FAIRness assessment challenge

FAIRness assessment challenge: Datasets and semantic artefacts

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More harmonised use of semantic artefacts such as ontologies, terminologies, taxonomies, thesauri, vocabularies, metadata schemas and standards is a key element to achieving a high level of FAIRness. However, it can often be difficult to find and use semantic artefacts as they themselves are not always FAIR.

Building on the successful “FAIRness hackathon approach” that was used by the French agri-food project FooSIN as well as on the FAIRsFAIR iterative FAIR pilot assessment and consultation experience, this targeted support action will help a cohort of dataset providers or semantic artefact developers to self-assess the level of FAIRness of their resources ( datasets, semantic artefacts, or any collections of those) with several FAIRness assessment tools and methods put at their disposal by FAIR-IMPACT. The cohort will participate in a joint challenge that will last one month during which they will apply a variety of assessment tools including F-UJI, O’FAIRe and FOOPS, and methods such as the FAIR Data Maturity Model (FDMM) and the Ten simple rules for FAIR vocabularies to self-assess their resource(s). The objective for all participants will be to maximise the FAIRness of their own resources as expressed by the scores obtained using the various tools used during the span of the challenge.

During the event, FAIR-IMPACT mentors will provide guidance and tips on how to improve their score by providing support either directly on the tools methodology available or with general FAIR-enabling feedback and advice. Based on their FAIRness score at the beginning of the challenge, participants will develop a plan to implement changes to improve those scores and their effort will be measured with the new score obtained at the end of the challenge period.

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Data Platform and Sensing Technologies for Environmental Sensing LAB

Duration: - Funding: FESR (EU funding / Project)

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