364

    +
    +
    +
    +

    [ELMI 2024] AI’s Dirty Little Secret: Without#

    +

    FAIR Data, It’s Just Fancy Math

    +

    Josh Moore, Susanne Kunis

    +

    Published 2024-05-21

    +

    Licensed CC-BY-4.0

    +

    Poster presented at the European Light Microscopy Initiative meeting in Liverpool (https://www.elmi2024.org/)

    +

    Tags: Nfdi4Bioimage, Research Data Management

    +

    https://zenodo.org/records/11235513

    +

    https://doi.org/10.5281/zenodo.11235513

    +
    +

    [ELMI 2024] AI’s Dirty Little Secret: Without FAIR Data, It’s Just Fancy Math#

    Josh Moore, Susanne Kunis

    @@ -724,6 +978,90 @@

    [Short Talk] NFDI4BIOIMAGE - A consortium in the National Research Data Infr

    Tags: Research Data Management, Bioimage Analysis, Nfdi4Bioimage

    Content type: Slides

    https://zenodo.org/doi/10.5281/zenodo.10939519

    +

    +
    +
    +

    [Workshop Material] Fit for OMERO - How imaging facilities and IT departments work together to enable RDM for bioimaging, October 16-17, 2024, Heidelberg#

    +

    Tom Boissonnet, Bettina Hagen, Susanne Kunis, Christian Schmidt, Stefanie Weidtkamp-Peters

    +

    Published 2024-11-18

    +

    Licensed CC-BY-4.0

    +

    Fit for OMERO: How imaging facilities and IT departments work together to enable RDM for bioimaging +Description: +Research data management (RDM) in bioimaging is challenging because of large file sizes, heterogeneous file formats and the variability of imaging methods. The image data management system OMERO (OME Remote Objects) allows for centralized and secure storage, organization, annotation, and interrogation of microscopy data by researchers. It is an internationally well-supported open-source software tool that has become one of the best-known image data management tools among bioimaging scientists. Nevertheless, the de novo setup of OMERO at an institute is a multi-stakeholder process that demands time, funds, organization and iterative implementation. In this workshop, participants learn how to begin setting up OMERO-based image data management at their institution. The topics include:

    +

    Stakeholder identification at the university / research institute +Process management, time line expectations, and resources planning +Learning about each other‘s perspectives on chances and challenges for RDM +Funding opportunities and strategies for IT and imaging core facilities +Hands-on: Setting up an OMERO server in a virtual machine environment

    +

    Target audience: +This workshop was directed at universities and research institutions who consider or plan to implement OMERO, or are in an early phase of implementation. This workshop was intended for teams from IT departments and imaging facilities to participate together with one person from the IT department, and one person from the imaging core facility at the same institution. +The trainers:

    +

    Prof. Dr. Stefanie Weidtkamp-Peters (Imaging Core Facility Head, Center for Advanced Imaging, Heinrich Heine University of Düsseldorf) +Dr. Susanne Kunis (Software architect, OMERO administrator, metadata specialist, University of Osnabrück) +Dr. Tom Boissonnet (OMERO admin and image metadata specialist, Center for Advanced Imaging, Heinrich Heine University of Düsseldorf) +Dr. Bettina Hagen (IT Administration and service specialist, Max Planck Institute for the Biology of Ageing, Cologne)  +Dr. Christian Schmidt (Science Manager for Research Data Management in Bioimaging, German Cancer Research Center (DKFZ), Heidelberg)

    +

    Time and place +The format was a two-day, in-person workshop (October 16-17, 2024). Location: Heidelberg, Germany +Workshop learning goals

    +

    Learn the steps to establish a local RDM environment fit for bioimaging data +Create a network of IT experts and bioimaging specialists for bioimage RDM across institutions +Establish a stakeholder process management for installing OMERO-based RDM +Learn from each other, leverage different expertise +Learn how to train users, establish sustainability strategies, and foster FAIR RDM for bioimaging at your institution

    +

    Tags: Nfdi4Bioimage, Research Data Management

    +

    https://zenodo.org/records/14178789

    +

    https://doi.org/10.5281/zenodo.14178789

    +
    +
    +
    +

    [Workshop] Bioimage data management and analysis with OMERO#

    +

    Riccardo Massei, Michele Bortolomeazzi, Christian Schmidt

    +

    Published 2024-05-13

    +

    Licensed CC-BY-4.0

    +

    Here we share the material used in a workshop held on May 13th, 2024, at the German Cancer Research Center in Heidelberg (on-premise) +Description:Microscopy experiments generate information-rich, multi-dimensional data, allowing us to investigate biological processes at high spatial and temporal resolution. Image processing and analysis is a standard procedure to retrieve quantitative information from biological imaging. Due to the complex nature of bioimaging files that often come in proprietary formats, it can be challenging to organize, structure, and annotate bioimaging data throughout a project. Data often needs to be moved between collaboration partners, transformed into open formats, processed with a variety of software tools, and exported to smaller-sized images for presentation. The path from image acquisition to final publication figures with quantitative results must be documented and reproducible. +In this workshop, participants learn how to use OMERO to organize their data and enrich the bioimage data with structured metadata annotations.We also focus on image analysis workflows in combination with OMERO based on the Fiji/ImageJ software and using Jupyter Notebooks. In the last part, we explore how OMERO can be used to create publication figures and prepare bioimage data for publication in a suitable repository such as the Bioimage Archive. +Module 1 (9 am - 10.15 am): Basics of OMERO, data structuring and annotation +Module 2 (10.45 am - 12.45 pm): OMERO and Fiji +Module 3 (1.45 pm - 3.45 pm): OMERO and Jupyter Notebooks +Module 4 (4.15 pm - 6. pm): Publication-ready figures and data with OMERO +The target group for this workshopThis workshop is directed at researchers at all career levels who plan to or have started to use OMERO for their microscopy research data management. We encourage the workshop participants to bring example data from their research to discuss suitable metadata annotation for their everyday practice. +Prerequisites:Users should bring their laptops and have access to the internet through one of the following options:- eduroam- institutional WiFi- VPN connection to their institutional networks to access OMERO +Who are the trainers? +Dr. Riccardo Massei (Helmholtz-Center for Environmental Research, UFZ, Leipzig) - Data Steward for Bioimaging Data in NFDI4BIOIMAGE +Dr. Michele Bortolomeazzi (DKFZ, Single cell Open Lab, bioimage data specialist, bioinformatician, staff scientist in the NFDI4BIOIMAGE project) +Dr. Christian Schmidt (Science Manager for Research Data Management in Bioimaging, German Cancer Research Center, Heidelberg, Project Coordinator of the NFDI4BIOIMAGE project)

    +

    Tags: Nfdi4Bioimage, Research Data Management

    +

    https://zenodo.org/records/11350689

    +

    https://doi.org/10.5281/zenodo.11350689

    +
    +
    +
    +

    [Workshop] Research Data Management for Microscopy and BioImage Analysis#

    +

    Christian Schmidt, Tom Boissonnet, Michele Bortolomeazzi, Ksenia Krooß

    +

    Published 2024-09-30

    +

    Licensed CC-BY-4.0

    +

    Research Data Management for Microscopy and BioImage Analysis

    +

    Introduction to BioImaging Research Data Management, NFDI4BIOIMAGE and I3D:bioChristian Schmidt /DKFZ Heidelberg +OMERO as a tool for bioimaging data managementTom Boissonnet /Heinrich-Heine Universität Düsseldorf +Reproducible image analysis workflows with OMERO software APIsMichele Bortolomeazzi /DKFZ Heidelberg +Publishing datasets in public archives for bioimage dataKsenia Krooß /Heinrich-Heine Universität Düsseldorf

    +

    Date & Venue:Thursday, Sept. 26, 5.30 p.m.Haus 22 / Paul Ehrlich Lecture Hall (H22-1)University Hospital Frankfurt

    +

    Tags: Nfdi4Bioimage, Research Data Management

    +

    https://zenodo.org/records/13861026

    +

    https://doi.org/10.5281/zenodo.13861026

    +
    +
    +
    +

    ome2024-ngff-challenge#

    +

    Will Moore, Josh Moore, sherwoodf, Jean-Marie Burel, Norman Rzepka, dependabot[bot], JensWendt, Joost de Folter, Torsten St\xF6ter, AybukeKY, Eric Perlman, Tom Boissonnet

    +

    Published 2024-08-30T12:00:53+00:00

    +

    Licensed BSD-3-CLAUSE

    +

    Project planning and material repository for the 2024 challenge to generate 1 PB of OME-Zarr data

    +

    Tags: Sharing, Nfdi4Bioimage, Research Data Management

    +

    Content type: Github Repository

    +

    ome/ome2024-ngff-challenge

    @@ -764,7 +1102,7 @@

    [Short Talk] NFDI4BIOIMAGE - A consortium in the National Research Data Infr

    previous

    -

    Neubias (26)

    +

    Neubias (27)

    [Short Talk] NFDI4BIOIMAGE - A consortium in the National Research Data Infr

    diff --git a/tags/omero.html b/tags/omero.html index 7f0af764..b2efd078 100644 --- a/tags/omero.html +++ b/tags/omero.html @@ -64,7 +64,7 @@ - + @@ -182,36 +182,29 @@

    By tag

    By content type

    @@ -236,11 +228,12 @@ @@ -507,7 +500,7 @@

    BIOMERO - A scalable and extensible image analysis frameworkhttps://doi.org/10.1016/j.patter.2024.101024

    @@ -528,7 +521,7 @@

    Browsing the Open Microscopy Image Data Resource with Pythonhttps://biapol.github.io/blog/robert_haase/browsing_idr/readme.html

    @@ -537,7 +530,7 @@

    Erick Martins Ratamero - Expanding the OME ecosystem for imaging data manage

    SciPy, Erick Martins Ratamero

    Published 2024-08-19

    Licensed YOUTUBE STANDARD LICENSE

    -

    Tags: Image Data Management, OMERO, Bioimage Analysis

    +

    Tags: OMERO, Bioimage Analysis

    Content type: Video, Presentation

    https://www.youtube.com/watch?v=GmhyDNm1RsM

    @@ -564,8 +557,8 @@

    Glencoe Software Webinarshttps://www.glencoesoftware.com/media/webinars/

    @@ -586,7 +579,7 @@

    I3D:bio’s OMERO training material: Re-usable, adjustable, multi-purpose sl

    Licensed CC-BY-4.0

    The open-source software OME Remote Objects (OMERO) is a data management software that allows storing, organizing, and annotating bioimaging/microscopy data. OMERO has become one of the best-known systems for bioimage data management in the bioimaging community. The Information Infrastructure for BioImage Data (I3D:bio) project facilitates the uptake of OMERO into research data management (RDM) practices at universities and research institutions in Germany. Since the adoption of OMERO into researchers’ daily routines requires intensive training, a broad portfolio of training resources for OMERO is an asset. On top of using the OMERO guides curated by the Open Microscopy Environment Consortium (OME) team, imaging core facility staff at institutions where OMERO is used often prepare additional material tailored to be applicable for their own OMERO instances. Based on experience gathered in the Research Data Management for Microscopy group (RDM4mic) in Germany, and in the use cases in the I3D:bio project, we created a set of reusable, adjustable, openly available slide decks to serve as the basis for tailored training lectures, video tutorials, and self-guided instruction manuals directed at beginners in using OMERO. The material is published as an open educational resource complementing the existing resources for OMERO contributed by the community.

    Tags: OMERO, Research Data Management, Nfdi4Bioimage, I3Dbio

    -

    Content type: Slide, Video

    +

    Content type: Slides, Video

    https://zenodo.org/records/8323588

    https://www.youtube.com/playlist?list=PL2k-L-zWPoR7SHjG1HhDIwLZj0MB_stlU

    https://doi.org/10.5281/zenodo.8323588

    @@ -595,9 +588,9 @@

    I3D:bio’s OMERO training material: Re-usable, adjustable, multi-purpose sl

    KNIME Image Processing#

    None

    -

    Licensed GPLV3

    +

    Licensed GPL-3.0

    The KNIME Image Processing Extension allows you to read in more than 140 different kinds of images and to apply well known methods on images, like preprocessing. segmentation, feature extraction, tracking and classification in KNIME.

    -

    Tags: Imagej, OMERO, Bioimage Data, Workflow

    +

    Tags: Imagej, OMERO, Workflow

    Content type: Tutorial, Online Tutorial, Documentation

    https://www.knime.com/community/image-processing

    @@ -649,7 +642,7 @@

    OMERO - HCS analysis pipeline using Jupyter Notebooks

    OMERO - QuPath#

    Rémy Jean Daniel Dornier

    -

    Licensed [‘CC-BY-NC-SA-4.0’]

    +

    Licensed CC-BY-NC-SA-4.0

    OMERO-RAW extension for QuPath allows to directly access to the raw pixels of images. All types of images (RGB, fluorescence, …) are supported with this extension.

    Tags: Bioimage Analysis, OMERO

    Content type: Online Tutorial

    @@ -729,7 +722,7 @@

    Overview of the Galaxy OMERO-suite - Upload images and metadata in OMERO usi

    Riccardo Massei, Björn Grüning

    Published 2024-12-02

    Licensed CC-BY-4.0

    -

    Tags: OMERO, Galaxy, Metadata

    +

    Tags: OMERO, Galaxy, Metadata, Nfdi4Bioimage

    Content type: Tutorial, Framework, Workflow

    https://training.galaxyproject.org/training-material/topics/imaging/tutorials/omero-suite/tutorial.html

    @@ -828,7 +821,7 @@

    Welcome to BioImage Town

    previous

    -

    Nfdi4bioimage (23)

    +

    Nfdi4bioimage (44)

    By tag

    By content type

    @@ -236,11 +228,12 @@ @@ -506,7 +499,7 @@

    Cultivating Open Training#

    Published 2023-07-05

    Licensed CC-BY-SA-4.0

    -

    Tags: Research Data Management, Bioimage Analysis, Image Data Management, Open Science

    +

    Tags: Research Data Management, Bioimage Analysis, Open Science

    Content type: Slides, Presentation

    https://omero-fbi.fr/slides/elmi23_cfd/main.html#/title-slide

    @@ -518,7 +511,7 @@

    Finding and using publicly available datahttps://www.ebi.ac.uk/training/online/courses/finding-using-public-data/

    @@ -538,7 +531,7 @@

    Making the most of bioimaging data through interdisciplinary interactionsVirginie Uhlmann, Matthew Hartley, Josh Moore, Erin Weisbart, Assaf Zaritsky

    Published 2024-10-23

    Licensed CC-BY-4.0

    -

    Tags: Bioimage Analysis, Open Science, Microscopy Image Analysis, Microscopy

    +

    Tags: Bioimage Analysis, Open Science, Microscopy

    Content type: Publication

    https://journals.biologists.com/jcs/article/137/20/jcs262139/362478/Making-the-most-of-bioimaging-data-through

    diff --git a/tags/open_source_software.html b/tags/open_source_software.html index d459e8b2..28cce0e5 100644 --- a/tags/open_source_software.html +++ b/tags/open_source_software.html @@ -182,36 +182,29 @@

    By tag

    By content type

    @@ -236,11 +228,12 @@ @@ -491,7 +484,7 @@

    Insights and Impact From Five Cycles of Essential Open Source Software for S

    NGFF Converter#

    Licensed GPL-2.0

    An easy to use and open source converter for bioimaging data. NGFF-Converter is a GUI application for conversion of bioimage formats into OME-NGFF (Next-Generation File Format) or OME-TIFF.

    -

    Tags: Bioimage Data, Open Source Software

    +

    Tags: Open Source Software

    Content type: Application

    https://www.glencoesoftware.com/products/ngff-converter/

    @@ -501,7 +494,7 @@

    Open Micoscropy Environment (OME) Youtube ChannelPublished None

    Licensed CC-BY-4.0

    OME develops open-source software and data format standards for the storage and manipulation of biological microscopy data

    -

    Tags: Open Source Software, Microscopy Image Analysis, Bioimage Data

    +

    Tags: Open Source Software

    Content type: Video, Collection

    https://www.youtube.com/@OpenMicroscopyEnvironment

    @@ -531,7 +524,7 @@

    bioformats2raw Converterglencoesoftware/bioformats2raw

    @@ -541,7 +534,7 @@

    raw2ometiff ConverterMelissa Linkert, Chris Allan, Sébastien Besson, Josh Moore

    Licensed GPL-2.0

    Java application to convert a directory of tiles to an OME-TIFF pyramid. This is the second half of iSyntax/.mrxs => OME-TIFF conversion.

    -

    Tags: Open Source Software, Bioimage Data

    +

    Tags: Open Source Software

    Content type: Application, Github Repository

    glencoesoftware/raw2ometiff

    diff --git a/tags/python.html b/tags/python.html index b310ee56..3f4bad7f 100644 --- a/tags/python.html +++ b/tags/python.html @@ -63,7 +63,7 @@ - + @@ -182,36 +182,29 @@

    By tag

    By content type

    @@ -236,11 +228,12 @@ @@ -553,7 +546,7 @@

    2022 MIC Workshop on Bioimage processing with PythonAnnotating 3D images in napari#

    Mara Lampert

    Tags: Python, Napari, Bioimage Analysis

    -

    Content type: Blog

    +

    Content type: Blog Post

    https://focalplane.biologists.com/2023/03/30/annotating-3d-images-in-napari/

    @@ -562,7 +555,7 @@

    BIDS-lecture-2024ScaDS/BIDS-lecture-2024

    @@ -582,8 +575,8 @@

    Bio-image Analysis with the Help of Large Language Modelshttps://zenodo.org/records/10815329

    https://doi.org/10.5281/zenodo.10815329

    @@ -593,7 +586,7 @@

    Bio-image Data ScienceRobert Haase

    Licensed CC-BY-4.0

    This repository contains training resources for Students at Uni Leipzig who want to dive into bio-image data science with Python.

    -

    Tags: Image Data Management, Deep Learning, Microscopy Image Analysis, Python

    +

    Tags: Research Data Management, Artificial Intelligence, Bioimage Analysis, Python

    Content type: Notebook

    ScaDS/BIDS-lecture-2024

    @@ -603,7 +596,7 @@

    Bio-image Data Science Lectures @ Uni Leipzig / https://zenodo.org/records/12623730

    @@ -622,7 +615,7 @@

    BioEngine DocumentationWei Ouyang, Nanguage, Jeremy Metz, Craig Russell

    Licensed MIT

    BioEngine, a Python package designed for flexible deployment and execution of bioimage analysis models and workflows using AI, accessible via HTTP API and RPC.

    -

    Tags: Workflow Engine, Deep Learning, Python

    +

    Tags: Workflow Engine, Artificial Intelligence, Python

    Content type: Documentation

    https://bioimage-io.github.io/bioengine/#/

    @@ -650,7 +643,7 @@

    Browsing the Open Microscopy Image Data Resource with Pythonhttps://biapol.github.io/blog/robert_haase/browsing_idr/readme.html

    @@ -757,7 +750,7 @@

    EMBL Deep Learning course 2023 exercises and materialsFeature extraction in napari#

    Mara Lampert

    Tags: Python, Napari, Bioimage Analysis

    -

    Content type: Blog

    +

    Content type: Blog Post

    https://focalplane.biologists.com/2023/05/03/feature-extraction-in-napari/

    @@ -783,7 +776,7 @@

    Generative artificial intelligence for bio-image analysishttps://f1000research.com/slides/12-971

    @@ -792,7 +785,7 @@

    Getting started with Mambaforge and Pythonhttps://biapol.github.io/blog/mara_lampert/getting_started_with_mambaforge_and_python/readme.html

    @@ -800,7 +793,7 @@

    Getting started with Mambaforge and Python#

    Mara Lampert

    Tags: Github, Python, Science Communication

    -

    Content type: Blog

    +

    Content type: Blog Post

    https://focalplane.biologists.com/2024/04/03/how-to-write-a-bug-report/

    @@ -809,7 +802,7 @@

    Image Processing with Pythonhttps://datacarpentry.org/image-processing/key-points.html

    @@ -856,7 +849,7 @@

    Introduction to Deep Learning for Microscopycomputational-cell-analytics/dl-for-micro

    @@ -875,7 +868,7 @@

    Managing Scientific Python environments using Conda, Mamba and friendsRobert Haase

    Licensed CC-BY-4.0

    Tags: Python, Conda, Mamba

    -

    Content type: Blog

    +

    Content type: Blog Post

    https://focalplane.biologists.com/2022/12/08/managing-scientific-python-environments-using-conda-mamba-and-friends/

    @@ -903,7 +896,7 @@

    Microscopy data analysis: machine learning and the BioImage ArchiveAndrii Iudin, Anna Foix-Romero, Anna Kreshuk, Awais Athar, Beth Cimini, Dominik Kutra, Estibalis Gomez de Mariscal, Frances Wong, Guillaume Jacquemet, Kedar Narayan, Martin Weigert, Nodar Gogoberidze, Osman Salih, Petr Walczysko, Ryan Conrad, Simone Weyend, Sriram Sundar Somasundharam, Suganya Sivagurunathan, Ugis Sarkans

    Licensed CC-BY-4.0

    The Microscopy data analysis: machine learning and the BioImage Archive course, which focused on introducing programmatic approaches used in the analysis of bioimage data via the BioImage Archive, ran in May 2023.

    -

    Tags: Microscopy Image Analysis, Python, Deep Learning

    +

    Tags: Bioimage Analysis, Python, Artificial Intelligence

    Content type: Video, Slides

    https://www.ebi.ac.uk/training/materials/microscopy-data-analysis-machine-learning-and-the-bioimage-archive-materials/

    @@ -931,7 +924,7 @@

    Neubias Academy 2020: Introduction to Nuclei Segmentation with StarDistMartin Weigert, Olivier Burri, Siân Culley, Uwe Schmidt

    Licensed UNKNOWN

    Tags: Python, Neubias, Artificial Intelligence, Bioimage Analysis

    -

    Content type: Slide, Notebook

    +

    Content type: Slides, Notebook

    maweigert/neubias_academy_stardist

    @@ -1000,7 +993,7 @@

    PoL Bio-Image Analysis Training School on GPU-Accelerated Image AnalysisPrompt Engineering in Bio-image Analysis#

    Mara Lampert

    Tags: Python, Jupyter, Bioimage Analysis, Prompt Engineering, Biabob

    -

    Content type: Blog

    +

    Content type: Blog Post

    https://focalplane.biologists.com/2024/07/18/prompt-engineering-in-bio-image-analysis/

    @@ -1035,7 +1028,7 @@

    QI 2024 Analysis Lab Manualhttps://bethac07.github.io/qi_2024_analysis_lab_manual/intro.html

    @@ -1065,7 +1058,7 @@

    QuPath for Python programmers#

    Mara Lampert

    Tags: Python, Napari, Bioimage Analysis

    -

    Content type: Blog

    +

    Content type: Blog Post

    https://focalplane.biologists.com/2023/04/13/quality-assurance-of-segmentation-results/

    @@ -1073,7 +1066,7 @@

    Quality assurance of segmentation results#

    Mara Lampert

    Tags: Python, Napari, Bioimage Analysis

    -

    Content type: Blog

    +

    Content type: Blog Post

    https://focalplane.biologists.com/2023/03/02/rescaling-images-and-pixel-anisotropy/

    @@ -1082,7 +1075,7 @@

    Running Deep-Learning Scripts in the BiA-PoL Omero Serverhttps://biapol.github.io/blog/marcelo_zoccoler/omero_scripts/readme.html

    @@ -1127,7 +1120,7 @@

    Teaching Bioimage Analysis with Python#

    Mara Lampert

    Tags: Python, Napari, Bioimage Analysis

    -

    Content type: Blog

    +

    Content type: Blog Post

    https://focalplane.biologists.com/2023/06/01/tracking-in-napari/

    @@ -1147,7 +1140,7 @@

    YMIA - Python-Based Event Series Training MaterialPublished None

    Licensed MIT

    This repository offer access to teaching material and useful resources for the YMIA - Python-Based Event Series.

