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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 multiple data stewards. Make sure to consider the relevant competencies that can or 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 teammay oversee all areas, or responsibilities may be shared across multiple data stewards. Some organisations also have department- or division-based data stewards who focus on local RDM needs while aligning with central policies. When assembling your team, consider which competencies should be covered internally 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 instituteinstitute’s data.

A formalised Dutch data steward profile has been adopted by many research performing organisations to professionalise data stewardship roles and create consistency across institutions. This profile is further detailed in Step 3.

Step 2

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- Determine the steward’s position in the organisation

Decide where the data steward will be based within your organisation. Depending on the structure and needs of your research team, a data steward may be:

• Part-time within a central unit. Hired from a library, knowledge hub or IT

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• team to support multiple projects.

• Dedicated to a project. Embedded within the research team for hands-on data stewardship.

• Department- or division-based. Assigned within a specific department or research unit to align local practices with institutional policies.

• External support. Engaged as a consultant to cover missing competencies.

In some organisations, data stewardship is an extended role for existing data specialists or data managers. Departments may choose to train their existing data specialists to take on data steward responsibilities, rather than hiring a separate data steward. This approach helps integrate data stewardship into existing research workflows.

Some researchers may allocate their own budget to hire a data steward. Some In addition, some funders allow costs for data stewardship in the project’s budgetcosts within project budgets. If there is no budget to hire a dedicated data steward for a research project, you may consider using existing data steward support services in your organisation to guarantee that data stewardship is covered.The steward cannot be hired, check whether your organisation provides existing data stewardship support to ensure proper coverage.

For further details on data steward allocation, see the NPOS report on professionalising data stewardship in the Netherlands provides further information on both step 1 and 2 (chapter 2.3).

Step 3 - Hire or consult a data steward

Hire or consult your a data steward following the formalised Dutch data steward profile adopted in the Netherlands. This contributes to professionalising the role of the data steward in the Netherlands and strengthens the career perspective of your data steward. The below areas indicate what a data steward potentially could know or do according to the formalised profile. More information on the profiles in the formal Dutch job classification systems can be found in the earlier cited report (Annex 5 and further, which has been adopted to professionalise data stewardship roles and create consistency across institutions. This profile ensures clear role definitions and career prospects for data stewards. The areas below outline what a data steward may be responsible for, based on the formal Dutch job classification system (see the earlier cited report, Annex 5 and beyond).

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• Policy &

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• strategy. Design strategies for raising awareness of RDM policies and regulations.

• Compliance. Advise on institutional compliance with RDM policies and regulations.

• Facilitating good RDM practices.

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• Support stakeholders in adopting effective RDM practices.

• RDM services.

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• Develop, implement and monitor RDM workflows and practices.

• Data infrastructure. Identify

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• requirements for adequate RDM infrastructure and tools.

• Knowledge management.

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• Assess and ensure the sustainability of RDM knowledge and skills.

• Network & communication.

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• Build and

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• maintain (inter)national RDM

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• collaborations.

• Data sharing & publishing.

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• Identify gaps in support for data sharing and publishing.

• Coordination of work.

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• Supervise and support less experienced colleagues.

• Coaching &

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• process improvement.

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• Improve work processes at different levels.

• Soft skills.

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• Apply competencies such as accuracy, persuasiveness, communication, collaboration and networking

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• .

Step 4 - Ensure support and compliance

If extending your team with includes a FAIR data steward, also take the considerations below into account.

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ensure they have access to the right support and resources.

• Training opportunities. Ensure data stewards receive continuous training to stay updated on best practices, tools and policies.

• Institutional and national networks. Connect data stewards with institutional, national and international networks to foster collaboration and professional growth. Central networks should be the first point of contact, including institutional data steward groups (via the Local Digital Competence Centers (LDCC)), regionally (e.g. your regional or the Open Science CommunityCommunities), nationally national networks (e.g. the Data Stewards Interest Group) or the and international level initiatives (e.g. RDA professionalising data stewardship Interest Group or the ELIXIR RDM Community). See also the Building your Community page page . Depending on the nature of your project, it's beneficial for the FAIR data steward to have domain specific knowledge relevant to your research field. This enables them to better understand the context and requirements of the data being generated and ensures that they can effectively communicate for more details.

• Domain-specific knowledge. A data steward with expertise in your research field can better understand the data’s context and communicate effectively with researchers and stakeholders.

• Funders more and more often Funder requirements. Many funders now require a data steward to be consulted or be part of a the project team. Check for specific responsibilities and tasks in the grant proposal and make sure your team’s FAIR data steward is able to meet them. Make sure data is handled in compliance with journal and institutional policies, and with (inter)national laws and regulations. Discuss in an early stage the condition of the journal you potentially consider to publish in, as well as the FAIR requirements by your institute. Ensure your team has the required FAIR data stewardship knowledge and skills. These could relate to, for instance, the use of relevant standards and uploading your (meta)data to a certain repository or cataloguegrant conditions to ensure compliance with specific expectations.

