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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Invasive pulmonary aspergillosis in critically ill patients with severe COVID-19 pneumonia: Results from the prospective AspCOVID-19 study
This article has 15 authors:Reviewed by ScreenIT
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Proteo-Genomic Analysis of SARS-CoV-2: A Clinical Landscape of Single-Nucleotide Polymorphisms, COVID-19 Proteome, and Host Responses
This article has 7 authors:Reviewed by ScreenIT
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Modeling and Forecasting of COVID-19 Growth Curve in India
This article has 2 authors:Reviewed by ScreenIT
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Prediction of the time evolution of the COVID-19 disease in Guadeloupe with a stochastic evolutionary model
This article has 4 authors:Reviewed by ScreenIT
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Classification and specific primer design for accurate detection of SARS-CoV-2 using deep learning
This article has 9 authors:Reviewed by ScreenIT
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Neutralization assay with SARS-CoV-1 and SARS-CoV-2 spike pseudotyped murine leukemia virions
This article has 13 authors:Reviewed by ScreenIT
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A Crystallographic Snapshot of SARS-CoV-2 Main Protease Maturation Process
This article has 15 authors:Reviewed by ScreenIT
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How did socio-demographic status and personal attributes influence compliance to COVID-19 preventive behaviours during the early outbreak in Japan? Lessons for pandemic management
This article has 5 authors:Reviewed by ScreenIT
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Preparedness and vulnerability of African countries against importations of COVID-19: a modelling study
This article has 13 authors:Reviewed by ScreenIT
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Occupational risk of COVID-19 in the first versus second epidemic wave in Norway, 2020
This article has 5 authors:Reviewed by ScreenIT