    -

    Tags: Python, Large Language Models, Prompt Engineering, Biabob, Bioimage Analysis, Microscopy Image Analysis

    +

    Tags: Python, Artifical Intelligence, Bioimage Analysis

    Content type: Github Repository, Slides

    rmassei/ymia_python_event_series_material

    @@ -1224,7 +1217,7 @@

    numpy pandas course

    next

    -

    Research data management (110)

    +

    Research data management (128)

     diff --git a/tags/readme.html b/tags/readme.html index 34dca709..6d31416b 100644 --- a/tags/readme.html +++ b/tags/readme.html @@ -182,36 +182,29 @@

    By tag

    By content type

    @@ -236,11 +228,12 @@ diff --git a/tags/research_data_management.html b/tags/research_data_management.html index 81660e64..465bd5ca 100644 --- a/tags/research_data_management.html +++ b/tags/research_data_management.html @@ -8,7 +8,7 @@ - Research data management (110) — NFDI4BioImage Training Materials + Research data management (128) — NFDI4BioImage Training Materials @@ -63,7 +63,7 @@ - + @@ -182,36 +182,29 @@

    By tag

    By content type

    @@ -236,11 +228,12 @@ @@ -447,7 +440,7 @@
    -

    Research data management (110)

    +

    Research data management (128)

    @@ -461,16 +454,20 @@

    Contents

  • 6 Steps Towards Reproducible Research
  • A Cloud-Optimized Storage for Interactive Access of Large Arrays
  • A call for public archives for biological image data
    • +
  • A journey to FAIR microscopy data
  • A practical guide to bioimaging research data management in core facilities
  • Abstract - NFDI Basic Service for Data Management Plans
  • Alles meins – oder!? Urheberrechte klären für Forschungsdaten
    • +
  • Angebote der NFDI für die Forschung im Bereich Zoologie
  • Best practice data life cycle approaches for the life sciences
  • BigDataProcessor2: A free and open-source Fiji plugin for inspection and processing of TB sized image data
  • Bio-Image Data Strudel for Workshop on Research Data Management in TU Dresden Core Facilities
    • +
  • Bio-image Data Science
  • Bring your own data workshops
  • Building a FAIR image data ecosystem for microscopy communities
  • Challenges and opportunities for bio-image analysis core-facilities
  • Challenges and opportunities for bioimage analysis core-facilities
    • +
  • Collaborative Working and Version Control with git[hub]
  • Collaborative bio-image analysis script editing with git
  • Combining the BIDS and ARC Directory Structures for Multimodal Research Data Organization
  • Community-developed checklists for publishing images and image analyses
    • @@ -510,6 +507,8 @@

    Contents

  • I3D:bio’s OMERO training material: Re-usable, adjustable, multi-purpose slides for local user training
  • If you license it, it’ll be harder to steal it. Why we should license our work
  • Introduction to Research Data Management and Open Research
    • +
  • Key-Value pair template for annotation in OMERO for light microscopy data acquired with AxioScan7 - Core Facility Cellular Imaging (CFCI)
    • +
  • Key-Value pair template for annotation of datasets in OMERO (PERIKLES study)
  • Kollaboratives Arbeiten und Versionskontrolle mit Git
  • Leitlinie? Grundsätze? Policy? Richtlinie? – Forschungsdaten-Policies an deutschen Universitäten
  • Lund Declaration on Maximising the Benefits of Research Data
    • @@ -521,10 +520,13 @@

    Contents

  • NFDI4BIOIMAGE - An Initiative for a National Research Data Infrastructure for Microscopy Data
  • NFDI4BIOIMAGE - National Research Data Infrastructure for Microscopy and BioImage Analysis - Online Kick-Off 2023
  • NFDI4BIOIMAGE - National Research Data Infrastructure for Microscopy and BioImage Analysis [conference talk: The Pelagic Imaging Consortium meets Helmholtz Imaging, 5.10.2023, Hamburg]
    • +
  • NFDI4BIOIMAGE data management illustrations by Henning Falk
  • NFDI4Bioimage - TA3-Hackathon - UoC-2023 (Cologne Hackathon)
  • NFDI4Bioimage - TA3-Hackathon - UoC-2023 (Cologne-Hackathon-2023, GitHub repository)
    • +
  • NFDI4Bioimage Calendar 2024 October; original image
  • OME Event Database
  • OME-NGFF: a next-generation file format for expanding bioimaging data-access strategies
    • +
  • OME2024 NGFF Challenge Results
  • OMERO for microscopy research data management
  • Open Image Data Handbook
  • Open Science, Sharing & Licensing
    • @@ -556,16 +558,25 @@

    Contents

  • Tess Search for Data Life Cycle
  • The BioImage Archive – Building a Home for Life-Sciences Microscopy Data
  • The FAIR Guiding Principles for scientific data management and stewardship
    • +
  • The Information Infrastructure for BioImage Data (I3D:bio) project to advance FAIR microscopy data management for the community
    • +
  • The role of Helmholtz Centers in NFDI4BIOIMAGE - A national consortium enhancing FAIR data management for microscopy and bioimage analysis
  • Thinking data management on different scales
    • +
  • Towards Preservation of Life Science Data with NFDI4BIOIMAGE
  • Train-the-Trainer Concept on Research Data Management
  • Using Glittr.org to find, compare and re-use online training materials
  • Who you gonna call? - Data Stewards to the rescue
  • [CORDI 2023] Zarr: A Cloud-Optimized Storage for Interactive Access of Large Arrays
    • +
  • [Community Meeting 2024] Overview Team Image Data Analysis and Management
    • +
  • [ELMI 2024] AI’s Dirty Little Secret: Without
  • [ELMI 2024] AI’s Dirty Little Secret: Without FAIR Data, It’s Just Fancy Math
  • [N4BI AHM] Welcome to BioImage Town
  • [SWAT4HCLS 2023] NFDI4BIOIMAGE: Perspective for a national bioimage standard
  • [Short Talk] NFDI4BIOIMAGE - A consortium in the National Research Data Infrastructure
    • +
  • [Workshop Material] Fit for OMERO - How imaging facilities and IT departments work together to enable RDM for bioimaging, October 16-17, 2024, Heidelberg
    • +
  • [Workshop] Bioimage data management and analysis with OMERO
    • +
  • [Workshop] Research Data Management for Microscopy and BioImage Analysis
  • cba-support-template
    • +
  • ome2024-ngff-challenge
  • re3data.org - registry of Research Data Repositories
    • @@ -578,8 +589,8 @@

    Contents

    -
    -

    Research data management (110)#

    +
    +

    Research data management (128)#

    “ZENODO und Co.” Was bringt und wer braucht ein Repositorium?#

    Elfi Hesse, Jan-Christoph Deinert, Christian Löschen

    @@ -619,6 +630,20 @@

    A call for public archives for biological image datahttps://www.nature.com/articles/s41592-018-0195-8

    +
    +

    A journey to FAIR microscopy data#

    +

    Stefanie Weidtkamp-Peters, Janina Hanne, Christian Schmidt

    +

    Published 2023-05-03

    +

    Licensed CC-BY-4.0

    +

    Oral presentation, 32nd MoMAN “From Molecules to Man” Seminar, Ulm, online. Monday February 6th, 2023

    +

    Abstract:

    +

    Research data management is essential in nowadays research, and one of the big opportunities to accelerate collaborative and innovative scientific projects. To achieve this goal, all our data needs to be FAIR (findable, accessible, interoperable, reproducible). For data acquired on microscopes, however, a common ground for FAIR data sharing is still to be established. Plenty of work on file formats, data bases, and training needs to be performed to highlight the value of data sharing and exploit its potential for bioimaging data.

    +

    In this presentation, Stefanie Weidtkamp-Peters will introduce the challenges for bioimaging data management, and the necessary steps to achieve data FAIRification. German BioImaging - GMB e.V., together with other institutions, contributes to this endeavor. Janina Hanne will present how the network of imaging core facilities, research groups and industry partners is key to the German bioimaging community’s aligned collaboration toward FAIR bioimaging data. These activities have paved the way for two data management initiatives in Germany: I3D:bio (Information Infrastructure for BioImage Data) and NFDI4BIOIMAGE, a consortium of the National Research Data Infrastructure. Christian Schmidt will introduce the goals and measures of these initiatives to the benefit of imaging scientist’s work and everyday practice.  

    +

    Tags: Nfdi4Bioimage, Research Data Management

    +

    https://zenodo.org/records/7890311

    +

    https://doi.org/10.5281/zenodo.7890311

    +
    +

    A practical guide to bioimaging research data management in core facilities#

    Christian Schmidt, Tom Boissonnet, Julia Dohle, Karen Bernhardt, Elisa Ferrando-May, Tobias Wernet, Roland Nitschke, Susanne Kunis, Stefanie Weidtkamp-Peters

    @@ -650,6 +675,17 @@

    Alles meins – oder!? Urheberrechte klären für Forschungsdatenhttps://doi.org/10.5281/zenodo.11472148

    +
    +

    Angebote der NFDI für die Forschung im Bereich Zoologie#

    +

    Birgitta König-Ries, Robert Haase, Daniel Nüst, Konrad Förstner, Judith Sophie Engel

    +

    Published 2024-12-04

    +

    Licensed CC-BY-4.0

    +

    In diesem Slidedeck geben wir einen Einblick in Angebote und Dienste der Nationalen Forschungsdaten Infrastruktur (NFDI), die Relevant für die Zoologie und angrenzende Disziplinen relevant sein könnten.

    +

    Tags: Nfdi4Bioimage, Research Data Management

    +

    https://zenodo.org/records/14278058

    +

    https://doi.org/10.5281/zenodo.14278058

    +
    +

    Best practice data life cycle approaches for the life sciences#

    Philippa C. Griffin, Jyoti Khadake, Kate S. LeMay, Suzanna E. Lewis, Sandra Orchard, et al.

    @@ -676,12 +712,22 @@

    Bio-Image Data Strudel for Workshop on Research Data Management in TU Dresde

    Published 2023-11-08

    Licensed CC-BY-4.0

    This presentation gives a short outline of the complexity of data and metadata in the bioimaging universe. It introduces NFDI4BIOIMAGE as a newly formed consortium as part of the German ‘Nationale Forschungsdateninfrastruktur’ (NFDI) and its goals and tools for data management including its current members on TU Dresden campus.  

    -

    Tags: Research Data Management, Tu Dresden, Bioimage Data, Nfdi4Bioimage

    -

    Content type: Slide

    +

    Tags: Research Data Management, Nfdi4Bioimage

    +

    Content type: Slides

    https://zenodo.org/records/10083555

    https://doi.org/10.5281/zenodo.10083555

    +
    +

    Bio-image Data Science#

    +

    Robert Haase

    +

    Licensed CC-BY-4.0

    +

    This repository contains training resources for Students at Uni Leipzig who want to dive into bio-image data science with Python.

    +

    Tags: Research Data Management, Artificial Intelligence, Bioimage Analysis, Python

    +

    Content type: Notebook

    +

    ScaDS/BIDS-lecture-2024

    +
    +

    Bring your own data workshops#

    Tags: Bioimage Analysis, Research Data Management

    @@ -702,7 +748,7 @@

    Challenges and opportunities for bio-image analysis core-facilitiesRobert Haase

    Licensed CC-BY-4.0

    Tags: Research Data Management, Bioimage Analysis, Nfdi4Bioimage

    -

    Content type: Slide

    +

    Content type: Slides

    https://f1000research.com/slides/12-1054

    @@ -716,13 +762,24 @@

    Challenges and opportunities for bioimage analysis core-facilitieshttps://onlinelibrary.wiley.com/doi/full/10.1111/jmi.13192

    +
    +

    Collaborative Working and Version Control with git[hub]#

    +

    Robert Haase

    +

    Published 2024-01-10

    +

    Licensed CC-BY-4.0

    +

    This slide deck introduces the version control tool git, related terminology and the Github Desktop app for managing files in Git[hub] repositories. We furthermore dive into:* Working with repositories* Collaborative with others* Github-Zenodo integration* Github pages* Artificial Intelligence answering Github Issues

    +

    Tags: Nfdi4Bioimage, Globias, Research Data Management, Research Software Management

    +

    https://zenodo.org/records/14626054

    +

    https://doi.org/10.5281/zenodo.14626054

    +
    +

    Collaborative bio-image analysis script editing with git#

    Robert Haase

    Licensed CC-BY-4.0

    Introduction to version control using git for collaborative, reproducible script editing.

