Networks, Funding & Communication

Chapter Summary

TBD

2.1 National and International Networks

Network Structure test maria

Short description

Data stewardship does not happen in isolation. To create FAIR and reusable data, professionals depend on a range of local, national, and international networks. These networks provide guidance, tools, training, expertise, and infrastructure that support researchers throughout the data life cycle. Understanding these networks helps data stewards navigate where to turn for support, find collaboration opportunities, and stay aligned with national and international standards.

Learning Objectives

• Identify key local, national, and international networks relevant to data stewardship.

• Explain how these networks support FAIR and responsible data stewardship.

• Recognize which networks are relevant based on the responsibilities, expertise areas, and function profile of the data steward.

(Step 1) Local networks

Local networks are the first line of support for researchers and data stewards. These networks operate within the institution and translate (inter)national guidelines into practical, day‑to‑day workflows. For most data stewards, this is where collaboration, problem‑solving, and decision‑making will happen most frequently.

Local networks are important because they directly support the key responsibilities defined in the Chapter 1 Role of Data Stewards, such as supporting compliance, guiding researchers, and coordinating infrastructure use.

Typical local networks include:

• Data Stewardship Teams - Departmental, faculty, or central data stewards who coordinate FAIR data practices, support researchers, and develop local policies.

• Privacy, Security, and Ethics Support - Includes DPOs, Privacy Officers, Information Security Officers, and Ethics/METC committees that help ensure legal and ethical handling of data.

• Research IT / Infrastructure Support - Offers secure storage solutions, backup services, and technical support for data collection, analysis, and data management tools.

• Cross‑Departmental and Cross-Organizational Teams – Collaborative groups that combine expertise to provide hands‑on support for data collection, analysis, and workflows.

• Other Research Support Offices - Assist with grant applications, contracts, project planning, related administrative tasks, and sometimes act as the first point of contact for routing questions to specialised networks.

Local networks support FAIR and responsible data management by providing the essential expertise, infrastructure, and governance needed to embed FAIR principles into daily research practice. These networks ensure that data are managed in ways that make them Findable, Accessible, Interoperable, and Reusable, while also meeting legal, ethical, and institutional requirements.

Together, these networks create a coherent ecosystem where data stewards can collaborate effectively, route researchers to the right specialists, and implement FAIR practices consistently across the institution. In the section Roles of Digital Competence Centers, you will learn how DCCs work with and support these networks.

Quiz

(Step 2) National networks

National networks play a key role in coordinating research data management, FAIR implementation, and responsible data sharing across institutions in the Netherlands. These networks provide shared infrastructure, common standards, training programmes, and policy alignment that support researchers and data stewards beyond their own institutions. They complement local networks by offering expertise, guidance, and infrastructures that a single institution cannot provide alone.

Important national networks include:

• Health‑RI – A national initiative building an integrated health data infrastructure for research and care. Health‑RI provides FAIR expertise, ELSI guidance, tools, and working groups that connect researchers and data stewards across Dutch Healthcare institutions.

• LCRDM (Landelijk Coördinatiepunt Research Data Management) – Coordinates national RDM activities, offering practical guidance, tool inventories, expert groups, and nationally aligned recommendations for research support staff.

• DCC–PO (DCC voor Praktijkgericht Onderzoek) – National knowledge center for research support in universities of applied sciences. Focus areas of this network are open science, FAIR data, data stewardship, and data-intensive research.Data Stewards Interest Group (DSIG) – A cross‑institutional community specifically focused on the roles, capacities, and professionalisation of data stewards across the Netherlands.

• Data Steward Interest Group (DGSI) - A cross‑institutional community specifically focused on the roles, capacities, and professionalisation of data stewards across the Netherlands.

• GO FAIR (NL and international nodes) – Promotes practical implementation of the FAIR principles through FAIR Implementation Profiles, FAIRification frameworks, and international alignment.

• ELIXIR‑NL (Dutch node of ELIXIR Europe) – Provides life science data services, tools, training, and interoperability standards that align with the broader European ELIXIR network.

