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Open data and open code for big science of science studies

Author

Listed:
  • Robert P. Light

    (Indiana University)

  • David E. Polley

    (Indiana University)

  • Katy Börner

    (Indiana University)

Abstract

Historically, science of science (Sci2) studies have been performed by single investigators or small teams. As the size and complexity of data sets and analyses scales up, a “Big Science” approach (Price, Little science, big science, 1963) is required that exploits the expertise and resources of interdisciplinary teams spanning academic, government, and industry boundaries. Big Sci2 studies utilize “big data”, i.e., large, complex, diverse, longitudinal, and/or distributed datasets that might be owned by different stakeholders. They apply a systems science approach to uncover hidden patterns, bursts of activity, correlations, and laws. They make available open data and open code in support of replication of results, iterative refinement of approaches and tools, and education. This paper introduces a database-tool infrastructure that was designed to support big Sci2 studies. The open access Scholarly Database ( http://sdb.cns.iu.edu ) provides easy access to 26 million paper, patent, grant, and clinical trial records. The open source Sci2 tool ( http://sci2.cns.iu.edu ) supports temporal, geospatial, topical, and network studies. The scalability of the infrastructure is examined. Results show that temporal analyses scale linearly with the number of records and file size, while the geospatial algorithm showed quadratic growth. The number of edges rather than nodes determined performance for network based algorithms.

Suggested Citation

  • Robert P. Light & David E. Polley & Katy Börner, 2014. "Open data and open code for big science of science studies," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(2), pages 1535-1551, November.
    • Handle: RePEc:spr:scient:v:101:y:2014:i:2:d:10.1007_s11192-014-1238-2
    DOI: 10.1007/s11192-014-1238-2
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    References listed on IDEAS

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    1. M.J. Cobo & A.G. López-Herrera & E. Herrera-Viedma & F. Herrera, 2011. "Science mapping software tools: Review, analysis, and cooperative study among tools," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 62(7), pages 1382-1402, July.
    2. Gavin LaRowe & Sumeet Ambre & John Burgoon & Weimao Ke & Katy Börner, 2009. "The Scholarly Database and its utility for scientometrics research," Scientometrics, Springer;Akadémiai Kiadó, vol. 79(2), pages 219-234, May.
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    Cited by:

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    10. McLevey, John & McIlroy-Young, Reid, 2017. "Introducing metaknowledge: Software for computational research in information science, network analysis, and science of science," Journal of Informetrics, Elsevier, vol. 11(1), pages 176-197.

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