Search (44 results, page 1 of 3)

  • × theme_ss:"Informetrie"
  • × year_i:[2000 TO 2010}
  1. Rostaing, H.; Barts, N.; Léveillé, V.: Bibliometrics: representation instrument of the multidisciplinary positioning of a scientific area : Implementation for an Advisory Scientific Committee (2007) 0.07
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    Date
    30.12.2007 11:22:39
  2. Larivière, V.; Gingras, Y.; Archambault, E.: ¬The decline in the concentration of citations, 1900-2007 (2009) 0.06
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    Date
    22. 3.2009 19:22:35
  3. Nicolaisen, J.: Citation analysis (2007) 0.02
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    Date
    13. 7.2008 19:53:22
  4. Pernik, V.; Schlögl, C.: Möglichkeiten und Grenzen von Web Structure Mining am Beispiel von informationswissenschaftlichen Hochschulinstituten im deutschsprachigen Raum (2006) 0.02
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  5. Van der Veer Martens, B.: Do citation systems represent theories of truth? (2001) 0.02
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    Date
    22. 7.2006 15:22:28
  6. Larivière, V.; Archambault, V.; Gingras, Y.; Vignola-Gagné, E.: ¬The place of serials in referencing practices : comparing natural sciences and engineering with social sciences and humanities (2006) 0.02
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  7. Pulgarin, A.; Gil-Leiva, I.: Bibliometric analysis of the automatic indexing literature : 1956-2000 (2004) 0.02
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    Abstract
    We present a bibliometric study of a corpus of 839 bibliographic references about automatic indexing, covering the period 1956-2000. We analyse the distribution of authors and works, the obsolescence and its dispersion, and the distribution of the literature by topic, year, and source type. We conclude that: (i) there has been a constant interest on the part of researchers; (ii) the most studied topics were the techniques and methods employed and the general aspects of automatic indexing; (iii) the productivity of the authors does fit a Lotka distribution (Dmax=0.02 and critical value=0.054); (iv) the annual aging factor is 95%; and (v) the dispersion of the literature is low.
  8. Cothey, V.: Web-crawling reliability (2004) 0.02
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  9. Della Mea, V.; Demartini, G.; Di Gaspero, L.; Mizzaro, S.: Measuring retrieval effectiveness with Average Distance Measure (ADM) (2006) 0.02
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  10. Petersen, A.; Münch, V.: STN® AnaVist(TM) holt verborgenes Wissen aus Recherche-Ergebnissen : Neue Software analysiert und visualisiert Marktaufteilung, Forschung und Patentaktivitäten (2005) 0.02
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    Abstract
    "Im 21. Jahrhundert ist die entscheidende Herausforderung an Informationsdienstleister nicht, Informationen zugänglich, sondern sie optimal nutzbar zu machen", sagt Sabine Brünger-Weilandt, Geschäftsführerin von FIZ Karlsruhe, das den Online-Dienst STN International in internationaler Kooperation betreibt. Informationsprofis, so Brünger-Weilandt weiter, bräuchten hockentwickelte Software für strategisches Informationsmanagement. Als "Antwort auf diesen Bedarf" hat STN International eine neue Software zur Analyse und Visualisierung (A&V) von Rechercheergebnissen aus STN-Datenbanken entwickelt. STN® AnaVistT(TM) wurde auf der DGI Online-Tagung Ende Mai in Frankfurt am Main und auf Benutzertreffen in Frankfurt am Main, München und Essen vorgestellt. Seit 18. Juli 2005 ist das neue A&V-Werkzeug für die öffentliche Nutzung freigegeben (www.stn-international.de).
  11. Lewison, G.: ¬The work of the Bibliometrics Research Group (City University) and associates (2005) 0.02
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    Date
    20. 1.2007 17:02:22
  12. Aris, A.; Shneiderman, B.; Qazvinian, V.; Radev, D.: Visual overviews for discovering key papers and influences across research fronts (2009) 0.02
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  13. Kuperman, V.: Productivity in the Internet mailing lists : a bibliometric analysis (2006) 0.01
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  14. Larivière, V.; Archambault, E.; Gingras, Y.: Long-term variations in the aging of scientific literature : from exponential growth to steady-state science (1900-2004) (2008) 0.01
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  15. Lisée, C.; Larivière, V.; Archambault, E.: Conference proceedings as a source of scientific information : a bibliometric analysis (2008) 0.01
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  16. Wallace, M.L.; Gingras, Y.; Duhon, R.: ¬A new approach for detecting scientific specialties from raw cocitation networks (2009) 0.01
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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.
  17. Archambault, E.; Campbell, D; Gingras, Y.; Larivière, V.: Comparing bibliometric statistics obtained from the Web of Science and Scopus (2009) 0.01
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  18. Raan, A.F.J. van: Statistical properties of bibliometric indicators : research group indicator distributions and correlations (2006) 0.01
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    Date
    22. 7.2006 16:20:22
  19. Thelwall, M.; Ruschenburg, T.: Grundlagen und Forschungsfelder der Webometrie (2006) 0.01
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    Date
    4.12.2006 12:12:22
  20. Levitt, J.M.; Thelwall, M.: Citation levels and collaboration within library and information science (2009) 0.01
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    Abstract
    Collaboration is a major research policy objective, but does it deliver higher quality research? This study uses citation analysis to examine the Web of Science (WoS) Information Science & Library Science subject category (IS&LS) to ascertain whether, in general, more highly cited articles are more highly collaborative than other articles. It consists of two investigations. The first investigation is a longitudinal comparison of the degree and proportion of collaboration in five strata of citation; it found that collaboration in the highest four citation strata (all in the most highly cited 22%) increased in unison over time, whereas collaboration in the lowest citation strata (un-cited articles) remained low and stable. Given that over 40% of the articles were un-cited, it seems important to take into account the differences found between un-cited articles and relatively highly cited articles when investigating collaboration in IS&LS. The second investigation compares collaboration for 35 influential information scientists; it found that their more highly cited articles on average were not more highly collaborative than their less highly cited articles. In summary, although collaborative research is conducive to high citation in general, collaboration has apparently not tended to be essential to the success of current and former elite information scientists.
    Date
    22. 3.2009 12:43:51