New uses of research data

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.

To enable new uses of research data, such as for AI and data-mining techniques, policymakers should emphasise responsible data management that aligns with the FAIR principles, ensuring data is reusable, as open as possible, and as closed as necessary.

This includes mandatory deposition of data in trusted repositories with open access unless exceptions apply. Developing the role of data stewards to assist in data re-use and combination can address technical and logistical challenges.

Policymakers should prioritise:

• Raising awareness of the economic and societal value of research data among public institutions.

• Preparing research data for machine-readability to support AI applications and automated processes, ensuring interoperability and adherence to community-approved standards.

• Promoting evidence-based policymaking by leveraging research data for designing, implementing, and validating policies, while also engaging citizens to foster innovative, science-driven solutions.

Supporting scientific advancement through new uses of research data involves several hurdles. 

• Interoperability challenges hinder the combination and re-use of existing datasets, requiring the adoption of community-approved standards and roles like data stewards to provide technical and logistical support. 
  • While the European Commission’s Horizon Europe framework and the FAIR principles mandate trusted repository deposition, ensuring compliance and enabling broad re-usability remains a challenge.

• Preparing data for machine-readability to facilitate AI and text- and data-mining techniques requires significant infrastructure development and alignment across disciplines. 
  • Programs like the Swiss Open Research Data Grants and efforts in fields like materials science, which unify data formats and analysis infrastructure, highlight the importance of consistent practices.

• Promoting the scientific merit of data re-use and raising awareness of its economic and societal value among stakeholders are critical. 
  • Knowledge Mobilization modules, as used by Social Sciences and Humanities Research Council (SSHRC), provide a model for targeting key audiences and assessing feasibility to enhance data re-use effectively.