In Principle I2 we referred to “vocabularies” as the methods that unambiguously represent concepts that exist in a given domain. The use of shared, and formally structured (principle I1), sets of terms is an essential part of FAIR. Terminology systems, including flat “vocabularies”, hierarchical “thesauri” and more granular specifications of knowledge such as data models and consistently structured ontologies, play an important role in community standards. However, the vocabularies used for metadata or data also need to be findable, accessible, interoperable, and reusable in their own right so that users (including machines) can fully understand the meaning of the terms used in the metadata. This principle has been criticized as “circular” but as has been made clear earlier in the Digital Intelligence article, the simple use of a “label” (e.g. “temperature”) is insufficient to enable a machine to understand both the intent of that label (Body temperature? Melting temperature?) and the contexts within which it can be properly linked – same-with-same – to other similarly-labelled data. I2, therefore, requires that the vocabulary terms used in the knowledge representation language (principle I1) can be sufficiently distinguished, by a machine, to resolve to the intended defined meaning and thus ensure detection and prevention of “false agreements” as well as “false disagreements” on exact meaning of the identifier