The FAIR principles describe characteristics intended to support access to and reuse of digital artifacts in the scientific research ecosystem. Persistent, globally unique identifiers, resolvable on the Web, and associated with a set of additional descriptive metadata, are foundational to FAIR data. Here we describe some basic principles and exemplars for their design, use and orchestration with other system elements to achieve FAIRness for digital research objects.
One of the key goals of the FAIR guiding principles is defined by its final principle – to optimize data sets for reuse by both humans and machines. To do so, data providers need to implement and support consistent machine readable metadata to describe their data sets. This can seem like a daunting task for data providers, whether it is determining what level of detail should be provided in the provenance metadata or figuring out what common shared vocabularies should be used. Additionally, for existing data sets it is often unclear what steps should be taken to enable maximal, appropriate reuse. Data citation already plays an important role in making data findable and accessible, providing persistent and unique identifiers plus metadata on over 16 million data sets. In this paper, we discuss how data citation and its underlying infrastructures, in particular associated metadata, provide an important pathway for enabling FAIR data reuse.