Responsibility, ownership and stewardship

Article V of the Recommendation recommends that

“Adherents take measures to ensure a clear delineation and allocation of responsibility, ownership, and stewardship for access to publicly funded research data and other research-relevant digital objects from public funding across the research data ecosystem, while also tailoring and implementing licensing and other management of intellectual property rights to optimise scientific discovery and innovation and protect research data and digital object producers’ rights.” 

Policymakers should adopt a bottom-up approach that aligns with researchers’ needs and best practices to ensure effective research data and software management.

This includes requiring all research projects to submit Data Management Plans (DMPs), promoting awareness through policies and training, and formally recognising data and software management skills in academic careers. Open access provisions in public-private partnerships should be clearly defined to balance commercial interests with public accessibility, including the development of data trustee models and anti-shelving clauses.

To enable responsible data reuse, including for artificial intelligence (AI) and data-mining applications, data should be prepared for machine-readability and deposited in trusted repositories under open licenses, with appropriate exceptions.

Clear guidelines on rights and licensing should be integrated into metadata standards, ensuring transparency and compliance. Policymakers should mandate open licenses for publicly funded research outputs while incentivising adoption through training, compliance monitoring, and funding advantages.

Effective research data and software management faces multiple challenges, including a lack of awareness, incentives, and standardised policies across disciplines. The shortage of training, infrastructure, and enforcement mechanisms further hinders adoption, while skills in data stewardship remain undervalued in academic evaluations.

In public-private partnerships, balancing openness with commercial and legal constraints, such as intellectual property rights and data privacy, complicates data sharing, requiring clear governance models.

The interoperability and machine-readability of research data for artificial intelligence (AI) and data-mining applications demand significant infrastructure and alignment efforts. Additionally, the inconsistent inclusion of rights and licensing metadata, coupled with the complexity of enforcing open licenses, poses barriers to accessibility and reusability. Addressing these hurdles requires strategic leadership, harmonised policies, and sustained investment in training, infrastructure, and compliance mechanisms.

Responsibility, ownership and stewardship pillar can be broken down into the following provisions. 

Foster, and require where appropriate, the adoption of good practice for research data and software management across the research system and work with communities of researchers, institutions, repositories, funders, and other stakeholders to support researchers in adopting coherent practices for management of research data and software. 

Promote access to research data and other research-relevant digital objects resulting from public-private partnerships in ways that helps ensure data collected with public funds is as open as possible while recognizing and protecting legal rights and legitimate interests of stakeholders, including private-sector partners. 

Support scientific advancement by taking the steps where necessary to enable new uses of research data and other research-relevant digital objects from public funding, such as for artificial intelligence and text- and data-mining techniques. 

Promote, and require where appropriate, the inclusion of information about rights and licensing in the metadata of all research data and other research-relevant digital objects from public funding as part of the implementation of Research Data Management principles. 

Encourage the widest use of open licences, where these are appropriate.