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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Undiagnosed SARS-CoV-2 seropositivity during the first 6 months of the COVID-19 pandemic in the United States
This article has 53 authors:Reviewed by ScreenIT
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SARS-CoV-2 genomic and quasispecies analyses in cancer patients reveal relaxed intrahost virus evolution
This article has 10 authors:Reviewed by ScreenIT
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Viral RNA level, serum antibody responses, and transmission risk in discharged COVID-19 patients with recurrent positive SARS-CoV-2 RNA test results: a population-based observational cohort study
This article has 35 authors:Reviewed by ScreenIT
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Countries with potential data misreport based on Benford’s law
This article has 2 authors:Reviewed by ScreenIT
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Self-learning on COVID-19 among medical students in Bhutan: A cross-sectional study
This article has 3 authors:Reviewed by ScreenIT
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The seroprevalence of severe acute respiratory syndrome coronavirus 2 in Delhi, India: a repeated population-based seroepidemiological study
This article has 11 authors:Reviewed by ScreenIT
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Development and performance evaluation of a rapid in-house ELISA for retrospective serosurveillance of SARS-CoV-2
This article has 11 authors:Reviewed by ScreenIT
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Inequalities in the decline and recovery of pathological cancer diagnoses during the first six months of the COVID-19 pandemic: a population-based study
This article has 9 authors:Reviewed by ScreenIT
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Studying the course of Covid-19 by a recursive delay approach
This article has 2 authors:Reviewed by ScreenIT
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Estimating effects of intervention measures on COVID-19 outbreak in Wuhan taking account of improving diagnostic capabilities using a modelling approach
This article has 4 authors:Reviewed by ScreenIT