Search (1380 results, page 1 of 69)

  • × theme_ss:"Informetrie"
  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) 0.05
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    Date
    22. 8.2014 17:11:29
    Source
    Journal of the Association for Information Science and Technology. 65(2014) no.9, S.1917-1928
  2. Li, T.-C.: Reference sources in periodicals : research note (1995) 0.05
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    Abstract
    Presents a list of 53 periodicals in 22 subject fields which regularly provide bibliographies of theses, research in progress and patents in their particular subject field. The fields of business, economics, history and literature have most periodical listings of dissertations and theses. Also lists 63 periodicals in 25 sub-disciplines which provide rankings or ratings. Rankings and ratings information predominates in the fields of business, sports and games, finance and banking, and library and information science
    Source
    Journal of information; communication; and library science. 2(1995) no.2, S.20-28
  3. Schlögl, C.: Internationale Sichtbarkeit der europäischen und insbesondere der deutschsprachigen Informationswissenschaft (2013) 0.04
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    Date
    22. 3.2013 14:04:09
    Source
    Information - Wissenschaft und Praxis. 64(2013) H.1, S.1-8
  4. D'Angelo, C.A.; Giuffrida, C.; Abramo, G.: ¬A heuristic approach to author name disambiguation in bibliometrics databases for large-scale research assessments (2011) 0.04
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    Date
    22. 1.2011 13:06:52
    Source
    Journal of the American Society for Information Science and Technology. 62(2011) no.2, S.257-269
  5. Chen, C.: CiteSpace II : detecting and visualizing emerging trends and transient patterns in scientific literature (2006) 0.04
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    Abstract
    This article describes the latest development of a generic approach to detecting and visualizing emerging trends and transient patterns in scientific literature. The work makes substantial theoretical and methodological contributions to progressive knowledge domain visualization. A specialty is conceptualized and visualized as a time-variant duality between two fundamental concepts in information science: research fronts and intellectual bases. A research front is defined as an emergent and transient grouping of concepts and underlying research issues. The intellectual base of a research front is its citation and co-citation footprint in scientific literature - an evolving network of scientific publications cited by research-front concepts. Kleinberg's (2002) burst-detection algorithm is adapted to identify emergent research-front concepts. Freeman's (1979) betweenness centrality metric is used to highlight potential pivotal points of paradigm shift over time. Two complementary visualization views are designed and implemented: cluster views and time-zone views. The contributions of the approach are that (a) the nature of an intellectual base is algorithmically and temporally identified by emergent research-front terms, (b) the value of a co-citation cluster is explicitly interpreted in terms of research-front concepts, and (c) visually prominent and algorithmically detected pivotal points substantially reduce the complexity of a visualized network. The modeling and visualization process is implemented in CiteSpace II, a Java application, and applied to the analysis of two research fields: mass extinction (1981-2004) and terrorism (1990-2003). Prominent trends and pivotal points in visualized networks were verified in collaboration with domain experts, who are the authors of pivotal-point articles. Practical implications of the work are discussed. A number of challenges and opportunities for future studies are identified.
    Date
    22. 7.2006 16:11:05
    Source
    Journal of the American Society for Information Science and Technology. 57(2006) no.3, S.359-377
  6. Nicholls, P.T.: Empirical validation of Lotka's law (1986) 0.04
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    Source
    Information processing and management. 22(1986), S.417-419
  7. Fiala, J.: Information flood : fiction and reality (1987) 0.04
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    Source
    Thermochimica acta. 110(1987), S.11-22
  8. Diodato, V.: Dictionary of bibliometrics (1994) 0.03
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    Footnote
    Rez. in: Journal of library and information science 22(1996) no.2, S.116-117 (L.C. Smith)
    Pages
    185 S
  9. Liu, S.; Chen, C.: ¬The differences between latent topics in abstracts and citation contexts of citing papers (2013) 0.03
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    Date
    22. 3.2013 19:50:00
    Source
    Journal of the American Society for Information Science and Technology. 64(2013) no.3, S.627-639
  10. Norris, M.; Oppenheim, C.: ¬The h-index : a broad review of a new bibliometric indicator (2010) 0.03
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    Date
    8. 1.2011 19:22:13
    Source
    Journal of documentation. 66(2010) no.5, S.681-705
  11. 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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    Abstract
    The Impact Factors (IFs) of the Institute for Scientific Information suffer from a number of drawbacks, among them the statistics-Why should one use the mean and not the median?-and the incomparability among fields of science because of systematic differences in citation behavior among fields. Can these drawbacks be counteracted by fractionally counting citation weights instead of using whole numbers in the numerators? (a) Fractional citation counts are normalized in terms of the citing sources and thus would take into account differences in citation behavior among fields of science. (b) Differences in the resulting distributions can be tested statistically for their significance at different levels of aggregation. (c) Fractional counting can be generalized to any document set including journals or groups of journals, and thus the significance of differences among both small and large sets can be tested. A list of fractionally counted IFs for 2008 is available online at http:www.leydesdorff.net/weighted_if/weighted_if.xls The between-group variance among the 13 fields of science identified in the U.S. Science and Engineering Indicators is no longer statistically significant after this normalization. Although citation behavior differs largely between disciplines, the reflection of these differences in fractionally counted citation distributions can not be used as a reliable instrument for the classification.
