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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Determining the Optimal SARS-CoV-2 mRNA Vaccine Dosing Interval for Maximum Immunogenicity
This article has 13 authors:Reviewed by ScreenIT
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Pegylated-interferon-λ treatment-induced peripheral interferon stimulated genes are associated with SARS-CoV-2 viral load decline despite delayed T cell response in older individuals
This article has 8 authors:Reviewed by ScreenIT
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Vaccine breakthrough infection leads to distinct profiles of neutralizing antibody responses by SARS-CoV-2 variant
This article has 29 authors:Reviewed by ScreenIT
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A predictive model for hospitalization and survival to COVID-19 in a retrospective population-based study
This article has 11 authors:Reviewed by ScreenIT
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Vaccine effectiveness and duration of protection against symptomatic and severe Covid-19 during the first year of vaccination in France
This article has 3 authors:Reviewed by ScreenIT
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Genomic epidemiology offers high resolution estimates of serial intervals for COVID-19
This article has 12 authors:Reviewed by ScreenIT
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Multicenter analysis of neutrophil extracellular trap dysregulation in adult and pediatric COVID-19
This article has 53 authors:Reviewed by ScreenIT
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Z-RNA and the flipside of the SARS Nsp13 helicase
This article has 2 authors:Reviewed by ScreenIT
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Recurrent SARS-CoV-2 mutations in immunodeficient patients
This article has 16 authors:Reviewed by ScreenIT
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Development of a vocal biomarker for fatigue monitoring in people with COVID-19
This article has 8 authors:Reviewed by ScreenIT