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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A cost/benefit analysis of clinical trial designs for COVID-19 vaccine candidates
This article has 7 authors:Reviewed by ScreenIT
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AIDCOV: An Interpretable Artificial Intelligence Model for Detection of COVID-19 from Chest Radiography Images
This article has 4 authors:Reviewed by ScreenIT
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A New Transmission Route for the Propagation of the SARS-CoV-2 Coronavirus
This article has 3 authors:Reviewed by ScreenIT
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Impact of COVID-19 on the indigenous population of Brazil: A geo-epidemiological study
This article has 6 authors:Reviewed by ScreenIT
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Selectively caring for the most severe COVID-19 patients delays ICU bed shortages more than increasing hospital capacity
This article has 4 authors:Reviewed by ScreenIT
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D614G Spike Mutation Increases SARS CoV-2 Susceptibility to Neutralization
This article has 25 authors: -
Aerosol and bioaerosol particle size and dynamics from defective sanitary plumbing systems
This article has 3 authors:Reviewed by ScreenIT
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COVID-19 most vulnerable Mexican cities lack the public health infrastructure to face the pandemic: a new temporally-explicit model
This article has 5 authors:Reviewed by ScreenIT
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Reconstructing SARS-CoV-2 infection dynamics through the phylogenetic inference of unsampled sources of infection
This article has 8 authors:Reviewed by ScreenIT
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Increased SAR-CoV-2 shedding associated with reduced disease severity despite continually emerging genetic variants
This article has 17 authors:Reviewed by ScreenIT