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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Recurring Spatiotemporal Patterns of COVID-19 in the United States
This article has 3 authors:Reviewed by ScreenIT
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Stabilization of the SARS-CoV-2 receptor binding domain by protein core redesign and deep mutational scanning
This article has 6 authors:Reviewed by ScreenIT
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Conserved recombination patterns across coronavirus subgenera
This article has 15 authors:Reviewed by ScreenIT
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Simulating the impact of vaccination rates on the initial stages of a COVID-19 outbreak in New Zealand (Aotearoa) with a stochastic model
This article has 1 author:Reviewed by ScreenIT
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Healthcare workers’ SARS-CoV-2 infection rates during the second wave of the pandemic: follow-up study
This article has 19 authors:Reviewed by ScreenIT
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Germany’s fourth COVID-19 wave was mainly driven by the unvaccinated
This article has 7 authors:Reviewed by ScreenIT
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Longitudinal SARS-CoV-2 RNA wastewater monitoring across a range of scales correlates with total and regional COVID-19 burden in a well-defined urban population
This article has 33 authors:Reviewed by ScreenIT
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Role of Body Mass and Physical Activity in Autonomic Function Modulation on Post-COVID-19 Condition: An Observational Subanalysis of Fit-COVID Study
This article has 12 authors:Reviewed by ScreenIT
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Severe COVID-19 induces molecular signatures of aging in the human brain
This article has 4 authors:Reviewed by ScreenIT
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Transmission of B.1.617.2 Delta variant between vaccinated healthcare workers
This article has 65 authors:Reviewed by ScreenIT