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Bulletin of Chinese Academy of Sciences (Chinese Version)

Keywords

innovation map,patent-cited papers,basic research,highly cited papers,technological innovation

Document Type

Information & Observation

Abstract

For the first time, this study draws an Innovation Science Structure Map using the scientific structure mapping analysis method. It takes Essential Science Indicators (ESI) highly cited papers referenced in patents as the foundatial analyzing data, revealing the impact of leading-edge fundamental research on the development of technological innovation worldwide. It identifies the hotspot areas that are driving the advancement of technological innovation and compares the performance between China and the United States in these areas, providing robust data support for informed scientific decision-making. The analysis reveals that papers with a high number of patent references are primarily concentrated in emerging biotechnology and biomedical fields, as well as in new information technology, energy, and materials sectors that are likely to trigger disruptive technological innovations. There is a noticeable growth trend in China for papers with high citation counts referenced in patents, with a five-year cumulative growth exceeding 60%, but the total number is still less than half of that in the United States. The number of papers from China in many research hotspots closely associated with technological innovation is higher than that of the United States. Still, the count of papers referenced in patents is usually lower. The United States tends to fundamental research that influences technological innovation, leading more towards industrial transformation-related R&D activities, and involves a large number of enterprises.

First page

588

Last Page

600

Language

Chinese

Publisher

Bulletin of Chinese Academy of Sciences

References

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2 Jefferson R. Comment:Turning science into social outcomes. Nature, 2017, 548:S8.

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4 王小梅, 韩涛, 李国鹏, 等. 科学结构图谱 2017. 北京:科学出版社, 2017. Wang X M, Han T, Li G P, et al. Mapping Science Structure 2017. Beijing:Science Press, 2017. (in Chinese)

5 陈挺, 李国鹏, 王小梅. 优化科学知识图谱方法绘制全领域科学结构图谱. 图书情报工作, 2022, 66(21):107-119. Chen T, Li G P, Wang X M. Optimizing science knowledge mapping methods to map the global science structure graph. Library and Information Service. 2022, 66(21):107-119. (in Chinese)

6 Chen T, Li G P, Deng Q P, et al. Using network embedding to obtain a richer and more stable network layout for a large scale bibliometric network. Journal of Data and Information Science, 2021, 6(1):154-177.

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