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FAIR assessment tools: evaluating use and performance
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Researchers evaluated ten FAIR (findable, accessible, interoperable, reusable) data assessment tools using nanomaterial and microplastic risk assessment datasets, finding that online self-assessment tools work best for quick individual dataset evaluations while semi-automated tools are more practical for assessing full databases. Large variation in outcomes across tools reflected differing implementations of the FAIR principles rather than dataset quality differences.
Publishing research data using a findable, accessible, interoperable, and reusable (FAIR) approach is paramount to further innovation in many areas of research. In particular in developing innovative approaches to predict (eco)toxicological risks in (nano or advanced) material design where efficient use of existing data is essential. The use of tools assessing the FAIRness of data helps the future improvement of data FAIRness and therefore their re-use. This paper reviews ten FAIR assessment tools that have been evaluated and characterized using two datasets from the nanomaterials and microplastics risk assessment domain. The tools were grouped into four categories: online and offline self-assessment survey based, online (semi-) automated and other tools. We found that the online self-assessment tools can be used for a quick scan of a user's dataset due to their ease of use, little need for experience and short time investment. When a user is looking to assess full databases, and not just datasets, for their FAIRness, (semi-)automated tools are more practical. The offline assessment tools were found to be limited and unreliable due to a lack of guidance and an under-developed state. To further characterize the usability, two datasets were run through all tools to check the similarity in the tools' results. As most of the tools differ in their implementation of the FAIR principles, a large variety in outcomes was obtained. Furthermore, it was observed that only one tool gives recommendations to the user on how to improve the FAIRness of the evaluated dataset. This paper gives clear recommendations for both the user and the developer of FAIR assessment tools.
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