FAIR-Checker: supporting digital resource findability and reuse with Knowledge Graphs and Semantic Web standards

The current rise of Open Science and Reproducibility in the Life Sciences requires the creation of rich, machine-actionable metadata in order to better share and reuse biological digital resources such as datasets, bioinformatics tools, training materials, etc. For this purpose, FAIR principles have been defined for both data and metadata and adopted by large communities, leading to the definition of specific metrics. However, automatic FAIRness assessment is still difficult because computational evaluations frequently require technical expertise and can be time-consuming. As a first step to address these issues, we propose FAIR-Checker, a web-based tool to assess the FAIRness of metadata presented by digital resources. FAIR-Checker offers two main facets: a Check module providing a thorough metadata evaluation and recommendations, and an Inspect module which assists users in improving metadata quality and therefore the FAIRness of their resource. FAIR-Checker leverages Semantic Web standards and technologies such as SPARQL queries and SHACL constraints to automatically assess FAIR metrics. Users are notified of missing, necessary, or recommended metadata for various resource categories. We evaluate FAIR-Checker in the context of improving the FAIRification of individual resources, through better metadata, as well as analyzing the FAIRness of more than 25 thousand bioinformatics software descriptions.

Automated summary

The rise of Open Science and Reproducibility in the Life Sciences necessitates the use of rich, machine-actionable metadata to facilitate the sharing and reuse of biological digital resources. To assess the FAIRness of metadata in digital resources, we propose FAIR-Checker, a web-based tool that offers comprehensive evaluations, recommendations, and metadata improvement assistance. Using Semantic Web standards and technologies, FAIR-Checker automatically assesses FAIR metrics and notifies users of missing or recommended metadata. We evaluate FAIR-Checker in the context of improving individual resource FAIRness and analyzing bioinformatics software descriptions.