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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Relative Burdens of the COVID-19, Malaria, Tuberculosis, and HIV/AIDS Epidemics in Sub-Saharan Africa
This article has 2 authors:Reviewed by ScreenIT
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Vaccine hesitancy and reasons for refusing the COVID-19 vaccination among the U.S. public: A cross-sectional survey
This article has 5 authors:Reviewed by ScreenIT
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A Combination Adjuvant for the Induction of Potent Antiviral Immune Responses for a Recombinant SARS-CoV-2 Protein Vaccine
This article has 11 authors:Reviewed by ScreenIT
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Study of the effectiveness of partial quarantines applied to control the spread of the Covid-19 virus
This article has 1 author:Reviewed by ScreenIT
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Racial disparities in the SOFA score among patients hospitalized with COVID-19
This article has 15 authors:Reviewed by ScreenIT
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Molecular interactions of the M and E integral membrane proteins of SARS-CoV-2
This article has 2 authors:Reviewed by ScreenIT
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HAVoC, a bioinformatic pipeline for reference-based consensus assembly and lineage assignment for SARS-CoV-2 sequences
This article has 6 authors:Reviewed by ScreenIT
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Single-Arm, Open-Label Phase 2 Trial of Preemptive Methylprednisolone to Avert Progression to Respiratory Failure in High-Risk Patients with COVID-19
This article has 9 authors:Reviewed by ScreenIT
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Asymptomatic or mild symptomatic SARS-CoV-2 infection elicits durable neutralizing antibody responses in children and adolescents
This article has 19 authors:Reviewed by ScreenIT
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Impact of COVID-19 on Pediatric Emergency Department Visits: A Retrospective Cohort Study
This article has 8 authors:Reviewed by ScreenIT