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.
The project team has made the source code of the tool available online in two versions, English and French, hosted by DANS and by Doranum. This source code can be modified to facilitate approval by other databases and also as part of FAIRsFAIR engagement and training activities. The FAIRsFAIR project partners and supporters invite everyone working with research data to use the tool, and spread the word to those who may benefit from it.
Tip: If you can’t see the changes to the tool yet, try emptying your cache and reloading the site.
FAIR-Aware has been developed by FAIRsFAIR partners DANS, DCC, UniHB and we’ve done our best to make using it as simple and time-efficient as possible. We’re looking to make further improvements so you’ll notice that some of the questions are directed at users. All feedback received will be taken on board and incorporated into a final version. An interim report was published in September 2020 detailing what we’ve learned from this initial phase and explaining the upgrades we have in mind.