FAIR Guiding Principle I1:
(meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation
Interpretation of I1
Consumers spend a disproportionate amount of time trying to make sense of the digital resources they need and designing accurate ways to combine them. This is most often due to a lack of suitably unambiguous content descriptors, or a lack of such descriptors entirely with respect to non-machine-interpretable data formats such as tables or “generic” XML. Community-defined data exchange formats work reasonably well within their original scope of a few types of data and a relatively homogeneous community, but not well beyond that. This makes interoperation and integration an expensive, often impossible task (even for humans), but also means that machines cannot efficiently make use of digital resources, which is the primary goal of the FAIR guiding principles. For example, when a machine visits two data files in which a field “temperature” is present, then it will need more contextual descriptions to distinguish between weather data in one file and body temperature measurements in another. Achieving a “common understanding” of digital resources through a globally understood “language” for machines is the purpose of principle I1, with an emphasis on “knowledge” and “knowledge representation”. This becomes critical when many differently formatted resources need to be visited or combined across organizations and countries and is especially challenging for interdisciplinary studies or for meta-analyses, where results from independent organizations, pertaining to the same topic, must be combined. In this context, the principle says that producers of digital resources are required to use a language (i.e., a representation of data/knowledge) that has a defined mechanism for mechanized interpretation – a machine-readable “grammar” – where, for example, the difference between an entity, as well as any relevant relationship between entities, is defined in the structure of the language itself. This allows machines to consume the information with at least a basic “understanding” of its content. It is a step towards a common understanding of digital resources by machines, which is a prerequisite for a functional Internet of FAIR Data and Services. Several technologies can be chosen for principle I1.
This interpretation of I1 is based on 'FAIR Principles: Interpretations and Implementation Considerations'. Jacobsen et al, Data Intelligence 2020; 2 (1-2): 10–29. doi: https://doi.org/10.1162/dint_r_00024