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.聽Jillian Anderson, Tyler Crick, and Rachel Wood聽have also contributed and to varying degrees are involved in maintaining the package.聽
About metaknowledge
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).
Install
We recommend using the because it comes with many other useful data analysis packages (typically referred to as the 鈥渟cientific stack鈥).
pip3 install metaknowledge
GitHub
Docs
Articles
John McLevey and Reid McIlroy-Young. 2017. 鈥.鈥 Journal of Informetrics. 11(1):176-197.
Note: Open access version coming soon!
@article{metaknowledge,
聽聽聽聽 title={Introducing metaknowledge: Software for computational research in information science, network analysis, and science of science},
聽聽聽聽 author={McLevey, John and McIlroy-Young, Reid},
聽聽聽聽 journal={Journal of Informetrics},
聽聽聽聽 volume={11},
聽聽聽聽 number={1},
聽聽聽聽 pages={176--197},
聽聽聽聽 year={2017}
}
Tutorials
There are tutorials on metaknowledge posted on the NetLab blog.
Jupyter Notebooks
There are a series of that follow along with the McLevey and McIlroy-Young 2017 article in Journal of Informetrics.