Roles & Responsibilities; Tasks & Competencies

Chapter summary

You have just started in your new role as a data steward in the health domain. Data steward is a relatively new profession in research institutions. What a data steward’s tasks are and what expertise is required for the job can vary depending on the type of organisation and the position within the organisation, the team in which the data steward operates, the research domain, the maturity of RDM services and policies in place etc. In this chapter you will learn about data stewardship, the different roles and competencies and you will hear how more experienced data stewards perceive their job. ## Section: Role of the Data Steward ## Short Description

In this module we would first define what is meant by Data Steward in the landscape of Research in the Life and Medical Sciences. Secondly, we will define the different roles and personas that you might develop into as a Data Steward.

Main Objective: To map the role of Data Steward and have an idea of the different profiles, competencies and possible career pathways.

Learning Objectives

Understand the Data Stewardship role
Know skills and competencies relevant for data stewards
Design a personal learning path

Step 1 Defining Data Stewardship

Data stewardship is about taking good care of data before, during, and after research projects; throughout the research data life cycle.

There are various definitions of Data Stewardship, in this e-learning we will use a definition that encompasses the roles, tasks and responsibilities mentioned in the other sections:

Data Stewardship refers to a:

“Course of action taken by a person or group to manage and supervise organisational data assets with responsibility and commitment. Good stewardship involves adequate care, making use of the FAIR principles, and holding ownership and regulation to provide high-quality data (including metadata), combining trust and ethical practice. “

Source: CODATA RDM Terminology: https://terms.codata.org/rdmt/data-stewardship

The goal is making research data findable, accessible, interoperable and reusable (FAIR) for the long term. However, regardless of where they fall on the spectrum, a data steward must always prioritize privacy and ethics while guiding researchers through the FAIR principles.

Data stewards in the health domain deal with domain specific types of data, requirements and regulations. E.g. for research in the Netherlands, they must distinguish between data governed by the Medical Research Involving Human Subjects Act (WMO) and non-WMO data, as this distinction significantly impacts the regulatory requirements for the project. Another example is the European Health Data Space, (EHDS) which will have a big impact on how organisations in the health sector handle and share data.

Quiz Question :

According to the definition provided in this chapter, what is the primary focus of “efficient” data stewardship?

A- The pure technical storage/archival of the data after a research project is completed

B- Applying the FAIR principles adequately or advising people on how to do it

C- Knowing enough about meta-data standards, ownership and regulations (e.g. GDPR)?

D- Personal Skills

Correct Answer: All of the above

Step 2 Data stewardship vs. Data steward

While data stewardship describes the overall process and philosophy of managing data responsibly, a data steward is the person (or sometimes a team) who is assigned specific roles and responsibilities to carry out data stewardship activities, sometimes within a team. Data stewards play a crucial role in making sure that the practices and standards associated with data stewardship are implemented in daily work. How the data steward role is shaped depends on the institute, department or even a specific project.

Quiz Question:

2. What is the key difference between ‘Data Stewardship’ and a ‘Data Steward’?

A) Data Stewardship is a software tool, while a Data Steward is the person who operates it.

B) Data Stewards only work in universities, while Data Stewardship only happens in hospitals.

C) Stewardship is the overall process of responsible management, while the Steward is the person assigned specific roles to implement it.

D) There is no difference; the terms are interchangeable in all professional contexts.

Correct Answer C: The steward is the actor who puts the standards and practices of stewardship into daily practice within an organization

Section: Data Steward’s Competencies

Short Description

To get a better understanding of the skills and competencies of data stewards, and the training needed, Research Data Netherlands (RDNL) developed a competency framework for data stewards3 . This framework can help data stewards and their employers draft learning paths that fit the data stewards’ personal as well as the organisation’s development.

Learning Objectives

Understand the competencies needed to be qualified data steward

Step 1 Developing Competencies

What you do in practice as a data steward depends largely on:

• the type of organisation, e.g., university, university medical centre, university of

applied sciences, another research institute;

• the position in the organisation, e.g., central, embedded in a research department

or group;

• the expertise in the data stewardship team, e.g. one multitasker, versus a large team

with a variety of expertise and specialisations;

• the maturity of the support structure;

• the scientific discipline. Fig. 1: Expertise areas for data stewards from the Professionalising Data Stewardship report (NPOS 2021)

The expertise areas illustrated in the figure above are also described in the RDNL Competency framework - RDNL competency framework for data stewards, developed by Research Data Netherlands (RNDL) in 2025. In this new framework, there are additional competencies to those described in Fig. 1, these include research software management, training and awareness raising, see fig. 2.

Fig. 2: Competency framework for data professionals, developed in the context of the RDNL project to build a national training & community platform for data professionals. The competency framework is described in 10.5281/zenodo.17952684

There are expertise areas that are commonly associated with these profiles. Data stewards in general have a basic understanding of all these areas, but mostly specialise in specific area(s), depending on their position as described above. Have a look at the competencies in more detail: https://researchdata.nl/en/about-training/competency-framework/.

RDM, FAIR principles & open science covers competencies required for providing tailored advice, curating datasets, and providing hands-on data management in line with good research data management (RDM) practices and the FAIR and open science principles.

Research Software Management covers competencies required for providing tailored advice about research software management and computational reproducibility and applying these topics hands-on.

Data infrastructure covers the availability of adequate digital infrastructure for RDM, such as for data storage, transfer, analysis and publication. It includes competencies required for providing tailored advice about infrastructure and tools and contributing to the infrastructure and tools available for researchers.

Policy and governance covers competencies required for the development, implementation and monitoring of RDM policy and strategy. We expect this area to be relevant as a further specialisation or suitable for data stewards who have an active role in RDM policy.

