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The FAIR Principles and the IMI FAIRplus project
The document outlines the FAIR principles aimed at enhancing the discoverability and reusability of life science data, emphasizing the importance of unique identifiers, descriptive metadata, and common terminologies. It discusses the challenges and requirements for implementing these principles within research ecosystems, highlighting ongoing projects and initiatives to improve data management and collaboration. Additionally, tools and guidelines such as the FAIR Cookbook are introduced to facilitate the integration of FAIR practices in various research domains.
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The FAIR Principles and the IMI FAIRplus project
- 1. The FAIR Principlesand FAIRplus: tools & guidelines for making life science data FAIR Susanna-Assunta Sansone ORCiD: 0000-0001-5306-5690 | Twitter: @SusannaASansone datareadiness.eng.ox.ac.uk Associate Professor, Engineering Science Associate Director, Oxford e-Research Centre UK Conference of Bioinformatics and Computational Biology 2020, 29-30 Sept 2020 Slides: https://www.slideshare.net/SusannaSansone
- 2. A set ofprinciples to enhance the value of all digital resources and its reuse by humans and machines Data that is discoverable and reusable at scale
- 3. Findable Accessible Interoperable Reusable • Globally unique,resolvable, and persistent identifiers ▪ To retrieve and connect data • Community defined descriptive metadata ▪ To enhance discoverability • Common terminologies ▪ To use the same term mean the same thing • Detailed provenance ▪ To contextualize the data and facilitate reproducibility • Terms of access ▪ Open as possible, closed as necessary • Terms of use ▪ Clear licences, ideally to enable innovation and reuse The FAIR Principles in a nutshell
- 4. doi.org/10.2777/02999 doi.org/10.2777/1524www.gov.uk/government/publications/open- research-data-task-force-final-report doi.org/10.5281/zenodo.1245568www.turing.ac.uk/research/impact-stories/changing- culture-data-science FAIR has defacto become a global norm; just some examples “…we estimate to be 10.2 billion euros per year…” www.fair-access.net.au doi.org/10.1787/25186167
- 5. The scholarly publishing ecosystemis changing Data-relates mandates by funders and institutions are growing Researchers need recognition and credit theconversation.com/how-robots-can-help-us-embrace-a-more-human-view-of-disability-76815 Human-machine collaboration is the future o 21% pharmacology data (doi.org/10.1038/nrd3439-c1) o 11% cancer data (doi.org/10.1038/483531a) o unsatisfactory in ML (openreview.net/pdf?id=By4l2PbQ-) towardsdatascience.com/scientific-data-analysis-pipelines-and-reproducibility-75ff9df5b4c5 Reproducibility of published studies is still problematic Responding to needs and crisis
- 6. Findable Accessible Interoperable Reusable Is NOT astandard but a set of guiding principles that provide for a continuum of features, attributes and behaviours, via many different implementations The FAIR Principles are aspirational
- 7. Depends upon severalstakeholders actively playing their parts to: • deliver research infrastructures and tools • use and harmonize the (meta)data standards • address policies, education and training • overcome technical, social and cultural challenges • identify motivators, credit and rewards mechanisms Making FAIR a reality in the research ecosystem
- 8. A crowded space,examples of FAIR evaluation tools
- 9. Credit to: A crowdedspace, examples of European projects
- 10. • Biopharma R&Dproductivity can be improved by implementing the FAIR Principles • FAIR enables powerful new AI analytics to access data for machine learning and prediction Ø Requirements § financial, technical, training Ø Challenges § change the culture, show business value, achieve the ‘FAIR enough’ Credit to: Ian Harrow, FAIR & OM projects FAIR as enabler for the digital transformation
- 11. Funded from January2019 to June 2022 23 participants: • 3 SMEs • 7 Pharmas • 13 Academics including ELIXIR-UK members: o University of Oxford (SA. Sansone, P. Rocca-Serra) o University of Manchester (G. Goble, N. Juty) o Heriot Watt University (A. Gray) Project Coordinator: ELIXIR Project Leader: Janssen www.fairplus-project.eu
- 12. Project types: • completed •ongoing • new Data types: • molecular • clinical IMI projects: Objectives and outputs
- 13. Project types: • completed •ongoing • new Data types: • molecular • clinical • non-clinical IMI projects: 1. Create the FAIR cookbook with best practices on data FAIRification and FAIR data management 2. Identify FAIRification processes and tools that work in the real world 3. Increase FAIR levels of at least 20 IMI projects and internal EFPIA datasets 4. Networking events for SMEs 5. FAIR Fellowship Programme - FAIR data training 6. Change data management culture - FAIR from the start, with explicit plans built into IMI projects
- 14. 1. Create theFAIR cookbook with best practices on data FAIRification and FAIR data management 2. Identify FAIRification processes and tools that work in the real world 3. Increase FAIR levels of at least 20 IMI projects and internal EFPIA datasets 4. Networking events for SMEs 5. FAIR Fellowship Programme - FAIR data training 6. Change data management culture - FAIR from the start, with explicit plans built into IMI projects Project types: • completed • ongoing • new IMI projects: Data types: • molecular • clinical • non-clinical
- 15. Address questions/issues, ratherthen perform technical duties Prioritization of the work based on pharma's needs The FAIRification process
- 16. Address questions/issues, ratherthen perform technical duties Prioritization of the work based on pharma's needs The FAIRification process
- 17. Address questions/issues, ratherthen perform technical duties Prioritization of the work based on pharma's needs The FAIRification process
- 18. 1. Ontologies 2. Standards 3.Versioning 4. Identifiers 5. Licensing Driver use cases: • Update data to new ontology versions • Capture ontology annotation provenance • Ontologies as a service • Ontology recommendations • Ontology annotation recommendations Top needs and challenges
- 19. ● To measuresthe FAIRness level of data ○ For use in the FAIRification processes to define initial/final level of data FAIRness => leveraging on ● To measures capability and performance of an organization for FAIR data generation and management ○ For use at the strategy level to identify investment areas, monitor processes FAIR indicators and capability maturity model
- 20. Publishing the FAIRifieddata and turning knowledge into recipes
- 21. ● A comprehensiveresource collating ‘recipes’ for making different types of data FAIR ● Examples of published recipes: ● Converting Excel files to frictionless data package readable by computers ● Request terms to be added to a public ontology What is it?
- 22. ● How toFAIRify or improve the FAIRness of exemplar datasets ● Which are the levels and indicators of FAIRness ● Which open source technologies, tools and services are available ● What skills are required ● Awareness of known challenges Learning outcomes
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- 26. 26 Technical infrastructure • TheCarpentries • Galaxy Training Community • The Alan Turing Institute’s Turing Way book • Elixir Training Platform Content • The NIH Common Fund Data Ecosystem • Pistoia Alliance’s FAIR Toolkit • IMI EDHEN for OMOP documentation Engaging and outreach
- 28. Before FAIR The roadto data management
- 29. Before FAIR After FAIR Theroad to data management
- 30. Before FAIR After FAIR ….fromchaos, comes order? http://blogs.nature.com/scientificdata/2019/10/22/the-layered-cake The road to data management
- 31. infrastructures standards tools policies education training cultural normalization incentives long terminvestment It is not simple, but it is no longer optional A FAIRY tale needs some magic