FAIR data
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The document discusses the FAIR data principles developed to enhance data sharing and reuse by assigning unique identifiers, using rich metadata, and ensuring accessibility. It emphasizes the distinction between FAIR and open data, highlighting that FAIR data can be shared with restrictions. Additionally, it outlines the recommendations and initiatives from the FAIR Data Expert Group to implement these principles effectively across various research communities.
FAIR data
- 1. S a r a h J o n e s A s s o c i a t e D i r e c t o r , D i g i t a l C u r a t i o n C e n t r e R a p p o r t e u r o f F A I R d a t a e x p e r t g r o u p s a r a h . j o n e s @ g l a s g o w . a c . u k T w i t t e r : @ s j D C C The FAIR data concept
- 2. What is FAIR? A set of principles to ensure that data are shared in a way that enables & enhances reuse, by humans and machines Image CC-BY-SA by SangyaPundir
- 3. Origins of FAIR • Emerged from a workshop held in Leiden in 2014 • Come from life sciences but intended for all data • Issued by FORCE11 community • Echo previous principles on open data & curation OECD Principles and Guidelines for Access to Research Data from Public Funding (2007) Science as an Open Enterprise (2012) notion of ‘intelligent openness’ where data are accessible, intelligible, assessable and useable G8 Science Ministers Statement (2013)
- 4. Why has FAIR gained traction? Meaningful and memorable articulation of concepts Natural desire to want to be ‘fair’ Easy to understand at a high-level, but… “We understand the basic principle of FAIR, but the terminology is often difficult to grasp immediately. Things could be explained better in plain language” “The term interoperable is quite confusing sometimes and mixed with re-use.” OpenAIRE & FAIR data Expert Group H2020 DMP survey https://doi.org/10.5281/zenodo.1120245
- 5. 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
- 6. 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.
- 7. FAIR data checklist Findable - Persistent ID - 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.1065991
- 8. FAIR vs open data • FAIR data does not have to be open • Data can be shared under restrictions & still be FAIR • Making data FAIR ensures it can be found, understood and reused • Open data is a subset of all the data shared "As open as possible, as closed as necessary"
- 9. Implementing FAIR • The principles do not specify particular technologies or implementations e.g. semantic web • FAIR is not a standard to be followed or strict criteria – it’s a spectrum / continuum • Supporting FAIR will require investment in infrastructure, coordination across initiatives and engagement with research communities
- 10. Context is king Practices varies across research communities particle physics mostly shares its data inside the large consortia attached to its experiments social scientists typically provide lots of documentation and methodological information supporting reuse Communities need to self-organise and define what FAIR data means in their context
- 11. Emerging initiatives Pilot project and several infrastructure investments https://eoscpilot.eu http://tiny.cc/EOSC-projects https://www.go-fair.org https://www.rd-alliance.org
- 12. Remit of the FAIR data expert group 1. To develop recommendations on what needs to be done to turn each component of the FAIR data principles into reality 2. To propose indicators to measure progress on each of the FAIR components 3. To provide input to the proposed European Open Science Cloud (EOSC) action plan on how to make data FAIR 4. To contribute to the evaluation of the Horizon 2020 Data Management Plan (DMP) template and development of associated sector / discipline-specific guidance 5. To provide input on the issue of costing and financing data management activities http://tinyurl.com/FAIR-EG
- 13. FAIR EG progress and plans Most of the report text is already written Preliminary recommendations Working meeting to initiate FAIR Data Action plan next week Draft due end May Public consultation around EOSC Summit in June Final Report and Action Plan due end September Announce / launch at Austrian Presidency events in October
- 14. Recommendations to facilitate change Define what actions are required by different stakeholders (EC, member states, disciplines, international) and when Structure and prioritise actions across key topics: policy, skills, standards, infrastructure, costs, rewards… Use these recommendations to develop a core FAIR Data Action Plan Support the definition of more detailed FAIR Data Action Plans at domain and member state level
- 15. Thanks - questions? Sarah Jones sarah.jones@glasgow.ac.uk Twitter: @sjDCC www.dcc.ac.uk http://tinyurl.com/FAIR-EG www.force11.org/group/fairgroup/fairprinciples
Editor's Notes
- #4: OECD – 13 principles e.g. openness, flexible, transparent, legal, interoperable, quality, secure, accountable, efficient… OECD preconditions: ‘data must be accessible and readily located; they must be intelligible to those who wish to scrutinise them; data must be assessable so that judgments can be made about their reliability and the competence of those who created them; and they must be usable by others.’ G8 statement adopted verbatim in the European Commission’s first data guidelines for the Horizon 2020 framework programme later the same year.