Search (117 results, page 1 of 6)

  • × year_i:[2010 TO 2020}
  • × theme_ss:"Informetrie"
  1. Scholarly metrics under the microscope : from citation analysis to academic auditing (2015) 0.05 
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    Classification
    010.72/7 / dc23
    Date
    22. 1.2017 17:12:50
    DDC
    010.72/7 / dc23
  2. Marx, W.; Bornmann, L.: On the problems of dealing with bibliometric data (2014) 0.02 
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    Date
    18. 3.2014 19:13:22
  3. Engqvist, L.; Frommen, J.G.: New insights into the relationship between the h-index and self-citations? (2010) 0.02 
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    Source
    Journal of the American Society for Information Science and Technology. 61(2010) no.7, S.1514-1516
  4. Prathap, G.: ¬The inconsistency of the H-index (2012) 0.02 
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    Journal of the American Society for Information Science and Technology. 63(2012) no.7, S.1466-1470
  5. Bertoli-Barsotti, L.: Improving a decomposition of the h-index (2013) 0.02 
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    Journal of the American Society for Information Science and Technology. 64(2013) no.7, S.1522
  6. Glänzel, W.: Bibliometrics-aided retrieval - where information retrieval meets scientometrics (2015) 0.02 
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    Content
    Vgl.: doi:10.1007/s11192-014-1480-7
  7. Rousseau, R.: Egghe's g-index is not a proper concentration measure (2015) 0.02 
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    Source
    Journal of the Association for Information Science and Technology. 66(2015) no.7, S.1518-1519
  8. Freistetter, F.: Warum jeder (fast) jeden kennt (2017) 0.02 
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    Source
    Spektrum der Wissenschaft. 2017, H.7, S.79
  9. Prathap, G.: ¬A three-class, three-dimensional bibliometric performance indicator (2014) 0.01 
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    Journal of the Association for Information Science and Technology. 65(2014) no.7, S.1506-1508
  10. Bornmann, L.; Mutz, R.: From P100 to P100' : a new citation-rank approach (2014) 0.01 
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    Date
    22. 8.2014 17:05:18
  11. Ohly, P.: Dimensions of globality : a bibliometric analysis (2016) 0.01 
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    Date
    20. 1.2019 11:22:31
  12. Cobo, M.J.; López-Herrera, A.G.; Herrera-Viedma, E.; Herrera, F.: Science mapping software tools : review, analysis, and cooperative study among tools (2011) 0.01 
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    Journal of the American Society for Information Science and Technology. 62(2011) no.7, S.1382-1402
  13. Zhang, L.; Rousseau, R.; Glänzel, W.: Document-type country profiles (2011) 0.01 
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    Journal of the American Society for Information Science and Technology. 62(2011) no.7, S.1403-1411
  14. Glänzel, W.; Rousseau, D.; Zhang, L.: ¬A visual representation of relative first-citation times (2012) 0.01 
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    Journal of the American Society for Information Science and Technology. 63(2012) no.7, S.1420-1425
  15. Zhang, L.; Thijs, B.; Glänzel, W.: What does scientometrics share with other "metrics" sciences? (2013) 0.01 
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    Journal of the American Society for Information Science and Technology. 64(2013) no.7, S.1515-1518
  16. Vogler, E.; Schindler, C.; Botte, A.; Rittberger, M.: Are altmetrics effective in transdisciplinary research fields? : altmetric coverage of outputs in educational research (2017) 0.01 
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    Date
    7. 4.2017 19:21:01
  17. Smolinsky, L.J.: Discrete power law with exponential cutoff and Lotka's law (2017) 0.01 
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    Journal of the Association for Information Science and Technology. 68(2017) no.7, S.1792-1795
  18. 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) 0.01 
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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.
  19. Yan, E.: Finding knowledge paths among scientific disciplines (2014) 0.01 
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    Date
    26.10.2014 20:22:22
  20. Zhu, Q.; Kong, X.; Hong, S.; Li, J.; He, Z.: Global ontology research progress : a bibliometric analysis (2015) 0.01 
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    Date
    20. 1.2015 18:30:22
    17. 9.2018 18:22:23

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