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§ LAW · DATA TERMS BATCH 2

Explanation of Common Terms in the Field of Data (Second Batch).

数据领域常用名词解释（第二批）

FILED UNDER · Data Economy Data Security

Promulgated by: National Data Administration.
Issued by the National Data Administration on March 29, 2025 by the Drafting Expert Team for Explanation of Terms in the Field of Data. Effective March 29, 2025.

DCC translation. The National Data Administration (国家数据局) released this second batch of standardized term explanations on March 29, 2025, continuing the consensus-building effort that began with the First Batch of December 30, 2024. The 20 terms in this batch focus on data property rights vocabulary, data trading institutions and market structure, the data industry and data-labeling sub-industry, trusted data spaces, data infrastructure, and computing-power scheduling and pooling. Translated against DCC’s bilingual glossary, with terminology aligned to the public-data property-rights registration documents and the First Batch translations where shared concepts appear.

Background

In order to build broad consensus, with the strong support of all walks of life, we have carefully studied and developed the Explanation of Common Terms in the Field of Data (Second Batch). We will continue to iteratively improve the term explanations in light of practical needs and development requirements, and welcome the continuous attention of the community.

— Drafting Expert Team for Explanation of Terms in the Field of Data, March 29, 2025

Annex: Explanation of Common Terms in the Field of Data (Second Batch)

1. 数据产权 (Data Property Rights). “Data Property Rights” refer to the property rights enjoyed by a rights-holder over specific data, including the Right to Hold Data, the Right to Use Data, the Right to Operate Data, and so on.

2. 数据产权登记 (Data Property Rights Registration). “Data Property Rights Registration” refers to the act of a Data Property Rights registration institution reviewing, in accordance with unified rules, the authenticity, compliance, and accuracy of the source, description, content, and other aspects of data, recording information such as the attribution of data rights, and issuing a registration certificate.

3. 数据持有权 (Right to Hold Data). “Right to Hold Data” refers to the right of a rights-holder to hold lawfully acquired data, either by itself or through another person it entrusts. Its purpose is to prevent others from illegally or in violation of regulations stealing, tampering with, leaking, or destroying data held by the rights-holder.

4. 数据使用权 (Right to Use Data). “Right to Use Data” refers to the right of a rights-holder to use data — through methods such as processing, aggregation, and analysis — to optimize production and operations, provide social services, form derived data, and the like. Generally speaking, the Right to Use Data is the right of a rights-holder to use data for internal use, on the premise of not providing the data externally.

5. 数据经营权 (Right to Operate Data). “Right to Operate Data” refers to the right of a rights-holder to provide data externally — for consideration or without consideration — through methods such as transfer, licensing, capital contribution, or the creation of security interests.

6. 衍生数据 (Derived data). “Derived data” refer to data formed when a data handler, in respect of data over which it enjoys the Right to Use Data, achieves substantive changes to the content, form, structure, or other aspects of the data — through methods such as the application of specialized knowledge for processing, model analysis, and extraction of key information — thereby significantly enhancing the value of the data, while protecting the legitimate rights and interests of all parties.

7. 企业数据 (Enterprise data). “Enterprise data” refer to data formed by enterprises in the course of production and operation activities, or lawfully acquired and held by enterprises.

8. 数据交易机构 (Data trading institution). “Data trading institution” refers to a specialized institution that provides data trading services to data suppliers and data demanders.

9. 数据场内交易 (On-exchange data trading). “On-exchange data trading” refers to the act of a data supplier and a data demander concluding a data transaction through a data trading institution.

10. 数据场外交易 (Off-exchange data trading). “Off-exchange data trading” refers to the act of a data supplier and a data demander concluding a data transaction without going through a data trading institution.

11. 数据交易撮合 (Data trading matching). “Data trading matching” refers to the act of helping a data supplier and a data demander conclude a data transaction.

12. 数据第三方专业服务机构 (Data third-party professional service institution). “Data third-party professional service institution” refers to a specialized organization that, in order to promote the compliant and efficient conduct of data trading activities, provides third-party services such as data integration, quality evaluation, data brokerage, compliance certification, security audit, data notarization, data insurance, data custody, asset evaluation, dispute mediation, risk assessment, talent training, and consulting services.

