Search (157 results, page 2 of 8)

  • × theme_ss:"Informetrie"
  • × year_i:[2010 TO 2020}
  1. Yan, E.: Finding knowledge paths among scientific disciplines (2014) 0.01
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    Date
    26.10.2014 20:22:22
  2. 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
  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. Schlögl, C.: Internationale Sichtbarkeit der europäischen und insbesondere der deutschsprachigen Informationswissenschaft (2013) 0.01
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    Date
    22. 3.2013 14:04:09
  6. 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
  7. Abbasi, M. K.; Frommholz, I.: Cluster-based polyrepresentation as science modelling approach for information retrieval (2015) 0.01
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  8. 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
  9. Albarrán, P.; Ruiz-Castillo, J.: References made and citations received by scientific articles (2011) 0.01
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    Abstract
    This article studies massive evidence about references made and citations received after a 5-year citation window by 3.7 million articles published in 1998 to 2002 in 22 scientific fields. We find that the distributions of references made and citations received share a number of basic features across sciences. Reference distributions are rather skewed to the right while citation distributions are even more highly skewed: The mean is about 20 percentage points to the right of the median, and articles with a remarkable or an outstanding number of citations represent about 9% of the total. Moreover, the existence of a power law representing the upper tail of citation distributions cannot be rejected in 17 fields whose articles represent 74.7% of the total. Contrary to the evidence in other contexts, the value of the scale parameter is above 3.5 in 13 of the 17 cases. Finally, power laws are typically small, but capture a considerable proportion of the total citations received.
  10. 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
  11. Hicks, D.; Wang, J.: Coverage and overlap of the new social sciences and humanities journal lists (2011) 0.01
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    Date
    22. 1.2011 13:21:28
  12. Frandsen, T.F.; Nicolaisen, J.: ¬The ripple effect : citation chain reactions of a nobel prize (2013) 0.01
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    Date
    22. 3.2013 16:21:09
  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. 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
  16. 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
  17. Kumar, S.: Co-authorship networks : a review of the literature (2015) 0.01
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    Date
    20. 1.2015 18:30:22
  18. Li, J.; Shi, D.: Sleeping beauties in genius work : when were they awakened? (2016) 0.01
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    Date
    22. 1.2016 14:13:32
  19. 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.
  20. Leydesdorff, L.; Bornmann, L.; Wagner, C.S.: ¬The relative influences of government funding and international collaboration on citation impact (2019) 0.01
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    Date
    8. 1.2019 18:22:45

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