STATUS: IN DEVELOPMENT
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.
Why is this step important
[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.
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)
[Sander] Towards FAIR Data Steward as profession for the Lifesciences (co-written by Mijke, 2019):
Appendix 5 - 7 contain information about Data steward workshops and training. Probably outdated, but perhaps useful?
[Hannah] From 2022 publication FAIR assessment tools: evaluating use and performance:
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?] |
[Sander] From HANDS:
Clinical researchers that are working at UMCs are obliged to complete the ‘Basiscursus Regelgeving en Organisatie voor Klinisch onderzoekers (BROK®)’. After four years, a re-registration course should be followed. Every UMC organises a centre-specific BROK for employees that perform clinical research. This centre-specific BROK offers information about data management as well as information about local policies, facilities and expertise. Many universities and UMCs organise additional datastewrdship training and/or data management courses that are not obligated, but highly recommended. HANDS’ toolbox can help you find such courses.
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.
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.
Tools and resources on this page
Add the tools and resources mentioned on this page. This should be a list of usable content and does not include textual resources such as journal references.
Training
Relevant training will be added in the future if available.
Suggestions
Visit our How to contribute page for information on how to get in touch if you have any suggestions about this page.