FAIR data principles refer to a set of guiding principles that aim to make data Findable, Accessible, Interoperable, and Reusable. The term FAIR was proposed by Wilkinson et al. (2016) in a paper accessible here.
One of the most important challenges in data-driven science is the way researchers share knowledge. Knowledge passes through the exploitation of data that need to be collected, analyzed and stored. Sharing knowledge in a FAIR way means offering knowledge discovery, access to, integration and analysis of task-appropriate scientific data and their associated algorithms and workflows.
Making data findable means that: they are described with rich metadata that specify the data identifier, assigned a globally unique and eternally persistent identifier, registered or indexed in a searchable resource.
Making data accessible means that data and their metadata are retrievable by their identifier using a standardized communications protocol, that the protocol is open, free, and universally implementable allowing for an authentication and authorization procedure, where necessary.
Making data interoperable means that: a formal, accessible, shared, and broadly applicable language for knowledge representation is used, including qualified references to other (meta)data.
Making data reusable means that: (meta)data have a plurality of accurate and relevant attributes, are released with a clear and accessible data usage license, associated with their provenance and meet domain-relevant community standards.
For more information about the FAIR principles and guidelines to make your data FAIR, see here.
References
WILKINSON, M., DUMONTIER, M., AALBERSBERG, I. et al. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 3, 160018. https://doi.org/10.1038/sdata.2016.18