Open Source Software for Efficient and Transparent Reviews
Abstract
To help researchers conduct a systematic review or meta-analysis as efficiently and transparently as possible, we designed a tool (ASReview) to accelerate the step of screening titles and abstracts. For many tasks - including but not limited to systematic reviews and meta-analyses - the scientific literature needs to be checked systematically. Currently, scholars and practitioners screen thousands of studies by hand to determine which studies to include in their review or meta-analysis. This is error prone and inefficient because of extremely imbalanced data: only a fraction of the screened studies is relevant. The future of systematic reviewing will be an interaction with machine learning algorithms to deal with the enormous increase of available text. We therefore developed an open source machine learning-aided pipeline applying active learning: ASReview. We demonstrate by means of simulation studies that ASReview can yield far more efficient reviewing than manual reviewing, while providing high quality. Furthermore, we describe the options of the free and open source research software and present the results from user experience tests. We invite the community to contribute to open source projects such as our own that provide measurable and reproducible improvements over current practice.
Cite
@article{arxiv.2006.12166,
title = {Open Source Software for Efficient and Transparent Reviews},
author = {Rens van de Schoot and Jonathan de Bruin and Raoul Schram and Parisa Zahedi and Jan de Boer and Felix Weijdema and Bianca Kramer and Martijn Huijts and Maarten Hoogerwerf and Gerbrich Ferdinands and Albert Harkema and Joukje Willemsen and Yongchao Ma and Qixiang Fang and Sybren Hindriks and Lars Tummers and Daniel Oberski},
journal= {arXiv preprint arXiv:2006.12166},
year = {2021}
}
Comments
All code for the software ASReview is available under an Apache-2.0 license at Github: https://github.com/asreview