Package: asreview-covid19
Extension to add publications on COVID-19 toASReview.ASReview against COVID-19 (Deprecated)This extension is deprecated. It still works for version 0.x of ASReview but datasets are no longer updated.The Active learning for Systematic Reviews softwareASReviewimplements learning algorithms that interactively query the researcher during the title and abstract reading phase of a systematic search. 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. The software can be used for traditional systematic reviews for which the user uploads a dataset of papers, or one can make use of the built-in datasets.To help combat the COVID-19 crisis, the ASReview team released an extension that integrates the latest scientific datasets on COVID-19 in the ASReview software. Experts canstart reviewing the latest scientific literature on COVID-19 immediately!Seedatasetsfor an overview of the datasets (daily updates).Installation, update, and usageThe COVID-19 plug-in requires ASReview 0.9.4 or higher. Install ASReview by following the instructions inInstallation of ASReview.Install the extension with pip:pipinstallasreview-covid19The datasets are immediately available after starting ASReview (asreview oracle). The datasets are selectable in Step 2 of the project initialization. For more information on the usage of ASReview, please have a look at theQuick Tour.Older versions of the plugin are no longer supported by ASReview>=0.9.4. Please update the plugin with:pipinstall--upgradeasreview-covid19DatasetsThe following datasets are available:CORD-19 datasetCOVID19 preprints datasetThe datasets are checked for updates every couple of hours such that the latest collections are available in the ASReview COVID19 plugin and ASReview software.CORD-19 datasetTheCORD-19 datasetis a dataset with scientific publications on COVID-19 and coronavirus-related research (e.g. SARS, MERS, etc.) from PubMed Central, the WHO COVID-19 database of publications, the preprint servers bioRxiv, medRxiv and arXiv, and papers contributed by specific publishers (currently Elsevier). The dataset is compiled and maintained by a collaboration of the Allen Institute for AI, the Chan Zuckerberg Initiative, Georgetown University’s Center for Security and Emerging Technology, Microsoft Research, and the National Library of Medicine of the National Institutes of Health. The full dataset contains metadata of more than100K publicationson COVID-19 and coronavirus-related research.The CORD-19 dataset receives daily updates and is directly available in the ASReview software.The most recent versions of the dataset can be found here:https://ai2-semanticscholar-cord-19.s3-us-west-2.amazonaws.com/historical_releases.htmlCOVID19 preprints datasetTheCOVID19 preprints datasetis created byNicholas FraserandBianca Kramer, by collecting metadata of COVID19-related preprints from over 15 preprint servers with DOIs registered with Crossref or DataCite, and from arXiv. The dataset contains metadata of >10K preprints on COVID-19 and coronavirus-related research. All versions are archived onFigshare. The COVID19 preprints dataset receives weekly updates.The most recent version of the dataset can be found here:https://github.com/nicholasmfraser/covid19_preprints/blob/master/data/covid19_preprints.csv.License, citation and contactThe ASReview software and the plugin have an Apache 2.0 LICENSE. For the datasets, please see the license of the CORD-19 datasethttps://pages.semanticscholar.org/coronavirus-research. The COVID19 preprints dataset has aCC0 license.Visithttps://doi.org/10.5281/zenodo.3764749to get the citation style of your preference.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 (@J535D165) and Raoul Schram (@qubixes).Got ideas for improvement? For any questions or remarks, please send an email toasreview@uu.nl.