[F,A,I,R] -> [possible, plausible, probable, perishable]

Findability is making reuse possible. If no one can discover your data, if even basic metadata is hidden in a silo, then reuse is simply not possible.

Accessibility is making reuse plausible. People and their designated software agents can not only identify relevant resources via metadata, but they can actually retrieve full data for inspection and evaluation.

Interoperability is making reuse probable. Not only is your data findable and accessible, but there is not too heavy a burden on someone to make your data compatible with their own data and processing tools. Your data is accessible in an open, standard format. You include a schema with the data, preferably in a machine-actionable format as well. An interested consumer is significantly more likely to bite.

Reusability, finally, is making reuse…perishable. Using a food analogy, let’s say you have identified a potential ingredient for a recipe (findability); you know it is in stock at a nearby store (accessibility); and you have determined that it is indeed suitable for use with your kitchen tools, other ingredients you have on-hand for the recipe, etc. (interoperability). Finally, you can bring it home to your kitchen…but will you actually be able to use it?

The warranties on and terms of use for data – its licenses – are important; these are analogous to “best by” dates, expiration dates, etc. on food. The reusability concern of FAIR does not guarantee reuse, but it again increases the likelihood of reuse because the terms and conditions of reuse are made more explicit – a consumer of your data can make a decision to reuse with more confidence. Data with no license is like a food product with no expiration date – does that mean it’s forever usable, or will someone likely be unsure and thus put your data in the trash bin?

This post was adapted from a note sent to my email list on Scientific Data Unification.
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