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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Manufacturer Signal-to-Cutoff Threshold Underestimates Cumulative Incidence of SARS-CoV-2 Infection: Evidence from the Los Angeles Firefighters Study
This article has 7 authors:Reviewed by ScreenIT
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Hydrogel‐Based Slow Release of a Receptor‐Binding Domain Subunit Vaccine Elicits Neutralizing Antibody Responses Against SARS‐CoV‐2
This article has 13 authors:Reviewed by ScreenIT
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Exploring the short-term role of particulate matter in the COVID-19 outbreak in USA cities
This article has 6 authors:Reviewed by ScreenIT
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SARS-CoV-2 virus transfers to skin through contact with contaminated solids
This article has 5 authors:Reviewed by ScreenIT
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mRNA COVID-19 vaccine elicits potent adaptive immune response without the persistent inflammation seen in SARS-CoV-2 infection
This article has 21 authors:Reviewed by ScreenIT
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Assessment of Sample Pooling for Clinical SARS-CoV-2 Testing
This article has 3 authors:Reviewed by ScreenIT
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Network-based Modeling of COVID-19 Dynamics: Early Pandemic Spread in India
This article has 4 authors:Reviewed by ScreenIT
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The basis of a more contagious 501Y.V1 variant of SARS-CoV-2
This article has 15 authors:Reviewed by ScreenIT
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The impact of policy timing on the spread of COVID-19
This article has 4 authors:Reviewed by ScreenIT
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Computational investigation of the increased virulence and pathogenesis of SARS-CoV-2 lineage B.1.1.7
This article has 6 authors:Reviewed by ScreenIT