|کد مقاله||کد نشریه||سال انتشار||مقاله انگلیسی||ترجمه فارسی||نسخه تمام متن|
|4968118||1365184||2017||22 صفحه PDF||سفارش دهید||دانلود کنید|
- metaknowledge accepts input from the Web of Science, PubMed, Scopus, Proquest Dissertation and Theses, and administrative data from some funding agencies.
- metaknowledge producing tidy datasets for longitudinal research, reference publication year spectroscopy, computational text analysis, and network analysis.
- metaknowledge is open source and integrates well with open and reproducible workflows.
- metaknowledge interfaces seamlessly with other software, including R, VOSViewer, Gephi, etc.
- metaknowledge is very fast and computationally efficient with large datasets.
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.
Journal: Journal of Informetrics - Volume 11, Issue 1, February 2017, Pages 176-197