Trace on both sides: a two-step text mining method to identify academic inventors’ patent–paper pairs

Yuhang Wang, Lei Pei, Jianjun Sun and Lele Kang ()
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Yuhang Wang: Nanjing University, Laboratory of Data Intelligence and Interdisciplinary Innovation
Lei Pei: Nanjing University, Laboratory of Data Intelligence and Interdisciplinary Innovation
Jianjun Sun: Nanjing University, Laboratory of Data Intelligence and Interdisciplinary Innovation
Lele Kang: Nanjing University, Laboratory of Data Intelligence and Interdisciplinary Innovation

Scientometrics, 2025, vol. 130, issue 2, No 14, 833-860

Abstract: Abstract The convergence of knowledge in scientific discovery and technological development is one of the most concerning issues in the studies of technology innovation. However, due to the challenges in constructing the appropriate datasets, discussions surrounding the interaction between science and technology pay limited attention to understanding the origin and impact of the patent–paper pairs (PPP) that denote the similar finding. This paper develops a two-step method to combine the patent data in PATSTAT and paper data in Scopus to identify the PPP from 1980 to 2022. Our study investigates 67,579 academic inventors who are involved with 3,045,001 papers (of which 2,236,515 have corresponding patents) and 506,820 patents (of which 292,904 have corresponding papers) and then creates the most extensive database with 14,137,072 PPP. Our dataset reveals that there is rapid development of PPP worldwide since the year 2000. Furthermore, there has been a significant reduction in the diffusion time from papers to patents. Papers and patents in dual disclosure exhibit significantly higher level of academic and technical impact. The constructed dataset is compatible with current scientific and patent datasets, accelerating and expanding its utility as a data source for studying the relationship between industrial development and academic discovery.

Keywords: Patent–paper pairs; Academic inventor; Knowledge convergence; Knowledge disclosure; Patent dataset (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s11192-024-05207-9

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