China's Premier Research Body Unveils Ambitious Data Publishing Initiative
The Chinese Academy of Sciences has taken a significant step forward in scientific publishing with the launch of Data Express, its first English-language data journal. This open-access publication serves as the flagship for a coordinated cluster of 20 specialized titles designed to elevate the role of scientific data in research worldwide.
Launched in Beijing, the initiative underscores China's commitment to positioning scientific data as a strategic national resource, particularly in the age of artificial intelligence. The cluster includes one comprehensive journal and 19 discipline-specific publications spanning fields such as artificial intelligence corpus research, ecosystem science, ocean science, lightning data, biomedicine, materials science, and agriculture.
Strategic Alignment with National Priorities
The rollout aligns closely with China's broader efforts to build world-class scientific journals and strengthen data sovereignty. Officials from the Chinese Academy of Sciences emphasized that data journals provide a formal mechanism for sharing high-value datasets generated at large-scale facilities and field stations, ensuring contributors receive proper academic credit.
Zhang Yun, deputy director of the CAS Bureau of Basic Capacity for Science and Technology, highlighted plans to integrate data papers into professional promotion and degree-awarding evaluations. This recognition of data contributions as formal academic outputs marks a shift toward valuing the foundational elements of modern research alongside traditional articles.
Addressing Global Challenges in Data Sharing
Editor-in-chief Yu Guirui, an ecologist and CAS member, framed the launch as a direct response to the global challenge of making scientific data findable, accessible, interoperable, and reusable under the FAIR principles. Data Express aims to serve as a Chinese hub for international scientific data exchange, facilitating barrier-free collaboration.
Sun Degang, Party secretary of the CAS Computer Network Information Center, noted that data journals act as a crucial lever for encouraging open data sharing, improving governance standards, and supporting new research paradigms powered by artificial intelligence.
Integration with National Science Data Infrastructure
The cluster's structure draws directly from China's national science data center system. Each of the 19 discipline-specific journals corresponds to established sub-centers, creating an immediate pipeline of high-quality submissions. This integrated approach ensures consistent standards and leverages existing institutional strengths across the academy's network of institutes and facilities.
Zhang Fan, deputy general manager of China Science Publishing & Media, whose SciEngine platform hosts the cluster, explained that the initiative also tackles the longstanding issue of Chinese research data and publications residing primarily on foreign platforms. The domestically developed SciEngine system connects with major international databases while maintaining data sovereignty.
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Embracing Comprehensive Research Outputs
Unlike conventional journals that often prioritize positive findings, the new data journals explicitly welcome well-documented negative or null results. This approach proves especially valuable in fields like materials science, where such data helps researchers avoid redundant experiments and supplies high-quality training material for AI models.
The platform's design supports the growing emphasis on reproducibility and comprehensive data documentation, aligning with international trends in responsible research practices.
Implications for Chinese Researchers and Institutions
For academics at CAS institutes and partner universities, the new journals offer domestic venues that meet global visibility standards while addressing evaluation concerns. Chinese scholars already contribute nearly 40 percent of the world's high-quality scientific papers, positioning the cluster for strong international competitiveness from the outset.
The initiative supports the national strategy to reduce reliance on external platforms and fosters greater control over the data that increasingly drives AI-driven discoveries across disciplines.
Building on Prior Domestic Efforts
China's progress in data publishing builds on earlier milestones, including the 2016 launch of the Chinese-language China Scientific Data journal. The current English-language cluster represents a substantial expansion, moving from a single title to a comprehensive ecosystem tailored to multiple research domains.
Global data journals such as Elsevier's Data in Brief and Springer's Scientific Data emerged around 2014, and China's coordinated rollout places it competitively alongside these established international efforts.
Future Outlook and Institutional Support
CAS officials expressed optimism that the clustered approach will accelerate China's leadership in this emerging publishing niche. By anchoring the journals in the national data infrastructure and providing clear pathways for recognition in career evaluations, the academy aims to encourage widespread participation from researchers, engineers, and technicians.
The SciEngine platform's connections to databases including Web of Science and Scopus ensure that contributions gain international exposure while remaining rooted in domestic systems.
Broader Context in Chinese Scientific Publishing
This development occurs amid ongoing reforms in research evaluation and publishing practices across China. The focus on data as a distinct scholarly output complements efforts to promote high-quality, domestically supported journals and address challenges in open-access models.
University administrators and research leaders will likely monitor how these journals influence hiring, promotion, and funding decisions in the coming years, particularly as data-intensive AI research continues to expand.
