Metadata-Version: 2.1
Name: litstudy
Version: 1.0.2
Summary: Using the power of Python and Jupyter notebooks to automate analysis of scientific literature
Home-page: https://github.com/nlesc/litstudy
Author: Stijn Heldens
Author-email: s.heldens@esciencecenter.nl
License: Apache 2.0
Project-URL: Bug Tracker, https://github.com/nlesc/litstudy/issues
Description: # litstudy
        [![github](https://img.shields.io/badge/github-repo-000.svg?logo=github&labelColor=gray&color=blue)](https://github.com/NLeSC/litstudy/)
        [![DOI](https://zenodo.org/badge/206312286.svg)](https://zenodo.org/badge/latestdoi/206312286)
        [![License](https://img.shields.io/github/license/nlesc/litstudy)](https://github.com/NLeSC/litstudy/blob/master/LICENSE)
        [![Version](https://img.shields.io/pypi/v/litstudy)](https://pypi.org/project/litstudy/)
        [![Build and Test](https://github.com/NLeSC/litstudy/actions/workflows/python-app.yml/badge.svg)](https://github.com/NLeSC/litstudy/actions/)
        
        `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](https://nlesc.github.io/litstudy/example.html).
        
        [![Example notebook](https://raw.githubusercontent.com/NLeSC/litstudy/master/docs/images/notebook.png)](https://github.com/NLeSC/litstudy/blob/master/notebooks/example.ipynb)
        
        
        ## Installation Guide
        litstudy is available on PyPI!
        Full installation guide is available [here](https://nlesc.github.io/litstudy/installation.html).
        
        ```bash
        pip install litstudy
        ```
        
        Or install the lastest development version directly from GitHub:
        
        ```bash
        pip install git+https://github.com/NLeSC/litstudy
        ```
        
        
        ## Documentation
        
        Documentation is available [here](https://nlesc.github.io/litstudy/).
        
        
        ## Requirements
        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](https://dev.elsevier.com/index.jsp).
        
        
        ## License
        Apache 2.0. See [LICENSE](https://github.com/NLeSC/litstudy/blob/master/LICENSE).
        
        
        ## Change log
        See [CHANGELOG.md](https://github.com/NLeSC/litstudy/blob/master/CHANGELOG.md).
        
        
        ## Contributing
        See [CONTRIBUTING.md](https://github.com/NLeSC/litstudy/blob/master/CONTRIBUTING.md).
        
        
        ## Related work
        
        Don't forget to check out these other amazing software packages!
        
        * [ScientoPy](https://www.scientopy.com/): Open-source Python based scientometric analysis tool.
        * [pybliometrics](https://github.com/pybliometrics-dev/pybliometrics): API-Wrapper to access Scopus.
        * [ASReview](https://asreview.nl/): Active learning for systematic reviews.
        * [metaknowledge](https://github.com/UWNETLAB/metaknowledge): Python library for doing bibliometric and network analysis in science.
        * [tethne](https://github.com/diging/tethne): Python module for bibliographic network analysis.
        * [VOSviewer](https://www.vosviewer.com/): Software tool for constructing and visualizing bibliometric networks.
        
Platform: UNKNOWN
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Provides-Extra: doc
