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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Modelling the positive testing rate of COVID-19 in South Africa Using A Semi-Parametric Smoother for Binomial Data
This article has 6 authors:Reviewed by ScreenIT
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Mask-associated ‘de novo’ headache in healthcare workers during the COVID-19 pandemic
This article has 8 authors:Reviewed by ScreenIT
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Size Dependent Particle Removal Efficiency and Pressure Drop of a Dust Cleaning Material For Use as Facemask Filters for Protection during COVID-19
This article has 4 authors:Reviewed by ScreenIT
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Reprogrammed CRISPR-Cas13b suppresses SARS-CoV-2 replication and circumvents its mutational escape through mismatch tolerance
This article has 14 authors:Reviewed by ScreenIT
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COVID-19 hospitalizations in Brazil’s Unified Health System (SUS)
This article has 5 authors:Reviewed by ScreenIT
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Risk stratification for COVID-19 hospitalization: a multivariable model based on gradient-boosting decision trees
This article has 5 authors:Reviewed by ScreenIT
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The COVID-19 outbreak in Sichuan, China: Epidemiology and impact of interventions
This article has 12 authors:Reviewed by ScreenIT
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Host metabolic reprogramming in response to SARS-Cov-2 infection
This article has 4 authors:Reviewed by ScreenIT
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Forecasting Confirmed Cases and Mortalities of COVID-19 in the US
This article has 4 authors:Reviewed by ScreenIT
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On the use of growth models to understand epidemic outbreaks with application to COVID-19 data
This article has 3 authors:Reviewed by ScreenIT