Search (412 results, page 2 of 21)

  • × theme_ss:"Informetrie"
  • × type_ss:"a"
  1. Leydesdorff, L.; Bornmann, L.; Wagner, C.S.: ¬The relative influences of government funding and international collaboration on citation impact (2019) 0.03
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    Date
    8. 1.2019 18:22:45
  2. Zhang, Y.: ¬The impact of Internet-based electronic resources on formal scholarly communication in the area of library and information science : a citation analysis (1998) 0.03
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    Date
    30. 1.1999 17:22:22
  3. 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) 0.03
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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.
  4. Wallace, M.L.; Gingras, Y.; Duhon, R.: ¬A new approach for detecting scientific specialties from raw cocitation networks (2009) 0.03
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    Abstract
    We use a technique recently developed by V. Blondel, J.-L. Guillaume, R. Lambiotte, and E. Lefebvre (2008) to detect scientific specialties from author cocitation networks. This algorithm has distinct advantages over most previous methods used to obtain cocitation clusters since it avoids the use of similarity measures, relies entirely on the topology of the weighted network, and can be applied to relatively large networks. Most importantly, it requires no subjective interpretation of the cocitation data or of the communities found. Using two examples, we show that the resulting specialties are the smallest coherent groups of researchers (within a hierarchy of cluster sizes) and can thus be identified unambiguously. Furthermore, we confirm that these communities are indeed representative of what we know about the structure of a given scientific discipline and that as specialties, they can be accurately characterized by a few keywords (from the publication titles). We argue that this robust and efficient algorithm is particularly well-suited to cocitation networks and that the results generated can be of great use to researchers studying various facets of the structure and evolution of science.
  5. Li, R.; Chambers, T.; Ding, Y.; Zhang, G.; Meng, L.: Patent citation analysis : calculating science linkage based on citing motivation (2014) 0.03
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  6. Tu, Y.-N.; Hsu, S.-L.: Constructing conceptual trajectory maps to trace the development of research fields (2016) 0.03
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  7. Aung, H.H.; Zheng, H.; Erdt, M.; Aw, A.S.; Sin, S.-C.J.; Theng, Y.-L.: Investigating familiarity and usage of traditional metrics and altmetrics (2019) 0.03
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  8. Kulczycki, E.; Huang, Y.; Zuccala, A.A.; Engels, T.C.E.; Ferrara, A.; Guns, R.; Pölönen, J.; Sivertsen, G.; Taskin, Z.; Zhang, L.: Uses of the Journal Impact Factor in national journal rankings in China and Europe (2022) 0.03
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  9. Zhang, L.; Gou, Z.; Fang, Z.; Sivertsen, G.; Huang, Y.: Who tweets scientific publications? : a large-scale study of tweeting audiences in all areas of research (2023) 0.03
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  10. Tonta, Y.; Ünal, Y.: Scatter of journals and literature obsolescence reflected in document delivery requests (2005) 0.03
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    Date
    20. 3.2005 10:54:22
  11. Meho, L.I.; Rogers, Y.: Citation counting, citation ranking, and h-index of human-computer interaction researchers : a comparison of Scopus and Web of Science (2008) 0.03
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    Abstract
    This study examines the differences between Scopus and Web of Science in the citation counting, citation ranking, and h-index of 22 top human-computer interaction (HCI) researchers from EQUATOR - a large British Interdisciplinary Research Collaboration project. Results indicate that Scopus provides significantly more coverage of HCI literature than Web of Science, primarily due to coverage of relevant ACM and IEEE peer-reviewed conference proceedings. No significant differences exist between the two databases if citations in journals only are compared. Although broader coverage of the literature does not significantly alter the relative citation ranking of individual researchers, Scopus helps distinguish between the researchers in a more nuanced fashion than Web of Science in both citation counting and h-index. Scopus also generates significantly different maps of citation networks of individual scholars than those generated by Web of Science. The study also presents a comparison of h-index scores based on Google Scholar with those based on the union of Scopus and Web of Science. The study concludes that Scopus can be used as a sole data source for citation-based research and evaluation in HCI, especially when citations in conference proceedings are sought, and that researchers should manually calculate h scores instead of relying on system calculations.
  12. Chang, Y.-W.; Huang, M.-H.: ¬A study of the evolution of interdisciplinarity in library and information science : using three bibliometric methods (2012) 0.03
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    Source
    Journal of the American Society for Information Science and Technology. 63(2012) no.1, S.22-33
  13. Zhang, Y.; Wu, M.; Zhang, G.; Lu, J.: Stepping beyond your comfort zone : diffusion-based network analytics for knowledge trajectory recommendation (2023) 0.03
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    Date
    22. 6.2023 18:07:12
  14. Vakkari, P.; Järvelin, K.; Chang, Y.-W.: ¬The association of disciplinary background with the evolution of topics and methods in Library and Information Science research 1995-2015 (2023) 0.03
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    Date
    22. 6.2023 18:15:06
  15. Leydesdorff, L.; Bornmann, L.: How fractional counting of citations affects the impact factor : normalization in terms of differences in citation potentials among fields of science (2011) 0.03
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    Date
    22. 1.2011 12:51:07
  16. Egghe, L.; Rousseau, R.: Averaging and globalising quotients of informetric and scientometric data (1996) 0.03
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    Source
    Journal of information science. 22(1996) no.3, S.165-170
  17. Leydesdorff, L.: Can networks of journal-journal citations be used as indicators of change in the social sciences? (2003) 0.03
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    Date
    6.11.2005 19:02:22
  18. He, Z.-L.: International collaboration does not have greater epistemic authority (2009) 0.03
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    Date
    26. 9.2009 11:22:05
  19. 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) 0.03
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    Date
    22. 3.2013 19:44:17
  20. Kronegger, L.; Mali, F.; Ferligoj, A.; Doreian, P.: Classifying scientific disciplines in Slovenia : a study of the evolution of collaboration structures (2015) 0.03
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    Date
    21. 1.2015 14:55:22

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