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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Effectiveness of BNT162b2 and ChAdOx1 Vaccines against Symptomatic COVID-19 among Healthcare Workers in Kuwait: A Retrospective Cohort Study
This article has 4 authors:Reviewed by ScreenIT
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A cost–benefit analysis of COVID-19 lockdowns in Australia
This article has 1 author:Reviewed by ScreenIT
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Echocardiographic characterisation in critical Covid19 - an observational study
This article has 6 authors:Reviewed by ScreenIT
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A Partition-Based Group Testing Algorithm for Estimating the Number of Infected Individuals
This article has 2 authors:Reviewed by ScreenIT
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Amygdala connectivity as a predisposing neural feature of stress-induced behaviour during the COVID-2019 outbreak in Hubei
This article has 13 authors:Reviewed by ScreenIT
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COVID ‐19 Vaccine Response in People with Multiple Sclerosis
This article has 29 authors:Reviewed by ScreenIT
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CIGB-300 synthetic peptide, an antagonist of CK2 kinase activity, as a treatment for Covid-19. A computational biology approach
This article has 4 authors:Reviewed by ScreenIT
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Virological and serological kinetics of SARS-CoV-2 Delta variant vaccine breakthrough infections: a multicentre cohort study
This article has 19 authors: -
Geographic concentration of SARS-CoV-2 cases by social determinants of health in metropolitan areas in Canada: a cross-sectional study
This article has 16 authors:Reviewed by ScreenIT
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Changes in household food security, access to health services and income in northern Lao PDR during the COVID-19 pandemic: a cross-sectional survey
This article has 6 authors:Reviewed by ScreenIT