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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 the 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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