    Tags: Sharing, Research Data Management

    -

    Content type: Blog

    +

    Content type: Blog Post

    https://focalplane.biologists.com/2021/09/04/collaborative-bio-image-analysis-script-editing-with-git/

    @@ -739,7 +796,7 @@

    Combining the BIDS and ARC Directory Structures for Multimodal Research Data

    Community-developed checklists for publishing images and image analyses#

    Beth Cimini et al.

    -

    Licensed BSD LICENSE

    +

    Licensed BSD-3-CLAUSE

    This book is a companion to the Nature Methods publication Community-developed checklists for publishing images and image analyses. In this paper, members of QUAREP-LiMi have proposed 3 sets of standards for publishing image figures and image analysis - minimal requirements, recommended additions, and ideal comprehensive goals. By following this guidance, we hope to remove some of the stress non-experts may face in determining what they need to do, and we also believe that researchers will find their science more interpretable and more reproducible.

    Tags: Bioimage Analysis, Research Data Management

    Content type: Notebook, Collection

    @@ -765,8 +822,8 @@

    Creating a Research Data Management Plan using chatGPTPublished 2023-11-06

    Licensed CC-BY-4.0

    In this blog post the author demonstrates how chatGPT can be used to combine a fictive project description with a DMP specification to produce a project-specific DMP.

    -

    Tags: Research Data Management, Large Language Models, Artificial Intelligence

    -

    Content type: Blog

    +

    Tags: Research Data Management, Artificial Intelligence

    +

    Content type: Blog Post

    https://focalplane.biologists.com/2023/11/06/creating-a-research-data-management-plan-using-chatgpt/

    @@ -807,7 +864,7 @@

    Data Stewardship WizardData handling in large-scale electron microscopy#

    Job Fermie

    Tags: Research Data Management

    -

    Content type: Blog

    +

    Content type: Blog Post

    https://blog.delmic.com/data-handling-in-large-scale-electron-microscopy

    @@ -825,7 +882,7 @@

    Data life cycle#

    Published 2023-07-05

    Licensed CC-BY-SA-4.0

    -

    Tags: Research Data Management, Bioimage Analysis, Image Data Management, Open Science

    +

    Tags: Research Data Management, Bioimage Analysis, Open Science

    Content type: Slides, Presentation

    https://omero-fbi.fr/slides/elmi23_cfd/main.html#/title-slide

    @@ -835,7 +892,7 @@

    DataPLANT knowledge basehttps://nfdi4plants.org/nfdi4plants.knowledgebase/index.html

    @@ -984,7 +1041,7 @@

    FAIR High Content Screening in Bioimaginghttps://www.nature.com/articles/s41597-023-02367-w

    @@ -1023,7 +1080,7 @@

    Five great reasons to share your research datahttps://web.library.uq.edu.au/blog/2022/03/five-great-reasons-share-your-research-data

    @@ -1098,7 +1155,7 @@

    I3D bio – Information Infrastructure for BioImage Data - Bioimage Metadata

    Christian Schmidt

    Licensed UNKNOWN

    A Microscopy Research Data Management Resource.

    -

    Tags: Metadata, I3Dbio, Research Data Management, Bioimage Data

    +

    Tags: Metadata, I3Dbio, Research Data Management

    Content type: Collection

    https://gerbi-gmb.de/i3dbio/i3dbio-rdm/i3dbio-bioimage-metadata/

    @@ -1119,7 +1176,7 @@

    I3D:bio’s OMERO training material: Re-usable, adjustable, multi-purpose sl

    Licensed CC-BY-4.0

    The open-source software OME Remote Objects (OMERO) is a data management software that allows storing, organizing, and annotating bioimaging/microscopy data. OMERO has become one of the best-known systems for bioimage data management in the bioimaging community. The Information Infrastructure for BioImage Data (I3D:bio) project facilitates the uptake of OMERO into research data management (RDM) practices at universities and research institutions in Germany. Since the adoption of OMERO into researchers’ daily routines requires intensive training, a broad portfolio of training resources for OMERO is an asset. On top of using the OMERO guides curated by the Open Microscopy Environment Consortium (OME) team, imaging core facility staff at institutions where OMERO is used often prepare additional material tailored to be applicable for their own OMERO instances. Based on experience gathered in the Research Data Management for Microscopy group (RDM4mic) in Germany, and in the use cases in the I3D:bio project, we created a set of reusable, adjustable, openly available slide decks to serve as the basis for tailored training lectures, video tutorials, and self-guided instruction manuals directed at beginners in using OMERO. The material is published as an open educational resource complementing the existing resources for OMERO contributed by the community.

    Tags: OMERO, Research Data Management, Nfdi4Bioimage, I3Dbio

    -

    Content type: Slide, Video

    +

    Content type: Slides, Video

    https://zenodo.org/records/8323588

    https://www.youtube.com/playlist?list=PL2k-L-zWPoR7SHjG1HhDIwLZj0MB_stlU

    https://doi.org/10.5281/zenodo.8323588

    @@ -1131,7 +1188,7 @@

    If you license it, it’ll be harder to steal it. Why we should license our

    Licensed CC-BY-4.0

    Blog post about why we should license our work and what is important when choosing a license.

    Tags: Licensing, Research Data Management

    -

    Content type: Blog

    +

    Content type: Blog Post

    https://focalplane.biologists.com/2023/05/06/if-you-license-it-itll-be-harder-to-steal-it-why-we-should-license-our-work/

    @@ -1146,6 +1203,58 @@

    Introduction to Research Data Management and Open Researchhttps://zenodo.org/records/4778265

    +
    +

    Key-Value pair template for annotation in OMERO for light microscopy data acquired with AxioScan7 - Core Facility Cellular Imaging (CFCI)#

    +

    Silke Tulok, Anja Nobst, Anett Jannasch, Tom Boissonnet, Gunar Fabig

    +

    Published 2024-06-28

    +

    Licensed CC-BY-4.0

    +

    This Key-Value pair template is used for the data documentation during imaging experiments and the later data annotation in OMERO. It is tailored for the usage and image acquisition at the slide scanning system Zeiss AxioScan 7 in the Core Facility Cellular Imaging (CFCI). It contains important metadata of the imaging experiment, which are not saved in the corresponding imaging files. All users of the Core Facility Cellular Imaging are trained to use that file to document their imaging parameters directly during the data acquisition with the possibility for a later upload to OMERO. Furthermore, there is a corresponding public example image used in the publication “Setting up an institutional OMERO environment for bioimage data: perspectives from both facility staff and users” and is available here: +https://omero.med.tu-dresden.de/webclient/?show=image-33248 +This template was developed by the CFCI staff during the setup and usage of the AxioScan 7 and is based on the REMBI recommendations (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8606015). +With this template it is possible to create a csv-file, that can be used to annotate an image or dataset in OMERO using the annotation script (ome/omero-scripts). +How to use:

    +

    fill the template sheet  with your metadata +select and copy the data range containing the Keys and Values +open a new excel sheet and paste transpose in cell A1  +Important: cell A1 contains always the name ‘dataset’ and cell A2 contains the exact name of the image/dataset, which should be annotated in OMERO +save the new excel sheet in csv-file (comma separated values) format

    +

    An example can be seen in sheet 3 ‘csv_AxioScan’. +Important note: The code has to be 8-Bit UCS transformation format (UTF-8) otherwise several characters (for example µ, %,°) might be not able to decode by the annotation script. We encountered this issue with old Microsoft-Office versions (MS Office 2016).  +Note: By filling the values in the excel sheet, avoid the usage of comma as decimal delimiter. +See cross reference: +10.5281/zenodo.12547566 Key-Value pair template for annotation of datasets in OMERO for light- and electron microscopy data within the research group of Prof. Mueller-Reichert +10.5281/zenodo.12546808 Key-Value pair template for annotation of datasets in OMERO (PERIKLES study)

    +

    Tags: Nfdi4Bioimage, Research Data Management

    +

    https://zenodo.org/records/12578084

    +

    https://doi.org/10.5281/zenodo.12578084

    +
    +
    +
    +

    Key-Value pair template for annotation of datasets in OMERO (PERIKLES study)#

    +

    Anett Jannasch, Silke Tulok, Vanessa Aphaia Fiona Fuchs, Tom Boissonnet, Christian Schmidt, Michele Bortolomeazzi, Gunar Fabig, Chukwuebuka Okafornta

    +

    Published 2024-06-26

    +

    Licensed CC-BY-4.0

    +

    This is a Key-Value pair template used for the annotation of datasets in OMERO. It is tailored for a research study (PERIKLES project) on the biocompatibility of newly designed biomaterials out of pericardial tissue for cardiovascular substitutes (https://doi.org/10.1063/5.0182672) conducted in the research department of Cardiac Surgery at the Faculty of Medicine Carl Gustav Carus at the Technische Universität Dresden . A corresponding public example dataset is used in the publication “Setting up an institutional OMERO environment for bioimage data: perspectives from both facility staff and users” and is available here +(https://omero.med.tu-dresden.de/webclient/?show=dataset-1557). +The template is based on the REMBI recommendations (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8606015) and it was developed during the PoL-Bio-Image Analysis Symposium in Dresden Aug 28th- Sept 1th 2023.  +With this template it is possible to create a csv-file, that can be used to annotate a dataset in OMERO using the annotation script (ome/omero-scripts). +How to use: +select and copy the data range containing Keys and Values +open a new excel sheet and paste transpose in column B1 +type in A1 ‘dataset’ +insert in A2 the exact name of the dataset, which should be annotated in OMERO +save the new excel sheet in csv- (comma seperated values) file format

    +

    Example can be seen in sheet 1 ‘csv import’. Important note; the code has to be 8-Bit UCS transformation format (UTF-8) otherwise several characters (for example µ, %,°) might not be able to decode by the annotation script. We encountered this issue with old Microsoft Office versions (e.g. MS Office 2016).  +Note: By filling the values in the excel sheet, avoid the usage of decimal delimiter. +  +See cross reference: +10.5281/zenodo.12547566 Key-Value pair template for annotation of datasets in OMERO (light- and electron microscopy data within the research group of Prof. Mueller-Reichert) +10.5281/zenodo.12578084 Key-Value pair template for annotation in OMERO for light microscopy data acquired with AxioScan7 - Core Facility Cellular Imaging (CFCI)

    +

    Tags: Nfdi4Bioimage, Research Data Management

    +

    https://zenodo.org/records/12546808

    +

    https://doi.org/10.5281/zenodo.12546808

    +
    +

    Kollaboratives Arbeiten und Versionskontrolle mit Git#

    Robert Haase

    @@ -1204,7 +1313,7 @@

    Microscopy-BIDS - An Extension to the Brain Imaging Data Structure for Micro

    Published 2022-04-19

    Licensed CC-BY-4.0

    The Brain Imaging Data Structure (BIDS) is a specification for organizing, sharing, and archiving neuroimaging data and metadata in a reusable way.

    -

    Tags: Research Data Management, Image Data Management, Bioimage Data

    +

    Tags: Research Data Management

    Content type: Publication

    https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2022.871228/full

    @@ -1234,8 +1343,8 @@

    NFDI4BIOIMAGE - An Initiative for a National Research Data Infrastructure fo

    Published 2021-04-29

    Licensed CCY-BY-SA-4.0

    Align existing and establish novel services & solutions for data management tasks throughout the bioimage data lifecycle.

