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.
An example notebook is available in notebooks/example.ipynb
and here.
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 is available here.
The package has been tested for Python 3.6. Required packages are available in requirements.txt
.
To access the Scopus
API using litstudy
, you (or your institute) needs a Scopus subscription and you need to request an Elsevier Developer API key (see Elsevier Developers.
Apache 2.0. See LICENSE.
See CHANGELOG.md.
See CONTRIBUTING.md.
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.