FAIR data
![](https://upload.wikimedia.org/wikipedia/commons/thumb/a/aa/FAIR_data_principles.jpg/220px-FAIR_data_principles.jpg)
FAIR dataaredatawhich meet principles offindability,accessibility,interoperability,andreusability(FAIR).[1][2]The acronym and principles were defined in a March 2016 paper in the journalScientific Databy a consortium of scientists and organizations.[1]
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.[3]
The abbreviationFAIR/O datais sometimes used to indicate that the dataset or database in question complies with the FAIR principles and also carries an explicit data‑capableopen license.
FAIR principles published by GO FAIR
[edit]Findable
The first step in (re)using data is to find them.Metadataand data should be easy to find for both humans and computers.Machine-readablemetadata are essential for automaticdiscoveryof 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
Accessible
Once the user finds the required data, they need to know how they can be accessed, possibly includingauthenticationandauthorisation.
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
Interoperable
The data usually need to be integrated with other data. In addition, the data need to interoperate with applications or workflows foranalysis,storage,andprocessing.
I1. (Meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
I2. (Meta)data usevocabulariesthat follow FAIR principles
I3. (Meta)data include qualified references to other (meta)data
Reusable
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 Foundation, FAIR Principles,https:// gofair.foundation/
Acceptance and implementation
[edit]Before FAIR a 2007 paper was the earliest paper discussing similar ideas related to data accessibility.[4]
At the2016 G20 Hangzhou summit,theG20leaders issued a statement endorsing the application of FAIR principles to research.[5][6]
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.[7]
In 2017 Germany, Netherlands and France agreed to establish[8]an international office to support the FAIR initiative, the GO FAIR International Support and Coordination Office.[9]
Other international organisations active in the research data ecosystem, such asCODATAorResearch 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,[10]CODATA's strategic Decadal Programme "Data for Planet: Making data work for cross-domain challenges"[11]mentions FAIR data principles as a fundamental enabler of data driven science.
![](https://upload.wikimedia.org/wikipedia/commons/thumb/b/b7/Implementing_FAIR_Data_Principles_-_The_Role_of_Libraries.pdf/page1-220px-Implementing_FAIR_Data_Principles_-_The_Role_of_Libraries.pdf.jpg)
TheAssociation of European Research Librariesrecommends the use of FAIR principles.[12]
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.[13]
Guides on implementing FAIR data practices state that the cost of adata management planin compliance with FAIR data practices should be 5% of the total research budget.[14]
In 2019 the Global Indigenous Data Alliance (GIDA) released theCARE Principles for Indigenous Data Governanceas a complementary guide.[15]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", held 8 November 2018, inGaborone,Botswana.[16]
The lack of information on how to implement the guidelines have led to inconsistent interpretations of them.[17]
In January 2020, representatives of nine groups of universities around the world produced theSorbonne declaration on research data rights,[18]which included a commitment to FAIR data, and called on governments to provide support to enable it.[19]
In 2021, researchers identified the FAIR principles as a conceptual component of data catalog software tools, with the other components being metadata management, business context and data responsibility roles.[20]
In April 2022, Matthias Scheffler and colleagues argued inNaturethat FAIR principles are "a must" so thatdata miningandartificial intelligencecan extract useful scientific information from the data.[21]
However, making data (and research outcomes) FAIR is a challenging task as well as it is challenging to assess the FAIRness.[22]
See also
[edit]- Data management
- Open access
- Open data– datasets and databases carrying an explicit data‑capableopen license
- Open science
- Remix culture
References
[edit]- ^abMark D. Wilkinson;Michel Dumontier;IJsbrand Jan Aalbersberg; et al. (15 March 2016)."The FAIR Guiding Principles for scientific data management and stewardship".Scientific Data.3(1): 160018.doi:10.1038/SDATA.2016.18.ISSN2052-4463.PMC4792175.PMID26978244.WikidataQ27942822.
