Search (84 results, page 3 of 5)

  • × theme_ss:"Informetrie"
  • × year_i:[2010 TO 2020}
  1. 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
  2. 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
  3. 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
  4. 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
  5. Kumar, S.: Co-authorship networks : a review of the literature (2015) 0.01 
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    Date
    20. 1.2015 18:30:22
  6. 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
  7. 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
  8. 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.
  9. Thelwall, M.; Maflahi, N.: Guideline references and academic citations as evidence of the clinical value of health research (2016) 0.01 
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    Date
    19. 3.2016 12:22:00
  10. Thelwall, M.; Sud, P.: Mendeley readership counts : an investigation of temporal and disciplinary differences (2016) 0.01 
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    Date
    16.11.2016 11:07:22
  11. Didegah, F.; Thelwall, M.: Co-saved, co-tweeted, and co-cited networks (2018) 0.01 
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    Date
    28. 7.2018 10:00:22
  12. 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
  13. Marx, W.; Bornmann, L.; Cardona, M.: Reference standards and reference multipliers for the comparison of the citation impact of papers published in different time periods (2010) 0.01 
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  14. Bornmann, L.; Schier, H.; Marx, W.; Daniel, H.-D.: Is interactive open access publishing able to identify high-impact submissions? : a study on the predictive validity of Atmospheric Chemistry and Physics by using percentile rank classes (2011) 0.01 
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  15. Zhang, L.; Rousseau, R.; Glänzel, W.: Diversity of references as an indicator of the interdisciplinarity of journals : taking similarity between subject fields into account (2016) 0.01 
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  16. Pan, X.; Yan, E.; Hua, W.: Science communication and dissemination in different cultures : an analysis of the audience for TED videos in China and abroad (2016) 0.01 
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  17. Leydesdorff, L.; Moya-Anegón, F. de; Nooy, W. de: Aggregated journal-journal citation relations in scopus and web of science matched and compared in terms of networks, maps, and interactive overlays (2016) 0.01 
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  18. Bornmann, L.; Thor, A.; Marx, W.; Schier, H.: ¬The application of bibliometrics to research evaluation in the humanities and social sciences : an exploratory study using normalized Google Scholar data for the publications of a research institute (2016) 0.01 
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  19. Leydesdorff, L.; Nooy, W. de: Can "hot spots" in the sciences be mapped using the dynamics of aggregated journal-journal citation relations (2017) 0.01 
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  20. Leydesdorff, L.; Bornmann, L.; Mingers, J.: Statistical significance and effect sizes of differences among research universities at the level of nations and worldwide based on the Leiden rankings (2019) 0.01 
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    Abstract
    The Leiden Rankings can be used for grouping research universities by considering universities which are not statistically significantly different as homogeneous sets. The groups and intergroup relations can be analyzed and visualized using tools from network analysis. Using the so-called "excellence indicator" PPtop-10%-the proportion of the top-10% most-highly-cited papers assigned to a university-we pursue a classification using (a) overlapping stability intervals, (b) statistical-significance tests, and (c) effect sizes of differences among 902 universities in 54 countries; we focus on the UK, Germany, Brazil, and the USA as national examples. Although the groupings remain largely the same using different statistical significance levels or overlapping stability intervals, these classifications are uncorrelated with those based on effect sizes. Effect sizes for the differences between universities are small (w < .2). The more detailed analysis of universities at the country level suggests that distinctions beyond three or perhaps four groups of universities (high, middle, low) may not be meaningful. Given similar institutional incentives, isomorphism within each eco-system of universities should not be underestimated. Our results suggest that networks based on overlapping stability intervals can provide a first impression of the relevant groupings among universities. However, the clusters are not well-defined divisions between groups of universities.

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