From 89b6250187ce3ff66329866457208aee7dd563c0 Mon Sep 17 00:00:00 2001 From: Munazah Date: Tue, 26 Mar 2024 17:17:59 +0000 Subject: [PATCH] Update tool_and_resource_list.yml (#1472) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * Automatic tool/resource-list update (#1396) * Update tool/resource_list.yaml * Update plant_phenomics_assembly.md * Update plant_sciences.md --------- Co-authored-by: bedroesb Co-authored-by: Bert Droesbeke <44875756+bedroesb@users.noreply.github.com> * add editors to alumni (#1433) * Update csc_assembly.md (#1429) * Update csc_assembly.md We updated our tool assembly. We would also have a new picture, but I don't know how I can upload it. * New csc tool assembly picture New csc tool assembly picture * Update csc_assembly.md changed the picture name to fi_csc_assembly_v2.svg * Update csc_assembly.md * Update pages/tool_assembly/csc_assembly.md Co-authored-by: Federico Bianchini <72258479+bianchini88@users.noreply.github.com> * Update pages/tool_assembly/csc_assembly.md ok! Co-authored-by: Federico Bianchini <72258479+bianchini88@users.noreply.github.com> * Update pages/tool_assembly/csc_assembly.md Co-authored-by: Federico Bianchini <72258479+bianchini88@users.noreply.github.com> * Update pages/tool_assembly/csc_assembly.md Co-authored-by: Federico Bianchini <72258479+bianchini88@users.noreply.github.com> * Update pages/tool_assembly/csc_assembly.md Co-authored-by: Federico Bianchini <72258479+bianchini88@users.noreply.github.com> * delete old image * Update news.yml Adding news about the refreshed CSC tool assembly page. --------- Co-authored-by: Nazeeefa Co-authored-by: Federico Bianchini <72258479+bianchini88@users.noreply.github.com> Co-authored-by: bedroesb * Adding a bibliography (#1434) * Adding bibliography * Update style_guide.md (#1435) --------- Co-authored-by: Korbinian Bösl * Update nels_assembly.md (#1436) Using a country keyword for a tool assembly might create undesired results when courses for other independent national resources/tools are registered into TeSS. With the current suggestion some results of the query get lost, but maybe the annotations on TeSS should be improved (e.g. including explicitly NeLS as a keyword or similar). * Fairtracks assembly (#1419) * first draft of FAIRtracks tool assembly * adding EMBL-EBI to affiliations * Update no_resources.md * Update no_resources.md * Update fairtracks_assembly.md Added description metadata * Update fairtracks_assembly.md minor text improvements * adding link to training events on TeSS * Adding TeSS to the omnipy tool entry * revision from Sveinung * cross-referencing domain pages * revision of FAIRtracks assembly * replacing figure with new version based on feedback from the editors * Update news.yml news item on FAIRtracks tool assembly page * Update news.yml fixing wrong indentation * adding newline * Update news.yml updated date of news item --------- Co-authored-by: bedroesb * Slight clarification (#1438) * Update fi_resources.md (#1439) * Update fi_resources.md Filling out "instance_of" field for FEGA Finland (instance of EGA) * Update fi_resources.md --------- Co-authored-by: Nazeeefa * Update se_resources.md (#1430) * Update se_resources.md Review of all links and resources, and update where necessary * Update se_resources.md * Add FEGA-SE to metadata * Update tool/resource_list.yaml (#1440) Co-authored-by: bedroesb * Update style_guide.md (#1442) * Update style_guide.md make bibliography as header * Add suggestions * Add full stop --------- Co-authored-by: Bert Droesbeke <44875756+bedroesb@users.noreply.github.com> * fixing display of instructions for creating a bibliography (#1446) * fixing display of instructions for creating a bibliography * Update pages/contribute/style_guide.md Co-authored-by: Bert Droesbeke <44875756+bedroesb@users.noreply.github.com> --------- Co-authored-by: Bert Droesbeke <44875756+bedroesb@users.noreply.github.com> * Update news.yml (#1449) added news about RIR * Update tool/resource_list.yaml (#1451) Co-authored-by: bedroesb * Ml domain (#1443) * Adding md file for domain page on machine-learning * Adding domain page for ML to the sidebar * Changing authir name to match the one recorded in the contributors file * Update machine_learning.md * Update tool_and_resource_list.yml * Update machine_learning.md * Update references.bib * Update machine_learning.md * Fixing syntax for bibliography * minor polishing of bib file * Update machine_learning.md * Adding missing authors based on information from the draft Google document * Update machine_learning.md Minor text polishing and fixed indentation for nested lists. * Update tool_and_resource_list.yml * Update tool_and_resource_list.yml * updating page metadata * adding news item about ML domain page * minor tweaks of ML domain page * shortening title to avoid splitting in two lines * polishing list of tools * Update machine_learning.md * Update pages/your_domain/machine_learning.md * shortening long title, splitting wall of text in smaller paragraphs, adding again list that were lost in the conversion from Google document * polishing bib file * Update data_management.yml * Update news.yml * removed duplicate entry for Schema.org * removing license from tools and resources list * Update machine_learning.md --------- Co-authored-by: Stella Fragkouli <79847870+sfragkoul@users.noreply.github.com> * improved bibtex entries for ML domain page (#1455) * Replaced broken links in National page of Estonia (#1452) * Update ee_resources.md, changed broken links * Update ee_resources.md * Update ee_resources.md * Update ee_resources.md * Update ee_resources.md minor fix --------- Co-authored-by: Nazeeefa * Improved bibliography (#1448) * take away and * Update references.bib (#1454) --------- Co-authored-by: Korbinian Bösl * Update news.yml (#1453) Added linked PR to new item * Update tool/resource_list.yaml (#1459) Co-authored-by: bedroesb * Update email Bert Droesbeke * Update email Bert Droesbeke * Adding Daniel to past editors (#1460) * Take Daniel out of the CODEOWNERS * Update tool/resource_list.yaml (#1461) Co-authored-by: bedroesb * Update tool/resource_list.yaml (#1462) Co-authored-by: bedroesb * changing bibliography style to nature (#1456) * changing bibliography style to nature * custom nature bib tex style --------- Co-authored-by: bedroesb * Adding metadata standards to the table of tools and resources (#1463) * Update existing_data.md (#1465) * Update no_resources.md (#1444) * Update no_resources.md * added VID resources * added normen chapter on privacy in research * Update no_resources.md * Update tool/resource_list.yaml (#1467) Co-authored-by: bedroesb * Adding rss feed to track RDMkit news (#1466) * adding rss feed for news * fix xml * add ixon * Rounded video * Update tool/resource_list.yaml (#1468) Co-authored-by: bedroesb * Update nl_resources.md (#1469) * Update nl_resources.md Added cBioPortal as a Dutch National Resource, requested by Lygature/EOSC4Cancer * Update nl_resources.md fixed the faulty URL for cBioPortal --------- Co-authored-by: Diana Pilvar <125961725+diana-pilvar@users.noreply.github.com> * Improve navigation by aligning the related pages with navigation tiles (#1366) * same graphics * fix type config * with icons * better theming * fix assembly * less css * variables * fix white * gray background * absolute path * relative link * variable for white * anchor fix * with shadow * smaller anchor * fix popping shadow * no shadow * smaller font * add new version * default shadow * different sizes * replace fix * replace fix * latest evrsion * primary color buttons * add news item * Update _data/news.yml Co-authored-by: Marina Popleteeva <108409595+marikapop@users.noreply.github.com> * Update news.yml --------- Co-authored-by: Marina Popleteeva <108409595+marikapop@users.noreply.github.com> * Adding Gil Poiares-Oliveira as an editorial member (#1471) * add @GilOliveira * Change Gil's email to BioData.pt --------- Co-authored-by: Gil Poiares-Oliveira * Update tool_and_resource_list.yml Added the tools for the health data page. --------- Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: bedroesb Co-authored-by: Bert Droesbeke <44875756+bedroesb@users.noreply.github.com> Co-authored-by: Siiri Fuchs <71016867+siirif@users.noreply.github.com> Co-authored-by: Nazeeefa Co-authored-by: Federico Bianchini <72258479+bianchini88@users.noreply.github.com> Co-authored-by: bedroesb Co-authored-by: Korbinian Bösl Co-authored-by: Sveinung Gundersen Co-authored-by: YvonneKallberg <35992574+YvonneKallberg@users.noreply.github.com> Co-authored-by: floradanna <51486716+floradanna@users.noreply.github.com> Co-authored-by: wittigue Co-authored-by: Stella Fragkouli <79847870+sfragkoul@users.noreply.github.com> Co-authored-by: Diana Pilvar <125961725+diana-pilvar@users.noreply.github.com> Co-authored-by: bedroesb Co-authored-by: MijkeJetten <69034034+MijkeJetten@users.noreply.github.com> Co-authored-by: Marina Popleteeva <108409595+marikapop@users.noreply.github.com> Co-authored-by: Gil Poiares-Oliveira --- CODEOWNERS | 10 +- Gemfile | 3 +- _bibliography/nature_custom.csl | 168 +++++++++ _bibliography/references.bib | 281 +++++++++++++++ _config.yml | 14 +- _data/CONTRIBUTORS.yaml | 25 +- _data/affiliations.yaml | 6 + _data/news.yml | 23 +- _data/sidebars/data_management.yml | 4 + _data/tool_and_resource_list.yml | 341 ++++++++++++++++++- _sass/_bootstrap_variables.scss | 13 +- _sass/_custom_classes.scss | 73 ++-- _sass/_custom_variables.scss | 16 +- assets/img/background.svg | 24 ++ feed.xml | 32 ++ images/fairtracks_tool-assembly.png | Bin 0 -> 502667 bytes images/fi_csc_assembly.svg | 1 - images/fi_csc_assembly_v2.svg | 47 +++ images/institutions/Ebi_official_logo.png | Bin 0 -> 55619 bytes index.html | 2 +- pages/about/editorial_board.md | 4 +- pages/about/news.md | 2 + pages/contribute/editorial_board_guide.md | 4 +- pages/contribute/style_guide.md | 8 +- pages/national_resources.md | 2 +- pages/national_resources/ee_resources.md | 12 +- pages/national_resources/fi_resources.md | 6 +- pages/national_resources/nl_resources.md | 10 + pages/national_resources/no_resources.md | 15 +- pages/national_resources/se_resources.md | 38 ++- pages/tool_assembly.md | 2 +- pages/tool_assembly/csc_assembly.md | 22 +- pages/tool_assembly/fairtracks_assembly.md | 115 +++++++ pages/tool_assembly/molgenis_assembly.md | 2 +- pages/tool_assembly/nels_assembly.md | 2 +- pages/your_domain.md | 3 +- pages/your_domain/human_data.md | 2 +- pages/your_domain/machine_learning.md | 101 ++++++ pages/your_domain/microbial_biotechnology.md | 52 +-- pages/your_domain/plant_sciences.md | 2 +- pages/your_domain/rare_disease_data.md | 1 + pages/your_domain/single_cell_sequencing.md | 2 +- pages/your_role.md | 3 +- pages/your_tasks.md | 2 +- pages/your_tasks/existing_data.md | 1 + pages/your_tasks/metadata_management.md | 2 +- 46 files changed, 1334 insertions(+), 164 deletions(-) create mode 100644 _bibliography/nature_custom.csl create mode 100644 _bibliography/references.bib create mode 100644 assets/img/background.svg create mode 100644 feed.xml create mode 100644 images/fairtracks_tool-assembly.png delete mode 100644 images/fi_csc_assembly.svg create mode 100644 images/fi_csc_assembly_v2.svg create mode 100644 images/institutions/Ebi_official_logo.png create mode 100755 pages/tool_assembly/fairtracks_assembly.md create mode 100644 pages/your_domain/machine_learning.md diff --git a/CODEOWNERS b/CODEOWNERS index 6b32823ef..ddf3faf48 100644 --- a/CODEOWNERS +++ b/CODEOWNERS @@ -5,17 +5,17 @@ * @bedroesb # Contribute -/pages/contribute @floradanna @pinarpink @bedroesb +/pages/contribute @floradanna @bedroesb /pages/contribute/copyright.md @martin-nc /pages/contribute/style_guide.md @martin-nc -CONTRIBUTING.md @floradanna @pinarpink +CONTRIBUTING.md @floradanna # Different Sections -/pages/your_tasks @DanFaria @floradanna @nazeeefa @marikapop @diana-pilvar -/pages/your_role @smza @pinarpink +/pages/your_tasks @floradanna @nazeeefa @marikapop @diana-pilvar +/pages/your_role @smza @GilOliveira /pages/data_life_cycle @wittigue @floradanna @smza @marikapop /pages/tool_assembly @korbinib @bianchini88 -/pages/your_domain @DanFaria @pinarpink @bianchini88 @lauportell +/pages/your_domain @bianchini88 @lauportell @GilOliveira /pages/national_resources @lauportell @nazeeefa @diana-pilvar #About diff --git a/Gemfile b/Gemfile index dd2b1788d..cf8b6ed17 100644 --- a/Gemfile +++ b/Gemfile @@ -1,6 +1,6 @@ source 'https://rubygems.org' -gem 'elixir-toolkit-theme-plugins', '~> 0.1.9' +gem 'elixir-toolkit-theme-plugins', '~> 0.1.10' gem 'webrick', '~> 1.7' gem 'jekyll', '~> 4.3.1' gem 'jemoji', '~> 0.13.0' @@ -13,6 +13,7 @@ group :jekyll_plugins do gem 'jekyll-github-metadata', '~> 2.15' gem 'jekyll-relative-links', '~> 0.6' gem 'jekyll-seo-tag', '~> 2.8' + gem 'jekyll-scholar', '~> 7.1.3' gem 'jekyll-remote-theme' end diff --git a/_bibliography/nature_custom.csl b/_bibliography/nature_custom.csl new file mode 100644 index 000000000..399740447 --- /dev/null +++ b/_bibliography/nature_custom.csl @@ -0,0 +1,168 @@ + + diff --git a/_bibliography/references.bib b/_bibliography/references.bib new file mode 100644 index 000000000..06c1fa1c2 --- /dev/null +++ b/_bibliography/references.bib @@ -0,0 +1,281 @@ +@article{walsh2021Author, + title = {Author {{Correction}}: {{DOME}}: Recommendations for Supervised Machine Learning Validation in Biology}, + shorttitle = {Author {{Correction}}}, + author = {Walsh, Ian and Fishman, Dmytro and {Garcia-Gasulla}, Dario and Titma, Tiina and Pollastri, Gianluca and {ELIXIR Machine Learning Focus Group} and Capriotti, Emidio and Casadio, Rita and {Capella-Gutierrez}, Salvador and Cirillo, Davide and Del Conte, Alessio and Dimopoulos, Alexandros C. and Del Angel, Victoria Dominguez and Dopazo, Joaquin and Fariselli, Piero and Fern{\'a}ndez, Jos{\'e} Maria and Huber, Florian and Kreshuk, Anna and Lenaerts, Tom and Martelli, Pier Luigi and Navarro, Arcadi and Broin, Pilib {\'O} and Pi{\~n}ero, Janet and Piovesan, Damiano and Reczko, Martin and Ronzano, Francesco and Satagopam, Venkata and Savojardo, Castrense and Spiwok, Vojtech and Tangaro, Marco Antonio and Tartari, Giacomo and Salgado, David and Valencia, Alfonso and Zambelli, Federico and Harrow, Jennifer and Psomopoulos, Fotis E. and Tosatto, Silvio C. E.