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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Using mixed-effects modeling to estimate decay kinetics of response to SARS-CoV-2 infection
This article has 6 authors:Reviewed by ScreenIT
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Increased ACE2, sRAGE, and Immune Activation, but Lowered Calcium and Magnesium in COVID-19
This article has 4 authors:Reviewed by ScreenIT
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Accuracy of novel antigen rapid diagnostics for SARS-CoV-2: A living systematic review and meta-analysis
This article has 15 authors:Reviewed by ScreenIT
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Analysis of Ten Microsecond simulation data of SARS-CoV-2 dimeric main protease
This article has 8 authors:Reviewed by ScreenIT
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Viral Architecture of SARS-CoV-2 with Post-Fusion Spike Revealed by Cryo-EM
This article has 18 authors:Reviewed by ScreenIT
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Multiplex Fragment Analysis for Flexible Detection of All SARS-CoV-2 Variants of Concern
This article has 19 authors:Reviewed by ScreenIT
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Human IgG and IgA responses to COVID-19 mRNA vaccines
This article has 3 authors:Reviewed by ScreenIT
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Naive human B cells engage the receptor binding domain of SARS-CoV-2, variants of concern, and related sarbecoviruses
This article has 15 authors:Reviewed by ScreenIT
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Mental Health during the COVID-19 Crisis in Africa: A Systematic Review and Meta-Analysis
This article has 12 authors:Reviewed by ScreenIT
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The acceptability of testing contacts of confirmed COVID-19 cases using serial, self-administered lateral flow devices as an alternative to self-isolation
This article has 7 authors:Reviewed by ScreenIT