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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Mathematical model study of a pandemic: Graded lockdown approach
This article has 3 authors:Reviewed by ScreenIT
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COVID-19 effective reproduction number dropped during Spain's nationwide dropdown, then spiked at lower-incidence regions
This article has 2 authors:Reviewed by ScreenIT
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The limits of estimating COVID-19 intervention effects using Bayesian models
This article has 2 authors:Reviewed by ScreenIT
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Clinical features associated with COVID-19 outcome in multiple myeloma: first results from the International Myeloma Society data set
This article has 25 authors:Reviewed by ScreenIT
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SARS-CoV-2 infection damages airway motile cilia and impairs mucociliary clearance
This article has 25 authors:Reviewed by ScreenIT
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The Use of Saliva as a Diagnostic Specimen for SARS CoV-2 Molecular Diagnostic Testing for Pediatric Patients
This article has 10 authors:Reviewed by ScreenIT
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An analysis of school absences in England during the COVID-19 pandemic
This article has 9 authors:Reviewed by ScreenIT
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Targeted adaptive isolation strategy for Covid-19 pandemic
This article has 3 authors:Reviewed by ScreenIT
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An Autoantigen Atlas From Human Lung HFL1 Cells Offers Clues to Neurological and Diverse Autoimmune Manifestations of COVID-19
This article has 5 authors:Reviewed by ScreenIT
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County Demographics and COVID-19 Death Rates: Comparison of relationship in the first and current stage of the pandemic in the United States of America
This article has 3 authors:Reviewed by ScreenIT