• LEARN-FAIR – Community and initiative offering training, materials, and knowledge exchange about implementing FAIR data practices in research.

• Netherlands eScience Center (NLeSC) – National organisation advancing research through high‑quality research software, RSE expertise, and digital skills training. Supports FAIR and reproducible research, coordinates the Dutch Research Software Engineer community, and collaborates on national Open Science initiatives.

• Research Software Training Network – A network that bringing together and facilitating training organizations in the Netherlands in the areas of research software, programming skills, applied data science, computational skills and open source.

• National Research Infrastructures – Such as BBMRI‑NL (biobanking) and SURF (computing, cloud services, secure storage, identity services).

• National ELSI Services – Such as the ELSI Servicedesk and Health‑RI’s ELSI working groups, which provide harmonised guidance on ethical, legal, and societal aspects of data sharing.

National networks are important because they directly influence institutional data policies, funding requirements, and the development of FAIR-aligned tools and services. They therefore also play a major role in shaping expectations for national funding calls, such as those from NWO and ZonMw, which often require alignment with national infrastructures or FAIR implementation strategies.

Some national networks also offer funding programmes and small grants to support FAIR implementation and Open Science, including calls related to FAIR data pilots, infrastructure development, or data stewardship capacity as you will read in the next section. These opportunities are valuable for data stewards and research groups looking to improve data workflows, develop FAIR tools, or strengthen institutional support.

More information on this connection can be found in the section in the last section of this chapter; “Funding Landscape in Research Data Management: “What Data Stewards Need to Know”.

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(Step 3) International Networks

International networks connect Dutch researchers, data stewards, and research support professionals to global standards, best practices, infrastructures, and communities. These networks help ensure that FAIR and responsible data management is aligned beyond national borders, enabling cross‑institutional and cross‑country collaboration. Many of these networks also shape global FAIR principles, interoperability frameworks, and Open Science policies, which influence national and institutional practices in turn.

European networks include:

• Skills4EOSC Consortium - Aims to professionalise data stewards across Europe, develop shared curricula, and create a network of competence centres.

• The Data Tank - Runs data steward bootcamps designed to upskill professionals and create a European network of trained data stewards.

• European Data Spaces – The European Commission’s initiative to build sector‑specific, interoperable data ecosystems. These spaces aim to create trusted environments for data access and reuse across national borders.

• ELIXIR Europe – A European research infrastructure that connects national nodes (including ELIXIR‑NL) to provide interoperable life science data services, tools, training, and standards across Europe.

• EOSC (European Open Science Cloud) – A major EU initiative building a federated ecosystem of data, tools, and services to support Open Science across Europe.

Some of the global networks are:

• RDA (Research Data Alliance) – A global community developing practical recommendations and standards for FAIR research data management. RDA runs multiple interest and working groups relevant for data stewards.

• GO FAIR Implementation Network - Although GO FAIR has regional nodes (e.g., NL, US, Brazil), its Implementation Networks (INs) operate internationally and bring together data stewards, FAIR experts, and domain specialists.

• FORRT (Framework for Open and Reproducible Research Training) – Community promoting open, transparent, and reproducible research practices and offering educational resources relevant to data stewards.

• FORCE11 - a global organisation that convenes researchers, librarians, data stewards, and publishers.

• International Research Software Engineering Society - A global network supporting Research Software Engineers. Many data stewards engage with this community due to overlap between data stewardship and software stewardship, including FAIR software, data pipelines, and reproducibility practices.

• CODATA (Committee on Data of the International Science Council) – Drives global policy development for data stewardship, FAIR data standards, and responsible data governance.

• GO FAIR International – Advocacy and implementation community promoting global FAIR principles, FAIR Digital Objects, and FAIR Implementation Profiles.

International networks are important because they provide access to global expertise, training, and tools that strengthen local and national efforts. They also play a key role in harmonising standards needed for cross‑border research, international funding calls, and large‑scale scientific collaborations.

