This indicator evaluates the extent to which an organisation has clearly defined, documented and communicated policies and procedures for research data management. It examines how consistently these policies are implemented across the institution, including the availability of support structures, tools and control systems in place.
Level 1 – Minimal or no documented RDM policies and procedures. Individual researchers or departments manage research data independently
- Lack of documentation: There are no formally documented policies or procedures. Individual researchers or groups rely on their own informal protocols.
- Lack of awareness: Leadership and researchers have limited understanding of why RDM policies matter or how they support research quality and compliance. Available information is not effectively promoted or communicated.
- Inconsistent practices: Data handling methods vary significantly across teams, resulting in fragmented and non-standardised workflows.
Impact: Inconsistent practices and lack of coordination significantly increase compliance risks, lead to duplicated effort, and reduce the likelihood that research data will remain reusable or aligned with FAIR principles over time.
Level 2 – Some RDM policies and procedures are defined but not yet fully implemented across the organization
- Implementation hurdles: The defined RDM policies and procedures are only partially implemented or vary significantly across departments.
- Awareness: Limited understanding of RDM principles prevents their consistent adoption across the organisation.
- Communication gaps: Existing materials are not well promoted or shared, reducing visibility and impact.
- Inconsistent support structures: Support for RDM is not yet standardised. Some departments may have support available, while others rely on ad hoc or informal practices.
Impact: Some progress is visible, but inconsistent implementation still causes gaps and duplication , and limits reliable FAIR data.
Level 3 – Well-defined and written RDM policies and processes, supported by technology and formal oversight. A unified approach to research data management in the organisation.
- Communication: Dedicated RDM personnel actively support researchers and help communicate established policies and procedures.
- Tool support: Institutional tools and systems are available to support researchers in implementing RDM practices.
- Oversight: While formal institution-wide oversight may still be limited, local support structures or review mechanisms provide guidance and feedback.
Impact: Tools and coordination help make RDM more consistent, but limited oversight still causes variation and limits how well data can be reused over time.
Level 4 – Proactive, and continuous improvement of well-established research data governance.
- Alignment: Institutional RDM policies and procedures are fully aligned with national, federal and disciplinary guidelines.
- Review: Policies and procedures are regularly reviewed and updated based on technological developments, regulatory changes and user feedback.
- Personnel: Dedicated RDM staff actively assess and ensure the continued effectiveness and relevance of RDM practices.
Impact: Regular reviews and strong alignment reduce overlap and risks, keeping research data reusable and in line with FAIR principles in a sustainable way. .
Contributors
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Barcelona Supercomputing Center (BSC), Spanish National Bioinformatics Institute (INB/ELIXIR-ES)
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Luxembourg Centre of Systems Biomedicine
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University of Tartu / ELIXIR-EE
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