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.