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FAIRcookbook: GSRS22-Singapore
Pharmas and academia are joining forces to make data FAIR (Findable, Accessible, Interoperable, and Reusable) through the development of the FAIR Cookbook. The FAIR Cookbook provides over 70 recipes and growing that give step-by-step guidance on improving the FAIRness of different data types through the use of tools, technologies, and best practices. It aims to provide practical examples and guidelines to support researchers, data managers, and others in managing data according to FAIR principles. The FAIR Cookbook is an open, community-developed resource overseen by an editorial board, with contributions from nearly 100 life sciences professionals.
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FAIRcookbook: GSRS22-Singapore
- 1. pharmas and academiajoin forces to make data FAIR 12th Global Summit on Regulatory Science (GSRS22), Bioinformatics Session, Singapore, 20 Oct, 2022 Slides: https://www.slideshare.net/SusannaSansone Professor of Data Readiness Associate Director, Oxford e-Research Centre Interoperability Platform Co-Lead elixir-europe.org Founding Academic Editor nature.com/sdata datareadiness.eng.ox.uk Susanna-Assunta Sansone ORCiD: 0000-0001-5306-5690 Twitter: @SusannaASansone Open, FAIR and reproducible science
- 2. The FAIR Principles Globallyunique and persistent identifiers Community defined descriptive metadata Community defined terminologies Detailed provenance Terms of access Terms of use
- 3. Rationale behind theFAIR Principles Globally unique and persistent identifiers Community defined descriptive metadata Community defined terminologies Detailed provenance Terms of access Terms of use https://www.forbes.com/sites/gilpress/2016/03/23/data-preparation-most-time-consuming-least-enjoyable-data-science-task-survey- says/#276a35e6f637 Discoveries are made using shared data, and this requires data that are: ● Cited and stored to be discoverable ● Retrievable and structured in standard format(s) ● Richly described to be understandable Data preparation accounts for about 80% of the work of data scientists
- 4. doi.org/10.2777/986252 www.gov.uk/government/publications/open- research-data-task-force-final-report www.fair-access.net.au doi.org/10.1787/25186167 ark:/48223/pf0000374837 FAIR hasaligned the broad community around common guidelines doi.org/10.7486/DRI.tq582c863 https://grants.nih.gov/grants/guide/notice-files/NOT-OD-21-013.html
- 5. FAIR as drivenof the digital transformation in (bio)phamas ● To improve biopharma R&D productivity ● To enables powerful new AI analytics to access data for ML and prediction ● Requirements o financial, technical, training ● Challenges o change the culture, show business value, achieve the ‘FAIR enough’ on an enterprise scale
- 6. The FAIR Principles: acontinuum of features, attributes, behaviours
- 7. The FAIR Principles: acontinuum of features, attributes, behaviours In practice, what I need to do?
- 8. The FAIR Cookbook: motivationsand ambitions beyond the hype Large body of generic FAIR guidance Motivations Non-specific guidance for the life sciences Ambitions Target specific situations to deliver a guide with applied examples Join academia and industry forces to make the case for FAIR data management Build capacity for high quality data management in the private and public sectors Lack of practical examples of ‘how-to’ with different data types and scenarios
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- 11. What it is? Acollection of recipes that cover the operation steps of FAIR data management
- 12. Who is itfor? Data Managers, Data Stewards, Data Curators Software Developers, Terminology Managers • A venue to document and share existing and new approaches or services to support FAIRification • A way to promote a participatory culture that enables sharing of expertise by getting exposure and credit Policymakers, Funders, Trainers • Practical examples to recommend in policies • To use in educational material to incentivize and guide FAIR in practice. • Introductory material • Hands-on, technical step-by-step examples Researchers, Data Scientists, Principal Investigators
- 13. ● Over 70recipes released and more content available ● Covering over 20 data types, incl: ○ omics ○ pre-clinical ○ clinical areas But not limited to it! Coverage and learning objectives Learn how to improve the FAIRness with exemplar datasets Understand the levels and indicators of FAIRness Discover open source technologies, tools and services Find out the required skills Acknowledge the challenges
- 14. Over 70 recipesand growing
- 15. Define what yourneeds are Goal: improving visibility of content Goal: semantic integration of datasets from multiple sources Goal: security compliance and with regulators
- 16. Define what yourneeds are Goal: improving visibility of content, e.g.: Goal: semantic integration of datasets from multiple sources, e.g.: Goal: security compliance and with regulators, e.g.: https://w3id.org/faircookbook/FCB010 https://w3id.org/faircookbook/FCB007 https://w3id.org/faircookbook/FCB006 https://w3id.org/faircookbook/FCB020 https://w3id.org/faircookbook/FCB004 https://w3id.org/faircookbook/FCB014 https://w3id.org/faircookbook/FCB035
- 17. https://w3id.org/faircookbook/FCB079 A recipe anda template for the FAIRification process
- 18. Anatomy of arecipe components Ingredients An idea of tools/skills needed Step by step process Guidelines, process, description Practical elements, code snippets #Python3 #zooma-annotator-script.py file def get_annotations(propertyType , propertyValues, filters = ""): " Examples Conclusions What should I read next?
