Search (137 results, page 1 of 7)

  • × theme_ss:"Informetrie"
  • × year_i:[2010 TO 2020}
  1. Ding, Y.: Applying weighted PageRank to author citation networks (2011) 0.04 
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    Date
    22. 1.2011 13:02:21
  2. Onodera, N.; Iwasawa, M.; Midorikawa, N.; Yoshikane, F.; Amano, K.; Ootani, Y.; Kodama, T.; Kiyama, Y.; Tsunoda, H.; Yamazaki, S.: ¬A method for eliminating articles by homonymous authors from the large number of articles retrieved by author search (2011) 0.04 
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  3. Larivière, V.; Gingras, Y.: ¬The impact factor's Matthew Effect : a natural experiment in bibliometrics (2010) 0.04 
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    Abstract
    Since the publication of Robert K. Merton's theory of cumulative advantage in science (Matthew Effect), several empirical studies have tried to measure its presence at the level of papers, individual researchers, institutions, or countries. However, these studies seldom control for the intrinsic quality of papers or of researchers - better (however defined) papers or researchers could receive higher citation rates because they are indeed of better quality. Using an original method for controlling the intrinsic value of papers - identical duplicate papers published in different journals with different impact factors - this paper shows that the journal in which papers are published have a strong influence on their citation rates, as duplicate papers published in high-impact journals obtain, on average, twice as many citations as their identical counterparts published in journals with lower impact factors. The intrinsic value of a paper is thus not the only reason a given paper gets cited or not, there is a specific Matthew Effect attached to journals and this gives to papers published there an added value over and above their intrinsic quality.
  4. Crespo, J.A.; Herranz, N.; Li, Y.; Ruiz-Castillo, J.: ¬The effect on citation inequality of differences in citation practices at the web of science subject category level (2014) 0.03 
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    Abstract
    This article studies the impact of differences in citation practices at the subfield, or Web of Science subject category level, using the model introduced in Crespo, Li, and Ruiz-Castillo (2013a), according to which the number of citations received by an article depends on its underlying scientific influence and the field to which it belongs. We use the same Thomson Reuters data set of about 4.4 million articles used in Crespo et al. (2013a) to analyze 22 broad fields. The main results are the following: First, when the classification system goes from 22 fields to 219 subfields the effect on citation inequality of differences in citation practices increases from ?14% at the field level to 18% at the subfield level. Second, we estimate a set of exchange rates (ERs) over a wide [660, 978] citation quantile interval to express the citation counts of articles into the equivalent counts in the all-sciences case. In the fractional case, for example, we find that in 187 of 219 subfields the ERs are reliable in the sense that the coefficient of variation is smaller than or equal to 0.10. Third, in the fractional case the normalization of the raw data using the ERs (or subfield mean citations) as normalization factors reduces the importance of the differences in citation practices from 18% to 3.8% (3.4%) of overall citation inequality. Fourth, the results in the fractional case are essentially replicated when we adopt a multiplicative approach.
  5. Chang, Y.-W.; Huang, M.-H.: ¬A study of the evolution of interdisciplinarity in library and information science : using three bibliometric methods (2012) 0.03 
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    Source
    Journal of the American Society for Information Science and Technology. 63(2012) no.1, S.22-33
  6. Stvilia, B.; Hinnant, C.C.; Schindler, K.; Worrall, A.; Burnett, G.; Burnett, K.; Kazmer, M.M.; Marty, P.F.: Composition of scientific teams and publication productivity at a national science lab (2011) 0.02 
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    Date
    22. 1.2011 13:19:42
  7. Ajiferuke, I.; Lu, K.; Wolfram, D.: ¬A comparison of citer and citation-based measure outcomes for multiple disciplines (2010) 0.02 
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    Date
    28. 9.2010 12:54:22
  8. ¬Die deutsche Zeitschrift für Dokumentation, Informationswissenschaft und Informationspraxis von 1950 bis 2011 : eine vorläufige Bilanz in vier Abschnitten (2012) 0.02 
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    Date
    22. 7.2012 19:35:26
    Footnote
    Besteht aus 4 Teilen: Teil 1: Eden, D., A. Arndt, A. Hoffer, T. Raschke u. P. Schön: Die Nachrichten für Dokumentation in den Jahren 1950 bis 1962 (S.159-163). Teil 2: Brose, M., E. durst, D. Nitzsche, D. Veckenstedt u. R. Wein: Statistische Untersuchung der Fachzeitschrift "Nachrichten für Dokumentation" (NfD) 1963-1975 (S.164-170). Teil 3: Bösel, J., G. Ebert, P. Garz,, M. Iwanow u. B. Russ: Methoden und Ergebnisse einer statistischen Auswertung der Fachzeitschrift "Nachrichten für Dokumentation" (NfD) 1976 bis 1988 (S.171-174). Teil 4: Engelage, H., S. Jansen, R. Mertins, K. Redel u. S. Ring: Statistische Untersuchung der Fachzeitschrift "Nachrichten für Dokumentation" (NfD) / "Information. Wissenschaft & Praxis" (IWP) 1989-2011 (S.164-170).
