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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Spread dynamics of SARS-CoV-2 epidemic in China: a phylogenetic analysis
This article has 3 authors:Reviewed by ScreenIT
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Mass Spectrometric detection of SARS-CoV-2 virus in scrapings of the epithelium of the nasopharynx of infected patients via Nucleocapsid N protein
This article has 10 authors:Reviewed by ScreenIT
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COVID-Align: Accurate online alignment of hCoV-19 genomes using a profile HMM
This article has 4 authors:Reviewed by ScreenIT
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Antihypertensive medication uses and serum ACE2 levels
This article has 9 authors:Reviewed by ScreenIT
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A comparative study of isothermal nucleic acid amplification methods for SARS-CoV-2 detection at point-of-care
This article has 14 authors:Reviewed by ScreenIT
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Bayesian modeling of COVID-19 cases with a correction to account for under-reported cases
This article has 6 authors:Reviewed by ScreenIT
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Early impact of COVID-19 pandemic on paediatric surgical practice in Nigeria: a national survey of paediatric surgeons
This article has 4 authors:Reviewed by ScreenIT
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Assessment of workers’ personal vulnerability to covid-19 using ‘covid-age’
This article has 4 authors:Reviewed by ScreenIT
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Spatiotemporal analysis of medical resource deficiencies in the U.S. under COVID-19 pandemic
This article has 8 authors:Reviewed by ScreenIT
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In-depth phenotyping of human peripheral blood mononuclear cells in convalescent COVID-19 patients following a mild versus severe disease course
This article has 5 authors:Reviewed by ScreenIT