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Rete-netzwerk-red: analyzing and visualizing scholarly networks using the Network Workbench Tool

Author

Listed:
  • Katy Börner

    (Indiana University)

  • Weixia Huang

    (Indiana University)

  • Micah Linnemeier

    (Indiana University)

  • Russell J. Duhon

    (Indiana University)

  • Patrick Phillips

    (Indiana University)

  • Nianli Ma

    (Indiana University)

  • Angela M. Zoss

    (Indiana University)

  • Hanning Guo

    (Indiana University)

  • Mark A. Price

    (Indiana University)

Abstract

The enormous increase in digital scholarly data and computing power combined with recent advances in text mining, linguistics, network science, and scientometrics make it possible to scientifically study the structure and evolution of science on a large scale. This paper discusses the challenges of this ‘BIG science of science’—also called ‘computational scientometrics’ research—in terms of data access, algorithm scalability, repeatability, as well as result communication and interpretation. It then introduces two infrastructures: (1) the Scholarly Database (SDB) ( http://sdb.slis.indiana.edu ), which provides free online access to 22 million scholarly records—papers, patents, and funding awards which can be cross-searched and downloaded as dumps, and (2) Scientometrics-relevant plug-ins of the open-source Network Workbench (NWB) Tool ( http://nwb.slis.indiana.edu ). The utility of these infrastructures is then exemplarily demonstrated in three studies: a comparison of the funding portfolios and co-investigator networks of different universities, an examination of paper-citation and co-author networks of major network science researchers, and an analysis of topic bursts in streams of text. The article concludes with a discussion of related work that aims to provide practically useful and theoretically grounded cyberinfrastructure in support of computational scientometrics research, education and practice.

Suggested Citation

  • Katy Börner & Weixia Huang & Micah Linnemeier & Russell J. Duhon & Patrick Phillips & Nianli Ma & Angela M. Zoss & Hanning Guo & Mark A. Price, 2010. "Rete-netzwerk-red: analyzing and visualizing scholarly networks using the Network Workbench Tool," Scientometrics, Springer;Akadémiai Kiadó, vol. 83(3), pages 863-876, June.
    • Handle: RePEc:spr:scient:v:83:y:2010:i:3:d:10.1007_s11192-009-0149-0
    DOI: 10.1007/s11192-009-0149-0
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    References listed on IDEAS

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    1. Chaomei Chen, 2006. "CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 57(3), pages 359-377, February.
    2. Lokman I. Meho & Kiduk Yang, 2007. "Impact of data sources on citation counts and rankings of LIS faculty: Web of science versus scopus and google scholar," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 58(13), pages 2105-2125, November.
    3. Gavin LaRowe & Sumeet Ambre & John Burgoon & Weimao Ke & Katy Börner, 2009. "The Scholarly Database and its utility for scientometrics research," Scientometrics, Springer;Akadémiai Kiadó, vol. 79(2), pages 219-234, May.
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    Cited by:

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    2. Juan Ruiz-Rosero & Gustavo Ramirez-Gonzalez & Jesus Viveros-Delgado, 2019. "Software survey: ScientoPy, a scientometric tool for topics trend analysis in scientific publications," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(2), pages 1165-1188, November.
    3. Chenjia Zhang & Yiping Fang & Xiujuan Chen & Tian Congshan, 2019. "Bibliometric Analysis of Trends in Global Sustainable Livelihood Research," Sustainability, MDPI, vol. 11(4), pages 1-28, February.
    4. Yuen-Hsien Tseng & Ming-Yueh Tsay, 2013. "Journal clustering of library and information science for subfield delineation using the bibliometric analysis toolkit: CATAR," Scientometrics, Springer;Akadémiai Kiadó, vol. 95(2), pages 503-528, May.
    5. Cynthia W. Cai & Martina K. Linnenluecke & Mauricio Marrone & Abhay K. Singh, 2019. "Machine Learning and Expert Judgement: Analyzing Emerging Topics in Accounting and Finance Research in the Asia–Pacific," Abacus, Accounting Foundation, University of Sydney, vol. 55(4), pages 709-733, December.
    6. Small, Henry & Boyack, Kevin W. & Klavans, Richard, 2014. "Identifying emerging topics in science and technology," Research Policy, Elsevier, vol. 43(8), pages 1450-1467.
    7. Feng Hu & Wei Liu & Sang-Bing Tsai & Junbin Gao & Ning Bin & Quan Chen, 2018. "An Empirical Study on Visualizing the Intellectual Structure and Hotspots of Big Data Research from a Sustainable Perspective," Sustainability, MDPI, vol. 10(3), pages 1-19, March.

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