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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Impact of circulating SARS-CoV-2 variants on mRNA vaccine-induced immunity
This article has 46 authors:Reviewed by ScreenIT
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Lives saved and hospitalizations averted by COVID-19 vaccination in New York City: a modeling study
This article has 9 authors:Reviewed by ScreenIT
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Sequencing SARS-CoV-2 from antigen tests
This article has 6 authors:Reviewed by ScreenIT
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Visualizing Amino Acid Substitutions in a Physicochemical Vector Space
This article has 1 author:Reviewed by ScreenIT
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Population antibody responses following COVID-19 vaccination in 212,102 individuals
This article has 14 authors:Reviewed by ScreenIT
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Vaccine Confidence and Hesitancy at the Start of COVID-19 Vaccine Deployment in the UK: An Embedded Mixed-Methods Study
This article has 6 authors:Reviewed by ScreenIT
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Sequencing SARS-CoV-2 in Slovakia: An Unofficial Genomic Surveillance Report
This article has 9 authors:Reviewed by ScreenIT
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Relative Ratios of Human Seasonal Coronavirus Antibodies Predict the Efficiency of Cross-Neutralization of SARS-CoV-2 Spike Binding to ACE2
This article has 14 authors:Reviewed by ScreenIT
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Validation of a novel molecular assay to the diagnostic of COVID-19 based on real time PCR with high resolution melting
This article has 13 authors:Reviewed by ScreenIT
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Variable susceptibility of intestinal organoid–derived monolayers to SARS-CoV-2 infection
This article has 9 authors:Reviewed by Rapid Reviews Infectious Diseases, ScreenIT