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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SI epidemic model applied to COVID-19 data in mainland China
This article has 3 authors:Reviewed by ScreenIT
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Association of corticosteroids use and outcomes in COVID-19 patients: A systematic review and meta-analysis
This article has 10 authors:Reviewed by ScreenIT
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Assessing the efficacy of interventions to control indoor SARS-Cov-2 transmission: An agent-based modeling approach
This article has 2 authors:Reviewed by ScreenIT
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The importance of the human factor during the evolution of SARS-CoV-2 pandemic: the successful case of the Italian strategy
This article has 3 authors:Reviewed by ScreenIT
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This article has 12 authors:
Reviewed by ScreenIT
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Functional landscape of SARS-CoV-2 cellular restriction
This article has 34 authors:Reviewed by ScreenIT
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Symptoms of COVID-19 infection and magnitude of antibody response in a large community-based study
This article has 13 authors:Reviewed by ScreenIT
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Negative Vaccine Attitudes and Intentions to Vaccinate Against Covid-19 in Relation to Smoking Status: A Population Survey of UK Adults
This article has 5 authors:Reviewed by ScreenIT
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Comparative evaluation of SARS-CoV-2 IgG assays in India
This article has 10 authors:Reviewed by ScreenIT
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Diabetes-related acute metabolic emergencies in COVID-19 patients: a systematic review and meta-analysis
This article has 7 authors:Reviewed by ScreenIT