‘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. |
Proper data stewardship ensures research data is Findable, Accessible, Interoperable and Reusable (FAIR) in the long term through data management, archiving and reuse. Creating FAIR data requires attention from the planning phase of a study 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 the local environment and FAIRification - should 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.
Good data stewardship incorporates the FAIR data principles and ensures sustainable integration in the research cycle. It is a collective effort requiring actions and competencies from researchers, project teams, institutes, research disciplines and funders. Each organisation tailors its research data management (RDM) policy to its own context, with funders and institutions defining roles and responsibilities differently, such as expectations for research teams, host institutions and third-party organisations like data centres (Towards FAIR Data Steward as profession for the Lifesciences).
Having a FAIR data steward in your multidisciplinary team (see Metroline Step: Build the team) offers key benefits (see FAIRopoly):
FAIR expertise. A team member with general FAIR knowledge and expertise helps achieve FAIR objectives (see Metroline Step: Define your FAIRification Objectives) more easily and faster.
FAIR standards. A FAIR data steward provides expertise in metadata standards and semantic modelling, essential for achieving interoperability between collections, studies and datasets.
FAIR technologies. They also bring knowledge of technologies and tools that automate or improve FAIRness within and beyond the institution.
FAIR use cases. Instead of reinventing the wheel, a FAIR data steward connects your team to community best practices and use cases for FAIRifying data.
To ensure effective data stewardship, consider which type of data steward best fits your team. The NPOS/ELIXIR Data Stewardship Competency Framework distinguishes three key roles, all with FAIR as their core focus:
Policy oriented. Develops and implements RDM policies within a team or organisation.
Research oriented. Works directly with researchers, providing hands-on support for data management.
Infrastructure oriented. Translates policy and research needs into suitable IT solutions, software, hardware and tools.
Each of these roles covers eight key competence areas (see below). A single data steward may 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.
The eight NPOS/ELIXIR competence areas are:
Policy & strategy. Development, implementation and monitoring of the RDM policy and strategy of the institute.
Compliance. Adherence to relevant codes of conduct, legislation and field specific standards.
Alignment with the FAIR data principles. Incorporating FAIR principles and open science practices.
Services. Availability of adequate RDM support in staff or services.
Infrastructure. Availability of adequate RDM infrastructure.
Knowledge management. Adequate level of knowledge and skills on RDM in the institute.
Network. Maintaining connections with aligned expertise areas and relevant organisations.
Data archiving. Adequate support and infrastructure for FAIR and long-term archiving of the institute’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.
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 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.
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 your data steward following the formalised 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).
Policy & 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. Advise relevant stakeholders on good practices of management of research data.
RDM services. Propose, implement and monitor RDM workflows and practices.
Data infrastructure. Identify the requirements for adequate RDM infrastructure and tools.
Knowledge management. Determine the adequate level and sustainability of RDM knowledge and skills.
Network & communication. Create and participate in (inter)national RDM networks.
Data sharing & publishing. Analyse gaps in support for data sharing and publishing.
Coordination of work. Lead, supervise and support less experienced colleagues.
Coaching & Process improvement. Make proposals for improving work processes at different levels.
Soft skills. This area comprises activities like accuracy, persuasiveness communication, collaboration and networking abilities.
Step 4
If extending your team with a FAIR data steward, also take the considerations below into account.
Community participation is essential to support your FAIR data steward. Let your data steward benefit from a broader network of data stewards, locally (e.g. the institutional data stewards network or Local Digital Competence Centers (LDCC)), regionally (e.g. your regional Open Science Community), nationally (e.g. the Data Stewards Interest Group) or the international level (e.g. RDA professionalising data stewardship Interest Group or the ELIXIR RDM Community). See also the Building your Community 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 with researchers and stakeholders.
Funders more and more often require a data steward to be consulted or be part of a 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 catalogue.
Having a clear overview of the added value of the role/function of the 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 areas.
Technical skills. A FAIR data steward will bring a strong technical background 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 privacy and security regulations to ensure compliance.
Collaboration with researchers. From the start of a project, a FAIR data steward will collaborate closely with researchers to help understand and provide support on data generation, collection, and analysis, as well as the 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.
Communication and advocacy. A 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 foster a culture of data sharing and transparency within the team.
Project management skills. The FAIR data steward will bring strong project management skills to oversee the implementation of FAIR data practices within a project. This includes developing data management plans, coordinating data sharing activities, tracking data quality and integrity, and ensuring compliance with funder and institutional policies.
European Joint Programme on Rare Diseases (EJP RD)
Has various FAIRification services, guidance, tooling and training, supported by a FAIRification stewards service of six people. The FAIRification stewards’ activities include, for example (see for more information the EJP RD FAIRification website):
participate in meetings to exchange knowledge and experience and develop FAIRification guidance;
identify FAIRification bottlenecks and help apply the FAIRification process;
identify training needs and organise workshops and hackathons.
Since data stewardship is a relatively new job profile and the field of data management and FAIR data practices are constantly evolving, a FAIR data steward will benefit from continuous learning and staying updated on emerging trends, tools, and standards. This will help the FAIR data steward to keep developing the necessary skills and expertise.
This may include training in 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 project.
The NPOS report on professionalising data stewardship in the Netherlands contains a list of training opportunities and materials (p. 148 - 162). Several organisations that deliver data stewardship training in the Netherlands are listed below.
Research Data Netherlands Essentials 4 Data Support. Training for a basic understanding of data management and data steward tasks (domain agnostic). Materials are publicly available.
LCRDM DCC Spring training days. Most materials are freely available afterwards.
Health-RI organises a FAIR Data Stewardship Basics course (2023 round, 2024 round). Contact fairservicedesk@health-ri.nl for next editions.
Training events and training materials on data management and FAIR can be found through Taxila or TeSS. Additionally, the RDMkit training resources might be helpful. See the general page to find training events and materials on data management and FAIR https://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: From Researcher to Data Steward: How to get started?
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