Quiz

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2.2 The role of digital competence centers

Short description

Requirements on data management have increased over time. To help researchers and facilitating staff to deal with data in a professional manner and in accordance with FAIR principles, the NWO gave multiple impulses for improving both the awareness and exchange of expertise within an organisation (by setting up a ‘local digital competence center’) and by promoting collaboration of experts on a national level per specific predefined goal for collaboration (thematic digital competence centers). In the section ‘Section: National and International Networks’ different types of experts in local and national networks were discussed. In this chapter we will help you understand how these networks were improved by each of these types of digital competence centers (DCCs) and help you navigate the available DCCs for you.

Learning Objectives

• Understand what digital competence centers (DCC) are

• Understand the difference between a local DCC (LDCC) and thematic DCC (TDCC)

• Is aware of whether or not a LDCC has been set up in their organization

• Gained knowledge of which TDCCs their organization is participating in and who from their organization are participating

• Recognize the role of Digital Competence Centers (DCCs) in coordinating data stewardship expertise and connecting institutional practices to wider networks.

• Knows who to contact if a desired LDCC or TDCC does not exist yet and they want to contribute to setting it up.

(Step 1) LDCCs

In the past 10 years a lot has changed in the research landscape. A publication by Wilkinson and colleagues in 2016 on the FAIR principles, the new European Privacy legislation in 2018, and the increase in Investigator Initiated Trials, which meant that researchers in the medical field took on more of the Sponsor responsibilities under the guidelines for Good Clinical Practice (GCP). Additionally, scientific advancement led to increased digitization of medical record (Electronic Health Records) and the (improved) ability of data extraction from the original source.

These are just some of the many changes to the research landscape that impacted how data is managed and what type of expertise is needed within an organisation.

People gathered information and consulted with experts within organizations, but much of the available expertise was spread throughout the organization with many experts working on their own, with limited established research support services compared to how we know them today. You would simply know Ms/Mr/Mx X in department Y may be able to help with one aspect of your research and you would then search for another expert on something different to help with a different part of your research. Some of the researchers and data experts collaborated more frequently, but support services and niche experts were not formally organized as a clear network yet.

Between 2019 and 2023 NWO had an impulse investment available of 8,5 million Euro for Local DCCs (LDCCs). The call was for non-competitive funding. This means that each knowledge institution in higher education would be rewarded with a grant if their proposal met the requirements.

Goals:

• Formalize local networks into LDCCs within knowledge institutions

• Safeguard structural support for data stewardship, software development and FAIR data principles

• Additionally, by funding both LDCCs and TDCCS the aim was to improve the alignment in scientific infrastructure and support and support organisations.

• On a national level, efforts were made to have a federated system that would connect both LDCCs and TDCCs.

The first round of funding and following top-up financing gave an impulse to the establishment and further enhancement of LDCCs. These funds enabled organisations to hire data stewards and software engineers, establish a clear digital network of the internal experts, and improve organisational aspects, develop clear policies and financially secure these networks.

In the Netherlands there are now 26 LDCCs: every university and university medical center has an LDCC. Applied universities on the other hand share one national hub, the DCC-PO.

< To add: a video of someone talking about the experience of what the funding helped change/ improve in their organization>

(Step 2) TDCCs

In Step 1 you learned about the improvement on collaboration within local networks by formalizing an LDCC. In thematic DCCs (TDCCs) experts from different organisations within the same field.

There are currently 3 TDCCs:

• TDCC of Life Sciences and Health (LSH)

• TDCC of Natural & Engineering Sciences (NES)

• TDCC of Social Sciences & Humanities (SSH)

The TDCC’s receive funding from NWO for 1. network activities 2. projects that address challenges in their domain. Each of these TDCCs can have projects on multiple topics. TDCC-LSH for example has 2 types of projects, divided in Bottleneck projects and Challenge projects. A bottleneck project currently ongoing is a FAIR fellowship programme in which each institution could enrol 1 data expert such as a local data steward to collaborate with other fellows and mentors from other organisations, to collectively work on building and improving knowledge related to data standards on a national level.

