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What it means to be FAIR

Presentation given at Macquarie University in support of the ARDC 'institutional role in the data commons' project on "Implementing FAIR: Standards in Research Data Management" https://ardc.edu.au/news/data-and-services-discovery-activities-successful-applicants/

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What it means to be FAIR

  1. 1. What it means to be FAIR Sarah Jones Digital Curation Centre sarah.jones@glasgow.ac.uk Twitter: @sjDCC FAIR session, Macquarie University, 7th August 2019
  2. 2. What is Digital Curation Centre? a centre of expertise in digital information curation with a focus on building capacity, capability and skills for research data management and open science www.dcc.ac.uk Training | Events | Tools | Advocacy | Consultancy | Guidance | Publications | Projects
  3. 3. Who am I? • Archivist with humanities background • Coordinator of DMPonline service • Heavily involved in Research Data Alliance • Co-Chair on Data Science Schools • Rapporteur of FAIR Expert Group • Independent member of EOSC Executive Board • From a seaside town – hence why I love beach and sunshine here :o) FAIR session, Macquarie University, 7th August 2019
  4. 4. All the fun of the FAIR Image Israel Palacio https://unsplash.com/photos/P6FgiDNe6W4
  5. 5. What is FAIR? A set of principles that describe the attributes data need to have to enable and enhance reuse, by humans and machines FAIR session, Macquarie University, 7th August 2019 Image CC-BY-SA by SangyaPundir
  6. 6. What FAIR means: 15 principles Findable F1. (meta)data are assigned a globally unique and eternally persistent identifier. F2. data are described with rich metadata. F3. (meta)data are registered or indexed in a searchable resource. F4. metadata specify the data identifier. Interoperable I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation. I2. (meta)data use vocabularies that follow FAIR principles. I3. (meta)data include qualified references to other (meta)data. Accessible A1 (meta)data are retrievable by their identifier using a standardized communications protocol. A1.1 the protocol is open, free, and universally implementable. A1.2 the protocol allows for an authentication and authorization procedure, where necessary. A2 metadata are accessible, even when the data are no longer available. Reusable R1. meta(data) have a plurality of accurate and relevant attributes. R1.1. (meta)data are released with a clear and accessible data usage license. R1.2. (meta)data are associated with their provenance. R1.3. (meta)data meet domain-relevant community standards. Slide CC-BY by Erik Schultes, Leiden UMC doi: 10.1038/sdata.2016.18 FAIR session, Macquarie University, 7th August 2019
  7. 7. The FAIR data principles explained • Clarifications from the Dutch Techcentre for Life Sciences • Each principle is a link to further clarification, examples and context https://www.dtls.nl/fair-data/fair- principles-explained R1. Meta(data) are richly described with a plurality of accurate and relevant attributes • By giving data many ‘labels’, it will be much easier to find and reuse the data. • Provide not just metadata that allows discovery, but also metadata that richly describes the context under which that data was generated • “plurality” indicates that metadata should be as generous as possible, even to the point of providing information that may seem irrelevant. FAIR session, Macquarie University, 7th August 2019
  8. 8. FAIR data checklist • Findable - Persistent Identifier - Metadata online • Accessible - Data online - Restrictions where needed • Interoperable - Use standards, controlled vocabs - Common (open) formats • Reusable - Rich documentation - Clear usage licence https://doi.org/10.5281/zenodo.1065991FAIR session, Macquarie University, 7th August 2019
  9. 9. FAIR is nothing new • Various research communities have been sharing their data in a ‘FAIR’ way long before the term emerged • Meaningful and memorable articulation of concepts • Natural desire to want to be ‘fair’ • FAIR is gaining significant international traction FAIR session, Macquarie University, 7th August 2019
  10. 10. Open, FAIR and RDM – setting FAIR in context Image Richard Balog https://unsplash.com/photos/P6FgiDNe6W4
  11. 11. Ultimately funders expect: • timely release of data - once patents are filed or on (acceptance for) publication • open data sharing - As open as possible as closed as necessary • preservation of data - typically 5-10+ years if of long-term value • evidence of following policy - a Data Management Plan or institutional policy and services See the SPARC Europe funder policy overview: https://sparceurope.org/latest-update-to-european-open-data- and-open-science-policies-released
