What does this mean?
When we are describing data or metadata, we often use vocabularies that provide the terms or concepts that are adequate to represent their content. However, if we use vocabularies in our data or metadata, we should make sure that they are also FAIR in their own right so that others, humans or machines, can find, access, interoperate and reuse them. The controlled vocabulary used to describe datasets needs to be documented and resolvable using globally unique and persistent identifiers. This documentation needs to be easily findable and accessible by anyone who uses the dataset.
Communities should define the required FAIRness level of the vocabularies used in their midst. Minimally, it is reasonable to expect that the vocabulary and its terms/concepts have globally unique and persistent identifiers (F1) that can be resolved using a standardised communication protocol (A1) and is described with a formal, accessible, shared and broadly applicable language for knowledge representation (I1).
Examples
- Using the FAIR Data Point ensures I2.
Links to resources