Abstract:

This paper presents models of the development, management, evolution of national science data center systems in the United States, the United Kingdom， and China. Our methods include network research and literature analysis to analyze the construction process, management mode and evaluation methods of these science data center sys‐ tems. In the United States, national data centers, domain-level data centers, and resource-node data centers exist and share data in an orderly manner, forming a data flow model from“the capillary to the aortic convergence.”In the United Kingdom, national level and field research data centers form a data flow model with“several parallel aortas”, in which data is directly obtained from the national and domain-level data centers. In China, similar to the US convergence model, national science data centers are established in key areas throughout the country to stan‐ dardize science data management and plan and build science data centers in the regions. The regional data cen‐ ters are encouraged to submit data to national data centers, thereby promoting the flow of scientific and technological resources from the relevant fields to the national platforms for convergence and integration. Our paper also considers the adjustment list released by the National Science Data Centers of China in June 2019, discussing the ecological correlation in scientific data management of National science data center and its science data management model relative to the new model that China's science data management may face. It is argued that the National Scientific Data Centers will play an important role in promoting the development of big science in China and provide support for the development of science and technology in the era of big data.

Key words: scientific data, National Science Data Center, management model, evolution model, comparative research, scientific data management, data resource, data management