The FAIR Principles are a set of guidelines aimed at improving the Findability, Accessibility, Interoperability, and Reusability of research data. Originally published in 2016, the FAIR Principles are designed to support data stewardship, enhance research transparency, and promote data reuse across disciplines.
The FAIR framework emphasizes machine-actionability, meaning that both humans and computers should be able to discover and use the data. FAIR is not a standard but a set of guiding principles to inform best practices.
| F |
Findable
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|---|---|
| A |
Accessible
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| I |
Interoperable
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R |
Reusable
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Implementing FAIR principles improves the quality, longevity, and impact of research data. By making data FAIR, researchers enhance reproducibility, enable new discoveries, and comply with funder or publisher data sharing policies (e.g., NIH Data Management and Sharing Policy).
While FAIR and Open are related, they are not synonymous. FAIR refers to data being well-described and accessible under defined conditions. Data can be FAIR but not openly available, such as when sensitive information is protected but metadata and access terms are clearly provided.
Foundational Article
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