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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The Approved Dose of Ivermectin Alone is not the Ideal Dose for the Treatment of COVID‐19
This article has 3 authors:Reviewed by ScreenIT
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Recruitment location influences bias and uncertainty in SARS-CoV-2 seroprevalence estimates
This article has 23 authors:Reviewed by ScreenIT
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Prospective surveillance study in a 1,400‐bed university hospital: COVID‐19 exposure at home was the main risk factor for SARS‐CoV‐2 point seroprevalence among hospital staff
This article has 19 authors:Reviewed by ScreenIT
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Susceptibility of midge and mosquito vectors to SARS-CoV-2 by natural route of infection
This article has 11 authors:Reviewed by ScreenIT
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Predicting Onset of COVID-19 with Mobility-Augmented SEIR Model
This article has 9 authors:Reviewed by ScreenIT
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Evaluation of the potential incidence of COVID-19 and effectiveness of containment measures in Spain: a data-driven approach
This article has 2 authors:Reviewed by ScreenIT
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Respiratory and non-respiratory manifestations in children admitted with COVID 19 in Rio de Janeiro city, Brazil
This article has 17 authors:Reviewed by ScreenIT
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Behavioural barriers to COVID-19 testing in Australia
This article has 12 authors:Reviewed by ScreenIT
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Initial Evaluation of a Mobile SARS-CoV-2 RT-LAMP Testing Strategy
This article has 31 authors:Reviewed by ScreenIT
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Production of ORF8 protein from SARS-CoV-2 using an inducible virus-mediated expression system in suspension-cultured tobacco BY-2 cells
This article has 5 authors:Reviewed by ScreenIT