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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Homo-harringtonine (HHT) – A highly effective drug against coronaviruses and the potential for large-scale clinical applications
This article has 22 authors:Reviewed by ScreenIT
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Transfer learning via multi-scale convolutional neural layers for human–virus protein–protein interaction prediction
This article has 5 authors:Reviewed by ScreenIT
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A critical update on the role of mild and serious vitamin D deficiency prevalence and the COVID-19 epidemic in Europe
This article has 2 authors:Reviewed by ScreenIT
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Homophily in risk and behavior complicate understanding the COVID-19 epidemic curve
This article has 4 authors:Reviewed by ScreenIT
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Seroprevalence of antibodies against SARS-CoV-2 in the adult population during the pre-vaccination period, Norway, winter 2020/21
This article has 14 authors:Reviewed by ScreenIT
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Dendritic cell deficiencies persist seven months after SARS-CoV-2 infection
This article has 20 authors:Reviewed by ScreenIT
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Safely Reopening K-12 Schools During the COVID-19 Pandemic
This article has 4 authors:Reviewed by ScreenIT
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Impact of Prior Infection on Protection and Transmission of SARS-CoV-2 in Golden Hamsters
This article has 12 authors:Reviewed by ScreenIT
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GIVE statistic for goodness of fit in instrumental variables models with application to COVID data
This article has 2 authors:Reviewed by ScreenIT
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Type I, II, and III Interferon Signatures Correspond to Coronavirus Disease 2019 Severity
This article has 13 authors:Reviewed by ScreenIT