13. 数据产业 (Data industry). “Data industry” refers to the emerging industry formed by using modern information technology to develop products or services from data resources and to promote their circulation and application, including data collection and aggregation, computing and storage, circulation and trading, development and utilization, security governance, and the construction of data infrastructure.

14. 数据标注产业 (Data labeling industry). “Data labeling industry” refers to the emerging industry that processes data through screening, cleaning, classification, annotation, marking, quality inspection, and similar processing.

15. 数字产业集群 (Digital industry cluster). “Digital industry cluster” refers to a new form of industrial organization characterized mainly by being driven by data elements, empowered by digital technology, supported by digital platforms, developed through industrial integration, and built on a shared cluster ecosystem.

16. 可信数据空间 (Trusted data space). “Trusted data space” refers to a data circulation and utilization infrastructure that, based on consensus rules, connects multiple participating subjects to enable the sharing and joint use of data resources. It is the application ecosystem for the co-creation of data-element value, and an important carrier supporting the construction of a nationally integrated data market. A trusted data space must possess three core capabilities: trusted data control, resource interaction, and value co-creation.

17. 数据使用控制 (Data use control). “Data use control” refers to the use of technical means to exert control over the transmission, storage, use, and destruction of data — for example, by using smart-contract technology to translate the data-use-control intent of the data-rights subject into machine-readable smart-contract terms — thereby resolving the precondition issue of data controllability and enabling control over factors such as the time, location, subjects, conduct, and objects of the use of data assets.

18. 数据基础设施 (Data infrastructure). “Data infrastructure”, from the perspective of unlocking the value of data elements, refers to a new category of infrastructure that provides data collection, aggregation, transmission, processing, circulation, utilization, operation, and security services to society. It is an organic whole that integrates hardware, software, model algorithms, standards and specifications, mechanism design, and similar elements.

19. 算力调度 (Computing-power scheduling). “Computing-power scheduling” is, in essence, the scheduling of computing tasks. It matches computing-power resources to user business needs and dispatches the relevant business, data, and applications to the matched computing-power resource pool for computation, thereby achieving the rational utilization of computing resources.

20. 算力池化 (Computing-power pooling). “Computing-power pooling” refers to the unified registration and management of various heterogeneous and geographically dispersed computing-power resources and equipment — through key technologies such as computing-power virtualization and application containerization — so as to enable on-demand application and use of computing resources within large-scale clusters.

§ COMMENTARY

Briefs on this law.

5 briefs reference this law.

  • § 01 · DATA-PROPERTY-RIGHTS

    Data 'Parallel Property Rights' — They Can Confer Status, but Can't Secure Control

    Part four — and the synthesis — of Hong Yanqing's (洪延青, 网安寻路人) study notes on China's 'separation of three rights' data-property framework takes up 'parallel property rights' (数据平行财产权): how to allocate rights when the *same* data is held, used, and operated by *multiple* parties at once. Building on Xiong Bingwan and Zhuang Hongshan's 'one-data, multiple-rights' (一数数权) idea — data is non-rivalrous and copyable, so the same right over the same data can sit with several parties without excluding each other — Hong argues parallel property rights are best understood as *default rules* for incomplete-contract, collaborative-production settings: internally, parallel use is presumed; externally, operation is classified by data type (by-products each party may operate alone; purpose-built or fused data needs the others' consent); and parallel holders share a *joint defensive* interest against third parties. But the substance, he shows, falls back on derivative data — and here Xiong, Xu Ke (许可), and Shen Weixing (申卫星), despite different scenarios and tests, all tilt the derivative-data right to the *processor*, leaving the data contributor with contract/compensation/tort/PI remedies rather than ownership of the new product. DCC's read for overseas counsel: parallel property rights cut *attribution* uncertainty (who may use, operate, defend) but not *control* uncertainty (future use, detection, tracing, modelled value, third-party chains, ongoing compliance) — status, not control.

    data-property-rights · parallel-property-rights · derivative-data
  • § 02 · DATA-PROPERTY-RIGHTS

    Why Upstream Won't Operate Its Data — Control Degradation, Derivative Data, and Irreducible Uncertainty

