Enabling FAIR - what works?
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The document discusses enabling FAIR (Findable, Accessible, Interoperable, Reusable) practices through both bottom-up and top-down approaches. It describes several EU and US biomedical data infrastructure programs and projects since 2014 that have taken bottom-up approaches through multi-institutional collaborations. It also discusses the FAIRsharing registry, a top-down approach that has been recommending standards, databases, and policies since 2011 to help researchers and improve data sharing policies of journals and funders. Lessons learned include the need to work both with technical and social aspects to define and measure success.
Enabling FAIR - what works?
- 1. Enabling FAIR: what works? Bottom up Susanna-Assunta Sansone ORCiD: 0000-0001-5306-5690 | Twitter: @SusannaASansone Fostering a FAIR research culture - what works | Open Science FAIR, Porto, Portugal, 16-18 September 2019 Slides: https://www.slideshare.net/SusannaSansone sansonegroup.eng.ox.ac.uk Associate Professor, Engineering Science Associate Director, Oxford e-Research Centre Principal Investigator and Group Leader
- 2. €3.3 billion programme 2014 - 2020 €300 million programme 2018 - 2020 European intergovernmental organisation 23 member countries and over 180 research organisations Since 2014 1 2 3 Started in 2019 FAIR-enabling EU and USA biomedical infrastructure programmes and projects, e.g. Since in 2014, several programs: 2014-2017 2017-2018
- 3. Organization and structure • Hub and (national) Nodes • Community-driven and rooted • Strong focus on interoperability • SMEs and Industry links • Cross-nodes funded activities
- 4. Academics from several ELIXIR Nodes, with Janssen, AstraZeneca, Eli Lilly, GSK, Novartis, Bayer, Boehringer Ingelheim Define and implement a data FAIRification process and infrastructure:
- 5. Working structure • Human capital maximization • Squads cross-cutting WPs & organizations • Three months sprint cycles • Prioritization based on pharma's needs ``` FAIRcookbook
- 6. Rocca-Serra and Sansone: 10.5281/zenodo.3274256 FAIRcookbook Practical recipes
- 7. 1 2014-2017 12 centres of excellence 2 2017-2018 3 Started in 2019 10 multi-PIs teams, forming one consortium around 3 data types/databases A consortium of 6 teams
- 8. 1 2014-2017 12 centres of excellence 2 2017-2018 3 Started in 2019 10 multi-PIs teams, forming one consortium around 3 data types/databases A consortium of 6 teams
- 9. 1 2014-2017 Building on previous work • Learn from positive and negative outcomes • Assessment of what did not work well and why • NIH centres/officers playing an active role • Evolving understanding of what a FAIR Data Commons is 12 centres of excellence 2 2017-2018 3 Started in 2019 10 multi-PIs teams, forming one consortium around 3 data types/databases A consortium of 6 teams
- 10. Stronger impact in discipline-specific efforts: • anchored to real use cases • closer to the (needs of the) practitioners • realistic on what can really be achieved but not easier, because e.g. biomedical sciences encompasses several sub-disciplines, with diverse long-standing norms, tools and standards Balancing social and technical engineering is an achievement per se: • work with and form the users to match expectations with promises • address questions/issues, rather then perform technical duties • pass evidence-based lessons learned to others, good and bad Defining success – lessons learned
- 11. Enabling FAIR: what works? Top-down Susanna-Assunta Sansone ORCiD: 0000-0001-5306-5690 | Twitter: @SusannaASansone Fostering a FAIR research culture - what works | Open Science FAIR, Porto, Portugal, 16-18 September 2019 Slides: https://www.slideshare.net/SusannaSansone sansonegroup.eng.ox.ac.uk Associate Professor, Engineering Science Associate Director, Oxford e-Research Centre Principal Investigator and Group Leader
- 12. Since 2011
- 13. Researchers in academia, industry, government Developers and curators of resources Journal publishers or organizations with data policy Research data facilitators, librarians, trainers Learned societies, unions and associations Funders and data policy makers A flagship output (and a WG) of the: Recommended by funders, e.g.: Core part of implementation networks in:
- 14. REPOSITORIES, databases and knowledgebases COMMUNITY STANDARDS DATA POLICIES by funders, journals and other organizations Inter-linked descriptions informative and educational resource
- 15. REPOSITORIES, databases and knowledgebases COMMUNITY STANDARDS DATA POLICIES by funders, journals and other organizations Inter-linked descriptions informative and educational resource All records are manually curated in-house, verified and claimed by the community behind each resource Ready for use, implementation, or recommendation In development Status uncertain Deprecated as subsumed or superseded
- 16. REPOSITORIES, databases and knowledgebases COMMUNITY STANDARDS DATA POLICIES by funders, journals and other organizations Inter-linked descriptions informative and educational resource We guide consumers to discover, select and use these resources with confidence We help producers to make their resources more visible, more widely adopted and cited
- 20. https://doi.org/10.1038/s41587-019-0080-8 Open Access CC-BY 69 authors (adopters, collaborators, users) representing different stakeholder groups Analysed the data policies by journals/publishers, and the standards and repositories they recommend Working with journal editors and publishers
- 21. Discrepancy in recommendation across the data policies • some repositories are named, but very few standards are • cautious approach due to the wealth of existing resources Recommendations are often driven by • the editor’s familiarity with one or more standards, notably for journals or publishers focusing on specific disciplines • the engagement with learned societies and researchers actively supporting and using certain resources Ø Consensus: FAIRsharing plays a key role in helping editors to discover and recommend appropriate resources What have we learned and are doing now
- 22. “The interactive browser will allow us to discover which databases and standards are not currently included in our author guidelines, enabling us to regularly monitor and refine our policies as appropriate, in support of our mission to help our authors enhance the reproducibility of their work.” H. Murray. Publishing Editor, F1000Research
- 23. In scope: • A shared list of recommended deposition repositories Out of scope: • Become or compete with • certification systems for repositories, such as CoreTrustSeal; • evaluation processes by a community ‘authority’ in a given area, e.g. by ELIXIR in the life sciences Collaboration: Harmonize journals and publishers’ data deposition guidelines by defining a common set of criteria for repository selection Document being approved internally by publishers; out before / to be presented at RDA’s 14th Plenary, Helsinki
- 24. Increase the number and the clarity of journals and funders data policies by classifying the recommendations these policies contain to improve their definition and guidance to researchers Collaboration: Workplan – phase 1: Curate and assess their compliance to the Transparency and Openness Promotion (TOP) guidelines and display the level in FAIRsharing