Legal and ethical responsibilities covers competencies required to advise about compliance with relevant scientific, legal, and ethical standards.

Training and awareness raising covers competencies to assess and ascertain an adequate level of knowledge and skills on RDM within the organisation.

Transversal skills, also known as ‘soft’ or ‘non-cognitive skills’, cover competencies that can be used in a wide variety of settings.

See Chapter 3 for information on available training to further develop skills and competencies.

Quiz Question:

1. The Multi-Tasker vs. The Specialist

Scenario: You have just been hired as the sole Data Steward for a small research institute. Because you are the only person in the role, your daily tasks range from helping a researcher write a Data Management Plan (DMP) to advising other researchers in the department on storage requirements.

Question: According to the “Developing Competencies” section, why is your role shaped this way?

A) Because the organization lacks maturity in RDM services.

B) Because the role is one in which multiple competences are needed daily.

C) Because the scientific discipline requires a generalist approach.

D) Because “Transversal skills” are more important than technical ones in small institutes.

Correct Answer: B. The text explicitly mentions that the expertise in the team (multitasker vs. large team) is a primary factor in how your practice is shaped.

Step 2 Different Work Contexts

Data Stewards can be organised in different ways within organisations, reflecting variations in institutional structures, research practices, and available expertise. In some settings, data stewards operate from a central unit, such as a Digital Competence Center (see Chapter 2), connected to internal and external networks of research support departments such as the library, IT services or Legal experts. These Data Stewards often work on complex data related questions and services that require their broad network of research support professionals. In other cases, data stewards are embedded directly within a research project, team or department. These dedicated/embedded Data Stewards focus on hands-on support throughout the research data lifecycle and are closely aligned with day-to-day research activities.

For more practical information about Data Steward roles and responsibilities in different work contexts you can have a look at “Have a FAIR data steward on board | FAIR Metroline”. For further background on data steward positionings, see the NPOS report Professionalising Data Stewardship in the Netherlands (Chapter 2.3).

1.1 The Data Steward Role in Health-RI Netherlands

What is a Data Steward? ma

UoAs have developed a job profile but not yet adopted

These profiles will be touched upon in more detailed in Chapter 3. However, at this initial point it might be a good idea to have them in mind, as you draft what Data Stewardship career journey you might want to embark on.

1. Identifying the Support Model

Scenario: A researcher is starting a new three-year project on clinical genomics and needs a dedicated person to manage their data daily, from the moment it is collected until it is archived. They hire a steward who sits in their office and attends all team meetings.

Quiz Question:

Question: Which organizational model does this represent?

A) A Centralized Unit

B) A Digital Competence Center

C) An Embedded Data Steward

D) An External Consultant

Correct Answer: C. Embedded stewards are hired directly within a research project or team and are alignedinvolved in with day-to-day activities.

Did you know:

Data stewardship personas are often defined by the specific roles stewards play and the scope of the research data they support. The nature of the data is one of the criteria that dictates the data steward’s focus:

Qualitative Data: This scope encompasses data collected via qualitative methods, such as interviews, life stories, ethnographies, and participant observations. These datasets often present unique challenges for FAIRification due to their deeply personal or sensitive nature, requiring the steward to balance data sharing with strict confidentiality.

Quantitative Data: This involves data collected through quantitative methods. For these datasets, the steward’s expertise must bridge the gap between privacy compliance (particularly for sensitive health-related data) and the technical aspects of interoperability and reusability.

Imaging data can be both qualitative (photos, videos, echos, CT, MRI, etc.) and quantitative (e.g. tumor measurements), depending on which aspects of the imaging are used.

Clinical data…

Omics….

Practical examples from the community

In the following interviews a variety of Data Stewards from UMCs in the Netherlands will talk about the devolopments made in their area the contributions they have made to the field:

## Test your knowledge ## Exercise 1: In the next exercise we invite you to reflect on the skills you would like to develop in your career as a Data Steward.? This exercise in the following form, should help you to describe these choices.

Take the following Quiz and reflect on your answers:

https://forms.office.com/pages/responsepage.aspx?id=CuPeS5QMZUi8Uh2fiUpJdTDeUMa8pLVFv-xgDXYKgmZUOUdSNlVBTFBTWDNGR0ZSWFVMWURIMUlFNC4u&origin=QRCode&route=shorturl

Training:

Take a look at:

HRI FAIR Metroline

The FAIR metroline is a collaborative effort by data stewards and other experts from the Health-RI network to help take all the steps necessary to achieve your goals in FAIRifying data. Each step contains information about the importance, a step-by-step guide, what expertise is required and information on relevant training .

Data Steward Handbook

The Elixir community (Life Sciences) has created a handbook for data stewards, with valuable information on all sorts of topics and use cases. It is work in progress but you can already consult it, and even volunteer to contribute to it.

Literature Used

Jetten, M., Grootveld, M., Mordant, A., Jansen, M., Bloemers, M., Miedema, M., & Van Gelder, C. W. G. (2021). Professionalising data stewardship in the Netherlands. Competences, training and education. Dutch roadmap towards national implementation of FAIR data stewardship (1.1). Zenodo. https://doi.org/10.5281/zenodo.4623713 (Chapter 2.3).

Huijser, D., Schoots, F., Grootveld, M., van Gelder, C. W. G., de Smaele, M., Thorpe, D. E., Saldner, S., & Clare, H. (2026). Curriculum for Data Professionals in Research-Performing Organisations in the Netherlands (v2025.12.31). Zenodo. https://doi.org/10.5281/zenodo.17952685

Navigation: Back to Overview

Next Chapter: Networks, Funding & Communication