Informatics Educational Institutions & Programs
FAIR data are data which meet principles of findability, accessibility, interoperability, and reusability. The acronym and principles were defined in a March 2016 paper in the journal Scientific Data by a consortium of scientists and organizations.
The FAIR principles emphasize 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.
The abbreviation FAIR/O data is sometimes used to indicate that the dataset or database in question complies with the FAIR principles and also carries an explicit data‑capable open license.
FAIR principles, as published by GO FAIR
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
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
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
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).— GO FAIR, FAIR Principles, https://www.go-fair.org/fair-principles/
Acceptance and implementation of FAIR data principles
Before FAIR a 2007 paper was the earliest paper discussing similar ideas related to data accessibility.
In 2016 a group of Australian organisations developed a Statement on FAIR Access to Australia's Research Outputs, which aimed to extend the principles to research outputs more generally.
In 2017 Germany, Netherlands and France agreed to establish 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, CODATA's strategic Decadal Programme "Data for Planet: Making data work for cross-domain challenges" mentions FAIR data principles as a fundamental enabler of data driven science.
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.
In 2019 the Global Indigenous Data Alliance (GIDA) released the CARE Principles for Indigenous Data Governance as a complementary guide. 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.
The lack of information on how to implement the guidelines have led to inconsistent interpretations of them.
In January 2020, representatives of nine groups of universities around the world produced the Sorbonne declaration on research data rights, which included a commitment to FAIR data, and called on governments to provide support to enable it.
- Data management
- Remix culture
- Open access
- Open data – datasets and databases carrying an explicit data‑capable open license
- Open science
- 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.
- "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.
- 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
- G20 leaders (5 September 2016). "G20 Leaders' Communique Hangzhou Summit". europa.eu. European Commission.
- "European Commission embraces the FAIR principles – Dutch Techcentre for Life Sciences". Dutch Techcentre for Life Sciences. 20 April 2016.
- "Australian FAIR Access Working Group". www.fair-access.net.au. Retrieved 2020-04-03.
- 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.
- "FAIR Data Maturity Model WG". RDA. 2018-09-23. Retrieved 2020-02-16.
- "Decadal Programme – CODATA". www.codata.org. Retrieved 2020-02-16.
- Association of European Research Libraries (13 July 2018). "Open Consultation on FAIR Data Action Plan – LIBER". LIBER.
- 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.
- Science Europe (May 2016). "Funding research data management and related infrastructures" (PDF).
- "CARE Principles of Indigenous Data Governance". Global Indigenous Data Alliance. Retrieved 2019-09-30.
- Jacobsen, Annika; de Miranda Azevedo, Ricardo; Juty, Nick; Batista, Dominique; Coles, Simon; Cornet, Ronald; Courtot, Mélanie; Crosas, Mercè; Dumontier, Michel; Evelo, Chris T.; Goble, Carole; Guizzardi, Giancarlo; Hansen, Karsten Kryger; Hasnain, Ali; Hettne, Kristina; Heringa, Jaap; Hooft, Rob W.W.; Imming, Melanie; Jeffery, Keith G.; Kaliyaperumal, Rajaram; Kersloot, Martijn G.; Kirkpatrick, Christine R.; Kuhn, Tobias; Labastida, Ignasi; Magagna, Barbara; McQuilton, Peter; Meyers, Natalie; Montesanti, Annalisa; van Reisen, Mirjam; Rocca-Serra, Philippe; Pergl, Robert; Sansone, Susanna-Assunta; da Silva Santos, Luiz Olavo Bonino; Schneider, Juliane; Strawn, George; Thompson, Mark; Waagmeester, Andra; Weigel, Tobias; Wilkinson, Mark D.; Willighagen, Egon L.; Wittenburg, Peter; Roos, Marco; Mons, Barend; Schultes, Erik (January 2020). "FAIR Principles: Interpretations and Implementation Considerations". Data Intelligence. 2 (1–2): 10–29. doi:10.1162/dint_r_00024.
- Sorbonne Declaration on Research Data Rights, Jan 27 2020
- Open data ‘tougher’ than open access and needs ‘mindset change’, Times Higher Education, January 31 2020
- FAIR Data and Semantic Publishing, a statement from the lab of the first author of the original paper
- Guide to FAIR Data from Dutch Techcentre for Life Sciences
- GO FAIR initiative website
- FAIR Principles with detailed description of each of the guiding principles by the GO FAIR initiative
- A FAIRy tale explaining the FAIR principles, published by the FAIR project