}, + year = {2021}, + month = nov, + journal = {Nature Methods}, + volume = {18}, + number = {11}, + pages = {1409--1410}, + issn = {1548-7091, 1548-7105}, + doi = {10.1038/s41592-021-01304-2}, + urldate = {2024-01-25}, + langid = {english} +} + +@article{williamson2023Data, + title = {Data Management Challenges for Artificial Intelligence in Plant and Agricultural Research}, + author = {Williamson, Hugh F. and Brettschneider, Julia and Caccamo, Mario and Davey, Robert P. and Goble, Carole and Kersey, Paul J. and May, Sean and Morris, Richard J. and Ostler, Richard and Pridmore, Tony and Rawlings, Chris and Studholme, David and Tsaftaris, Sotirios A. and Leonelli, Sabina}, + year = {2023}, + month = jan, + journal = {F1000Research}, + volume = {10}, + pages = {324}, + issn = {2046-1402}, + doi = {10.12688/f1000research.52204.2}, + urldate = {2024-01-25}, + langid = {english} +} + +@article{ravi2022FAIR, + title = {{{FAIR}} Principles for {{AI}} Models with a Practical Application for Accelerated High Energy Diffraction Microscopy}, + author = {Ravi, Nikil and Chaturvedi, Pranshu and Huerta, E. A. and Liu, Zhengchun and Chard, Ryan and Scourtas, Aristana and Schmidt, K. J. and Chard, Kyle and Blaiszik, Ben and Foster, Ian}, + year = {2022}, + month = nov, + journal = {Scientific Data}, + volume = {9}, + number = {1}, + pages = {657}, + issn = {2052-4463}, + doi = {10.1038/s41597-022-01712-9}, + urldate = {2024-01-25}, + langid = {english} +} + +@inbook{sansone2023FAIR, + title = {{{FAIR}}: {{Making Data AI-Ready}}}, + shorttitle = {{{FAIR}}}, + booktitle = {Artificial {{Intelligence}} for {{Science}}}, + author = {Sansone, Susanna-Assunta and {Rocca-Serra}, Philippe and Wilkinson, Mark and Harland, Lee}, + year = {2023}, + month = apr, + pages = {627--640}, + publisher = {{WORLD SCIENTIFIC}}, + doi = {10.1142/9789811265679_0033}, + urldate = {2024-01-25}, + collaborator = {Choudhary, Alok and Fox, Geoffrey and Hey, Tony}, + isbn = {9789811265662 9789811265679}, + langid = {english} +} + +@article{huerta2023FAIR, + title = {{{FAIR}} for {{AI}}: {{An}} Interdisciplinary and International Community Building Perspective}, + shorttitle = {{{FAIR}} for {{AI}}}, + author = {Huerta, E. A. and Blaiszik, Ben and Brinson, L. Catherine and Bouchard, Kristofer E. and Diaz, Daniel and Doglioni, Caterina and Duarte, Javier M. and Emani, Murali and Foster, Ian and Fox, Geoffrey and Harris, Philip and Heinrich, Lukas and Jha, Shantenu and Katz, Daniel S. and Kindratenko, Volodymyr and Kirkpatrick, Christine R. and {Lassila-Perini}, Kati and Madduri, Ravi K. and Neubauer, Mark S. and Psomopoulos, Fotis E. and Roy, Avik and R{\"u}bel, Oliver and Zhao, Zhizhen and Zhu, Ruike}, + year = {2023}, + month = jul, + journal = {Scientific Data}, + volume = {10}, + number = {1}, + pages = {487}, + issn = {2052-4463}, + doi = {10.1038/s41597-023-02298-6}, + urldate = {2024-01-25}, + langid = {english} +} + +@inbook{psomopoulos2023Roadmap, + title = {A {{Roadmap}} for {{Defining Machine Learning Standards}} in {{Life Sciences}}}, + booktitle = {Artificial {{Intelligence}} for {{Science}}}, + author = {Psomopoulos, Fotis and Goble, Carole and Castro, Leyla Jael and Harrow, Jennifer and Tosatto, Silvio C. E.}, + year = {2023}, + month = apr, + pages = {399--410}, + publisher = {{WORLD SCIENTIFIC}}, + doi = {10.1142/9789811265679_0022}, + urldate = {2024-01-25}, + collaborator = {Choudhary, Alok and Fox, Geoffrey and Hey, Tony}, + isbn = {9789811265662 9789811265679}, + langid = {english} +} + +@article{berisha2021Digital, + title = {Digital Medicine and the Curse of Dimensionality}, + author = {Berisha, Visar and Krantsevich, Chelsea and Hahn, P. Richard and Hahn, Shira and Dasarathy, Gautam and Turaga, Pavan and Liss, Julie}, + year = {2021}, + month = oct, + journal = {npj Digital Medicine}, + volume = {4}, + number = {1}, + pages = {153}, + issn = {2398-6352}, + doi = {10.1038/s41746-021-00521-5}, + urldate = {2024-01-25}, + langid = {english} +} + +@inproceedings{castro2021Working, + title = {Working {{Towards Understanding}} the {{Role}} of {{FAIR}} for {{Machine Learning}}}, + author = {Castro, Leyla Jael and Katz, Daniel S. and Psomopoulos, Fotis}, + year = {2021}, + publisher = {{PUBLISSO}}, + doi = {10.4126/FRL01-006429415}, + urldate = {2024-01-25}, + langid = {english}, + keywords = {FAIR,Machine Learning,Metadata} +} + +@article{comess2020Bringing, + title = {Bringing {{Big Data}} to {{Bear}} in {{Environmental Public Health}}: {{Challenges}} and {{Recommendations}}}, + shorttitle = {Bringing {{Big Data}} to {{Bear}} in {{Environmental Public Health}}}, + author = {Comess, Saskia and Akbay, Alexia and Vasiliou, Melpomene and Hines, Ronald N. and Joppa, Lucas and Vasiliou, Vasilis and Kleinstreuer, Nicole}, + year = {2020}, + month = may, + journal = {Frontiers in Artificial Intelligence}, + volume = {3}, + pages = {31}, + issn = {2624-8212}, + doi = {10.3389/frai.2020.00031}, + urldate = {2024-01-25} +} + +@article{field2008migs, + title={The minimum information about a genome sequence (MIGS) specification}, + author = {Field, Dawn and Garrity, George and Gray, Tanya and Morrison, Norman and Selengut, Jeremy and Sterk, Peter and Tatusova, Tatiana and Thomson, Nicholas and Allen, Michael J and Angiuoli, Samuel V and Ashburner, Michael and Axelrod, Nelson and Baldauf, Sandra and Ballard, Stuart and Boore, Jeffrey and Cochrane, Guy and Cole, James and Dawyndt, Peter and De Vos, Paul and {dePamphilis}, Claude and Edwards, Robert and Faruque, Nadeem and Feldman, Robert and Gilbert, Jack and Gilna, Paul and Gl{\"o}ckner, Frank Oliver and Goldstein, Philip and Guralnick, Robert and Haft, Dan and Hancock, David and Hermjakob, Henning and {Hertz-Fowler}, Christiane and Hugenholtz, Phil and Joint, Ian and Kagan, Leonid and Kane, Matthew and Kennedy, Jessie and Kowalchuk, George and Kottmann, Renzo and Kolker, Eugene and Kravitz, Saul and Kyrpides, Nikos and {Leebens-Mack}, Jim and Lewis, Suzanna E and Li, Kelvin and Lister, Allyson L and Lord, Phillip and Maltsev, Natalia and Markowitz, Victor and Martiny, Jennifer and Methe, Barbara and Mizrachi, Ilene and Moxon, Richard and Nelson, Karen and Parkhill, Julian and Proctor, Lita and White, Owen and Sansone, Susanna-Assunta and Spiers, Andrew and Stevens, Robert and Swift, Paul and Taylor, Chris and Tateno, Yoshio and Tett, Adrian and Turner, Sarah and Ussery, David and Vaughan, Bob and Ward, Naomi and Whetzel, Trish and San Gil, Ingio and Wilson, Gareth and Wipat, Anil}, + journal={Nature biotechnology}, + volume={26}, + number={5}, + pages={541--547}, + year={2008}, + doi={10.1038/nbt1360}, + url={https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2409278/} +} + +@article{ham2012jbeiice, + title={Design, implementation and practice of JBEI-ICE: an open source biological part registry platform and tools}, + author = {Ham, T. S. and Dmytriv, Z. and Plahar, H. and Chen, J. and Hillson, N. J. and Keasling, J. D.}, + journal={Nucleic acids research}, + volume={40}, + number={18}, + pages={e141}, + year={2012}, + doi={10.1093/nar/gks531}, + url={https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3467034/} +} + +@article{hecht2018bacterial, + title={A minimum information standard for reproducing bench-scale bacterial cell growth and productivity}, + author = {Hecht, Ariel and Filliben, James and Munro, Sarah A. and Salit, Marc}, + journal={Communications biology}, + volume={1}, + pages={219}, + year={2018}, + doi={10.1038/s42003-018-0220-6}, + url={https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6283831/} +} + +@article{kuwahara2017sbolme, + title={SBOLme: a Repository of SBOL Parts for Metabolic Engineering}, + author = {Kuwahara, Hiroyuki and Cui, Xuefeng and Umarov, Ramzan and Gr{\"u}nberg, Raik and Myers, Chris J. and Gao, Xin}, + journal={ACS synthetic biology}, + volume={6}, + number={4}, + pages={732--736}, + year={2017}, + doi={10.1021/acssynbio.6b00278}, + url={https://pubmed.ncbi.nlm.nih.gov/28076956/} +} + +@article{maloy2007strain, + title={Strain Collections and Genetic Nomenclature}, + author = {Maloy, Stanley R. and Hughes, Kelly T.}, + journal={Methods in Enzymology}, + pages={3--8}, + year={2007}, + doi={10.1016/s0076-6879(06)21001-2}, + url={https://pubmed.ncbi.nlm.nih.gov/17352909/} +} + +@article{parte2020lpsn, + title={List of Prokaryotic names with Standing in Nomenclature (LPSN) moves to the DSMZ}, + author = {Parte, Aidan C. and Sard{\`a} Carbasse, Joaquim and {Meier-Kolthoff}, Jan P. and Reimer, Lorenz C. and G{\"o}ker, Markus}, + journal={International journal of systematic and evolutionary microbiology}, + volume={70}, + number={11}, + pages={5607--5612}, + year={2020}, + doi={10.1099/ijsem.0.004332}, + url={https://pubmed.ncbi.nlm.nih.gov/32701423/} +} + +@article{sainzdemurieta2016toward, + title={Toward the First Data Acquisition Standard in Synthetic Biology}, + author = {Sainz De Murieta, I{\~n}aki and Bultelle, Matthieu and Kitney, Richard I}, + journal={ACS synthetic biology}, + volume={5}, + number={8}, + pages={817--826}, + year={2016}, + doi={10.1021/acssynbio.5b00222}, + url={https://pubmed.ncbi.nlm.nih.gov/26854090/} +} + +@article{sarkans2018biostudies, + title={The BioStudies database—one stop shop for all data supporting a life sciences study}, + author = {Sarkans, Ugis and Gostev, Mikhail and Athar, Awais and Behrangi, Ehsan and Melnichuk, Olga and Ali, Ahmed and Minguet, Jasmine and Rada, Juan Camillo and Snow, Catherine and Tikhonov, Andrew and Brazma, Alvis and McEntyre, Johanna}, + journal={Nucleic Acids Research}, + pages={D1266--D1270}, + year={2018}, + doi={10.1093/nar/gkx965}, + url={https://pubmed.ncbi.nlm.nih.gov/29069414/} +} + +@article{spidlen2021data, + title={Data File Standard for Flow Cytometry, Version FCS 3.2}, + author = {Spidlen, Josef and Moore, Wayne and Parks, David and Goldberg, Michael and Blenman, Kim and Cavenaugh, James S. and {the ISAC Data Standards Task Force} and Brinkman, Ryan}, + journal={Cytometry. 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https://fonts.googleapis.com/css2?family=Exo+2:ital,wght@0,100;0,200;0,300;0,400;0,500;0,600;0,700;0,800;0,900;1,400&display=swap - https://fonts.googleapis.com/css2?family=Open+Sans:wght@300;400;600;700;800&display=swap + headings: + resource-table-all-collapse: true exclude: - README.md @@ -98,31 +100,37 @@ defaults: path: "pages/data_life_cycle/" values: type: data_life_cycle + type_img: /assets/img/section-icons/arrows-spin-light.svg - scope: path: "pages/your_domain/" values: type: your_domain + type_img: /assets/img/section-icons/dna-light.svg - scope: path: "pages/your_tasks/" values: type: your_tasks + type_img: /assets/img/section-icons/list-check-light.svg - scope: path: "pages/your_role/" values: type: your_role + type_img: /assets/img/section-icons/address-card-light.svg - scope: path: "pages/tool_assembly/" values: type: tool_assembly + type_img: /assets/img/section-icons/arrows-to-dot-light.svg - scope: path: "pages/national_resources/" values: type: national_resources + type_img: /assets/img/section-icons/flag-light.svg datatable: true - scope: @@ -135,6 +143,9 @@ defaults: values: sidebar: about +scholar: + style: _bibliography/nature_custom.csl + plugins: - elixir-toolkit-theme-plugins - jemoji @@ -142,3 +153,4 @@ plugins: - jekyll-sitemap - jekyll-github-metadata - jekyll-octicons + - jekyll-scholar diff --git a/_data/CONTRIBUTORS.yaml b/_data/CONTRIBUTORS.yaml index 35c33e3f3..5d63212e0 100755 --- a/_data/CONTRIBUTORS.yaml +++ b/_data/CONTRIBUTORS.yaml @@ -12,7 +12,7 @@ Bert Droesbeke: git: bedroesb - email: bedro@psb.ugent.be + email: bert.droesbeke@vib.be orcid: 0000-0003-0522-5674 role: editor affiliation: VIB Data Core / ELIXIR-BE @@ -23,13 +23,11 @@ Flora D'Anna: affiliation: VIB Data Core / ELIXIR-BE Daniel Faria: git: DanFaria - role: editor orcid: 0000-0003-1511-277X affiliation: University of Lisbon / ELIXIR-PT Pinar Alper: git: pinarpink orcid: 0000-0002-2224-0780 - role: editor affiliation: LCSB / ELIXIR-LU Carole Goble: git: CaroleGoble @@ -65,7 +63,6 @@ Rob Hooft: git: rwwh email: rob.hooft@dtls.nl orcid: 0000-0001-6825-9439 - role: editor Karel Berka: git: KarelBerka email: karel.berka@upol.cz @@ -561,8 +558,9 @@ Polonca Ferk: Gil Poiares-Oliveira: git: GilOliveira orcid: 0000-0003-4638-2879 - email: gil.oliveira@tecnico.ulisboa.pt + email: gpo@biodata.pt affiliation: INESC-ID / BioData.pt / ELIXIR-PT + role: editor Uladzislau Vadadokhau: git: uladzislau21 email: uladzislau.vadadokhau@med.unideb.hu @@ -577,7 +575,12 @@ Styliani-Christina Fragkouli: git: sfragkoul orcid: 0000-0003-4067-7123 email: sfragkoul@certh.gr - affiliation: Institute of Applied Biosciences(INAB|CERTH) / University of Athens / ELIXIR-GR + affiliation: Institute of Applied Biosciences(INAB|CERTH) / University of Athens / ELIXIR-GR +Sveinung Gundersen: + git: sveinugu + orcid: 0000-0001-9888-7954 + email: sveinugu@ifi.uio.no + affiliation: ELIXIR Norway Diana Pilvar: git: diana-pilvar email: diana.pilvar@ut.ee @@ -598,4 +601,12 @@ Pavankumar Videm: git: pavanvidem email: videmp@informatik.uni-freiburg.de orcid: 0000-0002-5192-126X - affiliation: University of Freiburg / European Galaxy team \ No newline at end of file + affiliation: University of Freiburg / European Galaxy team +Edward Parkinson: + git: parkyed + orcid: 0009-0006-4552-9495 + affiliation: Cardiff University +Adriano Fonzino: + git: F0nz0 + orcid: 0009-0009-3325-8554 + affiliation: University of Bari diff --git a/_data/affiliations.yaml b/_data/affiliations.yaml index 502c519cf..7507da2f6 100644 --- a/_data/affiliations.yaml +++ b/_data/affiliations.yaml @@ -166,3 +166,9 @@ expose: yes type: infrastructure url: https://www.bbmri.nl/ +- name: EMBL-EBI + image_url: /images/institutions/Ebi_official_logo.png + pid: https://ror.org/02catss52 + expose: yes + type: project + url: https://www.ebi.ac.uk/ diff --git a/_data/news.yml b/_data/news.yml index e44d1a084..8369156fe 100644 --- a/_data/news.yml +++ b/_data/news.yml @@ -112,7 +112,7 @@ description: This page gives an insight in how we will govern and further develop the RDMkit in the future. [Discover the page here](rdmkit_alliance). - name: "New page: National resources Ireland" date: 2023-05-24 - linked_pr: 1076 + linked_pr: 1248 description: This page gives an insight in RDM on national level in Ireland. [Discover the page here](ie_resources) - name: "New page: Human pathogen genomics" date: 2023-08-04 @@ -150,3 +150,24 @@ date: 2023-12-06 linked_pr: 1413 description: A new "your domain" page about Single-cell sequencing was added. [Discover the page here](single_cell_sequencing). +- name: "Refreshed content: CSC tool assembly" + date: 2023-12-19 + linked_pr: 1429 + description: The content of the "tool assembly" page for CSC (Finnish IT Center for Science) was updated. [Discover the page here](csc_assembly). +- name: "New page: FAIRtracks tool assembly" + date: 2023-12-20 + linked_pr: 1419 + description: A new "tool assembly" page for FAIRtracks was added. [Discover the page here](fairtracks_assembly). +- name: "RDMkit selected as ELIXIR Recommended Interoperability Resource" + date: 2024-01-19 + linked_pr: 1449 + description: ELIXIR Recommended Interoperability Resources (RIR) are European tools and registries that help make scientific research findable, accessible, interoperable and reusable (FAIR). [ELIXIR News](https://elixir-europe.org/news/resource-announcement-2023). +- name: "New page: Machine learning" + date: 2024-01-25 + linked_pr: 1443 + description: A new "domain" page for machine learning was added. [Discover the page here](machine_learning). +- name: "Improved navigation and user experience" + date: 2024-03-24 + linked_pr: 