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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Impact of the COVID-19 pandemic on the mental health and well-being of UK healthcare workers
This article has 5 authors:Reviewed by ScreenIT
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Why do per capita COVID-19 Case Rates Differ Between U.S. States?
This article has 1 author:Reviewed by ScreenIT
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Clinical Outcome of Asymptomatic COVID-19 Infection Among a Large Nationwide Cohort of 5,621 Hospitalized Patients in Korea
This article has 7 authors:Reviewed by ScreenIT
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Introduction to and spread of COVID-19-like illness in care homes in Norfolk, UK
This article has 4 authors:Reviewed by ScreenIT
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Precision Mapping of COVID-19 Vulnerable Locales by Epidemiological and Socioeconomic Risk Factors, Developed Using South Korean Data
This article has 10 authors:Reviewed by ScreenIT
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Comparison of 16 Serological SARS-CoV-2 Immunoassays in 16 Clinical Laboratories
This article has 38 authors:Reviewed by ScreenIT
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Large-scale implementation of pooled RNA extraction and RT-PCR for SARS-CoV-2 detection
This article has 95 authors:Reviewed by ScreenIT
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Performance of Existing and Novel Surveillance Case Definitions for COVID-19 in the Community
This article has 14 authors:Reviewed by ScreenIT
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Relationships between Demographic, Geographic, and Environmental Statistics and the Spread of Novel Coronavirus Disease (COVID-19) in Italy
This article has 2 authors:Reviewed by ScreenIT
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Management strategies and prediction of COVID-19 by a fractional order generalized SEIR model
This article has 3 authors:Reviewed by ScreenIT