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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Projecting contact matrices in 177 geographical regions: An update and comparison with empirical data for the COVID-19 era
This article has 8 authors:Reviewed by ScreenIT
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Text Mining Approach to Analyze Coronavirus Impact: Mexico City as Case of Study
This article has 2 authors:Reviewed by ScreenIT
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A study of the utility and safety of bronchoscopy in mechanically ventilated COVID19 ARDS: Transgressing the conventional guidelines
This article has 6 authors:Reviewed by ScreenIT
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Role of meteorological factors in the transmission of SARS-CoV-2 in the United States
This article has 5 authors:Reviewed by ScreenIT
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The impact of disruptions due to COVID‐19 on HIV transmission and control among men who have sex with men in China
This article has 10 authors:Reviewed by ScreenIT
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Immune response to SARS-CoV-2 variants of concern in vaccinated individuals
This article has 26 authors:Reviewed by ScreenIT
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Identification of Natural SARS-CoV-2 Infection in Seroprevalence Studies Among Vaccinated Populations
This article has 14 authors:Reviewed by ScreenIT
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Surgical activity in England and Wales during the COVID-19 pandemic: a nationwide observational cohort study
This article has 10 authors:Reviewed by ScreenIT
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Rapalogs downmodulate intrinsic immunity and promote cell entry of SARS-CoV-2
This article has 16 authors:Reviewed by ScreenIT
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Epigallocatechin gallate from green tea effectively blocks infection of SARS-CoV-2 and new variants by inhibiting spike binding to ACE2 receptor
This article has 17 authors:Reviewed by ScreenIT