1366 + description: By collapsing secondary information we hope to give more attention to the main content of the RDMkit. Together with the improved navigation tiles, multiple bugfixes and some style tweaks, we hope to improve the user experience. + diff --git a/_data/sidebars/data_management.yml b/_data/sidebars/data_management.yml index 4fd352012..36e22f6d7 100755 --- a/_data/sidebars/data_management.yml +++ b/_data/sidebars/data_management.yml @@ -41,6 +41,8 @@ subitems: url: /human_pathogen_genomics - title: Intrinsically disordered proteins url: /intrinsically_disordered_proteins + - title: Machine learning + url: /machine_learning - title: Marine metagenomics url: /marine_metagenomics - title: Microbial biotechnology @@ -112,6 +114,8 @@ subitems: url: /covid19_data_portal - title: CSC url: /csc_assembly + - title: FAIRtracks + url: /fairtracks_assembly - title: Galaxy url: /galaxy_assembly - title: IFB diff --git a/_data/tool_and_resource_list.yml b/_data/tool_and_resource_list.yml index 6f873daff..f59603760 100755 --- a/_data/tool_and_resource_list.yml +++ b/_data/tool_and_resource_list.yml @@ -1,3 +1,51 @@ +- description: scikit-learn is a free software machine learning library for the Python programming language. + id: scikit-learn + name: scikit-learn + registry: + biotools: scikit-learn + tess: scikit-learn + url: https://scikit-learn.org +- description: The CUDA Toolkit from NVIDIA provides everything you need to develop GPU-accelerated applications + id: cuda + name: CUDA toolkit + url: https://developer.nvidia.com/cuda-toolkit +- description: A free and open-source software library for machine learning and artificial intelligence. + id: tensorflow + name: Tensorflow + registry: + biotools: tensorflow + tess: Tensorflow + url: https://www.tensorflow.org +- description: Keras is an open-source library that provides a Python interface for artificial neural networks. + id: keras + name: Keras + registry: + tess: Keras + url: https://keras.io/ +- description: BioImage is a collaborative effort to bring AI models to the bioimaging community. + id: bioimage + name: BioImage Model Zoo + registry: + tess: BioImage Model Zoo + url: https://bioimage.io/#/ +- description: Data, Optimisation, Model and Evaluation in Machine Learning (DOME-ML) is a set of community guidelines, recommendations and checklists for supervised ML validation in biology. + id: dome + name: DOME-ML + registry: + fairsharing: cf62c2 + url: https://dome-ml.org/ +- description: ONNX is an open format built to represent machine learning models. + id: onnx + name: Open Neural Network Exchange (ONNX) + url: https://onnx.ai/ +- description: The platform where the machine learning community collaborates on models, datasets, and applications. + id: huggingface + name: HuggingFace + url: https://huggingface.co/ +- description: CatBoost is a high-performance open source library for gradient boosting on decision trees. + id: catboost + name: CatBoost + url: https://catboost.ai/ - description: BRAVA is a tool designed to help developers test servers that comply with the BrAPI specifications. id: brava name: BRAVA @@ -93,6 +141,7 @@ id: addgene name: Addgene registry: + biotools: addgene fairsharing: 8hcczk url: https://www.addgene.org/browse/ - description: Browser for ontologies for agricultural science based on NBCO BioPortal. @@ -513,6 +562,8 @@ - description: Command line tool and library for transferring data with URLs id: curl name: cURL + registry: + tess: cURL url: https://curl.se - description: Image Data management id: cytomine-ims @@ -622,6 +673,8 @@ - description: Build your Data Management Plan id: dmptool name: DMPTool + registry: + tess: DMPTool url: https://dmptool.org - description: A database of DNA sequences id: dna-data-bank-of-japan @@ -664,12 +717,14 @@ name: e!DAL registry: biotools: edal + tess: e!DAL url: https://edal.ipk-gatersleben.de/ - description: Plant Genomics and Phenomics Research Data Repository id: e-dal-pgp name: e!DAL-PGP registry: fairsharing: rf3m4g + tess: e!DAL-PGP url: https://edal-pgp.ipk-gatersleben.de/ - description: The ECOTOXicology Knowledgebase (ECOTOX) is a comprehensive, publicly available Knowledgebase providing single chemical environmental toxicity data on aquatic life, terrestrial plants, and wildlife. id: ecotox @@ -838,10 +893,14 @@ name: FAIDARE registry: biotools: faidare + tess: FAIDARE url: https://urgi.versailles.inra.fr/faidare/ - description: FAIR Cookbook is an online resource for the Life Sciences with recipes that help you to make and keep data Findable, Accessible, Interoperable and Reusable (FAIR) id: fair-cookbook name: FAIR Cookbook + registry: + biotools: fair_cookbook + tess: FAIR Cookbook url: https://fairplus.github.io/the-fair-cookbook/content/recipes/assessing-fairness.html# - description: A FAIR Data Point stores metadata in a standardized and sharable way. id: fair-data-point @@ -1413,6 +1472,7 @@ name: Ontobee registry: fairsharing: q8fx1b + tess: Ontobee url: http://www.ontobee.org - description: Licenses that are conformant with the principles laid out in the Open Definition. id: open-definition-conformant-licenses @@ -1938,6 +1998,8 @@ - description: Wellcome Open Research requires that the source data underlying the results are made available as soon as an article is published. This page provides information about data you need to include, where your data can be stored, and how your data should be presented. id: wellcome-open-research-data-guidelines name: Wellcome Open Research - Data Guidelines + registry: + fairsharing: wf9q40 url: https://wellcomeopenresearch.org/for-authors/data-guidelines - description: WinSCP is a popular SFTP client and FTP client for Microsoft Windows! Copy file between a local computer and remote servers using FTP, FTPS, SCP, SFTP, WebDAV or S3 file transfer protocols. id: winscp @@ -2223,6 +2285,7 @@ registry: biotools: knetminer fairsharing: 826b4a + tess: KnetMiner url: https://knetminer.com - description: The RDF Knowledge-based Database for plant molecular networks id: agronomic-linked-data @@ -2297,11 +2360,39 @@ registry: biotools: dataplan url: https://plan.nfdi4plants.org +- description: Omnipy is a high level Python library for type-driven data wrangling and scalable workflow orchestration. + id: omnipy + name: Omnipy + registry: + biotools: omnipy + tess: Omnipy + url: https://github.com/fairtracks/omnipy +- description: TrackFind is a search and curation engine for metadata of geneomic tracks. It supports crawling of the TrackHub Registry and other portals. + id: trackfind + name: TrackFind + registry: + biotools: trackfind + url: https://trackfind.elixir.no/ +- description: Pydantic is the most widely used data validation library for Python. + id: pydantic + name: Pydantic + url: https://docs.pydantic.dev/latest/ +- description: Prefect is a workflow orchestration tool empowering developers to build, observe, and react to data pipelines. + id: prefect + name: Prefect + url: https://www.prefect.io/ +- description: A global centralised collection of publicly accessible track hubs + id: track-hub-registry + name: Track Hub Registry + registry: + fairsharing: a1de61 + url: https://www.trackhubregistry.org/ - description: Fast, sensitive and accurate integration of single-cell data. id: harmony name: Harmony registry: biotools: Harmony-R + tess: Harmony url: https://github.com/immunogenomics/harmony - description: A database of manually curated cell markers in human/mouse and web tools based on scRNA-seq data. id: cellmarker @@ -2314,22 +2405,29 @@ name: Seurat registry: biotools: seurat + tess: Seurat url: https://satijalab.org/seurat/ - description: Scalable toolkit for analyzing single-cell gene expression data. It includes preprocessing, visualization, clustering, pseudotime and trajectory inference and differential expression testing. id: scanpy name: Scanpy registry: biotools: scanpy + tess: Scanpy url: https://github.com/theislab/Scanpy - description: The Gene Ontology Consortium continues to develop, maintain and use a set of structured, controlled vocabularies for the annotation of genes, gene products and sequences. id: gene-ontology name: Gene Ontology registry: biotools: go + fairsharing: FGJ2T8 + tess: Gene Ontology url: http://www.geneontology.org/ - description: Python package for handling annotated data matrices in memory and on disk, positioned between pandas and xarray. id: anndata name: AnnData + registry: + biotools: anndata + tess: AnnData url: https://anndata.readthedocs.io/en/latest/ - description: Loom is an efficient file format for large omics datasets. id: loom @@ -2342,22 +2440,28 @@ - description: The SingleCellExperiment class is a lightweight Bioconductor container for storing and manipulating single-cell genomics data. id: singlecellexperiment name: SingleCellExperiment + registry: + biotools: singlecellexperiment url: https://www.bioconductor.org/packages/release/bioc/vignettes/SingleCellExperiment/inst/doc/intro.html - description: The Monocle 3 package provides a toolkit for analyzing single-cell gene expression experiments. id: monocle name: Monocle registry: biotools: monocle + tess: Monocle url: https://cole-trapnell-lab.github.io/monocle3/ - description: Scater offers a collection of tools for analysis (such as quality control) for single-cell gene expression data. id: scater name: Scater + registry: + biotools: scater + tess: Scater url: https://bioconductor.org/packages/release/bioc/html/scater.html - description: sceasy is a package that helps easy conversion of different single-cell data formats to each other. id: sceasy name: SCEasy url: https://github.com/cellgeni/sceasy -- description: Interactive portal for single-cell genomics data. +- description: Interactive portal for single-cell genomics data. id: single-cell-portal name: Single Cell Portal url: https://singlecell.broadinstitute.org/single_cell @@ -2367,20 +2471,241 @@ url: https://www.sc-best-practices.org/preamble.html - description: A set of analysis pipelines that perform sample demultiplexing, barcode processing, single cell gene counting, V(D)J transcript sequence assembly and annotation, and Feature Barcode analysis from single cell data. id: cellranger - name: CellRanger + name: CellRanger url: https://www.10xgenomics.com/support/software/cell-ranger - description: A comprehensive turnkey solution for quantifying gene expression in single-cell/nucleus RNA-seq data, built into RNA-seq aligner STAR. id: starsolo name: STARsolo + registry: + tess: STARsolo url: https://github.com/alexdobin/STAR/blob/master/docs/STARsolo.md - description: HDF5 consists of a file format for storing HDF5 data, a data model for logically organizing and accessing HDF5 data from an application, and the software (libraries, language interfaces, and tools) for working with this format. - id: hierarchical-data-format - name: Hierarchical Data Format + id: hierarchical-data-format + name: Hierarchical Data Format + registry: + fairsharing: wvgta9 url: https://www.10xgenomics.com/support/software/cell-ranger/analysis/outputs/cr-outputs-h5-matrices - description: Sequence Read Archive (SRA) data, available through multiple cloud providers and NCBI servers, is the largest publicly available repository of high throughput sequencing data. - id: sequence-read-archive - name: Sequence Read Archive - url: https://www.ncbi.nlm.nih.gov/sra + id: sequence-read-archive + name: Sequence Read Archive registry: fairsharing: g7t2hv - + tess: Sequence Read Archive + url: https://www.ncbi.nlm.nih.gov/sra +- description: Minimum Information about a high-throughput SEQuencing Experiment + id: minseqe + name: MINSEQE + registry: + fairsharing: a55z32 + tess: MINSEQE + url: https://doi.org/10.5281/zenodo.5706412 +- description: Minimum Information About a Microarray Experiment + id: miame + name: MIAME + registry: + fairsharing: 32b10v + tess: MIAME + url: https://www.fged.org/projects/miame/ +- description: A fully managed REDCap system based in the cloud, provided by nPhase. + id: redcapcloud + name: REDCap Cloud + registry: + biotools: + tess: + url: https://www.redcapcloud.com +- description: European Commission's official multilingual online survey-management system built for the creation and publishing of globally accessible forms. + id: eusurvey + name: EU Survey + registry: + biotools: + tess: + url: https://ec.europa.eu/eusurvey +- description: A free and open source platform to develop and publish online surveys. + id: limesurvey + name: LimeSurvey + registry: + biotools: + tess: + url: https://github.com/LimeSurvey/LimeSurvey +- description: Commercial online survey tool. + id: alchemer + name: Alchemer + registry: + biotools: + tess: + url: https://www.alchemer.com +- description: Commercial online survey tool. + id: jotform + name: Jotform + registry: + biotools: + tess: + url: https://www.jotform.com +- description: A standard used for health care data exchange. + id: hl7fhir + name: HL7 FHIR + registry: + biotools: + tess: + url: https://www.hl7.org/fhir/ +- description: A minimal set of clinical research FHIR resources and elements in an EHR that can be utilised in an interoperable and consistent manner for clinical research. + id: vulcan + name: Real World Data for Clinical Research (Vulcan) + registry: + biotools: + tess: + url: https://build.fhir.org/ig/HL7/vulcan-rwd/ +- description: Guidance on how FHIR can be used for supporting FAIR health data. + id: fhir4fair + name: FHIR4FAIR + registry: + biotools: + tess: + url: https://build.fhir.org/ig/HL7/fhir-for-fair/ +- description: A standard designed by the European Committee for Standardization to define a information architecture for communicating part or all of the EHR of a single patient between EHR systems or between EHR systems and a centralised EHR data repository. + id: iso13606 + name: ISO 13606 + registry: + biotools: + tess: + url: https://www.iso.org/standard/67868.html +- description: Recommended by the European Commission as the format to be used for the exchange of EHR data for cross-border healthcare. + id: eehrxf + name: European Electronic Health Record Exchange Format (EEHRxF) + registry: + biotools: + tess: + url: https://www.x-ehealth.eu/eehrxf/ +- description: Domain model for data interchange that enables semantic interoperability and intends to integrate biomedical, clinical research and routine healthcare data. + id: isodis14199 + name: ISO/DIS 14199 + registry: + biotools: + tess: + url: https://www.iso.org/standard/83433.html +- description: Non‑profit organisation that publishes technical standards for an EHR platform along with domain‑developed clinical models to define content. + id: openehr + name: OpenEHR + registry: + biotools: + tess: + url: https://openehr.org +- description: Consortium which defines several open standards for regulatory approval and case report forms in particular in the context of clinical trials meant for submission to FDA and EMA. + id: cdisc + name: Clinical Data Interchange Standards