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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.

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  • 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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    6. 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.
    7. Tosi, Mauro Dalle Lucca & dos Reis, Julio Cesar, 2021. "SciKGraph: A knowledge graph approach to structure a scientific field," Journal of Informetrics, Elsevier, vol. 15(1).
    8. Xin Li & Qiang Yao & Xuli Tang & Qian Li & Mengjia Wu, 2020. "How to investigate the historical roots and evolution of research fields in China? A case study on iMetrics using RootCite," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(2), pages 1253-1274, November.
    9. 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.

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