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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Percentage of reported Covid-19 cases in Colombia: Estimating the true scale of the pandemic
This article has 5 authors:Reviewed by ScreenIT
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Immunisation, asymptomatic infection, herd immunity and the new variants of COVID 19
This article has 2 authors:Reviewed by ScreenIT
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Clinical characteristics and mortality associated with COVID-19 in Jakarta, Indonesia: A hospital-based retrospective cohort study
This article has 17 authors:Reviewed by ScreenIT
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Investigation of COVID-19 comorbidities reveals genes and pathways coincident with the SARS-CoV-2 viral disease
This article has 6 authors:Reviewed by ScreenIT
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How to Make COVID-19 Contact Tracing Apps work: Insights From Behavioral Economics
This article has 3 authors:Reviewed by ScreenIT
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Individual variation in susceptibility or exposure to SARS-CoV-2 lowers the herd immunity threshold
This article has 10 authors:Reviewed by ScreenIT
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Creating a safe workplace by universal testing of SARS-CoV-2 infection in asymptomatic patients and healthcare workers in the electrophysiology units: a multi-center experience
This article has 21 authors:Reviewed by ScreenIT
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No Evidence for Temperature-Dependence of the COVID-19 Epidemic
This article has 4 authors:Reviewed by ScreenIT
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COVID-19 surveillance - a descriptive study on data quality issues
This article has 12 authors:Reviewed by ScreenIT
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COVID-19 in regions with low prevalence and low density of population. An uncertainty dynamic modeling approach
This article has 6 authors:Reviewed by ScreenIT