-
Nicolaisen, J.: Citation analysis (2007)
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- Date
- 13. 7.2008 19:53:22
-
Fiala, J.: Information flood : fiction and reality (1987)
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- Source
- Thermochimica acta. 110(1987), S.11-22
-
Marx, W.; Bornmann, L.: On the problems of dealing with bibliometric data (2014)
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- Date
- 18. 3.2014 19:13:22
-
Su, Y.; Han, L.-F.: ¬A new literature growth model : variable exponential growth law of literature (1998)
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- Date
- 22. 5.1999 19:22:35
-
Bornmann, L.; Bauer, J.; Haunschild, R.: Distribution of women and men among highly cited scientists (2015)
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Bornmann, L.; Mutz, R.: From P100 to P100' : a new citation-rank approach (2014)
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- Date
- 22. 8.2014 17:05:18
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Leydesdorff, L.; Bornmann, L.; Wagner, C.S.: ¬The relative influences of government funding and international collaboration on citation impact (2019)
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- Date
- 8. 1.2019 18:22:45
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Chung, Y.-K.: Bradford distribution and core authors in classification systems literature (1994)
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- Abstract
- Bradford's law of scatter was applied to the analysis of the authors of source documents on the subject of classification schemes, published in core periodicals over the period 1981-1990. Results indicated that: core authors of the international classification system literature are Library of Congress, M. Dewey, S. Ranganathan, J. Comaroni, A. Neelameghan, L. Chan and K. Markey; the highly cited authors are linked either to the developers of the classification schemes or to a research centre, or else they authored the most frequently cited books; and the data conforms to Bradford's Law of Scatter
-
Bornmann, L.; Bauer, J.: Which of the world's institutions employ the most highly cited researchers : an analysis of the data from highlycited.com (2015)
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-
Bornmann, L.; Bauer, J.: Which of the world's institutions employ the most highly cited researchers : an analysis of the data from highlycited.com (2015)
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-
Egghe, L.: Relations between the continuous and the discrete Lotka power function (2005)
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- Abstract
- The discrete Lotka power function describes the number of sources (e.g., authors) with n = 1, 2, 3, ... items (e.g., publications). As in econometrics, informetrics theory requires functions of a continuous variable j, replacing the discrete variable n. Now j represents item densities instead of number of items. The continuous Lotka power function describes the density of sources with item density j. The discrete Lotka function one obtains from data, obtained empirically; the continuous Lotka function is the one needed when one wants to apply Lotkaian informetrics, i.e., to determine properties that can be derived from the (continuous) model. It is, hence, important to know the relations between the two models. We show that the exponents of the discrete Lotka function (if not too high, i.e., within limits encountered in practice) and of the continuous Lotka function are approximately the same. This is important to know in applying theoretical results (from the continuous model), derived from practical data.
-
Egghe, L.: Empirical and combinatorial study of country occurrences in multi-authored papers (2006)
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- Abstract
- Papers written by several authors can be classified according to the countries of the author affiliations. The empirical part of this paper consists of two datasets. One dataset consists of 1,035 papers retrieved via the search "pedagog*" in the years 2004 and 2005 (up to October) in Academic Search Elite which is a case where phi(m) = the number of papers with m =1, 2,3 ... authors is decreasing, hence most of the papers have a low number of authors. Here we find that #, m = the number of times a country occurs j times in a m-authored paper, j =1, ..., m-1 is decreasing and that # m, m is much higher than all the other #j, m values. The other dataset consists of 3,271 papers retrieved via the search "enzyme" in the year 2005 (up to October) in the same database which is a case of a non-decreasing phi(m): most papers have 3 or 4 authors and we even find many papers with a much higher number of authors. In this case we show again that # m, m is much higher than the other #j, m values but that #j, m is not decreasing anymore in j =1, ..., m-1, although #1, m is (apart from # m, m) the largest number amongst the #j,m. The combinatorial part gives a proof of the fact that #j,m decreases for j = 1, m-1, supposing that all cases are equally possible. This shows that the first dataset is more conform with this model than the second dataset. Explanations for these findings are given. From the data we also find the (we think: new) distribution of number of papers with n =1, 2,3,... countries (i.e. where there are n different countries involved amongst the m (a n) authors of a paper): a fast decreasing function e.g. as a power law with a very large Lotka exponent.
-
Leydesdorff, L.; Bornmann, L.: How fractional counting of citations affects the impact factor : normalization in terms of differences in citation potentials among fields of science (2011)
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- Date
- 22. 1.2011 12:51:07
-
Egghe, L.; Rousseau, R.: Averaging and globalising quotients of informetric and scientometric data (1996)
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- Source
- Journal of information science. 22(1996) no.3, S.165-170
-
Leydesdorff, L.: Can networks of journal-journal citations be used as indicators of change in the social sciences? (2003)
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- Date
- 6.11.2005 19:02:22
-
Leydesdorff, L.; Sun, Y.: National and international dimensions of the Triple Helix in Japan : university-industry-government versus international coauthorship relations (2009)
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- Date
- 22. 3.2009 19:07:20
-
He, Z.-L.: International collaboration does not have greater epistemic authority (2009)
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- Date
- 26. 9.2009 11:22:05
-
Bornmann, L.: How to analyze percentile citation impact data meaningfully in bibliometrics : the statistical analysis of distributions, percentile rank classes, and top-cited papers (2013)
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- Date
- 22. 3.2013 19:44:17
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Kronegger, L.; Mali, F.; Ferligoj, A.; Doreian, P.: Classifying scientific disciplines in Slovenia : a study of the evolution of collaboration structures (2015)
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- Date
- 21. 1.2015 14:55:22
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Ridenour, L.: Boundary objects : measuring gaps and overlap between research areas (2016)
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- Abstract
- The aim of this paper is to develop methodology to determine conceptual overlap between research areas. It investigates patterns of terminology usage in scientific abstracts as boundary objects between research specialties. Research specialties were determined by high-level classifications assigned by Thomson Reuters in their Essential Science Indicators file, which provided a strictly hierarchical classification of journals into 22 categories. Results from the query "network theory" were downloaded from the Web of Science. From this file, two top-level groups, economics and social sciences, were selected and topically analyzed to provide a baseline of similarity on which to run an informetric analysis. The Places & Spaces Map of Science (Klavans and Boyack 2007) was used to determine the proximity of disciplines to one another in order to select the two disciplines use in the analysis. Groups analyzed share common theories and goals; however, groups used different language to describe their research. It was found that 61% of term words were shared between the two groups.
