From cd512c1823a94ab569fec4edc800580dd2e7ca51 Mon Sep 17 00:00:00 2001 From: Lea Gihlein <85543649+lea-33@users.noreply.github.com> Date: Wed, 29 Jan 2025 13:05:40 +0100 Subject: [PATCH 1/8] Create update-dalia-csv.yml --- .github/workflows/update-dalia-csv.yml | 46 ++++++++++++++++++++++++++ 1 file changed, 46 insertions(+) create mode 100644 .github/workflows/update-dalia-csv.yml diff --git a/.github/workflows/update-dalia-csv.yml b/.github/workflows/update-dalia-csv.yml new file mode 100644 index 00000000..b3672a60 --- /dev/null +++ b/.github/workflows/update-dalia-csv.yml @@ -0,0 +1,46 @@ +name: Update DALIA CSV on Resource Change + +on: + pull_request: + paths: + - 'resources/nfdi4bioimage.yml' + +jobs: + update-dalia-csv: + runs-on: ubuntu-latest + steps: + # Checkout repository + - name: Checkout repository + uses: actions/checkout@v2 + + # Set up Python environment + - name: Set up Python + uses: actions/setup-python@v4 + with: + python-version: '3.x' + + # Install dependencies + - name: Install dependencies + run: | + pip install -r requirements.txt + pip install nbconvert transformers torch + sudo apt-get install -y poppler-utils + + # Execute the Notebook to export CSV in DALIA format + - name: Execute Jupyter Notebook + run: | + jupyter nbconvert --to notebook --execute scripts/Export_to_DALIA.ipynb + + # Commit and push changes if any file was updated + - name: Commit and push changes + run: | + git config --global user.name "github-actions[bot]" + git config --global user.email "github-actions[bot]@users.noreply.github.com" + + git add . + git diff --staged --quiet || git commit -m "Auto-update CSV after modifying nfdi4bioimage.yml" + + # Authenticate and push + git push https://x-access-token:${{ secrets.GITHUB_TOKEN }}@github.com/${{ github.repository }}.git HEAD:${{ github.ref }} + + From 830a415bf9351a5afc072b92f031d22cab028a51 Mon Sep 17 00:00:00 2001 From: Lea Gihlein <85543649+lea-33@users.noreply.github.com> Date: Wed, 29 Jan 2025 13:08:25 +0100 Subject: [PATCH 2/8] Update nfdi4bioimage.yml just removed some spaces between entries to see if I could trigger the new action --- resources/nfdi4bioimage.yml | 4 ---- 1 file changed, 4 deletions(-) diff --git a/resources/nfdi4bioimage.yml b/resources/nfdi4bioimage.yml index 19dff2bc..59b2edd0 100644 --- a/resources/nfdi4bioimage.yml +++ b/resources/nfdi4bioimage.yml @@ -9564,10 +9564,6 @@ resources: - Wiki url: https://discourse.datamethods.org/t/reference-collection-to-push-back-against-common-statistical-myths/1787 - - - - - authors: - Tischer, Christian - Politi, Antonio From 0e5a6f0b15e3db2635f106f76694f9d3f46435c8 Mon Sep 17 00:00:00 2001 From: Lea Gihlein <85543649+lea-33@users.noreply.github.com> Date: Wed, 29 Jan 2025 13:09:20 +0100 Subject: [PATCH 3/8] Add Export_to_DALIA.ipynb --- scripts/Export_to_DALIA.ipynb | 2252 +++++++++++++++++++++++++++++++++ 1 file changed, 2252 insertions(+) create mode 100644 scripts/Export_to_DALIA.ipynb diff --git a/scripts/Export_to_DALIA.ipynb b/scripts/Export_to_DALIA.ipynb new file mode 100644 index 00000000..f8568ac0 --- /dev/null +++ b/scripts/Export_to_DALIA.ipynb @@ -0,0 +1,2252 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "7a19e5fa-6f3a-4a8a-9244-6bf9fdebad76", + "metadata": {}, + "source": [ + "### Test Conversion of yml to DALIA format" + ] + }, + { + "cell_type": "markdown", + "id": "2e055672-a937-4e46-926e-fdf6c527d628", + "metadata": {}, + "source": [ + "#### Load the Yml as a pandas DF" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "7396751e-9b56-4bf6-bc35-e6e38f6c108c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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authorsnametagstypeurllicenseevent_dateevent_locationdescriptionnum_downloadspublication_datefingerprintauthorsubmission_date
0[Elisabeth Kugler]Sharing Your Poster on Figshare: A Community G...[Sharing, Research Data Management][Blog]https://focalplane.biologists.com/2023/07/26/s...NaNNaNNaNNaNNaNNaNNaNNaNNaN
1[Marcelo Zoccoler]Running Deep-Learning Scripts in the BiA-PoL O...[Python, Artificial Intelligence, Bioimage Ana...[Blog]https://biapol.github.io/blog/marcelo_zoccoler...CC-BY-4.0NaNNaNNaNNaNNaNNaNNaNNaN
2[Robert Haase]Browsing the Open Microscopy Image Data Resour...[OMERO, Python][Blog]https://biapol.github.io/blog/robert_haase/bro...CC-BY-4.0NaNNaNNaNNaNNaNNaNNaNNaN
3[Mara Lampert]Getting started with Mambaforge and Python[Python, Conda, Mamba][Blog]https://biapol.github.io/blog/mara_lampert/get...CC-BY-4.0NaNNaNNaNNaNNaNNaNNaNNaN
4[Jennifer Waters]Promoting Data Management at the Nikon Imaging...[Research Data Management][Blog]https://datamanagement.hms.harvard.edu/news/pr...NaNNaNNaNNaNNaNNaNNaNNaNNaN
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authorsnametagstypeurllicenseevent_dateevent_locationdescriptionnum_downloadspublication_datefingerprintauthorsubmission_date
394NaNVirtual-I2K-2024-multiview-stitcher[Big Data, Bioimageanalysis][Github Repository, Tutorial][https://github.com/m-albert/Virtual-I2K-2024-...BSD-3-CLAUSENaNNaNRepository accompanying the multiview-stitcher...NaN2024-10-30T07:38:11+00:00NaNMarvin AlbertNaN
397NaNPrompt-Engineering-LLMs-Course[Llms, Prompt Engineering, Code Generation][Github Repository, Tutorial]https://github.com/HelmholtzAI-Consultants-Mun...MITNaNNaNNaN2024-09-11T07:45:30+00:00NaNIsra MekkiNaN
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" + ], + "text/plain": [ + " authors name \\\n", + "394 NaN Virtual-I2K-2024-multiview-stitcher \n", + "397 NaN Prompt-Engineering-LLMs-Course \n", + "\n", + " tags \\\n", + "394 [Big Data, Bioimageanalysis] \n", + "397 [Llms, Prompt Engineering, Code Generation] \n", + "\n", + " type \\\n", + "394 [Github Repository, Tutorial] \n", + "397 [Github Repository, Tutorial] \n", + "\n", + " url license \\\n", + "394 [https://github.com/m-albert/Virtual-I2K-2024-... BSD-3-CLAUSE \n", + "397 https://github.com/HelmholtzAI-Consultants-Mun... MIT \n", + "\n", + " event_date event_location \\\n", + "394 NaN NaN \n", + "397 NaN NaN \n", + "\n", + " description num_downloads \\\n", + "394 Repository accompanying the multiview-stitcher... NaN \n", + "397 NaN \n", + "\n", + " publication_date fingerprint author submission_date \n", + "394 2024-10-30T07:38:11+00:00 NaN Marvin Albert NaN \n", + "397 2024-09-11T07:45:30+00:00 NaN Isra Mekki NaN " + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#check which entries have 'author' column\n", + "df[df['author'].notna()]" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "1371bc6c-23d9-46db-857c-41dd73e861c2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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authorsnametagstypeurllicenseevent_dateevent_locationdescriptionnum_downloadspublication_datefingerprintauthorsubmission_date
394Marvin AlbertVirtual-I2K-2024-multiview-stitcher[Big Data, Bioimageanalysis][Github Repository, Tutorial][https://github.com/m-albert/Virtual-I2K-2024-...BSD-3-CLAUSENaNNaNRepository accompanying the multiview-stitcher...NaN2024-10-30T07:38:11+00:00NaNMarvin AlbertNaN
397Isra MekkiPrompt-Engineering-LLMs-Course[Llms, Prompt Engineering, Code Generation][Github Repository, Tutorial]https://github.com/HelmholtzAI-Consultants-Mun...MITNaNNaNNaN2024-09-11T07:45:30+00:00NaNIsra MekkiNaN
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" + ], + "text/plain": [ + " authors name \\\n", + "394 Marvin Albert Virtual-I2K-2024-multiview-stitcher \n", + "397 Isra Mekki Prompt-Engineering-LLMs-Course \n", + "\n", + " tags \\\n", + "394 [Big Data, Bioimageanalysis] \n", + "397 [Llms, Prompt Engineering, Code Generation] \n", + "\n", + " type \\\n", + "394 [Github Repository, Tutorial] \n", + "397 [Github Repository, Tutorial] \n", + "\n", + " url license \\\n", + "394 [https://github.com/m-albert/Virtual-I2K-2024-... BSD-3-CLAUSE \n", + "397 https://github.com/HelmholtzAI-Consultants-Mun... MIT \n", + "\n", + " event_date event_location \\\n", + "394 NaN NaN \n", + "397 NaN NaN \n", + "\n", + " description num_downloads \\\n", + "394 Repository accompanying the multiview-stitcher... NaN \n", + "397 NaN \n", + "\n", + " publication_date fingerprint author submission_date \n", + "394 2024-10-30T07:38:11+00:00 NaN Marvin Albert NaN \n", + "397 2024-09-11T07:45:30+00:00 NaN Isra Mekki NaN " + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Iterate over rows to change the information to the authors column\n", + "for index, entry in df[df['author'].notna()].iterrows():\n", + " df.loc[index, 'authors'] = entry['author']\n", + " \n", + "df[df['author'].notna()]" + ] + }, + { + "cell_type": "markdown", + "id": "51bdf851-201b-4c7d-8f9e-49ba5fb00eac", + "metadata": {}, + "source": [ + "#### 2. Exclude entries without mandatory attributes (License, Authors, Title, Link)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "11c39326-61e1-422d-99df-240f4b9b5c86", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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authorsnametagstypeurllicenseevent_dateevent_locationdescriptionnum_downloadspublication_datefingerprintauthorsubmission_date
1[Marcelo Zoccoler]Running Deep-Learning Scripts in the BiA-PoL O...[Python, Artificial Intelligence, Bioimage Ana...[Blog]https://biapol.github.io/blog/marcelo_zoccoler...CC-BY-4.0NaNNaNNaNNaNNaNNaNNaNNaN
2[Robert Haase]Browsing the Open Microscopy Image Data Resour...[OMERO, Python][Blog]https://biapol.github.io/blog/robert_haase/bro...CC-BY-4.0NaNNaNNaNNaNNaNNaNNaNNaN
3[Mara Lampert]Getting started with Mambaforge and Python[Python, Conda, Mamba][Blog]https://biapol.github.io/blog/mara_lampert/get...CC-BY-4.0NaNNaNNaNNaNNaNNaNNaNNaN
9[Robert Haase]Managing Scientific Python environments using ...[Python, Conda, Mamba][Blog]https://focalplane.biologists.com/2022/12/08/m...CC-BY-4.0NaNNaNNaNNaNNaNNaNNaNNaN
29[Robert Haase et al.]BioImage Analysis Notebooks[Python, Bioimage Analysis][Book, Notebook]https://haesleinhuepf.github.io/BioImageAnalys...[CC-BY-4.0, BSD-3-CLAUSE]NaNNaNNaNNaNNaNNaNNaNNaN
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" + ], + "text/plain": [ + " authors name \\\n", + "1 [Marcelo Zoccoler] Running Deep-Learning Scripts in the BiA-PoL O... \n", + "2 [Robert Haase] Browsing the Open Microscopy Image Data Resour... \n", + "3 [Mara Lampert] Getting started with Mambaforge and Python \n", + "9 [Robert Haase] Managing Scientific Python environments using ... \n", + "29 [Robert Haase et al.] BioImage Analysis Notebooks \n", + "\n", + " tags type \\\n", + "1 [Python, Artificial Intelligence, Bioimage Ana... [Blog] \n", + "2 [OMERO, Python] [Blog] \n", + "3 [Python, Conda, Mamba] [Blog] \n", + "9 [Python, Conda, Mamba] [Blog] \n", + "29 [Python, Bioimage Analysis] [Book, Notebook] \n", + "\n", + " url \\\n", + "1 https://biapol.github.io/blog/marcelo_zoccoler... \n", + "2 https://biapol.github.io/blog/robert_haase/bro... \n", + "3 https://biapol.github.io/blog/mara_lampert/get... \n", + "9 https://focalplane.biologists.com/2022/12/08/m... \n", + "29 https://haesleinhuepf.github.io/BioImageAnalys... \n", + "\n", + " license event_date event_location description \\\n", + "1 CC-BY-4.0 NaN NaN NaN \n", + "2 CC-BY-4.0 NaN NaN NaN \n", + "3 CC-BY-4.0 NaN NaN NaN \n", + "9 CC-BY-4.0 NaN NaN NaN \n", + "29 [CC-BY-4.0, BSD-3-CLAUSE] NaN NaN NaN \n", + "\n", + " num_downloads publication_date fingerprint author submission_date \n", + "1 NaN NaN NaN NaN NaN \n", + "2 NaN NaN NaN NaN NaN \n", + "3 NaN NaN NaN NaN NaN \n", + "9 NaN NaN NaN NaN NaN \n", + "29 NaN NaN NaN NaN NaN " + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = df[~df['license'].str.lower().isin(['unknown']) & df['license'].notna() & df['authors'].notna() & df['name'].notna()& df['url'].notna()]\n", + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "5e708904-0161-4fb6-8bf8-c2f6dc3dbbea", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total number of entries found: 536\n", + "Number of entries found with all mandatory entries: 330\n" + ] + } + ], + "source": [ + "print(f'Total number of entries found: {len(df)}')\n", + "print(f'Number of entries found with all mandatory entries: {len(data)}')" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "8ce34a4c-f14f-40b0-8254-a4234d1f9d23", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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authorsnametagstypeurllicenseevent_dateevent_locationdescriptionnum_downloadspublication_datefingerprintauthorsubmission_date
1[Marcelo Zoccoler]Running Deep-Learning Scripts in the BiA-PoL O...[Python, Artificial Intelligence, Bioimage Ana...[Blog]https://biapol.github.io/blog/marcelo_zoccoler...CC-BY-4.0NaNNaNNaNNaNNaNNaNNaNNaN
2[Robert Haase]Browsing the Open Microscopy Image Data Resour...[OMERO, Python][Blog]https://biapol.github.io/blog/robert_haase/bro...CC-BY-4.0NaNNaNNaNNaNNaNNaNNaNNaN
3[Mara Lampert]Getting started with Mambaforge and Python[Python, Conda, Mamba][Blog]https://biapol.github.io/blog/mara_lampert/get...CC-BY-4.0NaNNaNNaNNaNNaNNaNNaNNaN
9[Robert Haase]Managing Scientific Python environments using ...[Python, Conda, Mamba][Blog]https://focalplane.biologists.com/2022/12/08/m...CC-BY-4.0NaNNaNNaNNaNNaNNaNNaNNaN
29[Robert Haase et al.]BioImage Analysis Notebooks[Python, Bioimage Analysis][Book, Notebook]https://haesleinhuepf.github.io/BioImageAnalys...[CC-BY-4.0, BSD-3-CLAUSE]NaNNaNNaNNaNNaNNaNNaNNaN
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authorsnametagstypeurllicenseevent_dateevent_locationdescriptionnum_downloadspublication_datefingerprintauthorsubmission_date
1[Marcelo Zoccoler]Running Deep-Learning Scripts in the BiA-PoL O...Python * Artificial Intelligence * Bioimage An...[Blog]https://biapol.github.io/blog/marcelo_zoccoler...CC-BY-4.0NaNNaNNaNNaNNaNNaNNaNNaN
2[Robert Haase]Browsing the Open Microscopy Image Data Resour...OMERO * Python[Blog]https://biapol.github.io/blog/robert_haase/bro...CC-BY-4.0NaNNaNNaNNaNNaNNaNNaNNaN
3[Mara Lampert]Getting started with Mambaforge and PythonPython * Conda * Mamba[Blog]https://biapol.github.io/blog/mara_lampert/get...CC-BY-4.0NaNNaNNaNNaNNaNNaNNaNNaN
9[Robert Haase]Managing Scientific Python environments using ...Python * Conda * Mamba[Blog]https://focalplane.biologists.com/2022/12/08/m...CC-BY-4.0NaNNaNNaNNaNNaNNaNNaNNaN
29[Robert Haase et al.]BioImage Analysis NotebooksPython * Bioimage Analysis[Book, Notebook]https://haesleinhuepf.github.io/BioImageAnalys...CC-BY-4.0 * BSD-3-CLAUSENaNNaNNaNNaNNaNNaNNaNNaN
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" + ], + "text/plain": [ + " authors name \\\n", + "1 [Marcelo Zoccoler] Running Deep-Learning Scripts in the BiA-PoL O... \n", + "2 [Robert Haase] Browsing the Open Microscopy Image Data Resour... \n", + "3 [Mara Lampert] Getting started with Mambaforge and Python \n", + "9 [Robert Haase] Managing Scientific Python environments using ... \n", + "29 [Robert Haase et al.] BioImage Analysis Notebooks \n", + "\n", + " tags type \\\n", + "1 Python * Artificial Intelligence * Bioimage An... [Blog] \n", + "2 OMERO * Python [Blog] \n", + "3 Python * Conda * Mamba [Blog] \n", + "9 Python * Conda * Mamba [Blog] \n", + "29 Python * Bioimage Analysis [Book, Notebook] \n", + "\n", + " url \\\n", + "1 https://biapol.github.io/blog/marcelo_zoccoler... \n", + "2 https://biapol.github.io/blog/robert_haase/bro... \n", + "3 https://biapol.github.io/blog/mara_lampert/get... \n", + "9 https://focalplane.biologists.com/2022/12/08/m... \n", + "29 https://haesleinhuepf.github.io/BioImageAnalys... \n", + "\n", + " license event_date event_location description \\\n", + "1 CC-BY-4.0 NaN NaN NaN \n", + "2 CC-BY-4.0 NaN NaN NaN \n", + "3 CC-BY-4.0 NaN NaN NaN \n", + "9 CC-BY-4.0 NaN NaN NaN \n", + "29 CC-BY-4.0 * BSD-3-CLAUSE NaN NaN NaN \n", + "\n", + " num_downloads publication_date fingerprint author submission_date \n", + "1 NaN NaN NaN NaN NaN \n", + "2 NaN NaN NaN NaN NaN \n", + "3 NaN NaN NaN NaN NaN \n", + "9 NaN NaN NaN NaN NaN \n", + "29 NaN NaN NaN NaN NaN " + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data[\"tags\"] = data[\"tags\"].apply(lambda x: ' * '.join(x) if isinstance(x, list) else x) #Tags\n", + "data[\"license\"] = data[\"license\"].apply(lambda x: ' * '.join(x) if isinstance(x, list) else x) #License\n", + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "ac322332-6c61-4764-b8c2-760c33518429", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_12251/2387137408.py:21: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data[\"license\"] = data[\"license\"].replace(license_mapping)\n" + ] + } + ], + "source": [ + "# Map the License Entries to valid input\n", + "license_mapping = {\n", + " 'APACHE-2.0 LICENSE' : 'Apache-2.0',\n", + " 'CC0 1.0 UNIVERSAL' : 'CC0-1.0',\n", + " 'CC-BY-4.0 * BSD-3-CLAUSE' : 'CC-BY-4.0 * BSD-3-Clause',\n", + " 'CC0 (MOSTLY, BUT CAN DIFFER DEPENDING ON RESOURCE)' : 'CC0-1.0',\n", + " 'CCY-BY-SA-4.0' : 'CC-BY-SA-4.0',\n", + " 'YOUTTUBE STANDARD LICENSE' : 'YOUTUBE STANDARD LICENSE',\n", + " 'CC-BY-NC-SA' : 'CC-BY-NC-SA-4.0',\n", + " 'BSD3-CLAUSE' : 'BSD-3-Clause',\n", + " 'CC-ZERO' : 'CC0-1.0',\n", + " 'BSD 3-Clause \"New\" or \"Revised\" License' : 'BSD-3-Clause',\n", + " 'cc-by-4.0' : ' CC-BY-4.0',\n", + " 'Creative Commons Attribution Share Alike 4.0 International' : 'CC-BY-SA-4.0',\n", + " 'GNU General Public License v3.0' : 'GPL-3.0-only',\n", + " 'CC BY-NC-SA 4.0' : 'CC-BY-NC-SA-4.0',\n", + " 'BSD-3-CLAUSE' : 'BSD-3-Clause',\n", + " 'BSD-2-CLAUSE' : 'BSD-2-Clause',\n", + " 'APACHE-2.0' : 'Apache-2.0'\n", + "}\n", + "data[\"license\"] = data[\"license\"].replace(license_mapping)" + ] + }, + { + "cell_type": "markdown", + "id": "03fbeeb5-67d4-403c-a780-8757f738b9bb", + "metadata": {}, + "source": [ + "#### 4. Morph the **Type** Column into the **LearningResourceType** and **MediaType** Column" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "8a104889-190a-4504-af64-c5a019392ad3", + "metadata": {}, + "outputs": [], + "source": [ + "# Create Mapping for the Type Column:\n", + "type_to_learning_resource = {\n", + " \"Application\": \"Software Application\",\n", + " \"Big Data\": \"Data\",\n", + " \"Bioimage Analysis\": \"Other\",\n", + " \"Blog\": \"Web Page\",\n", + " \"Blog Post\": \"Text\",\n", + " \"Book\": \"Book\",\n", + " \"Book Chapter\": \"Book\",\n", + " \"Code\": None,\n", + " \"Collection\": \"Other\",\n", + " \"Conference Abstract\": \"Text\",\n", + " \"Data\": \"Data\",\n", + " \"Document\": \"Text\",\n", + " \"Documentation\": \"Text\",\n", + " \"Event\": \"Other\",\n", + " \"Forum Post\": \"Text\",\n", + " \"Github Repository\": \"Other\",\n", + " \"Jupyter Book\": \"Code Notebook\",\n", + " \"Notebook\": \"Code Notebook\",\n", + " \"Online Course\": \"Course\",\n", + " \"Online Tutorial\": \"Tutorial\",\n", + " \"Open Source Software\": \"Software Application\",\n", + " \"Poster\": \"Poster\",\n", + " \"Practicals\": \"Course\",\n", + " \"Preprint\": \"Text\",\n", + " \"Presentation\": \"Presentation\",\n", + " \"Publication\": \"Article\",\n", + " \"Python\": None,\n", + " \"Report\": \"Report\",\n", + " \"Slide\": \"Presentation\",\n", + " \"Slides\": \"Presentation\",\n", + " \"Tutorial\": \"Tutorial\",\n", + " \"Video\": None,\n", + " \"Videos\": None,\n", + " \"Website\": \"Web Page\",\n", + " \"Workshop\": \"Course\",\n", + " \"Youtube Channel\": \"Other\"\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "cd6e0ac8-2382-4a26-9e77-ee739081396f", + "metadata": {}, + "outputs": [], + "source": [ + "type_to_media_type = {\n", + " \"Application\": None,\n", + " \"Big Data\": None,\n", + " \"Bioimage Analysis\": None,\n", + " \"Blog\": \"text\",\n", + " \"Blog Post\": \"text\",\n", + " \"Book\": \"text\",\n", + " \"Book Chapter\": \"text\",\n", + " \"Code\": \"code\",\n", + " \"Collection\": None,\n", + " \"Conference Abstract\": \"text\",\n", + " \"Data\": None,\n", + " \"Document\": \"text\",\n", + " \"Documentation\": \"text\",\n", + " \"Event\": None,\n", + " \"Forum Post\": \"text\",\n", + " \"Github Repository\": None,\n", + " \"Jupyter Book\": \"code\",\n", + " \"Notebook\": \"code\",\n", + " \"Online Course\": None,\n", + " \"Online Tutorial\": None,\n", + " \"Open Source Software\": None,\n", + " \"Poster\": None,\n", + " \"Practicals\": None,\n", + " \"Preprint\": \"text\",\n", + " \"Presentation\": \"presentation\",\n", + " \"Publication\": \"text\",\n", + " \"Python\": None,\n", + " \"Report\": \"text\",\n", + " \"Slide\": \"presentation\",\n", + " \"Slides\": \"presentation\",\n", + " \"Tutorial\": None,\n", + " \"Video\": \"video\",\n", + " \"Videos\": \"video\",\n", + " \"Website\": None,\n", + " \"Workshop\": None,\n", + " \"Youtube Channel\": \"video\"\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "c32c15e5-2d12-4051-b238-44a94afcc5d1", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_12251/3151956629.py:30: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data[\"LearningResourceType\"] = data[\"type\"].apply(map_learning_resource)\n", + "/tmp/ipykernel_12251/3151956629.py:31: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data[\"MediaType\"] = data[\"type\"].apply(map_media_type)\n" + ] + }, + { + "data": { + "text/html": [ + "
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authorsnametagstypeurllicenseevent_dateevent_locationdescriptionnum_downloadspublication_datefingerprintauthorsubmission_dateLearningResourceTypeMediaType
1[Marcelo Zoccoler]Running Deep-Learning Scripts in the BiA-PoL O...Python * Artificial Intelligence * Bioimage An...[Blog]https://biapol.github.io/blog/marcelo_zoccoler...CC-BY-4.0NaNNaNNaNNaNNaNNaNNaNNaNWeb Pagetext
2[Robert Haase]Browsing the Open Microscopy Image Data Resour...OMERO * Python[Blog]https://biapol.github.io/blog/robert_haase/bro...CC-BY-4.0NaNNaNNaNNaNNaNNaNNaNNaNWeb Pagetext
3[Mara Lampert]Getting started with Mambaforge and PythonPython * Conda * Mamba[Blog]https://biapol.github.io/blog/mara_lampert/get...CC-BY-4.0NaNNaNNaNNaNNaNNaNNaNNaNWeb Pagetext
9[Robert Haase]Managing Scientific Python environments using ...Python * Conda * Mamba[Blog]https://focalplane.biologists.com/2022/12/08/m...CC-BY-4.0NaNNaNNaNNaNNaNNaNNaNNaNWeb Pagetext
29[Robert Haase et al.]BioImage Analysis NotebooksPython * Bioimage Analysis[Book, Notebook]https://haesleinhuepf.github.io/BioImageAnalys...CC-BY-4.0 * BSD-3-ClauseNaNNaNNaNNaNNaNNaNNaNNaNBook * Code Notebooktext * code
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" + ], + "text/plain": [ + " authors name \\\n", + "1 [Marcelo Zoccoler] Running Deep-Learning Scripts in the BiA-PoL O... \n", + "2 [Robert Haase] Browsing the Open Microscopy Image Data Resour... \n", + "3 [Mara Lampert] Getting started with Mambaforge and Python \n", + "9 [Robert Haase] Managing Scientific Python environments using ... \n", + "29 [Robert Haase et al.] BioImage Analysis Notebooks \n", + "\n", + " tags type \\\n", + "1 Python * Artificial Intelligence * Bioimage An... [Blog] \n", + "2 OMERO * Python [Blog] \n", + "3 Python * Conda * Mamba [Blog] \n", + "9 Python * Conda * Mamba [Blog] \n", + "29 Python * Bioimage Analysis [Book, Notebook] \n", + "\n", + " url \\\n", + "1 https://biapol.github.io/blog/marcelo_zoccoler... \n", + "2 https://biapol.github.io/blog/robert_haase/bro... \n", + "3 https://biapol.github.io/blog/mara_lampert/get... \n", + "9 https://focalplane.biologists.com/2022/12/08/m... \n", + "29 https://haesleinhuepf.github.io/BioImageAnalys... \n", + "\n", + " license event_date event_location description \\\n", + "1 CC-BY-4.0 NaN NaN NaN \n", + "2 CC-BY-4.0 NaN NaN NaN \n", + "3 CC-BY-4.0 NaN NaN NaN \n", + "9 CC-BY-4.0 NaN NaN NaN \n", + "29 CC-BY-4.0 * BSD-3-Clause NaN NaN NaN \n", + "\n", + " num_downloads publication_date fingerprint author submission_date \\\n", + "1 NaN NaN NaN NaN NaN \n", + "2 NaN NaN NaN NaN NaN \n", + "3 NaN NaN NaN NaN NaN \n", + "9 NaN NaN NaN NaN NaN \n", + "29 NaN NaN NaN NaN NaN \n", + "\n", + " LearningResourceType MediaType \n", + "1 Web Page text \n", + "2 Web Page text \n", + "3 Web Page text \n", + "9 Web Page text \n", + "29 Book * Code Notebook text * code " + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def map_learning_resource(entry):\n", + " # Skip empty or NaN rows\n", + " if entry is None or (isinstance(entry, float) and pd.isna(entry)):\n", + " return \"\"\n", + " # Use a set to avoid duplicates\n", + " matches = set()\n", + " if isinstance(entry, list):\n", + " for item in entry:\n", + " if item in type_to_learning_resource:\n", + " matches.add(type_to_learning_resource[item])\n", + " elif entry in type_to_learning_resource:\n", + " matches.add(type_to_learning_resource[entry])\n", + " return \" * \".join([m for m in matches if m is not None])\n", + "\n", + "def map_media_type(entry):\n", + " # Skip empty or NaN rows\n", + " if entry is None or (isinstance(entry, float) and pd.isna(entry)):\n", + " return \"\"\n", + " # Use a set to avoid duplicates\n", + " matches = set()\n", + " if isinstance(entry, list):\n", + " for item in entry:\n", + " if item in type_to_media_type:\n", + " matches.add(type_to_media_type[item])\n", + " elif entry in type_to_media_type:\n", + " matches.add(type_to_media_type[entry])\n", + " return \" * \".join([m for m in matches if m is not None])\n", + "\n", + "# Apply the mapping functions\n", + "data[\"LearningResourceType\"] = data[\"type\"].apply(map_learning_resource)\n", + "data[\"MediaType\"] = data[\"type\"].apply(map_media_type)\n", + "\n", + "data.head()" + ] + }, + { + "cell_type": "markdown", + "id": "19f96ee9-e203-4b19-82b2-96a0e4088383", + "metadata": {}, + "source": [ + "#### 5. Change the author names to fit the DALIA format (for persons: surname, prename and for organizations: organization-name)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "827776ce-3be9-4b28-b664-687c7d4fc4ab", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_12251/970863209.py:37: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data[\"Authors\"] = data[\"authors\"].apply(normalize_author_format)\n" + ] + }, + { + "data": { + "text/html": [ + "
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authorsnametagstypeurllicenseevent_dateevent_locationdescriptionnum_downloadspublication_datefingerprintauthorsubmission_dateLearningResourceTypeMediaTypeAuthors
1[Marcelo Zoccoler]Running Deep-Learning Scripts in the BiA-PoL O...Python * Artificial Intelligence * Bioimage An...[Blog]https://biapol.github.io/blog/marcelo_zoccoler...CC-BY-4.0NaNNaNNaNNaNNaNNaNNaNNaNWeb PagetextZoccoler, Marcelo
2[Robert Haase]Browsing the Open Microscopy Image Data Resour...OMERO * Python[Blog]https://biapol.github.io/blog/robert_haase/bro...CC-BY-4.0NaNNaNNaNNaNNaNNaNNaNNaNWeb PagetextHaase, Robert
3[Mara Lampert]Getting started with Mambaforge and PythonPython * Conda * Mamba[Blog]https://biapol.github.io/blog/mara_lampert/get...CC-BY-4.0NaNNaNNaNNaNNaNNaNNaNNaNWeb PagetextLampert, Mara
9[Robert Haase]Managing Scientific Python environments using ...Python * Conda * Mamba[Blog]https://focalplane.biologists.com/2022/12/08/m...CC-BY-4.0NaNNaNNaNNaNNaNNaNNaNNaNWeb PagetextHaase, Robert
29[Robert Haase et al.]BioImage Analysis NotebooksPython * Bioimage Analysis[Book, Notebook]https://haesleinhuepf.github.io/BioImageAnalys...CC-BY-4.0 * BSD-3-ClauseNaNNaNNaNNaNNaNNaNNaNNaNBook * Code Notebooktext * codeRobert Haase et al.
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" + ], + "text/plain": [ + " authors name \\\n", + "1 [Marcelo Zoccoler] Running Deep-Learning Scripts in the BiA-PoL O... \n", + "2 [Robert Haase] Browsing the Open Microscopy Image Data Resour... \n", + "3 [Mara Lampert] Getting started with Mambaforge and Python \n", + "9 [Robert Haase] Managing Scientific Python environments using ... \n", + "29 [Robert Haase et al.] BioImage Analysis Notebooks \n", + "\n", + " tags type \\\n", + "1 Python * Artificial Intelligence * Bioimage An... [Blog] \n", + "2 OMERO * Python [Blog] \n", + "3 Python * Conda * Mamba [Blog] \n", + "9 Python * Conda * Mamba [Blog] \n", + "29 Python * Bioimage Analysis [Book, Notebook] \n", + "\n", + " url \\\n", + "1 https://biapol.github.io/blog/marcelo_zoccoler... \n", + "2 https://biapol.github.io/blog/robert_haase/bro... \n", + "3 https://biapol.github.io/blog/mara_lampert/get... \n", + "9 https://focalplane.biologists.com/2022/12/08/m... \n", + "29 https://haesleinhuepf.github.io/BioImageAnalys... \n", + "\n", + " license event_date event_location description \\\n", + "1 CC-BY-4.0 NaN NaN NaN \n", + "2 CC-BY-4.0 NaN NaN NaN \n", + "3 CC-BY-4.0 NaN NaN NaN \n", + "9 CC-BY-4.0 NaN NaN NaN \n", + "29 CC-BY-4.0 * BSD-3-Clause NaN NaN NaN \n", + "\n", + " num_downloads publication_date fingerprint author submission_date \\\n", + "1 NaN NaN NaN NaN NaN \n", + "2 NaN NaN NaN NaN NaN \n", + "3 NaN NaN NaN NaN NaN \n", + "9 NaN NaN NaN NaN NaN \n", + "29 NaN NaN NaN NaN NaN \n", + "\n", + " LearningResourceType MediaType Authors \n", + "1 Web Page text Zoccoler, Marcelo \n", + "2 Web Page text Haase, Robert \n", + "3 Web Page text Lampert, Mara \n", + "9 Web Page text Haase, Robert \n", + "29 Book * Code Notebook text * code Robert Haase et al. " + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "import re\n", + "\n", + "def normalize_author_format(authors):\n", + " # Helper function to reformat a single name\n", + " def reformat_name(name):\n", + " # Check if it's already in \"Surname, Prename\" format\n", + " if \",\" in name:\n", + " return name.strip()\n", + " # If in \"Prename Surname\" format, convert to \"Surname, Prename\"\n", + " parts = name.split()\n", + " et_al = ['et', 'al.']\n", + " if len(parts) == 2 and all(p not in et_al for p in parts):\n", + " return f\"{parts[1]}, {parts[0]}\"\n", + " if len(parts) == 3 and all(p not in et_al for p in parts):\n", + " return f\"{parts[2]}, {parts[0]}{parts[1]}\"\n", + " return name.strip() # Return unchanged if not a simple name format\n", + "\n", + "\n", + " # Convert single strings to lists for uniform processing\n", + " if isinstance(authors, str):\n", + " # Split on commas for inline lists like \"Prename Surname, Prename Surname\"\n", + " authors = [a.strip() for a in re.split(r\",\\s*|\\*|\\band\\b\", authors)]\n", + " elif isinstance(authors, list):\n", + " authors = [str(a).strip() for a in authors] # Ensure all elements are strings\n", + "\n", + " # Process each author entry\n", + " formatted_authors = []\n", + " for author in authors:\n", + " formatted_authors.append(reformat_name(author))\n", + "\n", + " # Join all processed names with \"*\"\n", + " return \" * \".join(formatted_authors)\n", + "\n", + "\n", + "# Apply the normalization function\n", + "data[\"Authors\"] = data[\"authors\"].apply(normalize_author_format)\n", + "\n", + "data.head()" + ] + }, + { + "cell_type": "markdown", + "id": "0983e2a5-3f93-4cfa-9f40-3f41233fe77e", + "metadata": {}, + "source": [ + "#### 6. Change to names of the columns that already fit the DALIA format to their corresponding name in DALIA" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "4213ac0c-3274-408e-a86d-bc9e61832de8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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TitleKeywordsLinkLicenseDescriptionPublicationDateLearningResourceTypeMediaTypeAuthors
1Running Deep-Learning Scripts in the BiA-PoL O...Python * Artificial Intelligence * Bioimage An...https://biapol.github.io/blog/marcelo_zoccoler...CC-BY-4.0NaNNaNWeb PagetextZoccoler, Marcelo
2Browsing the Open Microscopy Image Data Resour...OMERO * Pythonhttps://biapol.github.io/blog/robert_haase/bro...CC-BY-4.0NaNNaNWeb PagetextHaase, Robert
3Getting started with Mambaforge and PythonPython * Conda * Mambahttps://biapol.github.io/blog/mara_lampert/get...CC-BY-4.0NaNNaNWeb PagetextLampert, Mara
9Managing Scientific Python environments using ...Python * Conda * Mambahttps://focalplane.biologists.com/2022/12/08/m...CC-BY-4.0NaNNaNWeb PagetextHaase, Robert
29BioImage Analysis NotebooksPython * Bioimage Analysishttps://haesleinhuepf.github.io/BioImageAnalys...CC-BY-4.0 * BSD-3-ClauseNaNNaNBook * Code Notebooktext * codeRobert Haase et al.
