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 highly conserved cryptic epitope in the receptor binding domains of SARS-CoV-2 and SARS-CoV
This article has 8 authors:Reviewed by ScreenIT
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The Architecture of SARS-CoV-2 Transcriptome
This article has 6 authors:Reviewed by ScreenIT
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Lack of Reinfection in Rhesus Macaques Infected with SARS-CoV-2
This article has 27 authors:Reviewed by ScreenIT
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Differential Antibody Recognition by Novel SARS-CoV-2 and SARS-CoV Spike Protein Receptor Binding Domains: Mechanistic Insights and Implications for the Design of Diagnostics and Therapeutics
This article has 3 authors:Reviewed by ScreenIT
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Analysis clinical features of COVID-19 infection in secondary epidemic area and report potential biomarkers in evaluation
This article has 4 authors:Reviewed by ScreenIT
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Age specificity of cases and attack rate of novel coronavirus disease (COVID-19)
This article has 3 authors:Reviewed by ScreenIT
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Genomic epidemiology of a densely sampled COVID-19 outbreak in China
This article has 22 authors:Reviewed by ScreenIT
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Knowledge and Perceptions of COVID-19 Among Health Care Workers: Cross-Sectional Study
This article has 5 authors:Reviewed by ScreenIT
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The prediction for development of COVID-19 in global major epidemic areas through empirical trends in China by utilizing state transition matrix model
This article has 6 authors:Reviewed by ScreenIT
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Estimation of instant case fatality rate of COVID-19 in Wuhan and Hubei based on daily case notification data
This article has 13 authors:Reviewed by ScreenIT