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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Epidemiological and clinical features of 291 cases with coronavirus disease 2019 in areas adjacent to Hubei, China: a double-center observational study
This article has 20 authors:Reviewed by ScreenIT
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Sixty‐eight consecutive patients assessed for COVID‐19 infection: Experience from a UK Regional infectious diseases Unit
This article has 11 authors:Reviewed by ScreenIT
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Nanopore Targeted Sequencing for the Accurate and Comprehensive Detection of SARS‐CoV‐2 and Other Respiratory Viruses
This article has 18 authors:Reviewed by ScreenIT
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Partial RdRp sequences offer a robust method for Coronavirus subgenus classification
This article has 4 authors:Reviewed by ScreenIT
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Clinical Features of Patients Infected with the 2019 Novel Coronavirus (COVID-19) in Shanghai, China
This article has 20 authors:Reviewed by ScreenIT
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Epidemiologic Characteristics of COVID-19 in Guizhou Province, China
This article has 11 authors:Reviewed by ScreenIT
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The effect of human mobility and control measures on the COVID-19 epidemic in China
This article has 18 authors:Reviewed by ScreenIT
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ddPCR: a more sensitive and accurate tool for SARS-CoV-2 detection in low viral load specimens
This article has 23 authors:Reviewed by ScreenIT
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Risk estimation and prediction of the transmission of coronavirus disease-2019 (COVID-19) in the mainland of China excluding Hubei province
This article has 3 authors:Reviewed by ScreenIT
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Transmission and clinical characteristics of coronavirus disease 2019 in 104 outside‐Wuhan patients, China
This article has 14 authors:Reviewed by ScreenIT