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FAIR-Aware is an online tool which helps researchers and data managers assess how much they know about the requirements for making datasets findable, accessible, interoperable, and reusable (FAIR) before uploading them into a data repository. The tool comprises 10 carefully designed questions, each generously supplied with additional information and practical tips which extend users' understanding of the FAIR principles as they work through the questionnaire with a target dataset in mind. Presented in a clear and informative way and suitable for different research domains, FAIR-Aware provides tips for each question, making it easier for users to understand difficult topics and helping them learn how to make their data more FAIR. Part of this guidance also supports researchers in the choices they need to make to choose a repository to deposit their data in, and how to collaborate with that repository to create a FAIR dataset.
FAIR Principles, FAIRsFAIR, Data Repository, Research Data
FAIR Principles, FAIRsFAIR, Data Repository, Research Data
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
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