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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Synergistic effects of anionic surfactants on coronavirus (SARS-CoV-2) virucidal efficiency of sanitizing fluids to fight COVID-19
This article has 4 authors:Reviewed by ScreenIT
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Synonymous sites in SARS-CoV-2 genes display trends affecting translational efficiency
This article has 6 authors:Reviewed by ScreenIT
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Assignment of coronavirus spike protein site-specific glycosylation using GlycReSoft
This article has 2 authors:Reviewed by ScreenIT
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Sofosbuvir protects human brain organoids against SARS-CoV-2
This article has 9 authors:Reviewed by ScreenIT
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A distinct phylogenetic cluster of Indian SARS-CoV-2 isolates
This article has 11 authors:Reviewed by ScreenIT
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COVID-3D: An online resource to explore the structural distribution of genetic variation in SARS-CoV-2 and its implication on therapeutic development
This article has 9 authors:Reviewed by ScreenIT
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Validation and Performance Comparison of Three SARS-CoV-2 Antibody Assays
This article has 10 authors:Reviewed by ScreenIT
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Structure-based design of prefusion-stabilized SARS-CoV-2 spikes
This article has 24 authors:Reviewed by ScreenIT
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Head-to-head comparison of four antigen-based rapid detection tests for the diagnosis of SARS-CoV-2 in respiratory samples
This article has 8 authors:Reviewed by ScreenIT
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Genomic analysis of early SARS-CoV-2 strains introduced in Mexico
This article has 31 authors:Reviewed by ScreenIT