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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Effectiveness of Covid-19 Vaccines against the SARS-COV-2-Delta (B.1.617.2) in China-A Real World Study
This article has 4 authors:Reviewed by ScreenIT
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Validating saliva as a biological sample for cost-effective, rapid and routine screening for SARS-CoV-2
This article has 12 authors:Reviewed by ScreenIT
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A “step too far” or “perfect sense”? A qualitative study of British adults’ views on mandating COVID-19 vaccination and vaccine passports
This article has 12 authors:Reviewed by ScreenIT
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Acoustic Epidemiology of Pulmonary Tuberculosis (TB) & Covid19 Leveraging explainable AI/ML
This article has 2 authors:Reviewed by ScreenIT
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Cardiovascular signatures of COVID-19 predict mortality and identify barrier stabilizing therapies
This article has 21 authors:Reviewed by ScreenIT
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A potent SARS-CoV-2 neutralizing antibody recognizing a conserved epitope with broad mutant variant and SARS-CoV activity
This article has 12 authors:Reviewed by ScreenIT
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Cumulative seroprevalence among healthcare workers after the first wave of the COVID-19 pandemic in El Salvador, Central America
This article has 10 authors:Reviewed by ScreenIT
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Mutations of Omicron Variant at the Interface of the Receptor Domain Motif and Human Angiotensin-Converting Enzyme-2
This article has 4 authors:Reviewed by ScreenIT
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SARS-CoV-2 Omicron (B.1.1.529) Infection of Wild White-Tailed Deer in New York City
This article has 34 authors:Reviewed by ScreenIT
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Clinical manifestations and disease severity of SARS-CoV-2 infection among infants in Canada
This article has 11 authors:Reviewed by ScreenIT