FAIR data principles

The FAIR data principles are a set of guidelines, published in 2016, stating that research data and the systems around it should be Findable (with rich metadata and a persistent identifier), Accessible (retrievable via a standard protocol, with clear conditions for access even when the data itself is restricted), Interoperable (using shared vocabularies and formats so it can be combined with other data), and Reusable (with clear licensing and enough context to be reused correctly).

FAIR is often confused with "open": FAIR data can be access-controlled, for example sensitive health data can be FAIR while remaining behind a permission process, as long as it is described, discoverable and retrievable under known conditions. This differs from open data, which specifically means data is freely available to anyone with no restriction.

FAIR principles guide research data management policy at funders, journals and institutions, and many grant bodies now require a data-management plan that addresses each of the four principles. They also support reproducibility, since a study cannot be checked if its data cannot be found or accessed. The common pitfall is treating FAIR compliance as a checkbox, adding a DOI and a license without genuinely documenting the data well enough for someone outside the original team to understand and correctly reuse it.