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Introducing metaknowledge: Software for computational research in information science, network analysis, and science of science

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  • McLevey, John
  • McIlroy-Young, Reid

Abstract

metaknowledge is a full-featured Python package for computational research in information science, network analysis, and science of science. It is optimized to scale efficiently for analyzing very large datasets, and is designed to integrate well with reproducible and open research workflows. It currently accepts raw data from the Web of Science, Scopus, PubMed, ProQuest Dissertations and Theses, and select funding agencies. It processes these raw data inputs and outputs a variety of datasets for quantitative analysis, including time series methods, Standard and Multi Reference Publication Year Spectroscopy, computational text analysis (e.g. topic modeling, burst analysis), and network analysis (including multi-mode, multi-level, and longitudinal networks). This article motivates the use of metaknowledge and explains its design and core functionality.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:infome:v:11:y:2017:i:1:p:176-197
    DOI: 10.1016/j.joi.2016.12.005
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    3. Andreas Thor & Lutz Bornmann & Werner Marx & Rüdiger Mutz, 2018. "Identifying single influential publications in a research field: new analysis opportunities of the CRExplorer," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(1), pages 591-608, July.
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    5. Klaus Kammerer & Manuel Göster & Manfred Reichert & Rüdiger Pryss, 2021. "Ambalytics: A Scalable and Distributed System Architecture Concept for Bibliometric Network Analyses," Future Internet, MDPI, vol. 13(8), pages 1-29, August.
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    7. Matthieu Ballandonne & Igor Cersosimo, 2021. "A note on reference publication year spectroscopy with incomplete information," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(6), pages 4927-4939, June.
    8. John McLevey & Alexander V. Graham & Reid McIlroy-Young & Pierson Browne & Kathryn S. Plaisance, 2018. "Interdisciplinarity and insularity in the diffusion of knowledge: an analysis of disciplinary boundaries between philosophy of science and the sciences," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(1), pages 331-349, October.

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