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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Early induction of functional SARS-CoV-2-specific T cells associates with rapid viral clearance and mild disease in COVID-19 patients
This article has 16 authors:Reviewed by ScreenIT
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SARS‐CoV‐2 sensing by RIG‐I and MDA5 links epithelial infection to macrophage inflammation
This article has 8 authors:Reviewed by ScreenIT
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Healthcare-associated COVID-19 in England: A national data linkage study
This article has 10 authors:Reviewed by ScreenIT
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Decontamination of SARS-CoV-2 and Other RNA Viruses from N95 Level Meltblown Polypropylene Fabric Using Heat under Different Humidities
This article has 10 authors:Reviewed by ScreenIT
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The true case fatality of COVID-19: An analytical solution
This article has 1 author:Reviewed by ScreenIT
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Epidemiology of Venous Thromboembolism in SARS-CoV-2 Infected Patients: A Systematic Review and Meta-Analysis
This article has 5 authors:Reviewed by ScreenIT
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Potential impact of COVID-19 related unemployment on increased cardiovascular disease in a high-income country: Modeling health loss, cost and equity
This article has 2 authors:Reviewed by ScreenIT
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Temporal and spatial heterogeneity of host response to SARS-CoV-2 pulmonary infection
This article has 33 authors:Reviewed by ScreenIT
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Systematic evaluation and external validation of 22 prognostic models among hospitalised adults with COVID-19: an observational cohort study
This article has 14 authors:Reviewed by ScreenIT
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Statistics-Based Predictions of Coronavirus Epidemic Spreading in Mainland China
This article has 1 author:Reviewed by ScreenIT