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Visualization of patents and papers in terahertz technology: a comparative study

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  • Guifeng Liu

    (Jiangsu University)

Abstract

Terahertz technology is one of the most promising research areas in the 21st century. In this work, we intend to compare the research status quo on terahertz technology between 1990 and 2010 using knowledge domain visualization techniques. Our data consists of 633 patents retrieved from Aureka management platform and 10,344 journal articles indexed in the ISI web of knowledge. Our analysis is a combination of two information visualization tools for analysis, Aureka and CiteSpace. Aureka is allowed for the analysis of patents filed/granted each year, priority country, inventors, assignees, citation counting, and cluster analysis, while networks of co-authors, countries, institutions, document co-citation networks and document co-citation clusters, are performed by CiteSpace. This research provides a comprehensive domain visualization map of innovation and knowledge in the area of terahertz technology. Our result shows that Aureka and CiteSpace are two promising visualization approaches to analyze patents and papers in any given field.

Suggested Citation

  • Guifeng Liu, 2013. "Visualization of patents and papers in terahertz technology: a comparative study," Scientometrics, Springer;Akadémiai Kiadó, vol. 94(3), pages 1037-1056, March.
    • Handle: RePEc:spr:scient:v:94:y:2013:i:3:d:10.1007_s11192-012-0782-x
    DOI: 10.1007/s11192-012-0782-x
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    References listed on IDEAS

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    Cited by:

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    2. Shu-Hao Chang, 2018. "A pilot study on the connection between scientific fields and patent classification systems," Scientometrics, Springer;Akadémiai Kiadó, vol. 114(3), pages 951-970, March.
    3. Xi Yang & Xiang Yu & Xin Liu, 2018. "Obtaining a Sustainable Competitive Advantage from Patent Information: A Patent Analysis of the Graphene Industry," Sustainability, MDPI, vol. 10(12), pages 1-25, December.
    4. Navonil Mustafee & Korina Katsaliaki & Paul Fishwick, 2014. "Exploring the modelling and simulation knowledge base through journal co-citation analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 98(3), pages 2145-2159, March.
    5. Jia-Yen Huang & Hung-Tu Hsu, 2017. "Technology–function matrix based network analysis of cloud computing," Scientometrics, Springer;Akadémiai Kiadó, vol. 113(1), pages 17-44, October.
    6. Chengliang Liu & Qinchang Gui, 2016. "Mapping intellectual structures and dynamics of transport geography research: a scientometric overview from 1982 to 2014," Scientometrics, Springer;Akadémiai Kiadó, vol. 109(1), pages 159-184, October.
    7. Shao-jie Zhang & Peng-hui Lyu & Yan Yan, 2015. "Global geographical and scientometric analysis of tourism-themed research," Scientometrics, Springer;Akadémiai Kiadó, vol. 105(1), pages 385-401, October.
    8. Ming Chen & Jiao Wu, 2023. "State ownership may not be bad: Based on bibliometric research (2002–2021)," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 44(2), pages 1285-1304, March.

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