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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Child mortality in England during the first year of the COVID-19 pandemic
This article has 5 authors:Reviewed by ScreenIT
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Closed doors: Predictors of stress, anxiety, depression, and PTSD during the onset of COVID-19 pandemic in Brazil
This article has 19 authors:Reviewed by ScreenIT
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COVID-19 related messaging, beliefs, information sources, and mitigation behaviors in Virginia: a cross-sectional survey in the summer of 2020
This article has 5 authors:Reviewed by ScreenIT
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The IHME vs Me: Modeling USA CoVID-19 Spread, Early Data to the Fifth Wave
This article has 1 author:Reviewed by ScreenIT
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SARS‐CoV‐2 infection induces soluble platelet activation markers and PAI‐1 in the early moderate stage of COVID‐19
This article has 9 authors:Reviewed by ScreenIT
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COVID-19 mitigation measures in primary schools and association with infection and school staff wellbeing: An observational survey linked with routine data in Wales, UK
This article has 15 authors:Reviewed by ScreenIT
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Federal Vaccine Policy and Interstate Variation in COVID-19 Vaccine Coverage in India
This article has 1 author:Reviewed by ScreenIT
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Emergence and phenotypic characterization of the global SARS-CoV-2 C.1.2 lineage
This article has 45 authors:Reviewed by ScreenIT
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“Urban Tertiary Care Centre Experience of Characteristics of Severe COVID-19 Pneumonia”
This article has 6 authors:Reviewed by ScreenIT
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Symptoms and SARS-CoV-2 positivity in the general population in the UK
This article has 16 authors:Reviewed by ScreenIT