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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Physician Perceptions of Catching COVID-19: Insights from a Global Survey
This article has 4 authors:Reviewed by ScreenIT
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An interactive viral genome evolution network analysis system enabling rapid large-scale molecular tracing of SARS-CoV-2
This article has 13 authors:Reviewed by ScreenIT
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Results from a survey in healthy blood donors in South Eastern Italy indicate that we are far away from herd immunity to SARS‐CoV‐2
This article has 15 authors:Reviewed by ScreenIT
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COVID-19: The Second Wave is not due to Cooling-down in Autumn
This article has 1 author:Reviewed by ScreenIT
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Low Covid-19 hospitalisation in Dumfries and Galloway: comparison with other Scottish health boards
This article has 3 authors:Reviewed by ScreenIT
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A comparative study of modified SIR and logistic predictors using local level database of COVID-19 in India
This article has 5 authors:Reviewed by ScreenIT
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Epidemiology of COVID-19 vs. influenza: Differential failure of COVID-19 mitigation among Hispanics, Cook County Health, Illinois
This article has 7 authors:Reviewed by ScreenIT
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Coronavirus Disease Model to Inform Transmission-Reducing Measures and Health System Preparedness, Australia
This article has 9 authors:Reviewed by ScreenIT
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Seven-month kinetics of SARS-CoV-2 antibodies and role of pre-existing antibodies to human coronaviruses
This article has 40 authors:Reviewed by ScreenIT
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Immuno-informatics approach for multi-epitope vaccine designing against SARS-CoV-2
This article has 5 authors:Reviewed by ScreenIT