litstudy 1.0.3

Using the power of Python and Jupyter notebooks to automate analysis of scientific literature

litstudy

litstudy is a Python package that allows analysis of scientific literature from the comfort of a Jupyter notebook. It enables selecting scientific publications and study their metadata using visualizations, network analysis, and natural language processing.

In essence, this package offers five features

• Extract metadata of scientific documents from various sources. The data is united by a standard interface, allowing data from different sources to be combined.
• Filter, select, deduplicate, and annotate collections of documents.
• Compute and plot general statistics of document sets (e.g., statistics on authors, venues, publication years, etc.)
• Generate and plot various bibliographic networks as an interactive visualization.
• Topic discovery based on natural language processing (NLP) allows automatic discovery of popular topics.

Example

An example notebook is available in notebooks/example.ipynb and here.

Installation Guide

litstudy is available on PyPI! Full installation guide is available here.

pip install litstudy

Or install the lastest development version directly from GitHub:

pip install git+https://github.com/NLeSC/litstudy

Documentation

Documentation is available here.

Requirements

The package has been tested for Python 3.6. Required packages are available in requirements.txt.

litstudy supports several data sources. Some of these sources (such as semantic Scholar, CrossRef, and arXiv) are openly available. However to access the Scopus API, you (or your institute) requires a Scopus subscription and you need to request an Elsevier Developer API key (see Elsevier Developers).

License

Apache 2.0. See LICENSE.

Change log

See CHANGELOG.md.

Contributing

See CONTRIBUTING.md.

Related work

Don't forget to check out these other amazing software packages!

• ScientoPy: Open-source Python based scientometric analysis tool.
• pybliometrics: API-Wrapper to access Scopus.
• ASReview: Active learning for systematic reviews.
• metaknowledge: Python library for doing bibliometric and network analysis in science.
• tethne: Python module for bibliographic network analysis.
• VOSviewer: Software tool for constructing and visualizing bibliometric networks.