The aim of this project is to implement the FAIR principles as a basis for a data assessment tool named FAIRdat so that every dataset which is deposited or reused from any data repository can be assessed for its score on the principles Findable, Accessible, Interoperable and overall ‘FAIRness’ on a scale of 5 stars. A couple of badge schemes for open data quality assessment already exist and enable reviewers to evaluate datasets based on certain indicators of quality. For example, the Open taiwan rcs data Data Certificate holds four badge levels (bronze, silver, gold and platinum) based on a set of criteria including legal, practical, technical and social requirements of a dataset, and Tim Burners-Lee’s employs a 5 star deployment scheme for open data quality. The development of our tool has been influenced by these schemes but makes use of star badges to represent the quality of a dataset based on each FAIR principle.
The FAIRdat tool will allow the assessment of a dataset and will associate a FAIR profile to each dataset in the FAIRdat database and/or in its location (i.e. repository). We aim to initially implement the tool into repositories at DANS (EASY and DataverseNL) and then to expand this externally so that any dataset in any location can be reviewed via the independent tool. This assessment tool is aimed for people, including data producers, re-users and archivists, who want to provide a quality assessment of a dataset.