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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Clinical Characteristics and Short-Term Outcomes of Severe Patients With COVID-19 in Wuhan, China
This article has 11 authors:Reviewed by ScreenIT
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Cotton-Tipped Plastic Swabs for SARS-CoV-2 RT-qPCR Diagnosis to Prevent Supply Shortages
This article has 7 authors:Reviewed by ScreenIT
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Modified full-face snorkel masks as reusable personal protective equipment for hospital personnel
This article has 42 authors:Reviewed by ScreenIT
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Genomics of Indian SARS-CoV-2: Implications in Genetic Diversity, Possible Origin and Spread of Virus
This article has 3 authors:Reviewed by ScreenIT
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Global Health Security Capacity Against COVID-19 Outbreak: An Analysis of Annual Data from 210 Countries and Territories
This article has 3 authors:Reviewed by ScreenIT
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Key predictors of attending hospital with COVID19: An association study from the COVID Symptom Tracker App in 2,618,948 individuals
This article has 26 authors:Reviewed by ScreenIT
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Bayesian Measurement of Diagnostic Accuracy of the RT-PCR Test for COVID-19
This article has 1 author:Reviewed by ScreenIT
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Demographic and socio-economic factors, and healthcare resource indicators associated with the rapid spread of COVID-19 in Northern Italy: An ecological study
This article has 6 authors:Reviewed by ScreenIT
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Unravelling the myths of R0 in controlling the dynamics of COVID-19 outbreak: A modelling perspective
This article has 2 authors:Reviewed by ScreenIT
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Predicting the COVID-19 epidemic in Algeria using the SIR model
This article has 2 authors:Reviewed by ScreenIT