The Automated Screening Working Groups is a group of software engineers and biologists passionate about improving scientific manuscripts on a large scale. Our members have created tools that check for common problems in scientific manuscripts, including information needed to improve transparency and reproducibility. We have combined our tools into a single pipeline, called ScreenIT. We're currently using our tools to screen COVID preprints.
Latest preprint reviews
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Environmental impact of personal protective equipment distributed for use by health and social care services in England in the first six months of the COVID-19 pandemic
This article has 3 authors:Reviewed by Rapid Reviews Infectious Diseases, ScreenIT
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COVID-19 mild cases determination from correlating COVID-line calls to reported cases
This article has 2 authors:Reviewed by ScreenIT
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Kinetics of antibody responses dictate COVID-19 outcome
This article has 30 authors:Reviewed by ScreenIT
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In search for the hotspots of Disease X: A biogeographic approach to mapping the predictive risk of WHO's blueprint priority diseases
This article has 4 authors:Reviewed by ScreenIT
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DeepCOVID: An Operational Deep Learning-driven Framework for Explainable Real-time COVID-19 Forecasting
This article has 8 authors:Reviewed by ScreenIT
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Protonation states in SARS-CoV-2 main protease mapped by neutron crystallography
This article has 8 authors:Reviewed by ScreenIT
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Reducing COVID-19 hospitalization risk through behavior change
This article has 4 authors:Reviewed by ScreenIT
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Real-time tracking and prediction of COVID-19 infection using digital proxies of population mobility and mixing
This article has 3 authors:Reviewed by ScreenIT
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Evidence and magnitude of the effects of meteorological changes on SARS-CoV-2 transmission
This article has 9 authors:Reviewed by ScreenIT
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Modelling COVID-19 dynamics and potential for herd immunity by vaccination in Austria, Luxembourg and Sweden
This article has 10 authors:Reviewed by ScreenIT