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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Unsupervised explainable AI for simultaneous molecular evolutionary study of forty thousand SARS-CoV-2 genomes
This article has 5 authors:Reviewed by ScreenIT
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Estimate of the actual number of COVID-19 cases from the analysis of deaths
This article has 2 authors:Reviewed by ScreenIT
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How well do face masks protect the wearer compared to public perceptions?
This article has 4 authors:Reviewed by ScreenIT
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Cutting Edge: Severe SARS-CoV-2 Infection in Humans Is Defined by a Shift in the Serum Lipidome, Resulting in Dysregulation of Eicosanoid Immune Mediators
This article has 13 authors:Reviewed by ScreenIT
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First and second waves of coronavirus disease-19: A comparative study in hospitalized patients in Reus, Spain
This article has 40 authors:Reviewed by ScreenIT
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Serial household serosurvey for COVID-19 in low and high transmission neighborhoods of urban Pakistan
This article has 16 authors:Reviewed by ScreenIT
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Effect of COVID-19 lockdown on child protection medical assessments: a retrospective observational study in Birmingham, UK
This article has 12 authors:Reviewed by ScreenIT
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Microsecond simulations and CD spectroscopy reveals the intrinsically disordered nature of SARS-CoV-2 spike-C-terminal cytoplasmic tail (residues 1242–1273) in isolation
This article has 4 authors:Reviewed by ScreenIT
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Impacts of K-12 school reopening on the COVID-19 epidemic in Indiana, USA
This article has 11 authors:Reviewed by ScreenIT
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Distant residues modulate conformational opening in SARS-CoV-2 spike protein
This article has 3 authors:Reviewed by ScreenIT