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Turning FAIR into Reality: Final outcomes from the European Commission FAIR Data Expert Group


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A multi-speaker presentation given by the European Commission FAIR Data Expert Group at ScieDataCon as part of International Data Week in Botswana in November 2018.

Simon Hodson, Chair of the Group explained the remit and background. Natalie Harrower outlined key concepts. Francoise Genova spoke on the recommendations related to research data culture. Daniel Mietchen addressed the infrastructure needed and our proposals for a FAIR ecosystem, and Sarah Jones spoke to the cultural aspects needed to drive change and outlined the FAIR Action Plan.

The report has been revised in light of the 500+ comments received as part of the open consultation and will be formally released on 23rd November as part of the Austrian Presidency events.

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Turning FAIR into Reality: Final outcomes from the European Commission FAIR Data Expert Group

  1. 1. “Turning FAIR into Reality” Final outcomes from the European Commission FAIR Data Expert Group Simon Hodson CODATA and Chair of Group Françoise Genova Observatoire Astronomique de Strasbourg Natalie Harrower Digital Repository of Ireland @natalieharrower Sarah Jones Digital Curation Centre and Rapporteur of Group Daniel Mietchen Data Science Institute, University of Virginia
  2. 2. 1. To develop recommendations on what needs to be done to turn the FAIR data principles into reality (EC, member states, international level). 2. Develop the FAIR Data Action Plan, by proposing a list of concrete actions as part of its Final Report. 3. To propose indicators to measure progress on making FAIR data a reality. 4. Run workshops with experts from relevant European and international initiatives, input to FAIR Data Action Plan. 5. Launch and disseminate FAIR Data Action Plan and support Commission communication in November 2018 6. To contribute to the evaluation of the Horizon 2020 Data Management Plan (DMP) template and development of associated sector / discipline-specific guidance 7. To provide input on the issue of costing and financing data management activities European Commission Expert Group on FAIR Data: Objectives
  3. 3. Simon Hodson, CODATA Chair of FAIR Data EG Rūta Petrauskaité, Vytautas Magnus University Peter Wittenburg, Max Planck Computing & Data Facility Sarah Jones, Digital Curation Centre (DCC), Rapporteur Daniel Mietchen, Data Science Institute, University of Virginia Françoise Genova, Observatoire Astronomique de Strasbourg Leif Laaksonen, CSC- IT Centre for Science Natalie Harrower, Digital Repository of Ireland – year 2 only Sandra Collins, National Library of Ireland – year 1 only FAIR Data EG: membership
  4. 4. FAIR Data EG: Timescale 4 May - June Interim report due end May Launch at EOSC Summit on 11th June in Brussels June - October Stakeholder workshop at EOSC Summit, 12 June Consultation until 5 August Revisions to report November Final Report and FAIR Data Action Plan Official launch and formal communications at Austrian Presidency event in Vienna, 23 Nov
  5. 5. Structure of the Report and Action Plan 1. Concepts: why FAIR? 2. Creating a culture of FAIR data 3. Creating a technical ecosystem for FAIR data 4. Skills and capacity building 5. Measuring change 6. Funding and sustaining FAIR data 7. FAIR action plan
  6. 6. Consultation 6 • Interim FAIR Data Action Plan: • Interim FAIR Data Report: • Lots of supportive feedback. • Areas where we needed to clarify our position: - Relation of FAIR and Open, Open Science - Need for FAIR services, role of trusted repositories and how we accredit these.
  7. 7. Key Points: To make FAIR a reality … • Report takes a holistic approach, not a data centric approach • Need to address the enabling practices and technologies – not just focus on the data and its attributes • Need to consider all digital outputs (data, code, metadata etc) • Objective is to make data and other digital research outputs FAIR for humans and machines. • Needs: concept of FAIR digital objects, FAIR ecosystem, interoperability frameworks for disciplines and across disciplines, FAIR services including trusted digital repositories, skills, metrics and sustainable funding.
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  10. 10. Define FAIR for Implementation, Apply it broadly 10 • To implement FAIR, research communities must define how the FAIR principles and related concepts apply in their context. • FAIR should be applied broadly to all objects (including metadata, identifiers, software and DMPs) that are essential to the practice of research, and should inform metrics relating directly to these objects.
  11. 11. Concepts Implied by the Principles 11 Making FAIR a reality depends on additional concepts that are implied by the principles, including: - The timeliness of sharing - Data appraisal and selection - Long-term preservation and stewardship - Assessability – to assess quality, accuracy, reliability - Legal interoperability – licenses, automated
  12. 12. FAIR Digital Objects 12 ● Implementing FAIR requires a model for FAIR digital objects ● Digital objects can include data, software, and other research resources ● Universal use of appropriate PIDs ● Use of common (ideally open) formats; data accompanied by code ● Rich metadata and clear licensing enables broadest reuse
  13. 13. The FAIR Ecosystem 13 • The realisation of FAIR relies on an ecosystem of components • Essential are: Policies Data Management Plans Identifiers Standards Repositories • Registries to catalogue each component of the ecosystem, and automated workflows between them. • Begin by enhancing existing registries; build those for DMPs and IDs
  14. 14. The FAIR Ecosystem National Geographic Society 14 • Ecosystem and its components should work for humans and machines • Need to clearly define infrastructure components essential in specific contexts and fields • Testbeds need to be used to evaluate, evolve, innovate the ecosystem
  15. 15. FAIR and Open 15 ● Concepts of FAIR and Open should not be conflated. Data can be FAIR or Open, both or neither ● The greatest potential reuse comes when data are both FAIR and Open ● Even internal or restricted data will benefit from being FAIR, and there are legitimate reasons for restriction which vary by discipline ● Align and harmonise FAIR and Open data policy to ensure that publicly-funded research data are made FAIR and Open, except for legitimate restrictions
  16. 16. FAIR and Open 16 ● ‘As Open as possible, as closed as necessary' ● By default, data created by publicly funded research projects, initiatives and infrastructures should be to made available as soon as possible. ● Policies could allow for (short) embargo periods to facilitate the right of first use for creators ● Guidance should be provided to researchers to help find optimal balance between sharing and privacy
