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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Survival and predictors of deaths of patients hospitalized due to COVID-19 from a retrospective and multicenter cohort study in Brazil
This article has 6 authors:Reviewed by ScreenIT
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Serological Assays Estimate Highly Variable SARS-CoV-2 Neutralizing Antibody Activity in Recovered COVID-19 Patients
This article has 20 authors:Reviewed by ScreenIT
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SARS-CoV-2 in first trimester pregnancy: a cohort study
This article has 24 authors:Reviewed by ScreenIT
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Households at Higher Risk of Losing at Least One Individual in India: if COVID-19 is a new normal
This article has 3 authors:Reviewed by ScreenIT
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Are women leaders significantly better at controlling the contagion?
This article has 3 authors:Reviewed by ScreenIT
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Changes in health promoting behavior during COVID-19 physical distancing: Utilizing wearable technology to examine trends in sleep, activity, and cardiovascular indicators of health
This article has 2 authors:Reviewed by ScreenIT
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Analysis of the Effect of Proton-Pump Inhibitors on the Course of COVID-19
This article has 6 authors:Reviewed by ScreenIT
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Transcriptional Start Site Coverage Analysis in Plasma Cell-Free DNA Reveals Disease Severity and Tissue Specificity of COVID-19 Patients
This article has 34 authors:Reviewed by ScreenIT
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Covid-19 Incidence Rate Evolution Modeling using Dual Wave Gaussian-Lorentzian Composite Functions
This article has 1 author:Reviewed by ScreenIT
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Repurposing of drugs for COVID-19: a systematic review and meta-analysis
This article has 8 authors:Reviewed by ScreenIT