Your first step towards your FAIR data(set)



Thank you for your participation!



F A I R

Awareness:


F A I R

Willingness to comply:

Guidance:

Based on your answers, you can find the guidance below to improve your awareness on some FAIR issues.





If you would like to view a summary of your responses, click here

Summary of your responses:

Course

Course identification


About you

Which research domain do you work in?


Which of the following describes your role? Please select all that apply.






 

Which of the following types of organisations best describe your employer? Please select all that apply.








 




F

FAIR questions

Glossary

FINDABLE



How likely are you willing to comply with this practice?
Very unlikely     Very likely


How likely are you willing to comply with this practice?
Very unlikely     Very likely


How likely are you willing to comply with this practice?
Very unlikely     Very likely
A

ACCESSIBLE



How likely are you willing to comply with this practice?
Very unlikely     Very likely


How likely are you willing to comply with this practice?
Very unlikely     Very likely




I

INTEROPERABLE



How likely are you willing to comply with this practice?
Very unlikely     Very likely









R

REUSABLE



How likely are you willing to comply with this practice?
Very unlikely     Very likely


How likely are you willing to comply with this practice?
Very unlikely     Very likely


How likely are you willing to comply with this practice?
Very unlikely     Very likely


How likely are you willing to comply with this practice?
Very unlikely     Very likely















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.

Do you have any questions or comments or would you like to use FAIR-Aware in your training? Send us an e-mail!