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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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Researchers: Researchers are responsible for conducting experiments and generating new knowledge. Responsibilities include collecting, cleaning, and analyzing data. Researchers need to have a good understanding of the FAIR principles in order to make sure that their data is accessible, interoperable, and reusable.
Data stewards: Data stewards are responsible for managing and curating data. They need to have a deep understanding of the FAIR principles in order to ensure that data is well-organized and easy to find.
IT professionals: IT professionals are responsible for developing and maintaining the infrastructure that supports data sharing. They need to be familiar with the FAIR principles in order to design systems that make data easy to find and use.
A comprehensive overview of roles can be found in https://health-ri.atlassian.net/wiki/x/BgBLE. Once you have identified your role within the research project, you can start to look for training that is specifically designed for your needs. There are many different training options available, including online courses, workshops, and conferences.
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[Fieke] Places to look for training events:
First: check within your organisation what training is available. Many institutions provide training on data management planning and FAIR for their researchers.
Taxila: Dutch portal that lists upcoming training events and materials
Tess portal: training portal for the life sciences maintained by Elixir
Health-RI website (in development)
FAIR training from the World Duchenne Organisation For recommend materials and general introduction into FAIR, see our general training page https://health-ri.atlassian.net/wiki/x/ewBXAg
Competencies frameworks
a. Competencies for data stewards developed by NPOS / Elixir: https://competency.ebi.ac.uk/framework/datasteward/1.0
b. https://doi.org/10.4126/FRL01-006441348 ]
c. Petersen, Britta, Claudia Engelhardt, Tanja Hörner, Juliane Jacob, Tatiana Kvetnaya, Andreas Mühlichen, Hermann Schranzhofer, et al. ‘Lernzielmatrix zum Themenbereich Forschungsdatenmanagement (FDM) für die Zielgruppen Studierende, PhDs und Data Stewards’. Zenodo, 14 June 2023. https://doi.org/10.5281/zenodo.8010617
d. Skills4EOSC
[Sander] Towards FAIR Data Steward as profession for the Lifesciences (co-written by Mijke, 2019):
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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.
Furthermore, the institution organises through the RTC department quarterly training FAIR RDM sessions where researchers can learn how to complement their Data management plans by introducing the FAIR principles.
Training
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If your role is to train your team members, there are resources to help you:
FAIR lesson plans (in development)
General train-the-trainer materials:
EOSC synergy learn platform https://learn.eosc-synergy.eu/get-started/
Training materials can themselves be FAIR. See for guidance:
https://elixir-europe-training.github.io/ELIXIR-TrP-FAIR-training-handbook/
Ten simple rules for making training materials FAIR, Castro, Leyla et al (2020) PLOS Computational Biology. 16. e1007854. 10.1371/journal.pcbi.1007854.
https://doi.org/10.5281/zenodo.7296920 Hernández Serrano, P. V., & Vivas Romero, M. (2022, november 1), Zenodo
Filiposka, S., Green, D., Mishev, A., Kjorveziroski, V., Corleto, A., Napolitano, E., Paolini, G., Di Giorgio, S., Janik, J., Schirru, L., Gingold, A., Hadrossek, C., Souyioultzoglou, I., Leister, C., Pavone, G., Sharma, S., Mendez Rodriguez, E. M., & Lazzeri, E. (2023). D2.2 Methodology for FAIR-by-Design Training Materials (1.4). Zenodo. https://doi.org/10.5281/zenodo.8305540
Suggestions
Visit our How to contribute page for information on how to get in touch if you have any suggestions about this page.
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