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" + ], + "text/plain": [ + " Title \\\n", + "1 Running Deep-Learning Scripts in the BiA-PoL O... \n", + "2 Browsing the Open Microscopy Image Data Resour... \n", + "3 Getting started with Mambaforge and Python \n", + "9 Managing Scientific Python environments using ... \n", + "29 BioImage Analysis Notebooks \n", + "\n", + " Keywords \\\n", + "1 Python * Artificial Intelligence * Bioimage An... \n", + "2 OMERO * Python \n", + "3 Python * Conda * Mamba \n", + "9 Python * Conda * Mamba \n", + "29 Python * Bioimage Analysis \n", + "\n", + " Link \\\n", + "1 https://biapol.github.io/blog/marcelo_zoccoler... \n", + "2 https://biapol.github.io/blog/robert_haase/bro... \n", + "3 https://biapol.github.io/blog/mara_lampert/get... \n", + "9 https://focalplane.biologists.com/2022/12/08/m... \n", + "29 https://haesleinhuepf.github.io/BioImageAnalys... \n", + "\n", + " License Description PublicationDate \\\n", + "1 CC-BY-4.0 NaN NaN \n", + "2 CC-BY-4.0 NaN NaN \n", + "3 CC-BY-4.0 NaN NaN \n", + "9 CC-BY-4.0 NaN NaN \n", + "29 CC-BY-4.0 * BSD-3-Clause NaN NaN \n", + "\n", + " LearningResourceType MediaType Authors \n", + "1 Web Page text Zoccoler, Marcelo \n", + "2 Web Page text Haase, Robert \n", + "3 Web Page text Lampert, Mara \n", + "9 Web Page text Haase, Robert \n", + "29 Book * Code Notebook text * code Robert Haase et al. " + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Rename columns\n", + "data = data.rename(columns={'name': 'Title', 'license': 'License', 'url': 'Link', 'description': 'Description', 'publication_date': 'PublicationDate', 'tags': 'Keywords'})\n", + "\n", + "# Remove unwanted columns with no important data\n", + "data = data.drop(columns=['event_date', 'event_location', 'num_downloads', 'submission_date', 'fingerprint', 'author', 'type', 'authors'])\n", + "\n", + "data.head()" + ] + }, + { + "cell_type": "markdown", + "id": "69d43abb-d409-49bf-ab21-b79729441d1f", + "metadata": {}, + "source": [ + "#### 7. Introduce the **Community Column**: NFDI4BioImage if it is listed in the tags" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "5a79c41e-6037-44c2-8cdd-0988197de047", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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TitleKeywordsLinkLicenseDescriptionPublicationDateLearningResourceTypeMediaTypeAuthorsCommunity
1Running Deep-Learning Scripts in the BiA-PoL O...Python * Artificial Intelligence * Bioimage An...https://biapol.github.io/blog/marcelo_zoccoler...CC-BY-4.0NaNNaNWeb PagetextZoccoler, MarceloNone
2Browsing the Open Microscopy Image Data Resour...OMERO * Pythonhttps://biapol.github.io/blog/robert_haase/bro...CC-BY-4.0NaNNaNWeb PagetextHaase, RobertNone
3Getting started with Mambaforge and PythonPython * Conda * Mambahttps://biapol.github.io/blog/mara_lampert/get...CC-BY-4.0NaNNaNWeb PagetextLampert, MaraNone
9Managing Scientific Python environments using ...Python * Conda * Mambahttps://focalplane.biologists.com/2022/12/08/m...CC-BY-4.0NaNNaNWeb PagetextHaase, RobertNone
29BioImage Analysis NotebooksPython * Bioimage Analysishttps://haesleinhuepf.github.io/BioImageAnalys...CC-BY-4.0 * BSD-3-ClauseNaNNaNBook * Code Notebooktext * codeRobert Haase et al.None
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" + ], + "text/plain": [ + " Title \\\n", + "1 Running Deep-Learning Scripts in the BiA-PoL O... \n", + "2 Browsing the Open Microscopy Image Data Resour... \n", + "3 Getting started with Mambaforge and Python \n", + "9 Managing Scientific Python environments using ... \n", + "29 BioImage Analysis Notebooks \n", + "\n", + " Keywords \\\n", + "1 Python * Artificial Intelligence * Bioimage An... \n", + "2 OMERO * Python \n", + "3 Python * Conda * Mamba \n", + "9 Python * Conda * Mamba \n", + "29 Python * Bioimage Analysis \n", + "\n", + " Link \\\n", + "1 https://biapol.github.io/blog/marcelo_zoccoler... \n", + "2 https://biapol.github.io/blog/robert_haase/bro... \n", + "3 https://biapol.github.io/blog/mara_lampert/get... \n", + "9 https://focalplane.biologists.com/2022/12/08/m... \n", + "29 https://haesleinhuepf.github.io/BioImageAnalys... \n", + "\n", + " License Description PublicationDate \\\n", + "1 CC-BY-4.0 NaN NaN \n", + "2 CC-BY-4.0 NaN NaN \n", + "3 CC-BY-4.0 NaN NaN \n", + "9 CC-BY-4.0 NaN NaN \n", + "29 CC-BY-4.0 * BSD-3-Clause NaN NaN \n", + "\n", + " LearningResourceType MediaType Authors Community \n", + "1 Web Page text Zoccoler, Marcelo None \n", + "2 Web Page text Haase, Robert None \n", + "3 Web Page text Lampert, Mara None \n", + "9 Web Page text Haase, Robert None \n", + "29 Book * Code Notebook text * code Robert Haase et al. None " + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def include_community(entry):\n", + " if isinstance(entry, list):\n", + " if any(e.lower() == 'nfdi4bioimage' for e in entry if isinstance(e, str)):\n", + " return 'NFDI4Bioimage'\n", + " elif isinstance(entry, str):\n", + " if entry.lower() == 'nfdi4bioimage':\n", + " return 'NFDI4Bioimage'\n", + " return None\n", + "\n", + "\n", + "# Apply the function\n", + "data['Community'] = data['Keywords'].apply(include_community)\n", + "data.head()" + ] + }, + { + "cell_type": "markdown", + "id": "78b5ec11-6e2a-4b6d-8ff3-faa58325b232", + "metadata": {}, + "source": [ + "### 8. Introduce the **FileFormat** Column by comparing the MediaType to a FileFormat list" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "89a3f72f-e614-4fe3-afc7-fc22345e104e", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "import requests\n", + "import re\n", + "import time\n", + "\n", + "# Function to extract record ID from a Zenodo link\n", + "def extract_zenodo_record_id(url):\n", + " # Regex to match Zenodo record links and extract the record ID\n", + " match = re.search(r\"https://zenodo.org/records/(\\d+)\", url)\n", + " return match.group(1) if match else None\n", + "\n", + "# Function to fetch file formats from Zenodo using the record ID\n", + "def fetch_file_formats(record_id):\n", + " if not record_id:\n", + " return None\n", + " api_url = f\"https://zenodo.org/api/records/{record_id}\"\n", + " try:\n", + " time.sleep(1) # Add a 1-second delay between requests\n", + " response = requests.get(api_url)\n", + " response.raise_for_status() # Raise an error for non-2xx responses\n", + " data = response.json()\n", + " file_types = {\n", + " file[\"key\"].split(\".\")[-1].lower()\n", + " for file in data.get(\"files\", [])\n", + " if \".\" in file[\"key\"]\n", + " }\n", + " return \" * \".join(sorted(file_types)) if file_types else None\n", + " except Exception as e:\n", + " print(f\"Error fetching file formats for record ID {record_id}: {e}\")\n", + " return None\n", + "\n", + "# Function to process a single URL or a list of URLs\n", + "def process_links(link_input):\n", + " if isinstance(link_input, str):\n", + " # Single URL case\n", + " record_id = extract_zenodo_record_id(link_input)\n", + " if record_id:\n", + " return fetch_file_formats(record_id)\n", + " elif isinstance(link_input, list):\n", + " # List of URLs case\n", + " for link in link_input:\n", + " record_id = extract_zenodo_record_id(link.strip())\n", + " if record_id:\n", + " file_format = fetch_file_formats(record_id)\n", + " if file_format: # Return on first valid result\n", + " return file_format\n", + " return None # Return None if no valid formats are found\n", + "\n", + "# Process the DataFrame\n", + "data[\"FileFormat\"] = data[\"Link\"].apply(process_links)" + ] + }, + { + "cell_type": "markdown", + "id": "74d9702a-00cb-4408-8815-c26fd9a4fdee", + "metadata": {}, + "source": [ + "Additionally map the Type Column to certain File Formats, if it is not already filled from the previous step. (only works for certain MediaTypes)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "965d0a41-9762-47cb-8bac-7042d35960c8", + "metadata": {}, + "outputs": [], + "source": [ + "def map_file_format(media_type, file_format):\n", + " # If FileFormat already has a valid entry, return it as is\n", + " if file_format is not None and file_format.strip() != \"\":\n", + " return file_format\n", + " # Map media types to specific file formats\n", + " if media_type == \"audio\":\n", + " return \".mp3\"\n", + " elif media_type == \"video\":\n", + " return \".mp4\"\n", + " else:\n", + " return \"\" # Return empty string if no mapping is needed\n", + "\n", + "# Apply the mapping function\n", + "data[\"FileFormat\"] = data.apply(\n", + " lambda row: map_file_format(row[\"MediaType\"], row[\"FileFormat\"]),\n", + " axis=1\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "461e7dc1-7572-4664-887d-ec36f4ed2656", + "metadata": {}, + "source": [ + "Now also correct the Format of the Link Column:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "0de3c9ba-a0b8-434d-bd79-896ad87cf1c1", + "metadata": {}, + "outputs": [], + "source": [ + "# Make * Delimiter for the Links if there is more than one for some entries\n", + "data[\"Link\"] = data[\"Link\"].apply(lambda x: ' * '.join(x) if isinstance(x, list) else x) #URL" + ] + }, + { + "cell_type": "markdown", + "id": "22a0d7cc-e4d7-4c77-807c-662fb44ffbe0", + "metadata": {}, + "source": [ + "#### 9. Extract the Language of each Entry\n", + "This is done using the [xlm-roberta-base-language-detection](https://huggingface.co/papluca/xlm-roberta-base-language-detection) model via the transformers package pipeline." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "5f3abf90-990f-4ccc-8da3-05bb26e6538e", + "metadata": {}, + "outputs": [], + "source": [ + "from transformers import pipeline\n", + "\n", + "model_ckpt = \"papluca/xlm-roberta-base-language-detection\"\n", + "pipe = pipeline(\"text-classification\", model=model_ckpt)\n", + "\n", + "def detect_language(text):\n", + " lang = pipe([text], top_k=1, truncation=True)[0][0][\"label\"]\n", + " return lang if lang in [\"en\", \"de\"] else \"\"\n", + "\n", + "data[\"Language\"] = data[\"Title\"].apply(detect_language)" + ] + }, + { + "cell_type": "markdown", + "id": "0cd61d11-5907-43ba-968c-bb851d003631", + "metadata": {}, + "source": [ + "### Export the data to a csv that now fits the DALIA Format" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "63071e24-8d4e-4885-ae78-74669bbe5557", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Exported 330 rows.\n" + ] + } + ], + "source": [ + "# save selected data\n", + "data.to_csv(destination, index=False)\n", + "\n", + "num_rows = data.shape[0]\n", + "print(f\"Exported {num_rows} rows.\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 84dcbc3ed3dce4dd163a10e2ee3d5cc99df19d16 Mon Sep 17 00:00:00 2001 From: Lea Gihlein <85543649+lea-33@users.noreply.github.com> Date: Wed, 29 Jan 2025 13:22:42 +0100 Subject: [PATCH 4/8] Update update-dalia-csv.yml changed the last line to enable pushing --- .github/workflows/update-dalia-csv.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/update-dalia-csv.yml b/.github/workflows/update-dalia-csv.yml index b3672a60..70d33ad7 100644 --- a/.github/workflows/update-dalia-csv.yml +++ b/.github/workflows/update-dalia-csv.yml @@ -41,6 +41,6 @@ jobs: git diff --staged --quiet || git commit -m "Auto-update CSV after modifying nfdi4bioimage.yml" # Authenticate and push - git push https://x-access-token:${{ secrets.GITHUB_TOKEN }}@github.com/${{ github.repository }}.git HEAD:${{ github.ref }} + git push https://x-access-token:${{ secrets.GITHUB_TOKEN }}@github.com/${{ github.repository }}.git HEAD:${{ github.head_ref }} From e269783c0d731a39d74c1d2bb44eab3ae24f8b13 Mon Sep 17 00:00:00 2001 From: Lea Gihlein <85543649+lea-33@users.noreply.github.com> Date: Wed, 29 Jan 2025 13:31:50 +0100 Subject: [PATCH 5/8] Update update-dalia-csv.yml add pull before pushing --- .github/workflows/update-dalia-csv.yml | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/.github/workflows/update-dalia-csv.yml b/.github/workflows/update-dalia-csv.yml index 70d33ad7..84b24ada 100644 --- a/.github/workflows/update-dalia-csv.yml +++ b/.github/workflows/update-dalia-csv.yml @@ -39,7 +39,9 @@ jobs: git add . git diff --staged --quiet || git commit -m "Auto-update CSV after modifying nfdi4bioimage.yml" - + + git pull --rebase origin ${{ github.head_ref }} + # Authenticate and push git push https://x-access-token:${{ secrets.GITHUB_TOKEN }}@github.com/${{ github.repository }}.git HEAD:${{ github.head_ref }} From b0aebf5009a97621137a18b48b43b9a16adbf1d0 Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" Date: Wed, 29 Jan 2025 12:38:25 +0000 Subject: [PATCH 6/8] Auto-update CSV after modifying nfdi4bioimage.yml --- docs/export/DALIA_training_materials.csv | 1413 +++++++++++++ scripts/Export_to_DALIA.nbconvert.ipynb | 2400 ++++++++++++++++++++++ 2 files changed, 3813 insertions(+) create mode 100644 docs/export/DALIA_training_materials.csv create mode 100644 scripts/Export_to_DALIA.nbconvert.ipynb diff --git a/docs/export/DALIA_training_materials.csv b/docs/export/DALIA_training_materials.csv new file mode 100644 index 00000000..2a28b4bc --- /dev/null +++ b/docs/export/DALIA_training_materials.csv @@ -0,0 +1,1413 @@ +Title,Keywords,Link,License,Description,PublicationDate,LearningResourceType,MediaType,Authors,Community,FileFormat,Language +Running Deep-Learning Scripts in the BiA-PoL Omero Server,Python * Artificial Intelligence * Bioimage Analysis,https://biapol.github.io/blog/marcelo_zoccoler/omero_scripts/readme.html,CC-BY-4.0,,,Web Page,text,"Zoccoler, Marcelo",,,en +Browsing the Open Microscopy Image Data Resource with Python,OMERO * Python,https://biapol.github.io/blog/robert_haase/browsing_idr/readme.html,CC-BY-4.0,,,Web Page,text,"Haase, Robert",,,en +Getting started with Mambaforge and Python,Python * Conda * Mamba,https://biapol.github.io/blog/mara_lampert/getting_started_with_mambaforge_and_python/readme.html,CC-BY-4.0,,,Web Page,text,"Lampert, Mara",,,en +"Managing Scientific Python environments using Conda, Mamba and friends",Python * Conda * Mamba,https://focalplane.biologists.com/2022/12/08/managing-scientific-python-environments-using-conda-mamba-and-friends/,CC-BY-4.0,,,Web Page,text,"Haase, Robert",,,en +BioImage Analysis Notebooks,Python * Bioimage Analysis,https://haesleinhuepf.github.io/BioImageAnalysisNotebooks/intro.html,CC-BY-4.0 * BSD-3-Clause,,,Book * Code Notebook,text * code,Robert Haase et al.,,,en +Introduction to Bioimage Analysis,Python * Imagej * Bioimage Analysis,https://bioimagebook.github.io/index.html,CC-BY-4.0,,,Book * Code Notebook,text * code,"Bankhead, Pete",,,en +Generative artificial intelligence for bio-image analysis,Python * Bioimage Analysis * Artificial Intelligence,https://f1000research.com/slides/12-971,CC-BY-4.0,,,Presentation,presentation,"Haase, Robert",,,en +Train-the-Trainer Concept on Research Data Management,Research Data Management,https://zenodo.org/record/4071471 * https://doi.org/10.5281/zenodo.4071471,CC-BY-4.0,"Within the project FDMentor, a German Train-the-Trainer Programme on Research Data Management (RDM) was developed and piloted in a series of workshops. The topics cover many aspects of research data management, such as data management plans and the publication of research data, as well as didactic units on learning concepts, workshop design and a range of didactic methods. + +After the end of the project, the concept was supplemented and updated by members of the Sub-Working Group Training/Further Education (UAG Schulungen/Fortbildungen) of the DINI/nestor Working Group Research Data (DINI/nestor-AG Forschungsdaten). The newly published English version of the Train-the-Trainer Concept contains the translated concept, the materials and all methods of the Train-the-Trainer Programme. Furthermore, additional English references and materials complement this version.",2020-11-04,Book,text,"Biernacka, Katarzyna * Bierwirth, Maik * Buchholz, Petra * Dolzycka, Dominika * Helbig, Kerstin * Neumann, Janna * Odebrecht, Carolin * Wiljes, Cord * Wuttke, Ulrike",,,en +Efficiently starting institutional research data management,Research Data Management,https://zenodo.org/record/3490058 * https://doi.org/10.5281/zenodo.3490058,CC-BY-4.0,"Researchers are increasingly often confronted with research data management (RDM) topics during their work. Higher education institutions therefore begin to offer services for RDM at some point to give support and advice. However, many groundbreaking decisions have to be made at the very beginning of RDM services. Priorities must be set and policies formulated. Likewise, the staff must first be qualified in order to provide advice and adequately deal with the manifold problems awaiting. +The FDMentor project has therefore bundled the expertise of five German universities with different experiences and levels of RDM knowledge to jointly develop strategies, roadmaps, guidelines, and open access training material. Humboldt-Universität zu Berlin, Freie Universität Berlin, Technische Universität Berlin, University of Potsdam, and European University Viadrina Frankfurt (Oder) have worked together on common solutions that are easy to adapt. With funding of the German Federal Ministry of Education and Research, the collaborative project addressed four problem areas: strategy development, legal issues, policy development, and competence enhancement. The aim of the project outcomes is to provide other higher education institutions with the best possible support for the efficient introduction of research data management. Therefore, all project results are freely accessible under the CC-BY 4.0 international license. The early involvement of the community in the form of workshops and the collection of feedback has proven its worth: the FDMentor strategies, roadmaps, guidelines, and training materials are applied and adapted beyond the partner universities.",2019-10-15,Text,text,"Biernacka, Katarzyna * Cortez, Katrin * Helbig, Kerstin",,,en +ONBI Image Analysis Course,Python * Bioimage Analysis,https://github.com/dwaithe/ONBI_image_analysis,GPL-2.0,This repository contains the materials for the University of Oxford DTC ONBI Image Analysis course.,,Code Notebook,code,"Jenkinson, Mark * Rittscher, Jens * Waithe, Dominic",,,en +A Fiji Scripting Tutorial,Imagej * Bioimage Analysis,https://syn.mrc-lmb.cam.ac.uk/acardona/fiji-tutorial/,CC0-1.0,,,Code Notebook,code,"Cardona, Albert",,,en +ImageJ2 API-beating,Neubias * Imagej * Bioimage Analysis,https://git.mpi-cbg.de/rhaase/lecture_imagej2_dev,BSD-3-Clause,,,Presentation,presentation,"Haase, Robert",,,en +Multi-view fusion,Neubias * Imagej * Bioimage Analysis,https://git.mpi-cbg.de/rhaase/lecture_multiview_registration,BSD-3-Clause,Lecture slides of a session on Multiview Fusion in Fiji,,Presentation,presentation,"Haase, Robert",,,en +"Tracking Theory, TrackMate, and Mastodon",Neubias * Imagej * Bioimage Analysis,https://git.mpi-cbg.de/rhaase/lecture_tracking_trackmate,BSD-3-Clause,Lecture slides of a session on Cell Tracking in Fiji,,Presentation,presentation,"Haase, Robert",,,en +Working with pixels,Neubias * Imagej * Bioimage Analysis,https://git.mpi-cbg.de/rhaase/lecture_working_with_pixels,BSD-3-Clause,,,Presentation,presentation,"Haase, Robert",,,en +Working with objects in 2D and 3D,Neubias * Imagej * Bioimage Analysis,https://git.mpi-cbg.de/rhaase/lecture_working_with_objects_in_2d_and_3d,BSD-3-Clause,,,Presentation,presentation,"Haase, Robert",,,en +Image processing with Python,Python,https://github.com/guiwitz/Python_image_processing,MIT,"Series of Notebooks exposing how to do mostly basic and some advanced image processing using Python. It uses standard packages (Numpy, Maplotlib) and for the image processing parts is heavily based on the scikit-image package.",,Code Notebook,code,"Witz, Guillaume",,,en +Bioimage analysis with Napari,Python * Napari * Bioimage Analysis,https://www.fabriziomusacchio.com/teaching/teaching_bioimage_analysis/,CC-BY-ND-SA-4.0,,,Other,,"Musacchio, Fabrizio",,,en +Research Data Management Seminar - Slides,Research Data Management,https://zenodo.org/record/6602101 * https://doi.org/10.5281/zenodo.6602101,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 + + + ",2022-05-18,Presentation,presentation,"Chiesa, Della * Stefano",,,en +A biologist’s guide to planning and performing quantitative bioimaging experiments,,https://doi.org/10.1371/journal.pbio.3002167 * https://www.bioimagingguide.org/,BSD-3-Clause,,,Article * Other,text,"Senft, RebeccaA. * Diaz-Rohrer, Barbara * Colarusso, Pina * Swift, Lucy * Jamali, Nasim * Jambor, Helena * Pengo, Thomas * Brideau, Craig * Llopis, PaulaMontero * Uhlmann, Virginie * Kirk, Jason * Gonzales, KevinAndrew * Bankhead, Peter * Edward L. Evans III * Eliceiri, KevinW. * Cimini, BethA.",,,en +Basics of Image Processing and Analysis,Bioimage Analysis,https://github.com/miura/ij_textbook1/raw/76b51338e1f006c580b6f0f5cfc48fe02fba38d7/CMCIBasicCourse201102Bib.pdf,ALL RIGHTS RESERVED,,,Book,text,"Miura, Kota",,,en +Introduction to Image Analysis with Fiji,Imagej * Fiji * Bioimage Analysis,https://github.com/mpicbg-scicomp/CourseIntroToIA,CC-BY-NC-4.0,,,,,"Haase, Robert * Lombardot, Benoit",,,en +Making your package available on conda-forge,Deployment * Python,https://kevinyamauchi.github.io/open-image-data/how_tos/conda_forge_packaging.html,CC-BY-4.0,,,Text,text,"Yamauchi, Kevin",,,en +"I3D:bio's OMERO training material: Re-usable, adjustable, multi-purpose slides for local user training",OMERO * Research Data Management * Nfdi4Bioimage * I3Dbio,https://zenodo.org/records/8323588 * https://www.youtube.com/playlist?list=PL2k-L-zWPoR7SHjG1HhDIwLZj0MB_stlU * https://doi.org/10.5281/zenodo.8323588,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.",2023-11-13,Presentation,presentation * video,"Schmidt, Christian * Bortolomeazzi, Michele * Boissonnet, Tom * Fortmann-Grote, Carsten * Dohle, Julia * Zentis, Peter * Kandpal, Niraj * Kunis, Susanne * Zobel, Thomas * Weidtkamp-Peters, Stefanie * Ferrando-May, Elisa",,odp * pdf * pptx,en +IAFIG-RMS Python for Bioimage Analysis Course,Bioimage Analysis,https://github.com/RMS-DAIM/Python-for-Bioimage-Analysis,GPL-3.0,,,Code Notebook,code,"Barbotin, Aurelien * Nelson, Chas * Waithe, Dominic * Tarkowska, Ola(Alexandra) * Kundegorski, Mikolaj * Cross, Stephen * Fallesen, Todd",,,en +numpy pandas course,Python,https://github.com/guiwitz/NumpyPandas_course,BSD-3-Clause,,,Code Notebook,code,"Witz, Guillaume",,, +Python for Microscopists,Python * Bioimage Analysis,https://github.com/bnsreenu/python_for_microscopists,MIT,,,Other * Code Notebook,code,"Bhattiprolu, Sreenivas",,,en +Scientific Visualization: Python + Matplotlib,Python,https://github.com/rougier/scientific-visualization-book * https://inria.hal.science/hal-03427242/document,CC-BY-ND-SA-4.0,,,Book,text,"Rougier, NicolasP.",,, +Teaching Bioimage Analysis with Python,Python * Bioimage Analysis,https://github.com/CamachoDejay/teaching-bioimage-analysis-python,MIT,,,Tutorial,,"Camacho, Rafael",,,en +Teaching ImageJ FIJI,Fiji * Bioimage Analysis,https://github.com/CamachoDejay/Teaching-ImageJ-FIJI,MIT,,,Tutorial,,"Camacho, Rafael",,, +"Fundamentals of image analysis in Python with scikit-image, napari, and friends",Python * Bioimage Analysis,https://github.com/jni/halfway-to-i2k-skimage-napari,BSD-3-Clause,,,Code Notebook,code,"Nunez-Iglesias, Juan",,,en +"Image analysis and visualization in Python with scikit-image, napari, and friends",Python * Bioimage Analysis,https://github.com/scipy-2023-image-analysis/tutorial,BSD-3-Clause,,,Code Notebook,code,"Nunez-Iglesias, Juan",,,en +quantixed/TheDigitalCell: First complete code set,Bioimage Analysis,https://github.com/quantixed/TheDigitalCell * https://zenodo.org/records/2643411 * https://doi.org/10.5281/zenodo.2643411,GPL-3.0,First complete code set for The Digital Cell book.,2019-04-17,,code,"Royle, Stephen",,zip,en +Python BioImage Analysis Tutorial,Python * Bioimage Analysis,https://github.com/WhoIsJack/python-bioimage-analysis-tutorial,MIT,,,,,"Hartmann, Jonas",,,en +Image Processing with Python,Python * Bioimage Analysis,https://datacarpentry.org/image-processing/ * https://github.com/datacarpentry/image-processing,CC-BY-4.0,,,Other,,"Deppen, Jacob * Meechan, Kimberly * Palmquist, David * Schiller, Ulf * Turner, Robert * Corvellec, Marianne * Hodges, Toby * et al.",,,en +Deep Vision and Graphics,Python * Artificial Intelligence,https://github.com/yandexdataschool/deep_vision_and_graphics,MIT,,,Code Notebook,code,"Yurchenko, Victor * Ratnikov, Fedor * Checkalina, Viktoriia",,,en +Collection of teaching material for deep learning for (biomedical) image analysis,Artificial Intelligence * Bioimage Analysis,https://github.com/constantinpape/dl-teaching-resources,MIT,,,,,"Pape, Constantin",,,en +ZeroCostDL4Mic: exploiting Google Colab to develop a free and open-source toolbox for Deep-Learning in microscopy,,https://github.com/HenriquesLab/ZeroCostDL4Mic * https://www.nature.com/articles/s41467-021-22518-0 * https://doi.org/10.1038/s41467-021-22518-0,MIT,,,Other * Code Notebook,code,"Chamier, Lucasvon * Laine, RomainF. * Jukkala, Johanna * Spahn, Christoph * Krentzel, Daniel * Nehme, Elias * Lerche, Martina * Hernández-pérez, Sara * Mattila, Pieta * Karinou, Eleni * Holden, Séamus * Solak, AhmetCan * Krull, Alexander * Buchholz, Tim-Oliver * Jones, MartinL * Royer, LoicAlain * Leterrier, Christophe * Shechtman, Yoav * Jug, Florian * Heilemann, Mike * Jacquemet, Guillaume * Henriques, Ricardo",,,en +DL4MicEverywhere,,https://github.com/HenriquesLab/DL4MicEverywhere,CC-BY-4.0,,,Other * Code Notebook,code,"Hidalgo, Iván * et al.",,,en +CellTrackColab,,https://www.biorxiv.org/content/10.1101/2023.10.20.563252v2 * https://github.com/guijacquemet/CellTracksColab,MIT,,,Other * Code Notebook,code,"Jacquemet, Guillaume",,, +Image analysis course material,,https://github.com/tischi/image-analysis-course-material,MIT,"Training materials about image registration, big warp and elastix",,,,"Tischer, Christian",,,en +Image processing for beginners,Python * Bioimage Analysis,https://github.com/guiwitz/PyImageCourse_beginner,BSD-3-Clause,,,Code Notebook,code,"Witz, Guillaume",,,en +Image-based Profiling Handbook,Bioimage Analysis,https://github.com/cytomining/profiling-handbook * https://cytomining.github.io/profiling-handbook/,CC0-1.0,,,Book,text,"Cimini, Beth * Becker, Tim * Singh, Shantanu * Way, Gregory * Abbasi, Hamdah * Tromans-Coia, Callum",,,en +Methods in bioimage analysis,Bioimage Analysis,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,CC-BY-4.0,,,Tutorial * Presentation,presentation * video,"Tischer, Christian",,,en +ilastik: interactive machine learning for (bio)image analysis,Artificial Intelligence * Bioimage Analysis,https://zenodo.org/doi/10.5281/zenodo.4330625,CC-BY-4.0,,,Presentation,presentation,"Kreshuk, Anna * Kutra, Dominik",,, +Nextflow: Scalable and reproducible scientific workflows,Workflow Engine,https://zenodo.org/records/4334697 * https://doi.org/10.5281/zenodo.4334697,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.",2020-12-17,Presentation,presentation,"Evan, Floden * Paolo, DiTommaso",,pdf,en +QuPath: Open source software for analysing (awkward) images,Bioimage Analysis,https://zenodo.org/records/4328911 * https://doi.org/10.5281/zenodo.4328911,CC-BY-4.0,Slides from the CZI/EOSS online meeting in December 2020.,2020-12-16,Presentation,presentation,"Bankhead, Peter",,pdf,en +Creating open computational curricula,,https://zenodo.org/records/4317149 * https://doi.org/10.5281/zenodo.4317149,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.",2020-12-11,Presentation,presentation,"Jordan, Kari * Kamvar, Zhian * Hodges, Toby",,pdf,en +Parallelization and heterogeneous computing: from pure CPU to GPU-accelerated image processing,,https://f1000research.com/slides/11-1171 * https://doi.org/10.7490/f1000research.1119154.1,CC-BY-4.0,,,Presentation,presentation,"Haase, Robert",,,en +Adding a Workflow to BIAFLOWS,Neubias * Bioimage Analysis,https://github.com/RoccoDAnt/Defragmentation_TrainingSchool_EOSC-Life_2022/blob/main/Slides/Adding_a_workflow_to_BIAFLOWS.pdf,BSD-2-Clause,,,Presentation,presentation,"Tosi, Sébastien * Baecker, Volker * Pavie, Benjamin",,,en +BioImage Data Analysis,Neubias * Bioimage Analysis,https://analyticalscience.wiley.com/do/10.1002/was.00050003/full/bioimagedataanalysis.pdf,ALL RIGHTS RESERVED,,,Book,text,"Miura, Kota",,,en +Open Image Data Handbook,Neubias * Research Data Management * Napari * Python * Bioimage Analysis,https://kevinyamauchi.github.io/open-image-data/intro.html,CC-BY-4.0,,,Book * Code Notebook,text * code,"Yamauchi, Kevin",,,en +"Bio-image analysis, biostatistics, programming and machine learning for computational biology",Python * Bioimage Analysis * Napari,https://github.com/BiAPoL/Bio-image_Analysis_with_Python,CC-BY-4.0,,,Code Notebook,code,"Poetsch, Anna * Dresden, Biotec * Zoccoler, MarceloLeomil * Müller, JohannesRichard * Haase, Robert",,,en +Bio-Image Data Strudel for Workshop on Research Data Management in TU Dresden Core Facilities,Research Data Management * Tu Dresden * Bioimage Data * Nfdi4Bioimage,https://zenodo.org/records/10083555 * https://doi.org/10.5281/zenodo.10083555,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.  ,2023-11-08,Presentation,presentation,"Wetzker, Cornelia",,pdf * pptx,en +Bio-image Analysis with the Help of Large Language Models,Large Language Models * Python,https://zenodo.org/records/10815329 * https://doi.org/10.5281/zenodo.10815329,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.",2024-03-13,Presentation,presentation,"Haase, Robert",,odp * pdf * pptx,en +PoL Bio-Image Analysis Training School on GPU-Accelerated Image Analysis,Gpu * Clesperanto * Dask * Python,https://github.com/BiAPoL/PoL-BioImage-Analysis-TS-GPU-Accelerated-Image-Analysis/,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.",,Code Notebook,code,"Rigaud, Stephane * Northan, Brian * Korten, Till * Jurenaite, Neringa * Kulkarni, ApurvDeepak * Steinbach, Peter * Starke, Sebastian * Soltwedel, Johannes * Albert, Marvin * Haase, Robert",,,en +Bio-image Data Science,Image Data Management * Deep Learning * Microscopy Image Analysis * Python,https://github.com/ScaDS/BIDS-lecture-2024,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.,,Code Notebook,code,"Haase, Robert",,, +Introduction to Deep Learning for Microscopy,Deep Learning * Pytorch * Segmentation * Python,https://github.com/computational-cell-analytics/dl-for-micro,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.,,Code Notebook,code,"Pape, Costantin",,,en +QM Course Lectures on Bio-Image Analysis with napari 2024,Napari * Python,https://zoccoler.github.io/QM_Course_Bio_Image_Analysis_with_napari_2024,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.",,Code Notebook,code,"Zoccoler, MarceloLeomil",,,en +QI 2024 Analysis Lab Manual,Segmentation * Python,https://bethac07.github.io/qi_2024_analysis_lab_manual/intro.html,CC-BY-4.0,"This book contains the quantitative analysis labs for the QI CSHL course, 2024",,Code Notebook,code,"Cimini, Beth * Jug, Florian * 2024, QI",,,en +My Journey Through Bioimage Analysis Teaching Methods From Classroom to Cloud,Teaching,https://zenodo.org/records/10679054 * https://doi.org/10.5281/zenodo.10679054,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. ",2024-02-19,Presentation,presentation,"Fazeli, Elnaz",,pdf,en +Cultivating Open Training,Teaching,https://zenodo.org/records/10654775 * https://doi.org/10.5281/zenodo.10654775,CC-BY-4.0,"In these slides introduce current challenges and potential solutions for openly sharing training materials, softly focusing on bio-image analysis. In this field a lot of training materials circulate in private channels, but openly shared, reusable materials, according to the FAIR-principles, are still rare. Using the CC-BY license and publicly acessible repositories are proposed to fill this gap.",2024-02-14,Presentation,presentation,"Haase, Robert",,pptx,en +[N4BI AHM] Welcome to BioImage Town,Research Data Management,https://zenodo.org/records/10008465 * https://doi.org/10.5281/zenodo.10008465,CC-BY-4.0,"Keynote at the NFDI4BIOIMAGE All-Hands Meeting in Düsseldorf, Germany, October 16, 2023.",2023-10-16,Presentation,presentation,"Moore, Josh",,pdf, +Elastix tutorial,Image Registration * Itk * Elastix,https://m-albert.github.io/elastix_tutorial/intro.html,BSD LICENSE,Tutorial material for teaching the basics of (itk-)elastix for image registration in microscopy images.,,Other * Code Notebook,code,"Albert, Marvin",,, +Intro napari slides,Napari,https://thejacksonlaboratory.github.io/intro-napari-slides/#/section,MIT,Introduction to napari workshop run at JAX (Spring 2024).,,Presentation,presentation,"Sobolewski, Peter",,, +NeubiasPasteur2023_AdvancedCellPose,Cellpose * Segmentation,https://github.com/gletort/NeubiasPasteur2023_AdvancedCellPose,BSD-3-Clause,Tutorial for running CellPose advanced functions,,Other,,"Letort, Gaelle",,, +Microscopy data analysis: machine learning and the BioImage Archive,Microscopy Image Analysis * Python * Deep Learning,https://www.ebi.ac.uk/training/materials/microscopy-data-analysis-machine-learning-and-the-bioimage-archive-materials/,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.",,Presentation,presentation * video,"Iudin, Andrii * Foix-Romero, Anna * Kreshuk, Anna * Athar, Awais * Cimini, Beth * Kutra, Dominik * Estibalis Gomez de Mariscal * Wong, Frances * Jacquemet, Guillaume * Narayan, Kedar * Weigert, Martin * Gogoberidze, Nodar * Salih, Osman * Walczysko, Petr * Conrad, Ryan * Weyend, Simone * Somasundharam, SriramSundar * Sivagurunathan, Suganya * Sarkans, Ugis",,,en +Kollaboratives Arbeiten und Versionskontrolle mit Git,Research Data Management * FAIR-Principles * Git * Zenodo,https://zenodo.org/records/10972692 * https://doi.org/10.5281/zenodo.10972692,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 +",2024-04-15,Presentation,presentation,"Haase, Robert",,pdf * pptx,de +"Open Science, Sharing & Licensing",Research Data Management * Open Access * FAIR-Principles * Licensing,https://zenodo.org/records/10990107 * https://doi.org/10.5281/zenodo.10990107,CC-BY-4.0,"Wir tauchen ein in die Welt der Open Science und definieren Begriffe wie Open Source, Open Access und die FAIR-Prinzipien (Findable, Accessible, Interoperable and Reuasable). Wir diskutieren, wie diese Methoden der [wissenschaftlichen] Kommunikation und des Datenmanagements die Welt verändern und wie wir sie praktisch in unsere Arbeit integrieren können. Dabei spielen Aspekte wie Copyright und Lizenzierung eine wichtige Rolle.",2024-04-18,Presentation,presentation,"Haase, Robert",,pdf * pptx,en +Datenmanagement,Research Data Management,https://zenodo.org/records/10970869 * https://doi.org/10.5281/zenodo.10970869,CC-BY-4.0,"In dieser Data Management Session wird der Lebenszyklus von Daten näher beleuchtet. Wie entstehen Daten, was passiert mit ihnen, wenn sie verarbeitet werden? Wem gehören die Daten und wer ist dafür verantwortlich, sie zu veröffentlichen, zu archivieren und gegebenenfalls wiederzuverwenden? Wir werden einen Datenmanagementplan in Gruppenarbeit entwerfen, ggf. mit Hilfe von ChatGPT.",2024-04-14,Presentation,presentation,"Haase, Robert",,docx * pdf * pptx, +Cultivating Open Training to advance Bio-image Analysis,Research Data Management * Licensing * FAIR-Principles,https://zenodo.org/records/11066250 * https://doi.org/10.5281/zenodo.11066250,CC-BY-4.0," +These slides introduce current challenges and potential solutions for openly sharing training materials, focusing on bio-image analysis. In this field a lot of training materials circulate in private channels, but openly shared, reusable materials, according to the FAIR-principles, are still rare. Using the CC-BY license and publicly acessible repositories are proposed to fill this gap. +",2024-04-25,Presentation,presentation,"Haase, Robert",,odp * pdf * pptx,en +FAIRy deep-learning for bioImage analysis,Deep Learning * FAIR-Principles * Microscopy Image Analysis,https://f1000research.com/slides/13-147,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.",,Presentation,presentation,Estibaliz Gómez de Mariscal,,,en +OMERO - HCS analysis pipeline using Jupyter Notebooks,Teaching * Bioimage Analysis * Notebooks * Python * OMERO,https://github.com/rmassei/2024-jn-omero-pipeline,MIT,"Material and solutions for the course 'Bioimage data management and analysis with OMERO' held in Heidelberg (13th May 2024) - Module 3 (1.45 pm - 3.45 pm): OMERO and Jupyter Notebooks. Main goal of the workflow is to show the potential of JN to perform reproducible image analysis in connection with an OMERO instance. In this specific example, we are performing a simple nuclei segmentation from raw images uploaded in OMERO.",,Other,,"Massei, Riccardo",,,en +Euro-BioImaging's Template for Research Data Management Plans,Bioimage Analysis * FAIR-Principles * Research Data Management,https://zenodo.org/records/11473803 * https://doi.org/10.5281/zenodo.11473803,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. ",2024-06-04,Tutorial * Other,,"Kemmer, Isabel * ERIC, Euro-BioImaging",,pdf,en +Euro-BioImaging's Guide to FAIR BioImage Data - Practical Tasks,Bioimage Analysis * FAIR-Principles * Research Data Management,https://zenodo.org/records/11474407 * https://doi.org/10.5281/zenodo.11474407,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",2024-06-04,Tutorial * Presentation,presentation,"Kemmer, Isabel * ERIC, Euro-BioImaging",,pdf,en +From Paper to Pixels: Navigation through your Research Data - presentations of speakers,Research Data Management,https://zenodo.org/records/11548617 * https://doi.org/10.5281/zenodo.11548617,CC-BY-4.0,"The workshop introduced key topics of research data management (RDM) and the implementation thereof on a life science campus. Internal and external experts of RDM including scientists that apply chosen software tools presented the basic concepts and their implementation to a broad audience.  +Talks covered general aspects of data handling and sorting, naming conventions, data storage repositories and archives, licensing of material, data and code management using git, data protection particularly regarding patient data and in genome sequencing and more. Two data management concepts and exemplary tools were highlighted in particular, being electronic lab notebooks with eLabFTW and the bio-image management software OMERO. Those were chosen because of three aspects: the large benefit of these management tools for a life science campus, their free availability as open source tools with the option of contribution of required functionalities and first existing use cases on campus already supported by CMCB/PoL IT. +Two talks by Robert Haase (ScaDS.AI/ Uni Leipzig) and Robert Müller (Kontaktstelle Forschungsdaten, TU Dresden with contributions from Denise Dörfel) that opened the symposium were shared independently: +https://zenodo.org/records/11382341 +https://zenodo.org/records/11261115 +The workshop organization was funded by the CMCB/PoL Networking Grant and supported by the consortium NFDI4BIOIMAGE (funded by DFG grant number NFDI 46/1, project number 501864659).",2024-06-10,Presentation,presentation,"Zoccoler, Marcelo * Bekemeier, Simon * Boissonnet, Tom * Parker, Simon * Bertinetti, Luca * Gentzel, Marc * Massei, Riccardo * Wetzker, Cornelia",,pdf * pptx,en +"RDF as a bridge to domain-platforms like OMERO, or There and back again.",Research Data Management * FAIR-Principles * Bioimage Analysis,https://zenodo.org/doi/10.5281/zenodo.10687658,CC-BY-4.0,"In 2005, the first version of OMERO stored RDF natively. However, just a year after the 1.0 release of RDF, performance considerations led to the development of a more traditional SQL approach for OMERO. A binary protocol makes it possible to query and retrieve metadata but the resulting information cannot immediately be combined with other sources. This is the adventure of rediscovering the benefit of RDF triples as a -- if not the -- common exchange mechanism.",,Presentation,presentation,"Moore, Josh * Waagmeester, Andra * Hettne, Kristina * Wolstencroft, Katherine * Kunis, Susanne",,,en +Developing (semi)automatic analysis pipelines and technological solutions for metadata annotation and management in high-content screening (HCS) bioimaging,Bioimage Analysis,https://doi.org/10.5281/zenodo.8434325,CC-BY-4.0,"High-content screening (HCS) bioimaging automates the imaging and analysis of numerous biological samples, generating extensive metadata that is crucial for effective image management and interpretation. Efficiently handling this complex data is essential to implementing FAIR principles and realizing HCS's full potential for scientific discoveries.",,Poster,,"Massei, Riccardo * Scholz, Stefan * Busch, Wibke * Schnike, Thomas * Bohring, Hannes * Bumberger, Jan",,,en +Combining the BIDS and ARC Directory Structures for Multimodal Research Data Organization,Research Data Management * FAIR-Principles,https://zenodo.org/doi/10.5281/zenodo.8349562,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.",,Poster,,"Stöter, Torsten * Gottschall, Tobias * Schrader, Andrea * Zentis, Peter * Valencia-Schneider, Monica * Kandpal, Niraj * Zuschratter, Werner * Schauss, Astrid * Dickscheid, Timo * Mühlhaus, Timo * Suchodoletz, Dirkvon",,,en +[CORDI 2023] Zarr: A Cloud-Optimized Storage for Interactive Access of Large Arrays,Research Data Management * Bioimage Analysis * Data Science,https://zenodo.org/doi/10.5281/zenodo.8340247,CC-BY-4.0,"For decades, the sharing of large N-dimensional datasets has posed issues across multiple domains. Interactively accessing terabyte-scale data has previously required significant server resources to properly prepare cropped or down-sampled representations on the fly. Now, a cloud-native chunked format easing this burden has been adopted in the bioimaging domain for standardization. The format — Zarr — is potentially of interest for other consortia and sections of NFDI.",,Poster,,"Moore, Josh",,,en +High throughput & automated data analysis and data management workflow with Cellprofiler and OMERO,OMERO * Data Analysis * Bioimage Analysis,https://zenodo.org/doi/10.5281/zenodo.8139353,CC-BY-4.0,"In this workshop a fully integrated data analysis solutions employing OMERO and commonly applied image analysis tools (e.g., CellProfiler, Fiji) using existing python interfaces (OMERO Python language bindings, ezOmero, Cellprofiler Python API) is presented.",,Other,,"Weischer, Sarah * Wendt, Jens * Zobel, Thomas",,,en +"A Glimpse of the Open-Source FLIM Analysis Software Tools FLIMfit, FLUTE and napari-flim-phasor-plotter",Bioimage Analysis * Flim,https://zenodo.org/doi/10.5281/zenodo.10886749,CC-BY-4.0,"The presentations introduce open-source software to read in, visualize and analyse fluorescence lifetime imaging microscopy (FLIM) raw data developed for life scientists. The slides were presented at German Bioimaging (GerBI) FLIM Workshop held February 26 to 29 2024 at the Biomedical Center of LMU München by Anca Margineanu, Chiara Stringari and Conni Wetzker. ",,Presentation,presentation,"Margineanu, Anca * Stringari, Chiara * Zoccoler, Marcelo * Wetzker, Cornelia",,,en +Hackaton Results - Conversion of KNIME image analysis workflows to Galaxy,Research Data Management,https://zenodo.org/doi/10.5281/zenodo.10793699,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),,Presentation,presentation,"Massei, Riccardo",,,en +MicroSam-Talks,Image Segmentation * Bioimage Analysis * Deep Learning,https://zenodo.org/records/11265038 * https://doi.org/10.5281/zenodo.11265038,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 +",2024-05-23,Presentation,presentation,"Pape, Constantin",,pdf * pptx,en +"Datenmanagement im Fokus: Organisation, Speicherstrategien und Datenschutz",Research Data Management,https://zenodo.org/records/11107798 * https://doi.org/10.5281/zenodo.11107798,CC-BY-4.0," Workshop zum Thema „Datenmanagement im Fokus: Organisation, Speicherstrategien und Datenschutz“ auf der Data Week Leipzig +Der Umgang mit Daten ist im Alltag nicht immer leicht: Wie und wo speichert man Daten idealerweise? Welche Strategien helfen, den Überblick zu behalten und wie geht man mit personenbezogenen Daten um? Diese Fragen möchten wir gemeinsam mit Ihnen anhand individueller Datenprobleme besprechen und Ihnen Lösungen aufzeigen, wie Sie ihr Datenmanagement effizient gestalten können.",2024-04-19,Presentation,presentation,"Voigt, Pia * Hundt, Carolin",,pdf,de +Sustainable Data Stewardship,Research Data Management * Data Stewardship,https://zenodo.org/records/10942559 * https://doi.org/10.5281/zenodo.10942559,CC-BY-4.0," These slides were presented at the 2. SaxFDM-Beratungsstammtisch and delve into the strategic integration of Research Data Management (RDM) within research organizations. The Leibniz IOER presented an insightful overview of RDM activities and approaches, emphasizing the criticality of embedding RDM strategically within research institutions. The presentation showcases some best practices in RDM implementation through practical examples, offering valuable insights for optimizing data stewardship processes.",2024-03-25,Presentation,presentation,"Chiesa, StefanoDella",,pdf,en +Cultivating Open Training,Open Science * Research Data Management * FAIR-Principles * Bioimage Analysis * Licensing,https://zenodo.org/records/10816895 * https://doi.org/10.5281/zenodo.10816895,CC-BY-4.0,"In this SaxFDM Digital Kitchen, I introduced current challenges and potential solutions for openly sharing training materials, softly focusing on bio-image analysis. In this field a lot of training materials circulate in private channels, but openly shared, reusable materials, according to the FAIR-principles, are still rare. Using the CC-BY license and uploading materials to publicly acessible repositories are proposed to fill this gap.",2024-03-14,Presentation,presentation,"Haase, Robert",,odp * pdf * pptx,en +"""ZENODO und Co."" Was bringt und wer braucht ein Repositorium?",Research Data Management,https://zenodo.org/records/4461261 * https://doi.org/10.5281/zenodo.4461261,CC-BY-4.0,Die Online-Veranstaltung fand am 21.01.2021 im Rahmen der SaxFDM-Veranstaltungsreihe "Digital Kitchen - Küchengespräche mit SaxFDM" statt. SaxFDM-Sprecherin Elfi Hesse (HTW Dresden) erläuterte zunächst Grundsätzliches zum Thema Repositorien. Anschließend teilten Nutzer (Jan Deinert – HZDR) und Anbieter (Christian Löschen – TU Dresden/ZIH) lokaler Repositorien ihre Erfahrungen mit uns.,2021-01-25,Presentation,presentation,"Hesse, Elfi * Deinert, Jan-Christoph * Löschen, Christian",,pdf,de +Alles meins – oder!? Urheberrechte klären für Forschungsdaten,Research Data Management * Licensing,https://zenodo.org/records/11472148 * https://doi.org/10.5281/zenodo.11472148,CC-BY-4.0,"Wem gehören Forschungsdaten? Diese Frage stellt sich bei Daten, an deren Entstehung mehrere Personen beteiligt waren, und besonders bei Textdaten, Bildern und Videos. Hier lernen Sie, für Ihr eigenes Forschungsvorhaben zu erkennen, wessen Urheber- und Leistungsschutzrechte zu berücksichtigen sind. Sie erfahren, wie Sie mit Hilfe von Vereinbarungen frühzeitig Rechtssicherheit herstellen, etwa um Daten weitergeben oder publizieren zu können. +  + ",2024-06-04,Presentation,presentation,"Wünsche, Stephan",,pdf,de +"So geschlossen wie nötig, so offen wie möglich - Datenschutz beim Umgang mit Forschungsdaten",Research Data Management * Data Protection * FAIR-Principles,https://zenodo.org/records/11396199 * https://doi.org/10.5281/zenodo.11396199,CC-BY-4.0,"Der Umgang mit personenbezogenen Daten stellt Forschende oft vor rechtliche Herausforderungen: Unter welchen Bedingungen dürfen personenbezogene Daten verarbeitet werden? Welche Voraussetzungen müssen erfüllt sein und welche Strategien können angewendet werden, um Daten sicher speichern, verarbeiten, teilen und aufbewahren zu können? Mit