Data and software management

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.

Policymakers should adopt a bottom-up approach that reflects researchers’ needs and best practices, ensuring coherent and effective research data and software management:

• Require all research projects, not just third-party funded ones, to submit Data Management Plans (DMPs) outlining strategies for managing data and software in compliance with best practices.

• Promote broader awareness of research data and software management through explicit policies, training programs, and infrastructure, targeting researchers across all levels of the research system.

• Develop and formally recognize data and software management skills as critical competencies in academic and public research careers.

• Encourage funders to establish clear criteria for funding allocation based on adherence to research data and software management standards.

• Include software explicitly in research policies and recognise it as a valuable research output in evaluation systems.

• Strengthen certification frameworks, integrating specific guidelines for research data and software management.

•  Many researchers lack awareness of best practices for research data and software management, and incentives for adopting these practices are often insufficient.

• The diverse requirements of different disciplines make it challenging to develop and enforce universal policies and standards.

• A shortage of training programs, infrastructure, and support staff (e.g., data stewards) hinders the widespread adoption of good practices.

• While some policies promote data and software management, enforcement mechanisms are often weak or absent, leading to inconsistent implementation.

• Skills and outputs related to data and software management are rarely recognised or rewarded in academic and research evaluation systems.

• The additional cost of implementing comprehensive data and software management practices, including certification, training, and infrastructure development, can be a barrier for T12nstitutions and funders.