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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COVID-19 Case Age Distribution: Correction for Differential Testing by Age
This article has 7 authors:Reviewed by ScreenIT
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Effects of Tocilizumab in Critically Ill Patients With COVID-19: A Quasi-Experimental Study
This article has 6 authors:Reviewed by ScreenIT
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Multiplexed detection of SARS-CoV-2 and other respiratory infections in high throughput by SARSeq
This article has 116 authors:Reviewed by ScreenIT
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Nsp1 of SARS-CoV-2 stimulates host translation termination
This article has 10 authors:Reviewed by ScreenIT
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A Rapid and Efficient Screening System for Neutralizing Antibodies and Its Application for SARS-CoV-2
This article has 37 authors:Reviewed by ScreenIT
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SARS-CoV-2 seroprevalence in a strictly-Orthodox Jewish community in the UK: A retrospective cohort study
This article has 13 authors:Reviewed by ScreenIT
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The strength of a NES motif in the nucleocapsid protein of human coronaviruses is related to genus, but not to pathogenic capacity
This article has 3 authors:Reviewed by ScreenIT
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Anakinra and Intravenous IgG versus Tocilizumab in the Treatment of COVID-19 Pneumonia
This article has 15 authors:Reviewed by ScreenIT
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Chest Computed Tomography Findings in Asymptomatic Patients with COVID-19
This article has 3 authors:Reviewed by ScreenIT
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This article has 13 authors:
Reviewed by ScreenIT