Scientometrics
Volume/Issue:
Volume 89: Issue 1
Mixed-indicators model for identifying emerging research areas
Authors:
Hanning Guo WISE Lab, Dalian University of Technology, Dalian, China
Cyberinfrastructure for Network Science Center, School of Library and Information Science, Indiana University, Bloomington, IN, USA scbweing@indiana.edukaty@indiana.edu

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,
Scott Weingart Cyberinfrastructure for Network Science Center, School of Library and Information Science, Indiana University, Bloomington, IN, USA scbweing@indiana.edukaty@indiana.edu

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, and
Katy Börner Cyberinfrastructure for Network Science Center, School of Library and Information Science, Indiana University, Bloomington, IN, USA scbweing@indiana.edukaty@indiana.edu

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Pages:
421–435
Online Publication Date:
21 Jun 2011
Publication Date:
01 Oct 2011
Article Category:
Research Article
DOI:
https://doi.org/10.1007/s11192-011-0433-7
Keywords:
Burst detection; Prediction; Emerging trend; Temporal dynamics; Science of science (Sci2) tool
Restricted access

Abstract

This study presents a mixed model that combines different indicators to describe and predict key structural and dynamic features of emerging research areas. Three indicators are combined: sudden increases in the frequency of specific words; the number and speed by which new authors are attracted to an emerging research area, and changes in the interdisciplinarity of cited references. The mixed model is applied to four emerging research areas: RNAi, Nano, h-Index, and Impact Factor research using papers published in the Proceedings of the National Academy of Sciences of the United States of America (1982–2009) and in Scientometrics (1978–2009). Results are compared in terms of strengths and temporal dynamics. Results show that the indicators are indicative of emerging areas and they exhibit interesting temporal correlations: new authors enter the area first, then the interdisciplinarity of paper references increases, then word bursts occur. All workflows are reported in a manner that supports replication and extension by others.

  • Adamson, I 1992 Access and retrieval of information as coordinates of scientific development and achievement in Nigeria. Scientometrics 23 1 191–199 .

  • Bettencourt, L, Kaiser, D, Kaur, J, Castillo-Chavez, C, Wojick, D 2008 Population modeling of the emergence and development of scientific fields. Scientometrics 75 3 495–518 .

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Boyack, KW 2004 Mapping knowledge domains: Characterizing PNAS. Proceedings of the National Academy of Sciences of the United States of America 101 Suppl 1 5192–5199 .

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Braun, T, Glänzel, W, Schubert, A 1987 One more version of the facts and figures on publication output and relative citation impact of 107 countries, 1978–1980. Scientometrics 11 1 9–15 .

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Braun, T, Glänzel, W, Schubert, A 1989 Assessing assessments of British science: Some facts and figures to accept or decline. Scientometrics 15 3 165–170 .

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Braun, T, Glänzel, W, Schubert, A 1989 The newest version of the facts and figures on publication output and relative citation impact: A collection of relational charts, 1981–1985. Scientometrics 15 1–2 13–20 .

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Braun, T, Schubert, A, Zsindely, S 1997 Nanoscience and nanotechnology on the balance. Scientometrics 38 2 321–325 .

  • Chen, C 2006 Citespace II: Detecting and visualizing emerging trends and transient patterns in scientific literature. Journal of the American Society for Information Science and Technology 57 3 359–377 .

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Chen, C, Chen, Y, Horowitz, M, Hou, H, Liu, Z, Pellegrino, D 2009 Towards an explanatory and computational theory of scientific discovery. Journal of Informetrics 3 3 191–209 .

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Chu, H 1992 Communication between Chinese and non-Chinese scientists in the discovery of high-TC superconductors: II. The informal perspective. Scientometrics 25 2 253–277 .

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Garfield, E., & Small, H. (1989). Identifying the change frontiers of science. In M. Kranzberg, Y. Elkana, & Z. Tadmor (eds.), Conference proceedings of innovation: At the crossroads between science and technology (pp. 51–65). Haifa, Israel: The S. Neaman Press.

    • Search Google Scholar
    • Export Citation

  • Goffman, W 1966 Mathematical approach to the spread of scientific ideas: The history of mast cell research. Nature 212 5061 452–499 .

  • Goffman, W 1971 A mathematical method for analyzing the growth of a scientific discipline. Journal of Association for Computing Machinery 18 2 173–185.

    • Search Google Scholar
    • Export Citation

  • Goffman, W, Harmon, G 1971 Mathematical approach to the prediction of scientific discovery. Nature 229 5280 103–104 .

  • Goffman, W, Newill, VA 1964 Generalization of epidemic theory: An application to the transmission of ideas. Nature 204 4955 225–228 .

  • Hirsch, JE 2005 An index to quantify an individual's scientific research output. Proceedings of the National Academy of Sciences of the USA 102 46 16569–16572 .

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Kajikawa, Y, Yoshikawaa, J, Takedaa, Y, Matsushima, K 2008 Tracking emerging technologies in energy research: Toward a roadmap for sustainable energy. Technological Forecasting and Social Change 75 6 771–782 .

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Kim, M-J 2001 A bibliometric analysis of physics publications in Korea, 1994–1998. Scientometrics 50 3 503–521 .

  • Klavans, R, Boyack, KW 2009 Toward a consensus map of science. Journal of the American Society for Information Science and Technology 60 3 455–476 .

