Research today involves more data than ever before, and funders, such as the Luxembourg National Research Fund (FNR) and Horizon Europe, want to know how that data will be managed. This is why researchers are increasingly asked to prepare a Data Management Plan (DMP), describing how their data will be handled from start to finish. At institutions as Luxembourg Institute of Science and Technology (LIST), this growing need led to a strategic effort to improve internal data management planning – ensuring researchers could meet requirements from funders, while embedding better data practices across project lifecycles.
Building on this effort, LIST partnered with LNDS team to initiate a data project focused on adopting and customising the Data Stewardship Wizard (DSW) – a tool that guides researchers step-by-step through writing clear, complete, and funder-aligned DMPs. In the article, we explore how this collaboration lead to real impact in the data management practices at LIST, through the adoption of the Data Stewardship Wizard tool, and how it supports key stages of the data provider journey within the broader End-to-End (E2E) data journey, in particular: Collect, Catalogue, Enhance, and Decide on Access.
See glossary of key definitions
Meeting evolving requirements in data management
When researchers apply for funding, whether from the FNR, Horizon Europe, or other agencies, they are often required to submit a Data Management Plan (DMP). Increasingly, funders require these plans not only at the start of projects but as living documents that are updated throughout the research lifecycle.
A DMP outlines how data will be handled during and after a research project, covering aspects such as data collection, storage, sharing, and preservation. For funders, DMPs are essential to ensure that research outputs remain accessible, reproducible, and responsibly managed.
As these requirements become more detailed and more frequent, research institutions need efficient tools and processes to support their researchers and strengthen data management practices across projects. This presents both a challenge and an opportunity: the challenge of supporting research teams in meeting funders’ requirements efficiently, and the opportunity to embed better data practices into the fabric of project planning.
Recognising the need for organisational solutions to address these challenges, in 2023 LIST reached out to LNDS to start a data project. The goal was to co-develop a sustainable and scalable solution for managing DMPs – one that aligned with funder expectations while responding to LIST’s internal needs.
A collaborative and practical methodology
Over the course of 2023 and 2024, LIST and LNDS teams collaborated on two interconnected data projects to address DMP-related challenges.
The process began with an in-depth assessment of existing data management planning practices, identifying both strengths and areas for improvement in current workflows.
Through structured workshops and working sessions, they explored possible tools and strategies to support LIST’s DMP creation. These engagements fostered a shared understanding of operational and technical needs.
After evaluating potential solutions, they identified the Data Stewardship Wizard (DSW) as the most suitable tool, thanks to its flexibility, funder alignment, and support for customisation.
Solutions delivered
From this decision, LNDS worked closely with LIST to tailor the DSW to their internal context. Together, they developed an extended knowledge model and two intelligent templates for FNR and Horizon Europe projects – dynamic, funder-specific guides that help researchers create compliant DMPs with built-in recommendations and validation.
Beyond customisation, the project delivered practical capabilities: providing support throughout the entire research data lifecycle, ensuring compliance with standards and policies for long-term accessibility and sharing, and centralising all DMPs in a single database for easier management and oversight.
Key phases of the data journey
These outputs directly addressed multiple phases of the data provider journey: Data Management Planning is a key precursor step for defining a project’s strategy for data management throughout the lifecycle:
- Data collection: by supporting structured planning for how data is gathered and organised.
- Data documentation and cataloguing: by embedding metadata models and classification standards in the DSW.
- Data processing and quality enhancement: through improved documentation quality and template customisation.
- Data sharing: by integrating access planning, licensing, and preservation considerations into the DMP templates.
The result was a solution grounded in real institutional needs, ready for integration into LIST’s daily research support workflows.
Impact: empowering autonomy and innovation
The implementation of the DSW tool, along with LIST’s extended knowledge model and funder-specific templates, marks a significant step forward in the organisation’s data management journey. These developments have enabled LIST to streamline its internal processes, reduce administrative burden, and foster greater autonomy among its research teams.
Importantly, LIST researchers are now actively using the DSW to create and manage Data Management Plans in collaboration with the internal Quality, Data and Knowledge Management Office. This project has not only helped LIST streamline internal workflows but also laid strong foundations for improving transparency and quality in data-related planning and access decisions.
With the success of these two data projects, LNDS have built a strong, forward-looking partnership with LIST. Together, they now share not only a common understanding of data management challenges, but also a tested methodology for addressing them.
Looking to strengthen your data management planning?
LNDS supports organisations with specific data-related needs through dedicated data projects. If your team is working to improve internal data processes, workflows, or governance, you can get in contact with LNDS to initiate a project with them. Together, LNDS will identify your needs and co-develop practical, tailored solutions.
LNDS Data Stewards team will work alongside you to identify your requirements and co-design practical solutions that align with your context and goals. LNDS supports you across the full End-to-End (E2E) data journey, including key stages of the data provider journey – from collecting and cataloguing data to planning access and enhancing documentation quality.
Source: LNDS