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 dispersion of SARS-CoV-2 on a dynamic network graph
This article has 2 authors:Reviewed by ScreenIT
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Estimating the COVID-19 epidemic trajectory and hospital capacity requirements in South West England: a mathematical modelling framework
This article has 15 authors:Reviewed by ScreenIT
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A cross-national study of factors associated with women’s perinatal mental health and wellbeing during the COVID-19 pandemic
This article has 9 authors:Reviewed by ScreenIT
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Should We Delay the Second COVID-19 Vaccine Dose in Order to Optimize Rollout? A Mathematical Perspective
This article has 5 authors:Reviewed by ScreenIT
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Association of Toll-like receptor 7 variants with life-threatening COVID-19 disease in males
This article has 24 authors:Reviewed by ScreenIT
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Small-Sample Estimation of the Mutational Support and Distribution of SARS-CoV-2
This article has 4 authors:Reviewed by ScreenIT
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Using mobile phone data for epidemiological simulations of lockdowns: government interventions, behavioral changes, and resulting changes of reinfections
This article has 8 authors:Reviewed by ScreenIT
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A simplified SARS-CoV-2 detection protocol for research laboratories
This article has 5 authors:Reviewed by ScreenIT
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Performance of nucleocapsid and spike-based SARS-CoV-2 serologic assays
This article has 10 authors:Reviewed by ScreenIT
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Testing and isolation to prevent overloaded healthcare facilities and reduce death rates in the SARS-CoV-2 pandemic in Italy
This article has 6 authors:Reviewed by ScreenIT