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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Secondary infections modify the overall course of hospitalized patients with COVID-19: a retrospective study from a network of hospitals across North India
This article has 101 authors:Reviewed by ScreenIT
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Durability of immune responses to the BNT162b2 mRNA vaccine
This article has 21 authors:Reviewed by ScreenIT
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A combination of two human neutralizing antibodies prevents SARS-CoV-2 infection in rhesus macaques
This article has 22 authors:Reviewed by ScreenIT
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Phylodynamics of a regional SARS-CoV-2 rapid spreading event in Colorado in late 2020
This article has 14 authors:Reviewed by ScreenIT
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Mucosal Antibody Response to SARS-CoV-2 in Paediatric and Adult Patients: A Longitudinal Study
This article has 14 authors:Reviewed by ScreenIT
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The effects of communicating uncertainty around statistics, on public trust
This article has 7 authors:Reviewed by ScreenIT
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Standardized incidence ratio of the COVID-19 pandemic: a case study in a Midwestern state
This article has 2 authors:Reviewed by ScreenIT
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Analysis of overdispersion in airborne transmission of COVID-19
This article has 7 authors:Reviewed by ScreenIT
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Initial analysis of viral dynamics and circulating viral variants during the mRNA-1273 Phase 3 COVE trial
This article has 20 authors:Reviewed by ScreenIT
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A PTX3/LDH/CRP signature correlates with lung injury CTs scan severity and disease progression in paucisymptomatic COVID-19
This article has 19 authors:Reviewed by ScreenIT