Consortium (CDISC) + registry: + biotools: + tess: + url: https://www.cdisc.org +- description: A CDISC Foundational Standard that defines dataset and metadata standards. + id: adam + name: Analysis Data Model for data and metadata of clinical trial statistical analysis (ADaM) + registry: + biotools: + tess: + url: https://www.cdisc.org/standards/foundational/adam +- description: A CDISC Data Exchange Standard that lets technology vendors implement tools that support a “write once, use many times” solution based on XML. + id: ctrxml + name: Clinical Trial Registry-XML (CTR-XML) + registry: + biotools: + tess: + url: https://www.cdisc.org/standards/data-exchange/ctr-xml +- description: A CDISC Data Exchange Standard that is a vendor-neutral, platform-independent format for exchanging and archiving clinical and translational research data, along with their associated metadata, administrative data, reference data, and audit information. + id: odmxml + name: Operational Data Model-XML (ODM-XML) + registry: + biotools: + tess: + url: https://www.cdisc.org/standards/data-exchange/odm +- description: A CDISC Foundational Standard that provides a standard for organizing and formatting data to streamline processes in collection, management, analysis and reporting. + id: sdtm + name: Study Data Tabulation Model (SDTM) + registry: + biotools: + tess: + url: https://www.cdisc.org/standards/foundational/sdtm +- description: Open community standard for observational health care data obtained from health records. + id: cdm + name: OMOP Common Data Model + registry: + biotools: + tess: + url: https://ohdsi.github.io/CommonDataModel/ +- description: A UML-based logical health information model defined by the Open Group, intended to achieve interoperability between multiple healthcare standards and protocols. + id: fhim + name: Federated Health Information Model (FHIM) + registry: + biotools: + tess: + url: https://fhim.org/fhim-model +- description: Defines data elements, the relationships between such data elements and terminologies for detailed small, reusable clinical models. + id: iso139722022 + name: ISO 13972:2022 + registry: + biotools: + tess: + url: https://www.iso.org/standard/79498.html +- description: REDCap feature which allows a REDCap project to interact with an EHR system and pull EHR data into REDCap. + id: cdis + name: Clinical Data Interoperability Services + registry: + biotools: + tess: + url: https://projectredcap.org/software/cdis/ +- description: Open-source tools to support data-analytics use cases on observational patient-level data. + id: ohdsi + name: Observational health data sciences and informatics (OHDSI) Software Tools + registry: + biotools: + tess: + url: https://www.ohdsi.org/software-tools/ +- description: Open-source tool to map datasets to the OMOP CDM. + id: carrotmapper + name: Convenient And Reusable Rapid OMOP Transformer (CaRROT-Mapper) + registry: + biotools: + tess: + url: https://github.com/Health-Informatics-UoN/CaRROT-Mapper +- description: Repository of ontologies for use with the OMOP CDM. + id: ohdsiathen + name: OHDSI Athena + registry: + biotools: + tess: + url: https://athena.ohdsi.org/search-terms/start +- description: Codes for diseases, signs and symptoms, abnormal findings, complaints, social circumstances, and external causes of injury or diseases. Replaced by ICD-11 on 1/1/2022. + id: icd10 + name: International Classification of Diseases 10th Revision (ICD-10) + registry: + biotools: + tess: + url: https://icd.who.int/browse10/2019/en +- description: Replaces the ICD-10 as the global standard for recording health information and causes of death. It is developed by the World Health Organization and is annually updated. + id: icd11 + name: International Classification of Diseases 11th Revision (ICD-11) + registry: + biotools: + tess: + url: https://icd.who.int/en +- description: Terminology standard for health measurements, observations, and documents. + id: loinc + name: Logical Observation Identifiers Names and Codes (LOINC) + registry: + biotools: + tess: + url: https://loinc.org +- description: Terminology standard consisting of codes, terms, synonyms and definitions used in clinical documentation and reporting. + id: snomedct + name: Systematized Nomenclature of Medicine – Clinical Terms (SNOMED-CT) + registry: + biotools: + tess: + url: https://www.snomed.org +- description: Standardised vocabulary of phenotypic abnormalities encountered in human disease. + id: hpo + name: Human Phenotype Ontology (HPO) + registry: + biotools: + tess: + url: https://hpo.jax.org/app/ diff --git a/_sass/_bootstrap_variables.scss b/_sass/_bootstrap_variables.scss index 4fad476a4..a912ebc55 100644 --- a/_sass/_bootstrap_variables.scss +++ b/_sass/_bootstrap_variables.scss @@ -15,7 +15,6 @@ $navbar-dark-toggler-border-color: transparent; $border-radius: 0.3rem; $btn-border-radius-sm: $border-radius; $card-border-width: 0; -$card-cap-bg: $light-x; $link-color: $blue; $info: $blue; $link-decoration: none; @@ -26,9 +25,9 @@ $alert-border-width: 0; $input-group-addon-bg: $light-x; $input-bg: $light; $input-border-width: 0; -$h1-font-size: 1rem * 3.5 !default; -$h2-font-size: 1rem * 2.2 !default; -$h3-font-size: 1rem * 1.5 !default; -$h4-font-size: 1rem * 1.1 !default; -$h5-font-size: 1rem !default; -$h6-font-size: 1rem !default; \ No newline at end of file +$h1-font-size: 1rem * 3.5; +$h2-font-size: 1rem * 2.2; +$h3-font-size: 1rem * 1.5; +$h4-font-size: 1rem * 1.1; +$h5-font-size: 1rem; +$h6-font-size: 1rem; diff --git a/_sass/_custom_classes.scss b/_sass/_custom_classes.scss index ffbfe354b..52b4d179f 100644 --- a/_sass/_custom_classes.scss +++ b/_sass/_custom_classes.scss @@ -11,6 +11,41 @@ background-color: $light-x; } +// Light gray background on the left +@media (min-width: 992px) { + body:not(:has(.landingpage)) { + .anchorjs-link { + font-size: 0.7em !important; + padding-top: 0.3em; + } + + #layout { + gap: 0 !important; + } + header nav.navbar { + margin-bottom: 0 !important; + } + aside#sidebar { + padding-top: $spacer * 3; + padding-right: 0.9rem; + } + main#main { + padding-top: $spacer * 3; + padding-left: 2.1rem; + background-color: $white; + } + background-image: url("../img/background.svg"); /* The image used */ + background-position: center; /* Center the image */ + background-repeat: no-repeat; /* Do not repeat the image */ + background-size: cover; /* Resize the background image to cover the entire container */ + } +} + +@media (max-width: 992px) { + .sidebar-collapse { + background-color: $light; + } +} #main { h2, @@ -23,7 +58,6 @@ h3 { font-weight: 600; } - } .octicon { @@ -45,18 +79,9 @@ } } -li ul .sidebar_rdm_sub { - border-color: $light; - padding: 0.75rem 0.5rem 0.5rem 0.5rem; - border-radius: 0px 0px $border-radius $border-radius; - border-style: none solid solid solid; - border-width: 0.2em; - margin-top: -0.25rem; -} - #side-nav .btn.sidebar-title { border: 0; - border-bottom: 0.2rem solid $primary!important; + border-bottom: 0.2rem solid $primary !important; font-weight: bold; padding-bottom: $input-btn-padding-y - 0.2rem; } @@ -68,7 +93,6 @@ li ul .sidebar_rdm_sub { /* -----Homepage----- */ .landingpage { - .h-icon-6 { height: 6em; } @@ -80,7 +104,7 @@ li ul .sidebar_rdm_sub { } .card { - &:hover{ + &:hover { box-shadow: $box-shadow; } @@ -98,14 +122,13 @@ li ul .sidebar_rdm_sub { h2 { font-weight: 700; - font-family: $font-family-theme; + font-family: $font-family-theme; } h3 { font-weight: 600; - font-family: $font-family-theme; + font-family: $font-family-theme; } - } // Search box @@ -121,8 +144,6 @@ section#search-section { .input-group-text { font-size: 1.5rem; } - - } // Counters on landing page @@ -132,25 +153,17 @@ section#search-section { font-weight: 700; } - /*-----Section navigation tiles-----*/ .navigation-tiles { - .card:hover { - box-shadow: $box-shadow; - } - - .card-header { - a:hover { - text-decoration: none; - } - h3 { - font-size: 1.2rem; + .card, + .btn { + &:hover { + box-shadow: $box-shadow; } } } - /*-----Top navigation-----*/ .navbar-nav .nav-item > a { diff --git a/_sass/_custom_variables.scss b/_sass/_custom_variables.scss index c626d92ce..a66acfce1 100644 --- a/_sass/_custom_variables.scss +++ b/_sass/_custom_variables.scss @@ -7,14 +7,20 @@ $topnav-bg: $dark; $topnav-brand-height: 40px; /*-----More information tiles-----*/ -$info-card-header-bg: $dark; +$info-card-bg: $light; +$info-card-color: $dark; +$info-card-bg-hover: $primary; +$info-card-color-hover: $white; + +/*-----Sidebar-----*/ +$sidebar-bg: transparent; +$sidebar-lvl2-bg: transparent; /*-----Section navigation tiles-----*/ $nav-card-bg: $light; -$nav-card-header-bg: $light-x; -$nav-card-header-color: $dark; -$nav-card-badge-color: $body-color; -$nav-card-badge-bg: $white; +$nav-card-color: $dark; +$nav-card-bg-hover: $light; +$nav-card-color-hover: $dark; /*-----Footer-----*/ $footer-bg: $dark; diff --git a/assets/img/background.svg b/assets/img/background.svg new file mode 100644 index 000000000..1711c30fb --- /dev/null +++ b/assets/img/background.svg @@ -0,0 +1,24 @@ + + + + + + + + + diff --git a/feed.xml b/feed.xml new file mode 100644 index 000000000..6a44c809b --- /dev/null +++ b/feed.xml @@ -0,0 +1,32 @@ +--- +layout: none +--- + + + + {{ site.title | xml_escape }} + {% if site.description %}{{ site.description | xml_escape }}{% endif %} + {{ site.feed_update_period | default: "daily" | xml_escape }} + {{ site.feed_update_frequency | default: 1 | xml_escape }} + {{ site.url }} + + {%- assign news = site.data.news | sort: "date" %} + {%- assign latest_news = news | last %} + {{ 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{{folder.title}}

Research Data Management at glance

- +
diff --git a/pages/about/editorial_board.md b/pages/about/editorial_board.md index bf2221d5b..7a7c8cdd5 100644 --- a/pages/about/editorial_board.md +++ b/pages/about/editorial_board.md @@ -4,7 +4,7 @@ title: Editorial board ## Meet the editorial board members -{% include contributor-carousel-selection.html custom="Bert Droesbeke, Carole Goble, Daniel Faria, Flora D'Anna, Frederik Coppens, Munazah Andrabi, Pinar Alper, Rob Hooft, Ulrike Wittig, Laura Portell Silva, Martin Cook, Korbinian Bösl, Federico Bianchini, Nazeefa Fatima, Ishwar Chandramouliswaran, Diana Pilvar, Marina Popleteeva" %} +{% include contributor-carousel-selection.html custom="Bert Droesbeke, Carole Goble, Flora D'Anna, Frederik Coppens, Munazah Andrabi, Ulrike Wittig, Laura Portell Silva, Martin Cook, Korbinian Bösl, Federico Bianchini, Nazeefa Fatima, Ishwar Chandramouliswaran, Diana Pilvar, Marina Popleteeva, Gil Poiares-Oliveira" %} ## Join as editorial board member @@ -54,4 +54,4 @@ Please, send your application to rdm-editors@elixir-europe.org. In this section we would like to thank contributions of our past editorial members. -{% include contributor-tiles-all.html custom="Mijke Jetten, Niclas Jareborg" %} +{% include contributor-tiles-all.html custom="Mijke Jetten, Niclas Jareborg, Pinar Alper, Rob Hooft, Daniel Faria" %} diff --git a/pages/about/news.md b/pages/about/news.md index 0ba0d0959..9e50d72c1 100644 --- a/pages/about/news.md +++ b/pages/about/news.md @@ -3,4 +3,6 @@ title: News custom_editme: _data/news.yml --- +[Subscribe to the RSS feed]({{ '/feed.xml' | relative_url }}){: class="btn btn-light rounded-pill"} + {% include news.html %} diff --git a/pages/contribute/editorial_board_guide.md b/pages/contribute/editorial_board_guide.md index 110e15aaf..22dd93476 100644 --- a/pages/contribute/editorial_board_guide.md +++ b/pages/contribute/editorial_board_guide.md @@ -171,10 +171,10 @@ Do you want that the GitHub picture of a contributor is shown next to their name ```yaml Bert Droesbeke: git: bedroesb - email: bedro@psb.ugent.be + email: bert.droesbeke@vib.be orcid: 0000-0003-0522-5674 role: editor - affiliation: VIB-UGent + affiliation: VIB Data Core / ELIXIR-BE ``` {% include callout.html type="important" content="Make sure that the name in the yaml file is identically the same as the one used in the metadata of the page." %} diff --git a/pages/contribute/style_guide.md b/pages/contribute/style_guide.md index a496614e1..09e5fa302 100644 --- a/pages/contribute/style_guide.md +++ b/pages/contribute/style_guide.md @@ -42,9 +42,6 @@ In general, we follow the European Commission's [Web Writing Style Guide](https: * This is item 3 of the list. * **Numbers:** spell the numbers one to ten out. After that, write the numbers (11, 12, 13, etc.). * **Quotations:** use double quotes for quotations, and single quotes for quotes within quotes. - * **References:** use the [Nature Author instructions](https://www.nature.com/srep/author-instructions/submission-guidelines#references) for books and papers. Use "*et al.*" for more than five authors. - * Bellin, D. L. *et al.* Electrochemical camera chip for simultaneous imaging of multiple metabolites in biofilms. Nat. Commun. 7, 10535; [10.1038/ncomms10535](http://www.nature.com/articles/ncomms10535) (2016). - * Lam, J. Data Management. (John Wiley & Sons, Inc., 2019). * **That/which:** use "that" when you are defining something and "which" when you are adding extra information about it e.g.: * "The cat that was on the table suddenly got up" is telling us which cat it was. It is important to the meaning of the sentence because you are not talking about any cat, just the cat on the table. * "The cat, which was sitting on the table, suddenly got up" is giving us extra information about the cat. The information is not necessary to understand the sentence. You can remove the clause and the sentence will still be clear. Clauses starting with "which" usually begin with a comma. @@ -52,6 +49,11 @@ In general, we follow the European Commission's [Web Writing Style Guide](https: * **Tool assembly:** there are multiple tools in **one** assembly. The plural is "tool assemblies". * **Training:** training is an uncountable noun and cannot have a plural. You can write "training courses" and "training materials" but not "trainings". +## Bibliography + * Add your citations as bibtex to the `_bibliography/references.bib` file. + * Add {% raw %} `{% cite reference_key %}` {% endraw %} to the text where you are citing the reference. + * Add {% raw %} `## Bibliography {% bibliography --cited %}` {% endraw %} to show a bibliography section containing the references in the page. + ## Graphic design * **White space:** make sure there is plenty of space so that the main elements stand out and the text does not appear overwhelming. * **Colours:**
diff --git a/pages/national_resources.md b/pages/national_resources.md index be30fe58a..981b1699a 100644 --- a/pages/national_resources.md +++ b/pages/national_resources.md @@ -9,4 +9,4 @@ Here, we point to non-exhaustive collection of country-specific information reso These country-specific RDM overviews will help readers to seek national advice and could be an inspirational guide for other countries to implement and promote data management practices at national level.
