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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Modelling the medium-term dynamics of SARS-CoV-2 transmission in England in the Omicron era
This article has 61 authors:Reviewed by ScreenIT
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Distribution of confirmed with COVID-19 by age and gender in Mexico
This article has 2 authors:Reviewed by ScreenIT
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Reactogenicity of COVID-19 vaccine in hemodialysis patients: a single-center retrospective study
This article has 15 authors:Reviewed by ScreenIT
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Unambiguous detection of SARS-CoV-2 subgenomic mRNAs with single-cell RNA sequencing
This article has 11 authors:Reviewed by ScreenIT
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A population framework for predicting the proportion of people infected by the far-field airborne transmission of SARS-CoV-2 indoors
This article has 5 authors:Reviewed by ScreenIT
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ACE2 expression and localization are regulated by CFTR: implications beyond cystic fibrosis
This article has 20 authors:Reviewed by ScreenIT
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High Burden of COVID-19 among Unvaccinated Law Enforcement Officers and Firefighters
This article has 47 authors:Reviewed by ScreenIT
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Dual Effects of Nanoviricides Platform Technology Based NV-CoV-2 Biomimetic Polymer Against COVID-19
This article has 10 authors:Reviewed by ScreenIT
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Neutralization of SARS-CoV-2 Variants by rVSV-ΔG-Spike-Elicited Human Sera
This article has 24 authors:Reviewed by ScreenIT
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Development and Performance verification of colloidal gold labeled SARS-CoV-2 antigen detection method for routine popular screening of COVID-19 with clinical samples in Poland and China
This article has 5 authors:Reviewed by ScreenIT