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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Association between overcrowded households, multigenerational households, and COVID-19: a cohort study
This article has 13 authors:Reviewed by ScreenIT
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Integrating epidemiological and clinical predictors of SARS-CoV-2 infection in students and school staff in the state of São Paulo
This article has 9 authors:Reviewed by ScreenIT
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Digital contact tracing contributes little to COVID-19 outbreak containment
This article has 3 authors:Reviewed by ScreenIT
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N4-hydroxycytidine and inhibitors of dihydroorotate dehydrogenase synergistically suppress SARS-CoV-2 replication
This article has 7 authors:Reviewed by ScreenIT
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Phylogenetic network analysis revealed the recombinant origin of the SARS-CoV-2 VOC202012/01 (B.1.1.7) variant first discovered in U.K.
This article has 4 authors:Reviewed by ScreenIT
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Recovery of Deleted Deep Sequencing Data Sheds More Light on the Early Wuhan SARS-CoV-2 Epidemic
This article has 1 author:Reviewed by ScreenIT
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Effectiveness of Tocilizumab, Sarilumab, and Anakinra for critically ill patients with COVID-19 The REMAP-CAP COVID-19 Immune Modulation Therapy Domain Randomized Clinical Trial
This article has 2 authors:Reviewed by ScreenIT
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The majority of the variation in COVID-19 rates between nations is explained by median age, obesity rate, and island status
This article has 3 authors:Reviewed by ScreenIT
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Can auxiliary indicators improve COVID-19 forecasting and hotspot prediction?
This article has 13 authors:Reviewed by ScreenIT
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Mutations within the Open Reading Frame (ORF) including Ochre stop codon of the Surface Glycoprotein gene of SARS-CoV-2 virus erase potential seed location motifs of human non-coding microRNAs
This article has 3 authors:Reviewed by ScreenIT