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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An explicit formula for minimizing the infected peak in an SIR epidemic model when using a fixed number of complete lockdowns
This article has 1 author:Reviewed by ScreenIT
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CovidEnvelope: A Fast Automated Approach to Diagnose COVID-19 from Cough Signals
This article has 3 authors:Reviewed by ScreenIT
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Estimation of the Reproduction Number for COVID-19 Based on Latest Vaccination Results and the Timing for Herd-Immunity: Prospect for 2021
This article has 2 authors:Reviewed by ScreenIT
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Upregulated miR-200c is associated with downregulation of the functional receptor for severe acute respiratory syndrome coronavirus 2 ACE2 in individuals with obesity
This article has 4 authors:Reviewed by ScreenIT
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A convergence based assessment of relative differences in age-stratified susceptibility and infectiousness for SARS-CoV-2 variants of B.1.1.7 lineage
This article has 1 author:Reviewed by ScreenIT
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Mathematical modeling suggests pre-existing immunity to SARS-CoV-2
This article has 3 authors:Reviewed by ScreenIT
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Effect of asymptomatic transmission and emergence time on multi-strain viral disease severity
This article has 2 authors:Reviewed by ScreenIT
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Modeling Substrate Coordination to Zn-Bound Angiotensin Converting Enzyme 2
This article has 3 authors:Reviewed by ScreenIT
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Differences in risk for SARS-CoV-2 infection among healthcare workers
This article has 10 authors:Reviewed by ScreenIT
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One size fits all?: A simulation framework for face-mask fit on population-based faces
This article has 3 authors:Reviewed by ScreenIT