Research data that is findable, accessible, interoperable and reusable (FAIR) enriches the research landscape and enables data-driven research. The FAIR principles are now widely acknowledged as best guidelines for data management.
It should be possible for others to discover your data. Rich metadata should be available online in a searchable resource, and the data should be assigned a persistent identifier.
It should be possible for humans and machines to gain access to your data, under specific conditions or restrictions where appropriate. FAIR does not mean that data need to be open! There should be metadata, even if the data aren’t accessible.
Data and metadata should conform to recognised formats and standards to allow them to be combined and exchanged.
Lots of documentation is needed to support data interpretation and reuse. The data should conform to community norms and be clearly licensed so others know what kinds of reuse are permitted.
Data not managed according to FAIR principles can cause many headaches. Learn about the troubles Dr. Judy Benign is facing in her quest to reuse some data in this short and entertaining video.
FAIR data does not necessarily need to be Open Data. Access can be restricted for example for ethical, legal or commercial reasons. The most valuable contribution to an open research landscape is however data that is both managed according to FAIR principles and made available without restrictions. (graphic by Sarah Jones)