FAIR evaluation of ten widely used chemical datasets: Lessons learned and recommendations
<|reference_start|>FAIR evaluation of ten widely used chemical datasets: Lessons learned and recommendations: This document focuses on databases disseminating data on (hazardous) substances found on the North American and the European (EU) market. The goal is to analyse the FAIRness (Findability, Accessibility, Interoperability and Reusability) of published open data on these substances and to qualitatively evaluate to what extend the selected databases already fulfil the criteria set out in the commission draft regulation on a common data chemicals platform. We implemented two complementary approaches: Manual, and Automatic. The manual approach is based on online questionnaires. These questionnaires provide a structured approach to evaluating FAIRness by guiding users through a series of questions related to the FAIR principles. They are particularly useful for initiating discussions on FAIR implementation within research teams and for identifying areas that require further attention. Automated tools for FAIRness assessment, such as F-UJI and FAIR Checker, are gaining prominence and are continuously under development. Unlike manual tools, automated tools perform a series of tests automatically starting from a dereferenceable URL to the data resource to be evaluated. We analysed ten widely adopted datasets managed in Europe and North America. The highest score from automatic analysis was 54/100. The manual analysis shows that several FAIR metrics were satisfied, but not detectable by automatic tools because there is no metadata, or the format of the information was not a standard one. Thus, it was not interpretable by the tool. We present the details of the analysis and tables summarizing the outcomes, the issues, and the suggestions to address these issues.<|reference_end|>
@article{da silveira2024fair,
    title={FAIR evaluation of ten widely used chemical datasets: Lessons learned
  and recommendations},
    author={Marcos Da Silveira and Oona Freudenthal and Louis Deladiennee},
    journal={arXiv preprint arXiv:2407.15591},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.15591},
    primaryClass={cs.CY cs.DB}
}