- 19. Links complementary resources Currentlinks with and references to:
- 20. CC BY-SA 4.0International MIAME MIRIAM MIQAS MIX MIGEN ARRIVE … MIAPE MIASE … MISFISHIE …. REMARK CONSORT SRAxml SDTM FASTA DICOM OMOP … SBRML SEDML … CDASH ISA CML MITAB … AAO CHEBI OBI PATO ENVO MOD BTO IDO … TEDDY PRO … XAO DO … VO EC number URL PURL LSID Handle ORCID RRID … InChI … IVOA ID … DOI Standard organizations, e.g.: Grass-roots groups, e.g.: Identifiers Terminologies Guidelines Formats 550 303 166 11 More than 1000 standards Standards in the life science
- 21. Tagging recipes with ‘DatasetMaturity Indicators’ Maturity level and indicators new feature! https://fairplus.github.io/Data-Maturity Provide insights into FAIR Maturity reached by applying a specific recipe to improve a dataset
- 22. Who developed it? Almost100 life sciences professionals, researchers and data managers FARIplus partners Industry + Academia ELIXIR Nodes represented
- 23. Current operations andEditorial Board Content prioritisation Identification of topics Review of drafts Call for contributions Monthly book-dash events Pre-defined focus areas Breakout on topics Housekeeping Technical platform Website Martin Cook Dominique Batista Office of Data Science Strategy
- 24. Become part ofa community of FAIR experts and write recipes! 1Identify a chapter and a topic Findability Accessibility Interoperability Reusability Infrastructure Applied examples Assessment 2 Choose a way of contributing and see our guidelines Google Docs HackMD Git Markdown cheat sheet Get recipe template Tips and tricks Submit an outline 3 You can discuss it with the Editorial Board
- 25. Credit and citability becauseall contributions matter! CreDiT attribution ontology w3id.org/faircookbook/FCB006
- 26. What has motivatedso many people to contribute? ● To stay engaged in a growing community and updated with the latest development ● To proof their FAIR competence (as individual and as an organisation) ● To expand their network of potential collaborators, clients and users ● To address common challenges and find common solutions at pre- competitive level
- 27. ● As aneducational material on FAIR in a training context (as part of the FAIRplus Fellowship Programme): o showed the validity of the recipes’ content towards the intended (learning) objectives o confirmed that some recipes require a greater amount of technical background knowledge, and a steeper learning curve Utility and value based on three uses: what we have learned
- 28. ● As apractical guidance to improve day-to-day tasks for FAIRer data ● And as contributor towards changing the culture in research data management (behind the pharmas’ firewall) o outcomes were expressed in terms of satisfaction of the value of the recipes, against specific tasks, or challenges addressed o they reported a positive contribution towards their discussion on return on investment to operationalize FAIR Utility and value based on three uses: what we have learned
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- 30. Thanks to Editorial Board SectionEditors FAIRplus partners All bookdashes’ participants All authors [email protected] faircookbook.elixir-europe.org fairplus-project.eu This project has received funding from the Innovative Medicines Initiative Joint Undertaking under grant agreement No 802750. This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA Companies. This communication reflects the views of the authors and neither IMI nor the European Union, EFPIA or any Associated Partners are liable for any use that may be made of the information contained herein.