- ^Annika Jacobsen; Ricardo de Miranda Azevedo; Nick Juty; et al. (31 January 2020). "FAIR Principles: Interpretations and Implementation Considerations".Data Intelligence:10–29.doi:10.1162/DINT_R_00024.ISSN2641-435X.WikidataQ76394974.
- ^"FAIR Principles".GO FAIR.Retrieved2020-02-16.
Material was copied from this source, which is available under aCreative 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.
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:CS1 maint: numeric names: authors list (link) - ^"European Commission embraces the FAIR principles – Dutch Techcentre for Life Sciences".Dutch Techcentre for Life Sciences.20 April 2016.
- ^"Australian FAIR Access Working Group".fair-access.net.au.Retrieved2020-04-03.
- ^Ministerie van Onderwijs, Cultuur en Wetenschap (2017-12-01)."Progress towards the European Open Science Cloud – GO FAIR – News item – Government.nl".government.nl(in Dutch).Retrieved2020-02-15.
- ^"GO FAIR Offices".GO FAIR.Retrieved2023-12-05.
- ^"FAIR Data Maturity Model WG".RDA.2018-09-23.Retrieved2020-02-16.
- ^"Decadal Programme – CODATA".codata.org.Retrieved2020-02-16.
- ^Association of European Research Libraries (13 July 2018)."Open Consultation on FAIR Data Action Plan – LIBER".LIBER.
- ^Barend Mons;Cameron Neylon;Jan Velterop;Michel Dumontier;Luiz Olavo Bonino da Silva Santos; Mark D. Wilkinson (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.ISSN0167-5265.WikidataQ29051495.
- ^Science Europe (May 2016)."Funding research data management and related infrastructures"(PDF).
- ^"CARE Principles of Indigenous Data Governance".Global Indigenous Data Alliance.Retrieved2019-09-30.
- ^O'Donnell, Dan (2021-12-16)."Thinking about the CARE Principles in the Digital Humanities".DARIAH-Campus.
- ^Annika Jacobsen; Ricardo de Miranda Azevedo; Nick Juty; et al. (31 January 2020). "FAIR Principles: Interpretations and Implementation Considerations".Data Intelligence:10–29.doi:10.1162/DINT_R_00024.ISSN2641-435X.WikidataQ76394974.
- ^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
- ^Ehrlinger, Lisa; Schrott, Johannes; Melichar, Martin; Kirchmayr, Nicolas; Wöß, Wolfram (2021), Kotsis, Gabriele; Tjoa, A Min; Khalil, Ismail; Moser, Bernhard (eds.),"Data Catalogs: A Systematic Literature Review and Guidelines to Implementation",Database and Expert Systems Applications - DEXA 2021 Workshops,Communications in Computer and Information Science, vol. 1479, Cham: Springer International Publishing, pp. 148–158,doi:10.1007/978-3-030-87101-7_15,ISBN978-3-030-87100-0,S2CID237621026,retrieved2022-06-26
- ^Scheffler, Matthias; Aeschlimann, Martin; Albrecht, Martin; Bereau, Tristan; Bungartz, Hans-Joachim; Felser, Claudia; Greiner, Mark; Groß, Axel; Koch, Christoph T.; Kremer, Kurt; Nagel, Wolfgang E. (2022-04-28)."FAIR data enabling new horizons for materials research".Nature.604(7907): 635–642.arXiv:2204.13240.Bibcode:2022Natur.604..635S.doi:10.1038/s41586-022-04501-x.ISSN0028-0836.PMID35478233.S2CID248415511.
- ^Candela, Leonardo; Mangione, Dario; Pavone, Gina (2024-05-27)."The FAIR Assessment Conundrum: Reflections on Tools and Metrics".Data Science Journal.23:33.doi:10.5334/dsj-2024-033.
External links
[edit]- FAIR Data and Semantic Publishing,a statement from the lab of the first author of the original paper
- Guide to FAIR Datafrom Dutch Techcentre for Life Sciences
- GO FAIRinitiative website
- FAIR Principleswith detailed description of each of the guiding principles by the GO FAIR initiative
- A FAIRy taleexplaining the FAIR principles, published by the FAIR project