Open Source Software for Efficient and Transparent Reviews. van de Schoot, R., de Bruin, J., Schram, R., Zahedi, P., de Boer, J., Weijdema, F., Kramer, B., Huijts, M., Hoogerwerf, M., Ferdinands, G., Harkema, A., Willemsen, J., Ma, Y., Fang, Q., Hindriks, S., Tummers, L., & Oberski, D. arXiv:2006.12166 [cs], December, 2020. arXiv: 2006.12166
Open Source Software for Efficient and Transparent Reviews [link]Paper  abstract   bibtex   
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.
@article{van_de_schoot_open_2020,
	title = {Open {Source} {Software} for {Efficient} and {Transparent} {Reviews}},
	copyright = {Creative Commons Attribution-ShareAlike 4.0 International License (CC-BY-SA)},
	url = {http://arxiv.org/abs/2006.12166},
	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.},
	urldate = {2021-01-25},
	journal = {arXiv:2006.12166 [cs]},
	author = {van de Schoot, Rens and de Bruin, Jonathan and Schram, Raoul and Zahedi, Parisa and de Boer, Jan and Weijdema, Felix and Kramer, Bianca and Huijts, Martijn and Hoogerwerf, Maarten and Ferdinands, Gerbrich and Harkema, Albert and Willemsen, Joukje and Ma, Yongchao and Fang, Qixiang and Hindriks, Sybren and Tummers, Lars and Oberski, Daniel},
	month = dec,
	year = {2020},
	note = {arXiv: 2006.12166},
	keywords = {Computer Science - Information Retrieval, Computer Science - Machine Learning},
}

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