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The Scholarly Database and its utility for scientometrics research

Author

Listed:
  • Gavin LaRowe

    (Indiana University)

  • Sumeet Ambre

    (Indiana University)

  • John Burgoon

    (Indiana University)

  • Weimao Ke

    (Indiana University)

  • Katy Börner

    (Indiana University)

Abstract

The Scholarly Database aims to serve researchers and practitioners interested in the analysis, modelling, and visualization of large-scale data sets. A specific focus of this database is to support macro-evolutionary studies of science and to communicate findings via knowledge-domain visualizations. Currently, the database provides access to about 18 million publications, patents, and grants. About 90% of the publications are available in full text. Except for some datasets with restricted access conditions, the data can be retrieved in raw or pre-processed formats using either a web-based or a relational database client. This paper motivates the need for the database from the perspective of bibliometric/scientometric research. It explains the database design, setup, etc., and reports the temporal, geographical, and topic coverage of data sets currently served via the database. Planned work and the potential for this database to become a global testbed for information science research are discussed at the end of the paper.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:scient:v:79:y:2009:i:2:d:10.1007_s11192-009-0414-2
    DOI: 10.1007/s11192-009-0414-2
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    Citations

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    Cited by:

    1. María de la Cruz del Río-Rama & Claudia Patricia Maldonado-Erazo & José Álvarez-García & Amador Durán-Sánchez, 2020. "Cultural and Natural Resources in Tourism Island: Bibliometric Mapping," Sustainability, MDPI, vol. 12(2), pages 1-26, January.
    2. Katy Börner & Weixia Huang & Micah Linnemeier & Russell J. Duhon & Patrick Phillips & Nianli Ma & Angela M. Zoss & Hanning Guo & Mark A. Price, 2010. "Rete-netzwerk-red: analyzing and visualizing scholarly networks using the Network Workbench Tool," Scientometrics, Springer;Akadémiai Kiadó, vol. 83(3), pages 863-876, June.
    3. 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.
    4. Muaz Niazi & Amir Hussain, 2011. "Agent-based computing from multi-agent systems to agent-based models: a visual survey," Scientometrics, Springer;Akadémiai Kiadó, vol. 89(2), pages 479-499, November.
    5. Milojević, Staša, 2015. "Quantifying the cognitive extent of science," Journal of Informetrics, Elsevier, vol. 9(4), pages 962-973.
    6. Jianbo Han & Edwin H. W. Chan & Esther H. K. Yung & Queena K. Qian & Patrick T. I. Lam, 2022. "A Policy Framework for Producing Age-Friendly Communities from the Perspective of Production of Space," IJERPH, MDPI, vol. 19(4), pages 1-23, February.
    7. 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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