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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Molecular evidence of SARS-CoV-2 in New York before the first pandemic wave
This article has 26 authors:Reviewed by ScreenIT
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The specificity of Japanese PCR assays for SARS-CoV-2 exceeds 99.7%
This article has 2 authors:Reviewed by ScreenIT
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Resistance of endothelial cells to SARS-CoV-2 infection in vitro
This article has 7 authors:Reviewed by ScreenIT
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Mobile outreach testing for COVID-19 in twenty homeless shelters in Toronto, Canada
This article has 9 authors:Reviewed by ScreenIT
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Empirical Assessment of U.S. Coronavirus Disease 2019 Crisis Standards of Care Guidelines
This article has 12 authors:Reviewed by ScreenIT
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Intranasal administration of SARS-CoV-2 neutralizing human antibody prevents infection in mice
This article has 6 authors:Reviewed by ScreenIT
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How Does Public Knowledge, Attitudes, and Behaviors Correlate in Relation to COVID-19? A Community-Based Cross-Sectional Study in Nepal
This article has 6 authors:Reviewed by ScreenIT
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The hidden variable in the dynamics of transmission of COVID-19: A Henon map approach
This article has 2 authors:Reviewed by ScreenIT
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Benchmarking the Covid-19 pandemic across countries and states in the USA under heterogeneous testing
This article has 3 authors:Reviewed by ScreenIT
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A common methodological phylogenomics framework for intra-patient heteroplasmies to infer SARS-CoV-2 sublineages and tumor clones
This article has 4 authors:Reviewed by ScreenIT