FAIR data are data which meet principles of findability, accessibility, interoperability, and reusability. 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 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.
FAIR principles
Findable
The first step in 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. data are assigned a globally unique and persistent identifier F2. Data are described with rich metadata F3. Metadata clearly and explicitly include the identifier of the data they describe F4. data are registered or indexed in a searchable resource
Accessible
Once the user finds the required data, she/he needs to know how can they be accessed, possibly including authentication and authorisation. A1. 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 for analysis, storage, and processing. I1. data use a formal, accessible, shared, and broadly applicable language for knowledge representation. I2. data use vocabularies that follow FAIR principles I3. data include qualified references to other 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 are richly described with a plurality of accurate and relevant attributes R1.1. data are released with a clear and accessible data usage license R1.2. data are associated with detailed provenance R1.3. data meet domain-relevant community standards The principles refer to three types of entities: data, metadata, and infrastructure. For instance, principle F4 defines that both metadata and data are registered or indexed in a searchable resource.
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. 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 . Other international organisations active in the research data ecosystem, such as CODATA or Research Data Alliance 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. The Association of European Research Libraries recommends the use of FAIR principles. 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. 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. Before FAIR a 2007 paper was the earliest paper discussing similar ideas related to data accessibility. In 2019 the Global Indigenous Data Alliance 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.
