Gregor Mendel whose discoveries in plant genetics (Mendel 1865) were so unprecedented that it took 34 years for the scientific community to catch up to it (Punnett 1907), although it has been even questioned if Mendel's paper is a real case of delayed recognition (Moore 2001) or a case of “typical” science only “rediscovered” for a later scientific dispute. As suggested by one of the reviewers, this also raises the question if the role of a “prince” (in the “Sleeping Beauties” metaphor) is to rediscover the idea or rediscover the paper, an issue that is worth studying in the future.
The CSIC is organized in eight main research areas: Agriculture; Biology and Biomedicine; Chemistry; Food, Science and Technology; Materials Science; Natural Resources; Physics; and Social Sciences and Humanities. For a description of the WoS subject categories that are related with the three CSIC research areas analyzed in this study we refer to Costas et al. (2009).
“Normal” in this context refers to the property of these papers of being the majority in their fields, thus these papers determine the pattern of ageing that can be considered the most common, standard or “typical” in the field.
Katherine McCain (2011) has observed this property for two publications of the Nobel Prize winner John Forbes Nash.
It is important to remind here that bibliometric indicators are an important tool in research evaluation due to their quantitative and objective nature. However they only measure the dimension of the research activity, not accounting other activities such teaching, mentoring, patenting, consultancy, etc.
For the determination of “Years after publication”, the citation history of each document was analyzed and the citations of each paper to their corresponding year after publication were assigned (note that year 1 after publication refers to the same year of publication of the source paper). With this approach it is possible to work with all the papers of the set although they were published in different years.
Note that Figs. 4, 5, 6, 7, 8, 9, 1011 are based on the distribution of researchers and their values on the different indicators calculated. This means that although some publications can share researchers from different classes or ages, this has no influence on the results of the figures as they are based on the distribution of the individual scores of researchers.
The process consists on a first step where k centroids are defined, one for each cluster (the centroids should be placed as much as possible far away from each other). The next step is to assign each point to the nearest centroid, and when all objects have been assigned the centroids are recalculated. The two previous steps are repeated (iterations) until the centroids no longer move.
The same analysis has been performed with other sorting options and the results are basically the same.
The solution of three clusters can be viewed also as the best solution because when four clusters are forced, two of them tend to be very big, while the other two remain very small.
Natural Resources needed 4 iterations, Biology and Biomedicine 17 and Material Sciences 13 iterations, until no more changes in the centroids were found.
The authors are strongly grateful to Prof. Maria Bordons from CSIC and Martijn Visser from CWTS for their advice and comments on an earlier draft of this paper, as well as to the two anonymous reviewers who with their comments and views have significantly contributed to the improvement of the original manuscript.
Aksnes, DW, Taxt, RE 2004 Peer reviews and bibliometric indicators: A comparative study at a Norwegian university. Research Evaluation 13 1 33–41 .
Amat, CB, Yegros Yegros, A 2009 Median age difference of references as indicator of information update of research groups: A case study in Spanish food research. Scientometrics 78 3 447–465 .
Aversa, ES 1985 Citation patterns of highly cited papers and their relationships to literature aging: A study of the working literature. Scientometrics 7 3–6 383–389 .
Bordons, M., Zulueta, M. A., Cabrero, A., & Barrigon, S. (1995a). Identifying research teams with bibliometric tools. Proceedings of the Fifth Biennial Conference of the International Society for Scientometrics and Informetrics (pp. 83–92). River Forest, IL, USA: Rosary College.
Bordons, M, Zulueta, MA, Cabrero, A, Barrigon, S 1995 Research performance at the micro level: Analysis of structure and dynamics of pharmacological research teams. Research Evaluation 5 2 137–142.
Bornmann, L, Daniel, H-D 2010 Citation speed as a measure to predict the attention an article receives: An investigation of the validity of editorial decisions at Angewandte Chemie International Edition. Journal of Informetrics 4 1 83–88 .
Butler, L 2008 Using a balanced approach to bibliometrics: Quantitative performance measures in the Australian Research Quality Framework. Ethics in Science and Environmental Politics 8:83–92 .
Campanario, JM 2002 The parallelism between scientists’ and students’ resistance to new scientific ideas. International Journal of Science Education 24 10 1095–1110 .
Campanario, JM, Acedo, E 2007 Rejecting Highly Cited Papers: The views of scientists who encounter resistance to their discoveries from other scientists. Journal of the American Society from Information Science and Technology 58 5 734–743 .
