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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No SARS-CoV-2 Neutralization by Intravenous Immunoglobulins Produced From Plasma Collected Before the 2020 Pandemic
This article has 5 authors:Reviewed by ScreenIT
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Early epidemiological and clinical manifestations of COVID-19 in Japan
This article has 8 authors:Reviewed by ScreenIT
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Seroprevalence of SARS-CoV-2 antibodies among 925 staff members in an urban hospital accepting COVID-19 patients in Osaka prefecture, Japan
This article has 8 authors:Reviewed by ScreenIT
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Obesity, walking pace and risk of severe COVID-19 and mortality: analysis of UK Biobank
This article has 7 authors:Reviewed by ScreenIT
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Transmission dynamics and control measures of COVID-19 outbreak in China: a modelling study
This article has 6 authors:Reviewed by ScreenIT
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Major alterations in the mononuclear phagocyte landscape associated with COVID-19 severity
This article has 92 authors:Reviewed by ScreenIT
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Health workers’ psychological distress during early phase of the covid-19 pandemic in Morocco
This article has 7 authors:Reviewed by ScreenIT
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Comprehensive analysis of T cell immunodominance and immunoprevalence of SARS-CoV-2 epitopes in COVID-19 cases
This article has 22 authors:Reviewed by ScreenIT
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The basic reproduction number of SARS-CoV-2: a scoping review of available evidence
This article has 13 authors:Reviewed by ScreenIT
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Estimating the end of the first wave of epidemic for COVID-19 outbreak in mainland China
This article has 3 authors:Reviewed by ScreenIT