Open data and open code for big science of science studies
Identifieur interne :
000131 ( PascalFrancis/Curation );
précédent :
000130;
suivant :
000132
Open data and open code for big science of science studies
Auteurs : Robert P. Light [
États-Unis] ;
David E. Polley [
États-Unis] ;
Katy Börner [
États-Unis]
Source :
-
Scientometrics : (Print) [ 0138-9130 ] ; 2014.
RBID : Pascal:14-0270434
Descripteurs français
English descriptors
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 stake-holders. 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.
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Le document en format XML
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</keywords><front><div type="abstract" xml:lang="en">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 stake-holders. 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.</div>
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