China’s Data Express: A Strategic Bet on AI-Ready Scientific Data

When China launched Data Express (《数据快报（英文）》) on June 23, 2026, it was easy to see it as just another academic journal. But the ambition behind the country’s first English-language data journal runs far deeper — it is a calculated move to position China at the center of a global transformation in how scientific data is produced, shared, and consumed in the age of artificial intelligence.

Data as Fuel for the AI Era

The launch event at the Chinese Academy of Sciences (CAS) in Beijing made the strategic calculus explicit. CAS academician Yu Guirui, the journal’s editor-in-chief, framed the initiative in terms that left little room for ambiguity: “Future competition in artificial intelligence is essentially competition for high-quality data resources,” he told Xinhua News Agency. “No matter how advanced an AI model is, without large-scale, high-standard data as ‘fuel,’ it cannot produce truly reliable scientific discoveries.”

This is not a passive observation. Data Express is engineered from the ground up to serve as a pipeline for what the journal calls “AI-Ready” datasets — standardized, machine-readable data that can directly train machine learning models without the costly preprocessing that currently bottlenecks AI-driven research.

The Data Publishing Gap

The journal addresses a problem that has long frustrated Chinese scientists. As Yu explained to Guangming Daily, “A vast amount of high-quality scientific data obtained at great cost by our country can be stored but cannot be published; or if published, lacks influence. Behind this is the absence of a publishing platform that can represent the national level and has international discourse power.”

Data Express fills that void. Unlike traditional research articles that present conclusions drawn from data, data papers focus on describing the data itself — its collection methodology, processing pipeline, and validation. As Yu put it, “Traditional papers answer ‘what was discovered,’ while data answers ‘what we actually observed.’”

A Journal Cluster, Not a Solo Effort

Data Express is the flagship of a far more ambitious plan. According to the CAS official website, the academy intends to build a “1 flagship + 19 specialized journals” data journal cluster in 2026, covering mathematics and physics, ecology and environment, marine and atmospheric science, engineering and technology, life and health, and modern agriculture.

Sun Degang, Party Secretary of the CAS Computer Network Information Center, described this as representing “two leaps” — from “single journal exploration to cluster leadership,” and from “domestic pioneering exploration to international collaborative leadership.” The cluster will be backed by infrastructure CAS has spent a decade building: SciEngine (a publishing platform), ScienceDB (a data repository recognized by UNESCO as a global open science best practice), CSTR (a resource identifier system), and DarXiv (a data preprint platform).

Built on a Decade of Standards

China is not new to data journals. CAS launched the country’s first Chinese-language data journal, China Scientific Data, in 2015. What distinguishes Data Express is the institutional scaffolding beneath it. Over the past decade, CAS led the development of national standards for scientific data citation (GB/T35294-2017), data paper publishing metadata (GB/T42813-2023), and data paper writing rules (GB/T7713.4-2025).

This groundwork gives Data Express a structural advantage. The journal does not need to invent its workflows from scratch — it inherits a mature standards ecosystem that few new publications can claim.

Implications for Global Science

The launch carries implications that extend well beyond academic publishing:

• Scientific data sovereignty: By creating its own English-language platform, China reduces dependence on Western journals and repositories for disseminating its scientific data.
• Standard-setting: China is positioning itself to influence how data papers are structured, reviewed, and cited globally.
• AI competition: Control over high-quality training data is increasingly recognized as a form of scientific power. Data Express is infrastructure for that competition.

As Yu told People’s Daily, “Science knows no borders, and data circulation and collaboration require global sharing. Choosing to launch an English-language journal is to let China’s high-value data directly step onto the international stage.”

The Road Ahead

Data Express faces genuine challenges. It will compete with established international platforms such as Nature Scientific Data, Dryad, and Zenodo. International researchers may approach a Chinese-operated platform with caution amid ongoing geopolitical tensions. And the journal must demonstrate that its peer-review processes and data standards meet the highest international benchmarks.

Yet the strategic direction is clear. As Yu framed it, “If the past century was recorded by papers documenting the history of scientific discovery, then the next century will be recorded by both data and papers together.” Data Express is China’s infrastructure bet on that future — and it is designed to ensure that Chinese data shapes the coming century of discovery.