<To add video from someone talking about the TDCC they are involved in – e.g. Karen Sap from Amsterdam UMC – with a fellowship related to ISA standards, and her mentors from other knowledge institutions for this programme. Or Meron Maas (policymaker on DCCs at NWO>

Even if you work in one field, say you work in LSH, it could still be that you are an expert that could contribute to a topic addressed by another TDCC. An LSH expert on research software development in an academic hospital for example, could be involved in TDCC-NES for the alignment on how to further development of career paths for Research Software Engineers. Or if you are a data steward involved in policies for dealing with interview data, you could be involved in the TDCC-SSH, even though you are a data steward in a hospital.

(Step 3) Getting involved

If you are new in your organisation and/or the role of data steward, you may still look for a predefined role for you specifically. In Chapter 1 you learned more on the many possible tasks for a data steward and to navigate what is expected from you in your role. It could be that your expertise (either the expertise you have or are building) would be beneficial in an LDCC and perhaps also in a TDCC, but... *How do you get involved?*

Question: Do you know the experts in your LDCC?

When you are familiar with one or some of the experts in the LDCC in your organisation, they could help you understand who else is part of the LDCC. Your organisation may have a list of people involved in the local network of experts and / or support services.

Even within your own team, your colleagues could be in a wide range of work groups that you have not yet heard about. Is there an overview of the workgroups your team members are involved in? Asking for documents or talking about this more casually over coffee could be great ways to find out more.

If an overview does not exist yet or is outdated, mapping such a local network or types of workgroups for yourself in a way that can be shared within your organisation could be a great contribution to your LDCC.

Regarding TDCCs: there is a nice webpage on how to participate on the TDCC website.

By signing up to a mailing list or slack community you could learn what is going on and actively participate. In case you have trouble finding out who are involved in TDCCs in your organisation, you could also contact the community managers on the website to find out and get in touch.

Assignment

Let students write down (for themselves we will not review their answers) a main contact for them in their organisation to go to if they have questions on the LDCC and how they could contribute, and 1 type of TDCC and contactname/general e-mailaddress to contact if they want to find out more or contribute

Training

• See training in other sections

• https://fairmetroline.org/

• If your organisation has not set up data management services yet, or has not formalized a network yet, you may benefit from an online course on Delivering Research Data Management Services by Futurelearn.

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This page has been written and reviewed by field experts through a rigorous process and has reached the “released” status. Learn more about the contributors here and explore the development process here. If you have any suggestions, visit our How to contribute page to get in touch or fill in our Quick feedback form.

Literature used

• Wilkinson, M., Dumontier, M., Aalbersberg, I. et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 3, 160018 (2016). https://doi.org/10.1038/sdata.2016.18

• Algemene verordening gegevensbescherming vanaf 25 mei 2018 in hele EU van toepassing. - EUR-Lex

• [ICH E6 Good clinical practice - Scientific guideline

  European Medicines Agency (EMA)](https://www.ema.europa.eu/en/ich-e6-good-clinical-practice-scientific-guideline)

• [Digitale Competentie Centra

  NWO](https://www.nwo.nl/onderzoeksprogrammas/uitvoeringsplan-ict-infrastructuur)

• FutureLearn: Online Courses and Degrees from Top Universities
  • Research Data Management Services - Online Course - FutureLearn
• Home - TDCC.nl
  • https://tdcc.nl/dsig/how-to-participate/

2.3 Funding landscape in Research Data Management: what Data Stewards need to know

Short description

Research funders increasingly expect evidence that research data will be well managed and openly shared. Most project proposals now require a short data management section, and once funding is awarded, a full DMP and FAIR research outputs at the project’s end are mandatory. These requirements support responsible research and good data stewardship.

