Research assessment based on infrequent achievements: A comparison of the United States and Europe in terms of highly cited papers and Nobel Prizes

Journal of the Association for Information Science and Technology

Volumn 67, Issue 3, 2016, Pages 731-740

  Rodríguez Navarro, Alonso ^a  

^a UNIVERSIDAD POLITÉCNICA DE MADRID   (Spain)

Author keywords

bibliometrics; citation analysis; scientometrics

Indexed keywords

MOLECULAR BIOLOGY;

BIBLIOMETRIC INDICATORS; BIBLIOMETRICS; CITATION ANALYSIS; CITATION DISTRIBUTION; MATHEMATICAL PROPERTIES; RESEARCH PERFORMANCE; SCIENTIFIC PROGRESS; SCIENTOMETRICS;

PAPER;

AWARDS AND PRIZES; BIBLIOMETRICS; BIOCHEMISTRY; CITATION ANALYSIS; EUROPE; MEDICINE; MOLECULAR BIOLOGY; PHYSICS; PHYSIOLOGY; THEORETICAL MODEL; UNITED STATES;

EID: 84997785116     PISSN: 23301635     EISSN: 23301643     Source Type: Journal    
DOI: 10.1002/asi.23412     Document Type: Article

References (30)

1. Albarrán, P., Crespo, J.A., Ortuño, I., & Ruiz-Castillo, J. (2010). A comparison of the scientific performance of the U.S. and the European Union at the turn of the 21st century. Scientometrics, 85(1), 329–344.
2. Albarrán, P., Crespo, J.A., Ortuño, I., & Ruiz-Castillo, J. (2011). The skewness of science in 219 sub-fields and a number of aggregates. Scientometrics, 88(2), 385–397.
3. Bornmann, L., & Marx, W. (2013). How good is research really? EMBO Reports, 14(3), 226–230.
4. Campanario, J.M. (1996). Using citation classics to study the incidence of serendipity in scientific discovery. Scientometrics, 37(1), 3–24.
5. Charlton, B.G. (2007). Scientometric identification of elite “revolutionary science” research institutions by analysis of trends in Nobel prizes 1947–2006. Medical Hypotheses, 68(5), 931–934.
6. Charlton, B.G., & Andras, P. (2008). Down-shifting” among top UK scientists?—The decline of “revolutionary science” and the rise of “normal science” in the UK compared with the USA. Medical Hypotheses, 70(3), 465–472.
7. Chomczynski, P., & Sacchi, N. (1987). Single-step method of RNA isolation by acid guanidinium thiocyanate phenol chloroform extraction. Analytical Biochemistry, 162(1), 156–159.
8. Clauset, A., Shalizi, C.R., & Newman, M. (2009). Power-law distributions in empirical data. Society for Industrial and Applied Mathematics Review, 51(4), 661–703.
9. Delanghe, H., Sloan, B., & Muldur, U. (2011). European research policy and bibliometric indicators, 1990–2005. Scientometrics, 87(2), 389–398.
10. Dosi, G., Llerena, P., & Labini, M.S. (2006). The relationships between science, technologies and their industrial exploitation: An illustration through the myths and realities of the so-called “European Paradox”. Research Policy, 35(10), 1450–1464.
11. Garfield, E., & Welljams-Dorof, A. (1992). Citation data: their use as quantitative indicators for science and technology evaluations and policy-making. Science and Public Policy, 19(5), 321–327.
12. Godin, B. (2006). On the origins of bibliometrics. Scientometrics, 68(1), 109–133.
13. Herranz, N., & Ruiz-Castillo, J. (2013). The end of the “European paradox.” Scientometrics, 95(1), 453–464.
14. Jacsó, P. (2009). The h-index for countries in Web of Science and Scopus. Online Information Review, 33(4), 831–837.
15. Jacsó, P. (2011). Interpretations and misinterpretations of scientometric data in the report of the Royal Society about the scientific landscape in 2011. Online Information Review, 35(4), 669–682.
16. King, D.A. (2004). The scientific impact of nations. Nature, 430(6997), 311–316.
17. Leydesdorff, L. (2005). Evaluation of research and evolution of science indicators. Current Science, 89(9), 1510–1517.
18. Milojević, S. (2010). Power-law distributions in information science—Making the case for logarithmic binning. Journal of the American Society for Information Science and Technology, 61(12), 2417–2425.
19. Narin, F. (2012). Decades of progress, or the progress of decades? Scientometrics, 92(2), 391–393.
20. Redner, S. (1998). How popular is your paper? An empirical study of the citation distribution. European Physical Journal B, 4(2), 131–134.
21. Rodríguez-Navarro, A. (2011a). A simple index for the high-citation tail of citation distribution to quantify research performance in countries and institutions. PLoS ONE, 6(5), e20510.
22. Rodríguez-Navarro, A. (2011b). Measuring research excellence. Number of Nobel Prize achievements versus conventional bibliometric indicators. Journal of Documentation, 67(4), 582–600.
23. Rodríguez-Navarro, A. (2012). Counting highly cited papers for university research assessment: Conceptual and technical issues. PLoS ONE, 7(10), e47210.
24. Rodríguez-Navarro, A. (2013). Response to “How good is research really?”. EMBO Reports, 14(14), 493–493.
25. Ruiz-Castillo, J. (2012). The evaluation of citation distributions. SERIEs, 3(1–2), 291–310.
26. Saitou, N., & Nei, M. (1987). The neighbor-joining method—A new method for reconstructing phylogenetic trees. Molecular Biology and Evolution, 4(4), 406–425.
27. Sarli, C.C., Dubinsky, E.K., & Colmes, K.L. (2010). Beyond citation analysis: A model for assessment of research impact. Journal of the Medical Library Association, 98(4), 17–23.
28. The Royal Society (2011), Knowledge, networks and nations. Retrieved from http://royalsociety.org/policy/projects/knowledge-networks-nations/report/
29. Thompson, J.D., Higgins, D.G., & Gibson, T.J. (1994). Clustal-W—Improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Research, 22(22), 4673–4680.
30. Van Noorden, R. (2010). A profusion of measures. Nature, 465(7300), 864–866.