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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CD8+ T cell responses in convalescent COVID-19 individuals target epitopes from the entire SARS-CoV-2 proteome and show kinetics of early differentiation
This article has 22 authors:Reviewed by ScreenIT
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Contact tracing of COVID-19 in Karnataka, India: Superspreading and determinants of infectiousness and symptomatic infection
This article has 16 authors:Reviewed by ScreenIT
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Exploration of strategies to reduce aerosol-spread during chest compressions: A simulation and cadaver model
This article has 7 authors:Reviewed by ScreenIT
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A comprehensive transcriptome analysis reveals broader but weaker host response of SARS-CoV-2 than SARS-CoV
This article has 5 authors:Reviewed by ScreenIT
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Modeling population‐wide testing of SARS‐CoV‐2 for containing COVID‐19 pandemic in Okinawa, Japan
This article has 3 authors:Reviewed by ScreenIT
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Influence of Adherence to Social Distancing Due to the COVID-19 Pandemic on Physical Activity Level in Post-bariatric Patients
This article has 13 authors:Reviewed by ScreenIT
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nanoDoc: RNA modification detection using Nanopore raw reads with Deep One-Class Classification
This article has 1 author:Reviewed by ScreenIT
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Evidence for immunity to SARS-CoV-2 from epidemiological data series
This article has 3 authors:Reviewed by ScreenIT
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Significantly longer Covid-19 incubation times for the elderly, from a case study of 136 patients throughout China
This article has 3 authors:Reviewed by ScreenIT
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The Genetic Risk for COVID-19 Severity Is Associated With Defective Immune Responses
This article has 18 authors:Reviewed by ScreenIT