Abstract
Emerging scientific fields are commonly identified by different citation based bibliometric parameters. However, their main shortcoming is the existence of a time lag needed for a publication to receive citations. In the present study, we assessed the relationship between the age of references in scientific publications and the change in publication rate within a research field. Two indices based on the age of references are presented, the relative age of references and the ratio of references published during the preceding 2 years, and applied thereafter on four datasets from the previously published studies, which assessed eutrophication research, sturgeon research, fisheries research, and the general field of ecology. We observed a consistent pattern that the emerging research topics had a lower median age of references and a higher ratio of references published in the preceding 2 years than their respective general research fields. The main advantage of indices based on the age of references is that they are not influenced by a time lag, and as such they are able to provide insight into current scientific trends. The best potential of the presented indices is to use them combined with other approaches, as each one can reveal different aspects and properties of the assessed data, and provide validation of the obtained results. Their use should be however assessed further before they are employed as standard tools by scientists, science managers and policy makers.
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Althouse, B. M., West, J. D., Bergstrom, T., & Bergstrom, C. T. (2009). Differences in impact factor across fields and over time. Journal of the American Society for Information Science and Technology, 60(1), 27–34.
Bador, P., & Lafouge, T. (2010). Comparative analysis between impact factor and h-index for pharmacology and psychiatry journals. Scientometrics, 84, 65–97.
Bergstrom, C. (2007). Eigenfactor. Measuring the value and prestige of scholarly journals. College & Research Libraries News, 68(5), 314–316.
Bornmann, L., & Daniel, H. D. (2008). What do citation counts measure? A review of studies on citing behavior. Journal of Documentation, 64(1), 45–80.
Bornmann, L., Mutz, R., Hug, S. E., & Daniel, H. D. (2011). A multilevel meta-analysis of studies reporting correlations between the h index and 37 different h index variants. Journal of Informetrics, 5, 346–359.
Braam, R. R., Moed, H. F., & van Raan, A. F. J. (1991). Mapping of science by combined co-citation and word analysis. I. Structural aspects. Journal of the American Society for Information Science, 42(4), 233–251.
Braun, T., Glänzel, W., & Schubert, A. (2006). A Hirsch-type index for journals. Scientometrics, 69(1), 169–173.
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.
Costas, R., & Bordons, M. (2007). The h-index: advantages, limitations and its relation with other bibliometric indicators at the micro level. Journal of Informetrics, 1, 193–203.
de Solla Price, D. J. (1965a). Networks of scientific papers. Science, 149(3683), 510–515.
de Solla Price, D. J. (1965b). Is technology historically independent of science? A study in statistical historiography. Technology and Culture, 6(4), 553–568.
de Solla Price, D. J. (1970). Citation measures of hard science, soft science, technology, and non-science. In C. E. Nelson & D. K. Pollock (Eds.), Communication among scientists and engineers (pp. 3–22). Lexington, MA.: Heath Lexington.
Egghe, L. (1997). Price index and its relation to the mean and median reference age. Journal of the American Society for Information Science, 48(6), 564–573.
Egghe, L. (2006). An improvement of the h-index: The g-index. ISSI Newsletter, 2(1), 8–9.
Glänzel, W., & Schoepflin, U. (1995). A bilbiometric ageing study based on serial and non-serial reference literature in the sciences. In Proceedings of 5th International Conference on Scientometrics and Informetrics, June 7–10, River Forest, IL (pp. 177–185). Medford, NJ: Learned Information.
Glänzel, W., & Schoepflin, U. (1999). A bibliometric study of reference literature in the sciences and social sciences. Information Processing and Management, 35, 31–44.
Hirsch, J. E. (2010). An index to quantify an individual’s scientific research output that takes into account the effect of multiple coauthorship. Scientometrics, 85, 741–754.
Jarić, I., Cvijanović, G., Knežević-Jarić, J., & Lenhardt, M. (2012). Trends in fisheries science during 2000–2009: a bibliometric study. Reviews in Fisheries Science, 20(2), 70–79.
Jarić, I., & Gessner, J. (2012). Analysis of publications on sturgeon research between 1996 and 2010. Scientometrics, 90(2), 715–735.
Kajikawa, Y., Yoshikawa, J., Takeda, Y., & Matsushima, K. (2008). Tracking emerging technologies in energy research: toward a roadmap for sustainable energy. Technological Forecasting and Social Change, 75, 771–782.
Krell, F. T. (2002). Why impact factors don’t work for taxonomy. Nature, 415(6875), 957.
Lee, W. H. (2008). How to identify emerging research fields using scientometrics: an example in the field of Information Security. Scientometrics, 76(3), 503–525.
McCain, K. W., Verner, J. M., Hislop, G. W., Evanco, W., & Cole, V. (2005). The use of bibliometric and knowledge elicitation techniques to map a knowledge domain: Software engineering in the 1990s. Scientometrics, 65(1), 131–144.
Morris, S. A., Yen, G., Wu, Z., & Asnake, B. (2003). Time line visualization of research fronts. Journal of the American Society for Information Science and Technology, 54(5), 413–422.
Natale, F., Fiore, G., & Hofherr, J. (2012). Mapping the research on aquaculture. A bibliometric analysis of aquaculture literature. Scientometrics, 90(3), 983–999.
Neff, M. W., & Corley, E. A. (2009). 35 years and 160,000 articles: a bibliometric exploration of the evolution of ecology. Scientometrics, 80(3), 657–682.
Qiu, H., & Chen, Y. F. (2009). Bibliometric analysis of biological invasions research during the period of 1991–2007. Scientometrics, 81(3), 601–610.
Romo-Fernández, L. M., Guerrero-Bote, V. P., & Moya-Anegón, F. (2013). Co-word based thematic analysis of renewable energy (1991–2010). Scientometrics,. doi:10.1007/s11192-013-1009-5.
Shibata, N., Kajikawa, Y., Takeda, Y., & Matsushima, K. (2008). Detecting emerging research fronts based on topological measures in citation networks of scientific publications. Technovation, 28, 758–775.
Small, H. (2006). Tracking and predicting growth areas in science. Scientometrics, 68(3), 595–610.
Small, H., & Upham, P. (2009). Citation structure of an emerging research area on the verge of application. Scientometrics, 79(2), 365–375.
Takeda, Y., & Kajikawa, Y. (2009). Optics: a bibliometric approach to detect emerging research domains and intellectual bases. Scientometrics, 78(3), 543–558.
Todeschini, R. (2011). The j-index: a new bibliometric index and multivariate comparisons between other common indices. Scientometrics, 87, 621–639.
Yi, H., & Jie, W. (2011). A bibliometric study of the trend in articles related to eutrophication published in science citation index. Scientometrics, 89, 919–927.
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The authors acknowledge the support by the Project No. 173045, funded by the Ministry of Education, Science and Technological Development of the Republic of Serbia. The authors would like to thank two anonymous referees for providing helpful comments and suggestions that improved the quality of the paper.
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Jarić, I., Knežević-Jarić, J. & Lenhardt, M. Relative age of references as a tool to identify emerging research fields with an application to the field of ecology and environmental sciences. Scientometrics 100, 519–529 (2014). https://doi.org/10.1007/s11192-014-1268-9
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DOI: https://doi.org/10.1007/s11192-014-1268-9