Costas, R, Bordons, M 2005 Bibliometric indicators at the micro-level: Some results in the area of natural resources at the Spanish CSIC. Research Evaluation 14 2 110–120 .
Costas, R, Bordons, M TN van Leeuwen AFJ van Raan 2009 Scaling rules in the science system: Influence of field-specific citation characteristics on the impact of individual researchers. Journal of the American Society for Information Science and Technology 60 4 740–753 .
Costas, R TN van Leeuwen Bordons, M 2010 A bibliometric classificatory approach for the study and assessment of research performance at the individual level: The effects of age on productivity and impact. Journal of the American Society for Information Science and Technology 61 8 1564–1581.
Costas, R TN van Leeuwen AFJ van Raan 2010 Is scientific literature subject to a ‘sell-by-date’? A general methodology to analyze the ‘durability’ of scientific documents. Journal of the American Society for Information Science and Technology 61 2 329–339.
DJ de Solla Price 1965 Networks of scientific papers: The pattern of bibliographic references indicates the nature of the scientific research front. Science 149 3683 510–515 .
Foss, NJ 1995 The economic thought of an Austrian Marshallian: George Barclay Richardson. Journal of Economics Studies 22 1 23–44 .
Garfield, E 1970 Would Mendel's work have been ignored if the Science Citation Index was available 100 years ago?. Current Contents 2:69–70.
Garfield, E 1989 Delayed recognition in scientific discovery: Citation frequency analysis aids the search for case histories. Essays of an Information Scientist 12:154–160.
Garfield, E., & Malin, M. V. (1968). Can Nobel Prize winners be predicted? 135th Annual Meeting, American Association for the Advancement of Science. Dallas, TX: AAAS.
Glänzel, W, Schoepflin, U 1995 A bibliometric study on ageing and reception process of scientific literature. Journal of Information Science 21 1 37–53 .
Glänzel, W, Schlemmer, B, Thijs, B 2003 Better late than never? On the chance to become highly cited beyond the standard bibliometric time horizon. Scientometrics 58 3 571–586 .
Glass, B 1974 The long neglect of genetic discoveries and the criterion of prematurity. Journal of the History of Biology 7 1 101–110 .
Graham, MH, Dayton, PK 2002 On the evolution of ecological ideas: Paradigms and scientific progress. Ecology 83 6 1481–1489 .
MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. Proceedings of the Fifth Symposium on Mathematical Statistics and Probability (pp. 281–297). Berkeley, CA: University of California Press.
Magri, M-H, Solari, A 1996 The SCI Journal Citation Reports: A potential tool for studying journals?. Scientometrics 35 1 93–117 .
McCain, K. W. (2011). Eponymy and obliteration by incorporation: the case of the “Nash Equilibrium”. Journal of the American Society for Information Science and Technology, 62 (7), 1412–1424.
Moed, HF 1989 Bibliometric measurement of research performance and Price's theory of differences among the sciences. Scientometrics 15 5–6 473–483 .
Moed, HF RE De Bruin TN van Leeuwen 1995 New bibliometric tools for the assessment of national research performance: Database description, overview of indicators and first applications. Scientometrics 33 3 381–422 .
Moed, HF TN van Leeuwen Reedijk, J 1998 A new classification system to describe the ageing of scientific journals and their impact factors. Journal of Documentation 54 4 387–419 .
Nederhof, AJ AFJ van Raan 1987 Peer review and bibliometric indicators of scientific performance: A comparison of cum laude doctorates with ordinary doctorates in physics. Scientometrics 36 2 185–206.
Rodriguez-Ruiz, O 2009 The citation indexes and the quantification of knowledge. Journal of Educational Administration 47 2 250–266 .
Sandström, U., & Sandström, E., (2009). Meeting the micro-level challenges: Bibliometrics at the individual level. 12th International Conference on Scientometrics and Informetrics (845–856). Rio de Janeiro: BIREME/PAHO/WHO.
Tahai, A, Rigsby, JT 1998 Information processing using citations to investigate journal influence in accounting. Information Processing and Management 34 2/3 341–359 .
HP Van Dalen Henkens, K 2004 Demographers and their journals: Who remains uncited after ten years?. Population and Development review 30 3 489–506 .
HP Van Dalen Henkens, K 2005 Signals in science—On the importance of signaling in gaining attention in science. Scientometrics 64 2 209–233 .
Weingart, P 2005 Impact of bibliometrics upon the science system: inadvertent consequences?. Scientometrics 62 1 11–21 .