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 deductive approach to modeling the spread of COVID-19
This article has 2 authors:Reviewed by ScreenIT
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Perceived Stress by Students of the Medical Sciences in Cuba Toward the COVID-19 Pandemic: Results of an Online Survey
This article has 6 authors:Reviewed by ScreenIT
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Baricitinib restrains the immune dysregulation in patients with severe COVID-19
This article has 30 authors:Reviewed by ScreenIT
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Genomic diversity analysis of SARS-CoV-2 genomes in Rwanda
This article has 3 authors:Reviewed by ScreenIT
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SCoVMod – a spatially explicit mobility and deprivation adjusted model of first wave COVID-19 transmission dynamics
This article has 14 authors:Reviewed by ScreenIT
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Evaluating the Effects of SARS-CoV-2 Spike Mutation D614G on Transmissibility and Pathogenicity
This article has 581 authors:Reviewed by ScreenIT
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Heat stress and PPE during COVID-19: impact on healthcare workers' performance, safety and well-being in NHS settings
This article has 5 authors:Reviewed by ScreenIT
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Comparison of seroprevalence of SARS-CoV-2 infections with cumulative and imputed COVID-19 cases: Systematic review
This article has 7 authors:Reviewed by ScreenIT
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Boceprevir, Calpain Inhibitors II and XII, and GC-376 Have Broad-Spectrum Antiviral Activity against Coronaviruses
This article has 6 authors:Reviewed by ScreenIT
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Anxiety Levels among Healthcare Professionals during Covid-19 Pandemic: A Multifactorial Study
This article has 5 authors:Reviewed by ScreenIT