-{% include section-navigation-tiles-simple.html type="national_resources" %} +{% include section-navigation-tiles-simple.html type="national_resources" col=3 %} diff --git a/pages/national_resources/ee_resources.md b/pages/national_resources/ee_resources.md index badb7edfc..c34aef9bd 100644 --- a/pages/national_resources/ee_resources.md +++ b/pages/national_resources/ee_resources.md @@ -2,7 +2,7 @@ title: Estonia country_code: EE -contributors: [Heleri Inno] +contributors: [Heleri Inno, Diana Pilvar] coordinators: [Heleri Inno] national_resources: @@ -33,7 +33,7 @@ national_resources: This page provides an overview of the data management resources and initiatives in Estonia. All these references are meant for Estonian researchers and their collaborators. -The Estonian government has released an [“Estonian Research and Development, Innovation and Entrepreneurship Strategy 2021-2035”](https://www.hm.ee/sites/default/files/taie_arengukava_kinnitatud_15.07.2021_211109a_en_final.pdf). +The Estonian government has released an “Estonian Research and Development, Innovation and Entrepreneurship Strategy 2021-2035” [in Estonian](https://www.hm.ee/korgharidus-ja-teadus/teadus-ja-arendustegevus/taie-arengukava-2021-2035) and [in English](https://www.hm.ee/en/ministry/ministry/strategic-planning-2021-2035#rdie). ## Funders @@ -45,21 +45,23 @@ The Estonian government has released an [“Estonian Research and Development, I ### Organisations * [Estonian Research Council](https://etag.ee/en/activities/open-science/) open science policies, surveys and documentation. -* ELIXIR Estonia [Data Management page](https://elixir.ut.ee/datamanagement): general information about data management and specific recommendations and references on how to manage your scientific data in Estonia. -* [Open Knowledge Estonia dataclub](https://okee.ee/andmeklubi/): monthly seminars on different topics of data management. +* ELIXIR Estonia [Data Management page](https://elixir.ut.ee/data_management/introduction/): general information about data management and specific recommendations and references on how to manage your scientific data in Estonia. +* [Open Knowledge Estonia](https://okee.ee/andmeklubi/) Promoter of data competence and digital literacy in Estonia. ### Libraries * [University of Tartu Library](https://utlib.ut.ee/en/open-science) open science page. The main focus is on Open Science and Open Access and how to write a Data Management Plan * [Library of Estonian University of Life Sciences](https://library.emu.ee/en/research/open-science/) open science page. * [Tallinn University of Technology](https://taltech.ee/en/library/open-science) open science page. + ### Other initiatives * [Open Science in Estonia](https://www.avatudteadus.ee/en/home/): the official Open Science branding of Estonia, developed by the University of Tartu Library. Contains information about community, initiatives, infrastructure and services. + ## Domain-specific infrastructures or resources * [ELIXIR Estonia](https://elixir.ut.ee/services) list of services that are provided. -* [Estonian Research Information System](https://www.etis.ee/Portal/News/Index/?IsLandingPage=true&lang=ENG#) contains information about the researchers and the projects. +* [Estonian Research Information System](https://www.etis.ee/) contains information about the researchers and the projects. * [DataDOI](https://datadoi.ee/) is an Estonian National repository. * [PlutoF](https://plutof.ut.ee/) is a data management and publishing platform. * [The High Performance Computing Center](https://hpc.ut.ee/) diff --git a/pages/national_resources/fi_resources.md b/pages/national_resources/fi_resources.md index b97b25b16..a18af6474 100644 --- a/pages/national_resources/fi_resources.md +++ b/pages/national_resources/fi_resources.md @@ -31,9 +31,9 @@ national_resources: your_tasks: [storage, data_publication, existing_data] url: https://research.csc.fi/-/fairdata-services - name: Federated EGA Finland - description: FEGA allows you to store and shaare sensitive data in Finland in a way that fulfils all the requirements of the General Data Protection Regulation (GDPR). - instance_of: - how_to_access: + description: FEGA allows you to store and share sensitive data in Finland in a way that fulfils all the requirements of the General Data Protection Regulation (GDPR). + instance_of: the-european-genome-phenome-archive + how_to_access: related_pages: tool_assembly: [csc] your_role: [researcher, data_steward] diff --git a/pages/national_resources/nl_resources.md b/pages/national_resources/nl_resources.md index 5411498d1..3ae809ba5 100644 --- a/pages/national_resources/nl_resources.md +++ b/pages/national_resources/nl_resources.md @@ -40,6 +40,16 @@ national_resources: your_role: [researcher] your_tasks: [data_analysis, existing_data, storage] url: https://www.bbmri.nl + - name: cBioPortal for Cancer Genomics + description: cBioPortal provides a web-based resource for researchers to explore, visualize, analyze, and share multidimensional cancer genomic datasets, as well as other studies involving multidimensional genomic data. + how_to_access: + instance_of: + related_pages: + tool_assembly: + your_domain: [human_data] + your_role: [researcher] + your_tasks: [data_analysis, existing_data, storage] + url: https://www.cbioportal.org - name: CBS, Statistics Netherlands description: The national statistical office, Statistics Netherlands (CBS), provides reliable statistical information and data in the life sciences and health domain. how_to_access: diff --git a/pages/national_resources/no_resources.md b/pages/national_resources/no_resources.md index b35d68cc1..8d6ca372f 100644 --- a/pages/national_resources/no_resources.md +++ b/pages/national_resources/no_resources.md @@ -6,7 +6,7 @@ contributors: [Nazeefa Fatima,Federico Bianchini,Korbinian Bösl,Erin Calhoun] coordinators: [Korbinian Bösl, Nazeefa Fatima] related_pages: - tool_assembly: [tsd, nels, marine_assembly] + tool_assembly: [tsd, nels, marine_assembly, fairtracks] training: - name: Training in TeSS @@ -28,6 +28,7 @@ national_resources: tool_assembly: [tsd, nels, marine_assembly] url: https://www.feide.no/ - name: DS-Wizard ELIXIR-Norway + id: dsw-no description: DS-Wizard is a tool to aid the creation, organisation and sharing of data management plans. It provides scientists with guidance, facilitating the understanding of the key components of FAIR-oriented Data Stewardship. The template in this instance provides additional guidance on resources, laws and regulations in Norway. how_to_access: Life Science Login (LS Login) with Feide or upon registration instance_of: data-stewardship-wizard @@ -84,7 +85,7 @@ national_resources: how_to_access: A formal application is required to gain access to the storage services. related_pages: your_tasks: [transfer, storage] - tool_assembly: [nels] + tool_assembly: [nels, fairtracks] url: https://documentation.sigma2.no/files_storage/nird.html - name: Sigma2 HPC systems description: The current Norwegian academic HPC infrastructure consists of three systems for different purposes. The Norwegian academic high-performance computing and storage infrastructure is maintained by [Sigma2 NRIS](https://sigma2.no/nris), which is a joint collaboration between UiO, UiB, NTNU, UiT, and [UNINETT Sigma2 (SIKT)](https://www.sigma2.no/). @@ -174,7 +175,7 @@ The Norwegian Ministry of Education and Research's "[National strategy on access ## Funder policies on research data -[Norges Forskningsråd (Research Council of Norway)](https://www.forskningsradet.no/en/) is the primary funding body in Norway for research. The [research data management policy of RCN requires a Data Management Plan (DMP)](https://www.forskningsradet.no/en/research-policy-strategy/open-science/research-data/) after a positive funding decision for each project. A DMP has to be submitted as part of a final report. RCN recommends following the ‘[Practical Guide to the International Alignment of Research Data Management](https://scienceeurope.org/our-resources/practical-guide-to-the-international-alignment-of-research-data-management/)’ by Science Europe, the organisation of research funders and performers. In addition to advising policies for open science and open access to publications, [RCN provides recommendations on how to make research data available](https://www.forskningsradet.no/siteassets/publikasjoner/2021/how-should-we-share-research-data.v2.pdf). From 2023 and onwards, a [project grant application submitted to RCN is assessed for open science best practices](https://www.forskningsradet.no/en/processing-grant-applications/processing-applications/assessment-open-science/). +[Norges Forskningsråd (Research Council of Norway)](https://www.forskningsradet.no/en/) is the primary funding body in Norway for research. The [research data management policy of RCN requires a Data Management Plan (DMP)](https://www.forskningsradet.no/en/research-policy-strategy/open-science/research-data/), preferably also available in the [DMP Common Standard](https://www.rd-alliance.org/group/dmp-common-standards-wg/outcomes/rda-dmp-common-standard-machine-actionable-data-management) as supported by for example by {% tool "data-stewardship-wizard" %} after a positive funding decision for each project. A DMP has to be submitted as part of a final report. RCN recommends following the ‘[Practical Guide to the International Alignment of Research Data Management](https://scienceeurope.org/our-resources/practical-guide-to-the-international-alignment-of-research-data-management/)’ by Science Europe, the organisation of research funders and performers. In addition to advising [policies for open science and open access](https://www.forskningsradet.no/siteassets/forskningspolitisk-radgivning/apen-forskning/nfr-policy-open-science-eng.pdf), [RCN provides recommendations on how to make research data available](https://www.forskningsradet.no/siteassets/publikasjoner/2021/how-should-we-share-research-data.v2.pdf). From 2023 and onwards, a [project grant application submitted to RCN is assessed for open science best practices](https://www.forskningsradet.no/en/processing-grant-applications/processing-applications/assessment-open-science/). ## Institutional policies on research data @@ -190,6 +191,7 @@ We provide here a non-exhaustive list of research institutions with Data Managem * [University of Agder](https://libguides.uia.no/c.php?g=653927&p=4778251#) * [Nord University](https://www.nord.no/en/research/researchers/research-data) * [Inland Norway University of Applied Sciences](https://www.inn.no/english/library/research-support/research-data-management/publish-research-data/) +* [VID Specialized University](https://www.vid.no/en/files/guidelines-for-managing-research-data-at-vid/) * [Svalbard Integrated Arctic Earth Observing System, SIOS](https://sios-svalbard.org/Documents#docSDMS) ### Institutional storage guidelines @@ -206,14 +208,18 @@ Details and provided solutions vary according to each institution: * [University of Bergen (UiB)](https://www.uib.no/en/it/153608/storage-guide) * [University of Oslo (UiO)](https://www.uio.no/english/for-employees/support/research/funding/units/hf/imv/data-ethics/storage-guide.html) * [The Arctic University of Norway (UiT)](https://en.uit.no/research/research-dataportal/art?p_document_id=729174) +* [VID Specialized University](https://www.vid.no/en/research/research-data/data-storage-guide/) ### Institutional guidelines related to personal data +* [Guidance for personal research in the healthcare system](https://www.ehelse.no/normen/normen-dokumenter/Veileder-i-personvern-og-informasjonssikkerhet-i-forskningsprosjekter) * [Norwegian University of Life Sciences (NMBU)](https://www.nmbu.no/forskning/forskningsdata) * [Norwegian University of Science and Technology (NTNU) - Privacy in research guidelines](https://innsida.ntnu.no/wiki/-/wiki/English/Collection+of+personal+data+for+research+projects), also available [in Norwegian](https://i.ntnu.no/wiki/-/wiki/Norsk/Behandle+personopplysninger+i+student-+og+forskningsprosjekt) * [Norwegian University of Science and Technology - Health research guidelines](https://i.ntnu.no/helseforskning) * [University of Bergen (UiB)](https://www.uib.no/en/personaldata/130126/privacy-policy-university-bergen), also available [in Norwegian](https://www.uib.no/personvern) * [University of Oslo (UiO)](https://www.uio.no/english/for-employees/support/privacy-dataprotection/personal-data-in-research.html) * [The Arctic University of Norway (UiT)](https://en.uit.no/research/ethics/art?p_document_id=754184), [full guidelines](https://uit.no/Content/800249/cache=1673451716000/Guidelines%20for%20processing%20of%20personal%20data%20in%20research%20and%20student%20projects%20at%20UiT.PDF) available as pdf +* [VID Specialized University](https://www.vid.no/en/privacy-and-gdpr/research-and-publishing/) + ## Support services @@ -234,6 +240,7 @@ The [ELSI helpdesk for biobanking](https://bbmri.no/help-desk-elsi) facilitates * [University of Agder](https://libguides.uia.no/c.php?g=653927&p=4778251#) * [Nord University](https://www.nord.no/en/research/researchers/research-data) * [Inland Norway University of Applied Sciences](https://www.inn.no/english/library/research-support/research-data-management/publish-research-data/) +* [VID Specialized University](https://www.vid.no/en/research/research-data/) * [Svalbard Integrated Arctic Earth Observing System, SIOS](https://sios-svalbard.org/DMsupport) ### Other data management infrastructures @@ -353,7 +360,7 @@ These are some of the laws relevant for research data management in Norway. You * [Medicines Act](http://www.lovdata.no/all/nl-19921204-132.html) * [Regulations on clinical trials of medical products for human use](http://www.lovdata.no/for/sf/ho/ho-20091030-1321.html) * [Biotechnology Act (on the medical use of biotechnology)](https://lovdata.no/dokument/NL/lov/2003-12-05-100) -* [e-helse Direktoratet: “Normen”: Norms for health data](https://ehelse.no/normen) +* [e-helse Direktoratet: “Normen”: Norms for health research data](https://www.ehelse.no/normen/normen-dokumenter/Veileder-i-personvern-og-informasjonssikkerhet-i-forskningsprosjekter) ### Other laws of potential relevance to life sciences research data diff --git a/pages/national_resources/se_resources.md b/pages/national_resources/se_resources.md index 6aded0b5a..cfcc5dc5e 100644 --- a/pages/national_resources/se_resources.md +++ b/pages/national_resources/se_resources.md @@ -33,20 +33,20 @@ national_resources: how_to_access: Available to everyone with an affiliation to a Swedish academic institution. related_pages: your_tasks: [dmp, data_publication, sensitive] - url: https://nbis.se/support/supportform/index.php?form=consultation - - name: Swedish COVID-19 Data Portal - description: The Swedish COVID-19 Data Portal provides information, guidelines, tools and services to support researchers to utilise Swedish and European infrastructures for data sharing. + url: https://nbis.se/services/guidance-on-data-handling/apply + - name: Swedish Pathogens Portal + description: The Swedish Pathogens Portal provides information, guidelines, tools and services to support researchers to utilise Swedish and European infrastructures for data sharing. related_pages: tool_assembly: [covid-19] your_domain: [human_data] your_tasks: [sensitive, existing_data, data_publication] - url: https://covid19dataportal.se/ - - name: SNIC - description: The Swedish National Infrastructure for Computing (SNIC) is a national research infrastructure that makes available large-scale high-performance computing resources, storage capacity, and advanced user support, for Swedish research. + url: https://pathogens.se/ + - name: NAISS + description: The National Academic Infrastructure for Super­computing in Sweden (NAISS) is a national research infrastructure that makes available large-scale high-performance computing resources, storage capacity, and advanced user support, for Swedish research. how_to_access: An application is required to gain access to the compute and storage services. related_pages: your_tasks: [data_analysis, storage] - url: https://www.snic.se/ + url: https://www.naiss.se/ - name: SciLifeLab RDM Guidelines description: Knowledge hub for the management of life science research data in Sweden. url: https://data-guidelines.scilifelab.se/ @@ -55,13 +55,19 @@ national_resources: related_pages: your_domain: [human_data] your_tasks: [sensitive] - url: https://nbis.se/support/human-data.html - + url: https://data-guidelines.scilifelab.se/topics/research-involving-human-data/ + - name: Federated EGA Sweden node + description: Secure archiving and sharing of genetic and phenotypic data resulting from Swedish biomedical research projects. + instance_of: the-european-genome-phenome-archive + related_pages: + your_domain: [human_data] + your_tasks: [sensitive, existing_data, data_publication] + url: https://fega.nbis.se/ --- ## Introduction -This page provides a general overview of national resources on research data management (RDM) in Sweden, directed towards researchers and official collaborators. National goals and long term data management achievements are provided in the [**Research Bill 2020/21-60**](https://www.regeringen.se/4af915/contentassets/da8732af87a14b689658dadcfb2d3777/forskning-frihet-framtid--kunskap-och-innovation-for-sverige.pdf). +This page provides a general overview of national resources on research data management (RDM) in Sweden, directed towards researchers and official collaborators. National goals and long term data management achievements are provided in the [**Research Bill 2020/21-60**](https://www.regeringen.se/contentassets/da8732af87a14b689658dadcfb2d3777/forskning-frihet-framtid--kunskap-och-innovation-for-sverige.pdf). The Swedish ELIXIR node National Bioinformatics Infrastructure Sweden ([**NBIS**](https://nbis.se/)) offers support and training in data management to life science researchers in Sweden, in collaboration with the [**Data Centre**](https://www.scilifelab.se/data/) at Science for Life Laboratory (SciLifeLab). ## Funders with policies @@ -89,18 +95,18 @@ If personal data is processed in your research, contact your institute’s Data ## Domain-specific infrastructures and resources -The [**SciLifeLab Data Centre**](https://www.scilifelab.se/data) provides services for IT and data management, including Data Stewardship Wizard instance (for writing data management plans), the Swedish COVID-19 data portal, and the SciLifeLab Data Repository. +The [**SciLifeLab Data Centre**](https://www.scilifelab.se/data) provides services for IT and data management, including Data Stewardship Wizard instance (for writing data management plans), the Swedish Pathogens Portal, and the SciLifeLab Data Repository. -Data stewards at [**NBIS**](https://nbis.se/) (ELIXIR-SE) provide consultation and support services regarding data management questions, including e.g. guidance when writing data management plans and when doing submissions to domain-specific repositories. For information about this and other resources at NBIS please see the [**Data Management**](https://nbis.se/infrastructure/data-management/) page. An upcoming resource is the [**Swedish Sensitive Data Archive**](https://nbis.se/infrastructure/sensitive-data-archive.html), a secure data archive and sharing platform for sensitive datasets, which will be integrated with the {% tool "the-european-genome-phenome-archive" %}. +Data stewards at [**NBIS**](https://nbis.se/) (ELIXIR-SE) provide consultation and support services regarding data management questions, including e.g. guidance when writing data management plans and when doing submissions to domain-specific repositories. For information about this and other resources at NBIS please see the [**Data Management**](https://nbis.se/services/guidance-on-data-handling) page. An upcoming resource is the [**FEGA Sweden**](https://fega.nbis.se/), a secure data archive and sharing platform for sensitive datasets, which will be integrated with the {% tool "the-european-genome-phenome-archive" %}. -The [**Swedish National Infrastructure for Computing**](https://snic.se/) (SNIC) is a national research infrastructure that provides resources and user support for large scale computation and data storage to meet the needs of researchers from all scientific disciplines and from all over Sweden. Of particular use for life science researchers is the [**SNIC-SENS**](https://www.uppmax.uu.se/projects-and-collaborations/snic-sens/) project which provides high-performance computing resources for analyzing sensitive data. +The [**National Academic Infrastructure for Super­computing in Sweden**](https://www.naiss.se/) (NAISS) is a national research infrastructure that provides resources and user support for large scale