Hilfe dieses Foliensatzes erhalten Sie Einblicke in datenschutzrechtliche Aspekte beim Umgang mit Ihren Forschungsdaten. ",2024-05-30,Presentation,presentation,"Voigt, Pia",,pdf * pptx,de +Einblicke ins Forschungsdatenmanagement - Darf ich das veröffentlichen? Rechtsfragen im Umgang mit Forschungsdaten,Research Data Management * Data Protection,https://zenodo.org/records/4748510 * https://doi.org/10.5281/zenodo.4748510,CC-BY-4.0,"Diese Präsentation wurde im Zuge der digitalen Veranstaltungsreihe "Einblicke ins Forschungsdatenmanagement" erstellt. Diese findet seit dem SS 2020 an der Universität Leipzig für alle Interessierten zu verschiedenen Themen des Forschungsdatenmanagements statt. + +Dieser Teil der Reihe dreht sich um Rechtsfragen im Umgang mit Forschungsdaten und deren Bedeutung für die wissenschaftliche Praxis. Sie finden in der vorliegenden Präsentation einen Überblick über relevante Rechtsbereiche sowie Erläuterungen zum Datenschutz, Urheberrecht und den Grundsätzen der guten wissenschaftlichen Praxis mit Fokus auf deren Bedeutung im Forschungsdatenmanagement.",2021-05-11,Presentation,presentation,"Wünsche, Stephan * Voigt, Pia",,pdf,de +Datenmanagementpläne erstellen - Teil 1,Research Data Management,https://zenodo.org/records/4630788 * https://doi.org/10.5281/zenodo.4630788,CC-BY-4.0,"Was ist ein Datenmanagementplan? Welche Vorgaben sollte ich beachten? Wie erstelle ich einen solchen für mein Forschungsprojekt und welche nützlichen Tools kann ich hierfür verwenden? + +Die Anforderungen der Forschungsförderer zum Datenmanagement steigen stetig. Damit verbunden ist häufig auch das Erstellen eines Datenmanagementplans. Dabei erwarten DFG, BMBF oder die EU jeweils unterschiedliche Angaben zur Erhebung, Speicherung und Veröffentlichung von projektbezogenen Forschungsdaten. Zudem bietet das Erstellen eines Datenmanagementplans viele Vorteile und hilft Ihnen nicht zuletzt, die Anforderungen der guten wissenschaftlichen Praxis strukturiert umzusetzen. + +Was im ersten Moment unübersichtlich und überfordernd wirkt, soll in diesem Kurs anhand einer grundlegenden theoretischen Einführung im ersten und praxisorientierter Beispiele im zweiten Teil der Veranstaltung handhabbar gemacht werden. Sie lernen, was hinter den Anforderungen der Forschungsförderer steckt, welche Elemente ein Datenmanagementplan enthalten sollte und wie sie einen solchen mithilfe interaktiver Tools selbst erstellen können.",2021-03-23,Presentation,presentation,"Voigt, Pia * Weiner, Barbara",,pdf,de +Datenmanagementpläne erstellen - Teil 2,Research Data Management,https://zenodo.org/records/4748534 * https://doi.org/10.5281/zenodo.4748534,CC-BY-4.0,"Was ist ein Datenmanagementplan? Welche Vorgaben sollte ich beachten? Wie erstelle ich einen solchen für mein Forschungsprojekt und welche nützlichen Tools kann ich hierfür verwenden? + +Die Anforderungen der Forschungsförderer zum Datenmanagement steigen stetig. Damit verbunden ist häufig auch das Erstellen eines Datenmanagementplans. Dabei erwarten DFG, BMBF oder die EU jeweils unterschiedliche Angaben zur Erhebung, Speicherung und Veröffentlichung von projektbezogenen Forschungsdaten. Zudem bietet das Erstellen eines Datenmanagementplans viele Vorteile und hilft Ihnen nicht zuletzt, die Anforderungen der guten wissenschaftlichen Praxis strukturiert umzusetzen. + +Was im ersten Moment unübersichtlich und überfordernd wirkt, soll in diesem Kurs anhand einer grundlegenden theoretischen Einführung im ersten und praxisorientierter Beispiele im zweiten Teil der Veranstaltung handhabbar gemacht werden. Sie lernen, was hinter den Anforderungen der Forschungsförderer steckt, welche Elemente ein Datenmanagementplan enthalten sollte und wie sie einen solchen mithilfe interaktiver Tools selbst erstellen können. + +Version 2 enthält aktuelle Links und weiterführende Hinweise zu einzelnen Aspekten eines Datenmanagementplans. + +Version 3 ist die überarbeitete und aktualisierte Version der ersten beiden und enthält u.a. Hinweise zur Lizenzierung und zu Nutzungsrechten an Forschungsdaten.",2021-03-30,Presentation,presentation,"Voigt, Pia * Weiner, Barbara",,pdf,de +Crashkurs Forschungsdatenmanagement,Research Data Management,https://zenodo.org/records/3778431 * https://doi.org/10.5281/zenodo.3778431,CC-BY-4.0,"Diese Präsentation bietet einen Einstieg in alle relevanten Bereiche des Forschungsdatenmanagements an der Universität Leipzig. Behandelt werden Grundlagen des Forschungsdatenmanagements, technische, ethische und rechtliche Aspekte sowie die Archivierung und Publikation von Forschungsdaten. Die Präsentation enthält zahlreiche weiterführende Links (rot) und Literaturhinweise. + +Ergänzend hierzu wird eine Präsentation mit Übungsaufgaben angeboten, die helfen soll, das Gelernte zu festigen und in der eigenen Forschungspraxis umzusetzen. Den Aufgaben folgen jeweils eine Antwortfolie sowie deren Auflösung.",2020-04-30,Presentation,presentation,"Weiner, Barbara * Wünsche, Stephan * Kühne, Stefan * Voigt, Pia * Frericks, Sebastian * Hoffmann, Clemens * Elze, Romy * Gey, Ronny",,pdf,de +Bio-image Data Science Lectures @ Uni Leipzig / ScaDS.AI,Bioimage Analysis * Deep Learning * Microscopy Image Analysis * Python,https://zenodo.org/records/12623730,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.,,Presentation,presentation,"Haase, Robert",,pdf * pptx, +BIDS-lecture-2024,Bioimage Analysis * Deep Learning * Microscopy Image Analysis * Python,https://github.com/ScaDS/BIDS-lecture-2024/,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.,,Other,,"Haase, Robert",,, +Insights and Impact From Five Cycles of Essential Open Source Software for Science,Open Source Software * Funding * Sustainability,https://zenodo.org/records/11201216,CC-BY-4.0,"Open source software (OSS) is essential for advancing scientific discovery, particularly in biomedical research, yet funding to support these vital tools has been limited. The Chan Zuckerberg Initiative's Essential Open Source Software for Science (EOSS) program has significantly contributed to this field by providing $51.8 million in funding over five years to support the maintenance, growth, and community engagement of critical OSS tools. The program has impacted scientific OSS projects by improving their technical outputs, community building, and sustainability practices, and fostering collaborations within the OSS community. Additionally, EOSS funding has enhanced diversity, equity, and inclusion within the OSS community, although changes in principal investigator demographics were not observed. The funded projects have had a substantial impact on biomedical research by improving the usability and accessibility of scientific software, which has led to increased adoption and advancements in various biomedical fields.",,Article,text,"Hertweck, Kate * Strasser, Carly * Taraborelli, Dario",,csv * md * pdf,en +6 Steps Towards Reproducible Research,Reproducibility * Research Data Management,https://zenodo.org/records/12744715,CC-BY-4.0,A short book with 6 steps that get you closer to making your work reproducible.,,Book,text,"Seibold, Heidi",,epub * jpg * pdf * png,en +NFDI4BIOIMAGE - An Initiative for a National Research Data Infrastructure for Microscopy Data,Nfdi4Bioimage * Research Data Management,https://archiv.ub.uni-heidelberg.de/volltextserver/29489/,CC-BY-SA-4.0,,,Poster * Article,text,"Schmidt, Christian * Ferrando-May, Elisa",,,en +Research data management for bioimaging: the 2021 NFDI4BIOIMAGE community survey,Nfdi4Bioimage * Research Data Management,https://f1000research.com/articles/11-638,CC-BY-4.0,,,Article,text,"Schmidt, Christian * Hanne, Janina * Moore, Josh * Meesters, Christian * Ferrando-May, Elisa * Weidtkamp-Peters, Stefanie * members of the NFDI4BIOIMAGE initiative",,,en +Sharing and licensing material,Sharing * Research Data Management,https://f1000research.com/slides/10-519,CC-BY-4.0,Introduction to sharing resources online and licensing,,Presentation,presentation,"Haase, Robert",,,en +"If you license it, it’ll be harder to steal it. Why we should license our work",Licensing * Research Data Management,https://focalplane.biologists.com/2023/05/06/if-you-license-it-itll-be-harder-to-steal-it-why-we-should-license-our-work/,CC-BY-4.0,Blog post about why we should license our work and what is important when choosing a license.,,Web Page,text,"Haase, Robert",,,en +Sharing research data with Zenodo,Sharing * Research Data Management,https://focalplane.biologists.com/2023/02/15/sharing-research-data-with-zenodo/,CC-BY-4.0,Blog post about how to share data using zenodo.org,,Web Page,text,"Haase, Robert",,,en +Collaborative bio-image analysis script editing with git,Sharing * Research Data Management,https://focalplane.biologists.com/2021/09/04/collaborative-bio-image-analysis-script-editing-with-git/,CC-BY-4.0,"Introduction to version control using git for collaborative, reproducible script editing.",,Web Page,text,"Haase, Robert",,,en +OMERO for microscopy research data management,Nfdi4Bioimage * OMERO * Research Data Management,https://analyticalscience.wiley.com/do/10.1002/was.0004000267/,ALL RIGHTS RESERVED,A use case example from the Münster Imaging Network,,Article,text,"Zobel, Thomas * Weischner, Sarah * Wendt, Jens",,,en +A Cloud-Optimized Storage for Interactive Access of Large Arrays,Nfdi4Bioimage * Research Data Management,https://doi.org/10.52825/cordi.v1i.285,CC-BY-4.0,,,Article * Text,text,"Moore, Josh * Kunis, Susanne",,,en +Structuring of Data and Metadata in Bioimaging: Concepts and technical Solutions in the Context of Linked Data,Nfdi4Bioimage * Research Data Management,https://zenodo.org/record/7018929 * https://doi.org/10.5281/zenodo.7018929,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.",2022-08-24,,video,"Kunis, Susanne",,.mp4,en +The Fiji Updater,Imagej,https://analyticalscience.wiley.com/do/10.1002/was.0004000112/,ALL RIGHTS RESERVED,Article about the Fiji Updater explaining how it works in the background.,,Article,text,"Haase, Robert",,,en +CLIJ: GPU-accelerated image processing for everyone,Imagej * Bioimage Analysis,https://doi.org/10.1038/s41592-019-0650-1,ALL RIGHTS RESERVED,CLIJ is a collection of image processing functions that use graphics processing units for accelerated processing.,2020,Article,text,"Haase, Robert * Royer, Loic * et al.",,,en +A Hitchhiker's guide through the bio-image analysis software universe,Bioimage Analysis,https://febs.onlinelibrary.wiley.com/doi/full/10.1002/1873-3468.14451,CC-BY-4.0,This article gives an overview about commonly used bioimage analysis software and which aspects to consider when choosing a software for a specific project.,,Article,text,"Haase, Robert * Fazeli, Elnaz * Legland, David * Doube, Michael * Culley, Siân * Belevich, Ilya * Jokitalo, Eija * Schorb, Martin * Klemm, Anna * Tischer, Christian",,,en +Challenges and opportunities for bioimage analysis core-facilities,Bioimage Analysis * Research Data Management,https://onlinelibrary.wiley.com/doi/full/10.1111/jmi.13192,CC-BY-4.0,"This article outlines common reasons for founding bioimage analysis core-facilities, services they can provide to fulfill certain need and conflicts of interest that arise from these services.",,Article,text,"Soltwedel, JohannesRichard * Haase, Robert",,,en +Open microscopy in the life sciences: quo vadis?,,https://doi.org/10.1038/s41592-022-01602-3,ALL RIGHTS RESERVED,This comment article outlines the current state of the art in open hardware publishing in the context of microscopy.,2022,Article,text,"Hohlbein, Johannes * Diederich, Benedict * Marsikova, Barbora * Reynaud, EmmanuelG. * Holden, Séamus * Jahr, Wiebke * Haase, Robert * Prakash, Kirti",,,en +Developing open-source software for bioimage analysis: opportunities and challenges,Neubias,https://f1000research.com/articles/10-302,CC-BY-4.0,This article outlines common challenges and practices when developing open-source software for bio-image analysis.,,Article,text,"Levet, Florian * Carpenter, AnneE. * Eliceiri, KevinW. * Kreshuk, Anna * Bankhead, Peter * Haase, Robert",,,en +Meeting in the Middle: Towards Successful Multidisciplinary Bioimage Analysis Collaboration,Bioimage Analysis,https://www.frontiersin.org/articles/10.3389/fbinf.2022.889755/full,CC-BY-4.0,,,Article,text,"Schlaeppi, Anjalie * Adams, Wilson * Haase, Robert * Huisken, Jan * MacDonald, RyanB. * Eliceiri, KevinW. * Kugler, ElisabethC.",,,en +Highlights from the 2016-2020 NEUBIAS training schools for Bioimage Analysts: a success story and key asset for analysts and life scientists,Bioimage Analysis * Neubias,https://f1000research.com/articles/10-334/v1,CC-BY-4.0,,2021,Article,text,"Martins, GabrielG. * Cordelières, FabriceP. * Colombelli, Julien * D’Antuono, Rocco * Golani, Ofra * Guiet, Romain * Haase, Robert * Klemm, AnnaH. * Louveaux, Marion * Paul-Gilloteaux, Perrine * Tinevez, Jean-Yves * Miura, Kota",,,en +MDEmic: a metadata annotation tool to facilitate management of FAIR image data in the bioimaging community,Research Data Management * Metadata,https://www.nature.com/articles/s41592-021-01288-z,ALL RIGHTS RESERVED,,,Article,text,"Kunis, Susanne * Hänsch, Sebastian * Schmidt, Christian * Wong, Frances * Strambio-De-Castillia, Caterina * Weidtkamp-Peters, Stefanie",,,en +QUAREP-LiMi: A community-driven initiative to establish guidelines for quality assessment and reproducibility for instruments and images in light microscopy,Quareo-Limi,https://onlinelibrary.wiley.com/doi/10.1111/jmi.13041,CC-BY-4.0,,,Article,text,"Nelson, Glyn * Boehme, Ulrike * et al.",,,en +OME-NGFF: a next-generation file format for expanding bioimaging data-access strategies,Nfdi4Bioimage * Research Data Management,https://www.nature.com/articles/s41592-021-01326-w,CC-BY-4.0,,,Article,text,"Moore, Josh * Allan, Chris * Besson, Sébastien * Burel, Jean-Marie * Diel, Erin * Gault, David * Kozlowski, Kevin * Lindner, Dominik * Linkert, Melissa * Manz, Trevor * Moore, Will * Pape, Constantin * Tischer, Christian * Swedlow, JasonR.",,,en +NFDI4BIOIMAGE: Perspective for a national bioimaging standard,Nfdi4Bioimage,https://ceur-ws.org/Vol-3415/paper-27.pdf,CC-BY-4.0,,,Article,text,"Moore, Josh * Kunis, Susanne",NFDI4Bioimage,, +SpatialData: an open and universal data framework for spatial omics,Python,https://www.biorxiv.org/content/10.1101/2023.05.05.539647v1.abstract,CC-BY-4.0,,,Article * Text,text,"Marconato, Luca * Palla, Giovanni * Yamauchi, KevinA * Virshup, Isaac * Heidari, Elyas * Treis, Tim * Toth, Marcella * Shrestha, Rahul * Vöhringer, Harald * Huber, Wolfgang * Gerstung, Moritz * Moore, Josh * Theis, FabianJ * Stegle, Oliver",,,en +Community-developed checklists for publishing images and image analyses,Bioimage Analysis,https://www.nature.com/articles/s41592-023-01987-9,ALL RIGHTS RESERVED,,,Article,text,"Schmied, Christopher * Nelson, MichaelS * Avilov, Sergiy * Bakker, Gert-Jan * Bertocchi, Cristina * Bischof, Johanna * Boehm, Ulrike * Brocher, Jan * Carvalho, MarianaT * Chiritescu, Catalin * Christopher, Jana * Cimini, BethA * Conde-Sousa, Eduardo * Ebner, Michael * Ecker, Rupert * Eliceiri, Kevin * Fernandez-Rodriguez, Julia * Gaudreault, Nathalie * Gelman, Laurent * Grunwald, David * Gu, Tingting * Halidi, Nadia * Hammer, Mathias * Hartley, Matthew * Held, Marie * Jug, Florian * Kapoor, Varun * Koksoy, AyseAslihan * Lacoste, Judith * Dévédec, SylviaLe * Guyader, SylvieLe * Liu, Penghuan * Martins, GabrielG * Mathur, Aastha * Miura, Kota * Llopis, PaulaMontero * Nitschke, Roland * North, Alison * Parslow, AdamC * Payne-Dwyer, Alex * Plantard, Laure * Ali, Rizwan * Schroth-Diez, Britta * Schütz, Lucas * Scott, RyanT * Seitz, Arne * Selchow, Olaf * Sharma, VedP * Spitaler, Martin * Srinivasan, Sathya * Strambio-De-Castillia, Caterina * Taatjes, Douglas * Tischer, Christian * Jambor, HelenaKlara",,,en +BigDataProcessor2: A free and open-source Fiji plugin for inspection and processing of TB sized image data,Research Data Management * Bioimage Analysis,https://doi.org/10.1093/bioinformatics/btab106,CC-BY-4.0,,,Article,text,"Tischer, Christian * Ravindran, Ashis * Reither, Sabine * Chiaruttini, Nicolas * Pepperkok, Rainer * Norlin, Nils",,,en +"EDAM-bioimaging: The ontology of bioimage informatics operations, topics, data, and formats (update 2020)",Metadata,https://f1000research.com/posters/9-162,CC-BY-4.0,,,Poster * Article,text,"Kalaš, Matúš * Plantard, Laure * Lindblad, Joakim * Jones, Martin * Sladoje, Nataša * Kirschmann, MoritzA * Chessel, Anatole * Scholz, Leandro * Rössler, Fabianne * Sáenz, LauraNicolás * Estibaliz Gómez de Mariscal * Bogovic, John * Dufour, Alexandre * Heiligenstein, Xavier * Waithe, Dominic * Domart, Marie-Charlotte * Karreman, Matthia * Raf Van de Plas * Haase, Robert * Hörl, David * Paavolainen, Lassi * Madunić, IvanaVrhovac * Karaica, Dean * Muñoz-Barrutia, Arrate * Sampaio, Paula * Sage, Daniel * Munck, Sebastian * Golani, Ofra * Moore, Josh * Levet, Florian * Ison, Jon * Gaignard, Alban * Ménager, Hervé * Zhang, Chong * Miura, Kota * Colombelli, Julien * Paul-Gilloteaux, Perrine",,,en +Thinking data management on different scales,Research Data Management * Nfdi4Bioimage,https://zenodo.org/records/8329306 * https://doi.org/10.5281/zenodo.8329306,CC-BY-4.0,Presentation given at PoL BioImage Analysis Symposium Dresden 2023,2023-08-31,Presentation,presentation,"Kunis, Susanne",,pdf * pptx,en +Challenges and opportunities for bio-image analysis core-facilities,Research Data Management * Bioimage Analysis * Nfdi4Bioimage,https://f1000research.com/slides/12-1054,CC-BY-4.0,,,Presentation,presentation,"Haase, Robert",,,en +NFDI4Bioimage - TA3-Hackathon - UoC-2023 (Cologne Hackathon),Arc * Dataplant * Hackathon * Nfdi4Bioimage * OMERO * Python * Research Data Management,https://github.com/NFDI4BIOIMAGE/Cologne-Hackathon-2023 * https://doi.org/10.5281/zenodo.10609770,CC-BY-4.0,,,Article * Other * Text,text,"Abdrabbou, MohamedM. * Babaki, Mehrnaz * Boissonnet, Tom * Bortolomeazzi, Michele * Dahms, Eik * Vanessa A. F. Fuchs * Hoevels, Moritz * Kandpal, Niraj * Möhl, Christoph * Moore, JoshuaA. * Schauss, Astrid * Schrader, Andrea * Stöter, Torsten * Thönnißen, Julia * Valencia-S., Monica * Weil, H.Lukas * Jens Wendt and Peter Zentis",,, +Welcome to BioImage Town,OMERO * Bioimage Analysis * Nfdi4Bioimage,https://zenodo.org/doi/10.5281/zenodo.10008464,CC-BY-4.0,"Welcome at NFDI4BIOIMAGE All-Hands Meeting in Düsseldorf, Germany, October 16, 2023",,Presentation,presentation,"Moore, Josh",,,en +NFDI4BIOIMAGE - National Research Data Infrastructure for Microscopy and BioImage Analysis - Online Kick-Off 2023,Research Data Management * FAIR-Principles * Bioimage Analysis * Nfdi4Bioimage,https://doi.org/10.5281/zenodo.8070038,CC-BY-4.0,"NFDI4BIOIMAGE core mission, bioimage data challenge, task areas, FAIR bioimage workflows.",,Presentation,presentation,"Weidtkamp-Peters, Stefanie",,,en +"NFDI4Bioimage - TA3-Hackathon - UoC-2023 (Cologne-Hackathon-2023, GitHub repository)",Research Data Management * FAIR-Principles * Bioimage Analysis * Nfdi4Bioimage,https://zenodo.org/doi/10.5281/zenodo.10609770,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.",,Other,,"Abdrabbou, Mohamed * Babaki, Mehrnaz * Boissonnet, Tom * Bortolomeazzi, Michele * Dahms, Eik * Fuchs, Vanessa * A. F. Moritz Hoevels * Kandpal, Niraj * Möhl, Christoph * Moore, JoshuaA. * Schauss, Astrid * Schrader, Andrea * Stöter, Torsten * Thönnißen, Julia * Valencia-S., Monica * Weil, H.Lukas * Wendt, Jens * Zentis, Peter",,, +"[ELMI 2024] AI's Dirty Little Secret: Without FAIR Data, It's Just Fancy Math",Research Data Management * FAIR-Principles * Bioimage Analysis * Nfdi4Bioimage,https://zenodo.org/doi/10.5281/zenodo.11235512,CC-BY-4.0,Poster presented at the European Light Microscopy Initiative meeting in Liverpool (https://www.elmi2024.org/),,Poster,,"Moore, Josh * Kunis, Susanne",,,en +Thinking data management on different scales,Research Data Management * Nfdi4Bioimage,https://zenodo.org/doi/10.5281/zenodo.8329305,CC-BY-4.0,Presentation given at PoL BioImage Analysis Symposium Dresden 2023,,Presentation,presentation,"Kunis, Susanne",,,en +[SWAT4HCLS 2023] NFDI4BIOIMAGE: Perspective for a national bioimage standard,Research Data Management * FAIR-Principles * Nfdi4Bioimage,https://zenodo.org/doi/10.5281/zenodo.7928332,CC-BY-4.0,"Poster presented at Semantic Web Applications and Tools for Health Care and Life Sciences (SWAT4HCLS 2023), Feb 13--16, 2023, Basel, Switzerland. NFDI4BIOIMAGE is a newly established German consortium dedicated to the FAIR representation of biological imaging data. A key deliverable is the definition of a semantically-compatible FAIR image object integrating RDF metadata with web-compatible storage of large n-dimensional binary data in OME-Zarr. We invite feedback from and collaboration with other endeavors during the soon-to-begin 5 year funding period.",,Poster,,"Moore, Josh * Kunis, Susanne",,,en +"NFDI4BIOIMAGE - National Research Data Infrastructure for Microscopy and BioImage Analysis [conference talk: The Pelagic Imaging Consortium meets Helmholtz Imaging, 5.10.2023, Hamburg]",Research Data Management * Bioimage Analysis * Nfdi4Bioimage,https://zenodo.org/doi/10.5281/zenodo.8414318,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.",,Presentation,presentation,"Massei, Riccardo",,,en +Who you gonna call? - Data Stewards to the rescue,Research Data Management * Bioimage Analysis * Data Stewardship * Nfdi4Bioimage,https://zenodo.org/doi/10.5281/zenodo.10730423,CC-BY-4.0,The Data Steward Team of the NFDI4BIOIMAGE consortium presents themselves and the services (including the Helpdesk) that we offer.,,Poster,,"Vanessa Aphaia Fiona Fuchs * Wendt, Jens * Müller, Maximilian * Ahmadi, Mohsen * Massei, Riccardo * Wetzker, Cornelia",,,en +[Short Talk] NFDI4BIOIMAGE - A consortium in the National Research Data Infrastructure,Research Data Management * Bioimage Analysis * Nfdi4Bioimage,https://zenodo.org/doi/10.5281/zenodo.10939519,CC-BY-4.0,"Short Talk about the NFDI4BIOIMAGE consortium presented at the RDM in (Bio-)Medicine Information Event on April 10th, 2024, organized C³RDM & ZB MED.",,Presentation,presentation,"Schmidt, Christian",,,en +NFDI4BIOIMAGE,Research Data Management * Bioimage Analysis * FAIR-Principles * Zarr * Nfdi4Bioimage,https://zenodo.org/doi/10.5281/zenodo.11031746,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.",,Presentation,presentation,"Fortmann.Grote, Carsten",,, +NFDI4BIOIMAGE - National Research Data Infrastructure for Microscopy and Bioimage Analysis,,https://zenodo.org/records/13168693 * https://doi.org/10.5281/zenodo.13168693,CC-BY-4.0,"Bioimaging refers to a collection of methods to visualize the internal structures and mechanisms of living organisms. The fundamental tool, the microscope, has enabled seminal discoveries like that of the cell as the smallest unit of life, and continues to expand our understanding of biological processes. Today, we can follow the interaction of single molecules within nanoseconds in a living cell, and the development of complete small organisms like fish and flies over several days starting from the fertilized egg. Each image pixel encodes multiple spatiotemporal and spectral dimensions, compounding the massive volume and complexity of bioimage data. Proper handling of this data is indispensable for analysis and its lack has become a growing hindrance for the many disciplines of the life and biomedical sciences relying on bioimaging. No single domain has the expertise to tackle this bottleneck alone. +As a method-specific consortium, NFDI4BIOMAGE seeks to address these issues, enabling bioimaging data to be shared and re-used like they are acquired, i.e., independently of disciplinary boundaries. We will provide solutions for exploiting the full information content of bioimage data and enable new discoveries through sharing and re-analysis. Our RDM strategy is based on a robust needs analysis that derives not only from a community survey but also from over a decade of experience in German BioImaging, the German Society for Microscopy and Image Analysis. It considers the entire lifecycle of bioimaging data, from acquisition to archiving, including analysis and enabling re-use. A foundational element of this strategy is the definition of a common, cloud-compatible, and interoperable digital object that bundles binary images with their descriptive and provenance metadata. With members from plant biology to neuroscience, NFDI4BIOIMAGE will champion the standardization of bioimage data to create a framework that answers discipline-specific needs while ensuring communication and interoperability with data types and RDM systems across domains. Integration of bioimage data with, e.g., omics data as the basis for spatial omics, holds great promise for fields such as cancer medicine. Unlocking the full potential of bioimage data will rely on the development and broad availability of exceptional analysis tools and training sets. NFDI4BIOIMAGE will make these accessible and usable including cutting-edge AI-based methods in scalable cloud environments. NFDI4BIOIMAGE intersects with multiple NFDI consortia, most prominently with GHGA for linking image and genomics data and with DataPLANT on the definition of FAIR data objects. Last but not least, NFDI4BIOIMAGE is internationally well connected and represents the opportunity for German scientists to keep path with and have a voice in several international initiatives focusing on the FAIRification of bioimage data as one of the main challenges for the advancement of knowledge in the life and biomedical sciences.",2024-08-07,,,"Zoccoler, Marcelo",,pdf,en +[GBI EOE VII] Five (or ten) must-have items for making IT infrastructure for managing bioimage data,,https://zenodo.org/records/11318151 * https://doi.org/10.5281/zenodo.11318151,CC-BY-4.0,Presentation made to the GBI Image Data Management Working Group during the 7th Exchange of Experience in Uruguay.,2024-05-26,,,"Moore, Josh",,pdf,en +DALIA Interchange Format,,https://zenodo.org/records/11521029 * https://doi.org/10.5281/zenodo.11521029,CC-BY-SA-4.0,"The DALIA (Data Literacy Alliance) project aims to develop a knowledge graph for FAIR teaching and learning materials on data literacy, data competencies and research data management (RDM) skills within the National Research Data Infrastructure (NFDI) and the RDM landscape. Such a platform thrives on the participation of users who want to search, create, manage or use teaching and learning materials. +A schematization of the metadata is necessary for the interoperability of teaching and learning materials. This is done by the DALIA Interchange Format (DIF), which provides a framework for making the metadata of teaching and learning materials transparent, comparable and smooth to integrate into the DALIA platform. It includes the description and explanation of the data fields for the online publication of educational resources. +The DIF was developed in close consultation with the scientific community. This development process included several feedback rounds in which valuable feedback was provided and subsequently incorporated into the DIF. This not only contributed to the clear, transparent and user-oriented definitions of the data fields, and to a clear structure, but also to the integration of many existing data standards and to the (special) requirements of the scientific community. The selection of elements is based on the Dublin Core Application Profile. +The DIF is provided as a PDF document and in table form (ODS) to convey the attributes of the teaching and learning materials and their definitions in an easily understandable form and to facilitate communication. It also includes a legend and an example in tabular form. In addition, a template (CSV) with the attributes as column headers is provided, which can be used for recording the metadata of the teaching and learning materials. The tables can also be transferred to technical application profiles. +We would like to thank all the commentators of the previous versions, especially Susanne Arndt, Sophie Boße, Sonja Felder, Marc Fuhrmans, Jan-Michael Haugwitz, Marina Lemaire, Karoline Lemke, Birte Lindstädt, Juliane Röder, and Jakob Voß. Without their feedback and advice, the DIF would be less transparent.",2024-06-07,,,"Geiger, Jonathan * Steiner, Petra * Desouki, AbdelmoneimAmer * Lange, Frank",,csv * ods * pdf, +2020 BioImage Analysis Survey: Community experiences and needs for the future,Bioimage Analysis,https://doi.org/10.1017/s2633903x21000039 * https://github.com/ciminilab/2021_Jamali_BiologicalImaging,BSD-3-Clause,,2021,Article,text,"Jamali, Nasim * Ellen T. A. Dobson * Eliceiri, KevinW. * Carpenter, AnneE. * Cimini, BethA.",,,en +Bridging Imaging Users to Imaging Analysis - A community survey,Bioimage Analysis,https://www.biorxiv.org/content/10.1101/2023.06.05.543701v1 * https://github.com/COBA-NIH/2023_ImageAnalysisSurvey,BSD-3-Clause,,2023,Article * Text,text,"Sivagurunathan, Suganya * Marcotti, Stefania * Nelson, CarlJ * Jones, MartinL * Barry, DavidJ * Thomas J A Slater * Eliceiri, KevinW * Cimini, BethA",,,en +Photonic data analysis in 2050,FAIR-Principles * Machine Learning * Research Data Management,https://doi.org/10.1016/j.vibspec.2024.103685,CC-BY-4.0,"Photonic data analysis, combining imaging, spectroscopy, machine learning, and computer science, requires flexible methods and interdisciplinary collaborations to advance. Essential developments include standardizing data infrastructure for comparability, optimizing data-driven models for complex investigations, and creating techniques to handle limited or unbalanced data and device variations.",,Article,text,"Ryabchykov, Oleg * Guo, Shuxia * Bocklitz, Thomas",,, +BioEngine Documentation,Workflow Engine * Deep Learning * Python,https://bioimage-io.github.io/bioengine/#/,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.",,Text,text,"Ouyang, Wei * Nanguage * Metz, Jeremy * Russell, Craig",,,en +Large Language Models: An Introduction for Life Scientists,,https://zenodo.org/records/13379394 * https://doi.org/10.5281/zenodo.13379394,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.",2024-08-27,,,"Haase, Robert",,pdf * pptx,en +ChatGPT for Image Analysis,,https://zenodo.org/records/13371196 * https://doi.org/10.5281/zenodo.13371196,CC-BY-4.0,"Large Language Models (LLMs) such as ChatGPT are changing the way we interact with computers, including how we analye microscopy imaging data. In this talk I introduce basic concepts of asking LLMs to write code and how to modify the questions to get the best out of it. For trying out these prompt engineering basics there are additional online resources available: https://scads.github.io/prompt-engineering-basics-2024/intro.html",2024-08-25,,,"Haase, Robert",,pdf * pptx * zip,en +Towards Preservation of Life Science Data with NFDI4BIOIMAGE,,https://zenodo.org/records/13506641 * https://doi.org/10.5281/zenodo.13506641,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.",2024-08-29,,,"Haase, Robert",,pdf * pptx,en +"EDAM-bioimaging - The ontology of bioimage informatics operations, topics, data, and formats",Ontology * Bioimage Analysis,https://hal.science/hal-02267597/document,CC-BY-4.0,"EDAM-bioimaging is an extension of the EDAM ontology, dedicated to bioimage analysis, bioimage informatics, and bioimaging.",,Poster,,Matúš Kalaš et al.,,,en +"Using Glittr.org to find, compare and re-use online training materials",Training * Bioimage Analysis * Research Data Management,https://www.biorxiv.org/content/10.1101/2024.08.20.608021v1,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.",,Article * Text,text,"Geest, Geertvan * Haefliger, Yann * Zahn-Zabal, Monique * Palagi, PatriciaM.",,,en +Omero-tools,OMERO * Bioimage Analysis,https://biapol.github.io/omero-tools/,CC-BY-4.0,"This repository contains a collection of tools for working with OMERO. Such tools can be working with the OMERO command line interface to transfer datasets between repositories, etc.",,Other,,"Soltwedel, Johannes",,,en +EmbedSeg Repository,Bioimage Analysis * Instance Segmentation,https://github.com/juglab/EmbedSeg,CC-BY-NC-4.0,"Code Implementation for EmbedSeg, an Instance Segmentation Method for Microscopy Images",,Other,,"Lalit, Manan * Deschamps, Joran * Jug, Florian * Kulkarni, Ajinkya",,, +Scipy Cookbook,Bioimage Analysis,https://github.com/scipy/scipy-cookbook,BSD-3-Clause,This is a conversion and second life of SciPy Cookbook as a bunch of Ipython notebooks.,,Other,,Pauli Virtanen et al.,,,en +SimpleITK-Notebooks,Bioimage Analysis * Simpleitk,https://github.com/InsightSoftwareConsortium/SimpleITK-Notebooks,Apache-2.0,Jupyter notebooks for learning how to use SimpleITK,,Other,,Ziv Yaniv et al.,,,en +skimage-tutorials,Bioimage Analysis * Scikit-Image,https://github.com/scikit-image/skimage-tutorials,CC0-1.0,skimage-tutorials - a collection of tutorials for the scikit-image package.,,Other,,Juan Nunez-Iglesias et al.,,,en +scikit-learn MOOC,Bioimage Analysis * Machine Learning,https://github.com/INRIA/scikit-learn-mooc,CC-BY-4.0,Machine learning in Python with scikit-learn MOOC,,Other,,Loïc Estève et al.,,, +NLP Course,Natural Language Processing,https://github.com/yandexdataschool/nlp_course,MIT,YSDA course in Natural Language Processing,,Other,,Yandex School of Data Analysis,,,en +Community-developed checklists for publishing images and image analyses,Bioimage Analysis * Research Data Management,https://quarep-limi.github.io/WG12_checklists_for_image_publishing/intro.html,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.",,Other * Code Notebook,code,Beth Cimini et al.,,,en +EMBL-EBI material collection,Bioinformatics * Training,https://www.ebi.ac.uk/training/on-demand?facets=type:Course%20materials&query=,CC0-1.0,"Online tutorial and webinar library, designed and delivered by EMBL-EBI experts",,Other,,EMBL-EBI,,, +[Workshop] FAIR data handling for microscopy: Structured metadata annotation in OMERO,,https://zenodo.org/records/11109616 * https://doi.org/10.5281/zenodo.11109616,CC-BY-4.0,"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 structured metadata annotations in the image data management platform OMERO (OME Remote Objects) to optimize their data handling. This strategy helps both with organizing data for easier processing and analysis and for the preparation of data publication in journal manuscripts and in public repositories such as the BioImage Archive. Participants learn the principles of leveraging object-oriented data organization in OMERO to enhance findability and usability of their data, also in collaborative settings. The integration of OMERO with image analysis tools, in particular ImageJ/Fiji, will be trained. Moreover, users learn about community-accepted metadata checklists (REMBI) to enrich the value of their data toward reproducibility and reusability. In this workshop, we will provide hands-on training and recommendations on: + +Structured metadata annotation features in OMERO and how to use them +Types of metadata in bioimaging: Technical metadata, sample metadata, analysis metadata +The use of ontologies and terminologies for metadata annotation +REMBI, the recommended metadata for biological images +Metadata-assisted image analysis streamlining +Tools for metadata annotation in OMERO + +The target group for this workshop +This workshop is directed at researchers at all career levels who have started using 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. +Who are the trainers (see trainer description below for more details) + +Dr. Vanessa Fuchs (NFDI4BIOIMAGE Data Steward, Center for Advanced Imaging, Heinrich-Heine University of Düsseldorf) +Dr. Tom Boissonnet (OMERO admin and image metadata specialist, Center for Advanced Imaging, Heinrich-Heine University of Düsseldorf) +Dr. Christian Schmidt (Science Manager for Research Data Management in Bioimaging, German Cancer Research Center, Heidelberg) + +Material Description +Published here are the presentation slides that were used for input from the trainers during the different sessions of the programme. Additionally, a Fiji Macro is published that depends on the OMERO Extensions Plugin by Pouchin et al, 2022, F100Research, https://doi.org/10.12688/f1000research.110385.2  +Programme Overview +Day 1 - April 29th, 2024 09.00 a.m. to 10.00 a.m.: Session 1 - Welcome and Introduction +10.00 a.m. to 10.30 a.m.:  Session 2 - Introduction to the FAIR principles & data annotation +10:30 a.m. to 10:45 a.m.: Coffee break +10.45 a.m. to 12.00 a.m.: Session 3 - Data structure (datasets in OMERO) and organization with Tags  +12.00 a.m. to 1.00 p.m.:  Lunch Break +1.00 p.m. to 2.00 p.m.:  Session 4 - REMBI, Key-Value pair annotations in bioimaging +2:00 p.m. to 2.30 p.m.:  Session 5 - Ontologies for Key-Value Pairs in OMERO +2:30 p.m. to 2:45 p.m. Coffee break +2.45 p.m. to 3.45 p.m.:  Wrap-up, discussion, outlook on day 2 +Day 2 - April 30th, 2024 +09.00 a.m. to 09.30 a.m.:  Arrival and Start into day 2 +09.30 a.m. to 11.30 a.m.:  Session 6 - Hands-on : REMBI-based Key-Value Pair annotation in OMERO +11.30 a.m. to 12.30 a.m.:  Lunch Break +12.30 a.m. to 1.15 p.m.: Session 7 - OMERO and OMERO.plugins +1.15 p.m. to 2.00 p.m.: Session 8 - Loading OMERO-hosted data into Fiji +2.00 p.m. to 2.15 p.m.: Coffee break  +2.15 p.m. to 3.00 p.m.: Discussion, Outlook",2024-05-06,,,"Vanessa Fiona Aphaia Fuchs * Schmidt, Christian * Boissonnet, Tom",,ijm * pdf * pptx, +Getting started with Python: intro and set-up a conda environment,,https://zenodo.org/records/13908480 * https://doi.org/10.5281/zenodo.13908480,CC-BY-4.0,"YMIA python event 2024 +Presentation :  ""Getting started with Python: intro and set-up a conda environment with Dr. Riccardo Massei""",2024-10-09,,,"Massei, Riccardo",,odp,en +Bio-image Analysis Code Generation using bia-bob,,https://zenodo.org/records/13908108 * https://doi.org/10.5281/zenodo.13908108,CC-BY-4.0,"In this presentation I introduce bia-bob, an AI-based code generator that integrates into Jupyter Lab and allows for easy generation of Bio-Image Analysis Python code. It highlights how to get started with using large language models and prompt engineering to get high-quality bio-image analysis code.",2024-10-09,,,"Haase, Robert",,pdf * pptx,en +RDM Starter Kit,Research Data Management,https://www.go-fair.org/resources/rdm-starter-kit/,CC-BY-4.0,This page is supposed to serve as a Starter Kit for research data management (RDM). It lists resources designed to help researchers get started to organize their data.,,Web Page,,"FAIR, GO",,, +Hitchhiking through a diverse Bio-image Analysis Software Universe,Bioimage Analysis,https://f1000research.com/slides/11-746 * https://doi.org/10.7490/f1000research.1119026.1,CC-BY-4.0,Overview about decision making and how to influence decisions in the bio-image analysis software context.,2022-07-22,Presentation,presentation,"Haase, Robert",,,en +"Research Data Reusability - Conceptual Foundations, Barriers and Enabling Technologies",Research Data Management * Open Science * Data Protection,https://www.mdpi.com/2304-6775/5/1/2,CC-BY-4.0,"This article discusses various aspects of data reusability in the context of scientific research, including technological, legal, and policy frameworks.",2017-01-09,Article,text,"Thanos, Costantino",,,en +The FAIR Guiding Principles for scientific data management and stewardship,FAIR-Principles * Research Data Management,https://www.nature.com/articles/sdata201618 * https://doi.org/10.1038/sdata.2016.18,CC-BY-4.0,"This Comment is the first formal publication of the FAIR Principles, and includes the rationale behind them, and some exemplar implementations in the community.",2016-03-15,Article,text,"Wilkinson, MarkD. * Dumontier, Michel * Aalbersberg, IJsbrandJan * Appleton, Gabrielle * Axton, Myles * al, et.",,,en +Modeling community standards for metadata as templates makes data FAIR,Data Stewardship * FAIR-Principles * Metadata,https://pubmed.ncbi.nlm.nih.gov/36371407/ * https://www.nature.com/articles/s41597-022-01815-3,CC-BY-4.0,"The authors have developed a model for scientific metadata, and they have made that model usable by both CEDAR and FAIRware. The approach shows that a formal metadata model can standardize reporting guidelines and that it can enable separate software systems to assist (1) in the authoring of standards-adherent metadata and (2) in the evaluation of existing metadata.",2022-11-12,Article,text,"Musen, MarkA * O'Connor, MartinJ * Schultes, Erik * Martínez-Romero, Marcos * Hardi, Josef * Graybeal, John",,,en +NFDI4BIOIMAGE - An Initiative for a National Research Data Infrastructure for Microscopy Data,Nfdi4Bioimage * Image Data Management * Bioimage Data * Research Data Management,https://doi.org/10.11588/heidok.00029489,CC-BY-SA-4.0,Align existing and establish novel services & solutions for data management tasks throughout the bioimage data lifecycle.,2021-04-29,Presentation * Text,text * presentation,"Schmidt, Christian * Ferrando-May, Elisa",,,en +Data life cycle,Data Life Cycle * Research Data Management,https://rdmkit.elixir-europe.org/data_life_cycle,CC-BY-4.0,"In this section, information is organised according to the stages of the research data life cycle.",,Web Page * Tutorial * Other,,ELIXIR (2021) Research Data Management Kit,,,en +Erstellung und Realisierung einer institutionellen Forschungsdaten-Policy,Research Data Management,https://bausteine-fdm.de/article/view/7945 * https://doi.org/10.17192/bfdm.2018.1.7945,CC-BY-4.0,Die vorliegende Empfehlung sowie die zugehörigen Erfahrungsberichte geben einen Überblick über die verschiedenen Möglichkeiten der Gestaltung einer Forschungsdatenmanagement Policy sowie Wege zu deren Erstellung., 2018-10-22,Article,text,"Hahn, Uli * Helbig, Kerstin * Jagusch, Gerald * Rex, Jessica",,,de +Leitlinie? Grundsätze? Policy? Richtlinie? – Forschungsdaten-Policies an deutschen Universitäten,Research Data Management * FAIR-Principles,https://www.o-bib.de/bib/article/view/2018H2S1-13,CC-BY-4.0,"As a methodological approach, research data policies of German universities are collected and evaluated, and compared to international recommendations on research data policies.",2018-07-13,Article,text,"Hiemenz, Bea * Kuberek, Monika",,,de +Creating a Research Data Management Plan using chatGPT,Research Data Management * Large Language Models * Artificial Intelligence,https://focalplane.biologists.com/2023/11/06/creating-a-research-data-management-plan-using-chatgpt/,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.,2023-11-06,Web Page,text,"Haase, Robert",,,en +Rechtsfragen bei Open Science - Ein Leitfaden,Open Science * Open Access * Copyright,https://hup.sub.uni-hamburg.de/oa-pub/catalog/book/205,CC-BY-4.0,"Die Digitalisierung ermöglicht eine offene Wissenschaft (Open Science). Diese hat viele Aspekte, insbesondere den freien Zugang zu wissenschaftlichen Veröffentlichungen und Materialien (Open Access), transparente Begutachtungsverfahren (Open Peer Review) oder quelloffene Technologien (Open Source). Das Programm Hamburg Open Science (Laufzeit 2018–2020) unterstützt unter anderem den Kulturwandel in der Wissenschaft. In diesem Kontext entstand der nun vorliegende Leitfaden, der das rechtliche Umfeld greifbar machen soll. Der Leitfaden erarbeitet die betroffenen Rechtsgebiete zunächst systematisch. Im zweiten Teil werden rechtliche Fragen zu Open Science beantwortet, die direkt aus den Universitäten und Bibliotheken kommen.",2021-05-25,Book,text,"Kreutzer, Till * Lahmann, Henning",,,de +The Open Microscopy Environment (OME) Data Model and XML file - open tools for informatics and quantitative analysis in biological imaging,Microscopy Image Analysis * Bioimage Analysis,https://genomebiology.biomedcentral.com/articles/10.1186/gb-2005-6-5-r47 * https://doi.org/10.1186/gb-2005-6-5-r47,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.",2005-05-03,Article,text,"Goldberg, IlyaG. * Allan, Chris * Burel, Jean-Marie * Creager, Doug * Falconi, Andrea * al, et.",,,en +Microscopy-BIDS - An Extension to the Brain Imaging Data Structure for Microscopy Data,Research Data Management * Image Data Management * Bioimage Data,https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2022.871228/full,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.",2022-04-19,Article,text,"Bourget, Marie-Hélène * Kamentsky, Lee * Ghosh, SatrajitS. * Mazzamuto, Giacomo * Lazari, Alberto * et al.",,,en +FAIR High Content Screening in Bioimaging,FAIR-Principles * Metadata * Research Data Management * Image Data Management * Bioimage Data,https://www.nature.com/articles/s41597-023-02367-w,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.",2023-07-17,Article,text,"Hosseini, Rohola * Vlasveld, Matthijs * Willemse, Joost * Bob van de Water * Sylvia E. Le Dévédec * Wolstencroft, KatherineJ.",,,en +Dokumentation und Anleitung zum elektronischen Laborbuch (eLabFTW),Research Data Management,https://www.fdm.tu-clausthal.de/fileadmin/FDM/documents/Manual_eLab_v0.3_20200323.pdf * https://www.elabftw.net/,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.",2020-03-23,Tutorial * Text,text,"Wegewitz, Lienhard * Strauß, F.",,,de +What is Open Data?,Open Science,http://opendatahandbook.org/guide/en/what-is-open-data/,CC-BY-4.0,"This handbook is about open data but what exactly is it? In particular what makes open data open, and what sorts of data are we talking about?",,Other,,"Dietrich, Daniel * Gray, Jonathan * McNamara, Tim * Poikola, Antti * Pollock, Rufus * et al.",,,en +Ten simple rules for making training materials FAIR,Metadata * Bioinformatics * FAIR-Principles * Training,https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1007854,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.",2020-05-21,Article,text,"Garcia, Leyla * Batut, Bérénice * Burke, MelissaL. * Kuzak, Mateusz * Psomopoulos, Fotis * et al.",,,en +The FAIR guiding principles for data stewardship - fair enough?,FAIR-Principles * Data Stewardship * Sharing,https://www.nature.com/articles/s41431-018-0160-0,CC-BY-4.0,"The FAIR guiding principles for research data stewardship (findability, accessibility, interoperability, and reusability) look set to become a cornerstone of research in the life sciences. A critical appraisal of these principles in light of ongoing discussions and developments about data sharing is in order.",2018-05-17,Article,text,"Boeckhout, Martin * Zielhuis, GerhardA. * Bredenoord, AnnelienL.",,,en +bioformats2raw Converter,Open Source Software * Bioimage Data,https://github.com/glencoesoftware/bioformats2raw,GPL-2.0,"Java application to convert image file formats, including .mrxs, to an intermediate Zarr structure compatible with the OME-NGFF specification.",,Software Application * Other,,"Linkert, Melissa * Allan, Chris * Moore, Josh * Besson, Sébastien * Gault, David * et al.",,,en +raw2ometiff Converter,Open Source Software * Bioimage Data,https://github.com/glencoesoftware/raw2ometiff,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.,,Software Application * Other,,"Linkert, Melissa * Allan, Chris * Besson, Sébastien * Moore, Josh",,,en +Checklists for publishing images and image analysis,Bioimage Data * Microscopy Image Analysis,https://forum.image.sc/t/checklists-for-publishing-images-and-image-analysis/86304,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.,2023-09-14,Text,text,"Schmied, Christopher",,,en +WorkflowHub,Bioinformatics * Workflow * Workflow Engine * Python * R,https://workflowhub.eu/,BSD-3-Clause,"A registry for describing, sharing and publishing scientific computational workflows.",,Other,,"Soiland-Reyes, Stian * Bacall, Finn * Droesbeke, Bert * Williams, AlanR * Gustafsson, Johan * et al.",,,en +Erick Martins Ratamero - Expanding the OME ecosystem for imaging data management | SciPy 2024,Image Data Management * OMERO * Bioimage Analysis,https://www.youtube.com/watch?v=GmhyDNm1RsM,YOUTUBE STANDARD LICENSE,,2024-08-19,Presentation,presentation * video,"SciPy * Ratamero, ErickMartins",,,en +"Plugin ""simple-omero-client""",OMERO * Github * Fiji,https://github.com/GReD-Clermont/simple-omero-client,GPL-2.0,"A wrapper library which can be called from scripts in Fiji, but can mostly be used in Maven projects to wrap calls to the underlying OMERO Java Gateway.",,Other,,"Pouchin, Pierre * Rdornier * kekunn * Burel, Jean-Marie",,,en +BIOMERO - A scalable and extensible image analysis framework,OMERO * Workflow * Bioimage Analysis * Image Data Management,https://doi.org/10.1016/j.patter.2024.101024,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.",,Article,text,"Luik, TorecT. * Rosas-Bertolini, Rodrigo * Reits, EricA.J. * Hoebe, RonA. * Krawczyk, PrzemekM.",,,en +Biomero,OMERO * Github,https://github.com/NL-BioImaging/biomero,Apache-2.0,"The BIOMERO framework, for BioImage analysis in OMERO, allows you to run (FAIR) bioimage analysis workflows directly from OMERO on a high-performance compute (HPC) cluster, remotely through SSH.",2024-07-24,Other,,"Luik, Torec * Soltwedel, Johannes",,, +"Plugin ""omero-cli-transfer""",OMERO,https://github.com/ome/omero-cli-transfer,GPL-2.0,An OMERO CLI plugin for creating and using transfer packets between OMERO servers.,2024-09-14,Other,,"Ratamero, ErickMartins * Burel, Jean-Marie * Moore, Will * Gay, Guillaume * Moehl, Christoph * et al.",,, +ome-ngff-validator,Bioimage Data,https://ome.github.io/ome-ngff-validator/ * https://github.com/ome/ome-ngff-validator,BSD-2-Clause,Web page for validating OME-NGFF files.,2022-09-29,Software Application * Other,,"Moore, Will * Moore, Josh * Halchenko, Yaroslav * Besson, Sébastien",,, +Towards Transparency and Knowledge Exchange in AI-assisted Data Analysis Code Generation,,https://zenodo.org/records/13928832 * https://doi.org/10.5281/zenodo.13928832,CC-BY-4.0,"The integration of Large Language Models (LLMs) in scientific research presents both opportunities and challenges for life scientists. Key challenges include ensuring transparency in AI-generated content and facilitating efficient knowledge exchange among researchers. These issues arise from the in-transparent nature of AI-driven code generation and the informal sharing of AI insights, which may hinder reproducibility and collaboration. This paper introduces git-bob, an innovative AI-assistant designed to address these challenges by fostering an interactive and transparent collaboration platform within GitHub. By enabling seamless dialogue between humans and AI, git-bob ensures that AI contributions are transparent and reproducible. Moreover, it supports collaborative knowledge exchange, enhancing the interdisciplinary dialogue necessary for cutting-edge life sciences research. The open-source nature of git-bob further promotes accessibility and customization, positioning it as a vital tool in employing LLMs responsibly and effectively within scientific communities.",2024-10-14,,,"Haase, Robert",,pdf,en +From Cells to Pixels: Bridging Biologists and Image Analysts Through a Common Language,,https://zenodo.org/records/13331351 * https://doi.org/10.5281/zenodo.13331351,CC-BY-4.0,"Bioimaging has transformed our understanding of biological processes, yet extracting meaningful information from complex datasets remains a challenge, particularly for early career scientists. This paper proposes a simplified, systematic approach to bioimage analysis, focusing on categorizing commonly observed structures and shapes, and providing relevant analysis methods. Our approach includes illustrative examples and a visual flowchart, enabling researchers to define analysis objectives clearly. By understanding the diversity of bioimage structures and aligning them with appropriate analysis approaches, the framework empowers researchers to navigate bioimage datasets more efficiently. It also aims to foster a common language between researchers and analysts, thereby enhancing mutual understanding and facilitating effective communication.",2024-08-16,,,"Fazeli, Elnaz * Robert, Haase * Michael, Doube * Kota, Miura * David, Legland",,pdf,en +OMERO - QuPath,Bioimage Analysis * OMERO,https://wiki-biop.epfl.ch/en/data-management/omero/qupath,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.",,Tutorial,,Rémy Jean Daniel Dornier,,, +Multimodal large language models for bioimage analysis,Bioimage Analysis * Large Language Models * FAIR-Principles * Workflow,https://www.nature.com/articles/s41592-024-02334-2 * https://arxiv.org/abs/2407.19778,CC-BY-NC-SA-4.0,"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",,Article,text,"Zhang, Shanghang * Dai, Gaole * Huang, Tiejun * Chen, Jianxu",,,en +Bio-image Analysis Code Generation,,https://zenodo.org/records/14001044 * https://doi.org/10.5281/zenodo.14001044,CC-BY-4.0,"Large Language Models are changing the way we interact with computers, especially how we write code. In this tutorial, we will generate bio-image analysis code using two LLM-based code generators, bia-bob and git-bob. +https://github.com/haesleinhuepf/bia-bob +https://github.com/haesleinhuepf/git-bob + ",2024-10-28,,,"Haase, Robert",,pdf * pptx,en +"I2K2024 workshop material - Lazy Parallel Processing and Visualization of Large Data with ImgLib2, BigDataViewer, the N5-API, and Spark",Training,https://saalfeldlab.github.io/i2k2024-lazy-workshop/ * https://github.com/saalfeldlab/i2k2024-lazy-workshop,Apache-2.0,,,Other * Course * Code Notebook,code,"Saalfeld, Stephan * Pietzsch, Tobias",,,en +Ultrack I2K 2024 Workshop Materials,Segmentation * Bioimage Analysis * Training,https://github.com/royerlab/ultrack-i2k2024 * https://royerlab.github.io/ultrack-i2k2024/,BSD-3-Clause,,,Tutorial * Other * Course,,"Bragantini, Jordão * Huijben, Teun",,,en +Multiplexed tissue imaging - tools and approaches,Bioimage Analysis * Microscopy Image Analysis,https://github.com/BIIFSweden/I2K2024-MTIWorkshop * https://docs.google.com/presentation/d/1R9-4lXAmTYuyFZpTMDR85SjnLsPZhVZ8/edit#slide=id.p1,CC-BY-4.0,"Material for the I2K 2024 ""Multiplexed tissue imaging - tools and approaches"" workshop",,Presentation * Other * Course,presentation,"Corbat, AgustínAndrés * OmFrederic * Windhager, Jonas * Lidayová, Kristína",,,en +I2K2024(virtual) - Bio-Image Analysis Code Generation,Bioimage Analysis * Notebooks * Biabob,https://github.com/haesleinhuepf/i2k2024-ai-code-generation,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.",,Tutorial * Other * Code Notebook,code,"Haase, Robert",,,en +Object Tracking and Track Analysis using TrackMate and CellTracksColab,Bioimage Analysis * Training,https://github.com/CellMigrationLab/I2K_2024,GPL-3.0,"I2K 2024 workshop materials for ""Object Tracking and Track Analysis using TrackMate and CellTracksColab""",,Tutorial * Presentation * Other * Course,presentation,"Pylvänäinen, Joanna",,,en +I2K 2024: clEsperanto - GPU-Accelerated Image Processing Library,Clesperanto * Training * Bioimage Analysis * Notebooks * Workflow,https://github.com/StRigaud/clesperanto_workshop_I2K24?tab=readme-ov-file,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.",,Tutorial * Other * Course * Code Notebook,code,"Rigaud, Stephane * Haase, Robert",,, +Example Pipeline Tutorial,Napari * Microscopy Image Analysis * Bioimage Analysis,https://timmonko.github.io/napari-ndev/tutorial/01_example_pipeline/ * https://github.com/timmonko/napari-ndev,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).,2024-10-28,Tutorial * Other * Text,text,"Monko, Tim",,,en +"[GBI EoE IX] NFDI4BIOIMAGE +National Research Data Infrastructure +for Microscopy and BioImage Analysis",,https://zenodo.org/records/14001388 * https://doi.org/10.5281/zenodo.14001388,CC-BY-4.0,"Presented at https://globalbioimaging.org/exchange-of-experience/exchange-of-experience-ix in Okazaki, Japan.",2024-10-29,,,"Moore, Josh",,pdf,en +[BINA CC] Scalable strategies for a next-generation of FAIR bioimaging,,https://zenodo.org/records/13831274 * https://doi.org/10.5281/zenodo.13831274,CC-BY-4.0,Presented at https://www.bioimagingnorthamerica.org/events/bina-2024-community-congress/,2024-09-24,,,"Moore, Josh",,pdf,en +[I2K] Scalable strategies for a next-generation of FAIR bioimaging,,https://zenodo.org/records/13991322 * https://doi.org/10.5281/zenodo.13991322,CC-BY-4.0,"or, ""OME-Zarr: 'even a talk on formats [can be] interesting'"" +Presented at https://events.humantechnopole.it/event/1/",2024-10-25,,,"Moore, Josh",,pdf,en +Excel template for adding Key-Value Pairs to images,,https://zenodo.org/records/14014252 * https://doi.org/10.5281/zenodo.14014252,CC-BY-4.0,"This Excel Workbook contains some simple Macros to help with the generation of a .csv in the necessary format for Key-Value pair annotations of images in OMERO. +The format is tailored for the OMERO.web script ""KeyVal_from_csv.py""  (from the version <=5.8.3 of the core omero-scripts). +Attached is also a video of Thomas Zobel, the head of the imaging core facility Uni Münster, showcasing the use of the Excel workbook.The video uses a slightly older version of the workbook and OMERO, but the core functionality remains unchanged. +Please keep in mind, that the OMERO.web script(s) to handle Key-Value Pairs from/to .csv files will undergo a major change very soon.This might break the compatibility with the format used now for the generated .csv from the workbook.",2024-10-30,,,"Zobel, Thomas * Wendt, Jens",,mp4 * xlsm,en +Research data management for bioimaging - the 2021 NFDI4BIOIMAGE community survey,Research Data Management * Image Data Management * Bioimage Data,https://f1000research.com/articles/11-638/v2,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.",2022-09-20,Article,text,"Schmidt, Christian * Hanne, Janina * Moore, Josh * Meesters, Christian * Ferrando-May, Elisa * et al.",,,en +Virtual-I2K-2024-multiview-stitcher,Big Data * Bioimageanalysis,https://github.com/m-albert/Virtual-I2K-2024-multiview-stitcher,BSD-3-Clause,Repository accompanying the multiview-stitcher tutorial for Virtual I2K 2024,2024-10-30T07:38:11+00:00,Tutorial * Other,,"Albert, Marvin",,,en +Forschungsdatenmanagement zukunftsfest gestalten – Impulse für die Strukturevaluation der Nationalen Forschungsdateninfrastruktur (NFDI),,https://zenodo.org/records/14032908 * https://doi.org/10.5281/zenodo.14032908,CC-BY-4.0,"Arbeitspapier des Steuerungsgremiums des Allianz-Schwerpunkts ""Digitalität in der Wissenschaft""",2024-11-04,,,"Allianz-Schwerpunkt, Steuerungsgremium * Alexander von Humboldt Foundation * Forschungsgemeinschaft, Deutsche * Society, Fraunhofer * Conference, GermanRectors' * Association, Leibniz * German National Academy of Sciences Leopoldina * German Academic Exchange Service * Helmholtz Association of German Research Centres * Society, MaxPlanck",,pdf,de +Stackview sliceplot example data,,https://zenodo.org/records/14030307 * https://doi.org/10.5281/zenodo.14030307,CC-BY-4.0,"This is a dataset of PNG images of [Bio-Image Data Science teaching slides](https://zenodo.org/records/12623730). The png_umap.yml file contains a list of all images and a dimensionality reduced embedding (Uniform Manifold Approximation Projection, UMAP) made using OpenAI's text-embedding-ada-002 model. +A notebook for visualizing this data is published here: https://github.com/haesleinhuepf/stackview/blob/main/docs/sliceplot.ipynb",2024-11-03,,,"Haase, Robert",,zip,en +Prompt-Engineering-LLMs-Course,Llms * Prompt Engineering * Code Generation,https://github.com/HelmholtzAI-Consultants-Munich/Prompt-Engineering-LLMs-Course,MIT,,2024-09-11T07:45:30+00:00,Tutorial * Other,,"Mekki, Isra",,,en +Building FAIR image analysis pipelines for high-content-screening (HCS) data using Galaxy,,https://zenodo.org/records/14044640 * https://doi.org/10.5281/zenodo.14044640 * https://galaxyproject.org/news/2024-11-08-galaxy-imaging-fair-pipelines/,CC-BY-4.0,"Imaging is crucial across various scientific disciplines, particularly in life sciences, where it plays a key role in studies ranging from single molecules to whole organisms. However, the complexity and sheer volume of image data, especially from high-content screening (HCS) experiments involving cell lines or other organisms, present significant challenges. Managing and analysing this data efficiently requires well-defined image processing tools and analysis pipelines that align with the FAIR principles—ensuring they are findable, accessible, interoperable, and reusable across different domains. +In the frame of NFDI4BioImaging (the National Research Data Infrastructure focusing on bioimaging in Germany), we want to find viable solutions for storing, processing, analysing, and sharing HCS data. In particular, we want to develop solutions to make findable and machine-readable metadata using (semi)automatic analysis pipelines. In scientific research, such pipelines are crucial for maintaining data integrity, supporting reproducibility, and enabling interdisciplinary collaboration. These tools can be used by different users to retrieve images based on specific attributes as well as support quality control by identifying appropriate metadata. +Galaxy, an open-source, web-based platform for data-intensive research, offers a solution by enabling the construction of reproducible pipelines for image analysis. By integrating popular analysis software like CellProfiler and connecting with cloud services such as OMERO and IDR, Galaxy facilitates the seamless access and management of image data. This capability is particularly valuable in bioimaging, where automated pipelines can streamline the handling of complex metadata, ensuring data integrity and fostering interdisciplinary collaboration. This approach not only increases the efficiency of HCS bioimaging but also contributes to the broader scientific community's efforts to embrace FAIR principles, ultimately advancing scientific discovery and innovation. +In the present study, we proposed an automated analysis pipeline for storing, processing, analysing, and sharing HCS bioimaging data. The (semi)automatic workflow was developed by taking as a case study a dataset of zebrafish larvae and cell lines images previously obtained from an automated imaging system generating data in an HCS fashion. In our workflows, images are automatically enriched with metadata (i.e. key-value pairs, tags, raw data, regions of interest) and uploaded to the UFZ-OME Remote Objects (OMERO) server using a novel OMERO tool suite developed with GALAXY. Workflows give the possibility to the user to intuitively fetch images from the local server and perform image analysis (i.e. annotation) or even more complex toxicological analyses (dose response modelling). Furthermore, we want to improve the FAIRness of the protocol by adding a direct upload link to the Image Data Resource (IDR) repository to automatically prepare the data for publication and sharing.",2024-11-06,,,"Massei, Riccardo * Berndt, Matthias * Serrano-Solano, Beatriz * Busch, Wibke * Scholz, Stefan * Bohring, Hannes * Nyffeler, Jo * Reger, Luise * Bumberger, Jan * Lopez-Delisle, Lucille",,odp,en +introduction-to-generative-ai,Artificial Intelligence,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,CC-BY-4.0,Course repository for Strategic Use of Generative AI,2024-09-27T14:38:51+00:00,Tutorial * Other,,"Piereck, Bruna * Botzki, Alexander",,, +nextflow-workshop,Workflow * Nextflow,https://github.com/vibbits/nextflow-workshop * https://liascript.github.io/course/?https://raw.githubusercontent.com/vibbits/nextflow-workshop/main/README.md#1,CC-BY-4.0,Nextflow workshop materials March 2023,2023-03-29T10:40:04+00:00,Tutorial * Other,,"Muyldermans, Tuur * Davie, Kris * Alexander * Vannieuwkerke, Nicolas * Lavaerts, Kobe * Ribeiro-Dantas, Marcel * Piereck, Bruna * Taelman, Steff",,,en +YMIA - Python-Based Event Series Training Material,Python * Large Language Models * Prompt Engineering * Biabob * Bioimage Analysis * Microscopy Image Analysis,https://github.com/rmassei/ymia_python_event_series_material,MIT,This repository offer access to teaching material and useful resources for the YMIA - Python-Based Event Series.,,Presentation * Other,presentation,"Massei, Riccardo * Haase, Robert * ENicolay",,,en +Finding and using publicly available data,Open Science * Teaching * Sharing,https://www.ebi.ac.uk/training/online/courses/finding-using-public-data/,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.",2024-01-01,Tutorial * Other,video,"Swan, Anna",,.mp4,en +"[Workshop Material] Fit for OMERO - How imaging facilities and IT departments work together to enable RDM for bioimaging, October 16-17, 2024, Heidelberg",,https://zenodo.org/records/14013026 * https://doi.org/10.5281/zenodo.14013026,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 +",2024-10-30,,,"Boissonnet, Tom * Hagen, Bettina * Kunis, Susanne * Schmidt, Christian * Weidtkamp-Peters, Stefanie",,pdf,en +training-resources,Bioimageanalysis * Neurobias,https://github.com/NEUBIAS/training-resources,CC-BY-4.0,Resources for teaching/preparing to teach bioimage analysis,2020-04-23T07:51:38+00:00,Other,,"Tischer, Christian * Politi, Antonio * Hodges, Toby * maulakhan * grinic * bugraoezdemir * Buchholz, Tim-Oliver * Fazeli, Elnaz * Halavatyi, Aliaksandr * Kutra, Dominik * Marcotti, Stefania * AnniekStok * Felix * jhennies * Klaus, Severina * Schorb, Martin * Vakili, Nima * Tirado, SebastianGonzalez * Helfrich, Stefan * Sun, Yi * Huang, Ziqiang * Eglinger, Jan * Pape, Constantin * Lüthi, Joel * McCormick, Matt * Gros, Oane",,,en +cba-support-template,Workflow * Research Data Management,https://git.embl.de/grp-cba/cba-support-template,MIT,,2021-12-01,Tutorial,,"Khan, Arif * Tischer, Christian * Gonzalez, Sebastian * Kutra, Dominik * Schneider, Felix * et al.",,, +Diátaxis - A systematic approach to technical documentation authoring.,Documentation,https://www.diataxis.fr/,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.",,Web Page * Tutorial,,"Procida, Daniele",,,en +Image Processing with Python,Segmentation * Bioimage Analysis * Training * Python * Scikit-Image * Image Segmentation,https://datacarpentry.org/image-processing/key-points.html,CC-BY-4.0,This lesson shows how to use Python and scikit-image to do basic image processing.,,Tutorial,,"Meysenburg, Mark * Hodges, Toby * Kutra, Dominik * Becker, Erin * Palmquist, David * et al.",,,en +Evident OIR sample files tiles + stitched image - FV 4000,,https://zenodo.org/records/13680725 * https://doi.org/10.5281/zenodo.13680725,CC-BY-4.0,"The files contained in this repository are confocal images taken with the Evident FV 4000 of a sample containing DAPI and mCherry stains, excited with a 405 nm laser and a 561 nm laser + +individual tiles are named `tiling-sample-brain-section_A01_G001_{i}.oir` +The stiched image is named `Stitch_A01_G001` and contains an extra file `Stitch_A01_G001_00001` +Some metadata like the tiles positions are stored in the extra files (omp2info) + + ",2024-09-04,,,"Chiaruttini, Nicolas",,hdf5 * oir * omp2info,en +Test Dataset for Whole Slide Image Registration,,https://zenodo.org/records/5675686 * https://doi.org/10.5281/zenodo.5675686,CC-BY-4.0,"Mouse duodenum fixed in 4% PFA overnight at 4°C, processed for paraffin infiltration using a standard histology procedure and cut at 4 microns were dewaxed, rehydrated, permeabilized with 0.5% Triton X-100 in PBS 1x and stained with Azide - Alexa Fluor 555 (Thermo Fisher) to detect EdU and DAPI for nuclei. The images were taken using a Leica DM5500 microscope with a 40X N.A.1 objective (black&white camera: DFC350FXR2, pixel dimension: 0.161 microns). Next, the slide was unmounted and stained using the fully automated Ventana Discovery xT autostainer (Roche Diagnostics, Rotkreuz, Switzerland). All steps were performed on automate with Ventana solutions. Sections were pretreated with heat using the CC1 solution under mild conditions. The primary rat anti BrDU (clone: BU1/75 (ICR1), Serotec, diluted 1:300) was incubated 1 hour at 37°C. After incubation with a donkey anti rat biotin diluted 1:200 (Jackson ImmunoResearch Laboratories), chromogenic revelation was performed with DabMap kit. The section was counterstained with Harris hematoxylin (J.T. Baker) before a second round of imaging on DM5500 PL Fluotar 40X N.A.1.0 oil (color camera: DFC 320 R2, pixel dimension: 0.1725 microns). Before acquisition, a white-balance as well as a shading correction is performed according to Leica LAS software wizard. The fluorescence and DAB images were converted in ome.tiff multiresolution file with the kheops Fiji Plugin. + +Sampled prepared in the EPFL histology core facility by Nathalie Müller and Gian-Filippo Mancini. + +Associated documents: + + + https://c4science.ch/w/bioimaging_and_optics_platform_biop/teaching/dab-intensity/ + https://imagej.net/plugins/bdv/warpy/warpy + + +This document contains a full QuPath project with an example of registered image. + + ",2021-04-12,,,"Guiet, Romain * Chiaruttini, Nicolas",,jpg * zip,en +Example Operetta Dataset,,https://zenodo.org/records/8153907 * https://doi.org/10.5281/zenodo.8153907,CC-BY-4.0,"This is a microscopy image dataset generated by the Perkin Elmer Operetta HCS microscope by of the user of the PTBIOP EPFL facility. +As of the 17th of July 2023, opening this file in ImageJ/Fiji using the BioFormats 6.14 library, this dataset generates a Null Pointer Exception. + +A post on forum.image.sc is linked to this issue: + +https://forum.image.sc/t/null-pointer-exception-in-perkin-elmer-operetta-dataset-with-bio-formats-6-14/83784 + +  + + ",2023-07-17,,,"Chiaruttini, Nicolas",,zip, +CZI file examples,,https://zenodo.org/records/8305531 * https://doi.org/10.5281/zenodo.8305531,CC-BY-4.0,"A set of public CZI files. These can be used for testing CZI readers. + +- Demo LISH 4x8 15pct 647.czi: A cleared mouse brain acquired with a Zeiss LightSheet Z1 with 32 tiles. Courtesy of the Carl Petersen lab LSENS (https://www.epfl.ch/labs/lsens). Sampled prepared by Yanqi Liu an imaged by Olivier Burri. +- test_gray.czi: a synthetically generated CZI file without metadata, made by Sebastian Rhode + +- Image_1_2023_08_18__14_32_31_964.czi: an example multi-part CZI file, containing only camera noise + +- a xt scan, xz scan, xzt scan + +- a set of multi angle, multi illumination, mutli tile acquisition, taken on the LightSheet Z1 microscope of the PTBIOP by Lorenzo Talà",2023-08-18,,,"Chiaruttini, Nicolas",,czi,en +LimeSeg Test Datasets,,https://zenodo.org/records/1472859 * https://doi.org/10.5281/zenodo.1472859,CC-BY-4.0,"Image datasets from the publication : LimeSeg: A coarse-grained lipid membrane simulation for 3D image segmentation + + + Vesicles.tif: spinning-disc confocal images of giant unilamellar vesicles + HelaCell-FIBSEM.tif: a 3D Electron Microscopy (EM) dataset of nearly isotropic sections of a Hela cell, acquired with a focused ion beam scanning electron microscope (FIB-SEM). Sections are aligned with TrackEm2 (doi: ), without additional preprocessing. + DrosophilaEggChamber.tif: point scanning confocal images of a Drosophila egg chamber. Channel 1: cell nuclei  stained with DAPI. Channel 2: cell membranes visualized with fused membrane proteins Nrg::GFP and Bsg::GFP.  + + +Image metadata contains extra information including voxel sizes. + + ",2018-10-27,,,"Machado, Sarah * Mercier, Vincent * Chiaruttini, Nicolas",,tif,en +Metadata Annotation Workflow for OMERO with Tabbles,,https://zenodo.org/records/8314968 * https://doi.org/10.5281/zenodo.8314968,CC-BY-4.0,Short presentation given at at PoL BioImage Analysis Symposium Dresden 2023,2023-09-04,,,"Jens, Wendt",,pdf * pptx,en +Image Repository Decision Tree - Where do I deposit my imaging data,,https://zenodo.org/records/13945179 * https://doi.org/10.5281/zenodo.13945179,CC-BY-4.0,"Depositing data in quality data repositories is one crucial step towards FAIR (Findable, Accessible, Interoperable, and Reusable) data. Accordingly, Euro-BioImaging strongly encourages sharing scientific imaging data in established, thematic repositories.  +To guide you in the selection of appropriate repositories, we have created an overview of available repositories for different types of image data, including their scope and requirements. This decision tree guides you through questions about your data and directs you to the correct repository, and/or provides instructions for further processing to meet the critera of the repositories.  +Three seperate trees are provided for different classes of imaging data: open bioimage data, preclinical data, and human imaging data. ",2024-10-22,,,"Kemmer, Isabel * Romdhane, Feriel * ERIC, Euro-BioImaging",,pdf,en +Example Microscopy Metadata JSON files produced using Micro-Meta App to document example microscopy experiments performed at individual core facilities,,https://zenodo.org/records/5847477 * https://doi.org/10.5281/zenodo.5847477,CC-BY-4.0,"Example Microscopy Metadata (Microscope.JSON and Settings.JSON) files produced using Micro-Meta App to document the Hardware Specifications of example Microscopes and the Image Acquisition Settings utilized to acquire example images as listed in the table below. + + +For each facility, the dataset contains two JSON files: + + + Microscope.JSON file (e.g., 01_marcello_uliverpool_cci_zeiss_axioobserz1_lsm710.json) + Settings.JSON file (indicated with the name of the image and with the _AS suffix) + + + +Micro-Meta App was developed as part of a global community initiative including the 4D Nucleome (4DN) Imaging Working Group, BioImaging North America (BINA) Quality Control and Data Management Working Group, and QUAlity and REProducibility for Instrument and Images in Light Microscopy (QUAREP-LiMi), to extend the Open Microscopy Environment (OME) data model. + + +The works of this global community effort resulted in multiple publications featured on a recent Nature Methods FOCUS ISSUE dedicated to Reporting and reproducibility in microscopy. + + + +Learn More! For a thorough description of Micro-Meta App consult our recent Nature Methods and BioRxiv.org publications! + + +  + + + + + Nr. + Manufacturer + Model + Tier + Εxperiment Type + Facility Name + Department and Institution + URL + References + + + 1 + Carl Zeiss Microscopy + Axio Observer Z1 (with LSM 710 scan head) + 1 + 3D visualization of superhydrophobic polymer-nanoparticles + Centre for Cell Imaging (CCI) + University of Liverpool + https://cci.liv.ac.uk/equipment_710.html + Upton et al., 2020 + + + 2 + Carl Zeiss Microscopy + Axio Observer (Axiovert 200M) + 2 + Μeasurement of illumination stability on Chinese Hamster Ovary cells expressing Paxillin-EGFP + Advanced BioImaging Facility (ABIF). + McGill University + https://www.mcgill.ca/abif/equipment/axiovert-1 + Kiepas et al., 2020 + + + 3 + Carl Zeiss Microscopy + Axio Observer Z1 (with Spinning Disk) + 2 + Immunofluorescence imaging of cryosection of Mouse kidney + Imagerie Cellulaire; Quality Control managed by Miacellavie (https://miacellavie.com/) + Centre de recherche du Centre Hospitalier Université de Montréal (CR CHUM), University of Montreal + https://www.chumontreal.qc.ca/crchum/plateformes-et-services  (the web site is for all core facilities, not specifically for the core facility hosting this microscope) + Pilliod et al., 2020 + + + 4 + Carl Zeiss Microscopy + Axio Imager Z2 (with Apotome) + 2 + Immunofluorescence imaging of mitotic division in Hela cells using   + Bioimaging Unit + Newcastle University + https://www.ncl.ac.uk/bioimaging/ + Watson et al., 2020 + + + 5 + Carl Zeiss Microscopy + Axio Observer Z1 + 2 + Fluorescence microscopy of human skin fibroblasts from Glycogen Storage Disease patients. + Life Imaging Center (LIC) + Centre for Integrative Signalling Analysis (CISA), University of Freiburg + https://miap.eu/equipments/sd-i-abl/ + Hannibal et al., 2020 + + + 6 + Leica Microsystems + DMI6000B + 2 + 3D immunofluorescence imaging  rhinovirus infected macrophages  + IMAG'IC Confocal Microscopy Facility + Institut Cochin, CNRS, INSERM, Université de Paris + https://www.institutcochin.fr/core_facilities/confocal-microscopy/cochin-imaging-photonic-microscopy/organigram_team/10054/view + Jubrail et al., 2020 + + + 7 + Leica Microsystems + DM5500B + 2 + Immunofluorescence analysis of the colocalization of PML bodies with DNA double-strand breaks + Bioimaging Unit + Edwardson Building on the Campus for Ageing and Vitality, Newcastle University + https://www.ncl.ac.uk/bioimaging/equipment/leica-dm5500/#overview + da Silva et al., 2019; Nelson et al., 2012 +    + + + 8 + Leica Microsystems + DMI8-CS (with TCS SP8 STED 3X) + 2 + Live-cell imaging of N. benthamiana leaves cells-derived protoplasts + Center for Advanced Imaging (CAi) + School of Mathematics/Natural Sciences, Heinrich-Heine-Universität Düsseldorf + https://www.cai.hhu.de/en/equipment/super-resolution-microscopy/leica-tcs-sp8-sted-3x + Singer et al., 2017; Hänsch et al., 2020 + + + 9 + Nikon Instruments + Eclipse Ti + 2 + Immunofluorescence analysis of the cytoskeleton structure in COS cells + Advanced Imaging Center (AIC) + Janelia Research Campus, Howard Hughes Medical Institute + https://www.janelia.org/support-team/light-microscopy/equipment + Abdelfattah et al., 2019; Qian et al., 2019; Grimm et al., 2020 + + + 10 + Nikon Instruments + Eclipse Ti-E (HCA) + 2 + Τime-lapse analysis of the bursting behavior of amine-functionalized vesicular assemblies + Light Microscopy Facility (IALS-LIF) + Institute for Applied Life Sciences, University of Massachusetts at Amherst + https://www.umass.edu/ials/light-microscopy + Fernandez et al., 2020 + + + 11 + Nikon Instruments/Coleman laboratory (customized) + TIRF HILO Epifluorescence light Microscope (THEM)/ Eclipse Ti + 2 + Single-particle tracking of Halo-tagged PCNA in Lox cells + Coleman laboratory + Anatomy and Structural Biology Department, The Albert Einstein College of Medicine + https://einsteinmed.org/faculty/12252/robert-coleman/ + Drosopoulos et al., 2020 + + + 12 + Nikon Instruments + Eclipse Ti (with Andor Dragon Fly Spinning Disk) + 2 + Investigation of the 3D structure of cerebral organoids + Montpellier Resources Imagerie + Centre de Recherche de Biologie cellulaire de Montpellier (MRI-CRBM), CNRS, Univerity of Montpellier + https://www.mri.cnrs.fr/en/optical-imaging/our-facilities/mri-crbm.html + Ayala-Nunez et al., 2019 + + + 13 + Nikon Instruments + Eclipse Ti2 + 2 + Ιmmunofluorescence imaging of cryosections of mouse hearth myocardium  + Neuroscience Center Microscopy Core + Neuroscience Center, University of North Carolina + https://www.med.unc.edu/neuroscience/core-facilities/neuro-microscopy/ + Aghajanian et al., 2021 + + + 14 + Nikon Instruments + Eclipse Ti2 + 2 + Live-cell imaging of bacterial cells expressing GFP-PopZ + Microscopy Resources on the North Quad (MicRoN) + Harvard Medical School  + https://micron.hms.harvard.edu/ + Lim and Bernhardt 2019; Lim et al., 2019 + + + 15 + Olympus/Biomedical Imaging Group (customized) + TIRF Epifluorescence Structured light Microscope (TESM)/IX71 + 3 + 3D distribution of HIV-1 in the nucleus of human cells + Biomedical Imaging Group + Program in Molecular Medicine, University of Massachusetts Medical School + https://trello.com/b/BQ8zCcQC/tirf-epi-fluorescence-structured-light-microscope + Navaroli et al., 2012 + + + 16 + Olympus/Computer Vision Laboratory (customized) + 3D BrightField Scanner/IX71 + 3 + Transmitted light brightfield visualization of swimming spermatocytes + Laboratorio Nacional de Microscopia Avanzada (LNMA) and Computer Vision Laboratory of the Institute of Biotechnology + Universidad Nacional Autonoma de Mexico (UNAM) + https://lnma.unam.mx/wp/ + Pimentel et al., 2012; Silva-Villalobos et al., 2014 + + + + +Getting started + +Use these videos to get started with using Micro-Meta App after installation into OMERO and downloading the example data files: + + + Video 1 + Video 2 + + +More information + + +For full information on how to use Micro-Meta App please utilize the following resources: + + + Micro-Meta App website + Full documentation + Installation instructions + Step-by-Step Instructions + Tutorial Videos + + + +Background + +If you want to learn more about the importance of metadata and quality control to ensure full reproducibility, quality and scientific value in light microscopy, please take a look at our recent publications describing the development of community-driven light 4DN-BINA-OME Microscopy Metadata specifications Nature Methods and BioRxiv.org and our overview manuscript entitled A perspective on Microscopy Metadata: data provenance and quality control. + +  + + ",2022-01-15,,,"Rigano, Alessandro * Boehm, Ulrike * Brown, ClaireM. * Ryan, Joel * Chambers, JamesJ. * Coleman, RobertA. * Faklaris, Orestis * Guilbert, Thomas * Itano, MichelleS. * Lacoste, Judith * Laude, Alex * Marcello, Marco * Montero-Llopis, Paula * Nelson, Glyn * Nitschke, Roland * Pimentel, JaimeA. * Weidtkamp-Peters, Stefanie * Strambio-De-Castillia, Caterina",,czi_ * json,en +Data stewardship and research data management tools for multimodal linking of imaging data in plasma medicine,,https://zenodo.org/records/10069368 * https://doi.org/10.5281/zenodo.10069368,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.",2023-11-03,,,"Ahmadi, Mohsen * Wagner, Robert * Mattern, Philipp * Plathe, Nick * Bekeschus, Sander * Becker, MarkusM. * Stöter, Torsten * Weidtkamp-Peters, Stefanie",,pdf,en +Leitfaden zur digitalen Datensparsamkeit (mit Praxisbeispielen),,https://zenodo.org/records/11445843 * https://doi.org/10.5281/zenodo.11445843,CC-BY-4.0,"Im Zuge der stetig wachsenden Brisanz des Forschungsdatenmanagements fallen immer größere Mengen an Forschungsdaten an. Diese an sich begrüßenswerte Entwicklung führt zu technischen und organisatorischen Herausforderungen nicht nur im Bereich der Speicherung von Forschungsdaten, sondern in allen Phasen des Forschungsdatenlebenszyklus. Der vorliegende Beitrag erläutert vor diesem Hintergrund mögliche Motivationen hinter digitaler Datensparsamkeit mit Blick auf organisatorische, technische und ethische Kriterien, Datenschutz und Nachhaltigkeit. Anschließend werden vor dem Hintergrund zentraler Herausforderungen Umsetzungsvorschläge für das Vorfeld sowie den Verlauf eines Forschungsvorhabens gemacht. Zudem werden grundlegende Empfehlungen zur digitalen Datensparsamkeit ausgesprochen. +Eine kürzere Ausgabe des Leitfadens ist im Mai 2024 in der Zeitschrift o | bib erschienen: https://doi.org/10.5282/o-bib/6036 +Diese Ausgabe enthält ein zusätzliches Kapitel (4.2) mit konkreten Praxisbeispielen. +Dieser Artikel wurde ins Englische übersetzt: +Heber, M., Jakob, M., Landwehr, M., Leendertse, J., Müller, M., Schneider, G., von Suchodoletz, D., & Ulrich, R. (2024). A Users' Guide to Economical Digital Data Usage. Zenodo. https://doi.org/10.5281/zenodo.13752220",2024-06-03,,,"Heber, Maximilian * Jakob, Moritz * Landwehr, Matthias * Leendertse, Jan * Müller, Maximilian * Schneider, Gabriel * Suchodoletz, Dirkvon * Ulrich, Robert",,pdf,de +Online_R_learning,Statistics,https://github.com/cxli233/Online_R_learning,CC0-1.0,Online R learning for applied statistics ,2023-07-09T06:27:14+00:00,Other,,"Li, C.",,, +FriendsDontLetFriends,Visualization,https://github.com/cxli233/FriendsDontLetFriends,MIT,Friends don't let friends make certain types of data visualization - What are they and why are they bad. ,2024-03-10T15:34:07+00:00,Other,,"Li, C.",,,en +"[Workshop Material] Fit for OMERO - How imaging facilities and IT departments work together to enable RDM for bioimaging, October 16-17, 2024, Heidelberg",,https://zenodo.org/records/14178789 * https://doi.org/10.5281/zenodo.14178789,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 +",2024-11-18,,,"Boissonnet, Tom * Hagen, Bettina * Kunis, Susanne * Schmidt, Christian * Weidtkamp-Peters, Stefanie",,pdf * xlsx,en +New Kid on the (NFDI) Block: NFDI4BIOIMAGE - A National Initiative for FAIR Data Management in Bioimaging and Bioimage Analysis,,https://zenodo.org/records/14006558 * https://doi.org/10.5281/zenodo.14006558,CC-BY-4.0,"The poster introduces the consortium NFDI4BIOIMAGE with its central objectives, provides an overview of challenges in bioimage data handling, sharing and analysis and lists support options by the consortium through its data stewardship team. +It is part of the work of the German consortium NFDI4BIOIMAGE funded by the Deutsche Forschungsgemeinschaft (DFG grant number NFDI 46/1, project number 501864659) and has been presented at the conference FDM@Campus held in Göttingen September 23-25, 2024.",2024-10-29,,,"Wetzker, Cornelia",,pdf,en +NFDI4Bioimage Calendar 2024 October; original image,,https://zenodo.org/records/13837146 * https://doi.org/10.5281/zenodo.13837146,CC-BY-4.0,Raw microscopy image from the NFDI4Bioimage calendar October 2024,2024-09-25,,,"Jüngst, Christian * Zentis, Peter",,lif,en +Insights from Acquiring Open Medical Imaging Datasets for Foundation Model Development,,https://zenodo.org/records/11503289 * https://doi.org/10.5281/zenodo.11503289,CC-BY-4.0,,2024-04-10,,,"Dvoretskii, Stefan",,pdf,en +RESEARCH DATA MANAGEMENT on Campus and in NFDI4BIOIMAGE,,https://zenodo.org/records/13684187 * https://doi.org/10.5281/zenodo.13684187,CC-BY-4.0,"The poster is part of the work of the German consortium NFDI4BIOIMAGE funded by the Deutsche Forschungsgemeinschaft (DFG grant number NFDI 46/1, project number 501864659).",2024-08-29,,,"Wetzker, Cornelia * Schlierf, Michael",,pdf, +Towards Preservation of Life Science Data with NFDI4BIOIMAGE,,https://zenodo.org/records/13640979 * https://doi.org/10.5281/zenodo.13640979,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.",2024-09-03,,,"Haase, Robert",,pdf * pptx,en +Insights from Acquiring Open Medical Imaging Datasets for Foundation Model Development,,https://zenodo.org/records/13380289 * https://doi.org/10.5281/zenodo.13380289,CC-BY-4.0,,2024-04-10,,,"Dvoretskii, Stefan",,pdf * pptx,en +Institutionalization and Collaboration as a Way of Addressing the Challenges Open Science Presents to Libraries: The University of Konstanz as a National Pioneer,,https://zenodo.org/records/12699637 * https://doi.org/10.5281/zenodo.12699637,CC-BY-4.0,"The rise of Open Science (OS) and the academic community’s needs that come with it bring about a range of challenges for academic libraries. To face these challenges, the University of Konstanz has created a competence unit called Team Open Science in the Communication, Information, Media Center (KIM) - a joint unit of library and IT infrastructure. The Team creates synergies within itself and across the library. In December 2023, it involved 12 staff members specialising in open access (OA), research data management (RDM), open educational resources (OER) and virtual research environments (VRE). It collaborates closely with other KIM departments. This submission shall serve as a best practice example for the impact of OS on research libraries and, beyond that, the impact of research libraries on universities. +To enhance and foster OS, the Team provides individual consultations, services and office hours for researchers. Here, it collaborates closely with other librarians like subject specialists and the Team University Publications. Along similar lines, the KIM offers institutional repositories for publications (KOPS) and research data (KonDATA). Beyond that, the Team provides solutions to host OA journals and analyses researchers’ VRE needs to decide on implementation options. In sum, the Team is the central OS contact point for the entire university, underlining the major role the library holds in making institutional impact. +Furthermore, the Team had the leading role in creating the University of Konstanz’ OS Policy, one of the first ones passed by a German university. This policy stands out because it encompasses various OS domains. It demands, among other things, that text publications be made OA and that research data be managed according to relevant subject-specific standards. If permissible and reasonable, it demands that research data should be made publicly available at the earliest possible time. Along these lines, the policy has a large impact on how the library handles closed access books and subscription-based journals. As a consequence, OA is pursued wherever possible, leading to the highest OA quota of all German universities. In that sense, the Team is a crucial driving force of OS in the University of Konstanz, which ties in with the library’s major role of open research transformation. +Beyond the University of Konstanz, the Team is involved in a range of national and international projects collaborating with other libraries. On a national level, they lead the project open.access-network which provides an information platform for researchers and librarians and connects the German-speaking OA community through events like bar camps. The project KOALA-AV supports libraries in establishing consortial solutions for financing Diamond OA publications. Moreover, the Team is involved in the federal state initiative for RDM in Baden-Württemberg (bwFDM). Here, the Team is in charge of forschungsdaten.info, the German-speaking countries’ leading RDM information platform, which will be offered in English within the next years. Internationally, the Team cooperates with librarians and other OS professionals from the European Reform University Alliance (ERUA) and the European University for Well-Being (EUniWell), establishing formats for best practice exchange, such as monthly OS Meet-Ups.",2024-07-09,,,"Habinger, Sophie * Heber, Maximilian * Kralj, Sonja * Mikautsch, Emilia",,pdf,en +The role of Helmholtz Centers in NFDI4BIOIMAGE - A national consortium enhancing FAIR data management for microscopy and bioimage analysis,,https://zenodo.org/records/11501662 * https://doi.org/10.5281/zenodo.11501662,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",2024-06-06,,,"Massei, Riccardo * Schmidt, Christian * Bortolomeazzi, Michele * Thoennissen, Julia * Bumberger, Jan * Dickscheid, Timo * Mallm, Jan-Philipp * Ferrando-May, Elisa",,pdf,en +[Workshop] Bioimage data management and analysis with OMERO,,https://zenodo.org/records/11350689 * https://doi.org/10.5281/zenodo.11350689,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)",2024-05-13,,,"Massei, Riccardo * Bortolomeazzi, Michele * Schmidt, Christian",,pdf,en +"[ELMI 2024] AI's Dirty Little Secret: Without +FAIR Data, It's Just Fancy Math",,https://zenodo.org/records/11235513 * https://doi.org/10.5281/zenodo.11235513,CC-BY-4.0,Poster presented at the European Light Microscopy Initiative meeting in Liverpool (https://www.elmi2024.org/),2024-05-21,,,"Moore, Josh * Kunis, Susanne",,pdf,en +LEO: Linking ELN with OMERO,,https://zenodo.org/records/11146807 * https://doi.org/10.5281/zenodo.11146807,CC-BY-4.0,First updates of LEO (Linking ELN with OMERO),2024-05-08,,,"Guerrero, EscobarDiaz * Rodrigo",,,en +NFDI4BIOIMAGE,,https://zenodo.org/records/11031747 * https://doi.org/10.5281/zenodo.11031747,CC-BY-4.0,This presentation was given at the 2nd MPG-NFDI Workshop on April 18th.,2024-04-22,,,"Fortmann-Grote, Carsten",,pdf, +[Short Talk] NFDI4BIOIMAGE - A consortium in the National Research Data Infrastructure,,https://zenodo.org/records/10939520 * https://doi.org/10.5281/zenodo.10939520,CC-BY-4.0,"Short Talk about the NFDI4BIOIMAGE consortium presented at the RDM in (Bio-)Medicine Information Event on April 10th, 2024, organized C³RDM & ZB MED.",2024-04-10,,,"Schmidt, Christian",,pdf,en +"A Glimpse of the Open-Source FLIM Analysis Software Tools FLIMfit, FLUTE and napari-flim-phasor-plotter",,https://zenodo.org/records/10886750 * https://doi.org/10.5281/zenodo.10886750,CC-BY-4.0,"The presentations introduce open-source software to read in, visualize and analyse fluorescence lifetime imaging microscopy (FLIM) raw data developed for life scientists. The slides were presented at German Bioimaging (GerBI) FLIM Workshop held February 26 to 29 2024 at the Biomedical Center of LMU München by Anca Margineanu, Chiara Stringari and Conni Wetzker. ",2024-03-27,,,"Margineanu, Anca * Stringari, Chiara * Zoccoler, Marcelo * Wetzker, Cornelia",,pdf * pptx,en +Linked (Open) Data for Microbial Population Biology,,https://zenodo.org/records/10808486 * https://doi.org/10.5281/zenodo.10808486,CC-BY-4.0,,2024-03-12,,,"Fortmann-Grote, Carsten",,pdf,en +Hackaton Results - Conversion of KNIME image analysis workflows to Galaxy,,https://zenodo.org/records/10793700 * https://doi.org/10.5281/zenodo.10793700,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) + ",2024-03-07,,,"Massei, Riccardo",,pptx,en +Who you gonna call? - Data Stewards to the rescue,,https://zenodo.org/records/10730424 * https://doi.org/10.5281/zenodo.10730424,CC-BY-4.0,The Data Steward Team of the NFDI4BIOIMAGE consortium presents themselves and the services (including the Helpdesk) that we offer.,2024-03-01,,,"Vanessa Aphaia Fiona Fuchs * Wendt, Jens * Müller, Maximilian * Ahmadi, Mohsen * Massei, Riccardo * Wetzker, Cornelia",,pdf,en +Key-Value pair template for annotation of datasets in OMERO (PERIKLES study),,https://zenodo.org/records/12546808 * https://doi.org/10.5281/zenodo.12546808,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)",2024-06-26,,,"Jannasch, Anett * Tulok, Silke * Vanessa Aphaia Fiona Fuchs * Boissonnet, Tom * Schmidt, Christian * Bortolomeazzi, Michele * Fabig, Gunar * Okafornta, Chukwuebuka",,xlsx,en +A journey to FAIR microscopy data,,https://zenodo.org/records/7890311 * https://doi.org/10.5281/zenodo.7890311,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.  ",2023-05-03,,,"Weidtkamp-Peters, Stefanie * Hanne, Janina * Schmidt, Christian",,pdf * pptx,en +Report on a pilot study: Implementation of OMERO for microscopy data management,,https://zenodo.org/records/10103316 * https://doi.org/10.5281/zenodo.10103316,CC-BY-4.0,"The Core Facility Cellular Imaging (CFCI) at the Faculty of Medicine Carl Gustav Carus (TU Dresden) is currently running a pilot project for testing the use and handling of the OMERO software. This is done together with interested users of the imaging facility and a research group. Currently, we are pushing forward this pilot study on a small scale without any data steward. Our experiences argue so far for giving data management issues into the hands of dedicated personnel not fully involved in research projects. As funding agencies will ask for higher and higher standards for implementing FAIRdata principles in the future, this will be a releva",2023-11-10,,,"Tulok, Silke * Fabig, Gunar * Vogelsang, Andy * Kugel, Thomas * Müller-Reichert, Thomas",,pdf,en +Key-Value pair template for annotation of datasets in OMERO for light- and electron microscopy data within the research group of Prof. Müller-Reichert,,https://zenodo.org/records/12547566 * https://doi.org/10.5281/zenodo.12547566,CC-BY-4.0,"This are a two Key-Value pair templates used for the annotation of datasets in OMERO. They are tailored for light- and electron microcopy data for all research projects of the research group of Prof. T. Mueller-Reichert.  All members of the Core Facility Cellular Imaging agreed for using these templates to annotate data in OMERO. Furthermore, there are a corresponding public example datasets used in the publication ""Setting up an institutional OMERO environment for bioimage data: perspectives from both facility staff and users"" and are available here: +https://omero.med.tu-dresden.de/webclient/?show=dataset-1552 --> for lattice-light sheet microscopy +https://omero.med.tu-dresden.de/webclient/?show=dataset-1555--> for electron microscopy data +That templates are based on the REMBI recommendations (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8606015) and were developed during the PoL-Bio-Image Analysis Symposium in Dresden Aug 28th- Sept 1st in 2023 and further adapeted during the usage of OMERO.  +With every 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: + +fill the template with 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 dataset, which should be annotated in OMERO +save the new excel sheet in csv-file (comma separated values) format + +Examples can be seen in sheet 3 'csv_TOMO' and sheet 5 csv_TEM'. +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.12546808 Key-Value pair template for annotation of datasets in OMERO (PERIKLES study) +10.5281/zenodo.12578084 Key-Value pair template for annotation in OMERO for light microscopy data acquired with AxioScan7 - Core Facility Cellular Imaging (CFCI) + ",2024-06-26,,,"Fabig, Gunar * Jannasch, Anett * Okafornta, Chukwuebuka * Boissonnet, Tom * Schmidt, Christian * Bortolomeazzi, Michele * Vanessa Aphaia Fiona Fuchs * Koeckert, Maria * Poddar, Aayush * Vogel, Martin * Schwarzbach, Hanna-Margareta * Vogelsang, Andy * Gerlach, Michael * Nobst, Anja * Müller-Reichert, Thomas * Tulok, Silke",,xlsx,en +Abdominal Imaging Window (AIW) for Intravital Imaging,,https://zenodo.org/records/14168603 * https://doi.org/10.5281/zenodo.14168603,CC-BY-4.0,"This upload features a simple model for the creation (Manufacturing/Prototyping) of an abdominal imaging window (AIW) for use in mice intravital microscopy. +Manufacture in titanium for chronic implantation. Measures in mm.",2024-11-15,,,"Gerlach, Michael",,3mf * f3d * pdf * png,en +Round Table Workshop 2 - Correction of Drift and Movement,,https://zenodo.org/records/14161633 * https://doi.org/10.5281/zenodo.14161633,CC-BY-4.0,Session 2 of a round table workshop. Features description of image processing methods useful in intravital imaging to compensate for the motion of living tissue.,2024-11-14,,,"Ishikawa-Ankerhold, Dr.Hellen * Nobis, Max",,pdf,en +Round Table Workshop 1 - Sample Stabilization in intravital Imaging,,https://zenodo.org/records/14161289 * https://doi.org/10.5281/zenodo.14161289,CC-BY-4.0,"Notes from a round table workshop on the 4th Day of Intravital Microscopy in Leuven, Belgium. +Contains hands-on tips, tricks and schemes to improve sample stability during various models of Intravital Miroscopy.",2024-11-14,,,"Gerlach, Michael * Fried, Hans-Ulrich * Peuckert, Christiane",,pptx, +Conference Slides - 4th Day of Intravital Microscopy,,https://zenodo.org/records/14113714 * https://doi.org/10.5281/zenodo.14113714,CC-BY-4.0,"Conference Slides for the presentation of GerBI e.V. at the 4th Day of Intravital Microscopy in Leuven, Belgium. +Features Structure, activities and Links to join GerBI e.V.",2024-11-13,,,"Ishikawa-Ankerhold, Dr.Hellen",,pptx,en +Implantation of abdominal imaging windows on the mouse liver,,https://zenodo.org/records/13683167 * https://doi.org/10.5281/zenodo.13683167,CC-BY-ND-4.0,"This video describes the surgical process of implanting an abdominal imaging window (AIW) on the liver of mice. This window can be used for acute or longitudinal imaging. All experiments have been reviewed and approved by the local authorities (Landesdirektion Sachsen). +Implantation of chronic abdominal windows allows for microscopical investigation of highly dynamic processes in physiological and pathological circumstances and is generally tolerated well by experimental animals. It enables insights which otherwise could only be obtained using high numbers of experimental animals. The method can be regarded as reduction approach in terms of 3R implementation. +This upload contains the full version and is distributed under CC BY-ND 4.0 license to inhibit decontextualized misuse. Please check license terms for usage, especially for remixing/transforming! If you want to remix the material, get in contact with the author.",2024-09-04,,,"Gerlach, Michael",,mp4,en +Implantation of abdominal imaging windows on the mouse liver - short version,,https://zenodo.org/records/13736218 * https://doi.org/10.5281/zenodo.13736218,CC-BY-ND-4.0,"This video describes the surgical process of implanting an abdominal imaging window (AIW) on the liver of mice. This window can be used for acute or longitudinal imaging. All experiments have been reviewed and approved by the local authorities (Landesdirektion Sachsen). +Implantation of chronic abdominal windows allows for microscopical investigation of highly dynamic processes in physiological and pathological circumstances and is generally tolerated well by experimental animals. It enables insights which otherwise could only be obtained using high numbers of experimental animals. The method can be regarded as reduction approach in terms of 3R implementation. +This upload contains the short version and is distributed under CC BY-ND 4.0 license to inhibit decontextualized misuse. Please check license terms for usage, especially for remixing/transforming! If you want to remix the material, get in contact with the author.",2024-09-09,,,"Gerlach, Michael",,mp4,en +Implantation of abdominal imaging windows on the mouse kidney - short version,,https://zenodo.org/records/13736240 * https://doi.org/10.5281/zenodo.13736240,CC-BY-ND-4.0,"This video describes the surgical process of implanting an abdominal imaging window (AIW) on the kidney of mice. This window can be used for acute or longitudinal imaging. All experiments have been reviewed and approved by the local authorities (Landesdirektion Sachsen). +Implantation of chronic abdominal windows allows for microscopical investigation of highly dynamic processes in physiological and pathological circumstances and is generally tolerated well by experimental animals. It enables insights which otherwise could only be obtained using high numbers of experimental animals. The method can be regarded as reduction approach in terms of 3R implementation. +This upload contains the shortened version and is distributed under CC BY-ND 4.0 license to inhibit decontextualized misuse. Please check license terms for usage, especially for remixing/transforming! If you want to remix the material, get in contact with the author.",2024-09-09,,,"Gerlach, Michael",,mp4,en +Implantation of abdominal imaging windows on the mouse kidney,,https://zenodo.org/records/13682928 * https://doi.org/10.5281/zenodo.13682928,CC-BY-ND-4.0,"This video describes the surgical process of implanting an abdominal imaging window (AIW) on the kidney of mice. This window can be used for acute or longitudinal imaging. All experiments have been reviewed and approved by the local authorities (Landesdirektion Sachsen). +Implantation of chronic abdominal windows allows for microscopical investigation of highly dynamic processes in physiological and pathological circumstances and is generally tolerated well by experimental animals. It enables insights which otherwise could only be obtained using high numbers of experimental animals. The method can be regarded as reduction approach in terms of 3R implementation. +This upload contains the full version and is distributed under CC BY-ND 4.0 license to inhibit decontextualized misuse. Please check license terms for usage, especially for remixing/transforming! If you want to remix the material, get in contact with the author.",2024-09-04,,,"Gerlach, Michael",,mp4,en +GerBI-Chat: Teil 2 - Wie schreibe ich am besten einen Großegräteantrag,,https://zenodo.org/records/13807114 * https://doi.org/10.5281/zenodo.13807114,CC-BY-4.0,"Die GermanBioImaging (GerBI-GMB) - Deutsche Gesellschaft für Mikroskopie und Bildanalyse e.V. bietet über regelmäßig stattfindende Treffen (GerBI-Chats) die Möglichkeit zum aktiven Austausch der Mitglieder untereinander. Das GerBI-GMB Team ""Legal und Finacial Framwork"", welches sich mit administrativen Aufgaben rund um das Core Facility Management beschäftigt, nutzt diese Möglichkeit zum aktiven Austausch innerhalb des Netzwerkes und darüber hinaus.  +Der Beschaffungsprozess von Forschungsgroßgeräten ist komplex und je nach Institution unterschiedlich geregelt. Aus unserer Sicht lässt sich dieser Prozess grob in drei Stufen aufteilen: + +Bedarfsanmeldung +Antragsvorbereitung und -fertigstellung +Antragsbewilligung und Nutzung  + +Nach dem Initialvortrag der GerBI-Chat Reihe, in dem das Thema Bedarfsanmeldung im Fokus stand, geht es im hier enthaltenen zweiten Teil „Antragsvorbereitung und -fertigstellung: Wie schreibe ich am besten einen Großgeräteantrag?“ um die Beantragung von Forschungsgroßgeräten nach Art. 91b GG.",2024-10-02,,,"Financial & Legal Framework of Core Facilities * Endl, Elmar * Hedrich, Jana * Hoth, Juliane * Nagy, Julia * Schauss, Astrid * Schulze, Nina * Tulok, Silke",,pdf,de +GerBI-Chat: Teil 1 - Vom Bedarf bis zum Großgeräteantrag-Schreiben,,https://zenodo.org/records/13810879 * https://doi.org/10.5281/zenodo.13810879,CC-BY-4.0,"Die GermanBioImaging (GerBI-GMB) - Deutsche Gesellschaft für Mikroskopie und Bildanalyse e.V. bietet über regelmäßig stattfindende Treffen (GerBI-Chats) die Möglichkeit zum aktiven Austausch der Mitglieder untereinander. Das GerBI-GMB Team ""Legal und Finacial Framwork"", welches sich mit administrativen Aufgaben rund um das Core Facility Management beschäftigt, nutzt diese Möglichkeit zum aktiven Austausch innerhalb des Netzwerkes und darüber hinaus.  +Der Beschaffungsprozess von Forschungsgroßgeräten ist komplex und je nach Institution unterschiedlich geregelt. Aus unserer Sicht lässt sich dieser Prozess grob in drei Stufen aufteilen: + +Bedarfsanmeldung +Antragsvorbereitung und -fertigstellung +Antragsbewilligung und Nutzung  + +Dieser hier enthaltene Beitrag ist der Initialvortrag des GerBi-Chats zum Teil 1 - Von der Bedarfsanmeldung bis zum Beginn der Antragststellung. Die weiteren Stufen der Großgerätebeschaffung werden in nachfolgenden Beiträgen behandelt.",2024-09-11,,,"Financial & Legal Framework of Core Facilities * Endl, Elmar * Hedrich, Jana * Hoth, Juliane * Nagy, Julia * Schauss, Astrid * Schulze, Nina * Tulok, Silke",,pdf,de +Key-Value pair template for annotation in OMERO for light microscopy data acquired with AxioScan7 - Core Facility Cellular Imaging (CFCI),,https://zenodo.org/records/12578084 * https://doi.org/10.5281/zenodo.12578084,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)",2024-06-28,,,"Tulok, Silke * Nobst, Anja * Jannasch, Anett * Boissonnet, Tom * Fabig, Gunar",,xlsx,en +Zeiss AxioZoom Stage Adapter - 12/6Well Plate,,https://zenodo.org/records/7944877 * https://doi.org/10.5281/zenodo.7944877,CC-BY-4.0,"A 3D- printable microscope stage adapter for the reproducible accomodation of 6 or 12-well plates at a Zeiss AxioZoom microscope. +4 cylindrical anchors are fixed to the glass plate of the stage. The stage adapter is reversibly placed on these anchors and acommodates a standard Greiner 6- or 12-well plate.",2024-06-20,,,"Gerlach, Michael",,3mf * png, +Zeiss AxioZoom Stage Adapter,,https://zenodo.org/records/7963020 * https://doi.org/10.5281/zenodo.7963020,CC-BY-4.0,"A 3D- printable microscope stage adapter for the reproducible accomodation of samples at a Zeiss AxioZoom stereomicroscope. +4 cylindrical anchors are fixed to the glass plate of the stage. The stage adapter is reversibly placed on these anchors. + ",2024-06-20,,,"Gerlach, Michael",,3mf * png, +Zeiss AxioZoom Stage Adapter - EM block holder,,https://zenodo.org/records/7963006 * https://doi.org/10.5281/zenodo.7963006,CC-BY-4.0,"A 3D- printable microscope stage adapter for the reproducible accomodation of EM Blocks at a Zeiss AxioZoom microscope. + +4 cylindrical anchors are fixed to the glass plate of the stage. The stage adapter is reversibly placed on these anchors and acommodates 70 standard resin EM blocks.",2024-06-20,,,"Gerlach, Michael",,3mf * png, +Zeiss AxioZoom Stage Adapter - Microscope slides,,https://zenodo.org/records/7945018 * https://doi.org/10.5281/zenodo.7945018,CC-BY-4.0,"A 3D- printable microscope stage adapter for the reproducible accomodation of microscopic slides at a Zeiss AxioZoom microscope. +4 cylindrical anchors are fixed to the glass plate of the stage. The stage adapter is reversibly placed on these anchors and acommodates 4 standard glass slides.",2024-06-21,,,"Gerlach, Michael",,3mf * png,en +Intravital microscopy contrasting agents for application - Database,,https://zenodo.org/records/12166710 * https://doi.org/10.5281/zenodo.12166710,CC-BY-4.0,"This is a set of databases containing published use of substances which can be applied to rodents in order to contrast specific structures for optical intravital microscopy. +The first dataset contains applied final dosages, calculated for 25g-mice, as well as the orignally published amounts, concentrations and application routes of agents directly applied into the target organism. +The second dataset contains dosages and cell numbers for the external contrastation and subsequent application of cells into the target organism. +Filtering possible for organ system and contrasted structure/cell type in both datasets, substance class and fluorescent detection windows can be filtered in the dataset for direct agent application. +Source publications are listed by DOI. + ",2024-06-19,,,"Gerlach, Michael",,xlsx,en +"Development of a platform for advanced optics education, training and prototyping",,https://zenodo.org/records/10925217 * https://doi.org/10.5281/zenodo.10925217,CC-BY-4.0,"In bio-medical research we often need to combine a broad range of expertise to run complex experiments and analyse and interpret their results. Also, it is desirable that all stakeholders of a project understand all parts of the experiment and analysis to draw and support the right conclusions. For imaging experiments this usually requires a basic understanding of the underlying physics. This has not necessarily been part of the professional training of all stakeholders, e.g. biologists or data scientists. Therefore an affordable platform for easily demonstrating and explaining imaging principles would be desirable. +Building up on a commercially available STEM Optics kit we developed extensions with widely available and affordable components to demonstrate advanced imaging techniques like e.g. confocal, lightsheet, OPT, spectral imaging. All models are quick and easy to build, yet demonstrate the important physical principles each imaging technique is based on. +Further use cases for this kit are training courses, demonstrations for imaging newbies when designing an experiment and outreach activities but also basic level prototyping.",2023-10-05,,,"Utz, Nadine * Reither, Sabine * Hans, Ruth * Feldhaus, Christian",,pdf,en +[Community Meeting 2024] Supporting and financing RDM projects within GerBI,,https://zenodo.org/records/10889694 * https://doi.org/10.5281/zenodo.10889694,CC-BY-4.0,Overview of GerBI RDM projects: why and how?,2024-03-28,,,"Weidtkamp-Peters, Stefanie * Moore, Josh * Schmidt, Christian * Nitschke, Roland * Kunis, Susanne * Zobel, Thomas",,pdf,en +Slides about FLUTE: a Python GUI for interactive phasor analysis of FLIM data,,https://zenodo.org/records/10839310 * https://doi.org/10.5281/zenodo.10839310,CC-BY-4.0,"This presentation introduces the open source software to analyze FLIM data: +FLUTE – (F)luorescence (L)ifetime (U)ltima(T)e (E)xplorer: +a Python GUI for interactive phasor analysis of FLIM data +  +The software is available on GitHub: https://github.com/LaboratoryOpticsBiosciences/FLUTE +and it is published on Biological imaging Journal: Gottlieb, D., Asadipour, B., Kostina, P., Ung, T., & Stringari, C. (2023). FLUTE: A Python GUI for interactive phasor analysis of FLIM data. Biological Imaging, 1-22. doi:10.1017/S2633903X23000211 +The lecture was part of the short talks on community developed FLIM-software at the German BioImaging workshop on FLIM in Munich.",2024-03-19,,,"Stringari, Chiara",,pdf * pptx,en +The Information Infrastructure for BioImage Data (I3D:bio) project to advance FAIR microscopy data management for the community,,https://zenodo.org/records/10805204 * https://doi.org/10.5281/zenodo.10805204,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.",2024-03-04,,,"Schmidt, Christian * Bortolomeazzi, Michele * Boissonnet, Tom * Dohle, Julia * Wernet, Tobias * Hanne, Janina * Nitschke, Roland * Kunis, Susanne * Bernhardt, Karen * Weidtkamp-Peters, Stefanie * Ferrando-May, Elisa",,pdf,en +[Community Meeting 2024] Overview Team Image Data Analysis and Management,,https://zenodo.org/records/10796364 * https://doi.org/10.5281/zenodo.10796364,CC-BY-4.0,"Overview of Activities of the Team Image Data Analysis and Management of German BioImaging e.V. + ",2024-03-08,,,"Kunis, Susanne * Zobel, Thomas",,pdf,en +Euro-BioImaging/BatchConvert: v0.0.4,,https://zenodo.org/records/10679318 * https://doi.org/10.5281/zenodo.10679318,CC-BY-4.0,"Changes implemented since v0.0.3 + +Support provided for file paths with spaces. +Support provided for globbing filenames from s3 for one-to-one conversion (parse_s3_filenames.py modified). +Support provided for single file import from s3 (parse_s3_filenames.py modified). +run_conversion.py replaces batchconvert_cli.sh and construct_cli.py, uniting them. +Error handling updated for each process +",2024-02-19,,,bugraoezdemir,,zip, +"Preprint: ""Be Sustainable"", Recommendations for FAIR Resources in Life Sciences research: EOSC-Life's Lessons",,https://zenodo.org/records/8338931 * https://doi.org/10.5281/zenodo.8338931,CC-BY-4.0,"""Be SURE - Be SUstainable REcommendations""The main goals and challenges for the Life Science (LS) communities in the Open Science framework are to increase reuse and sustainability of data resources, software tools, and workflows, especially in large-scale data-driven research and computational analyses. Here, we present key findings, procedures, effective measures and recommendations for generating and establishing sustainable LS resources based on the collaborative, cross-disciplinary work done within the EOSC-Life (European Open Science Cloud for Life Sciences) consortium. Bringing together 13 European LS Research Infrastructures (RIs), it has laid the foundation for an open, digital space to support biological and medical research. Using lessons learned from 27 selected projects, we describe the organisational, technical, financial and legal/ethical challenges that represent the main barriers to sustainability in the life sciences. We show how EOSC-Life provides a model for sustainable FAIR data management, including solutions for sensitive- and industry-related resources, by means of cross-disciplinary training and best practices sharing. Finally, we illustrate how data harmonisation and collaborative work facilitate interoperability of tools, data, solutions and lead to a better understanding of concepts, semantics and functionalities in the life sciences.IN PRESS EMBO Journal: https://www.embopress.org/journal/14602075 AVAILABLE SOON at : https://doi.org/10.15252/embj.2023115008 ",2023-09-12,,,"David, Romain * Rybina, Arina * Burel, Jean-Marie * Heriche, Jean-Karim * Audergon, Pauline * Boiten, Jan-Willem * Coppens, Frederik * Crockett, Sara * Katrina, Exter * Fahrener, Sven * Fratelli, Maddalena * Goble, Carole * Gormanns, Philipp * Grantner, Tobias * Gruning, Bjorn * Gurwitz, KimTamara * Hancock, John * Harmse, Henriette * Holub, Petr * Juty, Nick * Karnbach, Geoffrey * Karoune, Emma * Keppler, Antje * Klemeier, Jessica * Lancelotti, Carla * Legras, Jean-Luc * Lister, L.Allyson * Longo, DarioLivio * Ludwig, Rebecca * Madon, Benedicte * Massimi, Marzia * Matser, Vera * Matteoni, Rafaele * Th., MayrhoferMichaela * Ohmann, Christian * Panagiotopoulou, Maria * Parkinson, Helen * Perseil, Isabelle * Pfander, Claudia * Pieruschka, Roland * Raess, Michael * Rauber, Andreas * Richard, AudreyS. * Romano, Paolo * Rosato, Antonio * Sanchez-Pla, Alex * Sansone, Susanna-Assunta * Sarkans, Ugis * Serrano-Solano, Beatriz * Tang, Jing * Tanoli, Ziaurrehman * Tedds, Jonathan * Wagener, Harald * Weise, Martin * Westerhoff, HansV. * Wittner, Rudolf * Ewbank, Jonathan * Blomberg, Niklas * Gribbon, Philip",,pdf,en +Euro-BioImaging Scientific Ambassadors Program,,https://zenodo.org/records/8182154 * https://doi.org/10.5281/zenodo.8182154,CC-BY-4.0,Graduation presentation for the 7th cohort of the Open Seeds mentoring & training program for Open Science ambassadors. The project presented is called "Euro-BioImaging  Scientific Ambassadors Program".,2023-07-25,,,"Serrano-Solano, Beatriz",,pdf, +Euro-BioImaging ERIC Annual Report 2022,,https://zenodo.org/records/8146412 * https://doi.org/10.5281/zenodo.8146412,CC-BY-4.0,Euro-BioImaging ERIC is the European landmark research infrastructure for biological and biomedical imaging as recognized by the European Strategy Forum on Research Infrastructures (ESFRI). Euro-BioImaging is the gateway to world-class imaging facilities across Europe. This document is the Euro-BioImaging Annual Report for the year 2022.,2023-07-14,,,"ERIC, Euro-BioImaging",,pdf, +Building a FAIR image data ecosystem for microscopy communities,,https://zenodo.org/records/7788899 * https://doi.org/10.5281/zenodo.7788899,CC-BY-4.0,"Bioimaging has now entered the era of big data with faster than ever development of complex microscopy technologies leading to increasingly complex datasets. This enormous increase in data size and informational complexity within those datasets has brought with it several difficulties in terms of common and harmonized data handling, analysis and management practices, which are currently hampering the full potential of image data being realized. Here we outline a wide range of efforts and solutions currently being developed by the microscopy community to address these challenges on the path towards FAIR bioimage data. We also highlight how different actors in the microscopy ecosystem are working together, creating synergies that develop new approaches, and how research infrastructures, such as Euro-BioImaging, are fostering these interactions to shape the field. ",2023-03-31,,,"Kemmer, Isabel * Keppler, Antje * Serrano-Solano, Beatriz * Rybina, Arina * Özdemir, Bugra * Bischof, Johanna * Ghadraoui, AyoubEl * Eriksson, JohnE. * Mathur, Aastha",,pdf,en +Gut Analysis Toolbox,,https://zenodo.org/records/13739137 * https://doi.org/10.5281/zenodo.13739137,CC-BY-4.0,"What's Changed + +Updating User Dialogs by @mattyrowey in https://github.com/pr4deepr/GutAnalysisToolbox/pull/18 +Added Dialog Boxes and Grammar Corrections by @mattyrowey in https://github.com/pr4deepr/GutAnalysisToolbox/pull/19 +Updated Dialog Prompts for Clarity by @mattyrowey in https://github.com/pr4deepr/GutAnalysisToolbox/pull/20 +Batch analysis option added. +fixed a bunch of bugs related to ganglia segmentation and user workflow + +New Contributors + +@mattyrowey made their first contribution in https://github.com/pr4deepr/GutAnalysisToolbox/pull/18 + +Full Changelog: https://github.com/pr4deepr/GutAnalysisToolbox/compare/v0.6...v0.7",2024-09-10,,,"Sorensen, Luke * Saito, Ayame * Poon, Sabrina * Han, MyatNoe * Hamnett, Ryan * Neckel, Peter * Humenick, Adam * Mutunduwe, Keith * Glennan, Christie * Mahdavian, Narges * Brookes, SimonJH * McQuade, RachelM * Foong, JaimePP * Gómez-de-Mariscal, Estibaliz * Barrutia, ArrateMuñoz * Kaltschmidt, JuliaA. * King, SebastianK. * Haase, Robert * Carbone, Simona * Veldhuis, NicholasA. * Poole, DanielP. * Rajasekhar, Pradeep",,zip,en +"Gut Analysis Toolbox: Training data and 2D models for segmenting enteric neurons, neuronal subtypes and ganglia",,https://zenodo.org/records/10460434 * https://doi.org/10.5281/zenodo.10460434,CC-BY-4.0,"This upload is associated with the software, Gut Analysis Toolbox (GAT). +If you use it please cite: +Sorensen et al. Gut Analysis Toolbox: Automating quantitative analysis of enteric neurons. J Cell Sci 2024; jcs.261950. doi: https://doi.org/10.1242/jcs.261950 +The upload contains StarDist models for segmenting enteric neurons in 2D, enteric neuronal subtypes in 2D and UNet model for enteric ganglia in 2D in gut wholemount tissue. GAT is implemented in Fiji, but the models can be used in any software that supports StarDist and the use of 2D UNet models. The files here also consist of Python notebooks (Google Colab), training and test data as well as reports on model performance. +The model files are located in the respective folders as zip files. The folders have also been zipped: + +Neuron (Hu; StarDist model): + +Main folder: 2D_enteric_neuron_model_QA.zip +Model File:2D_enteric_neuron_v4_1.zip  + + +Neuronal subtype (StarDist model):  + +Main folder: 2D_enteric_neuron_subtype_model_QA.zip +Model File: 2D_enteric_neuron_subtype_v4.zip + + +Enteric ganglia (2D UNet model; Use in FIJI with deepImageJ) + +Main folder: 2D_enteric_ganglia_model_QA.zip +Model File: 2D_Ganglia_RGB_v2.bioimage.io.model.zip (Compatible with deepimageJ v3) + + + +For the all models, files included are: + +Model for segmenting cells or ganglia in 2D FIJI. StarDist or 2D UNet. +Training and Test datasets used for training. +Google Colab notebooks used for training and quality assurance (ZeroCost DL4Mic notebooks). +Quality assurance reports generated from above notebooks. +StarDist model exported for use in QuPath. + +The model files can be used within can be used within the software, StarDist. They are intended to be used within FIJI or QuPath, but can be used in any software that supports the implementation of StarDist in 2D. +Data: +All the images were collected from 4 different research labs and a public database (SPARC database) to account for variations in image acquisition, sample preparation and immunolabelling. +For enteric neurons the pan-neuronal marker, Hu has been used and the  2D wholemounts images from mouse, rat and human tissue. +For enteric neuronal subtypes, 2D images for nNOS, MOR, DOR, ChAT, Calretinin, Calbindin, Neurofilament, CGRP and SST from mouse tissue have been used.. +25 images were used from the following entries in the SPARC database: + +Howard, M. (2021). 3D imaging of enteric neurons in mouse (Version 1) [Data set]. SPARC Consortium. +Graham, K. D., Huerta-Lopez, S., Sengupta, R., Shenoy, A., Schneider, S., Wright, C. M., Feldman, M., Furth, E., Lemke, A., Wilkins, B. J., Naji, A., Doolin, E., Howard, M., & Heuckeroth, R. (2020). Robust 3-Dimensional visualization of human colon enteric nervous system without tissue sectioning (Version 1) [Data set]. SPARC Consortium. +Wang, L., Yuan, P.-Q., Gould, T. and Tache, Y. (2021). Antibodies Tested in theColon – Mouse (Version 1) [Data set]. SPARC Consortium. doi:10.26275/i7dl-58h + +The images have been acquired using a combination different microscopes. The images for the mouse tissue were acquired using:  + + +Leica TCS-SP8 confocal system (20x HC PL APO NA 1.33, 40 x HC PL APO NA 1.3)  + + +Leica TCS-SP8 lightning confocal system (20x HC PL APO NA 0.88)  + + +Zeiss Axio Imager M2 (20X HC PL APO NA 0.3)  + + +Zeiss Axio Imager Z1 (10X HC PL APO NA 0.45)  + + +Human tissue images were acquired using:  + + +IX71 Olympus microscope (10X HC PL APO NA 0.3)  + + +For more information, visit the Documentation website. +NOTE: The images for enteric neurons and neuronal subtypes have been rescaled to 0.568 µm/pixel for mouse and rat. For human neurons, it has been rescaled to 0.9 µm/pixel . This is to ensure the neuronal cell bodies have similar pixel area across images. The area of cells in pixels can vary based on resolution of image, magnification of objective used, animal species (larger animals -> larger neurons) and potentially how the tissue is stretched during wholemount preparation  +Average neuron area for neuronal model: 701.2 ± 195.9 pixel2 (Mean ± SD, 6267 cells) +Average neuron area for neuronal subtype model: 880.9 ± 316 pixel2 (Mean ± SD, 924 cells) +Software References: +Stardist +Schmidt, U., Weigert, M., Broaddus, C., & Myers, G. (2018, September). Cell detection with star-convex polygons. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 265-273). Springer, Cham. +deepImageJ +Gómez-de-Mariscal, E., García-López-de-Haro, C., Ouyang, W., Donati, L., Lundberg, E., Unser, M., Muñoz-Barrutia, A. and Sage, D., 2021. DeepImageJ: A user-friendly environment to run deep learning models in ImageJ. Nature Methods, 18(10), pp.1192-1195. +ZeroCost DL4Mic +von Chamier, L., Laine, R.F., Jukkala, J., Spahn, C., Krentzel, D., Nehme, E., Lerche, M., Hernández-Pérez, S., Mattila, P.K., Karinou, E. and Holden, S., 2021. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nature communications, 12(1), pp.1-18.",2022-02-15,,,"Sorensen, Luke * Saito, Ayame * Poon, Sabrina * Han, MyatNoe * Humenick, Adam * Neckel, Peter * Mutunduwe, Keith * Glennan, Christie * Mahdavian, Narges * Brookes, SimonJH * McQuade, RachelM * Foong, JaimePP * King, SebastianK. * Gómez-de-Mariscal, Estibaliz * Muñoz-Barrutia, Arrate * Haase, Robert * Carbone, Simona * Veldhuis, NicholasA. * Poole, DanielP. * Rajasekhar, Pradeep",,zip,en +Research Data Managemet and how not to get overwhelmed with data,,https://zenodo.org/records/8372703 * https://doi.org/10.5281/zenodo.8372703,CC-BY-4.0,"Research data management and how not to get overwhelmed with data presentation is an overview of bioimage analysis with a focus on the basics for data management planning, FAIR principles, and how to practically organize folders and prepares naming convention. The presentation includes an overview of metadata, Creative Common licenses, and a sum up of electronic laboratory notebooks. The last two slides go through how all of that works in practice in open access core microscopy facility.",2023-09-23,,,"Schätz, Martin",,pdf * pptx,en +Artificial Blobs and Labels image,,https://zenodo.org/records/7919117 * https://doi.org/10.5281/zenodo.7919117,CC-BY-4.0,"A groovy script to use in Fiji to generate artificial images and labels, with example images.",2023-05-10,,,Romain,,groovy * tif,en +ImageJ tool for percentage estimation of pneumonia in lungs,,https://zenodo.org/records/7885379 * https://doi.org/10.5281/zenodo.7885379,CC-BY-4.0,"The software tool is developed on demand of Radiological Department of Faculty Hospital of Královské Vinohrady, with the aim to provide a tool to estimate the percentage of pneumonia (or COVID-19 presence) in lungs. Paper Estimation of Covid-19 lungs damage based on computer tomography images analysis presenting the tool is available on F1000reserach DOI: 10.12688/f1000research.109020.1. The underlying dataset is published in Zenodo (DOI:10.5281/zenodo.5805939). One of the challenges was to design a tool that would be available without complicated install procedures and would process data in a reasonable time even on office computers. For this reason, 8-bit and 16-bit version of the tool exists. The FIJI software (or ImageJ with Bio-Formats plugin installed) was selected as the best candidate. Examples of use and tutorials are available at GitHub.  + +Underlying data: +DOI:10.5281/zenodo.5805939 +The first five datasets are analyzed using this tool, with results and parameters to repeat the analysis in results_csv.csv or results.xlsx. + +Contributions: +Martin SCHÄTZ:       Coding, tool testing, data curation, data set analysis +Olga RUBEŠOVÁ:    Code review, tutorial preparation, tool testing, data set analysis +Jan MAREŠ:             Tool testing, data set analysis +Alan SPARK:             Tool testing + +The work was funded by the Ministry of Education, Youth and Sports by grant ‘Development of Advanced Computational Algorithms for evaluating post-surgery rehabilitation’ number LTAIN19007. The work was also supported from the grant of Specific university research – grant No FCHI 2022-001. + + ",2023-05-02,,,"Schätz, Martin * Rubešová, Olga * Mareš, Jan * Spark, Alan",,csv * xlsx * zip,en +Multiplexed histology of COVID-19 post-mortem lung samples - CONTROL CASE 1 FOV1,,https://zenodo.org/records/7447491 * https://doi.org/10.5281/zenodo.7447491,CC-BY-4.0,"Image-based data set of a post-mortem lung sample from a non-COVID-related pneumonia donor (CONTROL CASE 1, FOV1) + +Each image shows the same field of view (FOV), sequentially stained with the depicted fluorescence-labelled antibodies, including surface proteins, intracellular proteins and transcription factors. Images contain 2024 x 2024 pixels and are generated using an inverted wide-field fluorescence microscope with a 20x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Images have been normalized and intensities adjusted.",2022-12-16,,,"Reguant, AnnaPascual * Mothes, Ronja * Radbruch, Helena * Hauser, AnjaE.",,tif,en +Measuring reporter activity domain in EPI aggregates and Gastruloids.ijm,,https://zenodo.org/records/7409423 * https://doi.org/10.5281/zenodo.7409423,CC-BY-4.0,This imagej macro analyses the reporter intensity activity and expression domain in EPI aggregates and Gastruloids.,2022-12-07,,,"Guiet, Romain * Burri, Olivier * Girgin, Mehmet * Lutolf, Matthias",,ijm * nd2,en +Interactive Image Data Flow Graphs,,https://zenodo.org/records/7215114 * https://doi.org/10.5281/zenodo.7215114,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.,2022-10-17,,,"Schätz, Martin * Schätz, Martin",,pptx,en +Optimisation and Validation of a Swarm Intelligence based Segmentation Algorithm for low Contrast Positron Emission Tomography,,https://zenodo.org/records/7209862 * https://doi.org/10.5281/zenodo.7209862,CC-BY-4.0,"In the field of radiooncological research, individualised therapy is one of the hot topics at the moment. As a key aspect biologically-adapted therapy is discussed. Therapy adaption based on biological parameters may include tomographic imaging to determine biological properties of the tumour. One often invoked imaging modality is positron emission tomography (PET) using the tracer [18F]-fluoromisonidazole (FMISO) for hypoxia imaging. Hypoxia imaging is of interest, because hypoxic tumours are known to be radiorestistant. Even further, patients with hypoxic tumours have worse prognosis compared to patients with normoxic tumours. Thus, hypoxia imaging appears promising for radiotherapy treatment adaption. For example, volumetric analysis of FMISO PET could deliver additional hypoxia target volumes, which may be irradiated with higher radiation doses to improve the therapeutic effect. However, limited contrast between target volume and background in FMISO PET images interferes image analysis.Established methods for target volume delineation in PET do not allow determination of reliable contours in FMISO PET. To tackle this aspect, this thesis focusses on an earlier developed swarm intelligence based segmentation algorithm for FMISO PET and rather, its optimisation and validation in a clinically relevant setting. In this setting, clinical FMISO PET images were used which were acquired as part of a clinical trial performed at the Clinic and Policlinic for Radiation Therapy and Radiooncology of the University Hospital Carl Gustav Carus Dresden. The segmentation algorithm was applied to these imaging data sets and optimised using a cross-validation approach incorporating reference contours from experienced observers who outlined FMISO PET positive volumes manually. Afterwards, the performance of the algorithm and the properties of the resulting contours were studied in more detail. The algorithm was shown to deliver contours which were similar to manually-created contours to a degree like manually-created contours were similar to each other. Thus, the application of the algorithm in clinical research is recommended to eliminate inter-observer-variabilities. Finally, it was shown that repeated FMISO PET imaging before and shortly after the beginning of combined radiochemotherapy lead to manually-created contours with significantly higher variations than the variations of automatically-created contours using the proposed algorithm. Increased contour similarity in subsequently acquired imaging data highlights the observer-independence of the algorithm. While several observers outline different volumes, in identical data sets as well as in subsequent imaging data sets, the algorithm outlines more stable volumes in both cases. Thus, increased contour reproducibility is reached by automation of the delineation process by the proposed algorithm. ",2014-04-01,,,"Haase, Robert",,pdf,en +CZI (Carl Zeiss Image) dataset with artificial test camera images with various dimension for testing libraries reading,,https://zenodo.org/records/7015307 * https://doi.org/10.5281/zenodo.7015307,CC-BY-4.0,"Set of CZI test images created by using a simulated microscope with a test grayscale camera (no LSM or AiryScan or RGB). The filename indicates the used dimension(s) for the acquisition experiment. The files can be used to test the basic functionality of libraries reading CZI files. + +Examples: + + + S=2_T=3_CH=1.czi = 2 Scenes, 3 TimePoints and 1 Channel + + Z-Stack was not activated inside acquisition experiment + + + S=2_T=3_Z=5_CH=2.czi = 2 Scenes, 3 TimePoints, 5-Z-Planes and 1 Channels + + Z-Stack was activated inside acquisition experiment + + + + +The test files (so far) contain not any data with more "advanced" dimensions like AiryScan rawdata, illumination angles etc. Also no CZI files with pixel type RGB are included yet. + +  + +  + + ",2022-08-22,,,"Rhode, Sebastian",,czi,en +SciAugment,,https://zenodo.org/records/6991106 * https://doi.org/10.5281/zenodo.6991106,OTHER-OPEN,"SciAugment v0.2.0 has pip installable version, channel-wise augmentation was added, and an option for all augmentations or no augmentation. Examples of how to use the tool are in README and in Google Colab notebooks. Practical examples of how to use results with YOLOv5 on scientific data can be found in the SciCount project. + +SciAugment aims to provide an option to create an augmented image set with similar changes in data as the imaging sensor and technique would do.",2022-07-29,,,"Schätz, Martin",,zip, +martinschatz-cz/SciCount: v1.0.0 with reusable example notebooks,,https://zenodo.org/records/6953610 * https://doi.org/10.5281/zenodo.6953610,OTHER-OPEN,"The first version contains an example of augmentation of scientific data and object detection with YOLO_v5 on colony counting (2 classes), object counting in blood smears (can be used as semisupervised learning for faster annotation), and wildlife detection from night records with a camera trap. + +The project is available on GitHub.",2022-08-02,,,"Schätz, Martin * Mrazík, Lukáš * Máhlerova, Karolina",,zip,en +Morphological analysis of neural cells with WEKA and SNT Fiji plugins,,https://zenodo.org/records/6834214 * https://doi.org/10.5281/zenodo.6834214,CC-BY-4.0,"A simple workflow to detect Soma and neurite paths, from light microscopy datasets. + +Using open-source tools for beginners.",2022-07-14,,,"Waiger, Daniel",,pdf,en +Large tiling confocal acquisition (rat brain),,https://zenodo.org/records/6646128 * https://doi.org/10.5281/zenodo.6646128,CC-BY-4.0,"Name: Large tiling confocal acquisition (rat brain) + +Microscope: Zeiss LSM700 + +Microscopy data type: 108 tiles, each with 62 z-slices and 2 channels : +Channel 1: DAPI +Channel 2: cck staining + +File format: .lsm (16-bit) + +Image size: 1024x1024x62 (Pixel size: 0.152 x 0.152 x 1 micron), 2 channels. + +  + +NOTE : Some tiles were annotated and used to train a StarDist3D model (https://doi.org/10.5281/zenodo.6645978   )",2022-06-15,,,"Meystre, Julie",,lsm,en +3D Nuclei annotations and StarDist 3D model(s) (rat brain),,https://zenodo.org/records/6645978 * https://doi.org/10.5281/zenodo.6645978,CC-BY-4.0,"Name: 3D Nuclei annotations and StarDist3D model(s) (rat brain) + +Images:  From a large tiling acquisition ( https://doi.org/10.5281/zenodo.6646128 ) individual Tile (xyz : 1024x1024x62) were downsampled and cropped (128x128x62). Four crops, from different tiles (./annotations_BIOP/images/) were manually annotated with ITK-SNAP (./annotations_BIOP/masks/) + +These four images, and their corresponding masks, were cropped into four quadrants (./crops_BIOP_v1/) in order to get 16 different images (64x64x62). + +Conda environment: A conda environment was created using the yml file  stardist0.8_TF1.15.yml + +Training : Training was performed using the jupyter notebook 1-Training_notebook.ipynb. +Three different trainings (with the same random seed, same anisotropy, patch size and grid) were performed and produced three different models (./models/) + +Validation images (from the random seed used) were exported to ease the visual inspection of the results(./val_rdm42/). + +Validation:  To save metrics in a csv file and compare predictions to the annotations the jupyter notebook 2-QC_notebook.ipynb can be used on the validation folder. + +Large images: To test the model on larger images one can use Whole_ds441.tif (or Crop_ds441.tif ) +These images were obtained using the plugin BigSticher on the raw data ( https://doi.org/10.5281/zenodo.6646128 ), resaved as h5 and exported the downsample by 4 version. + +  + + ",2022-06-15,,,"Guiet, Romain",,ipynb * png * tif * yml * zip,en +Multi-Template-Matching for object-detection (slides),,https://zenodo.org/records/6554166 * https://doi.org/10.5281/zenodo.6554166,CC-BY-4.0,"This presentations describes Multi-Template-Matching, a novel method extending on template-matching for object-detection in images. + +The project was part of the PhD project of Laurent Thomas between 2017 and 2020, under supervision of Jochen Gehrig. The project was hosted at ACQUIFER Imaging with collaboration of the medical university of Heidelberg, and part of the ImageInLife Horizon2020 ITN (PhD program). ",2022-05-16,,,"Thomas, Laurent",,pdf * pptx,en +Introduction to light-microscopy / Widefield microscopy,,https://zenodo.org/records/6535296 * https://doi.org/10.5281/zenodo.6535296,OTHER-AT,"This is a short introduction to light-microscopy, illustrated with widefield microscopy. + +It introduces : + +- upright and inverted widefield microscopes + +- the transmitted and fluorescent light-path + +- contrasting methods (optical and at the sample level) + +- the molecular principle of fluorescence (Perrin-Jablonski) + +- objective, resolution and limitations of the method (diffraction, diffusion/scattering) + +In addition to the PPT (with few animations), a lighter PDF version is provided for preview in Zenodo. + +  + +Illustrations are mostly extracted from the ThermoFisher Molecular Probes School of Fluorescence educator packet and from the course material from Micron Facility in Oxford. + +As stated in the presentation, illustrations are copyrighted but can be reproduced provided the original attribution is conserved.",2022-05-10,,,"Laurent, Thomas",,pdf * pptx,en +Liver Micrometastases area quantification using QuPath and pixel classifier,,https://zenodo.org/records/6523649 * https://doi.org/10.5281/zenodo.6523649,CC-BY-4.0,"Sample: Mouse (NSG) liver slices with human colorectal cancer cells metastases, stained with Hematoxylin & Eosin.  + +Image Acquisition: Images were acquired on an Olympus VS120 Whole Slide Scanner, using a 20x objective (UPLSAPO, N.A. 0.75) and a color camera (Pike F505 Color) with an image pixel size of 0.345 microns. + +Image Processing and Analysis: Obtained images were analyzed using the software QuPath [1] (version 0.3.2) using groovy scripts, making use of a pixel classifier to segment and measure cancer cell clusters. + +Files : + +Detailed_worflow.pdf : contains a detailed description of how pixel classifier was created + +images_for_classifier_training.zip : contains all the vsi file obtained from the microscope and used for the training + +project_for_classifier_training.zip : contains the QuPath project, with Training Image, annotations, classifiers and scripts for analysis + +PythonCode.txt : code ran to transform output results from QuPath to final results + +  + +[1] Bankhead, P. et al. QuPath: Open source software for digital pathology image analysis. Scientific Reports (2017). https://doi.org/10.1038/s41598-017-17204-5",2022-05-06,,,"Simó-Riudalbas, Laia * Guiet, Romain * Burri, Olivier * Duc, Julien * Trono, Didier",,pdf * png * txt * zip,en +Cellpose models for Label Prediction from Brightfield and Digital Phase Contrast images,,https://zenodo.org/records/6140111 * https://doi.org/10.5281/zenodo.6140111,CC-BY-4.0,"Name: Cellpose models for Brightfield and Digital Phase Contrast images + +Data type: Cellpose models trained via transfer learning from the ‘nuclei’ and ‘cyto2’ pretrained model with additional Training Dataset . Includes corresponding csv files with 'Quality Control' metrics(§) (model.zip). + +Training Dataset: Light microscopy (Digital Phase Contrast or Brightfield) and automatic annotations (nuclei or cyto) (https://doi.org/10.5281/zenodo.6140064) + +Training Procedure: The cellpose models were trained using cellpose version 1.0.0 with GPU support (NVIDIA GeForce K40) using default settings as per the Cellpose documentation . Training was done using a Renku environment (renku template). + +  + +Command Line Execution for the different trained models + +nuclei_from_bf: + +cellpose --train --dir 'data/train/' --test_dir 'data/test/' --pretrained_model nuclei  --img_filter _bf --mask_filter _nuclei --chan 0 --chan2 0 --use_gpu --verbose + +cyto_from_bf: + +cellpose --train --dir 'data/train/' --test_dir 'data/test/' --pretrained_model cyto2 --img_filter _bf --mask_filter _cyto --chan 0 --chan2 0 --use_gpu --verbose + +  + +nuclei_from_dpc: + +cellpose --train --dir 'data/train/' --test_dir 'data/test/' --pretrained_model nuclei  --img_filter _dpc --mask_filter _nuclei --chan 0 --chan2 0 --use_gpu --verbose + +cyto_from_dpc: + +cellpose --train --dir 'data/train/' --test_dir 'data/test/' --pretrained_model cyto2 --img_filter _dpc --mask_filter _cyto --chan 0 --chan2 0 --use_gpu --verbose + +  + +nuclei_from_sqrdpc: + +cellpose --train --dir 'data/train/' --test_dir 'data/test/' --pretrained_model nuclei --img_filter _sqrdpc --mask_filter _nuclei --chan 0 --chan2 0 --use_gpu --verbose + +cyto_from_sqrdpc: + +cellpose --train --dir 'data/train/' --test_dir 'data/test/' --pretrained_model cyto2 --img_filter _sqrdpc --mask_filter _cyto --chan 0 --chan2 0 --use_gpu --verbose + +  + +NOTE (§): We provide a notebook for Quality Control, which is an adaptation of the "Cellpose (2D and 3D)" notebook from ZeroCostDL4Mic . + +NOTE: This dataset used a training dataset from the Zenodo entry(https://doi.org/10.5281/zenodo.6140064) generated from the “HeLa “Kyoto” cells under the scope”  dataset Zenodo entry(https://doi.org/10.5281/zenodo.6139958) in order to automatically generate the label images. + +NOTE: Make sure that you delete the “_flow” images that are auto-computed when running the training. If you do not, then the flows from previous runs will be used for the new training, which might yield confusing results. + + ",2022-02-25,,,"Guiet, Romain * Burri, Olivier",,ipynb * zip,en +"HeLa ""Kyoto"" cells under the scope",,https://zenodo.org/records/6139958 * https://doi.org/10.5281/zenodo.6139958,CC-BY-4.0,"Name: HeLa “Kyoto” cells under the scope + +Microscope: Perkin Elmer Operetta microscope with a 20x N.A. 0.8 objective and an Andor Zyla 5.5 camera. + +Microscopy data type: The time-lapse datasets were acquired every 15 minutes, for 60 hours. From the individual plan images (channels, time-points, field of view exported by the PerkinElmer software Harmony) multi-dimension images were generated using the Operetta_Importer-0.1.21  with a downscaling of 4.  + +Channel 1 : Low Contrast DPC (Digital Phase Contrast) + +Channel 2 : High Contrast DPC + +Channel 3 : Brightfield + +Channel 4 : EGFP-α-tubulin + +Channel 5 : mCherry-H2B + +File format: .tif (16-bit) + +Image size: 540x540 (Pixel size: 0.299 nm), 5c, 1z , 240t + +  + +Cell type: HeLa “Kyoto” cells, expressing EGFP-α-tubulin and mCherry-H2B ( Schmitz et al, 2010 ) + +Protocol: Cells were resuspended in Imaging media and were seeded in a microscopy grade 96 wells plate ( CellCarrier Ultra 96, Perkin Elmer). The day after seeding, and for 60 hours, images were acquired in 3 wells, in 25 different fields of view, every 15 minutes. + +Imaging media: DMEM red-phenol-free media (FluoroBrite™ DMEM, Gibco) complemented with Fetal Calf Serum and Glutamax. + +  + +NOTE: This dataset was used to automatically generate label images in the following Zenodo entry:  https://doi.org/10.5281/zenodo.6140064 + +NOTE: This dataset was used to train the cellpose models in the following Zenodo entry: https://doi.org/10.5281/zenodo.6140111",2022-02-25,,,"Guiet, Romain",,png * tif * zip,en +Digital Phase Contrast on Primary Dermal Human Fibroblasts cells,,https://zenodo.org/records/5996883 * https://doi.org/10.5281/zenodo.5996883,CC-BY-4.0,"Name: Digital Phase Contrast on Primary Dermal Human Fibroblasts cells  + +Data type: Paired microscopy images (Digital Phase Contrast, square rooted) and corresponding labels/masks used for cellpose training (the corresponding Brightfield images are also present), organized as recommended by cellpose documentation. + +Microscopy data type: Light microscopy (Digital Phase Contrast and Brighfield ) + +Manual annotations: Labels/masks obtained via manual segmentation. For each region, all cells were annotated manually. Uncertain objects (Dust, fused cells) were left unannotated, so that the cellpose model (10.5281/zenodo.6023317) may mimic the same user bias during prediction. This was particularly necessary due to the accumulation of floating debris in the center of the well. + +Microscope: Perkin Elmer Operetta microscope with a 10x 0.35 NA objective + +Cell type: Primary Dermal Human Fibroblasts cells + +File format: .tif (16-bit for DPC and 16-bit for the masks) + +Image size: 1024x1024 (Pixel size: 634 nm) + +NOTE : This dataset was used to train cellpose model ( 10.5281/zenodo.6023317 ) + + ",2022-02-09,,,"Capolupo, Laura",,zip,en +Cellpose model for Digital Phase Contrast images,,https://zenodo.org/records/6023317 * https://doi.org/10.5281/zenodo.6023317,CC-BY-4.0,"Name: Cellpose model for Digital Phase Contrast images + +Data type: Cellpose model, trained via transfer learning from ‘cyto’ model. + +Training Dataset: Light microscopy (Digital Phase Contrast) and Manual annotations (10.5281/zenodo.5996883) + +Training Procedure: Model was trained using a Cellpose version 0.6.5 with GPU support (NVIDIA GeForce RTX 2080) using default settings as per the Cellpose documentation  + +python -m cellpose --train --dir TRAINING/DATASET/PATH/train --test_dir TRAINING/DATASET/PATH/test --pretrained_model cyto --chan 0 --chan2 0 + +The model file (MODEL NAME) in this repository is the result of this training. + +Prediction Procedure: Using this model, a label image can be obtained from new unseen images in a given folder with + +python -m cellpose --dir NEW/DATASET/PATH --pretrained_model FULL_MODEL_PATH --chan 0 --chan2 0 --save_tif --no_npy",2022-02-09,,,"Capolupo, Laura * Burri, Olivier * Guiet, Romain",,171894,en +LauLauThom/MaskFromRois-Fiji: Masks from ROIs plugins for Fiji - initial release,,https://zenodo.org/records/5121890 * https://doi.org/10.5281/zenodo.5121890,MIT,"Fiji plugins for the creation of binary and semantic masks from ROIs in the RoiManager. Works with stacks too. + +Installation in Fiji: activate the Rois from masks update site in Fiji. + +See GitHub readme for the documentation. + +Latest tested with Fiji 2.1.0/ImageJ 1.53j",2021-07-22,,,"Thomas, Laurent * Trehin, Pierre",,zip,en +Deconvolution Test Dataset,,https://zenodo.org/records/5101351 * https://doi.org/10.5281/zenodo.5101351,CC-BY-4.0,"This a test dataset, HeLa cells stained for action using Phalloidin-488 acquired on confocal Zeiss LSM710, which contains + +- Ph488.czi (contains all raw metadata) + +- Raw_large.tif ( is the tif version of Ph488.czi, provided for conveninence as tif doesn't need Bio-Formats to be open in Fiji ) + +- Raw.tif , is a crop of the large image + +- PSFHuygens_confocal_Theopsf.tif , is a theoretical PSF generated with HuygensPro + +- PSFgen_WF_WBpsf.tif  , is a theoretical PSF generated with PSF generator + +- PSFgen_WFsquare_WBpsf.tif, is the result of the square operation on PSFgen_WF_WBpsf.tif , to approximate a confocal PSF",2021-07-14,,,"Guiet, Romain",,czi * tif, +Ink in a dish,,https://zenodo.org/records/13642395 * https://doi.org/10.5281/zenodo.13642395,CC0-1.0,A test data set for troublshooting. no scientific meaning.,2024-09-03,,,Cavanagh,,zip, +Evident OIR sample files with lambda scan - FV 4000,,https://zenodo.org/records/12773657 * https://doi.org/10.5281/zenodo.12773657,CC-BY-4.0,"The files contained in this repository are confocal images taken with the Evident FV 4000 of a sample containing DAPI and mCherry stains, excited with the 405 nm laser and images for different emission windows (lambda scan). +They are public sample files which goal is to help test edge cases of the bio-formats library (https://www.openmicroscopy.org/bio-formats/), in particular for the proper handling of lambda scans. + +DAPI_mCherry_22Lambda-420-630-w10nm-s10nm.oir : 22 planes, each plane is an emission window, starting from 420 nm up to 630 nm by steps of 10 nm +DAPI_mCherry_4T_5Lambda-420-630-w10nm-s50nm.oir : 20 planes, 5 lambdas from 420 to 630 nm by steps of 50 nm, 4 timepoints +DAPI_mCherry_4Z_5Lambda-420-630-w10nm-s50nm.oir : 20 planes, 5 lambdas from 420 to 630 nm by steps of 50 nm, 4 slices +DAPI-mCherry_3T_4Z_5Lambda-420-630-w10nm-s50nm.oir : 60 planes, 5 lambdas from 420 to 630 nm by steps of 50 nm, 4 slices, 3 timepoints +",2024-07-18,,,"Chiaruttini, Nicolas",,oir,en +ICS/IDS stitched file,,https://zenodo.org/records/11637422 * https://doi.org/10.5281/zenodo.11637422,CC-BY-4.0,"Hi @ome team ! +We usually use ICS/IDS file formats as an output to our stitching pipeline as the reading and writing is pretty fast. However, it seems that since Bio-Formats 7.x opening the files is not working anymore. +I tried with a Fiji with Bio-Formats 6.10.1 and the files open, but more recent versions give an issue. +  +java.lang.NullPointerException + at loci.formats.in.ICSReader.initFile(ICSReader.java:1481) + at loci.formats.FormatReader.setId(FormatReader.java:1480) + at loci.plugins.in.ImportProcess.initializeFile(ImportProcess.java:498) + at loci.plugins.in.ImportProcess.execute(ImportProcess.java:141) + at loci.plugins.in.Importer.showDialogs(Importer.java:156) + at loci.plugins.in.Importer.run(Importer.java:77) + at loci.plugins.LociImporter.run(LociImporter.java:78) + at ij.IJ.runUserPlugIn(IJ.java:244) + at ij.IJ.runPlugIn(IJ.java:210) + at ij.Executer.runCommand(Executer.java:152) + at ij.Executer.run(Executer.java:70) + at ij.IJ.run(IJ.java:326) + at ij.IJ.run(IJ.java:337) + at ij.macro.Functions.doRun(Functions.java:703) + at ij.macro.Functions.doFunction(Functions.java:99) + at ij.macro.Interpreter.doStatement(Interpreter.java:281) + at ij.macro.Interpreter.doStatements(Interpreter.java:267) + at ij.macro.Interpreter.run(Interpreter.java:163) + at ij.macro.Interpreter.run(Interpreter.java:93) + at ij.macro.MacroRunner.run(MacroRunner.java:146) + at java.lang.Thread.run(Thread.java:750) + +You can find one example file at this link 1. +Thanks for your help !Best,Laurent",2024-06-13,,,IMCF,,ics * ids,en +Human DAB staining Axioscan BF 20x,,https://zenodo.org/records/11234863 * https://doi.org/10.5281/zenodo.11234863,CC-BY-4.0,Human brain tissue with DAB immunostaining. Image acquired by BF microscopy in  Zeiss Axioscan at 20x. ,2024-05-21,,,"Garcia, Mario",,czi, +Structuring of Data and Metadata in Bioimaging: Concepts and technical Solutions in the Context of Linked Data,,https://zenodo.org/records/7018750 * https://doi.org/10.5281/zenodo.7018750,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 + ... +",2022-07-12,,,"Weischer, Sarah * Wendt, Jens * Zobel, Thomas",,pdf,en +LZ4-compressed Imaris ims example datasets.,,https://zenodo.org/records/14197622 * https://doi.org/10.5281/zenodo.14197622,CC-BY-4.0,The files contained in this repository are cropped versions of Imaris demo images compressed with LZ4.,2024-11-21,,,"Stucchi, Marco",,ims, +OME2024 NGFF Challenge Results,,https://zenodo.org/records/14234608 * https://doi.org/10.5281/zenodo.14234608,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) + ",2024-11-01,,,"Moore, Josh",,pdf,en +Example Imaris ims datasets.,,https://zenodo.org/records/14235726 * https://doi.org/10.5281/zenodo.14235726,CC-BY-4.0,"The files contained in this repository are example Imaris ims images. +  +Initially related to https://github.com/ome/bioformats/pull/4249",2024-11-28,,,"Stucchi, Marco",,ims, +ome2024-ngff-challenge,sharing,https://github.com/ome/ome2024-ngff-challenge,BSD-3-Clause,Project planning and material repository for the 2024 challenge to generate 1 PB of OME-Zarr data,2024-08-30T12:00:53+00:00,,,"Moore, Will * Moore, Josh * sherwoodf * burel, jean-marie * Rzepka, Norman * dependabot[bot] * JensWendt * Folter, Joostde * Stöter, Torsten * AybukeKY * Perlman, Eric * Boissonnet, Tom",,, +Angebote der NFDI für die Forschung im Bereich Zoologie,,https://zenodo.org/records/14278058 * https://doi.org/10.5281/zenodo.14278058, 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.",2024-12-04,,,"König-Ries, Birgitta * Haase, Robert * Nüst, Daniel * Förstner, Konrad * Engel, Judith Sophie",,pdf * pptx,de +Astigmatic 4Pi bead stack,,https://zenodo.org/records/14287640 * https://doi.org/10.5281/zenodo.14287640, CC-BY-4.0,Bead stack taken on a 4Pi. DCIMG 0x1000000 file with a 4-pixel correction requirement.,2024-12-06,,,"Marin, Zach * Su, Maohan",,zip,en +10 frames of fluorescent particles,,https://zenodo.org/records/14281237 * https://doi.org/10.5281/zenodo.14281237, CC-BY-4.0,"10 frames of fluorescent particles. They don't do much, but they are a DCIMG version 0x7 file example.",2024-12-05,,,"Marin, Zach * Su, Maohan",,dcimg,en +Aberrated Bead Stack,,https://zenodo.org/records/14268554 * https://doi.org/10.5281/zenodo.14268554, CC-BY-4.0,"Bead stack taken on lower path of a 4Pi without deformable mirror corrections. DCIMG examples, not for other purposes.",2024-12-03,,,"Marin, Zach",,zip,en +patho_prompt_injection,histopathology * bioimage analysis,https://github.com/KatherLab/patho_prompt_injection,GPL-3.0-only,,2024-11-08T08:32:03+00:00,,,"JanClusmann * Lenz, Tim",,, +introduction-to-image-analysis,BioImage Analysis,https://github.com/RMS-DAIM/introduction-to-image-analysis,CC-BY-SA-4.0,,2024-10-23T14:05:55+00:00,,,"Barry, Dave * Marcotti, Stefania * Jones, Martin",,,en +rse-skills-workshop,Research Software Engineering,https://github.com/jatkinson1000/rse-skills-workshop,GPL-3.0-only,Teaching materials for improving research software writing abilities.,2023-12-22T17:39:48+00:00,Presentation,presentation,"Atkinson, Jack",,,en +Large Language Models: An Introduction for Life Scientists,,https://zenodo.org/records/14418209 * https://doi.org/10.5281/zenodo.14418209, 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.",2024-12-12,,,"Haase, Robert",,pdf * pptx,en +Terminology service for research data management and knowledge discovery in low-temperature plasma physics,,https://zenodo.org/records/14381522 * https://doi.org/10.5281/zenodo.14381522, CC-BY-4.0,"Abstract: +Terminology services (TS) [1,2] play a pivotal role in achieving structured metadata by providing controlled vocabularies and ontologies that standardize the description of data. This is a crucial aspect of research data management (RDM) in all scientific disciplines. In addition, TS facilitate the use of a common vocabulary within a scientific community also in a more general context, e.g. to annotate scientific papers, patents or other content for better discoverability, as envisaged by the Open Research Knowledge Graph (ORKG) [3] or the Patents4Science project [4].  +To make use of these opportunities, terminologies, ontologies and knowledge graphs must be developed and made available as TS where they do not yet exist. This step is currently being taken by the research community in low-temperature plasma (LTP) physics. LTP physics explores partially ionized gases and its technological applications. This vibrant field offers innovative solutions for societal challenges, ranging from developing efficient lighting and solar cells to revolutionizing healthcare through plasma medicine. Various activities and projects have been started in the past years to support the RDM in LTP research and development and to facilitate the application of data-driven research methods. These activities are supported in parts by the NFDI4BIOIMAGE consortium, active work in the NFDI section “(Meta)data, Terminologies, Provenance”, and the basic service Terminology Services 4 NFDI (TS4NFDI) funded by Base4NFDI.  +Recently, the ontology Plasma-O [5–7] for LTP physics has been developed at INP in collaboration with FIZ Karlsruhe – Leibniz Institute for Information Infrastructure, providing a framework for structuring metadata and building a knowledge graph for scientific information within the field. The present contribution will show how a TS utilizing this resource can support different aspects of RDM and knowledge discovery using concrete examples. The application cases include (i) standardizing data annotation: By providing researchers with a controlled vocabulary of LTP-specific terms and their relationships, ensuring consistent and unambiguous data descriptions; (ii) enabling semantic search: Moving beyond keyword-based searches, TS allow for complex queries based on the relationships between concepts, significantly improving data discoverability; (iii) facilitating data integration: By mapping data from different sources to a common ontology, TS enable seamless integration and analysis of heterogeneous datasets, which is crucial for data-driven research and development. The TS Suite of TS4NFDI with the provided widgets [8] fits perfectly to the requirements of these three application cases and will support the harmonization of metadata in LTP physics. The implementation of a public TS is required to provide the domain-specific metadata in a standardized format and will be instrumental in unlocking the full potential of the TS widgets for RDM and knowledge discovery by LTP researchers. Furthermore, the results can provide insights to other domains on how to apply TS to their specific needs. + The work was supported in parts by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under the National Research Data Infrastructure – [NFDI46/1] – 501864659 and project number 496963457 as well as by the Federal Ministry of Education and Research (BMBF), project number 16KOA013A. +References: + + + + +[1] + + +S. Jupp, T. Burdett, C. Leroy, H. Parkinson, “A new Ontology Lookup Service at EMBL-EBI”, Workshop on Semantic Web Applications and Tools for Life Sciences (2015), https://ceur-ws.org/Vol-1546/paper_29.pdf (accessed: 2024-09-20). + + + + +[2] + + +P. L. Whetzel, N. F. Noy, N. H. Shah, P. R. Alexander, C. Nyulas, T. Tudorache, M. A. Musen, “BioPortal: enhanced functionality via new Web services from the National Center for Biomedical Ontology to access and use ontologies in software applications”, Nucleic Acids Res. 39 (2011) W541–W545, https://doi.org/10.1093/nar/gkr469. + + + + +[3] + + +Open Research Knowledge Graph, https://orkg.org/ (accessed: 2024-09-20). + + + + +[4] + + +Patents4Science – Establishing an Information Infrastructure for the Use of Patent Knowledge in Science, https://www.patents4science.org/ (accessed: 2024-09-20). + + + + +[5] + + +H. Sack, F. Hoppe, “Verbundprojekt: Qualitätssicherung und Vernetzung von Forschungsdaten in der Plasmatechnologie - QPTDat; Teilvorhaben: Wissensgraph und Ontologieentwicklung zur Vernetzung von Metadaten : Schlussbericht des Teilvorhabens”, 2023, https://doi.org/10.2314/KXP:1883436974. + + + + +[6] + + +I. Chaerony Siffa, R. Wagner, L. Vilardell Scholten, M. M. Becker, “Semantic Information Management in Low-Temperature Plasma Science and Technology with VIVO”, 2024, preprint, https://doi.org/10.48550/arXiv.2409.11065. + + + + +[7] + + +I. Chaerony Siffa, R. Wagner, L. Vilardell Scholten, M. M. Becker, “Plasma Ontology and Knowledge Graph Initial Release v0.5.0”, 2024, Zenodo, https://doi.org/10.5281/zenodo.13325226. + + + + +[8] + + +J. Sasse, V. Kneip, R. Baum, P. Zimmermann, J. Darms, J. Schneider, V. Clemens, P. Oladazimi, L. Kühnel, “ts4nfdi/terminology-service-suite: v2.6.0”, 2024, Zenodo, https://doi.org/10.5281/zenodo.13692297. + + + +",2024-12-11,,,"Becker, Markus M. * Chaerony Siffa, Ihda * Baum, Roman",,pdf,en +NFDI4BIOIMAGE data management illustrations by Henning Falk,,https://zenodo.org/records/14186101 * https://doi.org/10.5281/zenodo.14186101, 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. + ",2024-11-29,,,"Consortium, NFDI4BIOIMAGE",,jpg * pdf, +Working Group Charter. RDM Helpdesk Network,,https://zenodo.org/records/14035822 * https://doi.org/10.5281/zenodo.14035822, 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.",2024-11-04,,,"Engel, Judith * Helling, Patrick * Herrenbrück, Robert * Lemaire, Marina * Mehrtens, Hela * Schmidt, Marcus * Stellmacher, Martha * Weimer, Lukas * Wiljes, Cord * Zinke, Wolf",,pdf,en +[Workshop] Research Data Management for Microscopy and BioImage Analysis,,https://zenodo.org/records/13861026 * https://doi.org/10.5281/zenodo.13861026, 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",2024-09-30,,,"Schmidt, Christian * Boissonnet, Tom * Bortolomeazzi, Michele * Krooß, Ksenia",,pdf,en +LSM example J. Dubrulle,,https://zenodo.org/records/14510432 * https://doi.org/10.5281/zenodo.14510432, CC-BY-4.0,,2024-12-17,,,Salama Lab Fred Hutchinson Cancer Center,,lsm, +Collaborative Working and Version Control with git[hub],,https://zenodo.org/records/14626054 * https://doi.org/10.5281/zenodo.14626054, 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",2024-01-10,,,Haase * Robert,,pdf * pptx,en +DataViz protocols - An introduction to data visualization protocols for wet lab scientists,Data Visualization * R,https://zenodo.org/records/7257808 * https://joachimgoedhart.github.io/DataViz-protocols/,CC-BY-NC-SA-4.0,,2024-12-10,Book,text,"Goedhart, Joachim",,zip,en +Overview of the Galaxy OMERO-suite - Upload images and metadata in OMERO using Galaxy,OMERO * Galaxy * Metadata,https://training.galaxyproject.org/training-material/topics/imaging/tutorials/omero-suite/tutorial.html,CC-BY-4.0,,2024-12-02,Tutorial,,"Massei, Riccardo * Grüning, Björn",,,en +Tracking of mitochondria and capturing mitoflashes,Bioinformatics * Bioimage Analysis,https://training.galaxyproject.org/training-material/topics/imaging/tutorials/detection-of-mitoflashes/tutorial.html#tracking-of-mitochondria-and-capturing-mitoflashes, CC-BY-4.0,,2024-11-20,Tutorial,,"Kostrykin, Leonid * Jurado, DianaChiang",,,en +Making the most of bioimaging data through interdisciplinary interactions,Bioimage Analysis * Open Science * Microscopy Image Analysis * Microscopy,https://journals.biologists.com/jcs/article/137/20/jcs262139/362478/Making-the-most-of-bioimaging-data-through, CC-BY-4.0,,2024-10-23,Article,text,"Uhlmann, Virginie * Hartley, Matthew * Moore, Josh * Weisbart, Erin * Zaritsky, Assaf",,,en +Introduction to Research Data Management and Open Research,Research Data Management * Open Science,https://zenodo.org/records/4778265,CC-BY-4.0,Introduction to RDM primarily for researchers. Can be seen as primer to all other materials in this catalogue.,2024-05-17,Presentation,presentation,Shanmugasundaram,,pdf,en +Statistical Rethinking,Statistics,https://github.com/rmcelreath/stat_rethinking_2024,CC0-1.0 license,"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.",2024-03-01,,,"McElreath, Richard",,,en +Modular training resources for bioimage analysis,,https://zenodo.org/records/14264885 * https://doi.org/10.5281/zenodo.14264885, CC-BY-4.0,Resources for teaching/preparing to teach bioimage analysis,2024-12-03,,,"Tischer, Christian * Politi, Antonio * Buchholz, Tim-Oliver * Fazeli, Elnaz * Gritti, Nicola * Halavatyi, Aliaksandr * Gonzalez Tirado, Sebastian * Hennies, Julian * Hodges, Toby * Khan, Arif * Kutra, Dominik * Marcotti, Stefania * Oezdemir, Bugra * Schneider, Felix * Schorb, Martin * Stokkermans, Anniek * Sun, Yi * Vakili, Nima",,zip,en +scanpy-tutorials,single-cell analysis * bioimage analysis,https://github.com/scverse/scanpy-tutorials,bsd-3,Scanpy Tutorials.,2018-12-16T03:42:46+00:00,,,"Wolf, Alex * pre-commit-ci[bot] * A., Philipp * Virshup, Isaac * Gold, Ilan * Palla, Giovanni * Ramirez, Fidel * Eraslan, Gökçen * Rybakov, Sergei * (Abe), Abolfazl * Gayoso, Adam * Palli, Dinesh * Sturm, Gregor * Lause, Jan * Hrovatin, Karin * Polanski, Krzysztof * RaphaelBuzzi * Zheng, Yimin * Miao, Yishen * evanbiederstedt",,,en +[CIDAS] Scalable strategies for a next-generation of FAIR bioimaging,,https://zenodo.org/records/14716546 * https://doi.org/10.5281/zenodo.14716546, 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",2025-01-23,,,"Moore, Josh",,pdf,en +[CMCB] Scalable strategies for a next-generation of FAIR bioimaging,,https://zenodo.org/records/14650434 * https://doi.org/10.5281/zenodo.14650434, 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 + ",2025-01-16,,,"Moore, Josh",,pdf,en +Optimized cranial window implantation for subcellular and functional imaging in vivo,,https://zenodo.org/records/14641777 * https://doi.org/10.5281/zenodo.14641777, CC-BY-4.0,Intravital workshop 15/11/2024,2025-01-13,,,"Vermaercke, Ben",,pptx,en +"Andor Dragonfly confocal image of BPAE cells stained for actin, IMS file format",,https://zenodo.org/records/14675120 * https://doi.org/10.5281/zenodo.14675120,cc-zero,,2025-01-16,,,"West-Foyle, Hoku",,ims,en diff --git a/scripts/Export_to_DALIA.nbconvert.ipynb b/scripts/Export_to_DALIA.nbconvert.ipynb new file mode 100644 index 00000000..c8f68735 --- /dev/null +++ b/scripts/Export_to_DALIA.nbconvert.ipynb @@ -0,0 +1,2400 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "7a19e5fa-6f3a-4a8a-9244-6bf9fdebad76", + "metadata": {}, + "source": [ + "### Test Conversion of yml to DALIA format" + ] + }, + { + "cell_type": "markdown", + "id": "2e055672-a937-4e46-926e-fdf6c527d628", + "metadata": {}, + "source": [ + "#### Load the Yml as a pandas DF" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "7396751e-9b56-4bf6-bc35-e6e38f6c108c", + "metadata": { + "execution": { + "iopub.execute_input": "2025-01-29T12:33:57.827529Z", + "iopub.status.busy": "2025-01-29T12:33:57.827136Z", + "iopub.status.idle": "2025-01-29T12:33:58.763316Z", + "shell.execute_reply": "2025-01-29T12:33:58.762783Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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1[Marcelo Zoccoler]Running Deep-Learning Scripts in the BiA-PoL O...[Python, Artificial Intelligence, Bioimage Ana...[Blog]https://biapol.github.io/blog/marcelo_zoccoler...CC-BY-4.0NaNNaNNaNNaNNaNNaNNaNNaN
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4[Jennifer Waters]Promoting Data Management at the Nikon Imaging...[Research Data Management][Blog]https://datamanagement.hms.harvard.edu/news/pr...NaNNaNNaNNaNNaNNaNNaNNaNNaN
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" + ], + "text/plain": [ + " authors name \\\n", + "394 Marvin Albert Virtual-I2K-2024-multiview-stitcher \n", + "397 Isra Mekki Prompt-Engineering-LLMs-Course \n", + "\n", + " tags \\\n", + "394 [Big Data, Bioimageanalysis] \n", + "397 [Llms, Prompt Engineering, Code Generation] \n", + "\n", + " type \\\n", + "394 [Github Repository, Tutorial] \n", + "397 [Github Repository, Tutorial] \n", + "\n", + " url license \\\n", + "394 [https://github.com/m-albert/Virtual-I2K-2024-... BSD-3-CLAUSE \n", + "397 https://github.com/HelmholtzAI-Consultants-Mun... MIT \n", + "\n", + " event_date event_location \\\n", + "394 NaN NaN \n", + "397 NaN NaN \n", + "\n", + " description num_downloads \\\n", + "394 Repository accompanying the multiview-stitcher... NaN \n", + "397 NaN \n", + "\n", + " publication_date fingerprint author submission_date \n", + "394 2024-10-30T07:38:11+00:00 NaN Marvin Albert NaN \n", + "397 2024-09-11T07:45:30+00:00 NaN Isra Mekki NaN " + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Iterate over rows to change the information to the authors column\n", + "for index, entry in df[df['author'].notna()].iterrows():\n", + " df.loc[index, 'authors'] = entry['author']\n", + " \n", + "df[df['author'].notna()]" + ] + }, + { + "cell_type": "markdown", + "id": "51bdf851-201b-4c7d-8f9e-49ba5fb00eac", + "metadata": {}, + "source": [ + "#### 2. Exclude entries without mandatory attributes (License, Authors, Title, Link)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "11c39326-61e1-422d-99df-240f4b9b5c86", + "metadata": { + "execution": { + "iopub.execute_input": "2025-01-29T12:33:58.792045Z", + "iopub.status.busy": "2025-01-29T12:33:58.791719Z", + "iopub.status.idle": "2025-01-29T12:33:58.806728Z", + "shell.execute_reply": "2025-01-29T12:33:58.806258Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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authorsnametagstypeurllicenseevent_dateevent_locationdescriptionnum_downloadspublication_datefingerprintauthorsubmission_date
1[Marcelo Zoccoler]Running Deep-Learning Scripts in the BiA-PoL O...[Python, Artificial Intelligence, Bioimage Ana...[Blog]https://biapol.github.io/blog/marcelo_zoccoler...CC-BY-4.0NaNNaNNaNNaNNaNNaNNaNNaN
2[Robert Haase]Browsing the Open Microscopy Image Data Resour...[OMERO, Python][Blog]https://biapol.github.io/blog/robert_haase/bro...CC-BY-4.0NaNNaNNaNNaNNaNNaNNaNNaN
3[Mara Lampert]Getting started with Mambaforge and Python[Python, Conda, Mamba][Blog]https://biapol.github.io/blog/mara_lampert/get...CC-BY-4.0NaNNaNNaNNaNNaNNaNNaNNaN
9[Robert Haase]Managing Scientific Python environments using ...[Python, Conda, Mamba][Blog]https://focalplane.biologists.com/2022/12/08/m...CC-BY-4.0NaNNaNNaNNaNNaNNaNNaNNaN
29[Robert Haase et al.]BioImage Analysis Notebooks[Python, Bioimage Analysis][Book, Notebook]https://haesleinhuepf.github.io/BioImageAnalys...[CC-BY-4.0, BSD-3-CLAUSE]NaNNaNNaNNaNNaNNaNNaNNaN
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" + ], + "text/plain": [ + " authors name \\\n", + "1 [Marcelo Zoccoler] Running Deep-Learning Scripts in the BiA-PoL O... \n", + "2 [Robert Haase] Browsing the Open Microscopy Image Data Resour... \n", + "3 [Mara Lampert] Getting started with Mambaforge and Python \n", + "9 [Robert Haase] Managing Scientific Python environments using ... \n", + "29 [Robert Haase et al.] BioImage Analysis Notebooks \n", + "\n", + " tags type \\\n", + "1 [Python, Artificial Intelligence, Bioimage Ana... [Blog] \n", + "2 [OMERO, Python] [Blog] \n", + "3 [Python, Conda, Mamba] [Blog] \n", + "9 [Python, Conda, Mamba] [Blog] \n", + "29 [Python, Bioimage Analysis] [Book, Notebook] \n", + "\n", + " url \\\n", + "1 https://biapol.github.io/blog/marcelo_zoccoler... \n", + "2 https://biapol.github.io/blog/robert_haase/bro... \n", + "3 https://biapol.github.io/blog/mara_lampert/get... \n", + "9 https://focalplane.biologists.com/2022/12/08/m... \n", + "29 https://haesleinhuepf.github.io/BioImageAnalys... \n", + "\n", + " license event_date event_location description \\\n", + "1 CC-BY-4.0 NaN NaN NaN \n", + "2 CC-BY-4.0 NaN NaN NaN \n", + "3 CC-BY-4.0 NaN NaN NaN \n", + "9 CC-BY-4.0 NaN NaN NaN \n", + "29 [CC-BY-4.0, BSD-3-CLAUSE] NaN NaN NaN \n", + "\n", + " num_downloads publication_date fingerprint author submission_date \n", + "1 NaN NaN NaN NaN NaN \n", + "2 NaN NaN NaN NaN NaN \n", + "3 NaN NaN NaN NaN NaN \n", + "9 NaN NaN NaN NaN NaN \n", + "29 NaN NaN NaN NaN NaN " + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = df[~df['license'].str.lower().isin(['unknown']) & df['license'].notna() & df['authors'].notna() & df['name'].notna()& df['url'].notna()]\n", + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "5e708904-0161-4fb6-8bf8-c2f6dc3dbbea", + "metadata": { + "execution": { + "iopub.execute_input": "2025-01-29T12:33:58.808471Z", + "iopub.status.busy": "2025-01-29T12:33:58.808129Z", + "iopub.status.idle": "2025-01-29T12:33:58.811379Z", + "shell.execute_reply": "2025-01-29T12:33:58.810845Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total number of entries found: 546\n", + "Number of entries found with all mandatory entries: 338\n" + ] + } + ], + "source": [ + "print(f'Total number of entries found: {len(df)}')\n", + "print(f'Number of entries found with all mandatory entries: {len(data)}')" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "8ce34a4c-f14f-40b0-8254-a4234d1f9d23", + "metadata": { + "execution": { + "iopub.execute_input": "2025-01-29T12:33:58.813033Z", + "iopub.status.busy": "2025-01-29T12:33:58.812705Z", + "iopub.status.idle": "2025-01-29T12:33:58.824446Z", + "shell.execute_reply": "2025-01-29T12:33:58.823915Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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authorsnametagstypeurllicenseevent_dateevent_locationdescriptionnum_downloadspublication_datefingerprintauthorsubmission_date
1[Marcelo Zoccoler]Running Deep-Learning Scripts in the BiA-PoL O...[Python, Artificial Intelligence, Bioimage Ana...[Blog]https://biapol.github.io/blog/marcelo_zoccoler...CC-BY-4.0NaNNaNNaNNaNNaNNaNNaNNaN
2[Robert Haase]Browsing the Open Microscopy Image Data Resour...[OMERO, Python][Blog]https://biapol.github.io/blog/robert_haase/bro...CC-BY-4.0NaNNaNNaNNaNNaNNaNNaNNaN
3[Mara Lampert]Getting started with Mambaforge and Python[Python, Conda, Mamba][Blog]https://biapol.github.io/blog/mara_lampert/get...CC-BY-4.0NaNNaNNaNNaNNaNNaNNaNNaN
9[Robert Haase]Managing Scientific Python environments using ...[Python, Conda, Mamba][Blog]https://focalplane.biologists.com/2022/12/08/m...CC-BY-4.0NaNNaNNaNNaNNaNNaNNaNNaN
29[Robert Haase et al.]BioImage Analysis Notebooks[Python, Bioimage Analysis][Book, Notebook]https://haesleinhuepf.github.io/BioImageAnalys...[CC-BY-4.0, BSD-3-CLAUSE]NaNNaNNaNNaNNaNNaNNaNNaN
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" + ], + "text/plain": [ + " authors name \\\n", + "1 [Marcelo Zoccoler] Running Deep-Learning Scripts in the BiA-PoL O... \n", + "2 [Robert Haase] Browsing the Open Microscopy Image Data Resour... \n", + "3 [Mara Lampert] Getting started with Mambaforge and Python \n", + "9 [Robert Haase] Managing Scientific Python environments using ... \n", + "29 [Robert Haase et al.] BioImage Analysis Notebooks \n", + "\n", + " tags type \\\n", + "1 [Python, Artificial Intelligence, Bioimage Ana... [Blog] \n", + "2 [OMERO, Python] [Blog] \n", + "3 [Python, Conda, Mamba] [Blog] \n", + "9 [Python, Conda, Mamba] [Blog] \n", + "29 [Python, Bioimage Analysis] [Book, Notebook] \n", + "\n", + " url \\\n", + "1 https://biapol.github.io/blog/marcelo_zoccoler... \n", + "2 https://biapol.github.io/blog/robert_haase/bro... \n", + "3 https://biapol.github.io/blog/mara_lampert/get... \n", + "9 https://focalplane.biologists.com/2022/12/08/m... \n", + "29 https://haesleinhuepf.github.io/BioImageAnalys... \n", + "\n", + " license event_date event_location description \\\n", + "1 CC-BY-4.0 NaN NaN NaN \n", + "2 CC-BY-4.0 NaN NaN NaN \n", + "3 CC-BY-4.0 NaN NaN NaN \n", + "9 CC-BY-4.0 NaN NaN NaN \n", + "29 [CC-BY-4.0, BSD-3-CLAUSE] NaN NaN NaN \n", + "\n", + " num_downloads publication_date fingerprint author submission_date \n", + "1 NaN NaN NaN NaN NaN \n", + "2 NaN NaN NaN NaN NaN \n", + "3 NaN NaN NaN NaN NaN \n", + "9 NaN NaN NaN NaN NaN \n", + "29 NaN NaN NaN NaN NaN " + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.head()" + ] + }, + { + "cell_type": "markdown", + "id": "1b18efe8-8833-478a-9971-8ec727580fa1", + "metadata": {}, + "source": [ + "#### 3. Change the format of the **Tags** and **License** columns to fit the DALIA format" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "5318cd0d-7b81-47df-bab6-e64a8afbf9a2", + "metadata": { + "execution": { + "iopub.execute_input": "2025-01-29T12:33:58.826290Z", + "iopub.status.busy": "2025-01-29T12:33:58.825890Z", + "iopub.status.idle": "2025-01-29T12:33:58.838539Z", + "shell.execute_reply": "2025-01-29T12:33:58.837987Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_2663/210055857.py:1: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data[\"tags\"] = data[\"tags\"].apply(lambda x: ' * '.join(x) if isinstance(x, list) else x) #Tags\n", + "/tmp/ipykernel_2663/210055857.py:2: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data[\"license\"] = data[\"license\"].apply(lambda x: ' * '.join(x) if isinstance(x, list) else x) #License\n" + ] + }, + { + "data": { + "text/html": [ + "
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authorsnametagstypeurllicenseevent_dateevent_locationdescriptionnum_downloadspublication_datefingerprintauthorsubmission_date
1[Marcelo Zoccoler]Running Deep-Learning Scripts in the BiA-PoL O...Python * Artificial Intelligence * Bioimage An...[Blog]https://biapol.github.io/blog/marcelo_zoccoler...CC-BY-4.0NaNNaNNaNNaNNaNNaNNaNNaN
2[Robert Haase]Browsing the Open Microscopy Image Data Resour...OMERO * Python[Blog]https://biapol.github.io/blog/robert_haase/bro...CC-BY-4.0NaNNaNNaNNaNNaNNaNNaNNaN
3[Mara Lampert]Getting started with Mambaforge and PythonPython * Conda * Mamba[Blog]https://biapol.github.io/blog/mara_lampert/get...CC-BY-4.0NaNNaNNaNNaNNaNNaNNaNNaN
9[Robert Haase]Managing Scientific Python environments using ...Python * Conda * Mamba[Blog]https://focalplane.biologists.com/2022/12/08/m...CC-BY-4.0NaNNaNNaNNaNNaNNaNNaNNaN
29[Robert Haase et al.]BioImage Analysis NotebooksPython * Bioimage Analysis[Book, Notebook]https://haesleinhuepf.github.io/BioImageAnalys...CC-BY-4.0 * BSD-3-CLAUSENaNNaNNaNNaNNaNNaNNaNNaN
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" + ], + "text/plain": [ + " authors name \\\n", + "1 [Marcelo Zoccoler] Running Deep-Learning Scripts in the BiA-PoL O... \n", + "2 [Robert Haase] Browsing the Open Microscopy Image Data Resour... \n", + "3 [Mara Lampert] Getting started with Mambaforge and Python \n", + "9 [Robert Haase] Managing Scientific Python environments using ... \n", + "29 [Robert Haase et al.] BioImage Analysis Notebooks \n", + "\n", + " tags type \\\n", + "1 Python * Artificial Intelligence * Bioimage An... [Blog] \n", + "2 OMERO * Python [Blog] \n", + "3 Python * Conda * Mamba [Blog] \n", + "9 Python * Conda * Mamba [Blog] \n", + "29 Python * Bioimage Analysis [Book, Notebook] \n", + "\n", + " url \\\n", + "1 https://biapol.github.io/blog/marcelo_zoccoler... \n", + "2 https://biapol.github.io/blog/robert_haase/bro... \n", + "3 https://biapol.github.io/blog/mara_lampert/get... \n", + "9 https://focalplane.biologists.com/2022/12/08/m... \n", + "29 https://haesleinhuepf.github.io/BioImageAnalys... \n", + "\n", + " license event_date event_location description \\\n", + "1 CC-BY-4.0 NaN NaN NaN \n", + "2 CC-BY-4.0 NaN NaN NaN \n", + "3 CC-BY-4.0 NaN NaN NaN \n", + "9 CC-BY-4.0 NaN NaN NaN \n", + "29 CC-BY-4.0 * BSD-3-CLAUSE NaN NaN NaN \n", + "\n", + " num_downloads publication_date fingerprint author submission_date \n", + "1 NaN NaN NaN NaN NaN \n", + "2 NaN NaN NaN NaN NaN \n", + "3 NaN NaN NaN NaN NaN \n", + "9 NaN NaN NaN NaN NaN \n", + "29 NaN NaN NaN NaN NaN " + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data[\"tags\"] = data[\"tags\"].apply(lambda x: ' * '.join(x) if isinstance(x, list) else x) #Tags\n", + "data[\"license\"] = data[\"license\"].apply(lambda x: ' * '.join(x) if isinstance(x, list) else x) #License\n", + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "ac322332-6c61-4764-b8c2-760c33518429", + "metadata": { + "execution": { + "iopub.execute_input": "2025-01-29T12:33:58.840535Z", + "iopub.status.busy": "2025-01-29T12:33:58.840107Z", + "iopub.status.idle": "2025-01-29T12:33:58.846010Z", + "shell.execute_reply": "2025-01-29T12:33:58.845581Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_2663/2387137408.py:21: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data[\"license\"] = data[\"license\"].replace(license_mapping)\n" + ] + } + ], + "source": [ + "# Map the License Entries to valid input\n", + "license_mapping = {\n", + " 'APACHE-2.0 LICENSE' : 'Apache-2.0',\n", + " 'CC0 1.0 UNIVERSAL' : 'CC0-1.0',\n", + " 'CC-BY-4.0 * BSD-3-CLAUSE' : 'CC-BY-4.0 * BSD-3-Clause',\n", + " 'CC0 (MOSTLY, BUT CAN DIFFER DEPENDING ON RESOURCE)' : 'CC0-1.0',\n", + " 'CCY-BY-SA-4.0' : 'CC-BY-SA-4.0',\n", + " 'YOUTTUBE STANDARD LICENSE' : 'YOUTUBE STANDARD LICENSE',\n", + " 'CC-BY-NC-SA' : 'CC-BY-NC-SA-4.0',\n", + " 'BSD3-CLAUSE' : 'BSD-3-Clause',\n", + " 'CC-ZERO' : 'CC0-1.0',\n", + " 'BSD 3-Clause \"New\" or \"Revised\" License' : 'BSD-3-Clause',\n", + " 'cc-by-4.0' : ' CC-BY-4.0',\n", + " 'Creative Commons Attribution Share Alike 4.0 International' : 'CC-BY-SA-4.0',\n", + " 'GNU General Public License v3.0' : 'GPL-3.0-only',\n", + " 'CC BY-NC-SA 4.0' : 'CC-BY-NC-SA-4.0',\n", + " 'BSD-3-CLAUSE' : 'BSD-3-Clause',\n", + " 'BSD-2-CLAUSE' : 'BSD-2-Clause',\n", + " 'APACHE-2.0' : 'Apache-2.0'\n", + "}\n", + "data[\"license\"] = data[\"license\"].replace(license_mapping)" + ] + }, + { + "cell_type": "markdown", + "id": "03fbeeb5-67d4-403c-a780-8757f738b9bb", + "metadata": {}, + "source": [ + "#### 4. Morph the **Type** Column into the **LearningResourceType** and **MediaType** Column" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "8a104889-190a-4504-af64-c5a019392ad3", + "metadata": { + "execution": { + "iopub.execute_input": "2025-01-29T12:33:58.847910Z", + "iopub.status.busy": "2025-01-29T12:33:58.847576Z", + "iopub.status.idle": "2025-01-29T12:33:58.851099Z", + "shell.execute_reply": "2025-01-29T12:33:58.850687Z" + } + }, + "outputs": [], + "source": [ + "# Create Mapping for the Type Column:\n", + "type_to_learning_resource = {\n", + " \"Application\": \"Software Application\",\n", + " \"Big Data\": \"Data\",\n", + " \"Bioimage Analysis\": \"Other\",\n", + " \"Blog\": \"Web Page\",\n", + " \"Blog Post\": \"Text\",\n", + " \"Book\": \"Book\",\n", + " \"Book Chapter\": \"Book\",\n", + " \"Code\": None,\n", + " \"Collection\": \"Other\",\n", + " \"Conference Abstract\": \"Text\",\n", + " \"Data\": \"Data\",\n", + " \"Document\": \"Text\",\n", + " \"Documentation\": \"Text\",\n", + " \"Event\": \"Other\",\n", + " \"Forum Post\": \"Text\",\n", + " \"Github Repository\": \"Other\",\n", + " \"Jupyter Book\": \"Code Notebook\",\n", + " \"Notebook\": \"Code Notebook\",\n", + " \"Online Course\": \"Course\",\n", + " \"Online Tutorial\": \"Tutorial\",\n", + " \"Open Source Software\": \"Software Application\",\n", + " \"Poster\": \"Poster\",\n", + " \"Practicals\": \"Course\",\n", + " \"Preprint\": \"Text\",\n", + " \"Presentation\": \"Presentation\",\n", + " \"Publication\": \"Article\",\n", + " \"Python\": None,\n", + " \"Report\": \"Report\",\n", + " \"Slide\": \"Presentation\",\n", + " \"Slides\": \"Presentation\",\n", + " \"Tutorial\": \"Tutorial\",\n", + " \"Video\": None,\n", + " \"Videos\": None,\n", + " \"Website\": \"Web Page\",\n", + " \"Workshop\": \"Course\",\n", + " \"Youtube Channel\": \"Other\"\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "cd6e0ac8-2382-4a26-9e77-ee739081396f", + "metadata": { + "execution": { + "iopub.execute_input": "2025-01-29T12:33:58.852970Z", + "iopub.status.busy": "2025-01-29T12:33:58.852638Z", + "iopub.status.idle": "2025-01-29T12:33:58.856089Z", + "shell.execute_reply": "2025-01-29T12:33:58.855672Z" + } + }, + "outputs": [], + "source": [ + "type_to_media_type = {\n", + " \"Application\": None,\n", + " \"Big Data\": None,\n", + " \"Bioimage Analysis\": None,\n", + " \"Blog\": \"text\",\n", + " \"Blog Post\": \"text\",\n", + " \"Book\": \"text\",\n", + " \"Book Chapter\": \"text\",\n", + " \"Code\": \"code\",\n", + " \"Collection\": None,\n", + " \"Conference Abstract\": \"text\",\n", + " \"Data\": None,\n", + " \"Document\": \"text\",\n", + " \"Documentation\": \"text\",\n", + " \"Event\": None,\n", + " \"Forum Post\": \"text\",\n", + " \"Github Repository\": None,\n", + " \"Jupyter Book\": \"code\",\n", + " \"Notebook\": \"code\",\n", + " \"Online Course\": None,\n", + " \"Online Tutorial\": None,\n", + " \"Open Source Software\": None,\n", + " \"Poster\": None,\n", + " \"Practicals\": None,\n", + " \"Preprint\": \"text\",\n", + " \"Presentation\": \"presentation\",\n", + " \"Publication\": \"text\",\n", + " \"Python\": None,\n", + " \"Report\": \"text\",\n", + " \"Slide\": \"presentation\",\n", + " \"Slides\": \"presentation\",\n", + " \"Tutorial\": None,\n", + " \"Video\": \"video\",\n", + " \"Videos\": \"video\",\n", + " \"Website\": None,\n", + " \"Workshop\": None,\n", + " \"Youtube Channel\": \"video\"\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "c32c15e5-2d12-4051-b238-44a94afcc5d1", + "metadata": { + "execution": { + "iopub.execute_input": "2025-01-29T12:33:58.857844Z", + "iopub.status.busy": "2025-01-29T12:33:58.857507Z", + "iopub.status.idle": "2025-01-29T12:33:58.872766Z", + "shell.execute_reply": "2025-01-29T12:33:58.872269Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_2663/3151956629.py:30: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data[\"LearningResourceType\"] = data[\"type\"].apply(map_learning_resource)\n", + "/tmp/ipykernel_2663/3151956629.py:31: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data[\"MediaType\"] = data[\"type\"].apply(map_media_type)\n" + ] + }, + { + "data": { + "text/html": [ + "
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authorsnametagstypeurllicenseevent_dateevent_locationdescriptionnum_downloadspublication_datefingerprintauthorsubmission_dateLearningResourceTypeMediaType
1[Marcelo Zoccoler]Running Deep-Learning Scripts in the BiA-PoL O...Python * Artificial Intelligence * Bioimage An...[Blog]https://biapol.github.io/blog/marcelo_zoccoler...CC-BY-4.0NaNNaNNaNNaNNaNNaNNaNNaNWeb Pagetext
2[Robert Haase]Browsing the Open Microscopy Image Data Resour...OMERO * Python[Blog]https://biapol.github.io/blog/robert_haase/bro...CC-BY-4.0NaNNaNNaNNaNNaNNaNNaNNaNWeb Pagetext
3[Mara Lampert]Getting started with Mambaforge and PythonPython * Conda * Mamba[Blog]https://biapol.github.io/blog/mara_lampert/get...CC-BY-4.0NaNNaNNaNNaNNaNNaNNaNNaNWeb Pagetext
9[Robert Haase]Managing Scientific Python environments using ...Python * Conda * Mamba[Blog]https://focalplane.biologists.com/2022/12/08/m...CC-BY-4.0NaNNaNNaNNaNNaNNaNNaNNaNWeb Pagetext
29[Robert Haase et al.]BioImage Analysis NotebooksPython * Bioimage Analysis[Book, Notebook]https://haesleinhuepf.github.io/BioImageAnalys...CC-BY-4.0 * BSD-3-ClauseNaNNaNNaNNaNNaNNaNNaNNaNBook * Code Notebooktext * code
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" + ], + "text/plain": [ + " authors name \\\n", + "1 [Marcelo Zoccoler] Running Deep-Learning Scripts in the BiA-PoL O... \n", + "2 [Robert Haase] Browsing the Open Microscopy Image Data Resour... \n", + "3 [Mara Lampert] Getting started with Mambaforge and Python \n", + "9 [Robert Haase] Managing Scientific Python environments using ... \n", + "29 [Robert Haase et al.] BioImage Analysis Notebooks \n", + "\n", + " tags type \\\n", + "1 Python * Artificial Intelligence * Bioimage An... [Blog] \n", + "2 OMERO * Python [Blog] \n", + "3 Python * Conda * Mamba [Blog] \n", + "9 Python * Conda * Mamba [Blog] \n", + "29 Python * Bioimage Analysis [Book, Notebook] \n", + "\n", + " url \\\n", + "1 https://biapol.github.io/blog/marcelo_zoccoler... \n", + "2 https://biapol.github.io/blog/robert_haase/bro... \n", + "3 https://biapol.github.io/blog/mara_lampert/get... \n", + "9 https://focalplane.biologists.com/2022/12/08/m... \n", + "29 https://haesleinhuepf.github.io/BioImageAnalys... \n", + "\n", + " license event_date event_location description \\\n", + "1 CC-BY-4.0 NaN NaN NaN \n", + "2 CC-BY-4.0 NaN NaN NaN \n", + "3 CC-BY-4.0 NaN NaN NaN \n", + "9 CC-BY-4.0 NaN NaN NaN \n", + "29 CC-BY-4.0 * BSD-3-Clause NaN NaN NaN \n", + "\n", + " num_downloads publication_date fingerprint author submission_date \\\n", + "1 NaN NaN NaN NaN NaN \n", + "2 NaN NaN NaN NaN NaN \n", + "3 NaN NaN NaN NaN NaN \n", + "9 NaN NaN NaN NaN NaN \n", + "29 NaN NaN NaN NaN NaN \n", + "\n", + " LearningResourceType MediaType \n", + "1 Web Page text \n", + "2 Web Page text \n", + "3 Web Page text \n", + "9 Web Page text \n", + "29 Book * Code Notebook text * code " + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def map_learning_resource(entry):\n", + " # Skip empty or NaN rows\n", + " if entry is None or (isinstance(entry, float) and pd.isna(entry)):\n", + " return \"\"\n", + " # Use a set to avoid duplicates\n", + " matches = set()\n", + " if isinstance(entry, list):\n", + " for item in entry:\n", + " if item in type_to_learning_resource:\n", + " matches.add(type_to_learning_resource[item])\n", + " elif entry in type_to_learning_resource:\n", + " matches.add(type_to_learning_resource[entry])\n", + " return \" * \".join([m for m in matches if m is not None])\n", + "\n", + "def map_media_type(entry):\n", + " # Skip empty or NaN rows\n", + " if entry is None or (isinstance(entry, float) and pd.isna(entry)):\n", + " return \"\"\n", + " # Use a set to avoid duplicates\n", + " matches = set()\n", + " if isinstance(entry, list):\n", + " for item in entry:\n", + " if item in type_to_media_type:\n", + " matches.add(type_to_media_type[item])\n", + " elif entry in type_to_media_type:\n", + " matches.add(type_to_media_type[entry])\n", + " return \" * \".join([m for m in matches if m is not None])\n", + "\n", + "# Apply the mapping functions\n", + "data[\"LearningResourceType\"] = data[\"type\"].apply(map_learning_resource)\n", + "data[\"MediaType\"] = data[\"type\"].apply(map_media_type)\n", + "\n", + "data.head()" + ] + }, + { + "cell_type": "markdown", + "id": "19f96ee9-e203-4b19-82b2-96a0e4088383", + "metadata": {}, + "source": [ + "#### 5. Change the author names to fit the DALIA format (for persons: surname, prename and for organizations: organization-name)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "827776ce-3be9-4b28-b664-687c7d4fc4ab", + "metadata": { + "execution": { + "iopub.execute_input": "2025-01-29T12:33:58.874569Z", + "iopub.status.busy": "2025-01-29T12:33:58.874216Z", + "iopub.status.idle": "2025-01-29T12:33:58.890853Z", + "shell.execute_reply": "2025-01-29T12:33:58.890405Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_2663/970863209.py:37: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data[\"Authors\"] = data[\"authors\"].apply(normalize_author_format)\n" + ] + }, + { + "data": { + "text/html": [ + "
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authorsnametagstypeurllicenseevent_dateevent_locationdescriptionnum_downloadspublication_datefingerprintauthorsubmission_dateLearningResourceTypeMediaTypeAuthors
1[Marcelo Zoccoler]Running Deep-Learning Scripts in the BiA-PoL O...Python * Artificial Intelligence * Bioimage An...[Blog]https://biapol.github.io/blog/marcelo_zoccoler...CC-BY-4.0NaNNaNNaNNaNNaNNaNNaNNaNWeb PagetextZoccoler, Marcelo
2[Robert Haase]Browsing the Open Microscopy Image Data Resour...OMERO * Python[Blog]https://biapol.github.io/blog/robert_haase/bro...CC-BY-4.0NaNNaNNaNNaNNaNNaNNaNNaNWeb PagetextHaase, Robert
3[Mara Lampert]Getting started with Mambaforge and PythonPython * Conda * Mamba[Blog]https://biapol.github.io/blog/mara_lampert/get...CC-BY-4.0NaNNaNNaNNaNNaNNaNNaNNaNWeb PagetextLampert, Mara
9[Robert Haase]Managing Scientific Python environments using ...Python * Conda * Mamba[Blog]https://focalplane.biologists.com/2022/12/08/m...CC-BY-4.0NaNNaNNaNNaNNaNNaNNaNNaNWeb PagetextHaase, Robert
29[Robert Haase et al.]BioImage Analysis NotebooksPython * Bioimage Analysis[Book, Notebook]https://haesleinhuepf.github.io/BioImageAnalys...CC-BY-4.0 * BSD-3-ClauseNaNNaNNaNNaNNaNNaNNaNNaNBook * Code Notebooktext * codeRobert Haase et al.