  9. Liu, D.-R.; Shih, M.-J.: Hybrid-patent classification based on patent-network analysis (2011) 0.02 
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    Abstract
    Effective patent management is essential for organizations to maintain their competitive advantage. The classification of patents is a critical part of patent management and industrial analysis. This study proposes a hybrid-patent-classification approach that combines a novel patent-network-based classification method with three conventional classification methods to analyze query patents and predict their classes. The novel patent network contains various types of nodes that represent different features extracted from patent documents. The nodes are connected based on the relationship metrics derived from the patent metadata. The proposed classification method predicts a query patent's class by analyzing all reachable nodes in the patent network and calculating their relevance to the query patent. It then classifies the query patent with a modified k-nearest neighbor classifier. To further improve the approach, we combine it with content-based, citation-based, and metadata-based classification methods to develop a hybrid-classification approach. We evaluate the performance of the hybrid approach on a test dataset of patent documents obtained from the U.S. Patent and Trademark Office, and compare its performance with that of the three conventional methods. The results demonstrate that the proposed patent-network-based approach yields more accurate class predictions than the patent network-based approach.
    Date
    22. 1.2011 13:04:21
  10. Song, M.; Kang, K.; An, J.Y.: Investigating drug-disease interactions in drug-symptom-disease triples via citation relations (2018) 0.02 
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    Date
    1.11.2018 18:19:22
  11. Bu, Y.; Ding, Y.; Xu, J.; Liang, X.; Gao, G.; Zhao, Y.: Understanding success through the diversity of collaborators and the milestone of career (2018) 0.02 
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  12. Zhang, C.; Bu, Y.; Ding, Y.; Xu, J.: Understanding scientific collaboration : homophily, transitivity, and preferential attachment (2018) 0.02 
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  13. Zhai, Y; Ding, Y.; Wang, F.: Measuring the diffusion of an innovation : a citation analysis (2018) 0.02 
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  14. Yan, E.; Ding, Y.: Weighted citation : an indicator of an article's prestige (2010) 0.02 
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  15. Liu, Y.; Rousseau, R.: Citation analysis and the development of science : a case study using articles by some Nobel prize winners (2014) 0.02 
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  16. Yan, E.; Ding, Y.: Discovering author impact : a PageRank perspective (2011) 0.02 
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  17. Zhang, C.; Liu, X.; Xu, Y.(C.); Wang, Y.: Quality-structure index : a new metric to measure scientific journal influence (2011) 0.01 
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  18. Tsay, M.-y.; Shu, Z.-y.: Journal bibliometric analysis : a case study on the Journal of Documentation (2011) 0.01 
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  19. Ye, F.Y.; Leydesdorff, L.: ¬The "academic trace" of the performance matrix : a mathematical synthesis of the h-index and the integrated impact indicator (I3) (2014) 0.01 
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    Abstract
    The h-index provides us with 9 natural classes which can be written as a matrix of 3 vectors. The 3 vectors are: X = (X1, X2, X3) and indicates publication distribution in the h-core, the h-tail, and the uncited ones, respectively; Y = (Y1, Y2, Y3) denotes the citation distribution of the h-core, the h-tail and the so-called "excess" citations (above the h-threshold), respectively; and Z = (Z1, Z2, Z3) = (Y1-X1, Y2-X2, Y3-X3). The matrix V = (X,Y,Z)T constructs a measure of academic performance, in which the 9 numbers can all be provided with meanings in different dimensions. The "academic trace" tr(V) of this matrix follows naturally, and contributes a unique indicator for total academic achievements by summarizing and weighting the accumulation of publications and citations. This measure can also be used to combine the advantages of the h-index and the integrated impact indicator (I3) into a single number with a meaningful interpretation of the values. We illustrate the use of tr(V) for the cases of 2 journal sets, 2 universities, and ourselves as 2 individual authors.
  20. Bu, Y.; Ding, Y.; Liang, X.; Murray, D.S.: Understanding persistent scientific collaboration (2018) 0.01 
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