As a data steward, understanding the major funding bodies and their data stewardship requirements helps you better support researchers within your organization and identify opportunities for funding your own work and ideas. This chapter provides an overview of key national and international funders for open science and data stewardship in the EU, along with practical guidance to help you navigate these resources effectively.

Learning Objectives

• Recognise key national and international funders

• Identify funding programmes supporting data stewardship through Open Science initiatives

• Identify key elements needed to support researchers in their grant proposals, as well as during and after their projects

• Understand major policies driving current funding programmes

(Step 1) National funders

The NWO (Dutch Research Council)

The NWO, or Nederlandse Organisatie voor Wetenschappelijk Onderzoek, is the main public organisation that funds scientific research in the Netherlands. Besides supporting researchers and research projects, it funds and supports RDM and data stewardship through several dedicated Open Science instruments, infrastructure investments, and policy requirements:

1. Open Science fund

The Open Science Fund is a key programme from NWO especially designed for researchers and support staff or organizations. It awards up to €50,000 for projects that promote practices, platforms and tools, for the adoption of Open Science, FAIR data and software.

2. Open Science NL (within NWO)

The Open Science NL is a dedicated branch of NWO that runs multiple funding actions that directly support RDM and data stewardship.

Key clusters of the programme include:

• Infrastructure funding: ensuring researchers have tools to make data findable, accessible, interoperable, and reusable.

• Digital Competence Centres (DCCs): funding research organisations to establish their local DCCs, including data steward positions that help researchers make data FAIR.

• Meetings: offering funding for the organization of meetings, workshops, symposia, and conferences, giving researchers, research support staff, and other stakeholders the opportunity to meet and discuss the challenges and opportunities of implementing Open Science.

• Publishing: Open Access funding schemes promote open publication and often require or enable FAIR data sharing.

• Replication studies: encouraging researchers to carry out replication research.

3. RDM requirements embedded in all NWO calls

Similarly to Horizon Europe, there are RDM requirements for all NWO projects. While not a programme on its own, these requirements apply to every project, NWO funding implicitly supports RDM work, including staffing and infrastructure costs needed to meet these requirements:

• All applicants must submit a data management section in the proposal.

• After award, they must submit a Data Management Plan (DMP) and deposit data in a trusted repository following the FAIR principles.

````{note} Did you know?

From 1 March 2026 institutions do not need to send their DMP templates for approval by NWO and ZonMw. Both organisations will continue to require a DMP for funded projects, and all DMPs must still comply with the Science Europe Core Requirements for DMPs and be reviewed by institutional data stewards.

#### Health domain funders: ZonMw and Health~Holland 

[ZonMw](https://www.zonmw.nl/nl) is the Netherlands Organisation for Health Research and Development, a national public funding body for research aimed at improving health, healthcare, and well‑being in the Netherlands. [Health~Holland](https://www.health-holland.com/nl) stimulates innovative research by financing public-private collaboration in the Life Sciences & Health sector.  Both fund data stewardship as part of research, infrastructure and innovation programmes: 

**1. FAIR data & RDM requirements **

Good data management and the implementation of the FAIR principles across all funded projects. Grantees must plan for FAIR data and submit a DMP. 

**2. Stimulating FAIRification, FAIR data infrastructure and data stewardship capacity** 

ZonMw promotes the development of FAIR metadata standards, FAIRification tools, and scalable FAIR services, including participation in [FAIR data and metadata initiatives](https://www.health-ri.nl/en/projects-collaborations/zonmw) (e.g., through GO FAIR and Health‑RI) and [FAIR Data Cube](https://www.medrxiv.org/content/10.1101/2023.04.23.23289000v2). Together with other major funders they co-signed a [2023 declaration](https://www.zonmw.nl/nl/nieuws/verklaring-financiers-gezondheidsonderzoek-samenwerking-rondom-nationale-data-infrastructuur) committing to harmonized practices and investment in a FAIR national health data infrastructure. Other projects included developing competence profiles and a professional description for data stewards in life sciences through the [Towards FAIR data steward as profession](https://zenodo.org/records/7225070) initiative. 

3. PPP Innovation subsidy (Public‑Private Partnerships) 

Health~Holland funds large public‑private [R&D data-driven ](https://preventivehealth.ewuu.nl/about-a-tailored-lifestyle/) where data infrastructure, FAIR data management, and digital health tooling are integral components. 

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<summary>Did you know?</summary>

