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 Vaccination Strategies Considering Hesitancy Using Particle-Based Epidemic Simulation
This article has 3 authors:Reviewed by ScreenIT
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Bayesian Inference of State-Level COVID-19 Basic Reproduction Numbers across the United States
This article has 7 authors:Reviewed by ScreenIT
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SARS-CoV-2 Cross-Reactivity in Prepandemic Serum from Rural Malaria-Infected Persons, Cambodia
This article has 18 authors:Reviewed by ScreenIT
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Racial and ethnic inequalities in COVID-19 mortality within Texas carceral settings
This article has 4 authors:Reviewed by ScreenIT
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The Kinetics of COVID-19 Vaccine Response in a Community-Vaccinated Population
This article has 5 authors:Reviewed by ScreenIT
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SARS-CoV-2 spike-specific memory B cells express higher levels of T-bet and FcRL5 after non-severe COVID-19 as compared to severe disease
This article has 10 authors:Reviewed by ScreenIT
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“The vaccination is positive; I don’t think it’s the panacea”: A qualitative study on COVID-19 vaccine attitudes among ethnically diverse healthcare workers in the United Kingdom
This article has 9 authors:Reviewed by ScreenIT
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Inflammasome activation in infected macrophages drives COVID-19 pathology
This article has 21 authors:Reviewed by ScreenIT
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Cytotoxic T-cell-based vaccine against SARS-CoV2: a hybrid immunoinformatic approach
This article has 2 authors:Reviewed by ScreenIT
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Sub-Saharan African Countries’ COVID-19 Research: An analysis of the External and Internal Contributions, Collaboration Patterns and Funding Sources
This article has 2 authors:Reviewed by ScreenIT