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You will learn the FAIR principles and how to apply them to your data.
You will learn the best practices for making data FAIR.
You will learn how to use the tools and technologies that can help you make your data FAIR.
You will be able to collaborate with others to make your data FAIR.
Expertise requirements for this step
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data FAIR
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Data management
Data modeling
Metadata
Interoperability
Reusability
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How To?
Check the page dedicated for Training FAIR Training and Capacity building
[Meriem] When getting training on FAIRification of data, it is important to first identify your specific role or roles within the research project. This will help you to focus on the training that is most relevant to your needs.
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Provided by DANS, this online survey gives a FAIRness score. Furthermore, it provides advice on how to improve the FAIRness of your (meta)data. [Hannah; according to the review paper, this tool ‘assesses the user's understanding of the FAIR principles rather than the FAIRness of his/her dataset. FAIR-aware is not further considered in this paper’. Maybe throw it out as well?] |
Expertise requirements for this step
[Meriem] The expertise requirements for making data FAIR vary depending on the specific dataset and the desired level of FAIRness. However, some of the key expertise areas include:
Data management
Data modeling
Metadata
Interoperability
Reusability
In addition to these technical skills, it is also important to have a good understanding of the research context in which the data is being used. This will help you to make decisions about how to make the data FAIR that are consistent with the needs of the researchers.
Practical Examples from the Community
[Inês and Milou] Researchers from RadboudUMC have mandatory induction days where they are presented with a variety of services available to them. During one of these presentations researchers are introduced to the application of the Findability and Acessibility principles into their studies.
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