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‘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. The core responsibilities and tasks vary, from policy advising and consultancy, to operational and technical support and IT related tasks. Responsibilities also vary between and among the different research-performing organisations, and data stewards (DS) often have different job titles.' (RDMkit) A FAIR data steward guides teams in organising, storing, and describing data to meet the FAIR principles. Having a data steward on board ensures research data can be understood and reused, making science more efficient and transparent. |
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Part-time within a central unit. Hired from a library, knowledge hub or IT 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. In addition, some funders allow data stewardship costs within project budgets. If a dedicated 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 (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, share experiences and experience 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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