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FAIR History and the Future

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A keynote given on the FAIR Data Principles at the FAIRplus Innovation and SME Forum, Hinxton Genome Campus, Cambridge, UK, 29 January 2020. The history of the principles, issues about the principles and speculations about the future

A keynote given on the FAIR Data Principles at the FAIRplus Innovation and SME Forum, Hinxton Genome Campus, Cambridge, UK, 29 January 2020. The history of the principles, issues about the principles and speculations about the future

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FAIR History and the Future

  1. 1. www.fairplus-project.eu Carole Goble The University of Manchester, UK FAIRplus WP2 ELIXIR-UK Head of Node & Interoperability Platform FAIRDOM Association e.V. carole.goble@manchester.ac.uk FAIRplus Innovation and SME Forum, 29 January 2020, Hinxton, UK FAIR History and the Future 1
  2. 2. Scientific Data 3, 160018 (2016) doi:10.1038/sdata.2016.18 2014 2015 2016 45 European, 8 USA, 1 South American 5 Companies, 3 Public Orgs
  3. 3. Government, Agencies, Policies
  4. 4. FAIR Metrics frameworks Automated Evaluation services Manual Evaluation services Wilkinson et al, Evaluating FAIR Maturity Through a Scalable, Automated, Community-Governed Framework https://doi.org/10.1101/649202 Evaluation Businesses FAIR evaluations FAIR support tools
  5. 5. 6
  6. 6. A Rallying Call
  7. 7. ELIXIR EOSC GO-FAIR CODATA Barend Mons RTD - DG Research and Innovation European Commission’s high level expert group advising regarding the shape of a European Open Science Cloud initiative. FAIRy GodFather
  8. 8. A Driver Community Data Commons Governed shared spaces for digital objects for a community. (Not lakes. Not warehouses)
  9. 9. FAIR Digital Objects FAIR DO Framework • Minimal metadata and identifier services Principles need to be developed for other objects, esp. living objects • RDA FAIR Software IG • FAIR Workflows in EOSC Life Workflow Hub EC’s Turning FAIR into Reality (2018) Ted Slater
  10. 10. Shout outs Mark Wilkinson Michel Dumontier Susanna Sansone Maryann Martone Erik Schultes
  11. 11. FAIR principles in that paper… … are in a break out box.
  12. 12. It’s not Gospel Monty Python’s Life of Brian, RIP Terry Jones
  13. 13. Jacobsen et all FAIR Principles: Interpretations and Implementation Considerations, J Data Intelligence (2020) “FAIR is non-trivial, and domain specific at anything other than the most superficial level” Mark Wilkinson 2019 Mons et al Cloudy, increasingly FAIR; Revisiting the FAIR Data guiding principles for the European Open Science Cloud. Information Services & Use. 37. 1-8. 10.3233/ISU-170824 (2017) Principles, not Precise Practice “the proposed implementation of these principles, with the goal of an Internet of FAIR Data and Services, is beginning to raise concern and confusion” “interpretation of the derived guiding principles for implementation is far from straightforward”
  14. 14. The Principles are… FAIR Mythology Summarised • An aspiration, a journey. • Ambiguous. • A spectrum. • Domain respectful / specific • Implementable with todays protocols and standards. • A small part of indicators. • A framework for prompting organisational change • Work in progress. The Principles are not… • A standard. • Strict. • One size fits all. • One domain • Inventing new protocols. • Technology specific • Anything to do with quality. • Synonymous with open. • An architecture • Tablets of stone. Mons et al Cloudy, increasingly FAIR; Revisiting the FAIR Data guiding principles for the European Open Science Cloud. Information Services & Use. 37. 1-8. 10.3233/ISU-170824, Dunning et al Are the FAIR Data Principles fair? IDCC17, Jacobsen et al FAIR Principles: Interpretations and Implementation Considerations Data Intelligence 2(2020), 10–29. doi: 10.1162/dint_r_000
  15. 15. The Second Wave Special Issue "FAIR Data, FAIR Services, and the European Open Science Cloud" Special Issue on FAIR Data, Systems and Analysis
  16. 16. The why and what of FAIR has things to say about FAIR today and the future.
  17. 17. Why FAIR? Knowledge Turning, Information Flow Josh Sommer, Chordoma Foundation, 2011 Flow of information across collaborating yet competing groups with churning membership Flow across all social groups the individual, the lab, the project, the organisation, the community Flow across all tech infra platforms, repositories, registries Reduce Knowledge Loss
