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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A Data-Informed Approach for Analysis, Validation, and Identification of COVID-19 Models
This article has 7 authors:Reviewed by ScreenIT
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Statistical Inference for Coronavirus Infected Patients in Wuhan
This article has 2 authors:Reviewed by ScreenIT
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Early Estimation Of Reproduction Number of Covid-19 in Vietnam
This article has 3 authors:Reviewed by ScreenIT
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Mechanistic modelling of COVID-19 and the impact of lockdowns on a short-time scale
This article has 5 authors:Reviewed by ScreenIT
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Regression tree modelling to predict total average extra costs in household spending during COVID-19 pandemic
This article has 1 author:Reviewed by ScreenIT
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A Generic, Scalable, and Rapid Time-Resolved Förster Resonance Energy Transfer-Based Assay for Antigen Detection—SARS-CoV-2 as a Proof of Concept
This article has 12 authors:Reviewed by ScreenIT
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Luminore CopperTouch Surface Coating Effectively Inactivates SARS-CoV-2, Ebola Virus, and Marburg Virus In Vitro
This article has 4 authors:Reviewed by ScreenIT
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Direct-to-Consumer Chat-Based Remote Care Before and During the COVID-19 Outbreak
This article has 4 authors:Reviewed by ScreenIT
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The COSEVAST Study Outcome: Evidence of COVID-19 Severity Proportionate to Surge in Arterial Stiffness
This article has 8 authors:Reviewed by ScreenIT
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SARS‐CoV‐2 outbreak in Iran: The dynamics of the epidemic and evidence on two independent introductions
This article has 37 authors:Reviewed by ScreenIT