Search
HemaFAIR SURVEYS

FAIR principles

‘FAIR’ is an acronym for (F)indable, (A)ccessible, (I)nteroperable, (R)eusable that is first launched at a workshop in 2014, resulting in development of FAIR principles in 2016.

Findable
  • (Meta)data are assigned a globally unique identifier
  • Data are described with rich metadata
  • (Meta)data are registered or indexed in a searchable resource
  • Metadata specify the data identifier

This first principle means the other humans and computers can find the data due to the presence of strong metadata and persistent identifiers (e.g., digital object identifiers, DOIs) that are registered or indexed in a searchable resource such as research data repository.

Accessible
  • (Meta)data are retrievable by their identifier using a standardized communications protocol
  • The protocol is open, free and universally implementable
  • The protocol allows for an authentication and authorization procedure, where necessary
  • Metadata are accessible, even when the data are no longer available

This principles states that the data should be available and downloadable from a reputable repository by both humans and computers. If the data cannot be shared openly, a metadata record detailing under what conditions the data can be shared should be created.

Interoperable
  • (Meta)data use a formal, accessible, shared, and broadly applicable language
  • (Meta)data use vocabularies that follow FAIR principles
  • (Meta)data include qualified references to other (meta)data

In order to speed up the discovery and innovation potential the data should be easily combined with other datasets, applications or workflows by humans and computer systems that are not inter-collaborative.

Reusable
  • (Meta)data have a plurality of accurate and relevant attributes
  • (Meta)data are released with clear accessible data usage licence
  • (Meta)data are associated with their provenance
  • (Meta)data meet domain-relevant community standards

The data should be ready for future research and processing for the community to build on new research questions. This can be achieved by using proper documentation to support interpretation of the data with clear data usage license, considering data sharing restrictions and intellectual property rights.