An automated solution for measuring the progress toward FAIR research data.

With a rising number of scientific datasets published and the need to test their Findable, Accessible, Interoperable, and Reusable (FAIR) compliance repeatedly, data stakeholders have recognized the importance of an automated FAIR assessment. This paper presents a programmatic solution for assessing the FAIRness of research data. We describe the translation of the FAIR data principles into measurable metrics and the application of the metrics in evaluating FAIR compliance of research data through an open-source tool we developed. For each metric, we conceptualized and implemented practical tests drawn upon prevailing data curation and sharing practices, and the paper discusses their rationales. We demonstrate the work by evaluating multidisciplinary datasets from trustworthy repositories, followed by recommendations and improvements. We believe our experience in developing and applying the metrics in practice and the lessons we learned from it will provide helpful information to others developing similar approaches to assess different types of digital objects and services.

A Research Graph dataset for connecting research data repositories using RD-Switchboard.

This paper describes the open access graph dataset that shows the connections between Dryad, CERN, ANDS and other international data repositories to publications and grants across multiple research data infrastructures. The graph dataset was created using the Research Graph data model and the Research Data Switchboard (RD-Switchboard), a collaborative project by the Research Data Alliance DDRI Working Group (DDRI WG) with the aim to discover and connect the related research datasets based on publication co-authorship or jointly funded grants. The graph dataset allows researchers to trace and follow the paths to understanding a body of work. By mapping the links between research datasets and related resources, the graph dataset improves both their discovery and visibility, while avoiding duplicate efforts in data creation. Ultimately, the linked datasets may spur novel ideas, facilitate reproducibility and re-use in new applications, stimulate combinatorial creativity, and foster collaborations across institutions.