FAIR or FAIRer? An integrated quantitative FAIRness assessment grid for semantic resources and ontologies

In open science, the expression "FAIRness assessment" refers to evaluating to which degree a digital object is Findable, Accessible, Interoperable, and Reusable. Standard vocabularies or ontologies are a key element to achieving a high level of FAIRness (FAIR Principle I2) but as any other data, ontologies have themselves to be FAIR. Despite the recent interest in the open science and semantic Web communities for this question, we have not seen yet a quantitative evaluation method to assess and score the level of FAIRness of ontologies or semantic resources in general (e.g., vocabularies, terminologies, and thesaurus). The main objective of this work is to provide such a method to guide semantic stakeholders for making their semantic resources FAIR. We present an integrated quantitative assessment grid for semantic resources and propose candidate metadata properties-taken from the MOD ontology metadata model-to be used to make a semantic resource FAIR. Aligned and nourished with relevant FAIRness assessment state-of-the-art initiatives, our grid distributes 478 credits to the 15 FAIR principles in a manner which integrates existing generic approaches for digital objects (i.e., FDMM, SHARC) and approaches dedicated to semantic resource or artefact (i.e., 5-stars V, MIRO, FAIRsFAIR, Poveda et al.). The credits of the grid can then be used for implementing FAIRness assessment methods and tools.