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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Modeling layered non-pharmaceutical interventions against SARS-CoV-2 in the United States with Corvid
This article has 4 authors:Reviewed by ScreenIT
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Estimation of the percentages of undiagnosed patients of the novel coronavirus (SARS-CoV-2) infection in Hokkaido, Japan by using birth-death process with recursive full tracing
This article has 3 authors:Reviewed by ScreenIT
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Clinical evaluation of a SARS-CoV-2 RT-PCR assay on a fully automated system for rapid on-demand testing in the hospital setting
This article has 8 authors:Reviewed by ScreenIT
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Epidemiological characteristics of COVID-19 cases and estimates of the reproductive numbers 1 month into the epidemic, Italy, 28 January to 31 March 2020
This article has 29 authors:Reviewed by ScreenIT
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Factors determining the diffusion of COVID-19 and suggested strategy to prevent future accelerated viral infectivity similar to COVID
This article has 1 author:Reviewed by ScreenIT
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SARS-CoV-2 infection in Health Care Workers in a large public hospital in Madrid, Spain, during March 2020
This article has 4 authors:Reviewed by ScreenIT
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Noisy Pooled PCR for Virus Testing
This article has 3 authors:Reviewed by ScreenIT
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Risk factors for mortality in patients with Coronavirus disease 2019 (COVID-19) infection: a systematic review and meta-analysis of observational studies
This article has 6 authors:Reviewed by ScreenIT
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A Very Flat Peak: Why Standard SEIR Models Miss the Plateau of COVID-19 Infections and How it Can be Corrected
This article has 2 authors:Reviewed by ScreenIT
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Liver injury is associated with severe coronavirus disease 2019 (COVID‐19) infection: A systematic review and meta‐analysis of retrospective studies
This article has 3 authors:Reviewed by ScreenIT