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 model to predict COVID-19 epidemics with applications to South Korea, Italy, and Spain
This article has 4 authors:Reviewed by ScreenIT
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A follow-up study shows that recovered patients with re-positive PCR test in Wuhan may not be infectious
This article has 9 authors:Reviewed by ScreenIT
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A valid protective immune response elicited in rhesus macaques by an inactivated vaccine is capable of defending against SARS-CoV-2 infection
This article has 55 authors:Reviewed by ScreenIT
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The Methodological Quality Score of COVID-19 Systematic Reviews is Low, Except for Cochrane Reviews: A Meta-epidemiological Study
This article has 5 authors:Reviewed by ScreenIT
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Hacking The Diversity Of SARS-CoV-2 And SARS-Like Coronaviruses In Human, Bat And Pangolin Populations
This article has 3 authors:Reviewed by ScreenIT
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Smoking increases the risk of COVID-19 positivity, while Never-smoking reduces the risk
This article has 4 authors:Reviewed by ScreenIT
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School closures reduced social mixing of children during COVID-19 with implications for transmission risk and school reopening policies
This article has 14 authors:Reviewed by ScreenIT
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Negative-Binomial and quasi-poisson regressions between COVID-19, mobility and environment in São Paulo, Brazil
This article has 5 authors:Reviewed by ScreenIT
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The Covid-19 epidemic in the UK
This article has 2 authors:Reviewed by ScreenIT
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Could seasonal influenza vaccination influence COVID-19 risk?
This article has 2 authors:Reviewed by ScreenIT