Data indicators: Technical research infrastructure

This indicator assesses the availability, integration and reliability of technical infrastructure supporting research data management across the data life cycle. It covers the hardware, software and digital environments used for data ingestion, analysis, storage, archiving and sharing. Maturity reflects the organisation’s capacity to provide secure, interoperable and FAIR-aligned systems that enable automation, reproducibility, and long-term stewardship through standardised procedures and usage guidelines.

Level 1 – No technical infrastructure for research / RDM is available

• Fragmented: There is no centralised or institutional infrastructure supporting research or RDM. Researchers rely on personal devices or ad hoc solutions.
• Independent data handling: Data are stored locally or on external drives, shared informally via email or portable media.
• Lack of protection: No formal security measures are in place.
• Undefined: There are no defined processes or best practices for backup, versioning or long-term archiving.

Impact: No specific guidance is available to help researchers with data management. Data are vulnerable to loss, corruption or misuse. Collaboration is inefficient, and achieving compliance with data security or FAIR principles is challenging.

Level 2 – Technical infrastructure for research / RDM is available; general usage recommendation

• Basic availability: Institutional or cloud-based storage solutions and recommendations are available.
• Basic backup and storage: Storage spaces with automated backup exist, but are inconsistently utilised. Data retention and archiving remain largely manual.
• Minimal standardisation: Metadata and file organisation practices vary widely; only basic sharing and versioning options are available.
• Emerging security: Security and access control rely mainly on passwords, with limited compliance checks.

Impact: Researchers have access to basic infrastructure but lack clear guidance or enforcement. Data remains inconsistently managed and difficult to find or reuse.

Level 3 – Technical infrastructure for research / RDM facilitates data handling along the research data life cycle; more specific usage recommendations

• Lifecycle support: Infrastructure facilitates storage, sharing, curation and archiving across the full data lifecycle.
• Improved reliability: Automated backups, redundancy systems and access controls with encryption ensure data protection.
• Structured organisation: Comprehensive metadata and indexing improve findability and accessibility.
• Integrated processes: Platforms are linked with research tools, and procedures follow established data management guidelines.

Impact: Data management becomes more efficient and secure. Infrastructure supports compliance with institutional and external standards, and researchers increasingly follow consistent RDM practices.

Level 4 – Usage guidelines (and/or SOPs) and automated processes are present in the technical infrastructure for research / RDM regarding, e.g., storage conditions, archiving rules, deletion rules

• Integrated and automated systems: The technical infrastructure encompasses the entire RDM workflow, featuring automation for storage, archiving and deletion in accordance with institutional policies.
• Standardised and enforced: Metadata and data organisation standards are enforced through built-in mechanisms and audit trails.
• Secure and compliant: Continuous compliance monitoring, encrypted storage and traceable access management are in place.
• Guided and supported use: Clear usage guidelines and SOPs are embedded in systems, supported by training and user documentation.

Impact: RDM infrastructure is robust, interoperable and FAIR-compliant. Automated processes reduce errors, ensure compliance and enable efficient long-term data stewardship across the organisation.

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