FAIRprincipi

FAIR principles, commonly referred to as the FAIR data principles, are guidelines for scientific data management and stewardship intended to improve Findability, Accessibility, Interoperability, and Reusability of data and metadata. Developed by the FORCE11 community and published in 2016, they have been adopted by many research institutions, funders, and publishers to enhance data discovery, interoperability, and long-term reuse across disciplines.

Findable: data and metadata should be assigned persistent identifiers and described with rich metadata to enable discovery by people and machines. Accessible: data and metadata should be retrievable by standard communication protocols, with appropriate authentication and authorization when necessary, and with metadata that remains accessible even if the data itself becomes unavailable. Interoperable: data should use shared vocabularies, ontologies, and formal formats, and include provenance information to enable integration with other datasets. Reusable: data should be richly described with accurate provenance, usage licenses, and documentation of format and standards to support replication and reuse in new studies.

Implementation and scope: the principles focus on making data machine-actionable and interoperable across platforms. They are not a blanket mandate for openness; data can be restricted or protected while still being FAIR. Implementing FAIR often involves assigning persistent identifiers, using metadata schemas, adopting interoperable formats, and providing clear licenses and provenance.

Impact and critique: FAIR has influenced policy developments, repository design, and funding program criteria worldwide. Critics note that achieving FAIR can require substantial resources, that the guidelines are not a one-size-fits-all solution, and that FAIR does not by itself ensure data quality or legal access.

The FAIR principles are widely used as a framework for evaluating and guiding data management practices in research.