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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COVID-19 classification of X-ray images using deep neural networks
This article has 28 authors:Reviewed by ScreenIT
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Impact of COVID-19 Pandemic on California Farmworkers’ Mental Health and Food Security
This article has 7 authors:Reviewed by ScreenIT
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A-to-I RNA editing in SARS-COV-2: real or artifact?
This article has 3 authors:Reviewed by ScreenIT
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GIS-Based Analysis Framework for identifying COVID-19 Incidence and Fatality Determinants at National Level Case study: Africa
This article has 4 authors:Reviewed by ScreenIT
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Application of COVID-19 pneumonia diffusion data to predict epidemic situation
This article has 1 author:Reviewed by ScreenIT
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Stay home and stay active? The impact of stay-at-home restrictions on physical activity routines in the UK during the COVID-19 pandemic
This article has 5 authors:Reviewed by ScreenIT
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Contribution to COVID-19 spread modelling: a physical phenomenological dissipative formalism
This article has 2 authors:Reviewed by ScreenIT
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Genomic epidemiology of the early stages of the SARS-CoV-2 outbreak in Russia
This article has 14 authors:Reviewed by ScreenIT
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Pay-as-you-go liquefied petroleum gas supports sustainable clean cooking in Kenyan informal urban settlement during COVID-19 lockdown
This article has 11 authors:Reviewed by ScreenIT
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Antigen rapid tests, nasopharyngeal PCR and saliva PCR to detect SARS-CoV-2: A prospective comparative clinical trial
This article has 14 authors:Reviewed by ScreenIT