Accessibility of research data

This indicator assesses how effectively the organisation ensures secure, transparent and technically optimised access to research data. It evaluates whether datasets can be discovered and retrieved through trusted and sustainable channels, with appropriate controls for sensitive content. It also considers the use of automation, audit trails, and standards such as persistent identifiers and licences to enable responsible, long-term reuse.

Level 1 – No access standards are in place on the actual format and SOPs are not in place for access decision making

• Lack of structure: No formal standards or procedures exist for managing or accessing data generated on site or received from partners. Access decisions are informal, undocumented and handled on a case-by-case basis.
• No defined access formats: Data formats and channels for sharing or retrieving data are not standardised, thus limiting discoverability, reusability and oversight.

Impact: Data access is fragmented and unreliable. Sensitive data may be shared without adequate review, and open data may remain undiscoverable or unusable due to inconsistent formats and missing metadata.

Level 2 – Standard access channel is in place. The Data access requests have to be submitted

• Basic access process: A standard mechanism for submitting and managing data access requests is available. Advice exists, but is high-level, and detailed guidance on specific questions is difficult to locate.
• Repository guidance: The institution offers basic guidance or lists of trusted repositories such as Zenodo and Figshare, which support metadata standards, persistent identifiers, and open access protocols.

Impact: Data access becomes more predictable and compliant with minimal requirements. Efficiency, governance and decision-making remain largely manual.

Level 3 – Data use conditions are reviewed upon request. Full audit trail is in place for access

• Integration with journals and funders: Data access and repository workflows align with funder and journal requirements, including data citations and availability statements.
• Support upon request: Some staff members possess the expertise needed to support researchers and review data use conditions. Access to this support is not standardised and depends on individual initiative.

Impact: Data access decisions are transparent and traceable. Governance mechanisms support ethical sharing while maintaining accountability and compliance.

Level 4 – Data is easily accessible and retrieval is optimised for efficiency. Access is (semi-)automated

• Efficient and seamless access: Data accessibility is streamlined, with efficient retrieval supported by APIs, secure transfer channels and domain-standard search and discovery capabilities.
• Licensing and reuse: Researchers are encouraged to publish data under standard open licences such as CC0 or CC-BY for non-sensitive data, promoting reuse and interoperability.
• Repository guidance: Curated lists of trusted or domain-specific repositories, such as Zenodo, Figshare, GenBank, ICPSR and PANGAEA, are maintained. These repositories support persistent identifiers, metadata standards and open access protocols.
• Access governance for sensitive data: Data access committees are established to review access requests. SOPs define roles and evaluation criteria, and vocabularies such as DUO are used to align permitted uses with access decisions.
• Automation and APIs: Access to non-sensitive or approved datasets is facilitated through automated retrieval mechanisms. Authentication and authorisation may be federated through systems such as AAI, eduGAIN or Passport, and metadata is indexed to support advanced discovery.

Impact: Data accessibility is seamless, efficient and scalable. Researchers and authorised users can retrieve data quickly and securely, while automation and interoperability ensure compliance, reusability and long-term sustainability.