LISC is a package for collecting and analyzing the scientific literature.
LISC acts as a wrapper and connector between available APIs, allowing users to collect data from and about scientific articles, and to do analyses on this data, such as performing automated meta-analyses.
A curated list of some projects enabled by LISC is available on the projects page.
Supported APIs through LISC includes:
- The NCBI EUtils provides programmatic access to the National Center for Biotechnology Information (NCBI), including the Pubmed database.
- The OpenCitations API provides access to citation data.
For data collection, LISC currently offers support for the following 'types' of literature data collection:
- 'Counts': collects counts and co-occurrences of specified search terms in the literature.
- 'Words': collects text data and meta-data from articles identified by specified search terms.
- 'Citations': collect citation and reference data for articles, based on DOIs.
In addition to connecting to external APIs, LISC also provides:
- Custom data objects for managing collected data
- A database structure, and save and load utilities for storing collected data
- Functions and utilities to analyze collected data
- Data visualization functions for plotting collected data and analysis outputs
Documentation for LISC available here.
The documentation also includes a set of tutorials.
For a curated list of projects that use LISC (or pre-cursors), check out the projects page.
LISC is written in Python 3, and requires Python 3.5 or greater to run.
Requirements:
Optional dependencies, used for plots, analyses & testing:
Stable Release Version
To install the latest stable release, you can install from pip:
$ pip install liscLISC can also be installed with conda, from the conda-forge channel:
$ conda install -c conda-forge liscDevelopment Version
To get the development version (updates that are not yet published to pip), you can clone this repo.
$ git clone https://github.com/lisc-tools/liscTo install this cloned copy of LISC, move into the directory you just cloned, and run:
$ pip install .Editable Version
If you want to install an editable version, for making contributions, download the development version as above, and run:
$ pip install -e .LISC includes an automated test suite, using pytest, and continuous integration on Travis.
Installing pytest
If you want to run the tests yourself, you will need pytest. You can install pytest with pip, as:
$ pip install pytestRunning tests on an installed copy of LISC
To run the test suite on an installed version of LISC, after installing, run:
$ pytest liscRunning tests on a local copy of LISC
To run the tests on a local copy of LISC, move into the LISC folder, and run:
$ pytest .Please use the Github issue tracker to file bug reports and/or ask questions about this project.
LISC welcomes and encourages contributions from the community!
If you have an idea of something to add to LISC, please start by opening an issue.
When working on LISC, please follow the Contribution Guidelines, and also make sure to follow our Code of Conduct.
If you use this code in your project, please cite
Donoghue, T. (2018) LISC: A Python Package for Scientific Literature Collection and Analysis. Journal of Open Source Software, 4(41), 1674. DOI: 10.21105/joss.01674
Direct Link: https://doi.org/10.21105/joss.01674
Bibtex:
@article{donoghue_lisc:_2019,
title = {{LISC}: {A} {Python} {Package} for {Scientific} {Literature} {Collection} and {Analysis}},
author = {Donoghue, Thomas},
journal = {Journal of Open Source Software},
year = {2019},
volume = {4},
number = {41},
issn = {2475-9066},
url = {https://joss.theoj.org/papers/10.21105/joss.01674},
doi = {10.21105/joss.01674}
}