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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Spike mutation D614G alters SARS-CoV-2 fitness
This article has 22 authors:Reviewed by ScreenIT
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Handling and accuracy of four rapid antigen tests for the diagnosis of SARS-CoV-2 compared to RT-qPCR
This article has 14 authors:Reviewed by ScreenIT
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Land-use change and the livestock revolution increase the risk of zoonotic coronavirus transmission from rhinolophid bats
This article has 4 authors:Reviewed by ScreenIT
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Clustering of age standardised COVID-19 infection fatality ratios and death trajectories
This article has 6 authors:Reviewed by ScreenIT
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Community prevalence of SARS-CoV-2 in England from April to November, 2020: results from the ONS Coronavirus Infection Survey
This article has 120 authors:Reviewed by ScreenIT
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Derivation and validation of a clinical severity score for acutely ill adults with suspected COVID-19: The PRIEST observational cohort study
This article has 24 authors:Reviewed by ScreenIT
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Neutralizing antibody responses to SARS-CoV-2 in symptomatic COVID-19 is persistent and critical for survival
This article has 20 authors:Reviewed by ScreenIT
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Multivariate spatio-temporal analysis of the global COVID-19 pandemic
This article has 2 authors:Reviewed by ScreenIT
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Associations of DMT therapies with COVID-19 severity in multiple sclerosis
This article has 47 authors:Reviewed by ScreenIT
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Modeling projections for COVID-19 pandemic by combining epidemiological, statistical, and neural network approaches
This article has 4 authors:Reviewed by ScreenIT