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" + ], + "text/plain": [ + " authors name \\\n", + "1 [Marcelo Zoccoler] Running Deep-Learning Scripts in the BiA-PoL O... \n", + "2 [Robert Haase] Browsing the Open Microscopy Image Data Resour... \n", + "3 [Mara Lampert] Getting started with Mambaforge and Python \n", + "9 [Robert Haase] Managing Scientific Python environments using ... \n", + "29 [Robert Haase et al.] BioImage Analysis Notebooks \n", + "\n", + " tags type \\\n", + "1 Python * Artificial Intelligence * Bioimage An... [Blog] \n", + "2 OMERO * Python [Blog] \n", + "3 Python * Conda * Mamba [Blog] \n", + "9 Python * Conda * Mamba [Blog] \n", + "29 Python * Bioimage Analysis [Book, Notebook] \n", + "\n", + " url \\\n", + "1 https://biapol.github.io/blog/marcelo_zoccoler... \n", + "2 https://biapol.github.io/blog/robert_haase/bro... \n", + "3 https://biapol.github.io/blog/mara_lampert/get... \n", + "9 https://focalplane.biologists.com/2022/12/08/m... \n", + "29 https://haesleinhuepf.github.io/BioImageAnalys... \n", + "\n", + " license event_date event_location description \\\n", + "1 CC-BY-4.0 NaN NaN NaN \n", + "2 CC-BY-4.0 NaN NaN NaN \n", + "3 CC-BY-4.0 NaN NaN NaN \n", + "9 CC-BY-4.0 NaN NaN NaN \n", + "29 CC-BY-4.0 * BSD-3-Clause NaN NaN NaN \n", + "\n", + " num_downloads publication_date fingerprint author submission_date \\\n", + "1 NaN NaN NaN NaN NaN \n", + "2 NaN NaN NaN NaN NaN \n", + "3 NaN NaN NaN NaN NaN \n", + "9 NaN NaN NaN NaN NaN \n", + "29 NaN NaN NaN NaN NaN \n", + "\n", + " LearningResourceType MediaType Authors \n", + "1 Web Page text Zoccoler, Marcelo \n", + "2 Web Page text Haase, Robert \n", + "3 Web Page text Lampert, Mara \n", + "9 Web Page text Haase, Robert \n", + "29 Book * Code Notebook text * code Robert Haase et al. " + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "import re\n", + "\n", + "def normalize_author_format(authors):\n", + " # Helper function to reformat a single name\n", + " def reformat_name(name):\n", + " # Check if it's already in \"Surname, Prename\" format\n", + " if \",\" in name:\n", + " return name.strip()\n", + " # If in \"Prename Surname\" format, convert to \"Surname, Prename\"\n", + " parts = name.split()\n", + " et_al = ['et', 'al.']\n", + " if len(parts) == 2 and all(p not in et_al for p in parts):\n", + " return f\"{parts[1]}, {parts[0]}\"\n", + " if len(parts) == 3 and all(p not in et_al for p in parts):\n", + " return f\"{parts[2]}, {parts[0]}{parts[1]}\"\n", + " return name.strip() # Return unchanged if not a simple name format\n", + "\n", + "\n", + " # Convert single strings to lists for uniform processing\n", + " if isinstance(authors, str):\n", + " # Split on commas for inline lists like \"Prename Surname, Prename Surname\"\n", + " authors = [a.strip() for a in re.split(r\",\\s*|\\*|\\band\\b\", authors)]\n", + " elif isinstance(authors, list):\n", + " authors = [str(a).strip() for a in authors] # Ensure all elements are strings\n", + "\n", + " # Process each author entry\n", + " formatted_authors = []\n", + " for author in authors:\n", + " formatted_authors.append(reformat_name(author))\n", + "\n", + " # Join all processed names with \"*\"\n", + " return \" * \".join(formatted_authors)\n", + "\n", + "\n", + "# Apply the normalization function\n", + "data[\"Authors\"] = data[\"authors\"].apply(normalize_author_format)\n", + "\n", + "data.head()" + ] + }, + { + "cell_type": "markdown", + "id": "0983e2a5-3f93-4cfa-9f40-3f41233fe77e", + "metadata": {}, + "source": [ + "#### 6. Change to names of the columns that already fit the DALIA format to their corresponding name in DALIA" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "4213ac0c-3274-408e-a86d-bc9e61832de8", + "metadata": { + "execution": { + "iopub.execute_input": "2025-01-29T12:33:58.892652Z", + "iopub.status.busy": "2025-01-29T12:33:58.892290Z", + "iopub.status.idle": "2025-01-29T12:33:58.901982Z", + "shell.execute_reply": "2025-01-29T12:33:58.901542Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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TitleKeywordsLinkLicenseDescriptionPublicationDateLearningResourceTypeMediaTypeAuthors
1Running Deep-Learning Scripts in the BiA-PoL O...Python * Artificial Intelligence * Bioimage An...https://biapol.github.io/blog/marcelo_zoccoler...CC-BY-4.0NaNNaNWeb PagetextZoccoler, Marcelo
2Browsing the Open Microscopy Image Data Resour...OMERO * Pythonhttps://biapol.github.io/blog/robert_haase/bro...CC-BY-4.0NaNNaNWeb PagetextHaase, Robert
3Getting started with Mambaforge and PythonPython * Conda * Mambahttps://biapol.github.io/blog/mara_lampert/get...CC-BY-4.0NaNNaNWeb PagetextLampert, Mara
9Managing Scientific Python environments using ...Python * Conda * Mambahttps://focalplane.biologists.com/2022/12/08/m...CC-BY-4.0NaNNaNWeb PagetextHaase, Robert
29BioImage Analysis NotebooksPython * Bioimage Analysishttps://haesleinhuepf.github.io/BioImageAnalys...CC-BY-4.0 * BSD-3-ClauseNaNNaNBook * Code Notebooktext * codeRobert Haase et al.
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" + ], + "text/plain": [ + " Title \\\n", + "1 Running Deep-Learning Scripts in the BiA-PoL O... \n", + "2 Browsing the Open Microscopy Image Data Resour... \n", + "3 Getting started with Mambaforge and Python \n", + "9 Managing Scientific Python environments using ... \n", + "29 BioImage Analysis Notebooks \n", + "\n", + " Keywords \\\n", + "1 Python * Artificial Intelligence * Bioimage An... \n", + "2 OMERO * Python \n", + "3 Python * Conda * Mamba \n", + "9 Python * Conda * Mamba \n", + "29 Python * Bioimage Analysis \n", + "\n", + " Link \\\n", + "1 https://biapol.github.io/blog/marcelo_zoccoler... \n", + "2 https://biapol.github.io/blog/robert_haase/bro... \n", + "3 https://biapol.github.io/blog/mara_lampert/get... \n", + "9 https://focalplane.biologists.com/2022/12/08/m... \n", + "29 https://haesleinhuepf.github.io/BioImageAnalys... \n", + "\n", + " License Description PublicationDate \\\n", + "1 CC-BY-4.0 NaN NaN \n", + "2 CC-BY-4.0 NaN NaN \n", + "3 CC-BY-4.0 NaN NaN \n", + "9 CC-BY-4.0 NaN NaN \n", + "29 CC-BY-4.0 * BSD-3-Clause NaN NaN \n", + "\n", + " LearningResourceType MediaType Authors \n", + "1 Web Page text Zoccoler, Marcelo \n", + "2 Web Page text Haase, Robert \n", + "3 Web Page text Lampert, Mara \n", + "9 Web Page text Haase, Robert \n", + "29 Book * Code Notebook text * code Robert Haase et al. " + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Rename columns\n", + "data = data.rename(columns={'name': 'Title', 'license': 'License', 'url': 'Link', 'description': 'Description', 'publication_date': 'PublicationDate', 'tags': 'Keywords'})\n", + "\n", + "# Remove unwanted columns with no important data\n", + "data = data.drop(columns=['event_date', 'event_location', 'num_downloads', 'submission_date', 'fingerprint', 'author', 'type', 'authors'])\n", + "\n", + "data.head()" + ] + }, + { + "cell_type": "markdown", + "id": "69d43abb-d409-49bf-ab21-b79729441d1f", + "metadata": {}, + "source": [ + "#### 7. Introduce the **Community Column**: NFDI4BioImage if it is listed in the tags" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "5a79c41e-6037-44c2-8cdd-0988197de047", + "metadata": { + "execution": { + "iopub.execute_input": "2025-01-29T12:33:58.903763Z", + "iopub.status.busy": "2025-01-29T12:33:58.903426Z", + "iopub.status.idle": "2025-01-29T12:33:58.912877Z", + "shell.execute_reply": "2025-01-29T12:33:58.912441Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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TitleKeywordsLinkLicenseDescriptionPublicationDateLearningResourceTypeMediaTypeAuthorsCommunity
1Running Deep-Learning Scripts in the BiA-PoL O...Python * Artificial Intelligence * Bioimage An...https://biapol.github.io/blog/marcelo_zoccoler...CC-BY-4.0NaNNaNWeb PagetextZoccoler, MarceloNone
2Browsing the Open Microscopy Image Data Resour...OMERO * Pythonhttps://biapol.github.io/blog/robert_haase/bro...CC-BY-4.0NaNNaNWeb PagetextHaase, RobertNone
3Getting started with Mambaforge and PythonPython * Conda * Mambahttps://biapol.github.io/blog/mara_lampert/get...CC-BY-4.0NaNNaNWeb PagetextLampert, MaraNone
9Managing Scientific Python environments using ...Python * Conda * Mambahttps://focalplane.biologists.com/2022/12/08/m...CC-BY-4.0NaNNaNWeb PagetextHaase, RobertNone
29BioImage Analysis NotebooksPython * Bioimage Analysishttps://haesleinhuepf.github.io/BioImageAnalys...CC-BY-4.0 * BSD-3-ClauseNaNNaNBook * Code Notebooktext * codeRobert Haase et al.None
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" + ], + "text/plain": [ + " Title \\\n", + "1 Running Deep-Learning Scripts in the BiA-PoL O... \n", + "2 Browsing the Open Microscopy Image Data Resour... \n", + "3 Getting started with Mambaforge and Python \n", + "9 Managing Scientific Python environments using ... \n", + "29 BioImage Analysis Notebooks \n", + "\n", + " Keywords \\\n", + "1 Python * Artificial Intelligence * Bioimage An... \n", + "2 OMERO * Python \n", + "3 Python * Conda * Mamba \n", + "9 Python * Conda * Mamba \n", + "29 Python * Bioimage Analysis \n", + "\n", + " Link \\\n", + "1 https://biapol.github.io/blog/marcelo_zoccoler... \n", + "2 https://biapol.github.io/blog/robert_haase/bro... \n", + "3 https://biapol.github.io/blog/mara_lampert/get... \n", + "9 https://focalplane.biologists.com/2022/12/08/m... \n", + "29 https://haesleinhuepf.github.io/BioImageAnalys... \n", + "\n", + " License Description PublicationDate \\\n", + "1 CC-BY-4.0 NaN NaN \n", + "2 CC-BY-4.0 NaN NaN \n", + "3 CC-BY-4.0 NaN NaN \n", + "9 CC-BY-4.0 NaN NaN \n", + "29 CC-BY-4.0 * BSD-3-Clause NaN NaN \n", + "\n", + " LearningResourceType MediaType Authors Community \n", + "1 Web Page text Zoccoler, Marcelo None \n", + "2 Web Page text Haase, Robert None \n", + "3 Web Page text Lampert, Mara None \n", + "9 Web Page text Haase, Robert None \n", + "29 Book * Code Notebook text * code Robert Haase et al. None " + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def include_community(entry):\n", + " if isinstance(entry, list):\n", + " if any(e.lower() == 'nfdi4bioimage' for e in entry if isinstance(e, str)):\n", + " return 'NFDI4Bioimage'\n", + " elif isinstance(entry, str):\n", + " if entry.lower() == 'nfdi4bioimage':\n", + " return 'NFDI4Bioimage'\n", + " return None\n", + "\n", + "\n", + "# Apply the function\n", + "data['Community'] = data['Keywords'].apply(include_community)\n", + "data.head()" + ] + }, + { + "cell_type": "markdown", + "id": "78b5ec11-6e2a-4b6d-8ff3-faa58325b232", + "metadata": {}, + "source": [ + "### 8. Introduce the **FileFormat** Column by comparing the MediaType to a FileFormat list" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "89a3f72f-e614-4fe3-afc7-fc22345e104e", + "metadata": { + "execution": { + "iopub.execute_input": "2025-01-29T12:33:58.914631Z", + "iopub.status.busy": "2025-01-29T12:33:58.914437Z", + "iopub.status.idle": "2025-01-29T12:37:57.248568Z", + "shell.execute_reply": "2025-01-29T12:37:57.248033Z" + }, + "scrolled": true + }, + "outputs": [], + "source": [ + "import requests\n", + "import re\n", + "import time\n", + "\n", + "# Function to extract record ID from a Zenodo link\n", + "def extract_zenodo_record_id(url):\n", + " # Regex to match Zenodo record links and extract the record ID\n", + " match = re.search(r\"https://zenodo.org/records/(\\d+)\", url)\n", + " return match.group(1) if match else None\n", + "\n", + "# Function to fetch file formats from Zenodo using the record ID\n", + "def fetch_file_formats(record_id):\n", + " if not record_id:\n", + " return None\n", + " api_url = f\"https://zenodo.org/api/records/{record_id}\"\n", + " try:\n", + " time.sleep(1) # Add a 1-second delay between requests\n", + " response = requests.get(api_url)\n", + " response.raise_for_status() # Raise an error for non-2xx responses\n", + " data = response.json()\n", + " file_types = {\n", + " file[\"key\"].split(\".\")[-1].lower()\n", + " for file in data.get(\"files\", [])\n", + " if \".\" in file[\"key\"]\n", + " }\n", + " return \" * \".join(sorted(file_types)) if file_types else None\n", + " except Exception as e:\n", + " print(f\"Error fetching file formats for record ID {record_id}: {e}\")\n", + " return None\n", + "\n", + "# Function to process a single URL or a list of URLs\n", + "def process_links(link_input):\n", + " if isinstance(link_input, str):\n", + " # Single URL case\n", + " record_id = extract_zenodo_record_id(link_input)\n", + " if record_id:\n", + " return fetch_file_formats(record_id)\n", + " elif isinstance(link_input, list):\n", + " # List of URLs case\n", + " for link in link_input:\n", + " record_id = extract_zenodo_record_id(link.strip())\n", + " if record_id:\n", + " file_format = fetch_file_formats(record_id)\n", + " if file_format: # Return on first valid result\n", + " return file_format\n", + " return None # Return None if no valid formats are found\n", + "\n", + "# Process the DataFrame\n", + "data[\"FileFormat\"] = data[\"Link\"].apply(process_links)" + ] + }, + { + "cell_type": "markdown", + "id": "74d9702a-00cb-4408-8815-c26fd9a4fdee", + "metadata": {}, + "source": [ + "Additionally map the Type Column to certain File Formats, if it is not already filled from the previous step. (only works for certain MediaTypes)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "965d0a41-9762-47cb-8bac-7042d35960c8", + "metadata": { + "execution": { + "iopub.execute_input": "2025-01-29T12:37:57.250658Z", + "iopub.status.busy": "2025-01-29T12:37:57.250377Z", + "iopub.status.idle": "2025-01-29T12:37:57.256536Z", + "shell.execute_reply": "2025-01-29T12:37:57.255991Z" + } + }, + "outputs": [], + "source": [ + "def map_file_format(media_type, file_format):\n", + " # If FileFormat already has a valid entry, return it as is\n", + " if file_format is not None and file_format.strip() != \"\":\n", + " return file_format\n", + " # Map media types to specific file formats\n", + " if media_type == \"audio\":\n", + " return \".mp3\"\n", + " elif media_type == \"video\":\n", + " return \".mp4\"\n", + " else:\n", + " return \"\" # Return empty string if no mapping is needed\n", + "\n", + "# Apply the mapping function\n", + "data[\"FileFormat\"] = data.apply(\n", + " lambda row: map_file_format(row[\"MediaType\"], row[\"FileFormat\"]),\n", + " axis=1\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "461e7dc1-7572-4664-887d-ec36f4ed2656", + "metadata": {}, + "source": [ + "Now also correct the Format of the Link Column:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "0de3c9ba-a0b8-434d-bd79-896ad87cf1c1", + "metadata": { + "execution": { + "iopub.execute_input": "2025-01-29T12:37:57.258392Z", + "iopub.status.busy": "2025-01-29T12:37:57.258014Z", + "iopub.status.idle": "2025-01-29T12:37:57.260962Z", + "shell.execute_reply": "2025-01-29T12:37:57.260567Z" + } + }, + "outputs": [], + "source": [ + "# Make * Delimiter for the Links if there is more than one for some entries\n", + "data[\"Link\"] = data[\"Link\"].apply(lambda x: ' * '.join(x) if isinstance(x, list) else x) #URL" + ] + }, + { + "cell_type": "markdown", + "id": "22a0d7cc-e4d7-4c77-807c-662fb44ffbe0", + "metadata": {}, + "source": [ + "#### 9. Extract the Language of each Entry\n", + "This is done using the [xlm-roberta-base-language-detection](https://huggingface.co/papluca/xlm-roberta-base-language-detection) model via the transformers package pipeline." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "5f3abf90-990f-4ccc-8da3-05bb26e6538e", + "metadata": { + "execution": { + "iopub.execute_input": "2025-01-29T12:37:57.262649Z", + "iopub.status.busy": "2025-01-29T12:37:57.262341Z", + "iopub.status.idle": "2025-01-29T12:38:24.372719Z", + "shell.execute_reply": "2025-01-29T12:38:24.372167Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/hostedtoolcache/Python/3.13.1/x64/lib/python3.13/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Device set to use cpu\n" + ] + } + ], + "source": [ + "from transformers import pipeline\n", + "\n", + "model_ckpt = \"papluca/xlm-roberta-base-language-detection\"\n", + "pipe = pipeline(\"text-classification\", model=model_ckpt)\n", + "\n", + "def detect_language(text):\n", + " lang = pipe([text], top_k=1, truncation=True)[0][0][\"label\"]\n", + " return lang if lang in [\"en\", \"de\"] else \"\"\n", + "\n", + "data[\"Language\"] = data[\"Title\"].apply(detect_language)" + ] + }, + { + "cell_type": "markdown", + "id": "0cd61d11-5907-43ba-968c-bb851d003631", + "metadata": {}, + "source": [ + "### Export the data to a csv that now fits the DALIA Format" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "63071e24-8d4e-4885-ae78-74669bbe5557", + "metadata": { + "execution": { + "iopub.execute_input": "2025-01-29T12:38:24.375031Z", + "iopub.status.busy": "2025-01-29T12:38:24.374768Z", + "iopub.status.idle": "2025-01-29T12:38:24.385129Z", + "shell.execute_reply": "2025-01-29T12:38:24.384611Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Exported 338 rows.\n" + ] + } + ], + "source": [ + "# save selected data\n", + "data.to_csv(destination, index=False)\n", + "\n", + "num_rows = data.shape[0]\n", + "print(f\"Exported {num_rows} rows.\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.1" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 61bce8804da48897229809c39d25f91ea0d1be17 Mon Sep 17 00:00:00 2001 From: Lea Gihlein <85543649+lea-33@users.noreply.github.com> Date: Wed, 29 Jan 2025 13:45:06 +0100 Subject: [PATCH 7/8] Update update-dalia-csv.yml added --inplace for the nbconvert command to stop the script from saving a second notebook each time --- .github/workflows/update-dalia-csv.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/update-dalia-csv.yml b/.github/workflows/update-dalia-csv.yml index 84b24ada..d7abe988 100644 --- a/.github/workflows/update-dalia-csv.yml +++ b/.github/workflows/update-dalia-csv.yml @@ -29,7 +29,7 @@ jobs: # Execute the Notebook to export CSV in DALIA format - name: Execute Jupyter Notebook run: | - jupyter nbconvert --to notebook --execute scripts/Export_to_DALIA.ipynb + jupyter nbconvert --execute --inplace scripts/Export_to_DALIA.ipynb # Commit and push changes if any file was updated - name: Commit and push changes From 96bcf90d8a3e91390ab3de238b0909d645c626c5 Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" Date: Wed, 29 Jan 2025 12:52:52 +0000 Subject: [PATCH 8/8] Auto-update CSV after modifying nfdi4bioimage.yml --- docs/export/DALIA_training_materials.csv | 50 ++--- scripts/Export_to_DALIA.ipynb | 230 +++++++++++++++++++---- 2 files changed, 214 insertions(+), 66 deletions(-) diff --git a/docs/export/DALIA_training_materials.csv b/docs/export/DALIA_training_materials.csv index 2a28b4bc..cf1ed7d9 100644 --- a/docs/export/DALIA_training_materials.csv +++ b/docs/export/DALIA_training_materials.csv @@ -3,8 +3,8 @@ Running Deep-Learning Scripts in the BiA-PoL Omero Server,Python * Artificial In Browsing the Open Microscopy Image Data Resource with Python,OMERO * Python,https://biapol.github.io/blog/robert_haase/browsing_idr/readme.html,CC-BY-4.0,,,Web Page,text,"Haase, Robert",,,en Getting started with Mambaforge and Python,Python * Conda * Mamba,https://biapol.github.io/blog/mara_lampert/getting_started_with_mambaforge_and_python/readme.html,CC-BY-4.0,,,Web Page,text,"Lampert, Mara",,,en "Managing Scientific Python environments using Conda, Mamba and friends",Python * Conda * Mamba,https://focalplane.biologists.com/2022/12/08/managing-scientific-python-environments-using-conda-mamba-and-friends/,CC-BY-4.0,,,Web Page,text,"Haase, Robert",,,en -BioImage Analysis Notebooks,Python * Bioimage Analysis,https://haesleinhuepf.github.io/BioImageAnalysisNotebooks/intro.html,CC-BY-4.0 * BSD-3-Clause,,,Book * Code Notebook,text * code,Robert Haase et al.,,,en -Introduction to Bioimage Analysis,Python * Imagej * Bioimage Analysis,https://bioimagebook.github.io/index.html,CC-BY-4.0,,,Book * Code Notebook,text * code,"Bankhead, Pete",,,en +BioImage Analysis Notebooks,Python * Bioimage Analysis,https://haesleinhuepf.github.io/BioImageAnalysisNotebooks/intro.html,CC-BY-4.0 * BSD-3-Clause,,,Code Notebook * Book,code * text,Robert Haase et al.,,,en +Introduction to Bioimage Analysis,Python * Imagej * Bioimage Analysis,https://bioimagebook.github.io/index.html,CC-BY-4.0,,,Code Notebook * Book,code * text,"Bankhead, Pete",,,en Generative artificial intelligence for bio-image analysis,Python * Bioimage Analysis * Artificial Intelligence,https://f1000research.com/slides/12-971,CC-BY-4.0,,,Presentation,presentation,"Haase, Robert",,,en Train-the-Trainer Concept on Research Data Management,Research Data Management,https://zenodo.org/record/4071471 * https://doi.org/10.5281/zenodo.4071471,CC-BY-4.0,"Within the project FDMentor, a German Train-the-Trainer Programme on Research Data Management (RDM) was developed and piloted in a series of workshops. The topics cover many aspects of research data management, such as data management plans and the publication of research data, as well as didactic units on learning concepts, workshop design and a range of didactic methods. @@ -37,7 +37,7 @@ Making your package available on conda-forge,Deployment * Python,https://kevinya "I3D:bio's OMERO training material: Re-usable, adjustable, multi-purpose slides for local user training",OMERO * Research Data Management * Nfdi4Bioimage * I3Dbio,https://zenodo.org/records/8323588 * https://www.youtube.com/playlist?list=PL2k-L-zWPoR7SHjG1HhDIwLZj0MB_stlU * https://doi.org/10.5281/zenodo.8323588,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.",2023-11-13,Presentation,presentation * video,"Schmidt, Christian * Bortolomeazzi, Michele * Boissonnet, Tom * Fortmann-Grote, Carsten * Dohle, Julia * Zentis, Peter * Kandpal, Niraj * Kunis, Susanne * Zobel, Thomas * Weidtkamp-Peters, Stefanie * Ferrando-May, Elisa",,odp * pdf * pptx,en IAFIG-RMS Python for Bioimage Analysis Course,Bioimage Analysis,https://github.com/RMS-DAIM/Python-for-Bioimage-Analysis,GPL-3.0,,,Code Notebook,code,"Barbotin, Aurelien * Nelson, Chas * Waithe, Dominic * Tarkowska, Ola(Alexandra) * Kundegorski, Mikolaj * Cross, Stephen * Fallesen, Todd",,,en numpy pandas course,Python,https://github.com/guiwitz/NumpyPandas_course,BSD-3-Clause,,,Code Notebook,code,"Witz, Guillaume",,, -Python for Microscopists,Python * Bioimage Analysis,https://github.com/bnsreenu/python_for_microscopists,MIT,,,Other * Code Notebook,code,"Bhattiprolu, Sreenivas",,,en +Python for Microscopists,Python * Bioimage Analysis,https://github.com/bnsreenu/python_for_microscopists,MIT,,,Code Notebook * Other,code,"Bhattiprolu, Sreenivas",,,en Scientific Visualization: Python + Matplotlib,Python,https://github.com/rougier/scientific-visualization-book * https://inria.hal.science/hal-03427242/document,CC-BY-ND-SA-4.0,,,Book,text,"Rougier, NicolasP.",,, Teaching Bioimage Analysis with Python,Python * Bioimage Analysis,https://github.com/CamachoDejay/teaching-bioimage-analysis-python,MIT,,,Tutorial,,"Camacho, Rafael",,,en Teaching ImageJ FIJI,Fiji * Bioimage Analysis,https://github.com/CamachoDejay/Teaching-ImageJ-FIJI,MIT,,,Tutorial,,"Camacho, Rafael",,, @@ -48,13 +48,13 @@ Python BioImage Analysis Tutorial,Python * Bioimage Analysis,https://github.com/ Image Processing with Python,Python * Bioimage Analysis,https://datacarpentry.org/image-processing/ * https://github.com/datacarpentry/image-processing,CC-BY-4.0,,,Other,,"Deppen, Jacob * Meechan, Kimberly * Palmquist, David * Schiller, Ulf * Turner, Robert * Corvellec, Marianne * Hodges, Toby * et al.",,,en Deep Vision and Graphics,Python * Artificial Intelligence,https://github.com/yandexdataschool/deep_vision_and_graphics,MIT,,,Code Notebook,code,"Yurchenko, Victor * Ratnikov, Fedor * Checkalina, Viktoriia",,,en Collection of teaching material for deep learning for (biomedical) image analysis,Artificial Intelligence * Bioimage Analysis,https://github.com/constantinpape/dl-teaching-resources,MIT,,,,,"Pape, Constantin",,,en -ZeroCostDL4Mic: exploiting Google Colab to develop a free and open-source toolbox for Deep-Learning in microscopy,,https://github.com/HenriquesLab/ZeroCostDL4Mic * https://www.nature.com/articles/s41467-021-22518-0 * https://doi.org/10.1038/s41467-021-22518-0,MIT,,,Other * Code Notebook,code,"Chamier, Lucasvon * Laine, RomainF. * Jukkala, Johanna * Spahn, Christoph * Krentzel, Daniel * Nehme, Elias * Lerche, Martina * Hernández-pérez, Sara * Mattila, Pieta * Karinou, Eleni * Holden, Séamus * Solak, AhmetCan * Krull, Alexander * Buchholz, Tim-Oliver * Jones, MartinL * Royer, LoicAlain * Leterrier, Christophe * Shechtman, Yoav * Jug, Florian * Heilemann, Mike * Jacquemet, Guillaume * Henriques, Ricardo",,,en -DL4MicEverywhere,,https://github.com/HenriquesLab/DL4MicEverywhere,CC-BY-4.0,,,Other * Code Notebook,code,"Hidalgo, Iván * et al.",,,en -CellTrackColab,,https://www.biorxiv.org/content/10.1101/2023.10.20.563252v2 * https://github.com/guijacquemet/CellTracksColab,MIT,,,Other * Code Notebook,code,"Jacquemet, Guillaume",,, +ZeroCostDL4Mic: exploiting Google Colab to develop a free and open-source toolbox for Deep-Learning in microscopy,,https://github.com/HenriquesLab/ZeroCostDL4Mic * https://www.nature.com/articles/s41467-021-22518-0 * https://doi.org/10.1038/s41467-021-22518-0,MIT,,,Code Notebook * Other,code,"Chamier, Lucasvon * Laine, RomainF. * Jukkala, Johanna * Spahn, Christoph * Krentzel, Daniel * Nehme, Elias * Lerche, Martina * Hernández-pérez, Sara * Mattila, Pieta * Karinou, Eleni * Holden, Séamus * Solak, AhmetCan * Krull, Alexander * Buchholz, Tim-Oliver * Jones, MartinL * Royer, LoicAlain * Leterrier, Christophe * Shechtman, Yoav * Jug, Florian * Heilemann, Mike * Jacquemet, Guillaume * Henriques, Ricardo",,,en +DL4MicEverywhere,,https://github.com/HenriquesLab/DL4MicEverywhere,CC-BY-4.0,,,Code Notebook * Other,code,"Hidalgo, Iván * et al.",,,en +CellTrackColab,,https://www.biorxiv.org/content/10.1101/2023.10.20.563252v2 * https://github.com/guijacquemet/CellTracksColab,MIT,,,Code Notebook * Other,code,"Jacquemet, Guillaume",,, Image analysis course material,,https://github.com/tischi/image-analysis-course-material,MIT,"Training materials about image registration, big warp and elastix",,,,"Tischer, Christian",,,en Image processing for beginners,Python * Bioimage Analysis,https://github.com/guiwitz/PyImageCourse_beginner,BSD-3-Clause,,,Code Notebook,code,"Witz, Guillaume",,,en Image-based Profiling Handbook,Bioimage Analysis,https://github.com/cytomining/profiling-handbook * https://cytomining.github.io/profiling-handbook/,CC0-1.0,,,Book,text,"Cimini, Beth * Becker, Tim * Singh, Shantanu * Way, Gregory * Abbasi, Hamdah * Tromans-Coia, Callum",,,en -Methods in bioimage analysis,Bioimage Analysis,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,CC-BY-4.0,,,Tutorial * Presentation,presentation * video,"Tischer, Christian",,,en +Methods in bioimage analysis,Bioimage Analysis,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,CC-BY-4.0,,,Presentation * Tutorial,presentation * video,"Tischer, Christian",,,en ilastik: interactive machine learning for (bio)image analysis,Artificial Intelligence * Bioimage Analysis,https://zenodo.org/doi/10.5281/zenodo.4330625,CC-BY-4.0,,,Presentation,presentation,"Kreshuk, Anna * Kutra, Dominik",,, Nextflow: Scalable and reproducible scientific workflows,Workflow Engine,https://zenodo.org/records/4334697 * https://doi.org/10.5281/zenodo.4334697,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.",2020-12-17,Presentation,presentation,"Evan, Floden * Paolo, DiTommaso",,pdf,en QuPath: Open source software for analysing (awkward) images,Bioimage Analysis,https://zenodo.org/records/4328911 * https://doi.org/10.5281/zenodo.4328911,CC-BY-4.0,Slides from the CZI/EOSS online meeting in December 2020.,2020-12-16,Presentation,presentation,"Bankhead, Peter",,pdf,en @@ -62,7 +62,7 @@ Creating open computational curricula,,https://zenodo.org/records/4317149 * http Parallelization and heterogeneous computing: from pure CPU to GPU-accelerated image processing,,https://f1000research.com/slides/11-1171 * https://doi.org/10.7490/f1000research.1119154.1,CC-BY-4.0,,,Presentation,presentation,"Haase, Robert",,,en Adding a Workflow to BIAFLOWS,Neubias * Bioimage Analysis,https://github.com/RoccoDAnt/Defragmentation_TrainingSchool_EOSC-Life_2022/blob/main/Slides/Adding_a_workflow_to_BIAFLOWS.pdf,BSD-2-Clause,,,Presentation,presentation,"Tosi, Sébastien * Baecker, Volker * Pavie, Benjamin",,,en BioImage Data Analysis,Neubias * Bioimage Analysis,https://analyticalscience.wiley.com/do/10.1002/was.00050003/full/bioimagedataanalysis.pdf,ALL RIGHTS RESERVED,,,Book,text,"Miura, Kota",,,en -Open Image Data Handbook,Neubias * Research Data Management * Napari * Python * Bioimage Analysis,https://kevinyamauchi.github.io/open-image-data/intro.html,CC-BY-4.0,,,Book * Code Notebook,text * code,"Yamauchi, Kevin",,,en +Open Image Data Handbook,Neubias * Research Data Management * Napari * Python * Bioimage Analysis,https://kevinyamauchi.github.io/open-image-data/intro.html,CC-BY-4.0,,,Code Notebook * Book,code * text,"Yamauchi, Kevin",,,en "Bio-image analysis, biostatistics, programming and machine learning for computational biology",Python * Bioimage Analysis * Napari,https://github.com/BiAPoL/Bio-image_Analysis_with_Python,CC-BY-4.0,,,Code Notebook,code,"Poetsch, Anna * Dresden, Biotec * Zoccoler, MarceloLeomil * Müller, JohannesRichard * Haase, Robert",,,en Bio-Image Data Strudel for Workshop on Research Data Management in TU Dresden Core Facilities,Research Data Management * Tu Dresden * Bioimage Data * Nfdi4Bioimage,https://zenodo.org/records/10083555 * https://doi.org/10.5281/zenodo.10083555,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.  ,2023-11-08,Presentation,presentation,"Wetzker, Cornelia",,pdf * pptx,en Bio-image Analysis with the Help of Large Language Models,Large Language Models * Python,https://zenodo.org/records/10815329 * https://doi.org/10.5281/zenodo.10815329,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.",2024-03-13,Presentation,presentation,"Haase, Robert",,odp * pdf * pptx,en @@ -74,7 +74,7 @@ QI 2024 Analysis Lab Manual,Segmentation * Python,https://bethac07.github.io/qi_ My Journey Through Bioimage Analysis Teaching Methods From Classroom to Cloud,Teaching,https://zenodo.org/records/10679054 * https://doi.org/10.5281/zenodo.10679054,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. ",2024-02-19,Presentation,presentation,"Fazeli, Elnaz",,pdf,en Cultivating Open Training,Teaching,https://zenodo.org/records/10654775 * https://doi.org/10.5281/zenodo.10654775,CC-BY-4.0,"In these slides introduce current challenges and potential solutions for openly sharing training materials, softly focusing on bio-image analysis. In this field a lot of training materials circulate in private channels, but openly shared, reusable materials, according to the FAIR-principles, are still rare. Using the CC-BY license and publicly acessible repositories are proposed to fill this gap.",2024-02-14,Presentation,presentation,"Haase, Robert",,pptx,en [N4BI AHM] Welcome to BioImage Town,Research Data Management,https://zenodo.org/records/10008465 * https://doi.org/10.5281/zenodo.10008465,CC-BY-4.0,"Keynote at the NFDI4BIOIMAGE All-Hands Meeting in Düsseldorf, Germany, October 16, 2023.",2023-10-16,Presentation,presentation,"Moore, Josh",,pdf, -Elastix tutorial,Image Registration * Itk * Elastix,https://m-albert.github.io/elastix_tutorial/intro.html,BSD LICENSE,Tutorial material for teaching the basics of (itk-)elastix for image registration in microscopy images.,,Other * Code Notebook,code,"Albert, Marvin",,, +Elastix tutorial,Image Registration * Itk * Elastix,https://m-albert.github.io/elastix_tutorial/intro.html,BSD LICENSE,Tutorial material for teaching the basics of (itk-)elastix for image registration in microscopy images.,,Code Notebook * Other,code,"Albert, Marvin",,, Intro napari slides,Napari,https://thejacksonlaboratory.github.io/intro-napari-slides/#/section,MIT,Introduction to napari workshop run at JAX (Spring 2024).,,Presentation,presentation,"Sobolewski, Peter",,, NeubiasPasteur2023_AdvancedCellPose,Cellpose * Segmentation,https://github.com/gletort/NeubiasPasteur2023_AdvancedCellPose,BSD-3-Clause,Tutorial for running CellPose advanced functions,,Other,,"Letort, Gaelle",,, Microscopy data analysis: machine learning and the BioImage Archive,Microscopy Image Analysis * Python * Deep Learning,https://www.ebi.ac.uk/training/materials/microscopy-data-analysis-machine-learning-and-the-bioimage-archive-materials/,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.",,Presentation,presentation * video,"Iudin, Andrii * Foix-Romero, Anna * Kreshuk, Anna * Athar, Awais * Cimini, Beth * Kutra, Dominik * Estibalis Gomez de Mariscal * Wong, Frances * Jacquemet, Guillaume * Narayan, Kedar * Weigert, Martin * Gogoberidze, Nodar * Salih, Osman * Walczysko, Petr * Conrad, Ryan * Weyend, Simone * Somasundharam, SriramSundar * Sivagurunathan, Suganya * Sarkans, Ugis",,,en @@ -95,7 +95,7 @@ FAIRy deep-learning for bioImage analysis,Deep Learning * FAIR-Principles * Micr OMERO - HCS analysis pipeline using Jupyter Notebooks,Teaching * Bioimage Analysis * Notebooks * Python * OMERO,https://github.com/rmassei/2024-jn-omero-pipeline,MIT,"Material and solutions for the course 'Bioimage data management and analysis with OMERO' held in Heidelberg (13th May 2024) - Module 3 (1.45 pm - 3.45 pm): OMERO and Jupyter Notebooks. Main goal of the workflow is to show the potential of JN to perform reproducible image analysis in connection with an OMERO instance. In this specific example, we are performing a simple nuclei segmentation from raw images uploaded in OMERO.",,Other,,"Massei, Riccardo",,,en Euro-BioImaging's Template for Research Data Management Plans,Bioimage Analysis * FAIR-Principles * Research Data Management,https://zenodo.org/records/11473803 * https://doi.org/10.5281/zenodo.11473803,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. ",2024-06-04,Tutorial * Other,,"Kemmer, Isabel * ERIC, Euro-BioImaging",,pdf,en -Euro-BioImaging's Guide to FAIR BioImage Data - Practical Tasks,Bioimage Analysis * FAIR-Principles * Research Data Management,https://zenodo.org/records/11474407 * https://doi.org/10.5281/zenodo.11474407,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",2024-06-04,Tutorial * Presentation,presentation,"Kemmer, Isabel * ERIC, Euro-BioImaging",,pdf,en +Euro-BioImaging's Guide to FAIR BioImage Data - Practical Tasks,Bioimage Analysis * FAIR-Principles * Research Data Management,https://zenodo.org/records/11474407 * https://doi.org/10.5281/zenodo.11474407,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",2024-06-04,Presentation * Tutorial,presentation,"Kemmer, Isabel * ERIC, Euro-BioImaging",,pdf,en From Paper to Pixels: Navigation through your Research Data - presentations of speakers,Research Data Management,https://zenodo.org/records/11548617 * https://doi.org/10.5281/zenodo.11548617,CC-BY-4.0,"The workshop introduced key topics of research data management (RDM) and the implementation thereof on a life science campus. Internal and external experts of RDM including scientists that apply chosen software tools presented the basic concepts and their implementation to a broad audience.  