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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Does TB Vaccination Reduce COVID-19 Infection? No Evidence from a Regression Discontinuity Analysis
This article has 3 authors:Reviewed by ScreenIT
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The Infection Rate of COVID-19 in Wuhan, China: Combined Analysis of Population Samples
This article has 5 authors:Reviewed by ScreenIT
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Exertional hypoxia in patients without resting hypoxia is an early predictor of moderate to severe COVID-19
This article has 6 authors:Reviewed by ScreenIT
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The incremental value of computed tomography of COVID-19 pneumonia in predicting ICU admission
This article has 32 authors:Reviewed by ScreenIT
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Infection and chronic disease activate a systemic brain-muscle signaling axis
This article has 17 authors:Reviewed by ScreenIT
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Viral genome sequencing places White House COVID-19 outbreak into phylogenetic context
This article has 15 authors:Reviewed by ScreenIT
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shinyCurves, a shiny web application to analyse multisource qPCR amplification data: a COVID-19 case study
This article has 7 authors:Reviewed by ScreenIT
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Mutations of SARS-CoV-2 nsp14 exhibit strong association with increased genome-wide mutation load
This article has 4 authors: -
Excess Mortality by Suicide Caused by COVID-19 in Japan
This article has 5 authors:Reviewed by ScreenIT
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A type I IFN, prothrombotic hyperinflammatory neutrophil signature is distinct for COVID-19 ARDS
This article has 34 authors:Reviewed by ScreenIT