Research pick: Just how FAIR are your digital, scientific resources? - "O’FAIRe makes you an offer: metadata-based automatic FAIRness assessment for ontologies and semantic resources"

Currently, there is no established method for automatically assessing the level of FAIRness (Findability, Accessibility, Interoperability, and Reusability) of semantic resources. The term “semantic resources” refers to various types of data, information, or knowledge artefacts that are represented in a structured and standardized way. These resources can include ontologies (technical, structured glossaries), vocabularies, data sets, and other relevant knowledge. An example is the AgroPortal semantic resource repository, an online platform for storing and organizing semantic resources related to the domain of agri-food and environment.

Writing in the International Journal of Metadata, Semantics and Ontologies, a team from France has used the AgroPortal as a case study to help them develop a metadata-based automatic assessment methodology for such resources, which they call Ontology FAIRness Evaluator (O’FAIRe).

Emna Amdouni, Syphax Bouazzouni, and Clement Jonquet of the University of Montpellier explain that making digital scientific data openly available remains an important challenge for the scientific community and funding agencies. The FAIR movement arose in 2014 to help address this challenge and has been largely embraced. However, FAIR, as many observers have pointed out, is only representing specifications for digital objects, or entities, rather than being a standardised or technically based system. There has thus been a need for a way to independently assess how well an entity adheres to the principles of FAIR.

In this context, the team’s proposal is aligned with existing initiatives and consists of 61 questions, primarily based on metadata descriptions, and using ontology libraries or repositories to ensure unified metadata for FAIRness assessment. The team implemented O’FAIRe in AgroPortal and successfully conducted a preliminary FAIRness analysis of 149 semantic resources in the agri-food/environment domain. The proposal should allow FAIR digital entities to be assessed objectively pushing us towards a more encompassing system in which entities and resources can be read and used competently equally well by humans and computers without barriers and problems arising because of inconsistencies across and within domains.

The researchers conclude that their work addresses many of the scientific and technical challenges regarding the implementation of the 15 FAIR principles for ontologies and semantic resources. The team writes that their work might now “guide the semantic community to put the FAIR principles into practice and enable them to qualify the degree of FAIRness of their semantic resource.”

Amdouni, E., Bouazzouni, S. and Jonquet, C. (2022) ‘O’FAIRe makes you an offer: metadata-based automatic FAIRness assessment for ontologies and semantic resources’, Int. J. Metadata Semantics and Ontologies, Vol. 16, No. 1, pp.16–46.