FAIR Guiding Principle R1:
(meta)data are richly described with a plurality of accurate and relevant attributes
Interpretation of R1
At first glance, principle R1 appears very similar to principle F2. However, the rationale behind principle F2 is to enable effective attribute-based search and query (findability), while the focus of R1 is to enable machines and humans to assess if the discovered resource is appropriate for intended reuse, given a specific task. For example, not all gene expression data for a given locus are relevant to a study of the effects of heat stress. While irrelevant data may be discovered by the agent's initial search (principle F2) for expression data about a given gene, here we address the ability to assess and filter the discovered data based on suitability-for-purpose. This reiterates the need for good data stewards to consider not only high-level metadata facets, that will assist in generic search, but also to consider more detailed metadata that will provide much more “operational” instructions for re-use. In this setting, a wide variety of factors may be needed to determine whether a resource is suitable for inclusion in an analysis, and how to adequately process it. The term “plurality” is used to indicate that the metadata author should be as generous as possible, not narrowly presuming who the secondary consumers might be, and therefore provide as much metadata as possible to support the widest variety of use-cases and agent needs. The sub-principles R1.1, R1.2 and R1.3 further define some critical types of attributes that contribute to R1.
This interpretation of R1 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