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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Homologous and Heterologous Covid-19 Booster Vaccinations
This article has 38 authors:Reviewed by ScreenIT
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A thermostable Cas12b from Brevibacillus leverages one-pot discrimination of SARS-CoV-2 variants of concern
This article has 11 authors:Reviewed by ScreenIT
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Comparative assessment of methods for short-term forecasts of COVID-19 hospital admissions in England at the local level
This article has 53 authors:Reviewed by ScreenIT
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Factors Associated With Severity of COVID-19 Disease in a Multicenter Cohort of People With HIV in the United States, March–December 2020
This article has 29 authors:Reviewed by ScreenIT
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Modeling the Influence of Vaccine Administration on COVID-19 Testing Strategies
This article has 2 authors:Reviewed by ScreenIT
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Mitoxantrone modulates a glycosaminoglycan-spike complex to inhibit SARS-CoV-2 infection
This article has 10 authors:Reviewed by ScreenIT
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Incidence of SARS-CoV-2 infection in a cohort of workers from the University of Porto, Portugal
This article has 4 authors:Reviewed by ScreenIT
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Comparison of Seroconversion in Children and Adults With Mild COVID-19
This article has 27 authors:Reviewed by ScreenIT
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A monoclonal antibody that neutralizes SARS-CoV-2 variants, SARS-CoV, and other sarbecoviruses
This article has 17 authors:Reviewed by ScreenIT
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Comparative effectiveness of ChAdOx1 versus BNT162b2 covid-19 vaccines in health and social care workers in England: cohort study using OpenSAFELY
This article has 42 authors:Reviewed by ScreenIT