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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Clinical characteristics and Outcomes of 500 patients with COVID Pneumonia – Results from a Single center (Southend University Hospital)
This article has 8 authors:Reviewed by ScreenIT
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The impact of COVID-19 nonpharmaceutical interventions on the future dynamics of endemic infections
This article has 6 authors:Reviewed by ScreenIT
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Immunogenicity of a new gorilla adenovirus vaccine candidate for COVID-19
This article has 28 authors:Reviewed by ScreenIT
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Detection of COVID-19 Disease from Chest X-Ray Images: A Deep Transfer Learning Framework
This article has 7 authors:Reviewed by ScreenIT
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An observational and Mendelian randomisation study on vitamin D and COVID-19 risk in UK Biobank
This article has 11 authors:Reviewed by ScreenIT
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The newly introduced SARS-CoV-2 variant A222V is rapidly spreading in Lazio region, Italy
This article has 8 authors:Reviewed by ScreenIT
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Structure of Nonstructural Protein 1 from SARS-CoV-2
This article has 3 authors:Reviewed by ScreenIT
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Large-scale sequencing of SARS-CoV-2 genomes from one region allows detailed epidemiology and enables local outbreak management
This article has 43 authors:Reviewed by ScreenIT
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Rates and predictors of uptake of mental health support during the COVID-19 pandemic: an analysis of 26,720 adults in the UK in lockdown
This article has 3 authors:Reviewed by ScreenIT
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Swab pooling: A new method for large-scale RT-qPCR screening of SARS-CoV-2 avoiding sample dilution
This article has 12 authors:Reviewed by ScreenIT