Behind the FAIR Brand: Thinkers, Doers and Dreamers
•
1 like•242 views
https://www.beilstein-institut.de/en/symposia/open-science/program
Recommended
Recommended
More Related Content
What's hot
What's hot (20)
Similar to Behind the FAIR Brand: Thinkers, Doers and Dreamers
Similar to Behind the FAIR Brand: Thinkers, Doers and Dreamers (20)
More from Susanna-Assunta Sansone
More from Susanna-Assunta Sansone (10)
Recently uploaded
Recently uploaded (20)
Behind the FAIR Brand: Thinkers, Doers and Dreamers
- 1. Behind the FAIR Brand: Thinkers, Doers and Dreamers Susanna-Assunta Sansone ORCiD: 0000-0001-5306-5690 | Twitter: @SusannaASansone Beilstein Open Science 2019 Symposium, 15 -17 October 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. A set of principles to enhance the value of all digital resources 2014 2016 Developed and endorsed by researchers, service providers, publishers, funding agencies, industry partners
- 3. And the FAIR brand is born
- 5. Everybody needs data that are • Discoverable by humans and machines • Retrievable and structured in standard format(s) • Self-described so that third parties can make sense of it Better data = better science and more efficiently Datasets SOPs Figures, Photos Workflows Slides Codes Tools DatabasesAlgorithmsDocument
- 6. • A crisis in confidence in research integrity in certain fields • Human-machine collaboration will be crucial to our future • Data-relates mandates and policies by funders • The changing world of scholarly publishing • The need for recognition and credit Driving factors Datasets SOPs Figures, Photos Workflows Slides Codes Tools DatabasesAlgorithmsDocument
- 7. Depends upon several stakeholders in the research ecosystem actively playing their parts • to deliver research infrastructures, tools and standards, policies, education and training • to overcome technical, social and cultural challenges It is not simple and it requires long term investment Making FAIR a reality
- 8. My fair share of the work
- 9. €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
- 10. Organization and structure • Hub and (national) Nodes • Community-driven and rooted • Strong focus on interoperability • SMEs and Industry links • Cross-nodes funded activities
- 11. model and related formats Initiated in 2003 Open source tools and formats to help researchers to: describe multi-modal experiments follow community-developed standards curate, analyze, release, share and publish
- 12. Nowadays ISA (format and/or tools) powers over 30 public resources, e.g., The ‘curse’ of success: • Time and (lack of) funds for: - Maintenance - Extensions - Community coordination/training • Not just about the software - data curation know-how
- 14. Funded by Part of the ISA-InterMine project Reproducibility – FAIR at the first mile From curated, structured metadata to data paper datascriptor.org
- 15. Academics from several ELIXIR Nodes, with Janssen, AstraZeneca, Eli Lilly, GSK, Novartis, Bayer, Boehringer Ingelheim Define, document and implement a data FAIRification process:
- 16. FAIR-driven biopharma R&D, e.g.
- 17. Human capital maximization • Work in squads cross-cutting working packages and partners • Address questions/issues, rather then perform technical duties • Prioritization of the work based on pharma's needs • Three months sprint cycles FAIRcookbook
- 18. Rocca-Serra and Sansone: 10.5281/zenodo.3274256 Scientific Data (accepted) Practical examples: data FAIRifications recipes FAIRcookbook
- 19. 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
- 20. 1 12 centres of excellence 2 3 10 multi-PIs teams, forming one consortium around 3 data types/databases A consortium of 6 teams 2014-2017 2017-2018 Started in 2019
- 21. 1 12 centres of excellence 2 3 10 multi-PIs teams, forming one consortium around 3 data types/databases A consortium of 6 teams 2014-2017 2017-2018 Started in 2019
- 22. 1 12 centres of excellence 2 3 10 multi-PIs teams, forming one consortium around 3 data types/databases A consortium of 6 teams 2014-2017 2017-2018 Started in 2019
- 23. 1 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 3 10 multi-PIs teams, forming one consortium around 3 data types/databases A consortium of 6 teams 2014-2017 2017-2018 Started in 2019
- 24. Data for machines – Use of data at scale 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
- 26. Formats Terminologies Guidelines Identifiers ID Conceptual model, conceptual schema, exchange formats Controlled vocabularies, thesauri, ontologies Minimum information reporting requirements, checklists Unambiguous, persistent and context-independent identifier schema Standards for metadata and identifiers metadata
- 27. 390+ 162+ 729+ ~1300 13 MIAME MIRIAM MIQAS MIX MIGEN ARRIVE MIAPE MIASE MIQE MISFISHIE …. REMARK CONSORT SRAxml SOFT FASTA DICOM MzML SBRML SEDML … GELML ISA CML MITAB … AAO CHEBIOBI PATO ENVO MOD BTO IDO … TEDDY PRO XAO DO VO EC number URL PURLLSID HandleORCID RRID InChI … IVOA ID DOI standard organizations grass-roots groups Formats Terminologies Guidelines Identifiers ID COMMUNITY STANDARDS for metadata and identifiers Domain-specific standards for datasets, e.g.
- 28. https://doi.org/10.6084/m9.figshare.3795816.v2 https://doi.org/10.6084/m9.figshare.4055496.v1 2013 2016 Analysis of the standards landscape Fragmentation, duplication and gaps: • Perspective and focus vary: • Motivation is diverse • Governance and participation vary
- 29. 2011-today doi: 10.1126/science.1180598 2007 doi:10.1038/nbt1346 2008 doi:10.1038/nbt1346 OBO Portal and Foundry Portal and Foundry 2009 doi: 10.1038/nbt.1411
- 31. Formats Terminologies Guidelines Identifiers ID REPOSITORIES, databases and knowledgebases DATA POLICIES by journals, funders, and other organizations COMMUNITY STANDARDS for metadata and identifiers informative and educational resource Curated inter-linked descriptions
- 32. Formats Terminologies Guidelines Identifiers ID informative and educational resource Curated inter-linked descriptions 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 REPOSITORIES, databases and knowledgebases DATA POLICIES by journals, funders, and other organizations COMMUNITY STANDARDS for metadata and identifiers
- 33. Formats Terminologies Guidelines Identifiers ID REPOSITORIES, databases and knowledgebases DATA POLICIES by journals, funders, and other organizations COMMUNITY STANDARDS for metadata and identifiers informative and educational resource Curated inter-linked descriptions 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
- 36. 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:
- 37. 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
- 38. 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
- 39. “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
- 40. 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 14th Plenary, Helsinki Criteria include: • Access conditions • Reuse condition • Deposition conditions • Unique ID schema • User support • Curation • …….
- 41. 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
- 43. The road to FAIRness Credit to: Robert Hanisch Before FAIR
- 44. The road to FAIRness Credit to: Robert Hanisch Before FAIR After FAIR
- 45. The road to FAIRness Credit to: Robert Hanisch Before FAIR After FAIR Congested and chaotic
- 46. infrastructures standards tools policies education training cultural normalization incentives long term investment It is hard work but any FAIRy tale needs some magic….
- 47. New member starting Jan 2020 sansonegroup.eng.ox.ac.uk and our collaborators