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Mapping the structure of science through usage

Author

Listed:
  • Johan Bollen

    (Research Library, Los Alamos National Laboratory)

  • Herbert van de Sompel

    (Research Library, Los Alamos National Laboratory)

Abstract

Summary Science has traditionally been mapped on the basis of authorship and citation data. Due to publication and citation delays such data represents the structure of science as it existed in the past. We propose to map science by proxy of journal relationships derived from usage data to determine research trends as they presently occur. This mapping is performed by applying a principal components analysis superimposed with a k-means cluster analysis on networks of journal relationships derived from a large set of article usage data collected for the Los Alamos National Laboratory research community. Results indicate that meaningful maps of the interests of a local scientific community can be derived from usage data. Subject groupings in the mappings corresponds to Thomson ISI subject categories. A comparison to maps resulting from the analysis of 2003 Thomson ISI Journal Citation Report data reveals interesting differences between the features of local usage and global citation data.

Suggested Citation

  • Johan Bollen & Herbert van de Sompel, 2006. "Mapping the structure of science through usage," Scientometrics, Springer;Akadémiai Kiadó, vol. 69(2), pages 227-258, November.
    • Handle: RePEc:spr:scient:v:69:y:2006:i:2:d:10.1007_s11192-006-0151-8
    DOI: 10.1007/s11192-006-0151-8
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    Citations

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    Cited by:

    1. Johan Bollen & Herbert Van de Sompel & Aric Hagberg & Luis Bettencourt & Ryan Chute & Marko A Rodriguez & Lyudmila Balakireva, 2009. "Clickstream Data Yields High-Resolution Maps of Science," PLOS ONE, Public Library of Science, vol. 4(3), pages 1-11, March.
    2. Kraker, Peter & Schlögl, Christian & Jack, Kris & Lindstaedt, Stefanie, 2015. "Visualization of co-readership patterns from an online reference management system," Journal of Informetrics, Elsevier, vol. 9(1), pages 169-182.
    3. Wencan Tian & Yongzhen Wang & Zhigang Hu & Ruonan Cai & Guangyao Zhang & Xianwen Wang, 2024. "Does Granger causality exist between article usage and publication counts? A topic-level time-series evidence from IEEE Xplore," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(6), pages 3285-3302, June.
    4. Ehsan Mohammadi, 2012. "Knowledge mapping of the Iranian nanoscience and technology: a text mining approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 92(3), pages 593-608, September.

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