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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A transferable deep learning approach to fast screen potential antiviral drugs against SARS-CoV-2
This article has 5 authors:Reviewed by ScreenIT
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Risk of COVID-19 hospital admission and COVID-19 mortality during the first COVID-19 wave with a special emphasis on ethnic minorities: an observational study of a single, deprived, multiethnic UK health economy
This article has 7 authors:Reviewed by ScreenIT
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Non-pharmaceutical interventions and inoculation rate shape SARS-COV-2 vaccination campaign success
This article has 9 authors:Reviewed by ScreenIT
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Data-driven optimized control of the COVID-19 epidemics
This article has 3 authors:Reviewed by ScreenIT
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A simple laboratory parameter facilitates early identification of COVID-19 patients
This article has 7 authors:Reviewed by ScreenIT
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Early evidence of effectiveness of digital contact tracing for SARS-CoV-2 in Switzerland
This article has 20 authors: -
Neuroinvasion and Encephalitis Following Intranasal Inoculation of SARS-CoV-2 in K18-hACE2 Mice
This article has 9 authors:Reviewed by ScreenIT
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News Sentiment Informed Time-series Analyzing AI (SITALA) to curb the spread of COVID-19 in Houston
This article has 1 author:Reviewed by ScreenIT
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Adjusting COVID-19 Reports for Countries’ Age Disparities: A Comparative Framework for Reporting Performances
This article has 3 authors:Reviewed by ScreenIT
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SARS-CoV-2 induces human plasmacytoid predendritic cell diversification via UNC93B and IRAK4
This article has 16 authors:Reviewed by ScreenIT