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Short description 

One of the goals of proper Proper data stewardship are activities that ensure ensures research data is Findable, Accessible, Interoperable , and Reusable (FAIR) in the long term . This includes through data management, archiving and reuse by third parties. Creating FAIR data requires attention from the planning phase of a scientific experiment to the life-long maintenance of the datastudy to its lifelong maintenance. Hence, according to FAIRification models such as FAIRopoly and A Generic Workflow for the Data FAIRification Process, a FAIR data steward - familiar with knowledge of the local environment and of the FAIRification process in general - should be part of your team and guide the FAIRification process. This could also mean that someone within the research team follows relevant training in order to take on specific data steward activities, and/or make use of existing (central) support data management services available in their own organisation or national organisations guide this process. This role can be filled by a trained team member or by using existing central support services within organisation or national initiatives such as Health-RI.

Why is this step important

Good data stewardship incorporates the FAIR data principles and implies ensures sustainable integration in the research cycle. For this reason, data stewardship It is a collective endeavour, with effort requiring actions ( and competencies ) needed from the individual researcher, from other scientists in the research project, from the institute, and also from the research discipline(s) and funders involved. Each organisation will implement its Research Data Management researchers, project teams, institutes, research disciplines and funders. Each organisation tailors its research data management (RDM) policy tailored to its own situation and there will be a variation in how funders and institutional policies (or legislation) define context, with funders and institutions defining roles and responsibilities , e.g. by placing differing expectations on the research team, host institution differently, such as expectations for research teams, host institutions and third-party organisation such as organisations like data centres (Towards FAIR Data Steward as profession for the Lifesciences).

Having a FAIR data steward as part of in your multidisciplinary team (see Metroline Step: Build the team) is beneficial for the reasons offers key benefits (see FAIRopoly) described below.:

• FAIR expertise. Having someone in the A team member with generic general FAIR knowledge and expertise will help you reach your helps achieve FAIR objectives (see Metroline Step: Define your FAIRification Objectives) more easily and faster.

• FAIR standards. A FAIR data steward often has, for instance, the required provides expertise in metadata standards and semantic modelling expertise, which is essential to for achieving Interoperability interoperability between collections, studies and /or data(sets)datasets.

• FAIR technologies. Additionally, a FAIR data steward often has extensive knowledge on technologies and tooling that help script or automate the FAIRness of data, in They also bring knowledge of technologies and tools that automate or improve FAIRness within and beyond the institution.

• FAIR use - cases. As you don’t want to reinvent Instead of reinventing the wheel, it is the a FAIR data steward that can link to the community’s good practices and uses case connects your team to community best practices and use cases for FAIRifying data.

How to 

Step 1 -

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Identify the right data steward

To ensure effective data stewardship, consider which type of data steward best fits your team would most benefit from. The NPOS/ELIXIR Data Stewardship Competency Framework distinguishes three data steward key roles, all with FAIR as the core of all roles.their core focus:

• Policy oriented. Focuses on policy development and the implementation of research data management practices in Develops and implements RDM policies within a team or organisation.

• Research oriented. Works directly with researchers and offering , providing hands-on support on for data management issues.

• Infrastructure oriented. Translates the requirements of policies and science policy and research needs into suitable IT solutions, software, soft- and hardware , and tools.

For Each of these data steward roles , consequently, covers eight key competence areas are distinguished (see below). Each task area requires different competencies. A single data steward can be responsible for all competence areas (including FAIR), but tasks can also be divided over may oversee all areas, or responsibilities may be shared across multiple data stewards. Make sure to consider the relevant competencies that can or When assembling your team, consider which competencies should be covered by your team and to be aware of available resources for roles and/or competencies that fall outside the scope of your teaminternally and identify resources for any gaps.

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The eight NPOS/ELIXIR competence areas are:

1. Policy & Strategystrategy. Development, implementation and monitoring of the research data management RDM policy and strategy of the institute.

2. Compliance. Compliance with Adherence to relevant codes of conduct, legislation and field specific standards.

3. Alignment with the FAIR data principles. Alignment to the Incorporating FAIR data principles and the principles of Open Scienceopen science practices.

4. Services. Availability of adequate RDM support on research data management, in staff or services.

5. Infrastructure. Availability of adequate RDM infrastructure for research data management.

6. Knowledge management. Adequate level of knowledge and skills on research data management RDM in the institute.

7. Network. Obtaining and maintaining a network of Maintaining connections with aligned expertise areas and relevant organisations by the institute.

8. Data archiving. Adequate support and infrastructure for FAIR and long-term archiving of the data of the institute.

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