  17. 17. 17 Conceptual understanding: degrees or spectrum
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  19. 19. A major change in the practice of research communities 19 ● Some communities share and use FAIR data, some are making progress, some are reluctant ● FAIR data availability does change the way science is done ● Disciplines know their data and have work to do to provide them FAIRly ● Interdisciplinary work should be enabled in particular to tackle the Grand Challenges ● Incentives and rewards are fundamental to enable the change
  20. 20. Interoperability Frameworks 20 • Support research communities to develop their interoperability frameworks for FAIR sharing • Examples of use cases and success stories to convince reluctant communities • Encourage disciplines and interdisciplinary research programmes to engage in international collaboration mechanisms to develop interoperability frameworks • Exchange of good practices and lessons learnt, define case studies • Common standards to support disciplinary frameworks and promote interoperability and reuse across disciplines
  21. 21. Interoperability Frameworks In Astronomy: The International Virtual Observatory Alliance (IVOA)  Created in 2002  National initiatives from the 5 continents  Researchers and IT specialists  An open and inclusive framework of standards and tools  100 “authorities” declared a resource in the Registry of Resources  Customized by planetary sciences, astroparticle physics, the Virtual Atomic and Molecular Data Center, Registry concepts reused for the Materials registry (RDA WG)
  22. 22. Interoperability Frameworks 22 The IVOA Architecture
  23. 23. Data Management via DMPs 23 A core element of research projects • Requirement, support and incentives to research communities. DMPs should cover all research outputs • DMPs should be living documents • DMPS should be tailored to disciplinary needs, research communities to provide input and agree • Harmonisation of DMP requirements across funders and organisations DMP acting as a hub of information on FAIR digital objects, connecting to the wider elements of the ecosystem
  24. 24. Recognition, Incentives and Rewards 24 Recognise provision of FAIR data, infrastructure and services in assessment of research contributions and career progression • Recognition of the diversity of research contributions and include them in CVs, researchers’ applications and activity reports • Credit should be given to all roles supporting FAIR data and definition of interoperability frameworks, whether for existing or new • Evidence of past practice in support to FAIR should be included in assessments of research contribution • Contribution to development and operation of certified and trusted infrastructures that support FAIR data should be recognized, rewarded and incentivised
  25. 25. Additional actions 25 COST DATA MANAGEMENT Data management, curation and publication costs should be included in grant applications. Detailed guidelines should be provided SELECTION/PRIORITIZATION OF FAIR DIGITAL OBJECTS Develop and implement processes to assist the appraisal and selection of outputs that will be retained and made FAIR DEPOSIT IN TRUSTED DIGITAL REPOSITORIES Research data should be made available in Trusted Digital Repositories, where possible those supporting specific disciplinary or interdisciplinary communities ENCOURAGE AND INCENTIVIZE REUSE OF FAIR OUTPUTS Funders should incentivize the reuse of FAIR outputs when appropriate in calls and require communities to build on existing content wherever possible
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  31. 31. Key drivers needed for change 31Incentives Metrics SkillsInvestment Cultural and social aspects that drive the ecosystem and enact change
  32. 32. Skills 32 • Two cohorts of professionals to support FAIR data: - data scientists embedded in research projects - data stewards who will ensure the curation of FAIR data • Coordinate, systematise and accelerate the pedagogy • Support formal and informal learning • Ensure researchers have foundational data skills Create / Analyse Preserve / Share
  33. 33. 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. • Certification schemes are needed to assess all components of the ecosystem as FAIR services. Existing frameworks like CoreTrustSeal for repository certification should be used and adapted rather than initiating new schemes based solely on FAIR, which is articulated for data rather than services.
  34. 34. Assessing FAIR services 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
  35. 35. 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
  36. 36. Investment 36 • Provide strategic and coordinated funding to maintain the components of the FAIR ecosystem • Ensure funding is sustainable – no unfunded mandates • Open EOSC to all providers, but ensure services are FAIR
  37. 37.  A short tweetable recommendation – Underpinned by several practical and specific action points – Action points to be linked to stakeholders and timeframes How is the Action Plan structured? 37 FAIR Action Plan is directed at the EC, Member States and international level, but will also apply in context of EOSC to inform this roadmap
  38. 38. ● Research communities: practitioners from all research fields, clustered around disciplinary interests, data types or cross-cutting grand challenges. ● Data service providers: domain repositories, research infrastructures and e-infrastructures, institutional, community and commercial tools and services. ● Data stewards: support staff from research communities and research libraries, and those managing data repositories. ● Standards bodies: formal organisations and consortia coordinating data standards and governing procedures relevant to FAIR ● Coordination fora: global and national bodies such as the Research Data Alliance, CODATA, WDS Communities of Excellence, GO FAIR. ● Policymakers: governments, international entities like OECD, research funders, institutions, publishers and others defining data policy. ● Research funders: the European Commission, national research funders, charitable organisations and foundations, and other funders of research activity. ● Institutions: universities and research performing organisations. ● Publishers: not-for-profit and commercial, Open Access and paywall publishers of research papers and data. Stakeholders with responsibilities
  39. 39. Context specific FAIR Action Plans 39  The Expert Group has developed an overarching FAIR Action Plan  Hope is that this will inspire the definition of more detailed FAIR Action Plans at research community and Member State level  What are the priority actions in your area and for which stakeholders?