Technical standards and practices

Article IV of the Recommendation recommends that

“Adherents take measures to promote, foster, and where appropriate, require compliance with technical standards and practices that make research data and other research-relevant digital objects from public funding findable, accessible, interoperable and re-usable.”

To enhance the findability, accessibility, interoperability, and reusability (FAIR) of publicly funded research data, it is essential to establish robust, internationally aligned technical standards and practices.

• Governments and funding agencies should mandate the use of open repositories, persistent identifiers (PIDs), and standardised metadata schemas such as OpenAIRE and DataCite, while promoting discipline-specific metadata supported by research communities.

• Investments should be made in sustainable, FAIR-compliant infrastructures, including repositories and secure data centers, ensuring appropriate access levels for sensitive data.

• To achieve interoperability, repositories must adopt common APIs, metadata exchange protocols, and semantic frameworks, while aligning national policies with global infrastructures like European Open Science Cloud (EOSC) and LA Referencia.

• Legal interoperability should be supported through standardised rights statements and open licenses, ensuring cross-border data sharing.

• Policymakers should fund and maintain technical standards, organise training on metadata and open licensing, and implement compliance checks in funding applications.

• Additionally, fostering community engagement, establishing feedback mechanisms, and continuously refining data-sharing policies in line with evolving global standards will ensure the long-term sustainability and effectiveness of these initiatives.

Implementing persistent identifiers (PIDs) and standardised metadata for research data under Article IV: Technical Standards and Practices faces several interconnected challenges.

• The absence of common or discipline-specific metadata schemas and the diversity of requirements across scientific fields complicate the development of interoperable, FAIR-compliant infrastructures.

• Establishing and maintaining these standards demands significant financial resources, technical expertise, and long-term funding, which are often inconsistent or insufficient. 

• Researchers face barriers in adopting FAIR principles due to complex tools, additional compliance efforts, and limited incentives.

• Moreover, achieving interoperability across institutions and countries requires harmonised technical standards, extensive coordination, and sustained international collaboration.

• Ethical, legal, and technical complexities in managing sensitive data, coupled with unclear ownership and licensing frameworks, further hinder data reuse and sharing.

• Lack of robust data discovery tools and the risk of fragmented, overly discipline-specific standards threaten the findability and broad accessibility of research data, underscoring the need for cohesive governance and community-driven engagement.

Technical standards and practices pillar can be broken down into the following provisions. 

Foster improved findability of research data and other research-relevant digital objects from public funding, for example, by assigning unique digital persistent identifiers and publishing descriptive metadata. 

Develop infrastructure and services to facilitate the accessibility of research data and other research-relevant digital objects from public funding within and across scientific domains and disciplines.  

Promote interoperability by requiring the use of semantic (including ontologies and scientific terminology), legal (rights of use), and technical (such as machine readability) standards as appropriate. 

Take steps to make research data and other research-relevant digital objects from public funding understandable and re-usable in the long term, including through the provision of high quality human-readable, machine-actionable, and open metadata and adequately maintained and supported bespoke algorithms, code, software, and workflows essential for re-use of data as free and open source.

Support the development, maintenance, adoption, dissemination, and implementation of technical standards that are open, freely accessible, and internationally agreed to the greatest possible extent.