• Publishing policies and local guidance. Ensure data handling aligns with institutional policies and national and international regulations. Discuss early stage requirements with local guidance bodies to meet FAIR standards and repository guidelines.

Expertise requirements for this step 

Having a clear overview of the added value of the role/function of the To determine the position of a FAIR data steward in relation to the FAIRification objectives of your project, department or team is required to be able to decide on the position of a FAIR data steward in your team. A data steward will bring added value in multiple areasyour team, it is essential to understand their added value in relation to your FAIRification objectives. While a data steward can contribute in multiple areas, their responsibilities vary across institutions. In some, a single steward may cover multiple roles, whereas in others, tasks are divided across departments. Aligning stewardship with existing support structures helps prevent duplication and inefficiencies.

• Technical skills. A FAIR data steward will bring a strong technical background brings expertise in data management and curation. This includes proficiency in data formatting, metadata standards, data integration techniques, and data repository platforms. They are familiar with data , ensuring compliance with privacy and security regulations to ensure compliance.

• Collaboration with researchers. From the start of a project, a FAIR data steward will collaborate They work closely with researchers to help understand and provide support on data generation, collection , and analysis, as well as the guiding them in selecting appropriate tools and platforms to use.

• Integration with existing infrastructure. A FAIR data steward will help evaluate your existing data infrastructure and workflows to identify opportunities for integrating FAIR data stewardship practices They assess institutional workflows and infrastructure to integrate FAIR data stewardship while ensuring alignment with existing roles to avoid duplication.

• Communication and advocacy. A They promote FAIR data steward will actively communicate the value of FAIR data practices to other team members, stakeholders, and funding agencies. They advocate for the importance of data stewardship and help practices within the team, engage stakeholders and funding agencies and foster a culture of data sharing and transparency within the team.

• Project management skills. The They oversee FAIR data steward will bring strong project management skills to oversee the implementation of FAIR data practices within a project. This includes developing implementation, including data management plans, coordinating data sharing activities, tracking data quality and integrity, and ensuring coordination and compliance with funder and institutional policies. In some institutions, these tasks are handled by dedicated departments, requiring clear coordination.

Practical examples from the community 

European Joint Programme on Rare Diseases (EJP RD)
Has EJP RD provides various FAIRification services, guidance, tooling tools and training, supported by a FAIRification stewards service team of six people. The FAIRification stewards’ FAIR data stewards. Their activities include , for example (see for more information the EJP RD FAIRification website for more details):

• participate participating in project, national and international meetings to exchange knowledge and experience , share experiences and develop FAIRification guidance;

• identify identifying FAIRification bottlenecks and help apply supporting the FAIRification processimplementation of FAIRification processes within the project;

• identify assessing training needs and organise organising workshops and hackathons at the project level.

Training

Since As data stewardship is a relatively new job profile and the field of data management and FAIR data practices are constantly evolving, a continues to evolve, FAIR data steward will stewards benefit from continuous learning and staying . Staying updated on emerging trends, tools , and standards . This will help the FAIR data steward to keep developing helps them develop the necessary skills and expertise.

• This may include training in Training for data stewards may cover 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 projectWhile many trainings and resources focus on general RDM, they can often be adapted to emphasise FAIR principles and tailored to specific project needs.

The NPOS report on professionalising data stewardship in the Netherlands contains a list of training opportunities and materials (ppp. 148 - 162148–162). Several organisations that deliver in the Netherlands provide data stewardship training in the Netherlands are listed below. , including:

• Research Data Netherlands (RDNL) Essentials 4 Data Support. Training for a basic understanding of : A foundational, domain agnostic training in data management and data steward tasks (domain agnostic)stewardship. Materials are publicly available.

• LCRDM DCC Spring training days: Offers sessions on FAIR data and research data management. Most materials are freely available afterwards.

• Health-RI organises a FAIR Data Stewardship Basics course: Provides training on core data stewardship principles (2023 round, 2024 round). Contact fairservicedesk@health-ri.nl for next upcoming editions.

Training events and training materials on data management and FAIR can also be found through Taxila or TeSS. Additionally, the RDMkit training resources might may be helpfuluseful. See the general Moreover, see the Health-RI FAIR Training and Capacity Building page to find training events and materials on data management and FAIRhttps://health-ri.atlassian.net/wiki/spaces/FSD/pages/39256187/FAIR+Training+and+Capacity+building#Resources-to-find-FAIR-training-events-and-materials.New to data stewardship? Read this blogpost from Esther Plomp, Bjørn Bartholdy, Lora Armstrong from TU Delft:

For those new to data stewardship, the blogpost From Researcher to Data Steward: How to get started?Get Started? provides insights into learning paths and practical steps. This resource may also be useful for researchers managing their own data in smaller projects without a dedicated data steward.

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