computation and data storage to meet the needs of researchers from all scientific disciplines and from all over Sweden. Of particular use for life science researchers is the [**NAISS-SENS**](https://www.uppmax.uu.se/projects-and-collaborations/naiss-sens/) project which provides high-performance computing resources for analyzing sensitive data. [**Swedish National Data Service**](https://snd.gu.se/en) (SND) with its network of almost 40 higher education institutions and public research institutes, provides researchers with a coordinated and quality-assured system for finding, describing, and sharing research data, nationally as well as internationally. The [**SND network**](https://snd.gu.se/en/about-us/snd-network) has agreed to create local units for managing research data (Data Access Units (DAUs)), with the main task to assist researchers in their respective organisation in making research data as accessible as possible, via training and support in data management. SND also provides a [**DMP checklist**](https://snd.gu.se/en/manage-data/guides/dmp-checklist) to support researchers in writing data management plans. List of universities with established Research Data Offices or Data Access Units (DAUs), with links to local online resources and contact information: -* Chalmers University of Technology - [Research Data Support](https://www.lib.chalmers.se/en/publish-and-analyse/open-access/research-data/) - -* Karolinska Institutet - [Research Data Support](https://staff.ki.se/about-research-data-office-rdo) - -* KTH Royal Institute of Technology - [Research Data Support](https://intra.kth.se/en/forskning/overgripande-stod) - +* Chalmers University of Technology - [Research Data Support](https://www.chalmers.se/en/infrastructure/ecommons/chalmers-data-office/) - +* Karolinska Institutet - [Research Data Support](https://staff.ki.se/research-data-management) - +* KTH Royal Institute of Technology - [Research Data Support](https://www.kth.se/en/biblioteket/publicera-analysera/hantera-forskningsdata/) - * Linköping University - [Research Data Support](https://ep.liu.se/en/datamanagement.aspx) - * Linnaeus University - [Research Data Support](https://lnu.se/en/medarbetare/researcher/researcher5/research-data/) - * Lund University - [Research Data Support](https://www.lub.lu.se/en/services-and-support/research-data/contacts-and-research-data-initiatives) - See web page for contact information diff --git a/pages/tool_assembly.md b/pages/tool_assembly.md index 31dd34653..64b3cbf53 100644 --- a/pages/tool_assembly.md +++ b/pages/tool_assembly.md @@ -5,4 +5,4 @@ search_exclude: true Tool Assemblies are examples of combining tools to cover data management tasks across several stages of the data life cycle. These can be tools that one or several communities combine to support RDM that can be picked up or accessed and used by others. The assemblies are aimed for users in a specific location and/or for users within a specific domain. -{% include section-navigation-tiles.html type="tool_assembly" affiliations="true" search=true %} +{% include section-navigation-tiles.html type="tool_assembly" affiliations="true" search=true col=3 %} \ No newline at end of file diff --git a/pages/tool_assembly/csc_assembly.md b/pages/tool_assembly/csc_assembly.md index 5f42a8ecd..3cadca516 100644 --- a/pages/tool_assembly/csc_assembly.md +++ b/pages/tool_assembly/csc_assembly.md @@ -1,6 +1,6 @@ --- title: CSC -contributors: [Siiri Fuchs, Minna Ahokas] +contributors: [Siiri Fuchs, Minna Ahokas, Janina Juuvinmaa] description: The Center of Science (CSC) provides high-quality ICT expert services for researchers in Finland and their collaborators. page_id: csc affiliations: [FI, CSC, ELIXIR Europe] @@ -34,7 +34,7 @@ training: ## Who can use the CSC data management tool assembly? -CSC and ELIXIR-FI services are accessible for researchers in Finland and to foreign collaborators of Finland-based research groups. Most of CSC's services are [free-of-charge](https://research.csc.fi/free-of-charge-use-cases) for academic research, education and training purposes in Finnish higher education institutions and in state research institutes. Researchers can start using services by registering an [account](https://research.csc.fi//accounts-and-projects) and get bioinformatics user support from our [service desk](mailto:servicedesk@csc.fi). +CSC and ELIXIR-FI services are available for researchers affiliated with a Finnish academic organisation or research institutes and their international collaborators. Most of CSC’s services are [free of charge](https://research.csc.fi/free-of-charge-use-cases) for academic research, education and training purposes in Finnish higher education institutions and state research institutes. Researchers can start using services by registering an account and get bioinformatics user support from our [service desk](mailto:servicedesk@csc.fi). ## How can you access the CSC data management tool assembly? @@ -43,27 +43,27 @@ You can access all CSC services through several secure authentication methods. S ## For what can you use the CSC data management tool assembly? -{% include image.html file="fi_csc_assembly.svg" caption="Figure 1. The CSC - IT Center for Science data management tool assembly." alt="CSC RDMkit" %} +{% include image.html file="fi_csc_assembly_v2.svg" caption="Figure 1. The CSC - IT Center for Science data management tool assembly." alt="CSC RDMkit" %} ### Data management planning -You can get support for data management [planning](planning) through [DMPTuuli](https://dmptuuli.fi/), a Finnish instance of DMPonline which includes guidance and templates provided by different organisations and funders. DMPs created in DMPTuuli are not yet machine actionable or linked to the CSC data management tool assembly services. However, a DMP is a valuable document when contacting the CSC research data management services. +Research funders often require a data management plan (DMP) as part of the funding application process or after funding has been approved. See e.g. guidance on creating a [DMP for the Research Council of Finland (former Academy of Finland)](https://www.aka.fi/en/research-funding/apply-for-funding/how-to-apply-for-funding/az-index-of-application-guidelines2/data-management-plan/data-management-plan/). There are several tools to guide you through the planning process, such as [DMPTuuli](https://dmptuuli.fi/), a Finnish instance of DMPonline which includes guidance and templates provided by different organisations and funders. DMPs created in DMPTuuli are not yet machine-actionable or linked to the CSC data management tool assembly services. However, a DMP is a valuable document when contacting and using the CSC research data management services. Other available DMP tools include {%tool "data-stewardship-wizard" %} and {%tool "dmponline" %}. Furthermore, research data support services at Finnish research organisations offer help and guidance through the planning process and when making decisions on data management. ### Data collection -When you start [collecting](collecting) data and need a storing environment where you can, for example, host cumulating or changing data, [Allas Object Storage](https://research.csc.fi/-/allas) is the recommended option. Indeed, Allas is CSC's general purpose research data storage server, which can be accessed on the CSC servers as well as from anywhere on the internet. Allas can be used both for static research data that needs to be available for analysis and to collect cumulating or changing data. For example, if you work with sequence data, the sequencing provider can transfer the data directly to Allas under your project. +When you start [collecting](collecting) data and need a storing environment where you can, for example, host cumulating data, [Allas Object Storage](https://research.csc.fi/-/allas) is the recommended option. Indeed, Allas is CSC’s general purpose research data storage server, which can be accessed on the CSC servers as well as from anywhere on the internet. Allas can be used both for static research data that needs to be available for analysis and to collect cumulating data. For example, if you work with sequence data, the sequencing provider can transfer the data directly to Allas under your project. However, as an object storage system, Allas is not suitable for very dynamic data like SQL databases. ### Data processing and analysis -For [processing](processing), [analysing](analysing) and [storing data](storage) during the research project, CSC offers several [computing platforms](https://research.csc.fi/computing). These include both environments for non-sensitive and [sensitive data](data_sensitivity). Depending on your needs, you can choose from a wide variety of computing resources: use [Chipster](https://chipster.csc.fi/) software for high-throughput data such as RNA-seq and single cell RNA-seq, build your own custom virtual machine, or utilise the full power of our world-class supercomputers. +For [processing](processing), [analysing](analysing) and [storing data](storage) during the research project, CSC offers several [computing platforms](https://research.csc.fi/computing). These include both environments for non-sensitive and [sensitive data](data_sensitivity). Depending on your needs, you can choose from a wide variety of computing resources: use [Chipster](https://chipster.csc.fi/) software for high-throughput data such as RNA-seq and single-cell RNA-seq, build your own custom virtual machine, or utilise the full power of our world-class supercomputers. -Supercomputers Puhti and Mahti can be used for larger scale analysis and simulations. They will soon be accompanied with the world-class supercomputer {% tool "lumi" %}. Pouta and Rahti cloud computing services offer more flexibility, allowing the user to manage the infrastructure. CSC's computers have a wide range of [preinstalled scientific software and databases](https://research.csc.fi/bioscience-programs) with usage instructions. +CSC is hosting  EuroHPC's world-class supercomputer [LUMI](https://www.lumi-supercomputer.eu/), which is available for researchers across Europe for projects requiring extreme computing capacity. LUMI is one of the world's most competitive supercomputers and one of the most advanced platforms for artificial intelligence. In addition, you can utilise two national supercomputers Puhti and Mahti for medium to large-scale simulations. Pouta and Rahti cloud computing services offer more flexibility, allowing the user to manage the infrastructure. CSC’s computers have a wide range of [preinstalled scientific software and databases](https://research.csc.fi/bioscience-programs) with usage instructions. -This summer, CSC will be releasing beta versions of new services for sensitive data management: Sensitive Data Desktop (SD Desktop) and Sensitive Data Connect (SD Connect). Sensitive Data Submit (SD Submit) will be available later this year. The new Sensitive Data Services are designed to facilitate collaborative research across Finland and between Finnish academics and their collaborators. [SD Desktop](https://research.csc.fi/-/sd-desktop) is a service that allows a user and their authorized colleagues to access a private computing environment workspace via a web browser and analyze the data within a secure cloud. [SD Connect](https://research.csc.fi/-/sd-connect) allows you to collect, organize and share your encrypted sensitive data in a secure manner via web browser. +For management of sensitive data, the [SD Connect](https://research.csc.fi/-/sd-connect) and [SD Desktop](https://research.csc.fi/-/sd-desktop) services are available. The Sensitive Data Services are designed to facilitate collaborative research across Finland and between Finnish academics and their collaborators.  SD Connect allows you to collect, organise and share your encrypted sensitive data in a secure manner via web browser or programmatically.  SD Desktop is a service that allows a user and their authorized colleagues to access a private computing environment workspace via a web browser and analyse the data within a secure cloud. A restricted version of SD Desktop is also available for processing health and social data for secondary use in compliance with the Finnish law and Findata regulation. ### Data sharing and publishing -It is recommended to [publish](data_publication) data in data specific repositories. You can find many options from {% tool "elixir-deposition-databases-for-biomolecular-data" %}. Furthermore, CSC and ELIXIR-FI will offer Federated EGA for sensitive human biomedical data that is linked to the central European Genome-phenome Archive and the SD Submit at the end of 2021. +It is recommended to [publish](data_publication) data in data specific repositories. You can find many options from {% tool "elixir-deposition-databases-for-biomolecular-data" %}. Furthermore, CSC and ELIXIR-FI will offer Federated EGA for sensitive human biomedical data that is linked to the central {%tool "the-european-genome-phenome-archive" %} -SD Submit allows you to publish sensitive data securely in a national repository. The service will give you the tools to describe your dataset (adding the appropriate metadata) and assign a permanent identifier (DOI). After publication, you will remain the data controller and decide according to specific policies, who can access the sensitive data for reuse. According to the GDPR, your data will remain within the Finnish borders and, at the same time, they will be accessible and discoverable according to FAIR data principles. +The Finnish Federated EGA is part of a European network of repositories for biomedical data. The service will give you the tools to describe your dataset (adding the appropriate metadata) and assigning an EGA accession number.  After publication, you will remain the data controller and decide according to specific policies, who can access the sensitive data for reuse. According to the GDPR, your data will remain within the Finnish borders and, at the same time, they will be accessible and discoverable according to FAIR data principles. -In addition to the above mentioned services, you can use national [Fairdata.fi services](https://research.csc.fi/-/fairdata-services). Fairdata IDA storage service enables saving, organising and sharing data within the project group and storing the data in an immutable state. After freezing your data in IDA, you can use Qvain, the research dataset description tool, to describe your data and thus create core metadata for your dataset, and publish it. Publishing means that your dataset will be published in Etsin, the research data finder, where you can discover and download any files you have associated with the dataset. Any published dataset is also made available to the [Research.fi portal](https://research.fi/) automatically by Fairdata services. +In addition to the above mentioned services, you can use national [Fairdata.fi services](https://research.csc.fi/-/fairdata-services). Fairdata IDA storage service enables saving, organising and sharing data within the project group and storing the data in an immutable state. After freezing your data in IDA, you can use Qvain, the research dataset description tool, to describe your data and thus create core metadata for your dataset, and publish it. Publishing means that your dataset will be published in Etsin, the research data finder, where you can discover and download any files you have associated with the dataset. Any published dataset is also made available to the [Research.fi](https://research.fi/en/) portal automatically by Fairdata services. diff --git a/pages/tool_assembly/fairtracks_assembly.md b/pages/tool_assembly/fairtracks_assembly.md new file mode 100755 index 000000000..3907095f8 --- /dev/null +++ b/pages/tool_assembly/fairtracks_assembly.md @@ -0,0 +1,115 @@ +--- +title: FAIRtracks +contributors: [Federico Bianchini, Sveinung Gundersen] +description: The FAIRtracks ecosystem provides technical solutions for the FAIRification of genome browser track files +page_id: fairtracks +affiliations: ["NO", "ES", "EMBL-EBI"] +related_pages: + your_tasks: [data_publication, data_transfer, metadata] + your_domain: [plants, rare_disease, single_cell_sequencing, human_data] +training: + - name: Training in TeSS + registry: TeSS + url: https://tess.elixir-europe.org/search?q=fairtracks +--- + +## What is the FAIRtracks tool assembly? + +The [FAIRtracks ecosystem](https://fairtracks.net/) is a set of services associated with a minimal +[metadata model](https://fairtracks.net/standards/#standards-01-fairtracks) for +[genomic annotations/tracks](https://fairtracks.net/tracks/#tracks-01-genomic-tracks), +implemented as a [set of JSON Schemas](https://github.com/fairtracks/fairtracks_standard/tree/master/json/schema). +The FAIRtracks model contains metadata fields particularly useful for data discovery, +harmonised through strict adherence to a selection of ontologies available through the {%tool "ontology-lookup-service" %}. +The usability of the model can be expanded through referencing the original records via Compact Uniform Resource Identifiers (CURIEs) +resolvable by {% tool "identifiers-org" %}. + +In the context of the Data Life Cycle and its stages, the FAIRtracks ecosystem covers [Collecting](collecting), [Processing](processing), +[Analysing](analysing), [Sharing](sharing), and [Reusing](reusing). It has to be noted, however, that the FAIRtracks ecosystem is structured +around a secondary data life cycle, as illustrated in Figure 1. As part of this secondary life cycle, the annotation/track data gets further distributed +and its discovery is enhanced through derived metadata. The FAIRtracks ecosystem aims at harmonising this process. +Primary data needs to be handled independently following domain best practices +(see e.g. the pages on [Single cell sequencing](single_cell_sequencing), [Plant sciences](plant_sciences), or [Rare disease data](rare_disease_data)). + +The FAIRtracks ecosystem is developed and provided as part of the national Service Delivery Plans by +[ELIXIR Norway](https://elixir.no/) and [ELIXIR Spain](https://elixir-europe.org/about-us/who-we-are/nodes/spain), +and is supported by the [Track Hub Registry group](https://trackhubregistry.org/) at [EMBL-EBI](https://www.ebi.ac.uk/). +FAIRtracks is endorsed by [ELIXIR Europe](https://elixir-europe.org/) as a +[Recommended Interoperability Resource](https://elixir-europe.org/platforms/interoperability/rirs). + +{% include image.html file="fairtracks_tool-assembly.png" caption="Figure 1. Illustration of the Data life cycle +for the FAIRtracks tool assembly. As genomic tracks/annotations often represent condensed summaries of the raw data, +this ecosystem covers a secondary cycle designed around the FAIRtracks metadata model. +The grey box shows the areas of relevance for the FAIRtracks ecosystem with its integrations, +and only a subset of the icons represents FAIRtracks services per se. Omnipy (dark grey box) is a general Python library +for scalable and reproducible data wrangling which can be used across several data models and research disciplines." +alt="FAIRtracks RDMkit" %} + +## Who can use the FAIRtracks tool assembly? + +There is no central authentication solution for the FAIRtracks services requiring login. +The entire FAIRtracks ecosystem is available to everyone. +Most of the services are accessible through Application Programming Interfaces (APIs). More details are provided in the description below. +Users of the FAIRtracks ecosystem belong to different categories, which could be summarised as: + +- Researchers and data analysts +- Data providers and biocurators +- Developers working on tooling for + - Research + - Implementation of the FAIR data principles + +Each of these categories benefits specifically from a subset of the global ecosystem. +The core services can be accessed both upstream (for data providers and biocurators) and downstream (for tool developers and analytical end users). + +## For what can you use the FAIRtracks tool assembly? + +The FAIRtracks tool assembly can be used for a large number of applications; we summarise the main ones below following the steps of the data life-cycle +and focusing on particular tools. + +While the assembly does not include a tool for [Data Management Planning](dmp), +the FAIRtracks metadata standard is registered in {%tool "fairsharing" %} +and, thus, formally connected to several other standards and databases. +The FAIRtracks standard can, thus, be selected on your Data Management Plan in all the instances of {% tool "data-stewardship-wizard" %} through +the integration with {%tool "fairsharing" %}. + +{%tool "omnipy" %} is a high-level Python library for type-driven data wrangling and scalable data flow