Open data and open code for big science of science studies

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 in...

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Bibliographic Details
Published in:Scientometrics 2014-11, Vol.101 (2), p.1535-1551
Main Authors: Light, Robert P., Polley, David E., Börner, Katy
Format: Article
Language:English
Subjects:
Bibliometrics. Scientometrics
Bibliometrics. Scientometrics. Evaluation
Computer Science
Data analysis
Datasets
Exact sciences and technology
Information and communication sciences
Information science. Documentation
Information Storage and Retrieval
Library and information science. General aspects
Library Science
Pattern recognition
Sciences and techniques of general use
Visualization
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Summary: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.
ISSN:0138-9130
1588-2861
DOI:10.1007/s11192-014-1238-2