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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.
How to
Check the page dedicated for Training general page on FAIR 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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Expertise requirements for this step
[Sander] This is should be in line with roles from Build the team if possible.
[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:
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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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If your role is to train your team members, there are resources to help you:
FAIR lesson plans FAIR lesson plans (in development)
General train-the-trainer materials:
EOSC synergy learn platform https://learn.eosc-synergy.eu/get-started/
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