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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On the COVID-19 Pandemic in Indian State of Maharashtra: Forecasting & Effect of different parameters
This article has 4 authors:Reviewed by ScreenIT
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Analysis of ACE2 genetic variants in 131 Italian SARS-CoV-2-positive patients
This article has 14 authors:Reviewed by ScreenIT
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Are We #Stayinghome to Flatten the Curve?
This article has 4 authors:Reviewed by ScreenIT
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Risk factors affecting COVID-19 case fatality rate: A quantitative analysis of top 50 affected countries
This article has 9 authors:Reviewed by ScreenIT
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Prevalence of SARS-CoV-2 infection among asymptomatic healthcare workers in greater Houston: a cross-sectional analysis of surveillance data from a large healthcare system
This article has 10 authors:Reviewed by ScreenIT
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Global Infodemiology of COVID-19: Analysis of Google Web Searches and Instagram Hashtags
This article has 2 authors:Reviewed by ScreenIT
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A scaling investigation of pattern in the spread of COVID-19: universality in real data and a predictive analytical description
This article has 1 author:Reviewed by ScreenIT
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Epidemiological study to detect active SARS-CoV-2 infections and seropositive persons in a selected cohort of employees in the Frankfurt am Main metropolitan area
This article has 11 authors:Reviewed by ScreenIT
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Is there an airborne component to the transmission of COVID-19? : a quantitative analysis study
This article has 1 author:Reviewed by ScreenIT
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Rising evidence of COVID-19 transmission potential to and between animals: do we need to be concerned?
This article has 3 authors:Reviewed by ScreenIT