```
Data stewards work with the research data life cycle, whereas project managers and grant support officers work with the research project life cycle, which includes a pre‑award  and a post‑award phase. The pre‑award phase involves identifying suitable funding opportunities and supporting the grant proposal process up to submission. The post‑award phase covers the support activities that take place after funding has been granted. These two phases require distinct types of data stewardship support. 
```
</details>

````{note} Did you know?
Data stewards work with the research data life cycle, whereas project managers and grant support officers work with the research project life cycle, which includes a pre‑award and a post‑award phase. The pre‑award phase involves identifying suitable funding opportunities and supporting the grant proposal process up to submission. The post‑award phase covers the support activities that take place after funding has been granted. These two phases require distinct types of data stewardship support. 

(Step 2) European funders

Horizon Europe

Horizon Europe is the EU’s main programme for research and innovation. RDM and data stewardship are required and funded across many parts of the programme through contractual obligations, thematic clusters, Open Science and infrastructure-focused calls. Below you can find an overview of the Horizon Europe requirements in RDM and data stewardship:

1. Open Science and RDM requirements

**Pre-award**

Horizon Europe treats Open Science and RDM as required standard practices, including them as part of applications and evaluation processes:

• Projects must outline how data will be managed and shared, for e.g., through the “Data management” and “Open Science” sections in the grant proposals

• Open Science requirements apply to all projects, and the programme directly funds practices such as publishing Open Access, citizen science, data and code FAIRification, open and transparent research.

</br>

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**Post-award**

There are RDM requirements for all funded projects, which represent a primary tool for putting RDM into practice:

• Prepare a DMP at the beginning of the project.

• DMPs must follow the FAIR principles (Findable, Accessible, Interoperable, Reusable).

• Regular updates to the DMP during the reporting periods of the project.

• Open Science practices ‘as open as possible, as closed as necessary’, such as publishing research outputs open access and in trusted repositories are also part of the contractual obligations.

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2. Project‑level allowances for data stewardship

Horizon Europe explicitly allows budgeting for RDM in the form of:

• Data stewards

• Data curation and cleaning

• Metadata creation

• Secure storage and preservation

• Persistent identifiers (PIDs)

• Ethical and legal data compliance

These expectations are documented in the Horizon Europe guidelines for research data management.

</br>

</br>

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**3. Calls supporting RDM **

All Horizon Europe calls, including those supporting research infrastructures, FAIR data practices, and Open Science, are published via the EU’s Funding & Tenders Portal. In this platform, you can find all open and upcoming calls under Horizon Europe, among these, typical call themes that fund RDM include:

• Research infrastructure calls (e.g., data repositories, data platforms)

• Open Science / FAIR data support calls

• EOSC‑related calls (European Open Science Cloud)

• Domain‑specific data-centred calls (health, environment, culture)

Did you know?

Calls are often collaborative, involving multiple project partners. The best way to find collaborators is through your national and international network of contacts, for example, the DCCs (link to Ch2 above) and the Data Stewardship Interest Group (DSIG). You can also use the Horizon Europe partner search tool that lists all organisations that have expressed an interest in collaborating in Horizon Europe funding opportunities. You are also welcome to register yourself in this tool.

Missing video

Test your knowledge

Quiz

Case-scenario: A Horizon Europe project has just started. The PI is unsure when the Data Management Plan should be written. They say: “We will write the DMP at the very end, after the final deliverables.”

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Expertise

Training

Pre-award Open Science and RDM requirements in Horizon Europe:

RDM in Horizon Europe grant proposals

Guide on Horizon Europe Open Science requirements in practice

Webinar - 8th Horizon Europe Open Science requirements in practice

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