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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Analysis of SARS-CoV-2 Transmission in Different Settings, Brunei
This article has 6 authors:Reviewed by ScreenIT
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Impact of complete lockdown on total infection and death rates: A hierarchical cluster analysis
This article has 3 authors:Reviewed by ScreenIT
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CoVID-19 prediction for India from the existing data and SIR(D) model study
This article has 5 authors:Reviewed by ScreenIT
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Importance of Interaction Structure and Stochasticity for Epidemic Spreading: A COVID-19 Case Study
This article has 3 authors:Reviewed by ScreenIT
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Evolution of COVID-19 pandemic: Power-law growth and saturation
This article has 6 authors:Reviewed by ScreenIT
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COVID-19 Infection in Children: Estimating Pediatric Morbidity and Mortality
This article has 7 authors:Reviewed by ScreenIT
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Health‐seeking behaviors of patients with acute respiratory infections during the outbreak of novel coronavirus disease 2019 in Wuhan, China
This article has 10 authors:Reviewed by ScreenIT
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More Than Smell—COVID-19 Is Associated With Severe Impairment of Smell, Taste, and Chemesthesis
This article has 123 authors:Reviewed by ScreenIT
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Robust and Specific Secretory IgA Against SARS-CoV-2 Detected in Human Milk
This article has 7 authors:Reviewed by ScreenIT
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A novel intervention recurrent autoencoder for real time forecasting and non-pharmaceutical intervention selection to curb the spread of Covid-19 in the world
This article has 6 authors:Reviewed by ScreenIT