Search (72 results, page 1 of 4)

  • × theme_ss:"Informetrie"
  • × year_i:[2010 TO 2020}
  1. 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
  2. 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
  3. 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
  4. 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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  5. Xie, Z.; Ouyang, Z.; Li, J.; Dong, E.: Modelling transition phenomena of scientific coauthorship networks (2018) 0.03 
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  6. He, Z.; Lei, Z.; Wang, D.: Modeling citation dynamics of "atypical" articles (2018) 0.03 
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  7. 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.
  8. Huang, M.-H.; Tang, M.-C.; Chen, D.-Z.: Inequality of publishing performance and international collaboration in physics (2011) 0.02 
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  9. Kuan, C.-H.; Huang, M.-H.; Chen, D.-Z.: ¬A two-dimensional approach to performance evaluation for a large number of research institutions (2012) 0.02 
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  10. Lancho Barrantes, B.S.; Guerrero Bote, V.P.; Chinchilla Rodríguez, Z.; Moya Anegón, F. de: Citation flows in the zones of influence of scientific collaborations (2012) 0.02 
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  11. Bornmann, L.: Lässt sich die Qualität von Forschung messen? (2013) 0.02 
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    Abstract
    Grundsätzlich können wir bei Bewertungen in der Wissenschaft zwischen einer 'qualitative' Form, der Bewertung einer wissenschaftlichen Arbeit (z. B. eines Manuskripts oder Forschungsantrags) durch kompetente Peers, und einer 'quantitative' Form, der Bewertung von wissenschaftlicher Arbeit anhand bibliometrischer Indikatoren unterscheiden. Beide Formen der Bewertung sind nicht unumstritten. Die Kritiker des Peer Review sehen vor allem zwei Schwächen des Verfahrens: (1) Verschiedene Gutachter würden kaum in der Bewertung ein und derselben wissenschaftlichen Arbeit übereinstimmen. (2) Gutachterliche Empfehlungen würden systematische Urteilsverzerrungen aufweisen. Gegen die Verwendung von Zitierhäufigkeiten als Indikator für die Qualität einer wissenschaftlichen Arbeit wird seit Jahren eine Vielzahl von Bedenken geäußert. Zitierhäufigkeiten seien keine 'objektiven' Messungen von wissenschaftlicher Qualität, sondern ein kritisierbares Messkonstrukt. So wird unter anderem kritisiert, dass wissenschaftliche Qualität ein komplexes Phänomen darstelle, das nicht auf einer eindimensionalen Skala (d. h. anhand von Zitierhäufigkeiten) gemessen werden könne. Es werden empirische Ergebnisse zur Reliabilität und Fairness des Peer Review Verfahrens sowie Forschungsergebnisse zur Güte von Zitierhäufigkeiten als Indikator für wissenschaftliche Qualität vorgestellt.
  12. Tüür-Fröhlich, T.: ¬Eine "autoritative" Datenbank auf dem Prüfstand : der Social Sciences Citation Index (SSCI) und seine Datenqualität (2018) 0.02 
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    Abstract
    Zitatdatenbanken bilden die Datengrundlagen für zahlreiche szientometrische Untersuchungen, Evaluationen wissenschaftlicher Leistungen und Uni-Rankings. In der Literatur finden sich kaum Hinweise auf endogene Fehler (Original richtig, Datenbankeintrag falsch) in den kostenpflichtigen Datenbanken. Banale Fehler (z. B. Falschschreibung der Namen von Autorinnen oder Autoren) in Datenbanken hätten nur geringe Relevanz. Die Fehlersuche zu Pierre Bourdieu als "cited author" im SSCI (Vergleich Original - SSCI-Record) ergab mehr als 85 Mutationen. Die Fallstudien zeigen eine hohe Anzahl endogener Datenbankfehler. In den Rechtswissenschaften übliche Referenzen in Fußnoten laufen große Gefahr, in Phantomreferenzen verwandelt zu werden (Fallstudie Harvard Law Review: 99 Prozent Fehler). Dem Anspruch des SSCI, die "relevanten" globalen Sozialwissenschaften abzubilden - für alle im SSCI erfassten Disziplinen -, stehen offenbar Mängel in Datenerfassung und -verarbeitung im Wege.
  13. Marx, W.; Bornmann, L.: On the problems of dealing with bibliometric data (2014) 0.02 
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    Date
    18. 3.2014 19:13:22
  14. Huang, M.-H.; Lin, C.-S.; Chen, D.-Z.: Counting methods, country rank changes, and counting inflation in the assessment of national research productivity and impact (2011) 0.02 
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  15. Tsay, M.-y.; Shu, Z.-y.: Journal bibliometric analysis : a case study on the Journal of Documentation (2011) 0.02 
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  16. Costas, R.; Zahedi, Z.; Wouters, P.: Do "altmetrics" correlate with citations? : extensive comparison of altmetric indicators with citations from a multidisciplinary perspective (2015) 0.02 
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  17. Amolochitis, E.; Christou, I.T.; Tan, Z.-H.; Prasad, R.: ¬A heuristic hierarchical scheme for academic search and retrieval (2013) 0.02 
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  18. Jiang, Z.; Liu, X.; Chen, Y.: Recovering uncaptured citations in a scholarly network : a two-step citation analysis to estimate publication importance (2016) 0.02 
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  19. Gazni, A.; Ghaseminik, Z.: Author practices in citing other authors, institutions, and journals (2016) 0.02 
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  20. Zahedi, Z.; Costas, R.; Wouters, P.: Mendeley readership as a filtering tool to identify highly cited publications (2017) 0.02 
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  • d 6

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  • a 71
  • m 1
  • s 1
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Themes

Subjects

Classifications