orchestration; +it is a self-standing subset of the FAIRtracks ecosystem covering several steps in the data life-cicle. +It can be used to extract metadata from specific portals and for [Processing](processing) of metadata entries to harmonise them into a unique model. +{%tool "omnipy" %} data flows are defined as transformations from specific input data models to specific output data models. +Input and output data are validated at each iteration through parsing based on {%tool "pydantic" %}. +Offloading of data flows to external compute resources is provided through the integration of {%tool "omnipy" %} with an orchestration engine based on {%tool "prefect" %}. + +There is ongoing work into adding {%tool "prefect" %} as one of the services available in the +[National Infrastructure for Research Data (NIRD) service platform](https://www.sigma2.no/nird-service-platform). +This would enable running {%tool "omnipy" %} on data and metadata stored in the [NIRD data storage](https://www.sigma2.no/data-storage). +Refer also to the [Norwegian national page](no_resources) for more details. Note that, while the usage of NIRD storage and services +is certainly convenient for Norwegian users, this is not a central or mandatory part of the tool assembly which is born as an international +service and aims at maintaining this status. + +Data [Sharing](sharing) and preservation is one of the key components of the FAIRtracks ecosystem. +Since genomic annotations/tracks typically consist of secondary data files referring to primary data sources, +they are often deposited together with the primary data. The aim of the minimal metadata model is to +offer a greater level of granularity, providing each track with an identifier and enabling the possibility of analysis across datasets +in an automatised fashion. A dedicated registry would typically be required to accomplish this. Given that such a registry does not yet exist, +the current recommendation is to deposit FAIRtracks-compliant metadata files to {%tool "zenodo" %}, +as this platform supports both Digital Object Identifier (DOI) versioning and DOI reservation before publication. +The identifiers on the metadata FAIRtracks object are then cross-linked with the actual data which is hosted +e.g. in a [Track Hub](https://genome.ucsc.edu/goldenPath/help/hgTrackHubHelp.html) and registered in +the {%tool "track-hub-registry" %}. + +Data and metadata organised in this fashion can be discovered for [Reusing](reusing) through {%tool "trackfind" %}, +a search and curation engine for genomic tracks. +{%tool "trackfind" %} will import FAIRtracks-compliant metadata from e.g. {%tool "zenodo" %}. +This metadata can be accessed through hierarchical browsing or by search queries both through a web-based user interface and as a RESTful API. +TrackFind supports advanced SQL-based queries that can be easily built into the user interface. + +Additional tools that comprise the core of the FAIRtracks ecosystem are the +[metadata validation](https://fairtracks.net/services/?category=Core%20services&tags%5B0%5D=Metadata%20validation) and the +[metadata augmentation](https://fairtracks.net/services/?category=Core%20services&tags%5B0%5D=Metadata%20augmentation) services. +The former is REST API that extends the standard JSON Schema validation technology to +e.g. validate ontology terms or check CURIEs against the registered entries. +The [FAIRtracks augmentation service](https://fairtracks.net/services/?category=Core%20services&tags%5B0%5D=Metadata%20augmentation) +is implemented as a REST API that expands on the information contained in a minimal FAIRtracks JSON by adding +a set of fields with human-readable values including ontology labels, versions, and summaries. +This service bridges the gap between data providers, which are required to submit only minimal information, and data consumers +who require richer information for data discovery and retrieval. diff --git a/pages/tool_assembly/molgenis_assembly.md b/pages/tool_assembly/molgenis_assembly.md index 834063978..1a73184e8 100644 --- a/pages/tool_assembly/molgenis_assembly.md +++ b/pages/tool_assembly/molgenis_assembly.md @@ -5,7 +5,7 @@ description: Molgenis is a modular web application for scientific data. Flexible page_id: molgenis affiliations: ["BBMRI-NL"] related_pages: - your_tasks: [data analysis, data publication ,storage, data quality, data transfer, metadata, sensitive data] + your_tasks: [data_analysis, data_publication ,storage, data_quality, transfer, metadata, sensitive_data] your_domain: [] training: - name: Intro diff --git a/pages/tool_assembly/nels_assembly.md b/pages/tool_assembly/nels_assembly.md index aa51f194c..cb64eccd2 100644 --- a/pages/tool_assembly/nels_assembly.md +++ b/pages/tool_assembly/nels_assembly.md @@ -10,7 +10,7 @@ related_pages: training: - name: Training in TeSS registry: TeSS - url: https://tess.elixir-europe.org/search?q=norway + url: https://tess.elixir-europe.org/search?q=NeLS+Norway --- ## What is the NeLS data management tool assembly? diff --git a/pages/your_domain.md b/pages/your_domain.md index 82e1e31bf..0d2389402 100644 --- a/pages/your_domain.md +++ b/pages/your_domain.md @@ -12,6 +12,5 @@ In this section, information is organised based on different domains in life sci -{% include section-navigation-tiles.html type="your_domain" search=true %} - +{% include section-navigation-tiles.html type="your_domain" search=true col=3 %} diff --git a/pages/your_domain/human_data.md b/pages/your_domain/human_data.md index 9628430e6..cb018f9d0 100644 --- a/pages/your_domain/human_data.md +++ b/pages/your_domain/human_data.md @@ -5,7 +5,7 @@ contributors: [Niclas Jareborg, Nirupama Benis, Ana Portugal Melo, Pinar Alper, page_id: human_data related_pages: your_tasks: [sensitive, gdpr_compliance] - tool_assembly: [tsd, covid-19, transmed] + tool_assembly: [tsd, covid-19, transmed, fairtracks] training: - name: Training in TeSS registry: TeSS diff --git a/pages/your_domain/machine_learning.md b/pages/your_domain/machine_learning.md new file mode 100644 index 000000000..f6ea361eb --- /dev/null +++ b/pages/your_domain/machine_learning.md @@ -0,0 +1,101 @@ +--- +title: Machine learning +description: This page is about setting best practices for data towards enabling FAIR ML. +contributors: [Styliani-Christina Fragkouli, Uladzislau Vadadokhau, Adriano Fonzino, Fotis Psomopoulos, Mihaly Varadi, +Edward Parkinson, Federico Bianchini, Munazah Andrabi] +page_id: machine_learning +related_pages: + your_tasks: [data_quality, sensitive] +training: + - name: Machine learning events and materials on TeSS + registry: TeSS + url: https://tess.elixir-europe.org/search?q=machine+learning#events + +--- +## What is machine learning (on this page)? + +### Description +The definition of Machine Learning (ML) can become so vague that it can reach the point of becoming a philosophical question. There are two main approaches; the first one considers the model as the centre of the definition. In this perspective, ML is defined mainly by the characteristics and capabilities of the model employed. This approach focuses on the technical aspects of ML, by emphasising factors such as algorithms, data processing and model performance {% cite ravi2022 %}. +In contrast, the second approach places the entire ML process at the centre of the definition of ML. In this form, the entire process is seen as a single entity, which includes all internal aspects/steps such as data collection, preprocessing, feature engineering, model training, evaluation and deployment. This approach acknowledges that all these stages are interconnected. + +For the purposes of this page, we will be using the first definition, i.e. the model-centred approach. The reasons behind this choice mainly include the technical focus with a strong emphasis on the technical aspects of ML and the performance evaluation because it provides a clear framework for comparing against other models. + +## What is FAIR in machine learning? + +### Description +As global standards for good data stewardship, the FAIR principles have become instrumental in ML, impacting policies and practices across sectors {% cite psomopoulos2023Roadmap %}. Embraced by policymakers and research institutes, implementing FAIR in ML enhances technical performance and proves economically advantageous. + +FAIR is an aspirational guideline, not a stringent standard, propelling better data utilisation in ML. Applying the FAIR principles in ML involves creating easily discoverable models with comprehensive metadata, openly accessible with clear licensing, compatible with other systems via standardised formats, and designed for reuse with meticulous documentation, licensing and versioning {% cite huerta2023FAIR %}. +Actualising these principles necessitates infrastructural adjustments and stakeholder education {% cite castro2021Working %}. + +FAIR ML holds immense potential in life sciences, from accelerating drug discovery processes to driving innovative bioinformatics applications through data interoperability from various sources. The transformative power of FAIR extends to enhancing predictive modelling, genetic research, disease diagnosis, and much more, demonstrating the criticality of its adoption for the future of life sciences {% cite ravi2022FAIR %}. + + +### Considerations + +There are several key considerations in applying the FAIR principles in ML and the life sciences: +- Findability: How can an ML model be easily identified? What metadata is necessary to make a model easily searchable? +- Accessibility: What measures must be in place to ensure the ML model is easily accessible? How does licensing impact the accessibility and usage of the model? +- Interoperability: How can an ML model effectively work with other systems or models? What role does data standardisation play in this regard? What documentation is needed to ensure seamless interoperability? What is the community-backed metadata schema used for annotating the model? +- Reusability: How can an ML model be designed for optimal reuse? What is the role of version control and comprehensive documentation in ensuring others can easily understand and build upon the model? +- Ethics and Privacy: What ethical considerations and privacy protections must be implemented when dealing with data, especially in sensitive areas like healthcare? How might these considerations impact the FAIRness of ML models? +- Quality Assurance: How can the data quality used for ML models be controlled? How does data quality impact the robustness and reliability of ML models? +- Sustainability: What are the strategies for maintaining models and their dependencies? +- Evaluation: How can FAIR models be transparently evaluated? What role does documentation of performance metrics, validation procedures, and evaluation results play in providing users with an understanding of a model’s capabilities and limitations? + +### Solutions +Applying the FAIR principles in ML is the focal point of initiatives such as the ELIXIR {% tool "dome" %} {% cite walsh2021Author %} recommendations and the [ELIXIR ML Focus Group](https://elixir-europe.org/focus-groups/machine-learning). The following solutions apply to the above-mentioned considerations at a high level: +- Use community-backed and standardised metadata that includes details about the model's authors, creation date, model type, training data, intended tasks, and performance metrics (such as {% tool "bioimage" %}, {% tool "schema-org" %}, {% tool "dome" %}, and {% tool "onnx" %}). +- Assign a unique and persistent identifier to each model. This identifier should be linked to the model's metadata to improve searchability. +- Ensure that models are open-sourced and shared on public platforms (e.g. {% tool "github" %} or {% tool "huggingface" %}) to improve accessibility. +- Specify the terms of use in the licensing information, or link to standard licenses. This must include what users are allowed to do with the model. The most commonly used license on {% tool "github" %} is the [MIT License](https://opensource.org/license/mit/), other options can be explored [here](https://choosealicense.com/). +- Implement Application Programming Interfaces (APIs) to allow easy interaction with the model. +- Use standardised formats for sharing models, making them compatible with different platforms and languages ({% tool "onnx" %} for neural networks, for example). +- Provide clear documentation about the model's requirements, design, usage, and output formats. This will ensure the model can interact with other systems more easily. +- Implement version control for the models. This allows for tracking changes, resolving conflicts, and supporting reusability. +- Provide comprehensive documentation about the model's training data, architecture, hyperparameters, and performance. This allows other researchers to reproduce the original work or build upon it. +- Design models modularly, separating data pre-processing, the model itself, and post-processing. This makes each component reusable in different contexts. +- Ensure that models and their dependencies (like libraries or data sources) are maintained regularly or provide versioned snapshots of your library and all its dependencies, e.g. by offering dockerised containers. +- Implement anonymisation techniques for sensitive data and always gain consent before using data, especially in sensitive areas like healthcare. Note that privacy considerations can affect the FAIRness of the data, but models can be trustworthy even when they compromise on specific FAIRness aspects. See also the [Data sensitivity page](data_sensitivity) for more information. + +## Data readiness for the ML models + +### Description +The success of an ML model depends on the input data. Finding an appropriate dataset can be a challenge. The data has to be cleaned, explored, and evaluated before ML model training. Data preparation is often the most time-consuming step in the AI lifecycle. The “Garbage in, Garbage out” principle (GIGO) has to be kept in mind at this stage: models trained on unreliable data will produce unreliable predictions {% cite sansone2023FAIR %}. + +To prepare data for ML models, several steps need to be followed. Firstly, data should be collected from reliable and diverse resources, ensuring it represents the problem domain accurately. Then, data cleaning techniques such as removing duplicates, handling missing values, and addressing outliers should be applied to enhance data quality. A commonly used practice for this task is the implementation of {% tool "scikit-learn" %}. Regarding features, on one hand there is the selection or extraction that can be performed to identify the most relevant and informative attributes for the model {% cite comess2020Bringing %}. On the other hand, there is the problem of the number of features which exceeds that of observations due to the nature of data in life sciences {% cite berisha2021Digital %}. Furthermore, data normalisation and standardisation might be necessary to scale features appropriately. Also, over or under-sampling can be used to handle an imbalanced dataset {% cite williamson2023Data %}. + +### Considerations +- The type of the data (text, images, time series, and others). +- Is there a “curse” of the data (more features than observations)? +- Are all variables converted to numeric type (data encoding)? +- Does the data need preprocessing? +- Are there outliers in the data? +- How are missing values treated (drop vs impute, random and non-random missing values)? +- Are there duplicates in the data? +- Does every subset (train, validation, and test) of the data represent the overall population, if data has been split? +- Data provenance: The source of the data, and whether it is recognised by the community. (source of data, involved points and more) +- Dataset size: Ensure the data set captures the full complexity of the underlying distribution, for example, the biological heterogeneity in the sample population. +- Balanced dataset: a dataset where all classes are properly and equally represented (i.e. same number of cases in each class and more). +- Availability of data/License associated with data +- Are the data processed and AI-ready? One of the possible ways to achieve this would be through a curation effort. + +### Solutions +- Classical ML algorithms have to be applied with caution to the high-dimensional data. For example, in a high-dimensional space distances between points have little difference meaning that algorithms based on distance measurement would fail. Additionally, the term blind spots can be defined here as a particular combination(s) of variables not covered by a given set of observations. Therefore, “cursed” data often represents a distorted picture of a real-world concept. One of the most direct solutions is to reduce the dimensional space. For instance, the theory of the domain can be applied to get a subset of features that are proven to be related to the target variable. Principal components analysis (PCA) and likewise methods do not result in domain-specific features but increase the model's general ability by reducing its variance. +- ML models take only numeric values as input with some exceptions (e.g. {% tool "catboost" %}). Categorical data has to be encoded into numbers via various strategies. The simplest is one-hot encoding. Namely, a new feature per every level of a categorical variable is created and the relation of that level to a particular observation is encoded with 1 (observation has this level of a variable) or 0. The drawback of this method is that the feature number is increased if the categorical variable has many levels. Other methods include label/ordinal encoding, target encoding, frequency encoding, and vectorisation (for text data). It is always a good idea to encode a variable in a way where encoding has a meaning which can help a model to recognise patterns in the data. For example, a variable storing temperature values with levels cold, warm, and hot can be encoded with 1 (cold), 2 (warm), and 3 (hot). However, the context of the task has to be considered in this case. +- Before dropping anomalies from the data, it is required to determine their nature and whether it is possible to convert them to regular values with a high level of confidence. +- Augmentation allows artificially increasing the diversity of the data by realistic data points to improve performance (decreasing overfitting, resolving class imbalance, rare events prediction) of the ML model or reducing the cost of data collection. Images can be augmented by random rotations, re-scaling, cropping, zooming, and noise addition. Several neural networks for this purpose are used (GANs, adversarial ML, neural style transfer). For NLP purposes word(s) deletion/insertion/swap and synonym replacement are used. For the regular table data, synthetic observations can be generated by drawing values from a distribution, utilising agent-based and deep-learning models. However, this process has to be planned carefully and performed with the assistance of an expert in the domain and with the utilisation of suitable tools like {% tool "keras" %}, {% tool "tensorflow" %} and {% tool "cuda" %}. +- For some ML algorithms, variables have to be put on the same scale. It is a crucial rule for unsupervised algorithms based on distance evaluation between data points (e.g. K-Means) and algorithms for dimensionality reduction (e.g. PCA). On the other hand, linear regression, tree-based algorithms, and neural networks can handle non-normalised data. The most popular technique is mean scaling, where each data point is converted to a z-score with a mean equal to 0 and a standard deviation equal to 1. In addition, data can be rescaled to a range [0, 1] by the min-max scaling technique. +- Source Verification: Establish robust mechanisms to verify the source of data and ensure its authenticity. Use cryptographic techniques or digital signatures to validate data integrity and origin. +- Metadata and Documentation: Maintain comprehensive metadata and documentation for each dataset, including information about data collection methodologies, ethical considerations, and any potential biases. This helps establish transparency and assists in community recognition. +- Depending on the type of data, certain preprocessing and transformation techniques can be followed: + - Text Data: Implement text preprocessing techniques such as tokenisation, stemming, and stop-word removal to prepare textual data for analysis. Apply techniques like term frequency-inverse document frequency (TF-IDF) or word embeddings to represent text data in a numerical format. + - Image Data: Utilise image preprocessing techniques such as resizing, cropping, and normalisation to standardise image