    -

    Tags: Nfdi4Bioimage, Image Data Management, Bioimage Data, Research Data Management

    -

    Content type: Conference Abstract, Slide

    +

    Tags: Nfdi4Bioimage, Research Data Management

    +

    Content type: Conference Abstract, Slides

    https://doi.org/10.11588/heidok.00029489

    @@ -1259,6 +1368,18 @@

    NFDI4BIOIMAGE - National Research Data Infrastructure for Microscopy and Bio

    https://zenodo.org/doi/10.5281/zenodo.8414318

    +
    +

    NFDI4BIOIMAGE data management illustrations by Henning Falk#

    +

    NFDI4BIOIMAGE Consortium

    +

    Published 2024-11-29

    +

    Licensed CC-BY-4.0

    +

    These illustrations were contracted by the Heinrich Heine University Düsseldorf in the frame of the consortium NFDI4BIOIMAGE from Henning Falk for the purpose of education and public outreach. The illustrations are free to use under a CC-BY 4.0 license.AttributionPlease include an attribution similar to: “Data annoation matters”, NFDI4BIOIMAGE Consortium (2024): NFDI4BIOIMAGE data management illustrations by Henning Falk, Zenodo, https://doi.org/10.5281/zenodo.14186100, is used under a CC-BY 4.0 license. Modifications to this illustration include cropping. + 

    +

    Tags: Nfdi4Bioimage, Research Data Management

    +

    https://zenodo.org/records/14186101

    +

    https://doi.org/10.5281/zenodo.14186101

    +
    +

    NFDI4Bioimage - TA3-Hackathon - UoC-2023 (Cologne Hackathon)#

    Mohamed M. Abdrabbou, Mehrnaz Babaki, Tom Boissonnet, Michele Bortolomeazzi, Eik Dahms, Vanessa A. F. Fuchs, Moritz Hoevels, Niraj Kandpal, Christoph Möhl, Joshua A. Moore, Astrid Schauss, Andrea Schrader, Torsten Stöter, Julia Thönnißen, Monica Valencia-S., H. Lukas Weil, Jens Wendt and Peter Zentis

    @@ -1279,6 +1400,17 @@

    NFDI4Bioimage - TA3-Hackathon - UoC-2023 (Cologne-Hackathon-2023, GitHub rep

    https://zenodo.org/doi/10.5281/zenodo.10609770

    +
    +

    NFDI4Bioimage Calendar 2024 October; original image#

    +

    Christian Jüngst, Peter Zentis

    +

    Published 2024-09-25

    +

    Licensed CC-BY-4.0

    +

    Raw microscopy image from the NFDI4Bioimage calendar October 2024

    +

    Tags: Nfdi4Bioimage, Research Data Management

    +

    https://zenodo.org/records/13837146

    +

    https://doi.org/10.5281/zenodo.13837146

    +
    +

    OME Event Database#

    Tags: OMERO, Research Data Management

    @@ -1295,6 +1427,19 @@

    OME-NGFF: a next-generation file format for expanding bioimaging data-access

    https://www.nature.com/articles/s41592-021-01326-w

    +
    +

    OME2024 NGFF Challenge Results#

    +

    Josh Moore

    +

    Published 2024-11-01

    +

    Licensed CC-BY-4.0

    +

    Presented at the 2024 FoundingGIDE event in Okazaki, Japan: https://founding-gide.eurobioimaging.eu/event/foundinggide-community-event-2024/ +Note: much of the presentation was a demonstration of the OME2024-NGFF-Challenge – https://ome.github.io/ome2024-ngff-challenge/ especially of querying an extraction of the metadata (ome/ome2024-ngff-challenge-metadata) + 

    +

    Tags: Nfdi4Bioimage, Research Data Management

    +

    https://zenodo.org/records/14234608

    +

    https://doi.org/10.5281/zenodo.14234608

    +
    +

    OMERO for microscopy research data management#

    Thomas Zobel, Sarah Weischner, Jens Wendt

    @@ -1340,7 +1485,7 @@

    Photonic data analysis in 2050#

    Jennifer Waters

    Tags: Research Data Management

    -

    Content type: Blog

    +

    Content type: Blog Post

    https://datamanagement.hms.harvard.edu/news/promoting-data-management-nikon-imaging-center-and-cell-biology-microscopy-facility

    @@ -1402,7 +1547,7 @@

    RDM_system_connectorSaibotMagd

    Licensed UNKNOWN

    This tool is intended to link different research data management platforms with each other.

    -

    Tags: Research Data Management, Image Data Management

    +

    Tags: Research Data Management

    Content type: Github Repository

    SaibotMagd/RDM_system_connector

    @@ -1413,7 +1558,7 @@

    REMBI - Recommended Metadata for Biological Images—enabling reuse of micro

    Published 2021-05-21

    Licensed UNKNOWN

    Bioimaging data have significant potential for reuse, but unlocking this potential requires systematic archiving of data and metadata in public databases. The authors propose draft metadata guidelines to begin addressing the needs of diverse communities within light and electron microscopy.

    -

    Tags: Metadata, Bioimage Data, Image Data Management, Research Data Management

    +

    Tags: Metadata, Research Data Management

    Content type: Publication

    https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8606015/

    https://www.nature.com/articles/s41592-021-01166-8

    @@ -1424,7 +1569,7 @@

    REMBI - Recommended Metadata for Biological Images—enabling reuse of micro

    REMBI Overview#

    Licensed CC0-1.0

    Recommended Metadata for Biological Images (REMBI) provides guidelines for metadata for biological images to enable the FAIR sharing of scientific data.

    -

    Tags: FAIR-Principles, Metadata, Image Data Management, Research Data Management, Bioimage Data

    +

    Tags: FAIR-Principles, Metadata, Research Data Management

    Content type: Collection

    https://www.ebi.ac.uk/bioimage-archive/rembi-help-overview/

    @@ -1443,7 +1588,7 @@

    Research Data Management Seminar - Slideshttps://zenodo.org/record/6602101

    https://doi.org/10.5281/zenodo.6602101

    @@ -1474,7 +1619,7 @@

    Research data management for bioimaging - the 2021 NFDI4BIOIMAGE community s

    Published 2022-09-20

    Licensed CC-BY-4.0

    As an initiative within Germany’s National Research Data Infrastructure, the authors conducted this community survey in summer 2021 to assess the state of the art of bioimaging RDM and the community needs.

    -

    Tags: Research Data Management, Image Data Management, Bioimage Data

    +

    Tags: Research Data Management

    Content type: Publication

    https://f1000research.com/articles/11-638/v2

    @@ -1501,7 +1646,7 @@

    Setting up a data management infrastructure for bioimaging#

    Elisabeth Kugler

    Tags: Sharing, Research Data Management

    -

    Content type: Blog

    +

    Content type: Blog Post

    https://focalplane.biologists.com/2023/07/26/sharing-your-poster-on-figshare/

    @@ -1511,7 +1656,7 @@

    Sharing and licensing materialhttps://f1000research.com/slides/10-519

    @@ -1521,7 +1666,7 @@

    Sharing research data with Zenodozenodo.org

    Tags: Sharing, Research Data Management

    -

    Content type: Blog

    +

    Content type: Blog Post

    https://focalplane.biologists.com/2023/02/15/sharing-research-data-with-zenodo/

    @@ -1547,23 +1692,37 @@

    Software Citation with CITATION.cff

    Structuring of Data and Metadata in Bioimaging: Concepts and technical Solutions in the Context of Linked Data#

    -

    Susanne Kunis

    -

    Published 2022-08-24

    -

    Licensed CC-BY-4.0

    -

    guided walkthrough of poster at https://doi.org/10.5281/zenodo.6821815

    -

    which provides an overview of contexts, frameworks, and models from the world of bioimage data as well as metadata and the techniques for structuring this data as Linked Data.

    -

    You can also watch the video in the browser on the I3D:bio website.

    +

    Sarah Weischer, Jens Wendt, Thomas Zobel

    +

    Published 2022-07-12

    +

    Licensed CC-BY-4.0

    +

    Provides an overview of contexts, frameworks, and models from the world of bioimage data as well as metadata. Visualizes the techniques for structuring this data as Linked Data. (Walkthrough Video: https://doi.org/10.5281/zenodo.7018928 )

    +

    Content:

    +
    Types of metadata
+Data formats
+Data Models Microscopy Data
+Tools to edit/gather metadata
+ISA Framework
+FDO Framework
+Ontology
+RDF
+JSON-LD
+SPARQL
+Knowledge Graph
+Linked Data
+Smart Data
+...
+
    +

    Tags: Nfdi4Bioimage, Research Data Management

    -

    Content type: Video

    -

    https://zenodo.org/record/7018929

    -

    https://doi.org/10.5281/zenodo.7018929

    +

    https://zenodo.org/records/7018750

    +

    https://doi.org/10.5281/zenodo.7018750

    Submitting data to the BioImage Archive#

    Licensed CC0-1.0

    To submit, you’ll need to register an account, organise and upload your data, prepare a file list, and then submit using our web submission form. These steps are explained here.

    -

    Tags: Research Data Management, Image Data Management, Bioimage Data

    +

    Tags: Research Data Management

    Content type: Tutorial, Video

    https://www.ebi.ac.uk/bioimage-archive/submit/

    @@ -1594,7 +1753,7 @@

    The BioImage Archive – Building a Home for Life-Sciences Microscopy DataPublished 2022-06-22

    Licensed UNKNOWN

    The BioImage Archive is a new archival data resource at the European Bioinformatics Institute (EMBL-EBI).

    -

    Tags: Image Data Management, Research Data Management, Bioimage Data

    +

    Tags: Research Data Management

    Content type: Publication

    https://www.sciencedirect.com/science/article/pii/S0022283622000791?via%3Dihub

    https://doi.org/10.1016/j.jmb.2022.167505

    @@ -1612,6 +1771,28 @@

    The FAIR Guiding Principles for scientific data management and stewardshiphttps://doi.org/10.1038/sdata.2016.18

    +
    +

    The Information Infrastructure for BioImage Data (I3D:bio) project to advance FAIR microscopy data management for the community#

    +

    Christian Schmidt, Michele Bortolomeazzi, Tom Boissonnet, Julia Dohle, Tobias Wernet, Janina Hanne, Roland Nitschke, Susanne Kunis, Karen Bernhardt, Stefanie Weidtkamp-Peters, Elisa Ferrando-May

    +

    Published 2024-03-04

    +

    Licensed CC-BY-4.0

    +

    Research data management (RDM) in microscopy and image analysis is a challenging task. Large files in proprietary formats, complex N-dimensional array structures, and various metadata models and formats can make image data handling inconvenient and difficult. For data organization, annotation, and sharing, researchers need solutions that fit everyday practice and comply with the FAIR (Findable, Accessible, Interoperable, Reusable) principles. International community-based efforts have begun creating open data models (OME), an open file format and translation library (OME-TIFF, Bio-Formats), data management software platforms, and microscopy metadata recommendations and annotation tools. Bringing these developments into practice requires support and training. Iterative feedback and tool improvement is needed to foster practical adoption by the scientific community. The Information Infrastructure for BioImage Data (I3D:bio) project works on guidelines, training resources, and practical assistance for FAIR microscopy RDM adoption with a focus on the management platform OMERO and metadata annotations.

    +

    Tags: Nfdi4Bioimage, Research Data Management

    +

    https://zenodo.org/records/10805204

    +

    https://doi.org/10.5281/zenodo.10805204

    +
    +
    +
    +

    The role of Helmholtz Centers in NFDI4BIOIMAGE - A national consortium enhancing FAIR data management for microscopy and bioimage analysis#

    +

    Riccardo Massei, Christian Schmidt, Michele Bortolomeazzi, Julia Thoennissen, Jan Bumberger, Timo Dickscheid, Jan-Philipp Mallm, Elisa Ferrando-May

    +

    Published 2024-06-06

    +

    Licensed CC-BY-4.0

    +

    Germany’s National Research Data Infrastructure (NFDI) aims to establish a sustained, cross-disciplinary research data management (RDM) infrastructure that enables researchers to handle FAIR (findable, accessible, interoperable, reusable) data. While FAIR principles have been adopted by funders, policymakers, and publishers, their practical implementation remains an ongoing effort. In the field of bio-imaging, harmonization of data formats, metadata ontologies, and open data repositories is necessary to achieve FAIR data. The NFDI4BIOIMAGE was established to address these issues and develop tools and best practices to facilitate FAIR microscopy and image analysis data in alignment with international community activities. The consortium operates through its Data Stewards team to provide expertise and direct support to help overcome RDM challenges. The three Helmholtz Centers in NFDI4BIOIMAGE aim to collaborate closely with other centers and initiatives, such as HMC, Helmholtz AI, and HIP. Here we present NFDI4BIOIMAGE’s work and its significance for research in Helmholtz and beyond

    +

    Tags: Nfdi4Bioimage, Research Data Management

    +

    https://zenodo.org/records/11501662

    +

    https://doi.org/10.5281/zenodo.11501662

    +
    +

    Thinking data management on different scales#

    Susanne Kunis

    @@ -1622,6 +1803,17 @@

    Thinking data management on different scaleshttps://zenodo.org/doi/10.5281/zenodo.8329305

    +
    +

    Towards Preservation of Life Science Data with NFDI4BIOIMAGE#

    +

    Robert Haase

    +

    Published 2024-09-03

    +

    Licensed CC-BY-4.0

    +

    This talk will present the initiatives of the NFDI4BioImage consortium aimed at the long-term preservation of life science data. We will discuss our efforts to establish metadata standards, which are crucial for ensuring data reusability and integrity. The development of sustainable infrastructure is another key focus, enabling seamless data integration and analysis in the cloud. We will take a look at how we manage training materials and communicate with our community. Through these actions, NFDI4BioImage seeks to enable FAIR bioimage data management for German researchers, across disciplines and embedded in the international framework.

