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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Identification of NPC1 as a novel SARS-CoV-2 intracellular target
This article has 10 authors:Reviewed by ScreenIT
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Long-term air pollution and other risk factors associated with COVID-19 at the census tract level in Colorado
This article has 3 authors:Reviewed by ScreenIT
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Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States
This article has 295 authors:Reviewed by ScreenIT
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Initial Model for the Impact of Social Distancing on COVID-19 Spread
This article has 1 author:Reviewed by ScreenIT
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Removing weekly administrative noise in the daily count of COVID-19 new cases. Application to the computation of Rt
This article has 3 authors:Reviewed by ScreenIT
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Detection of SARS-CoV-2 in Human Breast Milk
This article has 7 authors:Reviewed by ScreenIT
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Importance of Social Distancing: Modeling the spread of 2019-nCoV using Susceptible-Infected-Quarantined-Recovered-t model
This article has 1 author:Reviewed by ScreenIT
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Predicting the cumulative number of cases for the COVID-19 epidemic in China from early data
This article has 4 authors:Reviewed by ScreenIT
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Effect of emergency declaration for the COVID-19 outbreak in Tokyo, Japan in the first two weeks
This article has 3 authors:Reviewed by ScreenIT
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Outbreak dynamics of COVID-19 in China and the United States
This article has 4 authors:Reviewed by ScreenIT