Search (148 results, page 1 of 8)

  • × theme_ss:"Informetrie"
  1. Haiqi, Z.: ¬The literature of Qigong : publication patterns and subject headings (1997) 0.07
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    Source
    International forum on information and documentation. 22(1997) no.3, S.38-44
  2. He, Z.-L.: International collaboration does not have greater epistemic authority (2009) 0.06
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    Date
    26. 9.2009 11:22:05
  3. 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) 0.06
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    Date
    22. 8.2014 17:11:29
  4. Zhu, Q.; Kong, X.; Hong, S.; Li, J.; He, Z.: Global ontology research progress : a bibliometric analysis (2015) 0.06
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    Date
    20. 1.2015 18:30:22
    17. 9.2018 18:22:23
  5. Costas, R.; Zahedi, Z.; Wouters, P.: ¬The thematic orientation of publications mentioned on social media : large-scale disciplinary comparison of social media metrics with citations (2015) 0.05
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    Date
    20. 1.2015 18:30:22
  6. Wang, S.; Ma, Y.; Mao, J.; Bai, Y.; Liang, Z.; Li, G.: Quantifying scientific breakthroughs by a novel disruption indicator based on knowledge entities : On the rise of scrape-and-report scholarship in online reviews research (2023) 0.05
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    Date
    22. 1.2023 18:37:33
  7. Liu, Z.; Wang, C.: Mapping interdisciplinarity in demography : a journal network analysis (2005) 0.05
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  8. Bornmann, L.; Daniel, H.-D.: Universality of citation distributions : a validation of Radicchi et al.'s relative indicator cf = c/c0 at the micro level using data from chemistry (2009) 0.03
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    Abstract
    In a recently published PNAS paper, Radicchi, Fortunato, and Castellano (2008) propose the relative indicator cf as an unbiased indicator for citation performance across disciplines (fields, subject areas). To calculate cf, the citation rate for a single paper is divided by the average number of citations for all papers in the discipline in which the single paper has been categorized. cf values are said to lead to a universality of discipline-specific citation distributions. Using a comprehensive dataset of an evaluation study on Angewandte Chemie International Edition (AC-IE), we tested the advantage of using this indicator in practical application at the micro level, as compared with (1) simple citation rates, and (2) z-scores, which have been used in psychological testing for many years for normalization of test scores. To calculate z-scores, the mean number of citations of the papers within a discipline is subtracted from the citation rate of a single paper, and the difference is then divided by the citations' standard deviation for a discipline. Our results indicate that z-scores are better suited than cf values to produce universality of discipline-specific citation distributions.
  9. Zhang, J.; Yu, Q.; Zheng, F.; Long, C.; Lu, Z.; Duan, Z.: Comparing keywords plus of WOS and author keywords : a case study of patient adherence research (2016) 0.03
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  10. Xie, Z.; Ouyang, Z.; Li, J.; Dong, E.: Modelling transition phenomena of scientific coauthorship networks (2018) 0.03
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  11. He, Z.; Lei, Z.; Wang, D.: Modeling citation dynamics of "atypical" articles (2018) 0.03
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  12. 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.
  13. 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) 0.02
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  14. Tian, W.; Cai, R.; Fang, Z.; Geng, Y.; Wang, X.; Hu, Z.: Understanding co-corresponding authorship : a bibliometric analysis and detailed overview (2024) 0.02
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  15. Liu, Z.: Citation theories in the framework of international flow of information : new evidence with translation analysis (1997) 0.02
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  16. Morris, S.A.; Yen, G.; Wu, Z.; Asnake, B.: Time line visualization of research fronts (2003) 0.02
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  17. Nicholls, P.T.: Empirical validation of Lotka's law (1986) 0.02
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    Source
    Information processing and management. 22(1986), S.417-419
  18. Nicolaisen, J.: Citation analysis (2007) 0.02
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    Date
    13. 7.2008 19:53:22
  19. Fiala, J.: Information flood : fiction and reality (1987) 0.02
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    Source
    Thermochimica acta. 110(1987), S.11-22
  20. Su, Y.; Han, L.-F.: ¬A new literature growth model : variable exponential growth law of literature (1998) 0.02
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    Date
    22. 5.1999 19:22:35

Years

Languages

  • e 133
  • d 14
  • ro 1
  • More… Less…

Types

  • a 145
  • m 3
  • el 1
  • s 1
  • More… Less…