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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An observational cohort study on the incidence of SARS-CoV-2 infection and B.1.1.7 variant infection in healthcare workers by antibody and vaccination status
This article has 38 authors: -
SARS-CoV-2 RNA reverse-transcribed and integrated into the human genome
This article has 7 authors:Reviewed by ScreenIT
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Details of COVID-19 Disease Mitigation Strategies in 17 K-12 Schools in Wood County, Wisconsin
This article has 7 authors:Reviewed by ScreenIT
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Past SARS-CoV-2 infection elicits a strong immune response after a single vaccine dose
This article has 23 authors:Reviewed by ScreenIT
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Within-country age-based prioritisation, global allocation, and public health impact of a vaccine against SARS-CoV-2: A mathematical modelling analysis
This article has 20 authors:Reviewed by ScreenIT
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SARS-CoV-2 sculpts the immune system to induce sustained virus-specific naïve-like and memory B cell responses
This article has 12 authors:Reviewed by ScreenIT
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Cardiac Arrhythmias in Patients with COVID-19: A Systematic review and Meta-analysis
This article has 12 authors:Reviewed by ScreenIT
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Structural O-Glycoform Heterogeneity of the SARS-CoV-2 Spike Protein Receptor-Binding Domain Revealed by Top-Down Mass Spectrometry
This article has 8 authors:Reviewed by ScreenIT
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COVID-19 risk score as a public health tool to guide targeted testing: A demonstration study in Qatar
This article has 19 authors: -
Pediatric nasal epithelial cells are less permissive to SARS-CoV-2 replication compared to adult cells
This article has 25 authors:Reviewed by ScreenIT