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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What do we know about SARS-CoV-2 transmission? A systematic review and meta-analysis of the secondary attack rate and associated risk factors
This article has 12 authors:Reviewed by ScreenIT
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The Challenge of Using Epidemiological Case Count Data: The Example of Confirmed COVID-19 Cases and the Weather
This article has 5 authors:Reviewed by ScreenIT
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A biomarker based severity progression indicator for COVID-19: the Kuwait prognosis indicator score
This article has 9 authors:Reviewed by ScreenIT
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Homeless Shelter Characteristics and Prevalence of SARS-CoV-2
This article has 5 authors:Reviewed by ScreenIT
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Effects of a DPP-4 Inhibitor and RAS Blockade on Clinical Outcomes of Patients with Diabetes and COVID-19
This article has 6 authors:Reviewed by ScreenIT
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Age-determined expression of priming protease TMPRSS2 and localization of SARS-CoV-2 in lung epithelium
This article has 19 authors:Reviewed by ScreenIT
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Methods of inactivation of SARS-CoV-2 for downstream biological assays
This article has 8 authors:Reviewed by ScreenIT
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Immunity after COVID-19: Protection or sensitization?
This article has 2 authors:Reviewed by ScreenIT
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Optimising predictive models to prioritise viral discovery in zoonotic reservoirs
This article has 17 authors:Reviewed by Rapid Reviews Infectious Diseases, ScreenIT
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A Modular Microarray Imaging System for Highly Specific COVID-19 Antibody Testing
This article has 12 authors:Reviewed by ScreenIT