Data and AI for science: Key considerations

The first section introduces the foundational concepts and discusses the advantages and challenges of making scientific data AI-ready.

The second section examines the key considerations for data readiness for AI, and conversely, AI to curate data. We build on data standards while discussing AI-specific considerations such as machine-readability and bias mitigation, while highlighting ethical and environmental considerations around data readiness for AI in science.

The third section discusses data readiness within the framework of Open Science, presents two case studies that illustrate how Open Science practices can support AI-readiness for scientific research.

Recommendations

• Convergence to existing data frameworks and standards, for example, FAIR-R and Croissant, should be used by scientists and data stewards.
• Data governance structures should go beyond technical standards to promote equity, access to compute resources, and capacity-building.
• Investment in data infrastructure and skills development is a prerequisite for efficient and competitive use of AI in science.
• Recognition of data stewardship careers in science, and incentives to encourage these skills, is a cornerstone implementation pathway of the above investment.

Data and AI for science: Key considerations

September 2025

DOI: 10.24948/2025.11

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This work was carried out with the aid of a grant from the International Development Research Centre (IDRC), Ottawa, Canada. The views expressed herein do not necessarily represent those of IDRC or its Board of Governors.