    +

    Tags: Nfdi4Bioimage, Research Data Management

    +

    https://zenodo.org/records/13640979

    +

    https://doi.org/10.5281/zenodo.13640979

    +
    +

    Train-the-Trainer Concept on Research Data Management#

    Katarzyna Biernacka, Maik Bierwirth, Petra Buchholz, Dominika Dolzycka, Kerstin Helbig, Janna Neumann, Carolin Odebrecht, Cord Wiljes, Ulrike Wuttke

    @@ -1640,7 +1832,7 @@

    Using Glittr.org

    Geert van Geest, Yann Haefliger, Monique Zahn-Zabal, Patricia M. Palagi

    Licensed CC-BY-4.0

    Glittr.org is a platform that aggregates and indexes training materials on computational life sciences from public git repositories, making it easier for users to find, compare, and analyze these resources based on various metrics. By providing insights into the availability of materials, collaboration patterns, and licensing practices, Glittr.org supports adherence to the FAIR principles, benefiting the broader life sciences community.

    -

    Tags: Training, Bioimage Analysis, Research Data Management

    +

    Tags: Bioimage Analysis, Research Data Management

    Content type: Publication, Preprint

    https://www.biorxiv.org/content/10.1101/2024.08.20.608021v1

    @@ -1665,6 +1857,30 @@

    [CORDI 2023] Zarr: A Cloud-Optimized Storage for Interactive Access of Large

    https://zenodo.org/doi/10.5281/zenodo.8340247

    +
    +

    [Community Meeting 2024] Overview Team Image Data Analysis and Management#

    +

    Susanne Kunis, Thomas Zobel

    +

    Published 2024-03-08

    +

    Licensed CC-BY-4.0

    +

    Overview of Activities of the Team Image Data Analysis and Management of German BioImaging e.V. + 

    +

    Tags: Nfdi4Bioimage, Research Data Management

    +

    https://zenodo.org/records/10796364

    +

    https://doi.org/10.5281/zenodo.10796364

    +
    +
    +
    +

    [ELMI 2024] AI’s Dirty Little Secret: Without#

    +

    FAIR Data, It’s Just Fancy Math

    +

    Josh Moore, Susanne Kunis

    +

    Published 2024-05-21

    +

    Licensed CC-BY-4.0

    +

    Poster presented at the European Light Microscopy Initiative meeting in Liverpool (https://www.elmi2024.org/)

    +

    Tags: Nfdi4Bioimage, Research Data Management

    +

    https://zenodo.org/records/11235513

    +

    https://doi.org/10.5281/zenodo.11235513

    +
    +

    [ELMI 2024] AI’s Dirty Little Secret: Without FAIR Data, It’s Just Fancy Math#

    Josh Moore, Susanne Kunis

    @@ -1707,6 +1923,79 @@

    [Short Talk] NFDI4BIOIMAGE - A consortium in the National Research Data Infr

    https://zenodo.org/doi/10.5281/zenodo.10939519

    +
    +

    [Workshop Material] Fit for OMERO - How imaging facilities and IT departments work together to enable RDM for bioimaging, October 16-17, 2024, Heidelberg#

    +

    Tom Boissonnet, Bettina Hagen, Susanne Kunis, Christian Schmidt, Stefanie Weidtkamp-Peters

    +

    Published 2024-11-18

    +

    Licensed CC-BY-4.0

    +

    Fit for OMERO: How imaging facilities and IT departments work together to enable RDM for bioimaging +Description: +Research data management (RDM) in bioimaging is challenging because of large file sizes, heterogeneous file formats and the variability of imaging methods. The image data management system OMERO (OME Remote Objects) allows for centralized and secure storage, organization, annotation, and interrogation of microscopy data by researchers. It is an internationally well-supported open-source software tool that has become one of the best-known image data management tools among bioimaging scientists. Nevertheless, the de novo setup of OMERO at an institute is a multi-stakeholder process that demands time, funds, organization and iterative implementation. In this workshop, participants learn how to begin setting up OMERO-based image data management at their institution. The topics include:

    +

    Stakeholder identification at the university / research institute +Process management, time line expectations, and resources planning +Learning about each other‘s perspectives on chances and challenges for RDM +Funding opportunities and strategies for IT and imaging core facilities +Hands-on: Setting up an OMERO server in a virtual machine environment

    +

    Target audience: +This workshop was directed at universities and research institutions who consider or plan to implement OMERO, or are in an early phase of implementation. This workshop was intended for teams from IT departments and imaging facilities to participate together with one person from the IT department, and one person from the imaging core facility at the same institution. +The trainers:

    +

    Prof. Dr. Stefanie Weidtkamp-Peters (Imaging Core Facility Head, Center for Advanced Imaging, Heinrich Heine University of Düsseldorf) +Dr. Susanne Kunis (Software architect, OMERO administrator, metadata specialist, University of Osnabrück) +Dr. Tom Boissonnet (OMERO admin and image metadata specialist, Center for Advanced Imaging, Heinrich Heine University of Düsseldorf) +Dr. Bettina Hagen (IT Administration and service specialist, Max Planck Institute for the Biology of Ageing, Cologne)  +Dr. Christian Schmidt (Science Manager for Research Data Management in Bioimaging, German Cancer Research Center (DKFZ), Heidelberg)

    +

    Time and place +The format was a two-day, in-person workshop (October 16-17, 2024). Location: Heidelberg, Germany +Workshop learning goals

    +

    Learn the steps to establish a local RDM environment fit for bioimaging data +Create a network of IT experts and bioimaging specialists for bioimage RDM across institutions +Establish a stakeholder process management for installing OMERO-based RDM +Learn from each other, leverage different expertise +Learn how to train users, establish sustainability strategies, and foster FAIR RDM for bioimaging at your institution

    +

    Tags: Nfdi4Bioimage, Research Data Management

    +

    https://zenodo.org/records/14178789

    +

    https://doi.org/10.5281/zenodo.14178789

    +
    +
    +
    +

    [Workshop] Bioimage data management and analysis with OMERO#

    +

    Riccardo Massei, Michele Bortolomeazzi, Christian Schmidt

    +

    Published 2024-05-13

    +

    Licensed CC-BY-4.0

    +

    Here we share the material used in a workshop held on May 13th, 2024, at the German Cancer Research Center in Heidelberg (on-premise) +Description:Microscopy experiments generate information-rich, multi-dimensional data, allowing us to investigate biological processes at high spatial and temporal resolution. Image processing and analysis is a standard procedure to retrieve quantitative information from biological imaging. Due to the complex nature of bioimaging files that often come in proprietary formats, it can be challenging to organize, structure, and annotate bioimaging data throughout a project. Data often needs to be moved between collaboration partners, transformed into open formats, processed with a variety of software tools, and exported to smaller-sized images for presentation. The path from image acquisition to final publication figures with quantitative results must be documented and reproducible. +In this workshop, participants learn how to use OMERO to organize their data and enrich the bioimage data with structured metadata annotations.We also focus on image analysis workflows in combination with OMERO based on the Fiji/ImageJ software and using Jupyter Notebooks. In the last part, we explore how OMERO can be used to create publication figures and prepare bioimage data for publication in a suitable repository such as the Bioimage Archive. +Module 1 (9 am - 10.15 am): Basics of OMERO, data structuring and annotation +Module 2 (10.45 am - 12.45 pm): OMERO and Fiji +Module 3 (1.45 pm - 3.45 pm): OMERO and Jupyter Notebooks +Module 4 (4.15 pm - 6. pm): Publication-ready figures and data with OMERO +The target group for this workshopThis workshop is directed at researchers at all career levels who plan to or have started to use OMERO for their microscopy research data management. We encourage the workshop participants to bring example data from their research to discuss suitable metadata annotation for their everyday practice. +Prerequisites:Users should bring their laptops and have access to the internet through one of the following options:- eduroam- institutional WiFi- VPN connection to their institutional networks to access OMERO +Who are the trainers? +Dr. Riccardo Massei (Helmholtz-Center for Environmental Research, UFZ, Leipzig) - Data Steward for Bioimaging Data in NFDI4BIOIMAGE +Dr. Michele Bortolomeazzi (DKFZ, Single cell Open Lab, bioimage data specialist, bioinformatician, staff scientist in the NFDI4BIOIMAGE project) +Dr. Christian Schmidt (Science Manager for Research Data Management in Bioimaging, German Cancer Research Center, Heidelberg, Project Coordinator of the NFDI4BIOIMAGE project)

    +

    Tags: Nfdi4Bioimage, Research Data Management

    +

    https://zenodo.org/records/11350689

    +

    https://doi.org/10.5281/zenodo.11350689

    +
    +
    +
    +

    [Workshop] Research Data Management for Microscopy and BioImage Analysis#

    +

    Christian Schmidt, Tom Boissonnet, Michele Bortolomeazzi, Ksenia Krooß

    +

    Published 2024-09-30

    +

    Licensed CC-BY-4.0

    +

    Research Data Management for Microscopy and BioImage Analysis

    +

    Introduction to BioImaging Research Data Management, NFDI4BIOIMAGE and I3D:bioChristian Schmidt /DKFZ Heidelberg +OMERO as a tool for bioimaging data managementTom Boissonnet /Heinrich-Heine Universität Düsseldorf +Reproducible image analysis workflows with OMERO software APIsMichele Bortolomeazzi /DKFZ Heidelberg +Publishing datasets in public archives for bioimage dataKsenia Krooß /Heinrich-Heine Universität Düsseldorf

    +

    Date & Venue:Thursday, Sept. 26, 5.30 p.m.Haus 22 / Paul Ehrlich Lecture Hall (H22-1)University Hospital Frankfurt

    +

    Tags: Nfdi4Bioimage, Research Data Management

    +

    https://zenodo.org/records/13861026

    +

    https://doi.org/10.5281/zenodo.13861026

    +
    +

    cba-support-template#

    Arif Khan, Christian Tischer, Sebastian Gonzalez, Dominik Kutra, Felix Schneider, et al.

    @@ -1717,6 +2006,17 @@

    cba-support-templatehttps://git.embl.de/grp-cba/cba-support-template

    +
    +

    ome2024-ngff-challenge#

    +

    Will Moore, Josh Moore, sherwoodf, Jean-Marie Burel, Norman Rzepka, dependabot[bot], JensWendt, Joost de Folter, Torsten St\xF6ter, AybukeKY, Eric Perlman, Tom Boissonnet

    +

    Published 2024-08-30T12:00:53+00:00

    +

    Licensed BSD-3-CLAUSE

    +

    Project planning and material repository for the 2024 challenge to generate 1 PB of OME-Zarr data

    +

    Tags: Sharing, Nfdi4Bioimage, Research Data Management

    +

    Content type: Github Repository

    +

    ome/ome2024-ngff-challenge

    +
    +

    re3data.org - registry of Research Data Repositories#

    Licensed CC-BY-4.0

    @@ -1768,11 +2068,11 @@

    re3data.org - re

    next

    -

    Segmentation (5)

    +

    Sharing (12)

