Short Description Short Description
Once you’ve build your team, you can assess training needs. If expertise is missing from your team a variety of trainings are available. Literature??
Working Group
FAIR – training
Services
Why is this step important
This section should explain why this step is crucial [Meriem] Making data FAIR is a complex process that requires a deep understanding of the FAIR principles and the skills to implement them. By going through training focused specifically on FAIRIfication:
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
This section could describe the expertise required. Perhaps the Build Your Team step could then be an aggregation of all the “Expertise requirements for this step” steps that someone needs to fulfil his/her FAIRification goals.
How to
This section should help complete the step. It’s crucial that this is practical, doable and scalable.
Depending on the type of step, this can, for example, be a reference to one or more (doable) recipes, or perhaps some form of checklist? The recipes/best-practices presented should be based on experts from the field.
This should probably be a subpage so as not to have too much text on this page.
References, if relevant, to FAIRCookbook, RDMKit, GOFAIR?
Sub headers if relevant for specific domains?
Practical Examples from the Community
This section should show the step applied in a real project. Links to demonstrator projects.
References & Further reading
Authors / Contributors
Experts whom you can contact for further information [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.
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.
Here are some of the different roles that people play in research projects:
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.
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.
Here are some of the things to look for when choosing training on FAIRification of data:
The level of expertise required: Some training programs are designed for beginners, while others are designed for more experienced professionals.
The focus of the training: Some training programs focus on the technical aspects of FAIRification, while others focus on the policy and legal aspects.
The format of the training: Training can be delivered in a variety of formats, including online courses, workshops, and conferences.
The best way to choose the right training for your needs is to talk to other people who have been through the process. You can also ask your employer or research institution for recommendations.
[Fieke] Places to look for training:
Health-RI website (in development)
Duchenne FAIR training
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.
References & Further reading
Authors / Contributors
Meriem Manaï (Amsterdam UMC)
Fieke Schoots (Health-RI)