Promoting scientodiversity inspired by biodiversity
- 506 Downloads
Diversity of science (variety in and balance among research subjects) is often regarded as a key driver of innovation, but it is typically understood by means of heuristics, given the lack of precise formulations such as those found in biodiversity studies. From the policy perspective, a standard methodology for characterization of diversity of science is needed to enable the efficient management and breeding of diverse research responsive to socio-economic demands. We investigated the distribution of research subjects in a bibliographic database to develop a framework of diversity of science analogous to that of biodiversity. Our analysis of the distribution of research subjects among countries suggests that diversity of science has similar statistical characteristics as biodiversity. We find that number of research subjects follows log-normal distribution for almost all countries and indicates linear dependency on research budget in log–log plot. We also identify an inflection point in the subject–budget relationship curve. The results may validate the adoption of sophisticated concepts and techniques from biodiversity work in “scientodiversity” studies.
KeywordsDiversity of science Log-normal distribution Classification R&D expenditure Biodiversity
- Abbasi, A., Altmann, J., & Hossain, L. (2011). Identifying the effects of co-authorship networks on the performance of scholars: A correlation and regression analysis of performance measures and social network analysis measures. Journal of Informetrics, 5(4), 594–607. doi: 10.1016/j.joi.2011.05.007.CrossRefGoogle Scholar
- Aydinoglu, A. U., Allard, S., & Mitchell, C. (2015). Measuring diversity in disciplinary collaboration in research teams: An ecological perspective. doi: 10.1093/reseval/rvv028.
- Barjak, F. (2006). Team diversity and research collaboration in life sciences teams: Does a combination of research cultures pay off? University of Applied Sciences Northwestern Switzerland, Series A: Discussion Paper, W02. Retrieved from http://netreact-eu.org/documents/DPW2006-02_TeamDiversity_Barjak_Franz.pdf.
- Börner, K. (2010). Atlas of science: Visualizing what we know. Cambridge: The MIT Press.Google Scholar
- Bosman, J., van Mourik, I., Rasch, M., Sieverts, E., & Verhoeff, H. (2006). Scopus reviewed and compared. Utrecht: Utrecht University Library.Google Scholar
- Gibbons, M., Limoges, C., Nowotny, H., Schwartzman, S., Scott, P., & Trow, M. (1994). The new production of knowledge: The dynamics of science and research in contemporary societies. Contemporary sociology. London: Sage.Google Scholar
- Hubbell, S. P. (2001). The unified neutral theory of biodiversity and biogeography. Princeton: Princeton University Press.Google Scholar
- Irie, H., & Tokita, K. (2012). Species–area relationship for power-law species abundance distribution. International Journal of Biomathematics, 5(3), 1260014. Retrieved from http://arxiv.org/abs/q-bio/0609012.
- Leydesdorff, L., Rafols, I., & Chen, C. (2013b). Interactive overlays of journals and the measurement of interdisciplinarity on the basis of aggregated journal–journal citations. Journal of the American Society for Information Science and Technology, 64, 2573–2586. doi: 10.1002/asi.CrossRefGoogle Scholar
- Lund Declaration. (2009). Europe must focus on the grand challenges of our time. In Swedish Presidency Research Conference in Lund. New Times New Solutions. Lund. Retrieved from http://www.vr.se/download/18.7dac901212646d84fd38000336/.
- May, R. M. (1975). Patterns of species abundance and diversity. In M. L. Cody & J. M. Diamond (Eds.), Ecology and evolution of communities (pp. 81–120). Cambridge: The Belknap Press.Google Scholar
- Merton, R. K. (1973). The Normative Structure of Science. In N. Storer (Ed.), The sociology of sciene: Theoretical and empirical investigations (pp. 267–278). Chicago: The University Chicago Press.Google Scholar
- Mitesser, O., Heinz, M., Havemann, F., & Gläser, J. (2008). Measuring diversity of research by extracting latent themes from bipartite networks of papers and references. In H. Kretschmer & F. Havemann (Eds.), Proceedings of WIS 2008, 6th international conference on webometrics, informetrics and scientometrics & ninth COLLNET meeting. Berlin.Google Scholar
- Nelson, R. (Ed.). (1993). National innovation systems: A comparative analysis. Oxford: Oxford University Press.Google Scholar
- OECD. (2016). OECD Science, Technology and Industry Outlook. OECD. https://doi.org/10.1787/23129638.
- Rosenberg, N. (1996). Uncertainty and technological change. In G. W. R. Landau & T. Taylor (Eds.), The mosaic of economic growth (pp. 334–353). Stanford: Stanford University Press.Google Scholar
- Sakagami, Y. (1989). JICST science and technology classification. The Journal of Information Science and Technology Association, 39(11), 497–502.Google Scholar
- Schmidt, M., Glaser, J., Havemann, F., & Heinze, M. (2006). A methodological study for measuring the diversity of science. In Proceedings international workshop on webometrics, informetrics and scientometrics & seventh COLLNET meeting. Nancy.Google Scholar
- The World Bank. (2017). World Development Indicators. Retrieved from http://data.worldbank.org/data-catalog/world-development-indicators.
- Wagner, C. S., Roessner, J. D., Bobb, K., Klein, J. T., Boyack, K. W., Keyton, J., et al. (2011). Approaches to understanding and measuring interdisciplinary scientific research (IDR): A review of the literature. Journal of Informetrics, 5(1), 14–26. doi: 10.1016/j.joi.2010.06.004.CrossRefGoogle Scholar
- Williams, K. Y., & O’Reilly, C. A. (1998). Demography and diversity in organizations: A review of 40 years of research. Research in Organizational Behavior, 20, 77–140.Google Scholar