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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Modeling the interplay between demography, social contact patterns, and SARS-CoV-2 transmission in the South West Shewa Zone of Oromia Region, Ethiopia
This article has 14 authors:Reviewed by ScreenIT
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Design, Expression, Purification, and Characterization of a YFP-Tagged 2019-nCoV Spike Receptor-Binding Domain Construct
This article has 5 authors:Reviewed by ScreenIT
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High frequency of cerebrospinal fluid autoantibodies in COVID-19 patients with neurological symptoms
This article has 17 authors:Reviewed by ScreenIT
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Using excess deaths and testing statistics to determine COVID-19 mortalities
This article has 3 authors:Reviewed by ScreenIT
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Initial stage of the COVID-19 infection process in human population
This article has 1 author:Reviewed by ScreenIT
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Evolving Insights from SARS-CoV-2 Genome from 200K COVID-19 Patients
This article has 4 authors:Reviewed by ScreenIT
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Combining PCR and CT testing for COVID
This article has 5 authors:Reviewed by ScreenIT
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COVID-19 infections following physical school reopening
This article has 5 authors:Reviewed by ScreenIT
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Willingness-to-pay tuition and risk-taking proclivities among public health students
This article has 4 authors:Reviewed by ScreenIT
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Tissue-Specific Immunopathology in Fatal COVID-19
This article has 28 authors:Reviewed by ScreenIT