Modeling community standards for metadata as templates makes data FAIR

Determining whether datasets are FAIR (findable, accessible, interoperable, and reusable) is challenging due to idiosyncratic metadata criteria. The FAIR principles require rich metadata adhering to domain-relevant community standards. This paper explores a template-based approach using the CEDAR and FAIRware Workbenches. CEDAR assists investigators in authoring metadata, while FAIRware evaluates archived datasets for adherence to community standards. Representing metadata standards as machine-readable templates benefits data management and sharing by serving as a community reference for FAIR data and enabling distribution among various software applications.

Mark A. Musen, Martin J. O'Connor, Erik Schultes, Marcos Martínez-Romero, Josef Hardi, John Graybeal