  18. 18. Knowledge Exchange Accountability and Responsibility Producers and consumers. Retention and flow Who is judged FAIR? The repository owners? The content providers to the repositories? Why GUIDs are important! Researchers, Company Scientists, collaborators Neylon, Knowledge Exchange Report: http://www.knowledge- exchange.info/event/ke-approach-open-scholarship Organisations Businesses Senior Management Public Commons Data repositories
  19. 19. Neylon, Knowledge Exchange Report: http://www.knowledge- exchange.info/event/ke-approach-open-scholarship Good data management Rich metadata, open formats Prepare to share Adopt standards Submit to a repository … Persistent identifier Machine access Bidirectional links Future proofed formats Data citation Clear licensing … Knowledge Exchange Accountability and Responsibility Researchers, Company Scientists, collaborators Organisations Businesses Senior Management Public Commons Data repositories
  20. 20. Neylon, Knowledge Exchange Report: http://www.knowledge- exchange.info/event/ke-approach-open-scholarship Public Commons Beneficiaries outside Beneficiaries disconnected Dubious reciprocity Interop drivers speculative ROI tricky Commons Club Beneficiaries inside Beneficiaries connected (?) Enforced reciprocity? Interop drivers Competency Questions ROI calculable? Knowledge Exchange Accountability and Responsibility Researchers, Company Scientists, collaborators Organisations Businesses Senior Management Public Commons Data repositories
  21. 21. Open Science Automation Reproducible Science Scaled up Data-driven Science Team Science Distributed Data Influences on FAIR
  22. 22. The Science of Team Science Collaboration made up of individual effort, still individually rewarded. Even within big projects and company scientists “I” in FAIR means “I” want to find, access and reuse your/their data. https://www.nature.com/news/biology-needs-more-staff-scientists-1.21991
  23. 23. Open Science “accessible, assessable, intelligible, reusable” anyone, anything, anytime publication access, data, models, source codes, resources, transparent methods, standards, formats, identifiers, APIs, licenses, education, policies http://royalsociety.org/policy/projects/science- public-enterprise/report/
  24. 24. Data citation Publisher and Funder Policies Registry and repository explosion Data Management Planning at the three levels The same old concerns Sloooooooow cultural normalisation Over a decade, and today… Data Sharing that is “Open by Default, Closed as Necessary”
  25. 25. republic of science* regulation of science G8 Open Data Charter, 2013 Extrinsic drivers on • Institutions, “regular researchers” absent, middle management Regulation vs republic • Capitalising on investments • Accountability • Compliance auditing • Competitive advantages • Accelerating science
  26. 26. Data Parasites Data Flirters Sharing Spirals Sharing Enclaves Trust Reciprocity
  27. 27. I used to believe in carrots now I believe in sticks
  28. 28. FAIR is not the same as Open GDPR conundrums jumpy PIs and Deans Responsible FAIRness Promoting adoption Sharing -> CoP Retention
  29. 29. Automation for data at scale Distributed processing Data mining, Search Workflows, AI Machine Processable Metadata mark-up & self description Semantic Web -> Linked Data -> Knowledge Graphs. formats APIs persistent identifiers reporting checklists mark-up terms (aka ontologies) [Finn] [Sansone]
  30. 30. nanopublications & linksets 2012-2019 Licensing Identifier and Concept mapping Apps
  31. 31. FAIR https://www.natureindex.com/news-blog/what-scientists-need-to-know-about-fair-data FAIR is not about harmonising all metadata to one schema, or publishing everything in RDF. Interoperability requires a purpose. What is the business question? Most difficult, costly. Let it not be a blocker to FAIR overall. Personally, I think RDF is a red herring.
  32. 32. Find Lightweight mark-up of a few common terms A little semantics everywhere Dataset properties • 5 minimal • 8 recommended • What’s the license? • What’s the identifier?OWL ontologies -> Schema.org RDF -> JSON(-LD) mark-up SPARQL -> GraphQL Semantic Web -> Knowledge Graphs
  33. 33. FAIR is not about a resource’s Quality or Impact or Scientific value or Business value Cost/Benefit Analysis, Data Set Prioritisation, CMM ….
