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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Community vulnerability to epidemics in Nepal: A high-resolution spatial assessment amidst COVID-19 pandemic
This article has 3 authors:Reviewed by ScreenIT
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Using the infection fatality rate to predict the evolution of Covid-19 in Brazil
This article has 3 authors:Reviewed by ScreenIT
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Diverse local epidemics reveal the distinct effects of population density, demographics, climate, depletion of susceptibles, and intervention in the first wave of COVID-19 in the United States
This article has 4 authors:Reviewed by ScreenIT
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Identification of peptide candidate against COVID-19 through reverse vaccinology: An immunoinformatics approach
This article has 3 authors:Reviewed by ScreenIT
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High affinity binding of SARS-CoV-2 spike protein enhances ACE2 carboxypeptidase activity
This article has 2 authors:Reviewed by ScreenIT
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When the Best Pandemic Models are the Simplest
This article has 2 authors:Reviewed by ScreenIT
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Next-generation diagnostics: virus capture facilitates a sensitive viral diagnosis for epizootic and zoonotic pathogens including SARS-CoV-2
This article has 7 authors:Reviewed by ScreenIT
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Genetic architecture of host proteins interacting with SARS-CoV-2
This article has 18 authors:Reviewed by ScreenIT
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periscope: sub-genomic RNA identification in SARS-CoV-2 Genomic Sequencing Data
This article has 33 authors:Reviewed by ScreenIT
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Attenuated Subcomponent Vaccine Design Targeting the SARS-CoV-2 Nucleocapsid Phosphoprotein RNA Binding Domain: In Silico Analysis
This article has 10 authors:Reviewed by ScreenIT