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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Government Responses Matter: Predicting Covid-19 cases in US using an empirical Bayesian time series framework
This article has 2 authors:Reviewed by ScreenIT
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Seroprevalence of SARS-CoV-2 in the West Bank region of Palestine: a cross-sectional seroepidemiological study
This article has 8 authors:Reviewed by ScreenIT
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Predicting the growth and trend of COVID-19 pandemic using machine learning and cloud computing
This article has 4 authors:Reviewed by ScreenIT
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Saliva Is a Promising Alternative Specimen for the Detection of SARS-CoV-2 in Children and Adults
This article has 10 authors:Reviewed by ScreenIT
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A simple mathematical model for Coronavirus (COVID-19)
This article has 3 authors:Reviewed by ScreenIT
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Distribution of incubation periods of COVID-19 in the Canadian context
This article has 2 authors:Reviewed by ScreenIT
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Implications of monsoon season and UVB radiation for COVID-19 in India
This article has 2 authors:Reviewed by ScreenIT
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Reparations for Black American descendants of persons enslaved in the U.S. and their potential impact on SARS-CoV-2 transmission
This article has 11 authors:Reviewed by ScreenIT
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“There’s No Place Like Home for The Holidays:” Travel and SARS-CoV-2 Test Positivity Following Thanksgiving Weekend
This article has 4 authors:Reviewed by ScreenIT
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Is this the beginning or the end of COVID-19 outbreak in India? A data driven mathematical model-based analysis
This article has 3 authors:Reviewed by ScreenIT