These webpages provide an actionable list of the 15 FAIR Data Principles as a simple guide when publishing data. For each principle, we give a basic definition, examples, and links to useful resources. We hope that by working through the list, anyone wishing to maximize the reusability of their data, can prioritize their efforts and make more informed choices regarding a suitable repository. We hope that this list will also focus the growing public discourse around FAIR: what is FAIR exactly, and what is it not.
Findable: Data and metadata are easy to find by both humans and computers. Machine readable metadata is essential for automatic discovery of relevant datasets and services, and for this reason are essential to the FAIRification process.
Accessible: Limitations on the use of data, and protocols for querying or copying data are made explicit for both humans and machines.
Interoperable: The computer can interpret the data, so that they can be automatically combined with other data. There is a historical trend in computer science toward increased interoperation (for example, between different hardware designs, operating systems, programming languages, and communication protocols). Data interoperability can be seen as the ragged edge of this long-term trend. However, data interoperation is a non-trivial problem and the “I” will require the most creative effort in making FAIR Data.
- I1: (meta)data use a formal, accessible, shared and broadly applicable language for knowledge representation.
Reusable: Data and metadata are sufficiently well described for both humans and computers, so that they can be replicated or combined in future research.