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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SARS-CoV-2 productively infects primary human immune system cells in vitro and in COVID-19 patients
This article has 43 authors:Reviewed by ScreenIT
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Evaluating the impacts of tiered restrictions introduced in England, during October and December 2020 on COVID-19 cases: a synthetic control study
This article has 5 authors:Reviewed by ScreenIT
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Clinical Efficacy of Early Administration of Convalescent Plasma among COVID-19 Cases in Egypt
This article has 20 authors:Reviewed by ScreenIT
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COVID-19 antibody seroprevalence in Duhok, Kurdistan Region, Iraq: A population-based study
This article has 13 authors:Reviewed by ScreenIT
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Enhanced Binding of SARS-CoV-2 Spike Protein to Receptor by Distal Polybasic Cleavage Sites
This article has 2 authors:Reviewed by ScreenIT
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Functional interrogation of a SARS-CoV-2 host protein interactome identifies unique and shared coronavirus host factors
This article has 19 authors:Reviewed by ScreenIT
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Postinfection treatment with a protease inhibitor increases survival of mice with a fatal SARS-CoV-2 infection
This article has 13 authors:Reviewed by ScreenIT
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Mortality and Severity in COVID-19 Patients on ACEIs and ARBs—A Systematic Review, Meta-Analysis, and Meta-Regression Analysis
This article has 14 authors:Reviewed by ScreenIT
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LY-CoV1404 (bebtelovimab) potently neutralizes SARS-CoV-2 variants
This article has 69 authors:Reviewed by ScreenIT
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Modeling mutational effects on biochemical phenotypes using convolutional neural networks: application to SARS-CoV-2
This article has 2 authors:Reviewed by ScreenIT