Introducing metaknowledge: Software for computational research in information science, network analysis, and science of science
作者：
Highlights：
• 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.
摘要
Highlights•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.
论文关键词：Informetrics,Scientometrics,Bibliometrics,Networks,Computational,Big data,Software,RPYS,Gender,Topic models,Burst analysis,Python
论文评审过程：Received 20 July 2016, Revised 14 December 2016, Accepted 14 December 2016, Available online 29 December 2016, Version of Record 29 December 2016.
论文官网地址：https://doi.org/10.1016/j.joi.2016.12.005
