This training exercise was created to complement the FAIR-Aware Tool. FAIR-Aware (https://fairaware.dans.knaw.nl/) was created by DANS in the FAIRsFAIR1 project and is currently further developed in the FAIR-IMPACT2 project. FAIR-Aware is a versatile and flexible tool to use in education and training. The exercise contains instructions as well as worked examples that trainers can adapt for their own training purposes.

From the FAIR-Aware Tool:

Do you work with data? Are you looking to make it future-proof? The FAIR Principles can help you.

These principles stand for the Findability, Accessibility, Interoperability, and Reusability of data(sets). Applying these principles to your data(set) will help others to find, cite and reuse your data more easily.

FAIR-Aware helps you assess your knowledge of the FAIR Principles, and better understand how making your data(set) FAIR can increase the potential value and impact of your data.

The tool is discipline-agnostic, making it relevant to any scientific field. You can use this tool at any point during your research before depositing your data(set) in a data repository. It is also good to keep in mind that many FAIR-related decisions can already be made in the research planning phase, so you may want to use FAIR-Aware early on to help you make those decisions. Also, if you are a trainer, you can use FAIR-Aware to assess the knowledge of FAIR of your course participants.

The self-assessment consists of 10 questions with additional guidance texts to help you become more aware of what you can do to make your data(set) as FAIR as possible. The assessment will take between 10-30 minutes, after which you will receive an overview of your awareness level and additional tips on how you can further improve your FAIR skills.

The training exercise is distributed the same across three file formats: .docx., .pdf, and .odt. The .docx format is the most (web) accessible, while the .pdf serves as a stable visual version with accessible features, and the .odt file is the most open. In case of format issues, the .pdf would be considered the reference file.

1 FAIRsFAIR “Fostering FAIR Data Practices In Europe” has received funding from the European Union’s Horizon 2020 project call H2020-INFRAEOSC-2018-2020 Grant agreement 831558. The content of this document does not represent the opinion of the European Union, and the European Union is not responsible for any use that might be made of such content.

2 FAIR-IMPACT “Expanding FAIR solutions across EOSC” is funded by the European Union.