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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Optimal shutdown strategies for COVID-19 with economic and mortality costs: British Columbia as a case study
This article has 3 authors:Reviewed by ScreenIT
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Effective containment explains subexponential growth in recent confirmed COVID-19 cases in China
This article has 2 authors:Reviewed by ScreenIT
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This article has 7 authors:
Reviewed by ScreenIT
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Early mandated social distancing is a strong predictor of reduction in peak daily new COVID-19 cases
This article has 5 authors:Reviewed by ScreenIT
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Biological agents for rheumatic diseases in the outbreak of COVID-19: friend or foe?
This article has 7 authors:Reviewed by ScreenIT
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Influenza vaccine uptake, COVID-19 vaccination intention and vaccine hesitancy among nurses: A survey
This article has 6 authors:Reviewed by ScreenIT
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Genomic surveillance at scale is required to detect newly emerging strains at an early timepoint
This article has 6 authors:Reviewed by ScreenIT
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Forecasting Novel Corona Positive Cases in India using Truncated Information: A Mathematical Approach
This article has 1 author:Reviewed by ScreenIT
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DeepSOCIAL: Social Distancing Monitoring and Infection Risk Assessment in COVID-19 Pandemic
This article has 2 authors:Reviewed by ScreenIT
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COVID‐19: Generate and apply local modelled transmission and morbidity effects to provide an estimate of the variation in overall relative healthcare resource impact at general practice granularity
This article has 5 authors:Reviewed by ScreenIT