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Short Description 

To be able to reach your FAIRification goals, having a team with the right skillset is important [FAIRopoly]. The composition of the team depends on the exact goals and different skills may be necessary in different phases of the of the process [FAIRinAction]. The core of the team may be formed by one or more data stewards with expertise of the FAIRification process in general and knowledge of the local environment [Generic]. The team may, furthermore, contain (part-time) advisors with, for example domain expertise [FAIRopoly], as well as data managers, software developers, research scientists, project managers and legal support [FAIRinAction]. 

[Health-RI_FAIRification_Step_Report] In this section we describe the needed expertise for making data more FAIR. In general, FAIRification work requires consultancy with:

• Domain experts who know the domain-specific data - the meaning of the data, but also the provenance and relations to other data.

• FAIR experts or project managers that conducted a FAIRification project before (who know how to interpret and implement the FAIR principles).

Next to that, depending on your FAIRification goals, you might need more specific experts. To help you identify which expertise is required and available (or not) in your team, we present below a list of common roles and resources involved in FAIRification process by expertise and by FAIR principle. For the items that you do not have the expertise, please contact your local data stewards  or other data management services or Health RI to discuss a plan of action.

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Short Description 

To be able to reach your FAIRification goals, having a team with the right skillset is important [FAIRopoly]. The composition of the team depends on the exact goals and different skills may be necessary in different phases of the of the process [FAIRinAction]. . See, for example:

• FAIRopoly – FAIRification Guidance for ERN Patient Registries  

• FAIR in action - a flexible framework to guide FAIRification

To help you identify which expertise is required and available (or missing) in your team, in this step we present a list of common roles and resources involved in the FAIRification process listed by expertise and by FAIR principle.

[Sander] As a FAIRification FAIR data steward is essential for reaching the FAIRification goals, a full page has been dedicated to this role. See “Metroline “Metroline Step: Have a FAIRification FAIR data steward on board” board” for details on this crucial role.

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Expertise requirements for this step 

[Fieke] The data steward profile is often described according to three roles (policy, research and infrastructure) and eight task areas (policy & strategy; compliance; FAIR data; Services; Infrastructure; Knowledge management; network; data archiving). A single data steward can be responsible for all task areas, but tasks can also be divided among central and embedded / domain data stewards. Each task area requires different competencies. The EMBL-EBI competency hub describes activities, ksa’s (knowledge, skills & abilities) and learning objective for each rol and task area.

How to 

[Mijke: Another RDMkit page on this: https://rdmkit.elixir-europe.org/dm_coordination ]

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• Data Steward: Data stewardship is a relatively new profession and a catch-all term for numerous support functions, roles and activities. It implies professional and careful treatment of data throughout all stages of a research process.

• Policy maker: As a policy maker, you are responsible for the development of a strategic data management framework and the coordination and implementation of research data management guidelines and practices.

• Principal Investigator: As a Principal Investigator (PI), you may have recently acquired project funding. More and more funders require data management plans (DMP), stimulating the researcher to consider, from the beginning of a project, all relevant aspects of data management.

• Researcher: Your research data is a major output from your research project, it supports your research conclusions, and guides yourself and others towards future research. Therefore, managing the data well throughout the project, and sharing it, is a crucial aspect of research.

• Research Software Engineer: Research software engineers (RSE) in the life sciences design, develop and maintain software systems that help researchers manage their software and data. The RSE’s software tools and infrastructure are critical in enabling scientific research to be conducted effectively.

• Trainer: As a trainer, you design and deliver training courses in research data management with a focus on bioinformatics data. Your audience is mainly people in biomedical sciences: PhD students, postdocs, researchers, technicians and PIs.

[Generic] 

Data FAIRification requires different types of expertise and should therefore be carried out in a multidisciplinary team guided by FAIR data steward(s). The different sets of expertise are on i) the data to be FAIRified and how they are managed, ii) the domain and the aims of the data resource within it, iii) architectural features of the software that is (or will be) used for managing the data, iv) access policies applicable to the resource, v) the FAIRification process (guiding and monitoring it), vi) FAIR software services and their deployment, vii) data modelling, viii) global standards applicable to the data resource, and ix) global standards for data access. A good working approach is to organize a team that contains or has access to the required expertise. The core of such a team may be formed by data stewards, with at least expertise of the local environment and of the FAIRification process in general. 

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FAIR Principles and Example Resources

Resource glossary

Tool/Standard # can be used to #

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