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 Anaconda distribution of Python 3 because it comes with many other useful data analysis packages (typically referred to as the “scientific stack.”)

pip3 install metaknowledge

Github

https://github.com/networks-lab/gitnet

Docs

http://networkslab.org/metaknowledge/

Articles

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.

  • Open access version coming soon!
@article{metaknowledge,  
  title={Introducing metaknowledge: Software for computational research in   information science, network analysis, and science of science},  
  author={McLevey, John and McIlroy-Young, Reid},  
  journal={Journal of Informetrics},  
  volume={11},  
  number={1},  
  pages={176--197},  
  year={2017}  
}

Tutorials

There are tutorials on metaknowledge posted on the NetLab blog page.

Jupyter Notebooks

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.