In the previous section, you explored your Node context and identified the people involved in developing your data management strategy. The next step is to decide what your strategy should include.
Your Node already has services, roles and activities in place. The task is not to start from scratch, but to decide what matters most and how to structure this into a clear and manageable strategy.
There are different ways to approach this. In this module, we use two complementary approaches:
- Structuring your Node using the Global Open Research Commons (GORC) model
- Assessing and improving your setup using the Data Stewardship Handbook maturity model
The GORC model helps you organise your Node as a set of connected elements, such as governance, services, standards and human capacity. It answers the question: what is part of your system, and how does it fit together?
The maturity model helps you assess how well these elements are developed and where improvement is needed. It answers the question: how well is your Node performing, and what are the next steps?
In this chapter, you will use the GORC model to structure your Node and identify priorities. In the next chapter, you will use the maturity model to assess these areas and define concrete improvements.
You can read more about both approaches here:
- GORC model: https://datascience.codata.org/articles/10.5334/dsj-2024-056
- Data Stewardship Handbook maturity model: https://elixir-europe.github.io/ds-handbook/maturity-model
Why defining content matters
A Node data management strategy works best when it focuses on the right topics. It does not need to be long or detailed, but it should provide a clear picture of what your Node wants to achieve and how it supports researchers.
Without a clear structure, it is easy to overlook important areas or try to cover too much at once. Defining the content of your strategy helps you set priorities, connect existing work and make informed choices about where to focus effort.
What to include
The Global Open Research Commons (GORC) model provides a structured way to describe a research data ecosystem.
It was developed within the Research Data Alliance to show how different elements work together to support data sharing, reuse and coordination. You can read more in the original article:
https://datascience.codata.org/articles/10.5334/dsj-2024-056
The model describes the elements that together form the “commons”. These elements can be grouped into three connected areas:
Technical and infrastructure elements
These support how data is stored, processed and accessed:
- Infrastructure
- Services and tools
- Data and research objects
Standards and interoperability
These ensure that systems, data and services can work together:
- Standards and interoperability
Organisational and human elements
These shape how the system is governed, used and sustained:
- Governance
- Rules and access
- Human capacity
- Community engagement
- Sustainability
Together, these elements form the “commons”. They are connected: services depend on infrastructure, standards enable reuse, and governance and people determine how everything works in practice.
Why this matters
Using the GORC model helps you move from a fragmented view of services, roles and activities to a structured overview.
It allows you to:
- group related activities and responsibilities
- make connections between elements visible
- identify strengths in your current setup
- identify where coordination or development is needed
Exercise: map your Node using GORC
Select 2–3 GORC elements that are most relevant for your Node.
For each element, describe:
- what is already in place
- what works well
- what could be improved
Fill in the table below.
| GORC element | What exists | What works well | What could be improved |
|---|---|---|---|
What your result could look like
| GORC element | What exists | What works well | What could be improved |
|---|---|---|---|
| Governance | Defined roles such as Node Coordinator, Training Coordinator and Technical Coordinator; coordination embedded in national initiatives and European collaborations | Clear responsibilities at coordination level; strong connection to national and European structures | Decision-making routes not always explicit across organisations; stronger link needed between governance and service decisions |
| Rules and access | Use of national and European policies, FAIR principles and data access frameworks; alignment with initiatives such as EOSC and national infrastructures | Established policy frameworks and awareness of FAIR and access requirements | More consistent implementation across institutions; clearer translation of policies into practice |
| Services and tools | Tools such as data platforms, workflow tools and FAIR support services; contributions to shared service ecosystems and platforms | Strong technical expertise and integration with broader ecosystems (e.g. ELIXIR Platforms) | Visibility and reuse of services can be improved; clearer definition of core service portfolio needed |
| Infrastructure | Underlying compute, storage and data infrastructure across institutions; integration with national infrastructures | Strong institutional infrastructure and connection to national initiatives | Coordination across infrastructures can be improved; clearer alignment with long-term priorities |
| Data and research objects | Diverse datasets, metadata schemas and research outputs across domains; FAIRification workflows and data stewardship practices | Active work on FAIR data and metadata; availability of domain expertise | Alignment of data models and metadata across domains can be improved; better integration of data and tools |
| Standards and interoperability | Interoperability tools, FAIR workflows, metadata standards and identifier systems; contributions to shared standards and platforms | Strong collaboration and technical expertise; active contribution to interoperability efforts | Better alignment across projects and domains; stronger integration with policy and governance layers |
| Human capacity | Training programmes, data stewardship support, community networks and participation in European training platforms (e.g. TeSS) | Active communities and strong engagement in training and knowledge sharing | Coordination between training efforts can be improved; clearer career paths and sustainable capacity needed |
| Community engagement | Collaboration with research communities, participation in European networks and community-driven initiatives | Strong community involvement and cross-institutional collaboration | Engagement can be more structured; better feedback loops between communities and services needed |
| Sustainability | Combination of institutional, national and project-based funding; embedding in national and European infrastructures | Flexible and resilient model through distributed funding and integration | Long-term sustainability of services and roles not always clear; stronger structural funding needed |
What’s next
You will build on the overview you have created using the GORC model.
In the next section, you will assess these elements using the Data Stewardship Handbook maturity model. This will help you evaluate how well your current setup works and identify concrete next steps for improvement.
Read more:
- GORC model: https://datascience.codata.org/articles/10.5334/dsj-2024-056
- Data Stewardship Handbook maturity model: https://elixir-europe.github.io/ds-handbook/maturity-model
Examples from Nodes
These are examples of how Nodes might work with specific GORC elements:
Working on governance
A Node focused on governance as a priority area. They mapped existing roles such as Node coordination, training and technical leads, and realised that responsibilities were clear, but decision-making routes were not. They created a simple overview of how decisions are made across institutions, which helped improve coordination and communication.
Strengthening interoperability
A Node selected standards and interoperability as a focus. They listed their tools, FAIR workflows and metadata practices and identified that similar work was happening in parallel across projects. By bringing these together and aligning approaches, they improved visibility and coordination of interoperability activities.
Connecting training and services
A Node worked on human capacity. They mapped training activities and available services and noticed that these were not well connected. By involving both training coordinators and service leads, they improved alignment between training and the tools and support available to researchers.
Create your own example
✍ For contributors: Choose one GORC element you worked on (e.g. governance, interoperability, human capacity). Add your Node, name and role, and write a short example (max. 3 paragraphs): what was the situation, what did you do, and what did it help improve. You can describe a small step or first attempt. A full strategy is not required.
Node: [Your Node name]
Contributor: [Name]
Role: [e.g. Data steward, Node coordinator]
✍ Your example:
Describe the situation you encountered and what you did to address it. What was unclear or fragmented, and what did your action help improve?Make sure your example relates to one of the GORC elements, such as governance, interoperability, human capacity or sustainability.