This indicator assesses the extent to which RDM is supported by institutionally provided or recommended software, platforms and resources. It considers whether tools are recommended, documented, supported and consistently adopted by end users across the organisation and whether researchers have clear guidance on how to select and use them.
Level 1 - No software or resources for data management present or recommended
- No ecosystem provided: The organisation does not offer or coordinate RDM-related software or tools. Researchers independently choose their own solutions, and knowledge is not shared across the institution.
- No recommended systems: There is no curated list of approved, supported, or preferred tools or resources for managing research data.
Impact: RDM practices are inconsistent, fragmented and highly dependent on individual preferences for software and resources, leading to duplication of effort.
Level 2 - Reference to generic software and resources
- Ad hoc recommendations: The organisation references general or widely known tools (such as GitHub) without offering guidance tailored to research data workflows.
- Limited support: Tools are known but not centrally managed, evaluated or supported. Researchers are responsible for selecting, configuring and maintaining them independently.
Impact: Although some solutions exist, the lack of structured guidance leads to uneven usage and uncertainty about appropriate and secure tool choices.
Level 3 - Software and guidelines provided and documented for researchers / relevant groups / relevant RDM processes
- Institutionally supported tools: The organisation provides or licenses specific RDM tools, platforms or services (e.g., institutional storage, ELNs, repositories) that are recognised as relevant across organisations.
- Documented guidance: Clear instructions, use cases and guidelines describe how these tools should be used in relevant research workflows.
- Targeted support: Data stewards or IT/RDM support teams assist with tool onboarding and troubleshooting.
Impact: Researchers can follow consistent and well-documented guidelines, improving efficiency, data quality and compliance across projects.
Level 4 - Software and guidelines adopted by researchers / relevant groups / relevant RDM processes
- Consistent adoption: Institutionally recommended RDM tools and platforms are widely used and embedded in research workflows across departments.
- Integration: Tools and resources are interoperable and linked across the data life cycle.
- Continuous improvement: Tools, resources and guidance are regularly reviewed and upgraded based on new releases, emerging alternatives, researcher needs and evolving best practices.
- Ownership: Each tool or service has a designated responsible owner to ensure maintenance and support.
Impact: RDM workflows are efficient, coordinated and sustainable, supporting the production of high-quality and reusable data.
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University of Tartu / ELIXIR-EE
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