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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Impact of COVID-19 social distancing measures on future incidence of invasive pneumococcal disease in England and Wales: a mathematical modelling study
This article has 2 authors:Reviewed by ScreenIT
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Estimating the Impact of Daily Weather on the Temporal Pattern of COVID-19 Outbreak in India
This article has 3 authors:Reviewed by ScreenIT
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Fibrinolysis influences SARS-CoV-2 infection in ciliated cells
This article has 4 authors:Reviewed by ScreenIT
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Asymptomatic and symptomatic SARS-CoV-2 infections elicit polyfunctional antibodies
This article has 20 authors:Reviewed by ScreenIT
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Efficacy of face mask in preventing respiratory virus transmission: A systematic review and meta-analysis
This article has 7 authors:Reviewed by ScreenIT
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Oligonucleotide Capture Sequencing of the SARS-CoV-2 Genome and Subgenomic Fragments from COVID-19 Individuals
This article has 21 authors:Reviewed by ScreenIT
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Robust spike antibody responses and increased reactogenicity in seropositive individuals after a single dose of SARS-CoV-2 mRNA vaccine
This article has 4 authors: -
Rapid inactivation of SARS-CoV-2 on copper touch surfaces determined using a cell culture infectivity assay
This article has 3 authors:Reviewed by ScreenIT
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The human brain vasculature shows a distinct expression pattern of SARS-CoV-2 entry factors
This article has 8 authors: -
Analysis of mental and physical disorders associated with COVID-19 in online health forums: a natural language processing study
This article has 5 authors:Reviewed by ScreenIT