FAIR is gaining popularity as an acronym because its a great mnemonic for thinking about how to make data accessible. It's a way to consider your data sharing beyond just whether to put a dataset up on a website/repo or not. But what does FAIR mean? It stands for:
Findable
Accessible
Interoperable
Reusable
The FAIR Data principles include more details on making data more useful for future research, by you or your collaborators. The FAIR Principles are a set of guidelines for making data more effective for future replication or extension. FAIR is not an either/or proposition, but a process of moving data towards more usefulness for future science.
Some of the FAIR principles are about the technology and platform where the data (or their "metadata" such as keywords and abstract) are shared. Other FAIR principles have to do with how the researcher(s) handle and describe the data. Therefore, FAIR data depends both on your technological choices and on your data workflow.
Commit to taking a little step forward in FAIRification of your data with each study!
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