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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Evidence of SARS-CoV-2 Transcriptional Activity in Cardiomyocytes of COVID-19 Patients without Clinical Signs of Cardiac Involvement
This article has 10 authors:Reviewed by ScreenIT
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Temporal landscape of mutational frequencies in SARS-CoV-2 genomes of Bangladesh: possible implications from the ongoing outbreak in Bangladesh
This article has 6 authors:Reviewed by ScreenIT
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COVID-19 Among Patients With Hepatitis B or Hepatitis C: A Systematic Review
This article has 7 authors:Reviewed by ScreenIT
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Inhibitor binding influences the protonation states of histidines in SARS-CoV-2 main protease
This article has 19 authors:Reviewed by ScreenIT
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Examining the interplay between face mask usage, asymptomatic transmission, and social distancing on the spread of COVID-19
This article has 5 authors:Reviewed by ScreenIT
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Forecasting combination of hierarchical time series: A novel method with an application to COVID-19
This article has 1 author:Reviewed by ScreenIT
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Inflammatory leptomeningeal cytokines mediate delayed COVID-19 encephalopathy
This article has 12 authors:Reviewed by ScreenIT
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A student made MOOC for medical students during the SARS-CoV-2 pandemic.
This article has 1 author:Reviewed by ScreenIT
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Racial Disparities in Coronavirus Disease 2019 (COVID-19) Mortality Are Driven by Unequal Infection Risks
This article has 8 authors: -
Functional Effects of Cardiomyocyte Injury in COVID-19
This article has 33 authors:Reviewed by ScreenIT