Search (78 results, page 1 of 4)

  • × theme_ss:"Informetrie"
  • × year_i:[2010 TO 2020}
  1. Wan, X.; Liu, F.: Are all literature citations equally important? : automatic citation strength estimation and its applications (2014) 0.05 
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    Date
    22. 8.2014 17:12:35
  2. Zhu, Q.; Kong, X.; Hong, S.; Li, J.; He, Z.: Global ontology research progress : a bibliometric analysis (2015) 0.04 
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    Date
    20. 1.2015 18:30:22
    17. 9.2018 18:22:23
  3. Ye, F.Y.: ¬A theoretical approach to the unification of informetric models by wave-heat equations (2011) 0.03 
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    Abstract
    A simple distribution function f(x, t)=p(x+q)**-ße**alpha*t obeys wave and heat equations, that constructs a theoretical approach to the unification of informetric models, with which we can unify all informetric laws. While its space-type distributions deduce naturally Lotka-type laws in size approaches and Zipf-type laws in rank approaches, its time-type distributions introduce the mechanism of Price-type and Brookes-type laws.
  4. 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
  5. Prathap, G.: Fractionalized exergy for evaluating research performance (2011) 0.02 
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    Abstract
    The approach based on "thermodynamic" considerations that can quantify research performance using an exergy term defined as X = iC, where i is the impact and C is the number of citations is now extended to cases where fractionalized counting of citations is used instead of integer counting.
  6. 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) 0.02 
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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.
  7. Hu, X.; Rousseau, R.; Chen, J.: ¬A new approach for measuring the value of patents based on structural indicators for ego patent citation networks (2012) 0.02 
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  8. Egghe, L.: Influence of adding or deleting items and sources on the h-index (2010) 0.01 
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    Abstract
    Adding or deleting items such as self-citations has an influence on the h-index of an author. This influence will be proved mathematically in this article. We hereby prove the experimental finding in E. Gianoli and M.A. Molina-Montenegro ([2009]) that the influence of adding or deleting self-citations on the h-index is greater for low values of the h-index. Why this is logical also is shown by a simple theoretical example. Adding or deleting sources such as adding or deleting minor contributions of an author also has an influence on the h-index of this author; this influence is modeled in this article. This model explains some practical examples found in X. Hu, R. Rousseau, and J. Chen (in press).
  9. Zhou, P.; Su, X.; Leydesdorff, L.: ¬A comparative study on communication structures of Chinese journals in the social sciences (2010) 0.01 
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  10. Koulouri, X.; Ifrim, C.; Wallace, M.; Pop, F.: Making sense of citations (2017) 0.01 
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  11. Zheng, X.; Sun, A.: Collecting event-related tweets from twitter stream (2019) 0.01 
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  12. Hu, X.; Rousseau, R.: Do citation chimeras exist? : The case of under-cited influential articles suffering delayed recognition (2019) 0.01 
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  13. Scholarly metrics under the microscope : from citation analysis to academic auditing (2015) 0.01 
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    Date
    22. 1.2017 17:12:50
  14. 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
  15. Ohly, P.: Dimensions of globality : a bibliometric analysis (2016) 0.01 
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    Date
    20. 1.2019 11:22:31
  16. 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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  17. Liu, X.; Kaza, S.; Zhang, P.; Chen, H.: Determining inventor status and its effect on knowledge diffusion : a study on nanotechnology literature from China, Russia, and India (2011) 0.01 
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  18. Liu, X.; Zhang, J.; Guo, C.: Full-text citation analysis : a new method to enhance scholarly networks (2013) 0.01 
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  19. Xu, C.; Ma, B.; Chen, X.; Ma, F.: Social tagging in the scholarly world (2013) 0.01 
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  20. Wan, X.; Liu, F.: WL-index : leveraging citation mention number to quantify an individual's scientific impact (2014) 0.01 
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