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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COVID-19 vaccine booster induces a strong CD8 + T cell response against Omicron variant epitopes in HLA-A*02:01 + individuals
This article has 22 authors:Reviewed by ScreenIT
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matOptimize: a parallel tree optimization method enables online phylogenetics for SARS-CoV-2
This article has 9 authors:Reviewed by ScreenIT
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Estimation of age-stratified contact rates during the COVID-19 pandemic using a novel inference algorithm
This article has 3 authors:Reviewed by ScreenIT
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People with HIV have a higher risk of COVID‐19 diagnosis but similar outcomes to the general population
This article has 6 authors:Reviewed by ScreenIT
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A Distinct Dexamethasone-Dependent Gene Expression Profile in the Lungs of COVID-19 Patients
This article has 9 authors:Reviewed by ScreenIT
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Potent monoclonal antibodies neutralize Omicron sublineages and other SARS-CoV-2 variants
This article has 24 authors:Reviewed by ScreenIT
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COVID-19 Preventive Measures in Northern California Jails: Perceived Deficiencies, Barriers, and Unintended Harms
This article has 14 authors:Reviewed by ScreenIT
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Quantifying the impact of immune history and variant on SARS-CoV-2 viral kinetics and infection rebound: A retrospective cohort study
This article has 25 authors:This article has been curated by 1 group: -
The SARS-CoV-2 Omicron (B.1.1.529) variant exhibits altered pathogenicity, transmissibility, and fitness in the golden Syrian hamster model
This article has 27 authors:Reviewed by ScreenIT
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The mental health of staff working on intensive care units over the COVID-19 winter surge of 2020 in England: a cross sectional survey
This article has 12 authors:Reviewed by ScreenIT