  34. 34. Thanks to Wei Gu for the Analogy! Like PacMan not the Holy Grail A spectrum of indicators with different levels of maturity and importance to different players -> CMMI A mixed FAIR data portfolio at different maturity depths Requires communities to define their levels/depths and develop just in time / incremental delivery
  35. 35. Research Scientist view
  36. 36. FAIR is not one size fits all contextually dependent, community dependent priorities
  37. 37. The FAIR intentions of Data Providers. To improve the exchange of information and raise the bar. Contract Compliance Awareness Expectation setting Self-evaluation Reporting Comparison Monitoring Review Quality
  38. 38. Certification Endorsement Judgement Regulation Needed for Sticks and Carrots But by whom? Can’t shortcut community appropriate maturity levels, achievable indicators and transparent assessment. Credible and Responsible Assessment
  39. 39. The Tyranny of Metrics
  40. 40. From the Spirit to the Specific Scale up and Scale out Policy, Proclamations and Provocations Detailed Implementation Practice by Mortals precision FAIR Professionalisation Clarity
  41. 41. 1. What does FAIR really mean? 2. Isn’t this just for Data Repository Managers? 3. How do we do FAIR into our lab? What can we use? 4. Does everything have to be FAIR when most data I’m not going to share? 5. Should I bother with legacy data? 6. How do we resource it? 7. If I make the effort how will I benefit? Sounds Hard …
  42. 42. FAIR from the First Moving FAIR upstream The leaky data pipeline Support for metadata collection through research workflows Standardised Production vs Customised Exploration Rubbish data Handy data but not for this Processed Data Data in Paper Moremetadata
  43. 43. Challenges facing FAIR mortals • Granularity levels • Overthinking, analysis paralysis • Disconnect of providers from consumers • Examples to copy • Assembling a FAIR mixed skills football team • Process + People Execution Organisation MetricsCulture Process [Daron Green]
  44. 44. Practice by Mortals, not Purists Get Expert Help Skill your Team Publish your Data with a licence Use a data catalogue Register your repositories Cite others Use checklists Set FAIR governance Make a FAIR-aware patient consent framework. Annotate & Document for Strangers Use Standards Use IDs https://fair-software.nl/ Develop a Data Management Plan that fits into your workflow Professionalisation Corpas M et al (2018) A FAIR guide for data providers to maximise sharing of human genomic data, PLOS Comp Bio Boeckhout M et al (2018) The FAIR guiding principles for data stewardship: fair enough?, E J of Human Genetics
  45. 45. The Reality of FAIRification
  46. 46. Samiul Hasan, GSK, Biocuration need in Pharma: Drivers from a Translational Bioinformatics Perspective, EaSyM 2016 Is FAIR a one shot job?
  47. 47. FAIR Future? EC Picture PEST – political, economic, social, technical EC Turning FAIR into Reality
  48. 48. FAIR Future? Based on Matt Spritzer / Brian Nosek figure, COS A Data Provider Picture
  49. 49. Incentives To change behaviours
  50. 50. Eight FAIR Future Virtues 1. Lighten up on Principle Anxiety. 2. Community defined “FAIR enoughs” -> “GO-FAIR Profiles”. 3. Valuing FAIR in the organisations researchers actually work in OR disintermediation. 4. The rise of the FAIR profession. 5. FAIR methodologies that scales, with toolkits, templates & examples. 6. FAIR Digital Object Framework using todays conventions. 7. Selective FAIR data islands, with bridges. 8. Upstream FAIR via libertarian paternalism. simplify value support practice
  51. 51. FAIR inherits the properties of its influences. Let’s learn from them. FAIR is a means to an end. So lighten up. Just Do it.
  52. 52. www.fairplus-project.eu This project has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No. 802750. This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation and EFPIA companies. www.imi.europa.eu Thank you! 60 Wei Gu Oya Deniz Beyan Ibrahim Emam Nick Juty Mark Wilkinson Susanna Sansone Barend Mons Ian Harrow Helen Parkinson Kristian Garza
  53. 53. Get in touch • Website: www.fairplus-project.eu • Twitter: @FAIRplus_eu • LinkedIn: www.linkedin.com/company/fairplus • Newsletter: • Sign-up: http://eepurl.com/ghuHeT • Archive: http://bit.ly/2UG6mZI • Email:FAIRplus-PM@elixir-europe.org

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