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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Fourteen-days Evolution of COVID-19 Symptoms During the Third Wave in Non-vaccinated Subjects and Effects of Hesperidin Therapy: A randomized, double-blinded, placebo-controlled study
This article has 11 authors:Reviewed by ScreenIT
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Transmission of SARS-CoV-2 in Norwegian schools during academic year 2020-21: population wide, register based cohort study
This article has 7 authors:Reviewed by ScreenIT
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Prenatal Maternal Distress During the COVID-19 Pandemic and Associations With Infant Brain Connectivity
This article has 6 authors:Reviewed by ScreenIT
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Time trends in social contacts before and during the COVID-19 pandemic: the CONNECT study
This article has 15 authors:Reviewed by ScreenIT
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Seroprevalence of COVID-19 in Palestine in 2020
This article has 10 authors:Reviewed by ScreenIT
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A transfer learning framework to elucidate the clinical relevance of altered proximal tubule cell states in kidney disease
This article has 16 authors:Reviewed by ScreenIT
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SARS-CoV-2 causes human BBB injury and neuroinflammation indirectly in a linked organ chip platform
This article has 14 authors:Reviewed by ScreenIT
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How Does Temperature Affect the Dynamics of SARS-CoV-2 M Proteins? Insights from Molecular Dynamics Simulations
This article has 3 authors:Reviewed by ScreenIT
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SARS-CoV-2 PLpro whole human proteome cleavage prediction and enrichment/depletion analysis
This article has 1 author:Reviewed by ScreenIT
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Immunogenicity of SARS-CoV-2 trimetric spike protein associated to Poly(I:C) plus Alum
This article has 30 authors:Reviewed by ScreenIT