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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?? 

Why is this step important 

This section should explain why this step is crucial 

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. 

Further reading & References 

<since the descriptions are mostly based on the references below, I put them in the template...> 

[FAIRopoly] FAIRopoly https://www.ejprarediseases.org/fairopoly/  

[Generic] A Generic Workflow for the Data FAIRification Process: https://direct.mit.edu/dint/article/2/1-2/56/9988/A-Generic-Workflow-for-the-Data-FAIRification   

[Elixir] https://faircookbook.elixir-europe.org/content/recipes/introduction/fairification-process.html   

[Elixir2] A framework for FAIRification processes: https://faircookbook.elixir-europe.org/content/recipes/introduction/metadata-fair.html   

[GOFAIR] https://www.go-fair.org/fair-principles/f2-data-described-rich-metadata/  

[RDMkit] https://rdmkit.elixir-europe.org/machine_actionability.html  

Authors / Contributors 

Experts whom you can contact for further information [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 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.

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.

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.

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 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)

• 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):

• 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:

[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.

More from HANDS (toolbox)

Data-related courses

• 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 its employees. This centre-specific BROK offers information on data management and local policies, facilities, and expertise.

• Many universities and UMCs organise specialised data management courses. See resources at your UMC above.

• You can visit the European training portal TeSS for international courses and training materials in the field of life sciences data (ELIXIR).

• More and more training programmes for project data stewards are becoming available. At the national level, the Netherlands Bioinformatics and Systems Biology research school (BioSB) and the Dutch Techcentre for Life Science (DTL) organise data management-related workshops and courses, such as Data Carpentry workshops, the bi-annual course on Managing and Integrating life science information, and Bring Your Own Data workshops (BYODs). They also provide links to internationally organised training, for instance within the framework of the ELIXIR Training Platform.

• The DTL Course Directory includes Dutch courses, trainings, and workshops in the field of data and technologies for the life sciences. DTL also offers an extensive overview of online trainings.

• Essentials 4 Data Support (organised by Research Data Netherlands) is an introductory course for those that wish to support researchers in storing, managing, archiving and sharing their research data.

• Datacarpentry develops and teaches workshops on the fundamental data skills needed to conduct research.

• Netherlands Bioinformatics and Systems Biology research school (BioSB)

• Expert Tour Guide on Data Management by CESSDA ERIC (the Consortium of European Social Science Data Archives European Infrastructure Consortium) aims to put social scientists at the heart of making their research data findable, understandable, sustainably accessible and reusable.

• The Coursera MOOC 'Research Data Management and Sharing' by the University of North Carolina at Chapel Hill and The University of Edinburgh (MANTRA course content incorporated) provides learners with an introduction to research data management and sharing.

[Mijke, from the Have a FAIR data steward page]
Since data stewardship is a relatively new job profile and the field of data management and FAIR data practices are constantly evolving, a FAIR data steward will benefit from continuous learning and staying updated on emerging trends, tools, and standards. This will help the FAIR data steward to keep developing the necessary skills and expertise.

• This may include training in data management best practices, data curation techniques, metadata standards, and relevant tools and technologies. RDMKit provides an overview of data management best practices and guidelines.

• They should also be adaptable and able to tailor FAIR data solutions to meet the specific needs and constraints of your project.

The NPOS report on professionalising data stewardship in the Netherlands contains a list of training opportunities and materials (p. 148 - 162). Organisations that deliver data stewardship training in the Netherlands include:

• Research Data Netherlands Essentials 4 Data Support. Training for a basic understanding of data management and data steward tasks (domain agnostic). Materials are publicly available.

• LCRDM DCC Spring training days. Most materials freely available afterwards.

Training events and training materials on data management and FAIR can be found through Taxila or Tess. Additionally, the RDMkit training resources might be helpful.

[Mijke, from HANDS. I like the reference to a glossary, might be helpful]

The ‘Landelijk Coördinatiepunt Research Data Management (LCRDM)’ has developed a glossary of research data management terms.

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:

• 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.

Training

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:

  • https://elixir-europe.org/platforms/training/train-the-trainer

• 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

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