Search (132 results, page 2 of 7)

  • × theme_ss:"Informetrie"
  • × year_i:[2010 TO 2020}
  1. Ohly, P.: Dimensions of globality : a bibliometric analysis (2016) 0.01 
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    Date
    20. 1.2019 11:22:31
  2. Yan, E.: Finding knowledge paths among scientific disciplines (2014) 0.01 
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    Date
    26.10.2014 20:22:22
  3. Campanario, J.M.: Large increases and decreases in journal impact factors in only one year : the effect of journal self-citations (2011) 0.01 
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    Date
    22. 1.2011 12:53:00
  4. Ding, Y.: Applying weighted PageRank to author citation networks (2011) 0.01 
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    Date
    22. 1.2011 13:02:21
  5. Vieira, E.S.; Cabral, J.A.S.; Gomes, J.A.N.F.: Definition of a model based on bibliometric indicators for assessing applicants to academic positions (2014) 0.01 
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    Date
    18. 3.2014 18:22:21
  6. Huang, H.; Andrews, J.; Tang, J.: Citation characterization and impact normalization in bioinformatics journals (2012) 0.01 
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  7. 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) 0.01 
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  8. 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) 0.01 
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  9. Smolinsky, L.J.: Discrete power law with exponential cutoff and Lotka's law (2017) 0.01 
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    Abstract
    One of the first bibliometric laws appeared in Alfred J. Lotka's 1926 examination of author productivity in chemistry and physics. The result was a productivity distribution described by a power law. In this paper, Lotka's original data on author productivity in chemistry are reconsidered. We define a discrete power law with exponential cutoff, test Lotka's data, and compare the fit to the discrete power law.
  10. Renn, O.; Schnabl, J.: Forschungsmetriken: Ignorieren, boykottieren oder nutzen? : Forschungsmetriken in die Praxis gebracht (2017) 0.01 
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  11. Ajiferuke, I.; Lu, K.; Wolfram, D.: ¬A comparison of citer and citation-based measure outcomes for multiple disciplines (2010) 0.01 
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    Date
    28. 9.2010 12:54:22
  12. D'Angelo, C.A.; Giuffrida, C.; Abramo, G.: ¬A heuristic approach to author name disambiguation in bibliometrics databases for large-scale research assessments (2011) 0.01 
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    Date
    22. 1.2011 13:06:52
  13. 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) 0.01 
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    Date
    22. 3.2013 19:44:17
  14. Wan, X.; Liu, F.: Are all literature citations equally important? : automatic citation strength estimation and its applications (2014) 0.01 
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    Date
    22. 8.2014 17:12:35
  15. Huang, M.-H.; Huang, W.-T.; Chang, C.-C.; Chen, D. Z.; Lin, C.-P.: The greater scattering phenomenon beyond Bradford's law in patent citation (2014) 0.01 
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    Date
    22. 8.2014 17:11:29
  16. Kronegger, L.; Mali, F.; Ferligoj, A.; Doreian, P.: Classifying scientific disciplines in Slovenia : a study of the evolution of collaboration structures (2015) 0.01 
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    Date
    21. 1.2015 14:55:22
  17. Ntuli, H.; Inglesi-Lotz, R.; Chang, T.; Pouris, A.: Does research output cause economic growth or vice versa? : evidence from 34 OECD countries (2015) 0.01 
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    Date
    8. 7.2015 22:00:42
  18. Kumar, S.: Co-authorship networks : a review of the literature (2015) 0.01 
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    20. 1.2015 18:30:22
  19. Dobrota, M.; Dobrota, M.: ARWU ranking uncertainty and sensitivity : what if the award factor was Excluded? (2016) 0.01 
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    Date
    22. 1.2016 14:40:53
  20. Ridenour, L.: Boundary objects : measuring gaps and overlap between research areas (2016) 0.01 
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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.

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