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Stock, W.G.: Forschung im internationalen Vergleich - Wissenschaftsindikatoren auf Zitationsbasis : ISI Essential Science Indicators (2002)
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Oberhauser, O.: Fachspezifische Suche nach elektronischen Zeitschriften : Ein webliographischer Streifzug am Beispiel der Informationswissenschaft (2003)
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Ohly, H.P.: ¬Die Bibliometrie ist tot - es lebe die Bibliometrie (2003)
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Maier, S.: Wie Wissenschaftler berühmt werden : Anzahl der Veröffentlichungen zählt - "Google" ist das Maß aller Dinge (2004)
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Amitay, E.; Carmel, D.; Herscovici, M.; Lempel, R.; Soffer, A.: Trend detection through temporal link analysis (2004)
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Wagner-Döbler, R.: Kognitive Mobilität und Zipfs "Principle of Least Effort" (2004)
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Vaughan, L.; Shaw, D.: Web citation data for impact assessment : a comparison of four science disciplines (2005)
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Morris, S.A.: Manifestation of emerging specialties in journal literature : a growth model of papers, references, exemplars, bibliographic coupling, cocitation, and clustering coefficient distribution (2005)
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- Abstract
- A model is presented of the manifestation of the birth and development of a scientific specialty in a collection of journal papers. The proposed model, Cumulative Advantage by Paper with Exemplars (CAPE) is an adaptation of Price's cumulative advantage model (D. Price, 1976). Two modifications are made: (a) references are cited in groups by paper, and (b) the model accounts for the generation of highly cited exemplar references immediately after the birth of the specialty. This simple growth process mimics many characteristic features of real collections of papers, including the structure of the paper-to-reference matrix, the reference-per-paper distribution, the paper-per-reference distribution, the bibliographic coupling distribution, the cocitation distribution, the bibliographic coupling clustering coefficient distribution, and the temporal distribution of exemplar references. The model yields a great deal of insight into the process that produces the connectedness and clustering of a collection of articles and references. Two examples are presented and successfully modeled: a collection of 131 articles an MEMS RF (microelectromechnical systems radio frequency) switches, and a collection of 901 articles an the subject of complex networks.
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Ivanisevic, R.; Sapunar, D.: Multiple authorship in a small medical journal : a case study of the Croatian Medical Journal (2006)
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Vaughan, L.; Shaw , D.: Bibliographic and Web citations : what Is the difference? (2003)
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Parthey, H.: Strukturwandel der bibliometrischen Profile wissenschaftlicher Institutionen im 20. Jahrhundert (2006)
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Huntington, P.; Nicholas, D.; Jamali, H.R.; Tenopir, C.: Article decay in the digital environment : an analysis of usage of OhioLINK by date of publication, employing deep log methods (2006)
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Bornmann, L.; Daniel, H.-D.: Multiple publication on a single research study: does it pay? : The influence of number of research articles on total citation counts in biomedicine (2007)
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Zhao, D.; Strotmann, A.: Can citation analysis of Web publications better detect research fronts? (2007)
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Egghe, L.; Ravichandra Rao, I.K.: ¬The influence of the broadness of a query of a topic on its h-index : models and examples of the h-index of n-grams (2008)
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- Abstract
- The article studies the influence of the query formulation of a topic on its h-index. In order to generate pure random sets of documents, we used N-grams (N variable) to measure this influence: strings of zeros, truncated at the end. The used databases are WoS and Scopus. The formula h=T**1/alpha, proved in Egghe and Rousseau (2006) where T is the number of retrieved documents and is Lotka's exponent, is confirmed being a concavely increasing function of T. We also give a formula for the relation between h and N the length of the N-gram: h=D10**(-N/alpha) where D is a constant, a convexly decreasing function, which is found in our experiments. Nonlinear regression on h=T**1/alpha gives an estimation of , which can then be used to estimate the h-index of the entire database (Web of Science [WoS] and Scopus): h=S**1/alpha, , where S is the total number of documents in the database.
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Lucio-Arias, D.; Leydesdorff, L.: Main-path analysis and path-dependent transitions in HistCite(TM)-based historiograms (2008)
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Zhao, D.; Strotmann, A.: Evolution of research activities and intellectual influences in information science 1996-2005 : introducing author bibliographic-coupling analysis (2008)
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Bornmann, L.; Mutz, R.; Daniel, H.D.: Do we need the h index and its variants in addition to standard bibliometric measures? (2009)
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- Abstract
- In this study, we investigate whether there is a need for the h index and its variants in addition to standard bibliometric measures (SBMs). Results from our recent study (L. Bornmann, R. Mutz, & H.-D. Daniel, 2008) have indicated that there are two types of indices: One type of indices (e.g., h index) describes the most productive core of a scientist's output and informs about the number of papers in the core. The other type of indices (e.g., a index) depicts the impact of the papers in the core. In evaluative bibliometric studies, the two dimensions quantity and quality of output are usually assessed using the SBMs number of publications (for the quantity dimension) and total citation counts (for the impact dimension). We additionally included the SBMs into the factor analysis. The results of the newly calculated analysis indicate that there is a high intercorrelation between number of publications and the indices that load substantially on the factor Quantity of the Productive Core as well as between total citation counts and the indices that load substantially on the factor Impact of the Productive Core. The high-loading indices and SBMs within one performance dimension could be called redundant in empirical application, as high intercorrelations between different indicators are a sign for measuring something similar (or the same). Based on our findings, we propose the use of any pair of indicators (one relating to the number of papers in a researcher's productive core and one relating to the impact of these core papers) as a meaningful approach for comparing scientists.
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Archambault, E.; Campbell, D; Gingras, Y.; Larivière, V.: Comparing bibliometric statistics obtained from the Web of Science and Scopus (2009)
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Bornmann, L.; Daniel, H.-D.: Universality of citation distributions : a validation of Radicchi et al.'s relative indicator cf = c/c0 at the micro level using data from chemistry (2009)
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