Talks covered general aspects of data handling and sorting, naming conventions, data storage repositories and archives, licensing of material, data and code management using git, data protection particularly regarding patient data and in genome sequencing and more. Two data management concepts and exemplary tools were highlighted in particular, being electronic lab notebooks with eLabFTW and the bio-image management software OMERO. Those were chosen because of three aspects: the large benefit of these management tools for a life science campus, their free availability as open source tools with the option of contribution of required functionalities and first existing use cases on campus already supported by CMCB/PoL IT. Two talks by Robert Haase (ScaDS.AI/ Uni Leipzig) and Robert Müller (Kontaktstelle Forschungsdaten, TU Dresden with contributions from Denise Dörfel) that opened the symposium were shared independently: @@ -148,7 +148,7 @@ Bio-image Data Science Lectures @ Uni Leipzig / ScaDS.AI,Bioimage Analysis * Dee BIDS-lecture-2024,Bioimage Analysis * Deep Learning * Microscopy Image Analysis * Python,https://github.com/ScaDS/BIDS-lecture-2024/,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.,,Other,,"Haase, Robert",,, Insights and Impact From Five Cycles of Essential Open Source Software for Science,Open Source Software * Funding * Sustainability,https://zenodo.org/records/11201216,CC-BY-4.0,"Open source software (OSS) is essential for advancing scientific discovery, particularly in biomedical research, yet funding to support these vital tools has been limited. The Chan Zuckerberg Initiative's Essential Open Source Software for Science (EOSS) program has significantly contributed to this field by providing $51.8 million in funding over five years to support the maintenance, growth, and community engagement of critical OSS tools. The program has impacted scientific OSS projects by improving their technical outputs, community building, and sustainability practices, and fostering collaborations within the OSS community. Additionally, EOSS funding has enhanced diversity, equity, and inclusion within the OSS community, although changes in principal investigator demographics were not observed. The funded projects have had a substantial impact on biomedical research by improving the usability and accessibility of scientific software, which has led to increased adoption and advancements in various biomedical fields.",,Article,text,"Hertweck, Kate * Strasser, Carly * Taraborelli, Dario",,csv * md * pdf,en 6 Steps Towards Reproducible Research,Reproducibility * Research Data Management,https://zenodo.org/records/12744715,CC-BY-4.0,A short book with 6 steps that get you closer to making your work reproducible.,,Book,text,"Seibold, Heidi",,epub * jpg * pdf * png,en -NFDI4BIOIMAGE - An Initiative for a National Research Data Infrastructure for Microscopy Data,Nfdi4Bioimage * Research Data Management,https://archiv.ub.uni-heidelberg.de/volltextserver/29489/,CC-BY-SA-4.0,,,Poster * Article,text,"Schmidt, Christian * Ferrando-May, Elisa",,,en +NFDI4BIOIMAGE - An Initiative for a National Research Data Infrastructure for Microscopy Data,Nfdi4Bioimage * Research Data Management,https://archiv.ub.uni-heidelberg.de/volltextserver/29489/,CC-BY-SA-4.0,,,Article * Poster,text,"Schmidt, Christian * Ferrando-May, Elisa",,,en Research data management for bioimaging: the 2021 NFDI4BIOIMAGE community survey,Nfdi4Bioimage * Research Data Management,https://f1000research.com/articles/11-638,CC-BY-4.0,,,Article,text,"Schmidt, Christian * Hanne, Janina * Moore, Josh * Meesters, Christian * Ferrando-May, Elisa * Weidtkamp-Peters, Stefanie * members of the NFDI4BIOIMAGE initiative",,,en Sharing and licensing material,Sharing * Research Data Management,https://f1000research.com/slides/10-519,CC-BY-4.0,Introduction to sharing resources online and licensing,,Presentation,presentation,"Haase, Robert",,,en "If you license it, it’ll be harder to steal it. Why we should license our work",Licensing * Research Data Management,https://focalplane.biologists.com/2023/05/06/if-you-license-it-itll-be-harder-to-steal-it-why-we-should-license-our-work/,CC-BY-4.0,Blog post about why we should license our work and what is important when choosing a license.,,Web Page,text,"Haase, Robert",,,en @@ -176,10 +176,10 @@ NFDI4BIOIMAGE: Perspective for a national bioimaging standard,Nfdi4Bioimage,http SpatialData: an open and universal data framework for spatial omics,Python,https://www.biorxiv.org/content/10.1101/2023.05.05.539647v1.abstract,CC-BY-4.0,,,Article * Text,text,"Marconato, Luca * Palla, Giovanni * Yamauchi, KevinA * Virshup, Isaac * Heidari, Elyas * Treis, Tim * Toth, Marcella * Shrestha, Rahul * Vöhringer, Harald * Huber, Wolfgang * Gerstung, Moritz * Moore, Josh * Theis, FabianJ * Stegle, Oliver",,,en Community-developed checklists for publishing images and image analyses,Bioimage Analysis,https://www.nature.com/articles/s41592-023-01987-9,ALL RIGHTS RESERVED,,,Article,text,"Schmied, Christopher * Nelson, MichaelS * Avilov, Sergiy * Bakker, Gert-Jan * Bertocchi, Cristina * Bischof, Johanna * Boehm, Ulrike * Brocher, Jan * Carvalho, MarianaT * Chiritescu, Catalin * Christopher, Jana * Cimini, BethA * Conde-Sousa, Eduardo * Ebner, Michael * Ecker, Rupert * Eliceiri, Kevin * Fernandez-Rodriguez, Julia * Gaudreault, Nathalie * Gelman, Laurent * Grunwald, David * Gu, Tingting * Halidi, Nadia * Hammer, Mathias * Hartley, Matthew * Held, Marie * Jug, Florian * Kapoor, Varun * Koksoy, AyseAslihan * Lacoste, Judith * Dévédec, SylviaLe * Guyader, SylvieLe * Liu, Penghuan * Martins, GabrielG * Mathur, Aastha * Miura, Kota * Llopis, PaulaMontero * Nitschke, Roland * North, Alison * Parslow, AdamC * Payne-Dwyer, Alex * Plantard, Laure * Ali, Rizwan * Schroth-Diez, Britta * Schütz, Lucas * Scott, RyanT * Seitz, Arne * Selchow, Olaf * Sharma, VedP * Spitaler, Martin * Srinivasan, Sathya * Strambio-De-Castillia, Caterina * Taatjes, Douglas * Tischer, Christian * Jambor, HelenaKlara",,,en BigDataProcessor2: A free and open-source Fiji plugin for inspection and processing of TB sized image data,Research Data Management * Bioimage Analysis,https://doi.org/10.1093/bioinformatics/btab106,CC-BY-4.0,,,Article,text,"Tischer, Christian * Ravindran, Ashis * Reither, Sabine * Chiaruttini, Nicolas * Pepperkok, Rainer * Norlin, Nils",,,en -"EDAM-bioimaging: The ontology of bioimage informatics operations, topics, data, and formats (update 2020)",Metadata,https://f1000research.com/posters/9-162,CC-BY-4.0,,,Poster * Article,text,"Kalaš, Matúš * Plantard, Laure * Lindblad, Joakim * Jones, Martin * Sladoje, Nataša * Kirschmann, MoritzA * Chessel, Anatole * Scholz, Leandro * Rössler, Fabianne * Sáenz, LauraNicolás * Estibaliz Gómez de Mariscal * Bogovic, John * Dufour, Alexandre * Heiligenstein, Xavier * Waithe, Dominic * Domart, Marie-Charlotte * Karreman, Matthia * Raf Van de Plas * Haase, Robert * Hörl, David * Paavolainen, Lassi * Madunić, IvanaVrhovac * Karaica, Dean * Muñoz-Barrutia, Arrate * Sampaio, Paula * Sage, Daniel * Munck, Sebastian * Golani, Ofra * Moore, Josh * Levet, Florian * Ison, Jon * Gaignard, Alban * Ménager, Hervé * Zhang, Chong * Miura, Kota * Colombelli, Julien * Paul-Gilloteaux, Perrine",,,en +"EDAM-bioimaging: The ontology of bioimage informatics operations, topics, data, and formats (update 2020)",Metadata,https://f1000research.com/posters/9-162,CC-BY-4.0,,,Article * Poster,text,"Kalaš, Matúš * Plantard, Laure * Lindblad, Joakim * Jones, Martin * Sladoje, Nataša * Kirschmann, MoritzA * Chessel, Anatole * Scholz, Leandro * Rössler, Fabianne * Sáenz, LauraNicolás * Estibaliz Gómez de Mariscal * Bogovic, John * Dufour, Alexandre * Heiligenstein, Xavier * Waithe, Dominic * Domart, Marie-Charlotte * Karreman, Matthia * Raf Van de Plas * Haase, Robert * Hörl, David * Paavolainen, Lassi * Madunić, IvanaVrhovac * Karaica, Dean * Muñoz-Barrutia, Arrate * Sampaio, Paula * Sage, Daniel * Munck, Sebastian * Golani, Ofra * Moore, Josh * Levet, Florian * Ison, Jon * Gaignard, Alban * Ménager, Hervé * Zhang, Chong * Miura, Kota * Colombelli, Julien * Paul-Gilloteaux, Perrine",,,en Thinking data management on different scales,Research Data Management * Nfdi4Bioimage,https://zenodo.org/records/8329306 * https://doi.org/10.5281/zenodo.8329306,CC-BY-4.0,Presentation given at PoL BioImage Analysis Symposium Dresden 2023,2023-08-31,Presentation,presentation,"Kunis, Susanne",,pdf * pptx,en Challenges and opportunities for bio-image analysis core-facilities,Research Data Management * Bioimage Analysis * Nfdi4Bioimage,https://f1000research.com/slides/12-1054,CC-BY-4.0,,,Presentation,presentation,"Haase, Robert",,,en -NFDI4Bioimage - TA3-Hackathon - UoC-2023 (Cologne Hackathon),Arc * Dataplant * Hackathon * Nfdi4Bioimage * OMERO * Python * Research Data Management,https://github.com/NFDI4BIOIMAGE/Cologne-Hackathon-2023 * https://doi.org/10.5281/zenodo.10609770,CC-BY-4.0,,,Article * Other * Text,text,"Abdrabbou, MohamedM. * Babaki, Mehrnaz * Boissonnet, Tom * Bortolomeazzi, Michele * Dahms, Eik * Vanessa A. F. Fuchs * Hoevels, Moritz * Kandpal, Niraj * Möhl, Christoph * Moore, JoshuaA. * Schauss, Astrid * Schrader, Andrea * Stöter, Torsten * Thönnißen, Julia * Valencia-S., Monica * Weil, H.Lukas * Jens Wendt and Peter Zentis",,, +NFDI4Bioimage - TA3-Hackathon - UoC-2023 (Cologne Hackathon),Arc * Dataplant * Hackathon * Nfdi4Bioimage * OMERO * Python * Research Data Management,https://github.com/NFDI4BIOIMAGE/Cologne-Hackathon-2023 * https://doi.org/10.5281/zenodo.10609770,CC-BY-4.0,,,Article * Text * Other,text,"Abdrabbou, MohamedM. * Babaki, Mehrnaz * Boissonnet, Tom * Bortolomeazzi, Michele * Dahms, Eik * Vanessa A. F. Fuchs * Hoevels, Moritz * Kandpal, Niraj * Möhl, Christoph * Moore, JoshuaA. * Schauss, Astrid * Schrader, Andrea * Stöter, Torsten * Thönnißen, Julia * Valencia-S., Monica * Weil, H.Lukas * Jens Wendt and Peter Zentis",,, Welcome to BioImage Town,OMERO * Bioimage Analysis * Nfdi4Bioimage,https://zenodo.org/doi/10.5281/zenodo.10008464,CC-BY-4.0,"Welcome at NFDI4BIOIMAGE All-Hands Meeting in Düsseldorf, Germany, October 16, 2023",,Presentation,presentation,"Moore, Josh",,,en NFDI4BIOIMAGE - National Research Data Infrastructure for Microscopy and BioImage Analysis - Online Kick-Off 2023,Research Data Management * FAIR-Principles * Bioimage Analysis * Nfdi4Bioimage,https://doi.org/10.5281/zenodo.8070038,CC-BY-4.0,"NFDI4BIOIMAGE core mission, bioimage data challenge, task areas, FAIR bioimage workflows.",,Presentation,presentation,"Weidtkamp-Peters, Stefanie",,,en "NFDI4Bioimage - TA3-Hackathon - UoC-2023 (Cologne-Hackathon-2023, GitHub repository)",Research Data Management * FAIR-Principles * Bioimage Analysis * Nfdi4Bioimage,https://zenodo.org/doi/10.5281/zenodo.10609770,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.",,Other,,"Abdrabbou, Mohamed * Babaki, Mehrnaz * Boissonnet, Tom * Bortolomeazzi, Michele * Dahms, Eik * Fuchs, Vanessa * A. F. Moritz Hoevels * Kandpal, Niraj * Möhl, Christoph * Moore, JoshuaA. * Schauss, Astrid * Schrader, Andrea * Stöter, Torsten * Thönnißen, Julia * Valencia-S., Monica * Weil, H.Lukas * Wendt, Jens * Zentis, Peter",,, @@ -214,7 +214,7 @@ SimpleITK-Notebooks,Bioimage Analysis * Simpleitk,https://github.com/InsightSoft skimage-tutorials,Bioimage Analysis * Scikit-Image,https://github.com/scikit-image/skimage-tutorials,CC0-1.0,skimage-tutorials - a collection of tutorials for the scikit-image package.,,Other,,Juan Nunez-Iglesias et al.,,,en scikit-learn MOOC,Bioimage Analysis * Machine Learning,https://github.com/INRIA/scikit-learn-mooc,CC-BY-4.0,Machine learning in Python with scikit-learn MOOC,,Other,,Loïc Estève et al.,,, NLP Course,Natural Language Processing,https://github.com/yandexdataschool/nlp_course,MIT,YSDA course in Natural Language Processing,,Other,,Yandex School of Data Analysis,,,en -Community-developed checklists for publishing images and image analyses,Bioimage Analysis * Research Data Management,https://quarep-limi.github.io/WG12_checklists_for_image_publishing/intro.html,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.",,Other * Code Notebook,code,Beth Cimini et al.,,,en +Community-developed checklists for publishing images and image analyses,Bioimage Analysis * Research Data Management,https://quarep-limi.github.io/WG12_checklists_for_image_publishing/intro.html,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.",,Code Notebook * Other,code,Beth Cimini et al.,,,en EMBL-EBI material collection,Bioinformatics * Training,https://www.ebi.ac.uk/training/on-demand?facets=type:Course%20materials&query=,CC0-1.0,"Online tutorial and webinar library, designed and delivered by EMBL-EBI experts",,Other,,EMBL-EBI,,, [Workshop] FAIR data handling for microscopy: Structured metadata annotation in OMERO,,https://zenodo.org/records/11109616 * https://doi.org/10.5281/zenodo.11109616,CC-BY-4.0,"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. @@ -263,8 +263,8 @@ Hitchhiking through a diverse Bio-image Analysis Software Universe,Bioimage Anal "Research Data Reusability - Conceptual Foundations, Barriers and Enabling Technologies",Research Data Management * Open Science * Data Protection,https://www.mdpi.com/2304-6775/5/1/2,CC-BY-4.0,"This article discusses various aspects of data reusability in the context of scientific research, including technological, legal, and policy frameworks.",2017-01-09,Article,text,"Thanos, Costantino",,,en The FAIR Guiding Principles for scientific data management and stewardship,FAIR-Principles * Research Data Management,https://www.nature.com/articles/sdata201618 * https://doi.org/10.1038/sdata.2016.18,CC-BY-4.0,"This Comment is the first formal publication of the FAIR Principles, and includes the rationale behind them, and some exemplar implementations in the community.",2016-03-15,Article,text,"Wilkinson, MarkD. * Dumontier, Michel * Aalbersberg, IJsbrandJan * Appleton, Gabrielle * Axton, Myles * al, et.",,,en Modeling community standards for metadata as templates makes data FAIR,Data Stewardship * FAIR-Principles * Metadata,https://pubmed.ncbi.nlm.nih.gov/36371407/ * https://www.nature.com/articles/s41597-022-01815-3,CC-BY-4.0,"The authors have developed a model for scientific metadata, and they have made that model usable by both CEDAR and FAIRware. The approach shows that a formal metadata model can standardize reporting guidelines and that it can enable separate software systems to assist (1) in the authoring of standards-adherent metadata and (2) in the evaluation of existing metadata.",2022-11-12,Article,text,"Musen, MarkA * O'Connor, MartinJ * Schultes, Erik * Martínez-Romero, Marcos * Hardi, Josef * Graybeal, John",,,en -NFDI4BIOIMAGE - An Initiative for a National Research Data Infrastructure for Microscopy Data,Nfdi4Bioimage * Image Data Management * Bioimage Data * Research Data Management,https://doi.org/10.11588/heidok.00029489,CC-BY-SA-4.0,Align existing and establish novel services & solutions for data management tasks throughout the bioimage data lifecycle.,2021-04-29,Presentation * Text,text * presentation,"Schmidt, Christian * Ferrando-May, Elisa",,,en -Data life cycle,Data Life Cycle * Research Data Management,https://rdmkit.elixir-europe.org/data_life_cycle,CC-BY-4.0,"In this section, information is organised according to the stages of the research data life cycle.",,Web Page * Tutorial * Other,,ELIXIR (2021) Research Data Management Kit,,,en +NFDI4BIOIMAGE - An Initiative for a National Research Data Infrastructure for Microscopy Data,Nfdi4Bioimage * Image Data Management * Bioimage Data * Research Data Management,https://doi.org/10.11588/heidok.00029489,CC-BY-SA-4.0,Align existing and establish novel services & solutions for data management tasks throughout the bioimage data lifecycle.,2021-04-29,Text * Presentation,text * presentation,"Schmidt, Christian * Ferrando-May, Elisa",,,en +Data life cycle,Data Life Cycle * Research Data Management,https://rdmkit.elixir-europe.org/data_life_cycle,CC-BY-4.0,"In this section, information is organised according to the stages of the research data life cycle.",,Tutorial * Other * Web Page,,ELIXIR (2021) Research Data Management Kit,,,en Erstellung und Realisierung einer institutionellen Forschungsdaten-Policy,Research Data Management,https://bausteine-fdm.de/article/view/7945 * https://doi.org/10.17192/bfdm.2018.1.7945,CC-BY-4.0,Die vorliegende Empfehlung sowie die zugehörigen Erfahrungsberichte geben einen Überblick über die verschiedenen Möglichkeiten der Gestaltung einer Forschungsdatenmanagement Policy sowie Wege zu deren Erstellung., 2018-10-22,Article,text,"Hahn, Uli * Helbig, Kerstin * Jagusch, Gerald * Rex, Jessica",,,de Leitlinie? Grundsätze? Policy? Richtlinie? – Forschungsdaten-Policies an deutschen Universitäten,Research Data Management * FAIR-Principles,https://www.o-bib.de/bib/article/view/2018H2S1-13,CC-BY-4.0,"As a methodological approach, research data policies of German universities are collected and evaluated, and compared to international recommendations on research data policies.",2018-07-13,Article,text,"Hiemenz, Bea * Kuberek, Monika",,,de Creating a Research Data Management Plan using chatGPT,Research Data Management * Large Language Models * Artificial Intelligence,https://focalplane.biologists.com/2023/11/06/creating-a-research-data-management-plan-using-chatgpt/,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.,2023-11-06,Web Page,text,"Haase, Robert",,,en @@ -272,7 +272,7 @@ Rechtsfragen bei Open Science - Ein Leitfaden,Open Science * Open Access * Copyr The Open Microscopy Environment (OME) Data Model and XML file - open tools for informatics and quantitative analysis in biological imaging,Microscopy Image Analysis * Bioimage Analysis,https://genomebiology.biomedcentral.com/articles/10.1186/gb-2005-6-5-r47 * https://doi.org/10.1186/gb-2005-6-5-r47,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.",2005-05-03,Article,text,"Goldberg, IlyaG. * Allan, Chris * Burel, Jean-Marie * Creager, Doug * Falconi, Andrea * al, et.",,,en Microscopy-BIDS - An Extension to the Brain Imaging Data Structure for Microscopy Data,Research Data Management * Image Data Management * Bioimage Data,https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2022.871228/full,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.",2022-04-19,Article,text,"Bourget, Marie-Hélène * Kamentsky, Lee * Ghosh, SatrajitS. * Mazzamuto, Giacomo * Lazari, Alberto * et al.",,,en FAIR High Content Screening in Bioimaging,FAIR-Principles * Metadata * Research Data Management * Image Data Management * Bioimage Data,https://www.nature.com/articles/s41597-023-02367-w,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.",2023-07-17,Article,text,"Hosseini, Rohola * Vlasveld, Matthijs * Willemse, Joost * Bob van de Water * Sylvia E. Le Dévédec * Wolstencroft, KatherineJ.",,,en -Dokumentation und Anleitung zum elektronischen Laborbuch (eLabFTW),Research Data Management,https://www.fdm.tu-clausthal.de/fileadmin/FDM/documents/Manual_eLab_v0.3_20200323.pdf * https://www.elabftw.net/,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.",2020-03-23,Tutorial * Text,text,"Wegewitz, Lienhard * Strauß, F.",,,de +Dokumentation und Anleitung zum elektronischen Laborbuch (eLabFTW),Research Data Management,https://www.fdm.tu-clausthal.de/fileadmin/FDM/documents/Manual_eLab_v0.3_20200323.pdf * https://www.elabftw.net/,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.",2020-03-23,Text * Tutorial,text,"Wegewitz, Lienhard * Strauß, F.",,,de What is Open Data?,Open Science,http://opendatahandbook.org/guide/en/what-is-open-data/,CC-BY-4.0,"This handbook is about open data but what exactly is it? In particular what makes open data open, and what sorts of data are we talking about?",,Other,,"Dietrich, Daniel * Gray, Jonathan * McNamara, Tim * Poikola, Antti * Pollock, Rufus * et al.",,,en Ten simple rules for making training materials FAIR,Metadata * Bioinformatics * FAIR-Principles * Training,https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1007854,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.",2020-05-21,Article,text,"Garcia, Leyla * Batut, Bérénice * Burke, MelissaL. * Kuzak, Mateusz * Psomopoulos, Fotis * et al.",,,en The FAIR guiding principles for data stewardship - fair enough?,FAIR-Principles * Data Stewardship * Sharing,https://www.nature.com/articles/s41431-018-0160-0,CC-BY-4.0,"The FAIR guiding principles for research data stewardship (findability, accessibility, interoperability, and reusability) look set to become a cornerstone of research in the life sciences. A critical appraisal of these principles in light of ongoing discussions and developments about data sharing is in order.",2018-05-17,Article,text,"Boeckhout, Martin * Zielhuis, GerhardA. * Bredenoord, AnnelienL.",,,en @@ -294,13 +294,13 @@ Bio-image Analysis Code Generation,,https://zenodo.org/records/14001044 * https: https://github.com/haesleinhuepf/bia-bob https://github.com/haesleinhuepf/git-bob  ",2024-10-28,,,"Haase, Robert",,pdf * pptx,en -"I2K2024 workshop material - Lazy Parallel Processing and Visualization of Large Data with ImgLib2, BigDataViewer, the N5-API, and Spark",Training,https://saalfeldlab.github.io/i2k2024-lazy-workshop/ * https://github.com/saalfeldlab/i2k2024-lazy-workshop,Apache-2.0,,,Other * Course * Code Notebook,code,"Saalfeld, Stephan * Pietzsch, Tobias",,,en -Ultrack I2K 2024 Workshop Materials,Segmentation * Bioimage Analysis * Training,https://github.com/royerlab/ultrack-i2k2024 * https://royerlab.github.io/ultrack-i2k2024/,BSD-3-Clause,,,Tutorial * Other * Course,,"Bragantini, Jordão * Huijben, Teun",,,en -Multiplexed tissue imaging - tools and approaches,Bioimage Analysis * Microscopy Image Analysis,https://github.com/BIIFSweden/I2K2024-MTIWorkshop * https://docs.google.com/presentation/d/1R9-4lXAmTYuyFZpTMDR85SjnLsPZhVZ8/edit#slide=id.p1,CC-BY-4.0,"Material for the I2K 2024 ""Multiplexed tissue imaging - tools and approaches"" workshop",,Presentation * Other * Course,presentation,"Corbat, AgustínAndrés * OmFrederic * Windhager, Jonas * Lidayová, Kristína",,,en -I2K2024(virtual) - Bio-Image Analysis Code Generation,Bioimage Analysis * Notebooks * Biabob,https://github.com/haesleinhuepf/i2k2024-ai-code-generation,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.",,Tutorial * Other * Code Notebook,code,"Haase, Robert",,,en -Object Tracking and Track Analysis using TrackMate and CellTracksColab,Bioimage Analysis * Training,https://github.com/CellMigrationLab/I2K_2024,GPL-3.0,"I2K 2024 workshop materials for ""Object Tracking and Track Analysis using TrackMate and CellTracksColab""",,Tutorial * Presentation * Other * Course,presentation,"Pylvänäinen, Joanna",,,en -I2K 2024: clEsperanto - GPU-Accelerated Image Processing Library,Clesperanto * Training * Bioimage Analysis * Notebooks * Workflow,https://github.com/StRigaud/clesperanto_workshop_I2K24?tab=readme-ov-file,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.",,Tutorial * Other * Course * Code Notebook,code,"Rigaud, Stephane * Haase, Robert",,, -Example Pipeline Tutorial,Napari * Microscopy Image Analysis * Bioimage Analysis,https://timmonko.github.io/napari-ndev/tutorial/01_example_pipeline/ * https://github.com/timmonko/napari-ndev,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).,2024-10-28,Tutorial * Other * Text,text,"Monko, Tim",,,en +"I2K2024 workshop material - Lazy Parallel Processing and Visualization of Large Data with ImgLib2, BigDataViewer, the N5-API, and Spark",Training,https://saalfeldlab.github.io/i2k2024-lazy-workshop/ * https://github.com/saalfeldlab/i2k2024-lazy-workshop,Apache-2.0,,,Course * Other * Code Notebook,code,"Saalfeld, Stephan * Pietzsch, Tobias",,,en +Ultrack I2K 2024 Workshop Materials,Segmentation * Bioimage Analysis * Training,https://github.com/royerlab/ultrack-i2k2024 * https://royerlab.github.io/ultrack-i2k2024/,BSD-3-Clause,,,Course * Tutorial * Other,,"Bragantini, Jordão * Huijben, Teun",,,en +Multiplexed tissue imaging - tools and approaches,Bioimage Analysis * Microscopy Image Analysis,https://github.com/BIIFSweden/I2K2024-MTIWorkshop * https://docs.google.com/presentation/d/1R9-4lXAmTYuyFZpTMDR85SjnLsPZhVZ8/edit#slide=id.p1,CC-BY-4.0,"Material for the I2K 2024 ""Multiplexed tissue imaging - tools and approaches"" workshop",,Course * Presentation * Other,presentation,"Corbat, AgustínAndrés * OmFrederic * Windhager, Jonas * Lidayová, Kristína",,,en +I2K2024(virtual) - Bio-Image Analysis Code Generation,Bioimage Analysis * Notebooks * Biabob,https://github.com/haesleinhuepf/i2k2024-ai-code-generation,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.",,Code Notebook * Tutorial * Other,code,"Haase, Robert",,,en +Object Tracking and Track Analysis using TrackMate and CellTracksColab,Bioimage Analysis * Training,https://github.com/CellMigrationLab/I2K_2024,GPL-3.0,"I2K 2024 workshop materials for ""Object Tracking and Track Analysis using TrackMate and CellTracksColab""",,Course * Presentation * Tutorial * Other,presentation,"Pylvänäinen, Joanna",,,en +I2K 2024: clEsperanto - GPU-Accelerated Image Processing Library,Clesperanto * Training * Bioimage Analysis * Notebooks * Workflow,https://github.com/StRigaud/clesperanto_workshop_I2K24?tab=readme-ov-file,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.",,Course * Code Notebook * Tutorial * Other,code,"Rigaud, Stephane * Haase, Robert",,, +Example Pipeline Tutorial,Napari * Microscopy Image Analysis * Bioimage Analysis,https://timmonko.github.io/napari-ndev/tutorial/01_example_pipeline/ * https://github.com/timmonko/napari-ndev,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).,2024-10-28,Text * Tutorial * Other,text,"Monko, Tim",,,en "[GBI EoE IX] NFDI4BIOIMAGE National Research Data Infrastructure for Microscopy and BioImage Analysis",,https://zenodo.org/records/14001388 * https://doi.org/10.5281/zenodo.14001388,CC-BY-4.0,"Presented at https://globalbioimaging.org/exchange-of-experience/exchange-of-experience-ix in Okazaki, Japan.",2024-10-29,,,"Moore, Josh",,pdf,en @@ -357,7 +357,7 @@ Learn how to train users, establish sustainability strategies, and foster FAIR R ",2024-10-30,,,"Boissonnet, Tom * Hagen, Bettina * Kunis, Susanne * Schmidt, Christian * Weidtkamp-Peters, Stefanie",,pdf,en training-resources,Bioimageanalysis * Neurobias,https://github.com/NEUBIAS/training-resources,CC-BY-4.0,Resources for teaching/preparing to teach bioimage analysis,2020-04-23T07:51:38+00:00,Other,,"Tischer, Christian * Politi, Antonio * Hodges, Toby * maulakhan * grinic * bugraoezdemir * Buchholz, Tim-Oliver * Fazeli, Elnaz * Halavatyi, Aliaksandr * Kutra, Dominik * Marcotti, Stefania * AnniekStok * Felix * jhennies * Klaus, Severina * Schorb, Martin * Vakili, Nima * Tirado, SebastianGonzalez * Helfrich, Stefan * Sun, Yi * Huang, Ziqiang * Eglinger, Jan * Pape, Constantin * Lüthi, Joel * McCormick, Matt * Gros, Oane",,,en cba-support-template,Workflow * Research Data Management,https://git.embl.de/grp-cba/cba-support-template,MIT,,2021-12-01,Tutorial,,"Khan, Arif * Tischer, Christian * Gonzalez, Sebastian * Kutra, Dominik * Schneider, Felix * et al.",,, -Diátaxis - A systematic approach to technical documentation authoring.,Documentation,https://www.diataxis.fr/,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.",,Web Page * Tutorial,,"Procida, Daniele",,,en +Diátaxis - A systematic approach to technical documentation authoring.,Documentation,https://www.diataxis.fr/,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.",,Tutorial * Web Page,,"Procida, Daniele",,,en Image Processing with Python,Segmentation * Bioimage Analysis * Training * Python * Scikit-Image * Image Segmentation,https://datacarpentry.org/image-processing/key-points.html,CC-BY-4.0,This lesson shows how to use Python and scikit-image to do basic image processing.,,Tutorial,,"Meysenburg, Mark * Hodges, Toby * Kutra, Dominik * Becker, Erin * Palmquist, David * et al.",,,en Evident OIR sample files tiles + stitched image - FV 4000,,https://zenodo.org/records/13680725 * https://doi.org/10.5281/zenodo.13680725,CC-BY-4.0,"The files contained in this repository are confocal images taken with the Evident FV 4000 of a sample containing DAPI and mCherry stains, excited with a 405 nm laser and a 561 nm laser diff --git a/scripts/Export_to_DALIA.ipynb b/scripts/Export_to_DALIA.ipynb index f8568ac0..4ef6aa54 100644 --- a/scripts/Export_to_DALIA.ipynb +++ b/scripts/Export_to_DALIA.ipynb @@ -20,7 +20,14 @@ "cell_type": "code", "execution_count": 1, "id": "7396751e-9b56-4bf6-bc35-e6e38f6c108c", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2025-01-29T12:48:24.689597Z", + "iopub.status.busy": "2025-01-29T12:48:24.689416Z", + "iopub.status.idle": "2025-01-29T12:48:25.776301Z", + "shell.execute_reply": "2025-01-29T12:48:25.775768Z" + } + }, "outputs": [ { "data": { @@ -217,7 +224,14 @@ "cell_type": "code", "execution_count": 2, "id": "f2bf12bd-56dc-49b4-902d-e68050d715ce", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2025-01-29T12:48:25.778176Z", + "iopub.status.busy": "2025-01-29T12:48:25.777943Z", + "iopub.status.idle": "2025-01-29T12:48:25.792643Z", + "shell.execute_reply": "2025-01-29T12:48:25.792195Z" + } + }, "outputs": [ { "data": { @@ -339,7 +353,14 @@ "cell_type": "code", "execution_count": 3, "id": "1371bc6c-23d9-46db-857c-41dd73e861c2", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2025-01-29T12:48:25.794302Z", + "iopub.status.busy": "2025-01-29T12:48:25.794106Z", + "iopub.status.idle": "2025-01-29T12:48:25.805794Z", + "shell.execute_reply": "2025-01-29T12:48:25.805370Z" + } + }, "outputs": [ { "data": { @@ -472,7 +493,14 @@ "cell_type": "code", "execution_count": 4, "id": "11c39326-61e1-422d-99df-240f4b9b5c86", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2025-01-29T12:48:25.807643Z", + "iopub.status.busy": "2025-01-29T12:48:25.807278Z", + "iopub.status.idle": "2025-01-29T12:48:25.822302Z", + "shell.execute_reply": "2025-01-29T12:48:25.821845Z" + } + }, "outputs": [ { "data": { @@ -652,14 +680,21 @@ "cell_type": "code", "execution_count": 5, "id": "5e708904-0161-4fb6-8bf8-c2f6dc3dbbea", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2025-01-29T12:48:25.824115Z", + "iopub.status.busy": "2025-01-29T12:48:25.823768Z", + "iopub.status.idle": "2025-01-29T12:48:25.827071Z", + "shell.execute_reply": "2025-01-29T12:48:25.826559Z" + } + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Total number of entries found: 536\n", - "Number of entries found with all mandatory entries: 330\n" + "Total number of entries found: 546\n", + "Number of entries found with all mandatory entries: 338\n" ] } ], @@ -672,7 +707,14 @@ "cell_type": "code", "execution_count": 6, "id": "8ce34a4c-f14f-40b0-8254-a4234d1f9d23", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2025-01-29T12:48:25.828720Z", + "iopub.status.busy": "2025-01-29T12:48:25.828540Z", + "iopub.status.idle": "2025-01-29T12:48:25.840231Z", + "shell.execute_reply": "2025-01-29T12:48:25.839776Z" + } + }, "outputs": [ { "data": { @@ -859,19 +901,26 @@ "cell_type": "code", "execution_count": 7, "id": "5318cd0d-7b81-47df-bab6-e64a8afbf9a2", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2025-01-29T12:48:25.842062Z", + "iopub.status.busy": "2025-01-29T12:48:25.841720Z", + "iopub.status.idle": "2025-01-29T12:48:25.854664Z", + "shell.execute_reply": "2025-01-29T12:48:25.854191Z" + } + }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_12251/210055857.py:1: SettingWithCopyWarning: \n", + "/tmp/ipykernel_2661/210055857.py:1: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " data[\"tags\"] = data[\"tags\"].apply(lambda x: ' * '.join(x) if isinstance(x, list) else x) #Tags\n", - "/tmp/ipykernel_12251/210055857.py:2: SettingWithCopyWarning: \n", + "/tmp/ipykernel_2661/210055857.py:2: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", @@ -1058,13 +1107,20 @@ "cell_type": "code", "execution_count": 8, "id": "ac322332-6c61-4764-b8c2-760c33518429", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2025-01-29T12:48:25.856736Z", + "iopub.status.busy": "2025-01-29T12:48:25.856119Z", + "iopub.status.idle": "2025-01-29T12:48:25.861547Z", + "shell.execute_reply": "2025-01-29T12:48:25.860992Z" + } + }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_12251/2387137408.py:21: SettingWithCopyWarning: \n", + "/tmp/ipykernel_2661/2387137408.py:21: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", @@ -1109,7 +1165,14 @@ "cell_type": "code", "execution_count": 9, "id": "8a104889-190a-4504-af64-c5a019392ad3", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2025-01-29T12:48:25.863531Z", + "iopub.status.busy": "2025-01-29T12:48:25.863097Z", + "iopub.status.idle": "2025-01-29T12:48:25.866691Z", + "shell.execute_reply": "2025-01-29T12:48:25.866244Z" + } + }, "outputs": [], "source": [ "# Create Mapping for the Type Column:\n", @@ -1157,7 +1220,14 @@ "cell_type": "code", "execution_count": 10, "id": "cd6e0ac8-2382-4a26-9e77-ee739081396f", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2025-01-29T12:48:25.868460Z", + "iopub.status.busy": "2025-01-29T12:48:25.868106Z", + "iopub.status.idle": "2025-01-29T12:48:25.871751Z", + "shell.execute_reply": "2025-01-29T12:48:25.871300Z" + } + }, "outputs": [], "source": [ "type_to_media_type = {\n", @@ -1204,19 +1274,26 @@ "cell_type": "code", "execution_count": 11, "id": "c32c15e5-2d12-4051-b238-44a94afcc5d1", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2025-01-29T12:48:25.873492Z", + "iopub.status.busy": "2025-01-29T12:48:25.873131Z", + "iopub.status.idle": "2025-01-29T12:48:25.888822Z", + "shell.execute_reply": "2025-01-29T12:48:25.888264Z" + } + }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_12251/3151956629.py:30: SettingWithCopyWarning: \n", + "/tmp/ipykernel_2661/3151956629.py:30: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " data[\"LearningResourceType\"] = data[\"type\"].apply(map_learning_resource)\n", - "/tmp/ipykernel_12251/3151956629.py:31: SettingWithCopyWarning: \n", + "/tmp/ipykernel_2661/3151956629.py:31: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", @@ -1356,8 +1433,8 @@ " NaN\n", " NaN\n", " NaN\n", - " Book * Code Notebook\n", - " text * code\n", + " Code Notebook * Book\n", + " code * text\n", " \n", " \n", "\n", @@ -1404,7 +1481,7 @@ "2 Web Page text \n", "3 Web Page text \n", "9 Web Page text \n", - "29 Book * Code Notebook text * code " + "29 Code Notebook * Book code * text " ] }, "execution_count": 11, @@ -1460,13 +1537,20 @@ "cell_type": "code", "execution_count": 12, "id": "827776ce-3be9-4b28-b664-687c7d4fc4ab", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2025-01-29T12:48:25.890768Z", + "iopub.status.busy": "2025-01-29T12:48:25.890354Z", + "iopub.status.idle": "2025-01-29T12:48:25.906955Z", + "shell.execute_reply": "2025-01-29T12:48:25.906450Z" + } + }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_12251/970863209.py:37: SettingWithCopyWarning: \n", + "/tmp/ipykernel_2661/970863209.py:37: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", @@ -1611,8 +1695,8 @@ " NaN\n", " NaN\n", " NaN\n", - " Book * Code Notebook\n", - " text * code\n", + " Code Notebook * Book\n", + " code * text\n", " Robert Haase et al.\n", " \n", " \n", @@ -1660,7 +1744,7 @@ "2 Web Page text Haase, Robert \n", "3 Web Page text Lampert, Mara \n", "9 Web Page text Haase, Robert \n", - "29 Book * Code Notebook text * code Robert Haase et al. " + "29 Code Notebook * Book code * text Robert Haase et al. " ] }, "execution_count": 12, @@ -1722,7 +1806,14 @@ "cell_type": "code", "execution_count": 13, "id": "4213ac0c-3274-408e-a86d-bc9e61832de8", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2025-01-29T12:48:25.908840Z", + "iopub.status.busy": "2025-01-29T12:48:25.908503Z", + "iopub.status.idle": "2025-01-29T12:48:25.918391Z", + "shell.execute_reply": "2025-01-29T12:48:25.917916Z" + } + }, "outputs": [ { "data": { @@ -1813,8 +1904,8 @@ " CC-BY-4.0 * BSD-3-Clause\n", " NaN\n", " NaN\n", - " Book * Code Notebook\n", - " text * code\n", + " Code Notebook * Book\n", + " code * text\n", " Robert Haase et al.\n", " \n", " \n", @@ -1855,7 +1946,7 @@ "2 Web Page text Haase, Robert \n", "3 Web Page text Lampert, Mara \n", "9 Web Page text Haase, Robert \n", - "29 Book * Code Notebook text * code Robert Haase et al. " + "29 Code Notebook * Book code * text Robert Haase et al. " ] }, "execution_count": 13, @@ -1885,7 +1976,14 @@ "cell_type": "code", "execution_count": 14, "id": "5a79c41e-6037-44c2-8cdd-0988197de047", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2025-01-29T12:48:25.920397Z", + "iopub.status.busy": "2025-01-29T12:48:25.919906Z", + "iopub.status.idle": "2025-01-29T12:48:25.929469Z", + "shell.execute_reply": "2025-01-29T12:48:25.928922Z" + } + }, "outputs": [ { "data": { @@ -1981,8 +2079,8 @@ " CC-BY-4.0 * BSD-3-Clause\n", " NaN\n", " NaN\n", - " Book * Code Notebook\n", - " text * code\n", + " Code Notebook * Book\n", + " code * text\n", " Robert Haase et al.\n", " None\n", " \n", @@ -2024,7 +2122,7 @@ "2 Web Page text Haase, Robert None \n", "3 Web Page text Lampert, Mara None \n", "9 Web Page text Haase, Robert None \n", - "29 Book * Code Notebook text * code Robert Haase et al. None " + "29 Code Notebook * Book code * text Robert Haase et al. None " ] }, "execution_count": 14, @@ -2061,6 +2159,12 @@ "execution_count": 15, "id": "89a3f72f-e614-4fe3-afc7-fc22345e104e", "metadata": { + "execution": { + "iopub.execute_input": "2025-01-29T12:48:25.931479Z", + "iopub.status.busy": "2025-01-29T12:48:25.931033Z", + "iopub.status.idle": "2025-01-29T12:52:24.807650Z", + "shell.execute_reply": "2025-01-29T12:52:24.807108Z" + }, "scrolled": true }, "outputs": [], @@ -2128,7 +2232,14 @@ "cell_type": "code", "execution_count": 16, "id": "965d0a41-9762-47cb-8bac-7042d35960c8", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2025-01-29T12:52:24.809834Z", + "iopub.status.busy": "2025-01-29T12:52:24.809490Z", + "iopub.status.idle": "2025-01-29T12:52:24.815449Z", + "shell.execute_reply": "2025-01-29T12:52:24.815007Z" + } + }, "outputs": [], "source": [ "def map_file_format(media_type, file_format):\n", @@ -2162,7 +2273,14 @@ "cell_type": "code", "execution_count": 17, "id": "0de3c9ba-a0b8-434d-bd79-896ad87cf1c1", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2025-01-29T12:52:24.817245Z", + "iopub.status.busy": "2025-01-29T12:52:24.816850Z", + "iopub.status.idle": "2025-01-29T12:52:24.820195Z", + "shell.execute_reply": "2025-01-29T12:52:24.819659Z" + } + }, "outputs": [], "source": [ "# Make * Delimiter for the Links if there is more than one for some entries\n", @@ -2182,8 +2300,31 @@ "cell_type": "code", "execution_count": 18, "id": "5f3abf90-990f-4ccc-8da3-05bb26e6538e", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2025-01-29T12:52:24.822045Z", + "iopub.status.busy": "2025-01-29T12:52:24.821704Z", + "iopub.status.idle": "2025-01-29T12:52:51.513612Z", + "shell.execute_reply": "2025-01-29T12:52:51.513044Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/hostedtoolcache/Python/3.13.1/x64/lib/python3.13/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Device set to use cpu\n" + ] + } + ], "source": [ "from transformers import pipeline\n", "\n", @@ -2209,13 +2350,20 @@ "cell_type": "code", "execution_count": 19, "id": "63071e24-8d4e-4885-ae78-74669bbe5557", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2025-01-29T12:52:51.515754Z", + "iopub.status.busy": "2025-01-29T12:52:51.515479Z", + "iopub.status.idle": "2025-01-29T12:52:51.525412Z", + "shell.execute_reply": "2025-01-29T12:52:51.524976Z" + } + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Exported 330 rows.\n" + "Exported 338 rows.\n" ] } ], @@ -2244,7 +2392,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.12" + "version": "3.13.1" } }, "nbformat": 4,