    Date
    22. 1.2011 12:51:07
    Source
    Journal of the American Society for Information Science and Technology. 62(2011) no.2, S.217-229
  12. Schubert, T.; Michels, C.: Placing articles in the large publisher nations : is there a "free lunch" in terms of higher impact? (2013) 0.03
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    Date
    22. 3.2013 19:45:49
    Source
    Journal of the American Society for Information Science and Technology. 64(2013) no.3, S.596-611
  13. Kuan, C.-H.; Liu, J.S.: ¬A new approach for main path analysis : decay in knowledge diffusion (2016) 0.03
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    Date
    22. 1.2016 14:23:00
    Source
    Journal of the Association for Information Science and Technology. 67(2016) no.2, S.465-476
  14. Cerda-Cosme, R.; Méndez, E.: Analysis of shared research data in Spanish scientific papers about COVID-19 : a first approach (2023) 0.03
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    Abstract
    During the coronavirus pandemic, changes in the way science is done and shared occurred, which motivates meta-research to help understand science communication in crises and improve its effectiveness. The objective is to study how many Spanish scientific papers on COVID-19 published during 2020 share their research data. Qualitative and descriptive study applying nine attributes: (a) availability, (b) accessibility, (c) format, (d) licensing, (e) linkage, (f) funding, (g) editorial policy, (h) content, and (i) statistics. We analyzed 1,340 papers, 1,173 (87.5%) did not have research data. A total of 12.5% share their research data of which 2.1% share their data in repositories, 5% share their data through a simple request, 0.2% do not have permission to share their data, and 5.2% share their data as supplementary material. There is a small percentage that shares their research data; however, it demonstrates the researchers' poor knowledge on how to properly share their research data and their lack of knowledge on what is research data.
    Date
    21. 3.2023 19:22:02
    Source
    Journal of the Association for Information Science and Technology. 74(2023) no.4, S.402-414
  15. Bookstein, A.: Informetric distributions : I. Unified overview (1990) 0.03
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    Date
    22. 7.2006 18:55:29
    Source
    Journal of the American Society for Information Science. 41(1990) no.5, S.368-375
  16. Bookstein, A.: Informetric distributions : II. Resilience to ambiguity (1990) 0.03
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    Date
    22. 7.2006 18:55:55
    Source
    Journal of the American Society for Information Science. 41(1990) no.5, S.376-386
  17. Su, Y.; Han, L.-F.: ¬A new literature growth model : variable exponential growth law of literature (1998) 0.03
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    Date
    22. 5.1999 19:22:35
    Source
    Scientometrics. 42(1998) no.2, S.259-265
  18. Haustein, S.: Scientific interactions and research evaluation : from bibliometrics to Altmetrics (2015) 0.03
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    Pages
    S.36-42
    Source
    Re:inventing information science in the networked society: Proceedings of the 14th International Symposium on Information Science, Zadar/Croatia, 19th-21st May 2015. Eds.: F. Pehar, C. Schloegl u. C. Wolff
  19. Lewandowski, D.; Haustein, S.: What does the German-language information science community cite? (2015) 0.03
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    Pages
    S.93-104
    Source
    Re:inventing information science in the networked society: Proceedings of the 14th International Symposium on Information Science, Zadar/Croatia, 19th-21st May 2015. Eds.: F. Pehar, C. Schloegl u. C. Wolff
  20. Liu, Z.; Wang, C.: Mapping interdisciplinarity in demography : a journal network analysis (2005) 0.03
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    Source
    Journal of information science. 31(2005) no.4, S.308-316

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