     @@ -1797,16 +2097,20 @@

    re3data.org - re
  • 6 Steps Towards Reproducible Research
  • A Cloud-Optimized Storage for Interactive Access of Large Arrays
  • A call for public archives for biological image data
    • +
  • A journey to FAIR microscopy data
  • A practical guide to bioimaging research data management in core facilities
  • Abstract - NFDI Basic Service for Data Management Plans
  • Alles meins – oder!? Urheberrechte klären für Forschungsdaten
    • +
  • Angebote der NFDI für die Forschung im Bereich Zoologie
  • Best practice data life cycle approaches for the life sciences
  • BigDataProcessor2: A free and open-source Fiji plugin for inspection and processing of TB sized image data
  • Bio-Image Data Strudel for Workshop on Research Data Management in TU Dresden Core Facilities
    • +
  • Bio-image Data Science
  • Bring your own data workshops
  • Building a FAIR image data ecosystem for microscopy communities
  • Challenges and opportunities for bio-image analysis core-facilities
  • Challenges and opportunities for bioimage analysis core-facilities
    • +
  • Collaborative Working and Version Control with git[hub]
  • Collaborative bio-image analysis script editing with git
  • Combining the BIDS and ARC Directory Structures for Multimodal Research Data Organization
  • Community-developed checklists for publishing images and image analyses
    • @@ -1846,6 +2150,8 @@

    re3data.org - re
  • I3D:bio’s OMERO training material: Re-usable, adjustable, multi-purpose slides for local user training
  • If you license it, it’ll be harder to steal it. Why we should license our work
  • Introduction to Research Data Management and Open Research
    • +
  • Key-Value pair template for annotation in OMERO for light microscopy data acquired with AxioScan7 - Core Facility Cellular Imaging (CFCI)
    • +
  • Key-Value pair template for annotation of datasets in OMERO (PERIKLES study)
  • Kollaboratives Arbeiten und Versionskontrolle mit Git
  • Leitlinie? Grundsätze? Policy? Richtlinie? – Forschungsdaten-Policies an deutschen Universitäten
  • Lund Declaration on Maximising the Benefits of Research Data
    • @@ -1857,10 +2163,13 @@

    re3data.org - re
  • NFDI4BIOIMAGE - An Initiative for a National Research Data Infrastructure for Microscopy Data
  • NFDI4BIOIMAGE - National Research Data Infrastructure for Microscopy and BioImage Analysis - Online Kick-Off 2023
  • NFDI4BIOIMAGE - National Research Data Infrastructure for Microscopy and BioImage Analysis [conference talk: The Pelagic Imaging Consortium meets Helmholtz Imaging, 5.10.2023, Hamburg]
    • +
  • NFDI4BIOIMAGE data management illustrations by Henning Falk
  • NFDI4Bioimage - TA3-Hackathon - UoC-2023 (Cologne Hackathon)
  • NFDI4Bioimage - TA3-Hackathon - UoC-2023 (Cologne-Hackathon-2023, GitHub repository)
    • +
  • NFDI4Bioimage Calendar 2024 October; original image
  • OME Event Database
  • OME-NGFF: a next-generation file format for expanding bioimaging data-access strategies
    • +
  • OME2024 NGFF Challenge Results
  • OMERO for microscopy research data management
  • Open Image Data Handbook
  • Open Science, Sharing & Licensing
    • @@ -1892,16 +2201,25 @@

    re3data.org - re
  • Tess Search for Data Life Cycle
  • The BioImage Archive – Building a Home for Life-Sciences Microscopy Data
  • The FAIR Guiding Principles for scientific data management and stewardship
    • +
  • The Information Infrastructure for BioImage Data (I3D:bio) project to advance FAIR microscopy data management for the community
    • +
  • The role of Helmholtz Centers in NFDI4BIOIMAGE - A national consortium enhancing FAIR data management for microscopy and bioimage analysis
  • Thinking data management on different scales
    • +
  • Towards Preservation of Life Science Data with NFDI4BIOIMAGE
  • Train-the-Trainer Concept on Research Data Management
  • Using Glittr.org to find, compare and re-use online training materials
  • Who you gonna call? - Data Stewards to the rescue
  • [CORDI 2023] Zarr: A Cloud-Optimized Storage for Interactive Access of Large Arrays
    • +
  • [Community Meeting 2024] Overview Team Image Data Analysis and Management
    • +
  • [ELMI 2024] AI’s Dirty Little Secret: Without
  • [ELMI 2024] AI’s Dirty Little Secret: Without FAIR Data, It’s Just Fancy Math
  • [N4BI AHM] Welcome to BioImage Town
  • [SWAT4HCLS 2023] NFDI4BIOIMAGE: Perspective for a national bioimage standard
  • [Short Talk] NFDI4BIOIMAGE - A consortium in the National Research Data Infrastructure
    • +
  • [Workshop Material] Fit for OMERO - How imaging facilities and IT departments work together to enable RDM for bioimaging, October 16-17, 2024, Heidelberg
    • +
  • [Workshop] Bioimage data management and analysis with OMERO
    • +
  • [Workshop] Research Data Management for Microscopy and BioImage Analysis
  • cba-support-template
    • +
  • ome2024-ngff-challenge
  • re3data.org - registry of Research Data Repositories

    • diff --git a/tags/sharing.html b/tags/sharing.html index 5e969b47..18e51969 100644 --- a/tags/sharing.html +++ b/tags/sharing.html @@ -63,8 +63,8 @@ - - + + @@ -182,36 +182,29 @@

    By tag

    By content type

    @@ -236,11 +228,12 @@ @@ -499,7 +492,7 @@

    Collaborative bio-image analysis script editing with githttps://focalplane.biologists.com/2021/09/04/collaborative-bio-image-analysis-script-editing-with-git/

    @@ -510,7 +503,7 @@

    Finding and using publicly available datahttps://www.ebi.ac.uk/training/online/courses/finding-using-public-data/

    @@ -520,7 +513,7 @@

    Five great reasons to share your research datahttps://web.library.uq.edu.au/blog/2022/03/five-great-reasons-share-your-research-data

    @@ -536,7 +529,7 @@

    Making your project citable#

    Elisabeth Kugler

    Tags: Sharing, Research Data Management

    -

    Content type: Blog

    +

    Content type: Blog Post

    https://focalplane.biologists.com/2023/07/26/sharing-your-poster-on-figshare/

    @@ -546,7 +539,7 @@

    Sharing and licensing materialhttps://f1000research.com/slides/10-519

    @@ -556,7 +549,7 @@

    Sharing research data with Zenodozenodo.org

    Tags: Sharing, Research Data Management

    -

    Content type: Blog

    +

    Content type: Blog Post

    https://focalplane.biologists.com/2023/02/15/sharing-research-data-with-zenodo/

    @@ -589,11 +582,11 @@

    The FAIR guiding principles for data stewardship - fair enough?

    ome2024-ngff-challenge#

    -

    Will Moore, Josh Moore, sherwoodf, jean-marie burel, Norman Rzepka, dependabot[bot], JensWendt, Joost de Folter, Torsten Stöter, AybukeKY, Eric Perlman, Tom Boissonnet

    +

    Will Moore, Josh Moore, sherwoodf, Jean-Marie Burel, Norman Rzepka, dependabot[bot], JensWendt, Joost de Folter, Torsten St\xF6ter, AybukeKY, Eric Perlman, Tom Boissonnet

    Published 2024-08-30T12:00:53+00:00

    Licensed BSD-3-CLAUSE

    Project planning and material repository for the 2024 challenge to generate 1 PB of OME-Zarr data

    -

    Tags: Sharing

    +

    Tags: Sharing, Nfdi4Bioimage, Research Data Management

    Content type: Github Repository

    ome/ome2024-ngff-challenge

    @@ -631,20 +624,20 @@

    ome2024-ngff-challenge

    previous

    -

    Segmentation (5)

    +

    Research data management (128)

    next

    -

    Training (12)

    +

    Workflow (9)

     diff --git a/tags/training.html b/tags/training.html deleted file mode 100644 index dceee768..00000000 --- a/tags/training.html +++ /dev/null @@ -1,743 +0,0 @@ - - - - - - - - - - - Training (12) — NFDI4BioImage Training Materials - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    - - - - - - -
    - - - Ctrl+K -
    -     - -
    - -
    - - -
    -
    - - -
    -
    - - - - -
    - - - -
    - - - - -
    - -
    -
    - - - - - -
    -
    - -
    -
    -
    - - -
    -
    -
    -
    -
    - - -
    - -
    - - - -
    - -
    -
    - -
    -
    - -
    - -
    - -
    - - -
    - -
    - -
    - - - - - -
    - - -
    - - - - - - -
    - - -
    - - - - - - - - - - - - - -
    - -
    - -
    -
    - - - -
    -

    Training (12)

    - -
    - -
    -
    - - - - -
    - -
    -

    Training (12)#

    -
    -

    DataPLANT knowledge base#

    -

    Published 2022-12-14

    -

    Licensed CC-BY-4.0

    -

    Explore fundamental topics on research data management (RDM), how DataPLANT implements these aspects to support plant researchers with RDM tools and services, read guides and manuals or search for some teaching materials.

    -

    Tags: Research Data Management, Training, Dataplant

    -

    Content type: Collection

    -

    https://nfdi4plants.org/nfdi4plants.knowledgebase/index.html

    -
    -
    -
    -

    Docker Mastery - with Kubernetes + Swarm from a Docker Captain#

    -

    Bret Fisher

    -

    Licensed UNKNOWN

    -

    In this course you will learn how to use Docker, Compose and Kubernetes on your machine for better software building and testing.

    -

    Tags: Docker, Training

    -

    Content type: Videos, Tutorial, Online Course

    -

    https://www.udemy.com/course/docker-mastery/?srsltid=AfmBOornR5gRqOg-4v8Nsap1z24CaPPUPxg8JzyqEGZ6MvW_dh-sf4Af&couponCode=ST2MT110724BNEW

    -
    -
    -
    -

    EMBL-EBI material collection#

    -

    EMBL-EBI

    -

    Licensed CC0 (MOSTLY, BUT CAN DIFFER DEPENDING ON RESOURCE)

    -

    Online tutorial and webinar library, designed and delivered by EMBL-EBI experts

    -

    Tags: Bioinformatics, Training

    -

    Content type: Collection

    -

    https://www.ebi.ac.uk/training/on-demand?facets=type:Course%20materials&query=

    -
    -
    -
    -

    Glencoe Software Webinars#

    -

    Chris Allan, Emil Rozbicki

    -

    Licensed UNKNOWN

    -

    Example Workflows / usage of the Glencoe Software.

    -

    Tags: OMERO, Training

    -

    Content type: Videos, Tutorial, Collection

    -

    https://www.glencoesoftware.com/media/webinars/

    -
    -
    -
    -

    I2K 2024: clEsperanto - GPU-Accelerated Image Processing Library#

    -

    Stephane Rigaud, Robert Haase

    -

    Licensed BSD-3-CLAUSE

    -

    Course and material for the clEsperanto workshop presented at I2K 2024 @ Human Technopol (Milan, Italy). The workshop is an hands-on demo of the clesperanto project, focussing on how to use the library for users who want use GPU-acceleration for their Image Processing pipeline.

    -

    Tags: Clesperanto, Training, Bioimage Analysis, Notebooks, Workflow

    -

    Content type: Github Repository, Workshop, Tutorial, Notebook

    -

    StRigaud/clesperanto_workshop_I2K24

    -
    -
    -
    -

    I2K2024 workshop material - Lazy Parallel Processing and Visualization of Large Data with ImgLib2, BigDataViewer, the N5-API, and Spark#

    -

    Stephan Saalfeld, Tobias Pietzsch

    -

    Published None

    -

    Licensed APACHE-2.0

    -

    Tags: Training

    -

    Content type: Workshop, Notebook, Github Repository

    -

    https://saalfeldlab.github.io/i2k2024-lazy-workshop/

    -

    saalfeldlab/i2k2024-lazy-workshop

    -
    -
    -
    -

    Image Processing with Python#

    -

    Mark Meysenburg, Toby Hodges, Dominik Kutra, Erin Becker, David Palmquist, et al.

    -

    Licensed CC-BY-4.0

    -

    This lesson shows how to use Python and scikit-image to do basic image processing.

    -

    Tags: Segmentation, Bioimage Analysis, Training, Python, Scikit-Image, Image Segmentation

    -

    Content type: Tutorial, Workflow

    -

    https://datacarpentry.org/image-processing/key-points.html

    -
    -
    -
    -

    Object Tracking and Track Analysis using TrackMate and CellTracksColab#

    -

    Joanna Pylvänäinen

    -

    Published None

    -

    Licensed GPL-3.0

    -

    I2K 2024 workshop materials for “Object Tracking and Track Analysis using TrackMate and CellTracksColab”

    -

    Tags: Bioimage Analysis, Training

    -

    Content type: Github Repository, Tutorial, Workshop, Slides

    -

    CellMigrationLab/I2K_2024

    -
    -
    -
    -

    SWC/GCNU Software Skills#

    -

    Licensed CC-BY-4.0

    -

    Computational skills training at the UCL Sainsbury Wellcome Centre and Gatsby Computational Neuroscience Unit, delivered by members of the Neuroinformatics Unit.

