metaknowledge is a full-featured Python package for doing computational research on science and knowledge. It was designed and developed by John McLevey and Reid McIlroy-Young. Jillian Anderson, Tyler Crick, and Rachel Wood have also contributed and to varying degrees are involved in maintaining the package.
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).
We recommend using the Anaconda distribution of Python 3 because it comes with many other useful data analysis packages (typically referred to as the “scientific stack”).
John McLevey and Reid McIlroy-Young. 2017. “Introducing metaknowledge: Software for computational research in information science, network analysis, and science of science.” Journal of Informetrics. 11(1):176-197.
Note: Open access version coming soon!
There are tutorials on metaknowledge posted on the NetLab blog.
There are a series of Jupyter Notebooks available on GitHub that follow along with the McLevey and McIlroy-Young 2017 article in Journal of Informetrics.