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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Beta infection combined with Pfizer BNT162b2 vaccination leads to broadened neutralizing immunity against Omicron
This article has 22 authors:Reviewed by ScreenIT
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Mutual inhibition of airway epithelial responses supports viral and fungal co-pathogenesis during coinfection
This article has 8 authors:Reviewed by ScreenIT
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The Arabidopsis Framework Model version 2 predicts the organism-level effects of circadian clock gene mis-regulation
This article has 13 authors:Reviewed by ScreenIT
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SARS-CoV-2 Omicron BA.1 variant infection of human colon epithelial cells
This article has 6 authors:Reviewed by ScreenIT
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Genomic surveillance unfolds the dynamics of SARS-CoV-2 transmission and divergence in Bangladesh over the past two years
This article has 3 authors:Reviewed by ScreenIT
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This is GlycoQL
This article has 4 authors:Reviewed by ScreenIT
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Whole-body metabolic modelling predicts isoleucine dependency of SARS-CoV-2 replication
This article has 2 authors:Reviewed by ScreenIT
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Determinants of Spike infectivity, processing, and neutralization in SARS-CoV-2 Omicron subvariants BA.1 and BA.2
This article has 8 authors:Reviewed by ScreenIT
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Epitope Mapping of SARS-CoV-2 Spike Protein Reveals Distinct Antibody Binding Activity of Vaccinated and Infected Individuals
This article has 13 authors:Reviewed by ScreenIT
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Relative infectivity of the SARS-CoV-2 Omicron variant in human alveolar cells
This article has 13 authors:Reviewed by ScreenIT