A Comparison of Data FAIRness Evaluation Tools

Abstract

FAIR data principles represent a set of community-agreed guiding principles and practices for all researchers involved in the eScience ecosystem. The FAIR data principles were created to improve the reuse of data by making it findable, accessible, interoperable, and reusable. The goal of these principles is to ensure that the inputs and outputs from the computational analysis can be easily found and understood by data consumers, both humans, and machines. Since the introduction of FAIR Data Principles in 2016, the interest in these principles has been constantly increasing and several research groups have started developing tools for the evaluation of data FAIRness. In this paper, we aim to analyze the available online tools and checklists for data FAIRness evaluation and to provide tool comparison based on multiple features. Taking into account this analysis and the tools' advantages and disadvantages, we provide recommendations about the tools' usage. A FAIRness practical evaluation is also conducted on seven data sets from different data repositories using the analysed tools. Findings show that there are no commonly accepted requirements evaluation of data FAIRness. The conclusions of this study could be used for further improvement of the FAIRness criteria design and making FAIR feasible in daily practice.