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Measuring science–technology interaction using rare inventor–author names

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  • Boyack, Kevin W.
  • Klavans, Richard

Abstract

The relationship between science and technology has been extensively studied from both theoretical and quantitative perspectives. Quantitative studies typically use patents as proxy for technology and scientific papers as proxy for science, and investigate the relationship between the two. Most such studies have been limited to a single discipline or country. In this paper, we investigate science–technology interaction over a broad range of science and technology by identifying and validating a set of 18,251 inventor–authors through matching of rare names obtained from paper and patent data. These inventor–authors are listed as inventors on nearly 56,000 US patents between 2002 and 2006. Analysis of the distribution of these patents over classes shows that this 6.7% sample is a suitable sample for further analysis. In addition, a map of 290 IPC patent subclasses was created, showing the relationship between patent classes and industries as well as the distribution of patent classes with high science orientation and low science orientation.

Suggested Citation

  • Boyack, Kevin W. & Klavans, Richard, 2008. "Measuring science–technology interaction using rare inventor–author names," Journal of Informetrics, Elsevier, vol. 2(3), pages 173-182.
    • Handle: RePEc:eee:infome:v:2:y:2008:i:3:p:173-182
    DOI: 10.1016/j.joi.2008.03.001
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    References listed on IDEAS

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    4. Mariia Shkolnykova, 2021. "Who shapes plant biotechnology in Germany? Joint analysis of the evolution of co-authors’ and co-inventors’ networks," Review of Evolutionary Political Economy, Springer, vol. 2(1), pages 27-54, April.
    5. Sung, Hui-Yun & Wang, Chun-Chieh & Huang, Mu-Hsuan & Chen, Dar-Zen, 2015. "Measuring science-based science linkage and non-science-based linkage of patents through non-patent references," Journal of Informetrics, Elsevier, vol. 9(3), pages 488-498.
    6. Loet Leydesdorff & Lutz Bornmann, 2012. "Mapping (USPTO) patent data using overlays to Google Maps," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 63(7), pages 1442-1458, July.
    7. Loet Leydesdorff & Daniele Rotolo & Ismael Rafols, 2012. "Bibliometric perspectives on medical innovation using the medical subject Headings of PubMed," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 63(11), pages 2239-2253, November.
    8. Zhang, Yi & Shang, Lining & Huang, Lu & Porter, Alan L. & Zhang, Guangquan & Lu, Jie & Zhu, Donghua, 2016. "A hybrid similarity measure method for patent portfolio analysis," Journal of Informetrics, Elsevier, vol. 10(4), pages 1108-1130.
    9. Wang, Gangbo & Guan, Jiancheng, 2010. "The role of patenting activity for scientific research: A study of academic inventors from China's nanotechnology," Journal of Informetrics, Elsevier, vol. 4(3), pages 338-350.
    10. Hain, Daniel S. & Jurowetzki, Roman & Buchmann, Tobias & Wolf, Patrick, 2022. "A text-embedding-based approach to measuring patent-to-patent technological similarity," Technological Forecasting and Social Change, Elsevier, vol. 177(C).
    11. Ba, Zhichao & Liang, Zhentao, 2021. "A novel approach to measuring science-technology linkage: From the perspective of knowledge network coupling," Journal of Informetrics, Elsevier, vol. 15(3).
    12. Byungun Yoon & Sungjoo Lee & Gwanghee Lee, 2010. "Development and application of a keyword-based knowledge map for effective R&D planning," Scientometrics, Springer;Akadémiai Kiadó, vol. 85(3), pages 803-820, December.
    13. Xu, Haiyun & Yue, Zenghui & Pang, Hongshen & Elahi, Ehsan & Li, Jing & Wang, Lu, 2022. "Integrative model for discovering linked topics in science and technology," Journal of Informetrics, Elsevier, vol. 16(2).
    14. Loet Leydesdorff & Duncan Kushnir & Ismael Rafols, 2014. "Interactive overlay maps for US patent (USPTO) data based on International Patent Classification (IPC)," Scientometrics, Springer;Akadémiai Kiadó, vol. 98(3), pages 1583-1599, March.
    15. Guijie Zhang & Luning Liu & Fangfang Wei, 2019. "Key nodes mining in the inventor–author knowledge diffusion network," Scientometrics, Springer;Akadémiai Kiadó, vol. 118(3), pages 721-735, March.
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    17. Quirin, Arnaud & Cordón, Oscar & Vargas-Quesada, Benjamín & de Moya-Anegón, Félix, 2010. "Graph-based data mining: A new tool for the analysis and comparison of scientific domains represented as scientograms," Journal of Informetrics, Elsevier, vol. 4(3), pages 291-312.
    18. Kevin W. Boyack, 2017. "Thesaurus-based methods for mapping contents of publication sets," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 1141-1155, May.
    19. Xiaoling Sun & Kun Ding, 2018. "Identifying and tracking scientific and technological knowledge memes from citation networks of publications and patents," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(3), pages 1735-1748, September.
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    21. Anthony F. J. Raan, 2017. "Sleeping beauties cited in patents: Is there also a dormitory of inventions?," Scientometrics, Springer;Akadémiai Kiadó, vol. 110(3), pages 1123-1156, March.

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