Mixed-indicators model for identifying emerging research areas
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.
KeywordsBurst detection Prediction Emerging trend Temporal dynamics Science of science (Sci2) tool
We would like to thank Joseph Biberstine and Russell J. Duhon for developing custom queries and code and appreciate the expert comments from the three anonymous reviewers. This work is funded by the James S. McDonnell Foundation and the National Institutes of Health under awards R21DA024259 and U24RR029822.
- 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.Google Scholar
- Kuhn, T. S. (1970). The structure of scientific revolutions. Chicago: University of Chicago Press.Google Scholar
- Menard, H. W. (1971). Science: Growth and change. Cambridge, MA: Harvard Univ Press.Google Scholar
- Price, D. J. D. S. (1970). Citation measures of hard science, softscience, technology, and nonscience. In C. E. A. P. Nelson, D. (Ed.), Communication among scientists and engineers (pp. 3–12): Heath Lexington Books, Massachusetts.Google Scholar
- Scharnhorst, A., & Garfield, E. (2010 in press). Tracing scientific influence. Dynamic of Socio-Economic System, 2(1).Google Scholar
- Sci2 Team. (2009a). Science of Science (Sci2) Tool: Indiana University and SciTech Strategies, Inc. http://sci2.cns.iu.edu. Accessed 8 June 2010.
- 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.
- Thomson Reuters (2010). Web of science. http://scientific.thomsonreuters.com/products/wos/. Accessed 8 June 2010.
- Van Raan, A. F. J. (2000). On growth, ageing, and fractal differentiation of science. Scientometrics, 47(2), 1588–2861.Google Scholar
- 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.