China has launched its first English-language academic journal dedicated to publishing data papers, in a move aimed at strengthening international scientific data exchange and supporting the growing role of data in research and discovery.
According to a report by the China Daily, the new journal, Data Express, was officially launched at a conference in Beijing hosted by the Chinese Academy of Sciences (CAS), which oversees the publication.
CAS said the journal is intended to establish a “China hub” for global scientific data exchange and help address the challenge of making valuable research data more discoverable, shareable and reusable.
The journal’s editor-in-chief, Yu Guirui, an academician of CAS, described the publication as a response to a “historic shift” in scientific research. He noted that data has become a key foundation for AI-driven discovery and innovation and said Data Express would help fill a gap in China’s scientific publishing landscape by providing a dedicated outlet for data publications.
The launch forms part of a broader strategy by CAS to expand China’s research data publishing infrastructure. Within the next year, the academy plans to introduce additional specialist data journals covering disciplines including physics, ecology, biomedicine and engineering. Together, these publications will form a Data Express journal cluster designed to provide dedicated publication venues for datasets and data-driven research outputs across multiple fields.
According to CAS, the initiative signals a shift in China’s approach to scientific data sharing, moving from what it described as “individual exploration” towards greater international collaboration and leadership in global scientific data exchange.
The development comes as publishers, funders and research organisations worldwide place increasing emphasis on open science, research transparency and the publication of datasets as citable scholarly outputs. Dedicated data journals have become an important mechanism for improving data discoverability, reproducibility and reuse, particularly as artificial intelligence and data-intensive research continue to expand.