    Part three of Hong Yanqing's (洪延青, 网安寻路人) study notes on China's 'separation of three rights' framework turns to the Right to Operate Data (数据经营权) — the right to provide data externally by transfer, licence, capital contribution, or pledge — and asks a question prior to 'what does operation transfer?': in real conditions, *will* an upstream party operate its data at all? His answer: yes, but narrowly. Control-dependent upstreams (platforms, holders of core user or irreplaceable industrial/training data) tend not to provide open, raw, autonomous access, and shift to controlled use or simply decline. The reason is structural. Once a downstream party is licensed to use data, the derivative data it produces is a *new object*: the upstream's *erga omnes* (对世) control over the raw data does not reach it, leaving the upstream — at most — a contractual claim against one counterparty. Hong then catalogues the uncertainties an upstream faces *ex ante*: some that attribution rules could touch but can't eliminate (qualification of the output, default ownership, good-faith of the processor, measurement of remedy), and some no rule can reach (combinatorial/unforeseeable value, undetectable misuse, the privity-and-insolvency chain, fusion and co-ownership, abstraction leakage into model parameters and learned skills, personal-information exposure, and counterparty hold-up). DCC's read for overseas counsel: this is the rigorous explanation of why Chinese data 'supply' is thin and why sandbox / privacy-computing structures dominate — defining a right does not supply the conditions to exercise it.

    data-property-rights · data-operation-right · data-economy
  • § 03 · DATA-PROPERTY-RIGHTS

    When the 'Right to Use Data' Goes External — Provision, Derivative Data, and the Erosion of Upstream Control

    Part two of Hong Yanqing's (洪延青, 网安寻路人) study notes on China's 'separation of three rights' data-property framework turns to the Right to Use Data (数据使用权). The official definition (国家数据局, Common Data Terms Batch 2) makes the use right an *internal* power — 'I use my own data' to process, aggregate, analyse, and form derivative data — exercised on the premise of *not* providing data externally. So 'granting a use right to a downstream party' is not the use right travelling outward; it is the upstream party exercising its **operation right** to license, while the downstream party acquires a use right. That externalisation flips the downstream's legal position from PIPL **entrusted processor** (委托处理) to **provision** (提供) or **joint processing** — triggering notice and *separate consent* for personal information, and the Network Data Security Regulation's contracting duties. And because a strong use right lets the downstream form **derivative data** (衍生数据) — models, scores, indices, labels — value migrates downstream even though the raw data stays upstream. DCC's read for overseas counsel: in China data deals the use right is real but never self-bounding; whether a partner will grant an open, autonomous use right depends on its business model (control-dependent vs monetisation), and the default structure you should expect is *controlled use* (sandbox, privacy computing, federated modelling), not a clean copy.

    data-property-rights · data-use-right · data-economy
  • § 04 · TOKENS

    Cold Water on 'Token Trading' — Wang Qinglan on the NDA's High-Quality Data Set Initiative

    In March 2026, the National Data Administration released the *Implementation Plan for Promoting High-Quality Industry Data Set Construction (Draft for Public Consultation)*, which explores a 'token (词元) based value system' and 'token trading as a new transaction mode' for high-quality data sets. The Chinese AI policy community immediately heralded the move as 'revolutionizing data trading.' Wang Qinglan pours cold water: token is a measuring unit, not a magic transformer. AI tokens are not crypto tokens. The bottleneck in China's data-element market isn't measurement — it's supply, rights clarity, compliance cost, and data silos.

    tokens · ai-training-data · data-trading
  • § 05 · DATA-ECONOMY

    What Is Actually Traded on China's Data Exchanges — A Bakery Metaphor

    Per the Shenzhen Provisional Measures for Data Trading Administration, four categories of object can be traded on a Chinese data exchange: data products, data services, data tools, and other regulator-approved objects. Wang Qinglan walks through what each means in plain language with a bakery metaphor — wheat (raw data) becomes flour (data resources) becomes cakes (data products); a baker is a data service; the oven is a data tool. The piece is useful precisely because it answers a question overseas teams rarely think to ask: what are the data exchanges actually selling?

    data-economy · data-trading · data-products
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