Open data and open code for big science of science studies
Identifieur interne :
000013 ( PascalFrancis/Corpus );
précédent :
000012;
suivant :
000014
Open data and open code for big science of science studies
Auteurs : Robert P. Light ;
David E. Polley ;
Katy BörnerSource :
-
Scientometrics : (Print) [ 0138-9130 ] ; 2014.
RBID : Francis: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.
Notice en format standard (ISO 2709)
Pour connaître la documentation sur le format Inist Standard.
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| A11 | 03 | 1 | | @1 BÖRNER (Katy) |
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| A12 | 01 | 1 | | @1 GORRAIZ (Juan) @9 ed. |
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| A12 | 02 | 1 | | @1 GUMPENBERGER (Christian) @9 ed. |
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| A12 | 03 | 1 | | @1 HÖRLESBERGER (Marianne) @9 ed. |
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| A12 | 04 | 1 | | @1 MOED (Henk) @9 ed. |
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Format Inist (serveur)
| NO : | FRANCIS 14-0270434 INIST |
|---|
| ET : | Open data and open code for big science of science studies |
|---|
| AU : | LIGHT (Robert P.); POLLEY (David E.); BÖRNER (Katy); GORRAIZ (Juan); GUMPENBERGER (Christian); HÖRLESBERGER (Marianne); MOED (Henk); SCHIEBEL (Edgar) |
|---|
| AF : | Cyberinfrastructure for Network Science Center, School of Informatics and Computing, Indiana University/Bloomington, IN/Etats-Unis (1 aut., 2 aut., 3 aut.); Library and Archive Services, Bibliometrics Department, University of Vienna, Boltzmanngasse 5/1090 Vienna/Autriche (1 aut., 2 aut.); AIT Austrian Institute of Technology GmbH, Tech Gate Vienna, Donau-City-Strasse 1/1220 Vienna/Autriche (3 aut., 5 aut.); Elsevier B.V., Radarweg 29/1043 NX Amsterdam/Pays-Bas (4 aut.) |
|---|
| DT : | Publication en série; Congrès; Niveau analytique |
|---|
| SO : | Scientometrics : (Print); ISSN 0138-9130; Coden SCNTDX; Pays-Bas; Da. 2014; Vol. 101; No. 2; Pp. 1535-1551; Bibl. 3/4 p. |
|---|
| LA : | Anglais |
|---|
| EA : | 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. |
|---|
| CC : | 790B02 |
|---|
| FD : | Analyse donnée; Interdisciplinaire; Base de données; Brevet; Algorithme; Croissance; Recherche scientifique |
|---|
| ED : | Data analysis; Interdisciplinary field; Database; Patents; Algorithm; Growth; Scientific research |
|---|
| SD : | Análisis datos; Interdisciplinario; Base dato; Patente; Algoritmo; Crecimiento; Investigación científica |
|---|
| LO : | INIST-19049.354000504566570370 |
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| ID : | 14-0270434 |
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Links to Exploration step
Francis:14-0270434
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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<ET>Open data and open code for big science of science studies</ET>
<AU>LIGHT (Robert P.); POLLEY (David E.); BÖRNER (Katy); GORRAIZ (Juan); GUMPENBERGER (Christian); HÖRLESBERGER (Marianne); MOED (Henk); SCHIEBEL (Edgar)</AU>
<AF>Cyberinfrastructure for Network Science Center, School of Informatics and Computing, Indiana University/Bloomington, IN/Etats-Unis (1 aut., 2 aut., 3 aut.); Library and Archive Services, Bibliometrics Department, University of Vienna, Boltzmanngasse 5/1090 Vienna/Autriche (1 aut., 2 aut.); AIT Austrian Institute of Technology GmbH, Tech Gate Vienna, Donau-City-Strasse 1/1220 Vienna/Autriche (3 aut., 5 aut.); Elsevier B.V., Radarweg 29/1043 NX Amsterdam/Pays-Bas (4 aut.)</AF>
<DT>Publication en série; Congrès; Niveau analytique</DT>
<SO>Scientometrics : (Print); ISSN 0138-9130; Coden SCNTDX; Pays-Bas; Da. 2014; Vol. 101; No. 2; Pp. 1535-1551; Bibl. 3/4 p.</SO>
<LA>Anglais</LA>
<EA>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.</EA>
<CC>790B02</CC>
<FD>Analyse donnée; Interdisciplinaire; Base de données; Brevet; Algorithme; Croissance; Recherche scientifique</FD>
<ED>Data analysis; Interdisciplinary field; Database; Patents; Algorithm; Growth; Scientific research</ED>
<SD>Análisis datos; Interdisciplinario; Base dato; Patente; Algoritmo; Crecimiento; Investigación científica</SD>
<LO>INIST-19049.354000504566570370</LO>
<ID>14-0270434</ID>
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