  • Kleinberg, J 2003 Bursty and hierarchical structure in streams. Data Mining and Knowledge Discovery 7 4 373–397 .

  • Kuhn, T. S. (1970). The structure of scientific revolutions. Chicago: University of Chicago Press.

  • Lee, WH 2008 How to identify emerging research fields using scientometrics: An example in the field of information security. Scientometrics 76 3 1588–2861 .

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Lewison, G 1991 The scientific output of the EC's less favoured regions. Scientometrics 21 3 383–402 .

  • Leydesdorff, L, Schank, T 2008 Dynamic animations of journal maps: Indicators of structural changes and interdisciplinary developments. Journal of the American Society for Information Science and Technology 59 11 1810–1818 .

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Lucio-Arias, D, Leydesdorff, L 2007 Knowledge emergence in scientific communication: From “Fullerenes” to “nanotubes”. Scientometrics 70 3 603–632 .

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Mane, K, Börner, K 2004 Mapping topics and topic bursts in PNAS. Proceedings of the National Academy of Sciences of the United States of America (PNAS) 101 Suppl 1 5287–5290 .

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Menard, HW 1971 Science: Growth and change Harvard Univ Press Cambridge, MA.

  • Merton, RK 1968 The matthew effect in science: The reward and communication systems of science are considered. Science 159 3810 56–63 .

  • Porter, AL, Rafols, I 2009 Is science becoming more interdisciplinary? Measuring and mapping six research fields over time. Scientometrics 81 3 719–745 .

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Price, D. J. D. S. (1970). Citation measures of hard science, softscience, technology, and nonscience. In C. E. A. P. Nelson, (ed.), Communication among scientists and engineers (pp. 3–12): Heath Lexington Books, Massachusetts.

    • Search Google Scholar
    • Export Citation

  • Rao, CR 1982 Diversity: Its measurement, decomposition, apportionment and analysis. Sankhy: The Indian Journal of Statistics, Series A 44 1 1–22.

    • Search Google Scholar
    • Export Citation

  • Scharnhorst, A., & Garfield, E. (2010 in press). Tracing scientific influence. Dynamic of Socio-Economic System, 2(1).

  • Sci2 Team. (2009a). Science of Science (Sci2) Tool: Indiana University and SciTech Strategies, Inc. http://sci2.cns.iu.edu. Accessed 8 June 2010.

    • Search Google Scholar
    • Export Citation

  • Sci2 Team. (2009b). Stop word list. http://nwb.slis.indiana.edu/svn/nwb/trunk/plugins/preprocessing/edu.iu.nwb.preprocessing.text.normalization/src/edu/iu/nwb/preprocessing/text/normalization/stopwords.txt. Accessed 11 June 2010.

    • Search Google Scholar
    • Export Citation

  • Serenko, A, Bontis, N, Booker, L, Sadeddin, K, Hardie, T 2010 A scientometric analysis of knowledge management and intellectual capital academic literature (1994–2008). Journal of Knowledge Management 14 1 3–23 .

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Small, H 2006 Tracking and predicting growth areas in science. Scientometrics 63 3 595–610 .

  • Stirling, A 2007 A general framework for analysing diversity in science, technology and society. Journal of the Royal Society Interface 4 15 707–719 .

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Takeda, Y, Kajikawa, Y 2009 Optics: A bibliometric approach to detect emerging research domains and intellectual bases. Scientometrics 78 3 543–558 .

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Thomson Reuters (2010). Web of science. http://scientific.thomsonreuters.com/products/wos/. Accessed 8 June 2010.

  • Tsipouri, L 1991 Effects of EC R&D policy on Greece: Some thoughts in view of the stride programme. Scientometrics 21 3 403–416 .

  • AFJ Van Raan 2000 On growth, ageing, and fractal differentiation of science. Scientometrics 47 2 1588–2861.

  • Watts, RJ, Porter, AL 2003 R&D cluster quality measures and technology maturity. Technological Forecasting and Social Change 70 8 735–758 .

  • Weingart, S., Guo, H., Börner, K., Boyack, K. W., Linnemeier, M. W., & Duhon, R. J., et al. (2010). Science of Science (Sci2) Tool User Manual. http://sci2.wiki.cns.iu.edu. Accessed 28 Jan 2011.

    • Search Google Scholar
    • Export Citation

  • Zitt, M, Bassecoulard, E 2008 Challenges for scientometric indicators: Data de-mining, knowledge flows measurements and diversity issues. Ethics in science and environmental politics 8:49–60 .

    • Crossref
    • Search Google Scholar
    • Export Citation
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Cover Scientometrics
Scientometrics
Print ISSN:
0138-9130
Online ISSN:
1588-2861

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Scientometrics
Language English
Size B5
Year of
Foundation		 1978
Volumes
per Year		 1
Issues
per Year		 12
Founder Akadémiai Kiadó
Founder's
Address		 H-1117 Budapest, Hungary 1516 Budapest, PO Box 245.
Publisher Akadémiai Kiadó
Springer Nature Switzerland AG
Publisher's
Address		 H-1117 Budapest, Hungary 1516 Budapest, PO Box 245.
CH-6330 Cham, Switzerland Gewerbestrasse 11.
Responsible
Publisher		 Chief Executive Officer, Akadémiai Kiadó
ISSN 0138-9130 (Print)
ISSN 1588-2861 (Online)

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