 Introducing metaknowledge: Software for computational research in information science, network analysis, and science of scienceJohn McLevey and Reid McIlroy-Young Journal of Informetrics, 2017, vol. 11, issue 1, 176-197 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. Keywords: Informetrics; Scientometrics; Bibliometrics; Networks; Computational; Big data; Software; RPYS; Gender; Topic models; Burst analysis; Python (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:infome:v:11:y:2017:i:1:p:176-197 DOI: 10.1016/j.joi.2016.12.005 Access Statistics for this article Journal of Informetrics is currently edited by Leo Egghe More articles in Journal of Informetrics from Elsevier |
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