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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Kinetics of SARS-CoV-2 infection in the human upper and lower respiratory tracts and their relationship with infectiousness
This article has 4 authors:Reviewed by ScreenIT
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An in-silico study on SARS-CoV-2: Its compatibility with human tRNA pool, and the polymorphism arising in a single lineage over a month
This article has 3 authors:Reviewed by ScreenIT
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Sleeping under the waves: A longitudinal study across the contagion peaks of the COVID‐19 pandemic in Italy
This article has 4 authors:Reviewed by ScreenIT
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Medical Capacity Shortages Facilitated the Rapid Dissemination of COVID-19 in Wuhan, New York State, and Italy
This article has 3 authors:Reviewed by ScreenIT
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COVID-19 in Canada: Predictions for the future and control lessons from Asia
This article has 2 authors:Reviewed by ScreenIT
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The rapid, massive growth of COVID-19 authors in the scientific literature
This article has 4 authors:Reviewed by ScreenIT
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COVID-19 Pandemic Response Simulation: Impact of Non-pharmaceutical Interventions on Ending Lockdowns
This article has 4 authors:Reviewed by ScreenIT
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Fundamental principles of epidemic spread highlight the immediate need for large-scale serological surveys to assess the stage of the SARS-CoV-2 epidemic
This article has 5 authors:Reviewed by ScreenIT
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Immune cell residency in the nasal mucosa may partially explain respiratory disease severity across the age range
This article has 8 authors:Reviewed by ScreenIT
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Retrospective survey from vascular access team Lombardy net in COVID-19 era
This article has 22 authors:Reviewed by ScreenIT