This indicator assesses how well the organisation’s RDM practices align with relevant policies and guidelines at the organisational, local, national, European and international levels. It examines whether these policies are understood, translated into practical workflows and supported through guidance and oversight.
Level 1 – Governing policies not taken into consideration
- No systematic alignment: Research data practices are carried out without awareness of institutional, national or international RDM policies and regulations. Funder mandates, FAIR principles and legal or ethical requirements are not systematically considered.
- No institutional guidance: There is no central support, documentation or guidance available to translate external policies and requirements into practical steps for researchers. Researchers rely on personal judgement or ad hoc solutions for daily data management decisions, leading to inconsistent and non-reproducible practices.
- Lack of oversight: No mechanisms or workflows exist to verify whether research data practices meet external regulatory or strategic obligations.
Impact: High risk of non-compliance and inconsistent data practices undermines data quality, reuse and responsible research conduct.
Level 2 – Governing policies partially taken into consideration
- Basic awareness: Key external and organisational RDM policies are recognised on an institutional level but not systematically implemented. Some departments or projects implement them (e.g., where required by funders), while others continue to rely on ad hoc practices.
- Informal translation to practice: Guidance may exist but is fragmented or not tailored to research workflows. Support staff (e.g., data stewards, ethics committees) may offer advice but processes are not standardised or generically applied.
- Partial oversight: Project documentation (e.g., DMPs, ethics applications) may reference and adhere to relevant policies, but review and enforcement inconsistent and lack central coordination.
Impact: Compliance improves in some areas, but inconsistent adoption leads to gaps, duplicated effort and uncertainty for researchers.
Level 3 – Governing policies fully taken into consideration
- Systematic alignment: Institutional RDM practices and workflows are broadly aligned with relevant policies at organisational, national and international levels.
- Consistent implementation: Researchers increasingly follow shared procedures and standards that reflect these policies, though some variations still exist across departments.
- Supported translation: Policies are accompanied by practical guidance, examples, checklists and training materials that help staff apply them in daily work. Data stewards and related experts offer coordinated advice to support correct interpretation and application.
- Clear oversight and feedback: Basic monitoring (e.g., DMP reviews or repository checks) are in place, and feedback from practice is used to update supporting materials and guidance.
Impact: RDM practices are coherent, compliant and sustainable. They support reliable data reuse, reduced risk and alignment with FAIR principles.
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Luxembourg Centre of Systems Biomedicine
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