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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Identifying the Zoonotic Origin of SARS-CoV-2 by Modeling the Binding Affinity between the Spike Receptor-Binding Domain and Host ACE2
This article has 5 authors:Reviewed by ScreenIT
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Stress, Sleep and Psychological Impact in Healthcare Workers During the Early Phase of COVID-19 in India: A Factor Analysis
This article has 6 authors:Reviewed by ScreenIT
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Integrative genomics analysis reveals a 21q22.11 locus contributing risk to COVID-19
This article has 8 authors:Reviewed by ScreenIT
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REACT-1 round 9 interim report: downward trend of SARS-CoV-2 in England in February 2021 but still at high prevalence
This article has 16 authors:Reviewed by ScreenIT
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Effect of Vitamin D Deficiency on COVID-19 Status: A Systematic Review
This article has 7 authors:Reviewed by ScreenIT
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Epidemics Forecast From SIR-Modeling, Verification and Calculated Effects of Lockdown and Lifting of Interventions
This article has 2 authors:Reviewed by ScreenIT
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A vaccine built from potential immunogenic pieces derived from the SARS-CoV-2 spike glycoprotein
This article has 1 author:Reviewed by ScreenIT
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Simulating the infected population and spread trend of 2019-nCov under different policy by EIR model
This article has 2 authors:Reviewed by ScreenIT
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Immunogenicity and Protective Efficacy of a Highly Thermotolerant, Trimeric SARS-CoV-2 Receptor Binding Domain Derivative
This article has 34 authors:Reviewed by ScreenIT
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Why (and how) COVID-19 could move us closer to the “health information for all” goal
This article has 3 authors:Reviewed by ScreenIT