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 Comparative Analysis Of COVID-19 Mortality Rate Across the Globe: An Extensive Analysis of the Associated Factors
This article has 14 authors:Reviewed by ScreenIT
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Clinical and Immunological Factors That Distinguish COVID-19 From Pandemic Influenza A(H1N1)
This article has 40 authors:Reviewed by ScreenIT
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Evaluating scenarios for school reopening under COVID19
This article has 5 authors:Reviewed by ScreenIT
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Whole-genome sequencing of SARS-CoV-2 in the Republic of Ireland during waves 1 and 2 of the pandemic
This article has 15 authors:Reviewed by ScreenIT
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Preliminary Results of Seroprevalence of SARS-CoV-2 at Community Clinics in Tokyo
This article has 7 authors:Reviewed by ScreenIT
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Chromatin remodeling in peripheral blood cells reflects COVID-19 symptom severity
This article has 22 authors:Reviewed by ScreenIT
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A guideline to limit indoor airborne transmission of COVID-19
This article has 2 authors:Reviewed by ScreenIT
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SARS-CoV-2 Spike Glycoprotein S1 Induces Neuroinflammation in BV-2 Microglia
This article has 4 authors:Reviewed by ScreenIT
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Prevalence of bacterial pathogens and potential role in COVID-19 severity in patients admitted to intensive care units in Brazil
This article has 17 authors:Reviewed by ScreenIT
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Molecular dynamics simulations and functional studies reveal that hBD-2 binds SARS-CoV-2 spike RBD and blocks viral entry into ACE2 expressing cells
This article has 9 authors:Reviewed by ScreenIT