  12. 12. Shifting language: policy examples FAIR session, Macquarie University, 7th August 2019 c.2000 – 2008 • Data management • Data sharing • Preservation • Good research • conduct codes c.2010 on • Open Science • Open Data c.2016 on • FAIR data • Reproducibility • Ethical ? * Anecdotal, not scientific. Personal observation on how I feel global data policy rhetoric and terminology has changed
  13. 13. Advice Terminology changes but ideas persist. Focus on core concepts: • managing data well • ensuring ethical conduct • good quality, reusable data • open sharing where possible FAIR session, Macquarie University, 7th August 2019 Image by Headway https://unsplash.com/photos/5QgIuuBxKwM
  14. 14. Forerunners to FAIR OECD Principles and Guidelines for Access to Research Data from Public Funding (2007) A. Openness B. Flexibility C. Transparency D. Legal conformity E. Protection of IP F. Formal responsibility G. Professionalism H. Interoperability I. Quality J. Security K. Efficiency L. Accountability M. Sustainability Science as an Open Enterprise (2012) notion of ‘intelligent openness’ where data are accessible, intelligible, assessable and useable “Open scientific research data should be easily discoverable, accessible, assessable, intelligible, useable, and wherever possible interoperable to specific quality standards.” G8 Science Ministers Statement (2013) FAIR session, Macquarie University, 7th August 2019
  15. 15. How do Open, FAIR & RDM intersect? Open FAIR data Managed data Internal Self-interest External Community benefit FAIR session, Macquarie University, 7th August 2019
  16. 16. FAIR and Open • The greatest potential reuse comes when data are both FAIR and Open • Align and harmonise FAIR and Open data policy FAIR session, Macquarie University, 7th August 2019 Concepts of FAIR and Open should not be conflated. Data can be FAIR or Open, both or neither
  17. 17. Open, FAIR and RDM FAIR session, Macquarie University, 7th August 2019 • Paper explores overlaps between concepts of Open, FAIR and RDM. • Proposes using Open and FAIR as ways to engage researchers in managing data well, as this is a prerequisite for both. • Recommends making data FAIR and Open wherever possible Higman, R., Bangert, D. and Jones, S., 2019. Three camps, one destination: the intersections of research data management, FAIR and Open. Insights, 32(1), p.18. DOI: http://doi.org/10.1629/uksg.468
  18. 18. Turning FAIR into Reality Image Kid Circus https://unsplash.com/photos/7vSlK_9gHWA
  19. 19. FAIR Data Expert Group Take a holistic approach to lay out what needs to be done to make FAIR a reality, in general and for EOSC Addresses the following key areas: 1. Concepts for FAIR 2. Creating a FAIR culture 3. Creating a technical ecosystem for FAIR 4. Skills and capacity building 5. Incentives and metrics 6. Investment and sustainability Turning FAIR into Reality: Report and Action Plan https://doi.org/10.2777/1524
  20. 20. Address culture and technology FAIR session, Macquarie University, 7th August 2019 Incentives Metrics Skills Investment Cultural and social aspects that drive the ecosystem and enact change Cloudofregistries Two sides of one whole
  21. 21. FAIR Digital Objects • Can include data, software, and other research resources • Universal use of PIDs • Use of common formats • Data accompanied by code • Rich metadata • Clear licensing FAIR session, Macquarie University, 7th August 2019
  22. 22. FAIR EG recommendations FAIR session, Macquarie University, 7th August 2019 • Research communities • Data service providers • Standards bodies • Coordination fora • Policymakers • Research funders • Institutions • Publishers Recommendations aimed at multiple stakeholders:
  23. 23. FAIR metrics: data and services FAIR session, Macquarie University, 7th August 2019 DATA REPOSITORY F4. (meta)data are registered or indexed in a searchable resource + TECHNOLOGIES + PROCEDURES + EXPERTISE + PEOPLE (META)DATA F1. (meta)data are assigned a globally unique and persistent identifier F2. data are described with rich metadata F3. metadata clearly and explicitly include the identifier of the data it describes Assessing FAIRness of data Critical role of environment & services in making data FAIR
  24. 24. FAIR metrics • A set of metrics for FAIR Digital Objects should be developed and implemented, starting from the basic common core of descriptive metadata, PIDs and access. • Build on existing work in this space – RDA Working Group • https://www.rd-alliance.org/groups/fair-data-maturity-model-wg • Certification schemes are needed to assess all components of the ecosystem as services that enable FAIR FAIR session, Macquarie University, 7th August 2019
  25. 25. Services that enable FAIR Many aspects of FAIR apply to services (findability, accessibility, use of standards…) but you also want to check: • Appropriate policy is in place • Robustness of business processes • Expertise of current staff • Value proposition / business model • Succession plans • Trustworthiness FAIR session, Macquarie University, 7th August 2019
