Title: Build Teams and Capacity for FAIR Implementation

Original pages:

Metroline Step: Organise training - Health-RI data - Confluence

Metroline Step: Organise training - Health-RI data - Confluence

Rationale for merging these two pages: This merge aligns the process of building a team with the development of their expertise. By combining "Build the Team" and "Organize Training," we create a unified guide for Capacity Building. This ensures that institutional FAIR implementation is not just about filling roles, but about ensuring those roles possess the specific competencies required to succeed in a Health-RI environment.

Short description

To successfully execute a FAIR data project, you must not only assemble a multidisciplinary team but also ensure they possess the specific competencies required for Health-RI standards. This step guides you through identifying the necessary roles—centered around the Data Steward—and provides a framework for assessing and addressing expertise gaps through targeted training. By aligning recruitment with a continuous learning strategy, you ensure the team remains capable of navigating the technical and ethical complexities of the FAIRification process.

Why is this step important?

Assembling a team is only the first half of the equation; ensuring they are equipped with the right skills is what leads to project success. While building a team it is essential to assess the following:

• Clarity of Role vs. Skill: A person may have the right job title but may need specific training to perform their role in the different areas of data Farification.

• Standardization: Training ensures that everyone on the team—from researchers to IT—speaks the same "FAIR language," preventing communication and practical errors.

• Future-Proofing: Data standards and privacy regulations (like GDPR) change. A team that is built with a "training-first" mindset can adapt to new requirements without stalling in their role and their work in different projects.

Step 1: Building and upskilling your team

• Identify Core Roles: Define the Data Scientist, Data Steward, Training Experts and Technical leads.

• Assess Expertise: Use a skills-gap analysis to see where the team stands regarding FAIR principles.

• Onboard the Data Stewards: Ensure this central role is active before training begins.

• Curate Training Resources: Select relevant Health-RI or external modules (e.g., FAIR in action).

• Establish a Learning Schedule: Set milestones for when the team should reach specific competency levels.

• Create knowledge sharing communities: Create internal or cross-project "Communities of Practice." This ensures that solutions to common hurdles—like metadata mapping or privacy issues—are shared, documented, and reused rather than solved in isolation.

Step 2 Overview of roles and skills or competences

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-organized 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 emphasize 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.