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People, with their enduring cultures and practices, and the enormous variety of perspectives on their environment (including a concept such as data) still play a key role in whether technological developments and the information infrastructures that accompany them work as intended. Bowker and Star, 2000

This page brings forward the training and educational elements available to guide the team in their journey through the Metroline subsequent steps.

Short description

After building your team (Metroline Step: Build the Team), it is a good idea to assess their training needs. This vital step involves a comprehensive assessment to pinpoint any missing expertise or skill gaps within the different roles in your team. This page creates a broad overview of training solutions that support the development of core competencies in FAIR data management. This page offers guidance on how to locate targeted training options for all roles within a given team. Specialised training that is needed for a specific Metroline Step are listed in the corresponding pages.

Why is this step important

The journey to making data FAIR is intricate, demanding both a comprehensive understanding of the FAIR principles and the practical skills for their implementation. A specialised FAIRification training will empower your team members to:

  • Understand the FAIR principles and how to 1) apply them to your own data or to 2) support others in applying them effectively.
  • Learn the benefits and best practices for implementing FAIR data management, whether you are responsible for hands-on activities or for advising colleagues.
  • Gain experience with tools and technologies that support making data FAIR, either through direct use or by guiding others in their application.
  • Develop the skills needed to collaborate effectively in making data FAIR.

How to

Step 1 - Consider your roles and/or those of team members

Before diving into specific training modules, it’s beneficial to understand the broader landscape of FAIR training for each specific role in your team. A comprehensive overview of roles can be found in Metroline Step: Build the Team, and different types Data Stewards are described in Metroline step: Have a FAIR data steward on board. In case your FAIRification project is on existing data you can also consider doing a Pre-FAIR assessment (see Metroline step: Pre-FAIR Assessment). This assessment can help you identify potential knowledge gaps within your team, allowing you to determine appropriate roles and address any training needs. Once you have a clearer picture of the role(s) in your team and you have pinpointed knowledge gaps, you can more effectively suggest and/or select training modules that address specific needs.

Here are some of the different roles that people have in research projects and FAIRification of data:

  • 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 guide researchers to achieve the FAIR principles and meet funder’s requirements. They need to have a deep understanding of the FAIR principles in order to ensure that data is well-organised and easy to find. Within a research group, Data Stewards are often responsible for managing and curating data.
  • Trainer. If you yourself are the trainer or educator, you will need specific Train-the-Trainer resources. Also, joining other colleagues’ training and reviewing their materials can be very helpful to build your expertise. Lastly, any course on education and pedagogy can enrich the content and the dynamics of the training you provide.

Step 2 - Criteria for selecting appropriate FAIR training

The next step is to reflect on the requirements, expectations, and resources needed to participate in a particular training. Here are some of the things to consider:

  • 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 may emphasise technical aspects of FAIRification, while others focus on the policy and legal aspects. Relevant training programs may vary depending on an individual’s professional role and career trajectory.
  • The format of the training. Training can be delivered in a variety of formats, including online courses, workshops, and conferences.
  • The time and resources available. Consider the time you can realistically dedicate as well as practical requirements such as scheduling and registration. While many trainings are free, they may require advance sign-up.

For more tailored advice, it can be helpful to consult a FAIR training coordinator at your research institute for any training recommendations. If your institute does not have a FAIR training coordinator, you can contact your research support department or your local Digital Competence Center for assistance.

Step 3 - Where to find the appropriate training

In addition to identifying which skills are needed, it is also important to know where to look for training, depending on what particular resources you’re looking into. Here we present an overview of where to find certain resources.

Generic FAIR and RDM training

Check within your organisation what training is available. Many institutions provide training on data management and FAIR for their researchers. However, if these are not available some useful places to look into are described below.

