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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Identification of immunodominant linear epitopes from SARS-CoV-2 patient plasma
This article has 10 authors:Reviewed by ScreenIT
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No benefit of hydroxychloroquine on SARS-CoV-2 viral load reduction in non-critical hospitalized patients with COVID-19
This article has 6 authors:Reviewed by ScreenIT
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Genetic Spectrum and Distinct Evolution Patterns of SARS-CoV-2
This article has 8 authors:Reviewed by ScreenIT
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Endothelial Dysfunction and Thrombosis in Patients With COVID-19—Brief Report
This article has 9 authors:Reviewed by ScreenIT
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Essential epidemiological parameters of COVID-19 for clinical and mathematical modeling purposes: a rapid review and meta-analysis
This article has 6 authors:Reviewed by ScreenIT
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Community engagement for COVID-19 prevention and control: a rapid evidence synthesis
This article has 8 authors:Reviewed by ScreenIT
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The impact of the Covid-19 lockdown on the experiences and feeding practices of new mothers in the UK: Preliminary data from the COVID-19 New Mum Study
This article has 5 authors:Reviewed by ScreenIT
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Divergent SARS-CoV-2-specific T and B cell responses in severe but not mild COVID-19
This article has 16 authors:Reviewed by ScreenIT
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Genomic surveillance of SARS-CoV-2 reveals community transmission of a major lineage during the early pandemic phase in Brazil
This article has 23 authors:Reviewed by ScreenIT
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Experimental and natural evidence of SARS-CoV-2-infection-induced activation of type I interferon responses
This article has 28 authors:Reviewed by ScreenIT