This indicator assesses the existence, quality and practical use of guidelines or standards of how research data should be structured or organised. It includes file naming conventions, folder structures, metadata, version control and documentation. It also considers whether these are defined, communicated and consistently applied. The level of maturity reflects how effectively these standards are integrated into daily research and project management, from individual habits to systematic and institution-wide application.
Level 1 – No standard present or recommended
- Unstructured: Research data are stored according to individual habits without shared principles or conventions.
- Inconsistent: File naming, documentation and folder structures vary between researchers and projects, making data difficult to understand, navigate or reuse.
- Lack of awareness: Researchers are unaware of existing best practices for organising research data.
Impact: Data are inconsistently organised and often difficult to interpret or reuse. This results in data of limited quality, usability, reproducibility and discoverability for research purposes.
Level 2 – Reference to generic best practices
- Basic awareness: RDM personnel recognise the importance of research data organisation standards. General recommendations and external resources (e.g., FAIR principles) are known and occasionally shared, but guidance remains broad and not domain-specific.
- Non-specific: No organisation’s standards or templates exist.
- Limited implementation: Adoption of practices is voluntary and varies greatly between individuals or research groups.
Impact: Some improvements in data organisation appear, but practices remain inconsistent and dependent on individual initiative rather than institutional support.
Level 3 – Guidelines / standards provided and documented for researchers / relevant groups / relevant RDM processes
- Clear guidance: Institutional or departmental guidelines for research data organisation are documented, publicly available and tailored to disciplinary contexts.
- Structured approach: Templates, examples and recommendations cover key aspects, including folder hierarchy, file naming, metadata and version control.
- Supported by training: Awareness and use of standards are promoted through RDM training, consultation and/or onboarding.
Impact: Data management becomes more consistent across projects. However, adoption remains partial and is contingent upon researcher engagement.
Level 4 – Guidelines / standards adopted by researchers / relevant groups / relevant RDM processes
- Integrated into workflows: Data organisation standards are embedded in research processes, supported by tools, automation and infrastructure where relevant.
- Continuous improvement: Guidelines are periodically reviewed and updated to reflect best practices, domain-specific needs and user feedback.
- Supported by training: Staff and researchers receive regular refresher training to ensure the consistent application of standards.
Impact: Standards are widely adopted and seamlessly integrated into research workflows, increasing interoperability, automation and reproducibility. Data remain well-organised, accessible and reusable over time, ensuring compliance with FAIR principles.
Contributors
Contributor
University of Tartu / ELIXIR-EE
Contributor
Contributor