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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A model of supply‐chain decisions for resource sharing with an application to ventilator allocation to combat COVID ‐19
This article has 5 authors:Reviewed by ScreenIT
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Surge capacities and predicted demands of Brazil’s health system associated with severe COVID-19 cases
This article has 2 authors:Reviewed by ScreenIT
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Assessment of Specimen Pooling to Conserve SARS CoV-2 Testing Resources
This article has 6 authors:Reviewed by ScreenIT
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Automatic X-ray COVID-19 Lung Image Classification System based on Multi-Level Thresholding and Support Vector Machine
This article has 5 authors:Reviewed by ScreenIT
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Reliability of Self-Sampling for Accurate Assessment of Respiratory Virus Viral and Immunologic Kinetics
This article has 16 authors:Reviewed by ScreenIT
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Acute liver injury and its association with death risk of patients with COVID-19: a hospital-based prospective case-cohort study
This article has 13 authors:Reviewed by ScreenIT
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Public perspectives on protective measures during the COVID-19 pandemic in the Netherlands, Germany and Italy: A survey study
This article has 11 authors:Reviewed by ScreenIT
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COVID19-Tracker: una aplicación Shiny para analizar datos de la epidemia de SARS-CoV-2 en España
This article has 4 authors:Reviewed by ScreenIT
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Differential COVID-19-attributable mortality and BCG vaccine use in countries
This article has 5 authors:Reviewed by ScreenIT
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COVID-19 patients exhibit less pronounced immune suppression compared with bacterial septic shock patients
This article has 7 authors:Reviewed by ScreenIT