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
[maxbutton id=”4″ url=”https://doi.org/10.1162/dint_a_00030″ text=”Read more” linktitle=”Data Intelligence: FAIR Data Reuse – the Path through Data” ]Citation
Paul Groth, Helena Cousijn, Tim Clark and Carole Goble (2022):
FAIR Data Reuse – the Path through Data.
Data Intelligence 2(1-2)
https://doi.org/10.1162/dint_a_00030
