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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Analysis of 46,046 SARS-CoV-2 whole-genomes leveraging principal component analysis (PCA)
This article has 5 authors:Reviewed by ScreenIT
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Air pollution and COVID-19 mortality in the United States: Strengths and limitations of an ecological regression analysis
This article has 5 authors: -
Outcomes of Ivermectin in the treatment of COVID-19: a systematic review and meta-analysis
This article has 6 authors:Reviewed by ScreenIT
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How the COVID-19 pandemic affects transgender health care - A cross-sectional online survey in 63 upper-middle-income and high-income countries
This article has 16 authors:Reviewed by ScreenIT
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County-Level Factors That Influenced the Trajectory of COVID-19 Incidence in the New York City Area
This article has 2 authors:Reviewed by ScreenIT
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The flexibility of ACE2 in the context of SARS-CoV-2 infection
This article has 10 authors:Reviewed by ScreenIT
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Obesity and smoking as risk factors for invasive mechanical ventilation in COVID-19: A retrospective, observational cohort study
This article has 14 authors:Reviewed by ScreenIT
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Uncovering Survivorship Bias in Longitudinal Mental Health Surveys During the COVID-19 Pandemic
This article has 5 authors:Reviewed by ScreenIT
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Thiopurines activate an antiviral unfolded protein response that blocks viral glycoprotein accumulation in cell culture infection model
This article has 10 authors:Reviewed by ScreenIT
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Association of SARS-CoV-2 Seropositive Antibody Test With Risk of Future Infection
This article has 16 authors: