FAIR data

FAIR data are data which meet principles of findability, accessibility, interoperability, and reusability.[1] A March 2016 publication by a consortium of scientists and organizations specified the “FAIR Guiding Principles for scientific data management and stewardship” in Scientific Data, using FAIR as an acronym and making the concept easier to discuss.

The authors intended to provide guidelines to improve the findability, accessibility, interoperability, and reuse of digital assets. The FAIR principles emphasise machine-actionability (i.e., the capacity of computational systems to find, access, interoperate, and reuse data with none or minimal human intervention) because humans increasingly rely on computational support to deal with data as a result of the increase in volume, complexity, and creation speed of data.[2]

FAIR principles


The first step in (re)using data is to find them. Metadata and data should be easy to find for both humans and computers. Machine-readable metadata are essential for automatic discovery of datasets and services, so this is an essential component of the FAIRification process.

F1. (Meta)data are assigned a globally unique and persistent identifier

F2. Data are described with rich metadata (defined by R1 below)

F3. Metadata clearly and explicitly include the identifier of the data they describe

F4. (Meta)data are registered or indexed in a searchable resource[2]


Once the user finds the required data, she/he needs to know how can they be accessed, possibly including authentication and authorisation.

A1. (Meta)data are retrievable by their identifier using a standardised communications protocol

A1.1 The protocol is open, free, and universally implementable

A1.2 The protocol allows for an authentication and authorisation procedure, where necessary

A2. Metadata are accessible, even when the data are no longer available[2]


The data usually need to be integrated with other data. In addition, the data need to interoperate with applications or workflows for analysis, storage, and processing.

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[2]


The ultimate goal of FAIR is to optimise the reuse of data. To achieve this, metadata and data should be well-described so that they can be replicated and/or combined in different settings.

R1. Meta(data) are richly described with 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 detailed provenance

R1.3. (Meta)data meet domain-relevant community standards

The principles refer to three types of entities: data (or any digital object), metadata (information about that digital object), and infrastructure. For instance, principle F4 defines that both metadata and data are registered or indexed in a searchable resource (the infrastructure component).[2]

Acceptance and implementation of FAIR data principles

At the 2016 G20 Hangzhou summit, the G20 leaders issued a statement endorsing the application of FAIR principles to research.[3][4]

In 2017 Germany, Netherlands and France agreed to establish[5] an international office to support the FAIR initiative, the GO FAIR International Support and Coordination Office.

Other international organisations active in the research data ecosystem, such as CODATA or Research Data Alliance (RDA) also support FAIR implementations by their communities. FAIR principles implementation assessment is being explored by FAIR Data Maturity Model Working Group of RDA[6], CODATA’s strategic Decadal Programme “Data for Planet: Making data work for cross-domain challenges”[7] mentions FAIR data principles as a fundamental enabler of data driven science.

The Association of European Research Libraries recommends the use of FAIR principles.[8]

A 2017 paper by advocates of FAIR data reported that awareness of the FAIR concept was increasing among various researchers and institutes, but also, understanding of the concept was becoming confused as different people apply their own differing perspectives to it.[9]

Guides on implementing FAIR data practices state that the cost of a data management plan in compliance with FAIR data practices should be 5% of the total research budget.[10]

Before FAIR a 2007 paper was the earliest paper discussing similar ideas related to data accessibility.[11]

In 2019 the Global Indigenous Data Alliance (GIDA) released the CARE Principles for Indigenous Data Governance as a complementary guide[12]. The CARE principles extend principles outlined in FAIR data to include Collective benefit, Authority to control, Responsibility, and Ethics to ensure data guidelines address historical contexts and power differentials. The CARE Principles for Indigenous Data Governance were drafted at the International Data Week and Research Data Alliance Plenary co-hosted event “Indigenous Data Sovereignty Principles for the Governance of Indigenous Data Workshop,” 8 November 2018, Gaborone, Botswana.


  1. ^Wilkinson, Mark D.; Dumontier, Michel; Aalbersberg, IJsbrand Jan; Appleton, Gabrielle; et al. (15 March 2016). “The FAIR Guiding Principles for scientific data management and stewardship”. Scientific Data. 3: 160018. doi:10.1038/sdata.2016.18. OCLC 961158301. PMC 4792175. PMID 26978244.
  2. ^ Jump up to:ab c d e “FAIR Principles”. GO FAIR. Retrieved 2020-02-16. Material was copied from this source, which is available under a Creative Commons Attribution 4.0 International License.
  3. ^G20 leaders (5 September 2016). “G20 Leaders’ Communique Hangzhou Summit”. europa.eu. European Commission.
  4. ^“European Commission embraces the FAIR principles – Dutch Techcentre for Life Sciences”. Dutch Techcentre for Life Sciences. 20 April 2016.
  5. ^Ministerie van Onderwijs, Cultuur en Wetenschap (2017-12-01). “Progress towards the European Open Science Cloud – GO FAIR – News item – Government.nl”. www.government.nl (in Dutch). Retrieved 2020-02-15.
  6. ^“FAIR Data Maturity Model WG”. RDA. 2018-09-23. Retrieved 2020-02-16.
  7. ^“Decadal Programme – CODATA”. www.codata.org. Retrieved 2020-02-16.
  8. ^Association of European Research Libraries (13 July 2018). “Open Consultation on FAIR Data Action Plan – LIBER”. LIBER.
  9. ^Mons, Barend; Neylon, Cameron; Velterop, Jan; Dumontier, Michel; da Silva Santos, Luiz Olavo Bonino; Wilkinson, Mark D. (7 March 2017). “Cloudy, increasingly FAIR; revisiting the FAIR Data guiding principles for the European Open Science Cloud”. Information Services & Use. 37 (1): 49–56. doi:10.3233/ISU-170824. hdl:20.500.11937/53669.
  10. ^Science Europe (May 2016). “Funding research data management and related infrastructures” (PDF).
  11. ^Sandra Collins; Françoise Genova; Natalie Harrower; Simon Hodson; Sarah Jones; Leif Laaksonen; Daniel Mietchen; Rūta Petrauskaité; Peter Wittenburg (7 June 2018), “Turning FAIR data into reality: interim report from the European Commission Expert Group on FAIR data”, Zenodo, doi:10.5281/ZENODO.1285272
  12. ^“CARE Principles of Indigenous Data Governance”. Global Indigenous Data Alliance. Retrieved 2019-09-30.

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