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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Knowledge, Attitudes, and Perceptions of the Greek Population Regarding the COVID-19 Pandemic during the National Lockdown (March 23–May 03, 2020): A Web-Based Cross-Sectional Study
This article has 3 authors:Reviewed by ScreenIT
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Rapid Epidemiological Analysis of Comorbidities and Treatments as risk factors for COVID-19 in Scotland (REACT-SCOT): A population-based case-control study
This article has 18 authors:Reviewed by ScreenIT
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Use of Ivermectin Is Associated With Lower Mortality in Hospitalized Patients With Coronavirus Disease 2019
This article has 6 authors:Reviewed by ScreenIT
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A Particle-Based COVID-19 Simulator with Contact Tracing and Testing
This article has 5 authors:Reviewed by ScreenIT
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Model Calculations of Aerosol Transmission and Infection Risk of COVID-19 in Indoor Environments
This article has 10 authors:Reviewed by ScreenIT
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The link between vitamin D deficiency and Covid-19 in a large population
This article has 8 authors:Reviewed by ScreenIT
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Standard and Anomalous Waves of COVID-19: A Multiple-Wave Growth Model for Epidemics
This article has 6 authors:Reviewed by ScreenIT
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Monitoring COVID-19 Transmission Risks by Quantitative Real-Time PCR Tracing of Droplets in Hospital and Living Environments
This article has 19 authors:Reviewed by ScreenIT
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The potential effect of the African population age structure on COVID-19 mortality
This article has 3 authors:Reviewed by ScreenIT
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COVID-19 in India: Predictions, Reproduction Number and Public Health Preparedness
This article has 3 authors:Reviewed by ScreenIT