FAIR and RDM training aimed at researchers or hands-on Data Stewards in life sciences & health

Many universities and UMCs organise specialised data management courses. Check your local organisation for resources at your UMC as well as practical examples from the community:

Training for Data Stewards

The evolution of the Data Steward’s role within the research data lifecycle has brought a need for specific skill sets. Because data stewardship is a relatively new job profile and the areas of data management and FAIR data practices are constantly evolving, FAIR data stewards will significantly benefit from continuous learning. Staying on top of emerging trends, tools, and standards is key to developing and maintaining the necessary skills and expertise. In this context you might want to consider the following options:

  • Health-RI provides an annual introductory course for data stewards. For more information check the Health-RI service portal.
  • Several universities and UMCs have their own onboarding training for new data stewards, such as Maastricht University.
  • The ELIXIR RDMKit provides an overview of data management best practices data curation techniques, metadata standards, and relevant tools and technologies.
  • The Elixir RDM community is also developing the Data Steward Handbook, a resource that offers practical guidance to data stewards.
  • Essentials 4 Data Support, provided by Research Data Netherlands, is an introductory course for those that wish to support researchers in storing, managing, archiving and sharing their research data, regardless of their domain. All course materials are publicly available.
  • GDPR 4 Data Support, also provided by Research Data Netherlands, is an introductory course for data professionals / data stewards who help researchers deal with privacy sensitive data. All course materials are publicly available.
  • The National Coordination Point RDM (LCRDM) organises the DCC Spring training days. Most materials are freely available afterwards.
  • For other resources also check the FAIR Training and Capacity Building on the Health-RI Confluence.

Training for trainers

Training materials can themselves be FAIR. The following resources support trainers in enhancing the FAIRness and effectiveness of their materials.

You can also consider joining a community of trainers.

  • the LEARNFAIR community of FAIR trainers collaborates on Open Educational Resources (among which the FAIR lesson plans) for training in FAIR in the Life Sciences & Health domain;
  • the ELIXIR Training Platform aims to develop a training community that spans all ELIXIR member states;
  • Research Software Training NL is a network bringing together and facilitating training organisations in the Netherlands in the areas of research software, programming skills, applied data science, computational skills and open source.

All of these resources are emerging to adapt and tailor FAIR data solutions to meet the specific needs and constraints of every team. Additionally, every step of the Metroline contains training links or information that could be of further use. Ultimately, also contact the Training Coordinator of your institution for more relevant advise.

Expertise requirements for this step

The expertise required may depend on the training that will be followed, or the task that will be performed. Experts that may need to be involved, as described in Metroline Step: Build the Team, are described below.

  • Researcher. Provides research data and discipline specific knowledge.
  • Data steward. Often, data stewards act as trainers for their research group, department, faculty, or university.
  • Trainer. Your institutional Training Coordinator that focuses on research data management (if available) knows which training is available for the different roles in the team. They can find or create and organise tailor-made training and evaluate the effectiveness and impact of training.

Practical examples from the community

Radboud University Medical Center (RadboudUMC)
Researchers from RadboudUMC have mandatory induction days where they are presented with a variety of services available to them. As part of these sessions, researchers learn to apply the Findability and Acessibility principles within their studies. Furthermore, within the Radboudumc Technology Centers quarterly training FAIR RDM sessions get offered where researchers learn how to introduce the FAIR principles in their Data management practices.

Maastricht University’s Faculty of Health, Medicine and Life Sciences (FHML)
While previous initiatives primarily targeted researchers, the University of Maastricht FHML recognised the necessity of extending this knowledge to the diverse professional profiles mentioned earlier. Consequently, Maastricht University’s Faculty of Health, Medicine and Life Sciences, DataHub has developed a training session for data professionals. This session features experts in the previously discussed areas and addresses specific topics participants have identified as gaps in their expertise.

Leiden University Medical Center (LUMC)
At the LUMC, central Data Stewards are responsible for promoting data stewardship and FAIR research practices. They develop and maintain training materials for both researchers and support staff. A mandatory e-learning module on data stewardship, currently required for all PhD candidates, has enrolled over 200 participants. Additional in-house courses, such as Writing a Data Management Plan and From Data Export to Analysis, are offered quarterly. A program to train specialised data stewards per department is currently in progress. In collaboration with the BioSemantics group, new training resources on FAIR data are also in development.

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