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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)

Core of proper data stewardship are activities that ensure research data

is Findable, Accessible, Interoperable, and Reusable (FAIR) in the long term. This includes 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 data. Hence, according to FAIRification models such as FAIRopoly and A Generic Workflow for the Data FAIRification Process, a FAIR data steward - 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 such as Health-RI.

Why is this step important

Good data stewardship has to adhere to the FAIR data principles and by definition implies long-term and sustainable care across multiple research cycles. For this reason, data stewardship is a collective endeavour, with 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 (RDM) policy tailored to its own situation and there will be a variation in how funders and institutional policies (or legislation) define roles and responsibilities, e.g. by placing differing expectations on the research team, host institution and third-party organisation

such as data centres

(Towards FAIR Data Steward as profession for the Lifesciences).

Having a FAIR data steward as part of your multidisciplinary team (see Metroline Step: Build the team) is beneficial for the

reasons (see FAIRopoly)

described below.

• FAIR guidance

• . Having someone in the team with actual FAIR knowledge and expertise will help you reach your FAIR objectives (see

• Metroline Step: Define your FAIRification Objectives) more easily and faster.

• FAIR standards

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

• 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 and beyond the institution.

• FAIR use-cases

• . As you don’t want to reinvent the wheel, it is the FAIR data steward that can link to the community’s good practices and uses case for FAIRifying data.

How to 

Step 1

Consider what type of data steward your team would most benefit from. The NPOS/ELIXIR Data Stewardship Competency Framework distinguishes three data steward

roles, with FAIR as the core of all roles.

• Policy oriented

• . Focuses on policy development and the implementation of research data management practices in a team or organisation.

• Research oriented

• . Works directly with researchers and offering hands on support on data management issues.

• Infrastructure oriented

• . Translates the requirements of policies and science into suitable IT solutions, soft- and hardware, and tools.

For these data steward roles, consequently, eight 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 team.

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The eight NPOS/ELIXIR competence areas

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

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

3. Alignment with the FAIR data principles. Alignment to the FAIR data principles and the principles of Open Science.

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

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

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

7. Network. Obtaining and maintaining a network of 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.

Step 2

Discuss where in the organisation your data steward is allocated. Is it possible to (part-time) hire a data steward from a generic team (such as a central library, knowledge hub or IT team), or do you most benefit from a dedicated, project embedded data steward? Alternatively, you may want to consider consulting a dedicated data steward for missing competence areas in your team.

Depending on the project, there may

be budget to hire a data steward. Some funders allow costs for data stewardship in the project’s budget. 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

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 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).

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1. Policy & Strategy

1. . Design strategies for raising awareness of RDM policies and regulations.

2. Compliance

1. . Advise on institutional compliance with RDM policies and regulations.

2. Facilitating good RDM practices

1. . Advise relevant stakeholders on good practices of management of research data.

2. RDM services

1. . Propose, implement and monitor RDM workflows and practices.

2. Data infrastructure

1. . Identify the requirements for adequate RDM infrastructure and tools.

2. Knowledge management

1. . Determine the adequate level and sustainability of RDM knowledge and skills.

2. Network & communication

1. . Create and participate in (inter)national RDM networks.

2. Data sharing & publishing

1. . Analyse gaps in support for data sharing and publishing.

2. Coordination of work

1. . Lead, supervise and support less experienced colleagues.

2. Coaching & Process improvement

1. . Make proposals for improving work processes at different levels.

2. Soft skills

1. . 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 AUMC Data Stewards Network), 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. Extend your team with the required FAIR data stewardship knowledge and skills. These could relate to for instance uploading your (meta)data to a certain repository or catalog

• .

Expertise requirements for this step 

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 can 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

• .

Practical

examples from the

community 

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 bottle-necks and help apply the FAIRification process;

• identify training needs and

• organise workshops and hackathons

• .

Training

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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