    -

    Tags: Training

    -

    Content type: Collection, Online Course, Videos, Tutorial

    -

    https://software-skills.neuroinformatics.dev/index.html

    -
    -
    -
    -

    Ten simple rules for making training materials FAIR#

    -

    Leyla Garcia, Bérénice Batut, Melissa L. Burke, Mateusz Kuzak, Fotis Psomopoulos, et al.

    -

    Published 2020-05-21

    -

    Licensed CC-BY-4.0

    -

    The authors offer trainers some simple rules, to help make their training materials FAIR, enabling others to find, (re)use, and adapt them.

    -

    Tags: Metadata, Bioinformatics, FAIR-Principles, Training

    -

    Content type: Publication

    -

    https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1007854

    -
    -
    -
    -

    Ultrack I2K 2024 Workshop Materials#

    -

    Jordão Bragantini, Teun Huijben

    -

    Licensed BSD3-CLAUSE

    -

    Tags: Segmentation, Bioimage Analysis, Training

    -

    Content type: Workshop, Github Repository, Tutorial

    -

    royerlab/ultrack-i2k2024

    -

    https://royerlab.github.io/ultrack-i2k2024/

    -
    -
    -
    -

    Using Glittr.org to find, compare and re-use online training materials#

    -

    Geert van Geest, Yann Haefliger, Monique Zahn-Zabal, Patricia M. Palagi

    -

    Licensed CC-BY-4.0

    -

    Glittr.org is a platform that aggregates and indexes training materials on computational life sciences from public git repositories, making it easier for users to find, compare, and analyze these resources based on various metrics. By providing insights into the availability of materials, collaboration patterns, and licensing practices, Glittr.org supports adherence to the FAIR principles, benefiting the broader life sciences community.

    -

    Tags: Training, Bioimage Analysis, Research Data Management

    -

    Content type: Publication, Preprint

    -

    https://www.biorxiv.org/content/10.1101/2024.08.20.608021v1

    -
    -
    -
    - - - - -
    - - - - - - - - -
    - - - - - - - -
    -
    - -
    - -
    - -

    -By Robert Haase, Clément Caporal,... and the NFDI4BioImage Initiative -

    - -
    - -
    - - -
    - -
    -

    - Last updated on 2025-02-01. -
    -

    -
    - -
    - -
    -

    -Copyright: Licensed CC-BY 4.0 unless mentioned otherwise. -Contributions and feedback are welcome. -

    - -
    -
    - -
    -
    - - -
    -
    -
    - - - - - -
    -
    - - \ No newline at end of file diff --git a/tags/workflow.html b/tags/workflow.html index 2ff8fff2..df08ae58 100644 --- a/tags/workflow.html +++ b/tags/workflow.html @@ -8,7 +8,7 @@ - Workflow (10) — NFDI4BioImage Training Materials + Workflow (9) — NFDI4BioImage Training Materials @@ -64,7 +64,7 @@ - + @@ -182,36 +182,29 @@

    By tag

    By content type

    @@ -236,11 +228,12 @@ @@ -447,7 +440,7 @@
    -

    Workflow (10)

    +

    Workflow (9)

    @@ -460,7 +453,6 @@

    Contents

  • BIOMERO - A scalable and extensible image analysis framework
  • Creating Workflows and Advanced Workflow Options
  • Galaxy workflows
    • -
  • I2K 2024: clEsperanto - GPU-Accelerated Image Processing Library
  • KNIME Image Processing
  • Multimodal large language models for bioimage analysis
  • User friendly Image metadata annotation tool/workflow for OMERO
    • @@ -478,15 +470,15 @@

    Contents

    -
    -

    Workflow (10)#

    +
    +

    Workflow (9)#

    BIOMERO - A scalable and extensible image analysis framework#

    Torec T. Luik, Rodrigo Rosas-Bertolini, Eric A.J. Reits, Ron A. Hoebe, Przemek M. Krawczyk

    Published None

    Licensed CC-BY-4.0

    The authors introduce BIOMERO (bioimage analysis in OMERO), a bridge connecting OMERO, a renowned bioimaging data management platform, FAIR workflows, and high-performance computing (HPC) environments.

    -

    Tags: OMERO, Workflow, Bioimage Analysis, Image Data Management

    +

    Tags: OMERO, Workflow, Bioimage Analysis

    Content type: Publication

    https://doi.org/10.1016/j.patter.2024.101024

    @@ -508,22 +500,12 @@

    Galaxy workflowshttps://galaxy-au-training.github.io/tutorials/modules/workflows/

    -
    -

    I2K 2024: clEsperanto - GPU-Accelerated Image Processing Library#

    -

    Stephane Rigaud, Robert Haase

    -

    Licensed BSD-3-CLAUSE

    -

    Course and material for the clEsperanto workshop presented at I2K 2024 @ Human Technopol (Milan, Italy). The workshop is an hands-on demo of the clesperanto project, focussing on how to use the library for users who want use GPU-acceleration for their Image Processing pipeline.

    -

    Tags: Clesperanto, Training, Bioimage Analysis, Notebooks, Workflow

    -

    Content type: Github Repository, Workshop, Tutorial, Notebook

    -

    StRigaud/clesperanto_workshop_I2K24

    -
    -

    KNIME Image Processing#

    None

    -

    Licensed GPLV3

    +

    Licensed GPL-3.0

    The KNIME Image Processing Extension allows you to read in more than 140 different kinds of images and to apply well known methods on images, like preprocessing. segmentation, feature extraction, tracking and classification in KNIME.

    -

    Tags: Imagej, OMERO, Bioimage Data, Workflow

    +

    Tags: Imagej, OMERO, Workflow

    Content type: Tutorial, Online Tutorial, Documentation

    https://www.knime.com/community/image-processing

    @@ -531,9 +513,9 @@

    KNIME Image Processing

    Multimodal large language models for bioimage analysis#

    Shanghang Zhang, Gaole Dai, Tiejun Huang, Jianxu Chen

    -

    Licensed [‘CC-BY-NC-SA’]

    +

    Licensed CC-BY-NC-SA

    Multimodal large language models have been recognized as a historical milestone in the field of artificial intelligence and have demonstrated revolutionary potentials not only in commercial applications, but also for many scientific fields. Here we give a brief overview of multimodal large language models through the lens of bioimage analysis and discuss how we could build these models as a community to facilitate biology research

    -

    Tags: Bioimage Analysis, Large Language Models, FAIR-Principles, Workflow

    +

    Tags: Bioimage Analysis, FAIR-Principles, Workflow

    Content type: Publication

    https://www.nature.com/articles/s41592-024-02334-2

    https://arxiv.org/abs/2407.19778

    @@ -612,12 +594,12 @@

    nextflow-workshop

    previous

    -

    Training (12)

    +

    Sharing (12)

     nextflow-workshopBIOMERO - A scalable and extensible image analysis framework
  • Creating Workflows and Advanced Workflow Options
  • Galaxy workflows
    • -
  • I2K 2024: clEsperanto - GPU-Accelerated Image Processing Library
  • KNIME Image Processing
  • Multimodal large language models for bioimage analysis
  • User friendly Image metadata annotation tool/workflow for OMERO
    • diff --git a/tags/workflow_engine.html b/tags/workflow_engine.html index 87983296..0afe1039 100644 --- a/tags/workflow_engine.html +++ b/tags/workflow_engine.html @@ -63,8 +63,8 @@ - - + + @@ -182,36 +182,29 @@

    By tag

    By content type

    @@ -236,11 +228,12 @@ @@ -516,7 +509,7 @@

    BioEngine DocumentationWei Ouyang, Nanguage, Jeremy Metz, Craig Russell

    Licensed MIT

    BioEngine, a Python package designed for flexible deployment and execution of bioimage analysis models and workflows using AI, accessible via HTTP API and RPC.

    -

    Tags: Workflow Engine, Deep Learning, Python

    +

    Tags: Workflow Engine, Artificial Intelligence, Python

    Content type: Documentation

    https://bioimage-io.github.io/bioengine/#/

    @@ -582,7 +575,7 @@

    Nextflow: Scalable and reproducible scientific workflowshttps://zenodo.org/records/4334697

    https://doi.org/10.5281/zenodo.4334697

    @@ -644,15 +637,15 @@

    WorkflowHub

    previous

    -

    Workflow (10)

    +

    Workflow (9)

    next

    -

    Blog (19)

    +

    Blog post (23)

     diff --git a/whats_new.html b/whats_new.html index 0def5b16..7bb15678 100644 --- a/whats_new.html +++ b/whats_new.html @@ -182,36 +182,29 @@

    By tag

    By content type

    @@ -236,11 +228,12 @@ @@ -484,7 +477,7 @@

    Recently added (10)#

    Hoku West-Foyle

    Published 2025-01-16

    -

    Licensed CC-ZERO

    +

    Licensed CC0-1.0

    https://zenodo.org/records/14675120

    https://doi.org/10.5281/zenodo.14675120

    @@ -495,6 +488,7 @@

    Collaborative Working and Version Control with git[hub]Published 2024-01-10

    Licensed CC-BY-4.0

    This slide deck introduces the version control tool git, related terminology and the Github Desktop app for managing files in Git[hub] repositories. We furthermore dive into:* Working with repositories* Collaborative with others* Github-Zenodo integration* Github pages* Artificial Intelligence answering Github Issues

    +

    Tags: Nfdi4Bioimage, Globias, Research Data Management, Research Software Management

    https://zenodo.org/records/14626054

    https://doi.org/10.5281/zenodo.14626054

    @@ -520,10 +514,11 @@

    LSM example J. Dubrulle

    Modular training resources for bioimage analysis#

    -

    Christian Tischer, Antonio Politi, Tim-Oliver Buchholz, Elnaz Fazeli, Nicola Gritti, Aliaksandr Halavatyi, Gonzalez Tirado, Sebastian, Julian Hennies, Toby Hodges, Arif Khan, Dominik Kutra, Stefania Marcotti, Bugra Oezdemir, Felix Schneider, Martin Schorb, Anniek Stokkermans, Yi Sun, Nima Vakili

    +

    Christian Tischer, Antonio Politi, Tim-Oliver Buchholz, Elnaz Fazeli, Nicola Gritti, Aliaksandr Halavatyi, Sebastian Gonzalez Tirado, Julian Hennies, Toby Hodges, Arif Khan, Dominik Kutra, Stefania Marcotti, Bugra Oezdemir, Felix Schneider, Martin Schorb, Anniek Stokkermans, Yi Sun, Nima Vakili

    Published 2024-12-03

    Licensed CC-BY-4.0

    Resources for teaching/preparing to teach bioimage analysis

    +

    Tags: Neubias, Bioimage Analysis

    https://zenodo.org/records/14264885

    https://doi.org/10.5281/zenodo.14264885

    @@ -555,6 +550,7 @@

    [CIDAS] Scalable strategies for a next-generation of FAIR bioimagingLicensed CC-BY-4.0

    Talk given at Georg-August-Universität Göttingen Campus Institute Data Science23rd January 2025 https://www.uni-goettingen.de/en/653203.html

    +

    Tags: Nfdi4Bioimage

    https://zenodo.org/records/14716546

    https://doi.org/10.5281/zenodo.14716546

    @@ -567,6 +563,7 @@

    [CMCB] Scalable strategies for a next-generation of FAIR bioimagingCMCB LIFE SCIENCES SEMINARSTechnische Universität Dresden16th January 2025 https://tu-dresden.de/cmcb/crtd/news-termine/termine/cmcb-life-sciences-seminar-josh-moore-german-bioimaging-e-v-society-for-microscopy-and-image-analysis-constance  

    +

    Tags: Nfdi4Bioimage

    https://zenodo.org/records/14650434

    https://doi.org/10.5281/zenodo.14650434

    @@ -582,6 +579,7 @@

    [Workshop] Research Data Management for Microscopy and BioImage Analysis

    Date & Venue:Thursday, Sept. 26, 5.30 p.m.Haus 22 / Paul Ehrlich Lecture Hall (H22-1)University Hospital Frankfurt

    +

    Tags: Nfdi4Bioimage, Research Data Management

    https://zenodo.org/records/13861026

    https://doi.org/10.5281/zenodo.13861026