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Automated Systematic Review

Project description

ASReview bot

ASReview: Active learning for systematic reviews

Systematic Reviews are “top of the bill” in research. The number of scientific studies is increasing exponentially in many scholarly fields. Performing a sound systematic review is a time-consuming and sometimes boring task. Our software is designed to accelerate the step of screening abstracts and titles with a minimum of papers to be read by a human with no or very few false negatives.

The Active learning for Systematic Reviews (ASReview) project implements learning algorithms that interactively query the researcher. This way of interactive training is known as Active Learning. ASReview offers support for classical learning algorithms and state-of-the-art learning algorithms like neural networks.

ASReview software implements two different modes:

  • Oracle The oracle modus is used to perform a systematic review with interaction by the reviewer (the 'oracle' in literature on active learning). The software presents papers to the reviewer, whereafter the reviewer classifies them. See ASReview App.
  • Simulate The simulation modus is used to measure the performance of our software on existing systematic reviews. The software shows how many papers you could have potentially skipped during the systematic review.

Installation

The ASReview software requires Python 3.6+. The project is available on Pypi. Install the project with (for windows users add python -m):

pip install asreview

Upgrade ASReview with the following command:

pip install --upgrade asreview

ASReview app

The ASReview team developed a user-friendly user interface to replace the old command line interface. The new interface is still under development but is already available for testing and training purposes.

ASReview Command Line Interface

Covid-19 plugin

Covid-19 Plugin

The ASReview team developed a plugin for researchers and doctors to facilitate the reading of literature on the Coronavirus. The plugin makes the CORD-19 dataset available in the ASReview software. We also constructed a second database with studies published after December 1st 2019 to search for relevant papers published during the Covid-19 crisis.

Install the plugin with the command below.

pip install asreview-covid19

Documentation

Documentation is available at asreview.rtfd.io. Please have a look at https://asreview.readthedocs.io/en/latest/quicktour.html for a quick tour through the user interface.

  • automated-systematic-review-datasets A project with systematic review datasets optimized and processed for use with ASReview or other systematic review software. The project describes the preferred format to store systematic review datasets.
  • automated-systematic-review-simulations A repository with scripts for a simulation study and scripts for the aggregation and visualisation of the results.

Contributing

Build StatusDocumentation Status DOI

Contact

This project is coordinated by by Rens van de Schoot (@Rensvandeschoot) and Daniel Oberski (@daob) and is part of the research work conducted by the Department of Methodology & Statistics, Faculty of Social and Behavioral Sciences, Utrecht University, The Netherlands. Maintainers are Jonathan de Bruin (Lead engineer, @J535D165) and Raoul Schram (@qubixes).

Got ideas for improvement? We would love to hear about your suggestions! Get started here . See who have contributed to ASReview here. For any questions or remarks, please send an email to asreview@uu.nl.

License and Citation

The ASReview software has an Apache 2.0 LICENSE.

A research paper is coming up for this project. In the mean time, it can be cited with (fill in x and y for the version number):

ASReview Core Development Team (2019). ASReview: Software for automated systematic reviews [version 0.x.y]. Utrecht University, Utrecht, The Netherlands. Available at https://github.com/asreview/asreview.

BibTeX:

@Manual{,
    title = {ASReview: Active learning for systematic reviews},
    author = {{ASReview Core Development Team}},
    organization = {Utrecht University},
    address = {Utrecht, The Netherlands},
    year = 2019,
    doi = {10.5281/zenodo.3345592},
    url = {https://doi.org/10.5281/zenodo.3345592}
}

Project details


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