LitStudy

LitStudy is a Python package that enables analysis of scientific literature from the comfort of a Jupyter notebook. It provides the ability to select scientific publications and study their metadata through the use of visualizations, network analysis, and natural language processing.

In essence, this package offers five main features:

Extract metadata from scientific documents sourced from various locations. The data is presented in a standardized interface, allowing for the combination of data from different sources.
Filter, select, deduplicate, and annotate collections of documents.
Compute and plot general statistics for document sets, such as statistics on authors, venues, and publication years.
Generate and plot various bibliographic networks as interactive visualizations.
Topic discovery using natural language processing (NLP) allows for the automatic discovery of popular topics.

Frequently Asked Questions

If you have any questions or run into an error, see the Frequently Asked Questions section of the documentation. If your question or error is not on the list, please check the GitHub issue tracker for a similar issue or create a new issue.

Supported Source

LitStudy supports the following data sources. The table below lists which metadata is fully (✓) or partially (*) provided by each source.

Name	Title	Authors	Venue	Abstract	Citations	References
Scopus	✓	✓	✓	✓	✓	✓
SemanticScholar	✓	✓	✓	✓	* ^{(count only)}	✓
CrossRef	✓	✓	✓	✓	* ^{(count only)}	✓
DBLP	✓	✓	✓
arXiv	✓	✓		✓
IEEE Xplore	✓	✓	✓	✓	* ^{(count only)}
Springer Link	✓	✓	✓	✓	* ^{(count only)}
CSV file	✓	✓	✓	✓
bibtex file	✓	✓	✓	✓
RIS file	✓	✓	✓	✓

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 latest 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 3.7. 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). For more information, see the guide by pybliometrics.

License

Apache 2.0. See LICENSE.

Change log

See CHANGELOG.md.

Contributing

See CONTRIBUTING.md.

Citation

If you use LitStudy in your work, please cite the following publication:

S. Heldens, A. Sclocco, H. Dreuning, B. van Werkhoven, P. Hijma, J. Maassen & R.V. van Nieuwpoort (2022), "litstudy: A Python package for literature reviews", SoftwareX 20

As BibTeX:

@article{litstudy,
    title = {litstudy: A Python package for literature reviews},
    journal = {SoftwareX},
    volume = {20},
    pages = {101207},
    year = {2022},
    issn = {2352-7110},
    doi = {https://doi.org/10.1016/j.softx.2022.101207},
    url = {https://www.sciencedirect.com/science/article/pii/S235271102200125X},
    author = {S. Heldens and A. Sclocco and H. Dreuning and B. {van Werkhoven} and P. Hijma and J. Maassen and R. V. {van Nieuwpoort}},
}

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.

rfeinberg3/litstudy