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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A rapid systematic review and case study on test, contact tracing, testing, and isolation policies for Covid-19 prevention and control
This article has 6 authors:Reviewed by ScreenIT
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COVID-19 and HIV co-infection: a living systematic evidence map of current research
This article has 3 authors:Reviewed by ScreenIT
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Losing ground at the wrong time: trends in self-reported influenza vaccination uptake in Switzerland, Swiss Health Survey 2007–2017
This article has 5 authors:Reviewed by ScreenIT
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A systematic review of convalescent plasma treatment for COVID19
This article has 3 authors:Reviewed by ScreenIT
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The impact of COVID-19 control measures on social contacts and transmission in Kenyan informal settlements
This article has 58 authors:Reviewed by ScreenIT
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Containment of COVID-19: Simulating the impact of different policies and testing capacities for contact tracing, testing, and isolation
This article has 10 authors:Reviewed by ScreenIT
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BMI and future risk for COVID-19 infection and death across sex, age and ethnicity: Preliminary findings from UK biobank
This article has 11 authors:Reviewed by ScreenIT
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A shred of evidence that BCG vaccine may protect against COVID-19: Comparing cohorts in Spain and Italy
This article has 1 author:Reviewed by ScreenIT
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Is the psychological well-being of a population associated with COVID-19 related survival?
This article has 1 author:Reviewed by ScreenIT
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Heterogeneity is essential for contact tracing
This article has 4 authors:Reviewed by ScreenIT