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FAIR-Aware: Assess Your Knowledge of FAIR

Authors: Vesa Akerman; Linas Cepinskas; Maaike Verburg; Mokrane Mustapha;

FAIR-Aware: Assess Your Knowledge of FAIR

Abstract

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.

Keywords

FAIR Principles, FAIRsFAIR, Data Repository, Research Data

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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).
BIP!Citations provided by BIP!
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.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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