Applying a FAIRness questionnaire to characterize Materials databases

Abstract

The FAIR principles guidelines aim to enhance the discovery and usage of digital objects by humans and computational agents. They are formulated at a high level and, as such, are differently interpreted and implemented by distinct communities of practice, which often have to collaborate, such as in the context of the use of chemicals in scientific discovery. Practical approaches outlining FAIR-related characteristics of digital objects are few and far between, and most of these are domain-agnostic, i.e., they do not consider scientific communities’ varied needs and require specific implementations and combinations for better estimation. Questionnaires have been considered the main mechanism to systematically capture the implementation choices corresponding to each FAIR principle. However, existing questionnaires focus on FAIR assessment using identical questions for distinct communities, i.e., evaluating the digital objects in the same way and usually reckoning that the digital objects have passed through a FAIRification process. In other words, they do not aim at characterizing digital objects, which would give a current overview of the properties that most contribute to their FAIRness. This work builds on the FAIR principles while considering distinct proposed metrics and tools for manual, automated, and semi-automated FAIRness assessment, like a questionnaire specifically designed to assess a plurality of interrelated scientific domains and their possible integration. It reports on applying an improved questionnaire aiming to characterize digital objects’ properties towards their FAIRification on two Materials databases: Materials Cloud and PubChem. We investigate the hypothesis that this questionnaire instills digital objects’ characteristics with a richness of details about their current properties and outlines their main elements for FAIRification. We demonstrate that the improved questionnaire is a more suitable tool for both domain specialists and data stewards to investigate digital objects’ characteristics and improve on them.