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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Timing of exposure is critical in a highly sensitive model of SARS-CoV-2 transmission
This article has 8 authors:Reviewed by ScreenIT
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SARS-CoV-2 Omicron Variant Neutralization in Serum from Vaccinated and Convalescent Persons
This article has 5 authors:Reviewed by ScreenIT
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Exploring selection bias in COVID-19 research: Simulations and prospective analyses of two UK cohort studies
This article has 13 authors:Reviewed by ScreenIT
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Deep Mutational Engineering of broadly-neutralizing and picomolar affinity nanobodies to accommodate SARS-CoV-1 & 2 antigenic polymorphism
This article has 11 authors:Reviewed by ScreenIT
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A novel bacterial protease inhibitor adjuvant in RBD-based COVID-19 vaccine formulations increases neutralizing antibodies, specific germinal center B cells and confers protection against SARS-CoV-2 infection
This article has 17 authors:Reviewed by ScreenIT
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Graph Convolutional Network-Based Screening Strategy for Rapid Identification of SARS-CoV-2 Cell-Entry Inhibitors
This article has 8 authors:Reviewed by ScreenIT
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Combinatorial mRNA vaccination enhances protection against SARS-CoV-2 delta variant
This article has 20 authors:Reviewed by ScreenIT
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Nanotrap Particles Improve Nanopore Sequencing of SARS-CoV-2 and Other Respiratory Viruses
This article has 26 authors:Reviewed by ScreenIT
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Mutations in the spike RBD of SARS-CoV-2 omicron variant may increase infectivity without dramatically altering the efficacy of current multi-dosage vaccinations
This article has 4 authors:Reviewed by ScreenIT
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The Landscape-Based Protein Stability Analysis and Network Modeling of Multiple Conformational States of the SARS-CoV-2 Spike D614 Mutant: Conformational Plasticity and Frustration-Driven Allostery as Energetic Drivers of Highly Transmissible Spike Variant
This article has 4 authors:Reviewed by ScreenIT