Significance, challenges, and policy recommendations for strengthening database development to support AI for Science in China

Authors

Kaihua CHEN, School of Public Policy and Management, University of Chinese Academy of Sciences, Beijing 100049, ChinaFollow
Hongxin LIU, School of Public Policy and Management, University of Chinese Academy of Sciences, Beijing 100049, China
Rui GUO, School of Economics, Beijing Institute of Technology, Beijing 100081, ChinaFollow

Keywords

AI for Science (AI4S), scientific database, development autonomy, open source and open innovation, standardized management

Document Type

Policy & Management Research

Abstract

With the rapid emergence of research intelligence driven by big data and artificial intelligence (AI), high-quality, openly shared scientific databases have become a strategic focal point for scientific innovation and enhancing technological competitiveness. Major countries around the world are increasingly recognizing the foundational role of scientific databases in advancing basic research. While continuously strengthening their own scientific data infrastructure through a series of initiatives, they have simultaneously imposed restrictions and suppression on the development of AI technologies in China, including those involving research data. Against this backdrop, building an autonomous and controllable scientific data ecosystem to support research intelligence is of great significance for unlocking the potential of China’s scientific data, ensuring national research security, and achieving a leap in independent innovation capacity. This study reviews international experiences in constructing databases that support research intelligence, analyzes the current status and challenges of scientific database development in China—including mismatches between the quantity and quality of data storage, insufficient openness and sharing of databases, inadequate coordination within the policy support system, and an underdeveloped database application ecosystem. Based on these findings, the study proposes strategies for strengthening China’s scientific data ecosystem, including enhancing top-level design and government support, promoting open-source and standardized management, fostering diverse collaborations, and facilitating integration with industrial applications.

First page

194

Last Page

204

Language

Chinese

Publisher

Bulletin of Chinese Academy of Sciences

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Recommended Citation

CHEN, Kaihua; LIU, Hongxin; and GUO, Rui (2026) "Significance, challenges, and policy recommendations for strengthening database development to support AI for Science in China," Bulletin of Chinese Academy of Sciences (Chinese Version): Vol. 41 : Iss. 1 , Article 17.
DOI: https://doi.org/10.3724/j.issn.1000-3045.20250423004
Available at: https://bulletinofcas.researchcommons.org/journal/vol41/iss1/17