ScreenIT
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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Orthogonal SARS-CoV-2 Serological Assays Enable Surveillance of Low-Prevalence Communities and Reveal Durable Humoral Immunity
This article has 37 authors:Reviewed by ScreenIT
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SARS-CoV-2 uses a multipronged strategy to impede host protein synthesis
This article has 17 authors:Reviewed by ScreenIT
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Identification of Vulnerable Populations and Areas at Higher Risk of COVID-19-Related Mortality during the Early Stage of the Epidemic in the United States
This article has 5 authors:Reviewed by ScreenIT
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The IGNITE trial: Participant recruitment lessons prior to SARS-CoV-2
This article has 13 authors:Reviewed by ScreenIT
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Common variants at 21q22.3 locus influence MX1 and TMPRSS2 gene expression and susceptibility to severe COVID-19
This article has 21 authors:Reviewed by ScreenIT
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COVID-19 severity in asthma patients: a multi-center matched cohort study
This article has 8 authors:Reviewed by ScreenIT
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Propagation of viral bioaerosols indoors
This article has 4 authors:Reviewed by ScreenIT
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Multifaceted strategies for the control of COVID-19 outbreaks in long-term care facilities in Ontario, Canada
This article has 9 authors:Reviewed by ScreenIT
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Effects of the COVID-19 Pandemic on the Mental Health of Pregnant and Puerperal Women: A Systematic Review
This article has 8 authors:Reviewed by ScreenIT
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A population-based seroprevalence survey of severe acute respiratory syndrome coronavirus 2 infection in Beijing, China
This article has 11 authors:Reviewed by ScreenIT