sizes and enhance model performance. Apply image augmentation techniques like rotation, flipping, and colour jittering to increase dataset diversity. + - Time-Series Data: Perform time-series preprocessing techniques such as resampling, interpolation, and feature engineering to extract relevant temporal patterns. Apply sliding windows or lagged variables to capture temporal dependencies. +- License: It is of pivotal importance to work in agreement and compliance with the license of data especially if you are planning to share them or a part of those also if those have been preprocessed. +- Data Balance: The dataset must be balanced. For data in labelled classes, ensure each class is sufficiently represented in the dataset. For data with continuous labels, like in regression tasks, evaluate how well the range of outputs is covered. +- Curation: If you are sharing a dataset there is the need to provide information about the curation process performed or if the dataset has to be curated before its usage. + +## Bibliography + +{% bibliography --cited %} diff --git a/pages/your_domain/microbial_biotechnology.md b/pages/your_domain/microbial_biotechnology.md index f62391e16..c7f3489f8 100644 --- a/pages/your_domain/microbial_biotechnology.md +++ b/pages/your_domain/microbial_biotechnology.md @@ -71,15 +71,15 @@ It is recommended to publish and share information about biological hosts in ded #### Metadata schemas and ontologies * Current data standards to capture the taxonomic and phenotypic data are still evolving, with notable work on the {% tool "access-to-biological-collection-data-schema" %} and the activities of the {% tool "biodiversity-information-standards" %}. The Darwin Core standard from the {% tool "biodiversity-information-standards" %} is an appropriate standard to provide metadata about the taxonomic properties of a particular microorganism. * The {% tool "ncbi-taxonomy" %}homepage can also provide appropriate taxon IDs for recording taxonomic information. -* Information about proposed standardised nomenclature for prokaryotes can be found at the {% tool "list-of-prokaryotic-names-with-standing-in-nomenclature" %} ([Parte et al., 2020](https://pubmed.ncbi.nlm.nih.gov/32701423/)). -* Data standards for recording the information about where a microorganism was isolated from do exist and this topic is covered in other RDMkit pages such as the [marine metagenomics](marine_metagenomics) domain. Information can also be found in a publication by Ten Hoopen and colleagues ([Ten Hoopen et al., 2015](https://pubmed.ncbi.nlm.nih.gov/26203332/)). +* Information about proposed standardised nomenclature for prokaryotes can be found at the {% tool "list-of-prokaryotic-names-with-standing-in-nomenclature" %} {% cite parte2020lpsn %}. +* Data standards for recording the information about where a microorganism was isolated from do exist and this topic is covered in other RDMkit pages such as the [marine metagenomics](marine_metagenomics) domain. Information can also be found in a publication by Ten Hoopen and colleagues {% cite tenhoopen2015m2b3 %}. * {% tool "the-environment-ontology" %} is also relevant here to describe environmental entities of all kinds, from microscopic to intergalactic scales. -* A set of genetic nomenclature standards have been established by microbiologists and have been used for many years. These are still a useful way of communicating data about the genotype of a strain ([Maloy and Hughes, 2007](https://pubmed.ncbi.nlm.nih.gov/17352909/)). -* Minimal information standards have been established to specify this metadata, such as the MIGS standard ([Field et al., 2008](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2409278/)). +* A set of genetic nomenclature standards have been established by microbiologists and have been used for many years. These are still a useful way of communicating data about the genotype of a strain {% cite maloy2007strain %}. +* Minimal information standards have been established to specify this metadata, such as the MIGS standard {% cite field2008migs %}. #### (Meta)data publication and sharing * For sharing host information, you can use databases such as the {% tool "bacdive" %}. You can also deposit strains and associated information in a strain repository such as the {% tool "ncimb" %} or the {% tool "atcc" %}. There are also many organisations established for individual species of microorganisms, the {% tool "bacillus-genetic-stock-center" %} being one example. -* Databases such as {% tool "cellrepo" %} allow strains that have been barcoded to be tracked using a version control type system ([Tellechea-Luzardo et al., 2020](https://pubmed.ncbi.nlm.nih.gov/32078768/)). +* Databases such as {% tool "cellrepo" %} allow strains that have been barcoded to be tracked using a version control type system {% cite tellechealuzardo2020linking %}. * Genomic information can be captured at the nucleotide level using the well-known {% tool "european-nucleotide-archive" %} and submitted to the ENA database to allow the information to be shared. * The database collection from the {% tool "international-nucleotide-sequence-database-collaboration" %} provides an umbrella for gathering and sharing a variety of sequence data from different sequence databases internationally. * Other databases such as {% tool "genbank" %} and the {% tool "dna-data-bank-of-japan" %} also cater for sequence information. @@ -101,7 +101,7 @@ Appropriate and detailed description of the synthetic parts design is critical f #### Existing data * Sequences are characterised as parts which can be found with the assistance of various repositories such as: * {% tool "igem-parts-registry" %} - * {% tool "jbei-ice" %} ([Ham et al., 2012](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3467034/)) + * {% tool "jbei-ice" %} {% cite ham2012jbeiice %} * {% tool "synbiohub" %} * {% tool "freegenes" %} - Repository of IP-free synthetic biological parts * Sequences can be isolated from standard genetic databases such as {% tool "european-nucleotide-archive" %} and {% tool "genbank" %}. @@ -109,9 +109,9 @@ Appropriate and detailed description of the synthetic parts design is critical f #### Tools for metadata collection * You can manage the design stage using genetic computer aided design tools, such as {% tool "benchling" %} for example, where information can be shared within small teams. {% tool "benchling" %} supports a number of different data standards including FASTA, GenBank and SBOL1. * Sometimes FASTA will be the most relevant format, for example when sending for DNA synthesis. - * Formats like GenBank, DICOM-SB ([Sainz de Murieta, Bultelle and Kitney, 2016](https://pubmed.ncbi.nlm.nih.gov/26854090/)) or SBOL may be more applicable for instances where more information, such as functional annotation, would be useful to be shared. + * Formats like GenBank, DICOM-SB {% cite sainzdemurieta2016toward %} or SBOL may be more applicable for instances where more information, such as functional annotation, would be useful to be shared. * SBOL 2.0 and higher allows more than just the genetics of a system to be captured and shared. Using SBOL allows interactions between components in the design to be specified, information about RNA and proteins can be included and the provenance of a design can also be captured. Experimental information relating to the test and build of a system can also be captured and shared. -* SBOL data can be made using tools such as {% tool "benchling" %} (SBOL1 only), {% tool "sboldesigner" %} ([Zhang et al., 2017](https://pubmed.ncbi.nlm.nih.gov/28441476/)) and {% tool "shortbol" %} to name but a few. A more comprehensive list of SBOL tools can be found on the {% tool "synthetic-biology-open-language" %} website. +* SBOL data can be made using tools such as {% tool "benchling" %} (SBOL1 only), {% tool "sboldesigner" %} {% cite zhang2017sboldesigner %} and {% tool "shortbol" %} to name but a few. A more comprehensive list of SBOL tools can be found on the {% tool "synthetic-biology-open-language" %} website. * More generally, the [Investigation/Study/Assay (ISA)](https://isa-specs.readthedocs.io/) model can be used in systems biology, life sciences, environmental and biomedical domains to structure research outputs. The [ISA-Tab](https://isa-specs.readthedocs.io/en/latest/isatab.html) format provides a framework for capturing these data in CSV files. * {% tool "rightfield" %} provides a mechanism for capturing metadata using easy to use spreadsheets. @@ -142,7 +142,7 @@ Here we describe some of the available options to accurately represent and store * More generally, the {% tool "iupac-iubmb-joint-commission-on-biochemical-nomenclature" %} encourages the communication of biochemical information using generally understood terminology. #### (Meta)data publication -* Databases such as SBOLME ([Kuwahara et al., 2017](https://pubmed.ncbi.nlm.nih.gov/28076956/)) or {% tool "synbiohub" %} can be used to share the data. +* Databases such as SBOLME {% cite kuwahara2017sbolme %} or {% tool "synbiohub" %} can be used to share the data. * Metabolite information can also be submitted to, or referred to in, {% tool "chebi" %}. * {% tool "brenda" %} (BRENDA). @@ -210,13 +210,13 @@ The data arising from assays for product development is highly variable and beyo ### Solutions #### Metadata standards -* **Minimum Information Standard for Engineered Organism Experiments (MIEO).** Minimal information necessary to record the growth of an organism in culture, has been described by Hect and colleagues ([Hecht et al., 2018](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6283831/)). +* **Minimum Information Standard for Engineered Organism Experiments (MIEO).** Minimal information necessary to record the growth of an organism in culture, has been described by Hect and colleagues {% cite hecht2018bacterial %}. -* **Enzyme.** If your product is a protein such as an enzyme then some standards developed by the {% tool "standards-for-reporting-enzyme-data" %} may be helpful ([‘Standards for Reporting Enzyme Data: The STRENDA Consortium: What it aims to do and why it should be helpful’, 2014](https://www.sciencedirect.com/science/article/pii/S2213020914000135)). +* **Enzyme.** If your product is a protein such as an enzyme then some standards developed by the {% tool "standards-for-reporting-enzyme-data" %} may be helpful {% cite strenda2014standards %}. * **Microscopy.** Microscopy is often also used to characterise the behaviour of engineered microorganisms. Standards such as the [Open Microscopy Environment Ontology](https://fairsharing.org/bsg-s001430/) and the {% tool "cellular-microscopy-phenotype-ontology" %} can help provide standardised metadata terms. -* **Flow Cytometry data.** The {% tool "international-society-for-the-advancement-of-cytometry" %} provides information on a variety of appropriate data standards for capturing Flow Cytometry data (used to characterise microbial populations at a single cell level) ([Spidlen et al., 2021](https://pubmed.ncbi.nlm.nih.gov/32881398/)). +* **Flow Cytometry data.** The {% tool "international-society-for-the-advancement-of-cytometry" %} provides information on a variety of appropriate data standards for capturing Flow Cytometry data (used to characterise microbial populations at a single cell level) {% cite spidlen2021data %}. * **Nucleic acids information.** The {% tool "european-nucleotide-archive" %}, amongst others, provides guidance on the metadata for RNAseq datasets. @@ -234,33 +234,9 @@ The data arising from assays for product development is highly variable and beyo * {% tool "arrayexpress" %} * **Proteomics.** Proteomics data can be shared via {% tool "proteomics-standards-initiative" %}. * **Metabolic studies.** Metabolomic studies can be shared through the {% tool "metabolomexchange" %}, which provides a resource for sharing data from metabolic studies and guidance for the submission of metabolome data. -* **Biological sources.** Information about biological sources can be shared via the {% tool "biostudies" %} database, which has been set up to capture and share information about multi-omics and other biological studies ([Sarkans et al., 2018](https://pubmed.ncbi.nlm.nih.gov/29069414/)). +* **Biological sources.** Information about biological sources can be shared via the {% tool "biostudies" %} database, which has been set up to capture and share information about multi-omics and other biological studies {% cite sarkans2018biostudies %}. ## Bibliography -[Field, D. et al. (2008) ‘The minimum information about a genome sequence (MIGS) specification’, Nature biotechnology, 26(5), pp. 541–547. doi: 10.1038/nbt1360.](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2409278/) - -[Ham, T. S. et al. (2012) ‘Design, implementation and practice of JBEI-ICE: an open source biological part registry platform and tools’, Nucleic acids research, 40(18), p. e141. doi: 10.1093/nar/gks531.](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3467034/) - -[Hecht, A. et al. (2018) ‘A minimum information standard for reproducing bench-scale bacterial cell growth and productivity’, Communications biology, 1, p. 219. doi: 10.1038/s42003-018-0220-6.](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6283831/) - -[Kuwahara, H. et al. (2017) ‘SBOLme: a Repository of SBOL Parts for Metabolic Engineering’, ACS synthetic biology, 6(4), pp. 732–736. doi: 10.1021/acssynbio.6b00278.](https://pubmed.ncbi.nlm.nih.gov/28076956/) - -[Maloy, S. R. and Hughes, K. T. (2007) ‘Strain Collections and Genetic Nomenclature’, Methods in Enzymology, pp. 3–8. doi: 10.1016/s0076-6879(06)21001-2.](https://pubmed.ncbi.nlm.nih.gov/17352909/) - -[Parte, A. C. et al. (2020) ‘List of Prokaryotic names with Standing in Nomenclature (LPSN) moves to the DSMZ’, International journal of systematic and evolutionary microbiology, 70(11), pp. 5607–5612. doi: 10.1099/ijsem.0.004332.](https://pubmed.ncbi.nlm.nih.gov/32701423/) - -[Sainz de Murieta, I., Bultelle, M. and Kitney, R. I. (2016) ‘Toward the First Data Acquisition Standard in Synthetic Biology’, ACS synthetic biology, 5(8), pp. 817–826. doi: 10.1021/acssynbio.5b00222.](https://pubmed.ncbi.nlm.nih.gov/26854090/) - -[Sarkans, U. et al. (2018) ‘The BioStudies database—one stop shop for all data supporting a life sciences study’, Nucleic Acids Research, pp. D1266–D1270. doi: 10.1093/nar/gkx965.](https://pubmed.ncbi.nlm.nih.gov/29069414/) - -[Spidlen, J. et al. (2021) ‘Data File Standard for Flow Cytometry, Version FCS 3.2’, Cytometry. Part A: the journal of the International Society for Analytical Cytology, 99(1), pp. 100–102. doi: 10.1002/cyto.a.24225.](https://pubmed.ncbi.nlm.nih.gov/32881398/) - -[‘Standards for Reporting Enzyme Data: The STRENDA Consortium: What it aims to do and why it should be helpful’ (2014) Perspectives in Science, 1(1-6), pp. 131–137. doi: 10.1016/j.pisc.2014.02.012.](https://www.sciencedirect.com/science/article/pii/S2213020914000135) - -[Tellechea-Luzardo, J. et al. (2020) ‘Linking Engineered Cells to Their Digital Twins: A Version Control System for Strain Engineering’, ACS synthetic biology, 9(3), pp. 536–545. doi: 10.1021/acssynbio.9b00400.](https://pubmed.ncbi.nlm.nih.gov/32078768/) - -[Ten Hoopen, P. et al. (2015) ‘Marine microbial biodiversity, bioinformatics and biotechnology (M2B3) data reporting and service standards’, Standards in genomic sciences, 10, p. 20. doi: 10.1186/s40793-015-0001-5.](https://pubmed.ncbi.nlm.nih.gov/26203332/) - -[Zhang, M. et al. (2017) ‘SBOLDesigner 2: An Intuitive Tool for Structural Genetic Design’, ACS synthetic biology, 6(7), pp. 1150–1160. doi: 10.1021/acssynbio.6b00275.](https://pubmed.ncbi.nlm.nih.gov/28441476/) +{% bibliography --cited %} diff --git a/pages/your_domain/plant_sciences.md b/pages/your_domain/plant_sciences.md index c3e5a7b70..bfb0ee9aa 100644 --- a/pages/your_domain/plant_sciences.md +++ b/pages/your_domain/plant_sciences.md @@ -6,7 +6,7 @@ related_pages: page_id: plants related_pages: your_tasks: [metadata] - tool_assembly: [plant_geno_assembly, plant_pheno_assembly] + tool_assembly: [plant_geno_assembly, plant_pheno_assembly, fairtracks] training: - name: Training in TeSS registry: TeSS diff --git a/pages/your_domain/rare_disease_data.md b/pages/your_domain/rare_disease_data.md index 64aca3af9..f8c968c88 100644 --- a/pages/your_domain/rare_disease_data.md +++ b/pages/your_domain/rare_disease_data.md @@ -5,6 +5,7 @@ contributors: [Philip van Damme, Nirupama Benis, César Bernabé, Shuxin Zhang, page_id: rare_disease related_pages: your_domain: [human_data] + tool_assembly: [fairtracks] your_tasks: [dmp, data_publication, machine_actionability] --- diff --git a/pages/your_domain/single_cell_sequencing.md b/pages/your_domain/single_cell_sequencing.md index a4ad9ed39..1cc969198 100644 --- a/pages/your_domain/single_cell_sequencing.md +++ b/pages/your_domain/single_cell_sequencing.md @@ -4,7 +4,7 @@ description: "Managing data generated from single-cell sequencing experiments." contributors: [Johan Rollin, Pavankumar Videm, Mehmet Tekman] related_pages: your_tasks: [dmp, data_organisation, data_publication, metadata, storage] - tool_assembly: [galaxy] + tool_assembly: [galaxy, fairtracks] training: - name: Single-cell training on the Galaxy Training Network url: "https://usegalaxy.eu/training-material/topics/single-cell/" diff --git a/pages/your_role.md b/pages/your_role.md index e0c22698c..0e74e59e1 100644 --- a/pages/your_role.md +++ b/pages/your_role.md @@ -10,5 +10,4 @@ In this section, information is organised based on the different roles a profess -{% include section-navigation-tiles.html type="your_role" search=true %} - +{% include section-navigation-tiles.html type="your_role" search=true col=3 %} diff --git a/pages/your_tasks.md b/pages/your_tasks.md index 1a5c2f907..2fa9ba684 100644 --- a/pages/your_tasks.md +++ b/pages/your_tasks.md @@ -11,4 +11,4 @@ In this section, information is organised around regular research data managemen - A summary table of tools and resources relevant for the specific task and recommended by communities. -{% include section-navigation-tiles.html type="your_tasks" search=true %} +{% include section-navigation-tiles.html type="your_tasks" search=true col=3 %} diff --git a/pages/your_tasks/existing_data.md b/pages/your_tasks/existing_data.md index 7e87fac9b..cd69f0fd2 100644 --- a/pages/your_tasks/existing_data.md +++ b/pages/your_tasks/existing_data.md @@ -43,6 +43,7 @@ Many datasets could exist that you can reuse for your project. Even if you know * The {% tool "omicsdi" %} provides a knowledge discovery framework across heterogeneous omics data (genomics, proteomics, transcriptomics and metabolomics). * The {% tool "elixir-core-data-resources" %} list of knowledge resources recommended by ELIXIR. * {% tool "openaire-explore" %} provides linked open research datasets. + * {% tool "mendeley-data" %} is linked with the Mendeley social network. * Search through all repositories you found to identify what you could use. Give priority to curated repositories. diff --git a/pages/your_tasks/metadata_management.md b/pages/your_tasks/metadata_management.md index 8f494668b..5e6cddcfa 100644 --- a/pages/your_tasks/metadata_management.md +++ b/pages/your_tasks/metadata_management.md @@ -116,7 +116,7 @@ There are multiple standards for different types of data, ranging from generic d * On the repository website, go through the submission process (try to submit some dummy data) to identify metadata requirements. For instance, if you consider publishing your transcriptomic data in ArrayExpress, you can make your metadata spreadsheet by using [Annotare 2.0 submission tool](https://www.ebi.ac.uk/fg/annotare/), at the beginning of the project. * Be aware that data type specific repositories usually have check-lists for metadata. For example, the European Nucleotide Archive provides [sample checklists](https://www.ebi.ac.uk/ena/browser/checklists) that can also be downloaded as a spreadsheet after log in. -* If you do not know yet what repository you will use, look for what is the recommended minimal information (i.e. “Minimum Information ...your topic”, e.g. [MIAME](https://www.fged.org/projects/miame) or [MINSEQE](https://www.fged.org/projects/minseqe) or {% tool "miappe" %}) required for your type of data in your community, or other metadata, at the following resources: +* If you do not know yet what repository you will use, look for what is the recommended minimal information (i.e. “Minimum Information about your topic”, e.g. {%tool "miame" %}, {%tool "minseqe" %}, or {% tool "miappe" %}) required for your type of data in your community, or other metadata, at the following resources: * {% tool "rda-standards" %} * {% tool "fairsharing" %} at “Standards” and “Collections” * {% tool "data-curation-centre-metadata-list" %}