  26. 26. From metrics to incentives • Use metrics to measure practice but beware misuse • Generate genuine incentives – career progression for data sharing & curation, recognise all outputs of research, include in recruitment and project evaluation processes… • Implement ‘next-generation’ metrics • Automate reporting as far as possible FAIR session, Macquarie University, 7th August 2019
  27. 27. Many, many H2020 FAIR projects clusters National initiatives • EOSC-Nordic • EOSC-Pillar • EOSC-synergy • ExPaNDS • NI4OS-Europe FAIR session, Macquarie University, 7th August 2019
  28. 28. The European Open Science Cloud Image Kyle Hinkson https://unsplash.com/photos/xyXcGADvAwE
  29. 29. An open festival for science • Virtual space where science producers and science consumers come together • Federation of existing infrastructure and services • An open-ended range of content and services • Quality mark « Data made in Europe » A platform for European research FAIR session, Macquarie University, 7th August 2019
  30. 30. EOSC Governance 2019-2020 EOSC governance structure FAIR session, Macquarie University, 7th August 2019 FAIR session, Macquarie University, 7th August 2019
  31. 31. Executive Board FAIR session, Macquarie University, 7th August 2019 • Karel Luyben & Cathrin Stover as Co-Chairs • 8 representatives of stakeholder groups • 3 independent experts https://www.eoscsecretariat.eu/ eb-profiles FAIR session, Macquarie University, 7th August 2019
  32. 32. EOSC Exec Board Working Groups GB/EB comms and engagement sub-group Skills WG Going Global WG Others under consideration FAIR session, Macquarie University, 7th August 2019
  33. 33. What is each WG is doing? • Map EOSC-relevant national infrastructures • Analyse Member State readiness to provide financial resource (with Sustainability) • Propose mechanisms to facilitate convergence and alignment Landscape • Recommend a minimal set of Rules of Participation that define the rights, obligations and accountability governing all EOSC transactions • Embrace the principles of openness, transparency and inclusiveness Rules of P. • Define, agree and develop an interoperability layer to federate systems i.e. standards, open APIs and protocols • Offer a catalogue of EOSC datasets and services Architecture • FAIR practices  EOSC interoperability framework (with Arch. & RoP) • Persistent Identifier (PID) policy for EOSC (with Architecture) • Frameworks to assess FAIR data and certify services that enable FAIR FAIR • Provide a set of strategic and financing orientations for EOSC post 2020 • In-depth analysis of business models and their different implications • Options for a governance framework to steer & oversee EOSC operations Sustainability FAIR session, Macquarie University, 7th August 2019 https://www.eoscsecretariat.eu/eosc-working-groups FAIR session, Macquarie University, 7th August 2019
  34. 34. All the fun of the FAIR Keep up to date with progress on the EOSCsecretariat blog: https://www.eoscsecretariat.eu/news-events-opinion/opinion FAIR session, Macquarie University, 7th August 2019
  35. 35. Where next at Macquarie? Image David Iskander https://unsplash.com/photos/iWTamkU5kiI
  36. 36. Hook DMPs into existing processes • Good idea to use Infonethica • Do lots of user testing and be willing to iterate • Keep the DMP short – reuse info where possible • Focusing on HDR students can be a good way to seed good practice up – some UK unis get supervisors to review / approve DMPs
  37. 37. Use existing fora to get advice • RDA Active DMPs Interest Group • https://rd-alliance.org/groups/active-data-management-plans.html • ARDC DMP Community of Practice • https://ardc.edu.au/resources/communities-of-practice • Jiscmail list with global RDM community. 1751 subscribers, running since 2008, email archive… • https://www.jiscmail.ac.uk/cgi-bin/webadmin?A0=RESEARCH-DATAMAN FAIR session, Macquarie University, 7th August 2019
  38. 38. Focus on FAIR basics For researchers • Document data • Use standards • Deposit in a repository • Assign a licence • Get a PID For services • Get researchers thinking early – DMP to plan • Advise on standards • Offer / point to repositories • Assign and use PIDs • Foster a culture of sharing • Recognise and reward FAIR FAIR session, Macquarie University, 7th August 2019
  39. 39. Reuse existing training materials • MANTRA – https://mantra.edina.ac.uk • RDMS MOOC – https://www.coursera.org/learn/data- management • Zenodo RDM training collection - https://zenodo.org/communities/dcc-rdm-training- materials • FOSTER online toolkit – https://www.fosteropenscience.eu/toolkit • FOSTER trainer’s handbook - https://www.fosteropenscience.eu/node/2150 FAIR session, Macquarie University, 7th August 2019
  40. 40. KEEP CALM Lots of others may have done lots of FAIR things, but this is an opportunity. Learn from their mistakes and copy good practice. Don’t fret about being behind… Image Joe DeSousa https://unsplash.com/photos/0MGhdhObDXA
  41. 41. Thanks! Any questions? FAIR session, Macquarie University, 7th August 2019

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