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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Applying chemical reaction transition theory to predict the latent transmission dynamics of coronavirus outbreak in China
This article has 1 author:Reviewed by ScreenIT
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A Peptide-Based Magnetic Chemiluminescence Enzyme Immunoassay for Serological Diagnosis of Coronavirus Disease 2019
This article has 34 authors:Reviewed by ScreenIT
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SARS‐CoV‐2 can be detected in urine, blood, anal swabs, and oropharyngeal swabs specimens
This article has 11 authors:Reviewed by ScreenIT
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Real-time monitoring the transmission potential of COVID-19 in Singapore, March 2020
This article has 7 authors:Reviewed by ScreenIT
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Comparative Analysis of Early Dynamic Trends in Novel Coronavirus Outbreak: A Modeling Framework
This article has 5 authors:Reviewed by ScreenIT
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COVID-19 in hemodialysis (HD) patients: Report from one HD center in Wuhan, China
This article has 9 authors:Reviewed by ScreenIT
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Neurological Manifestations of Hospitalized Patients with COVID-19 in Wuhan, China: a retrospective case series study
This article has 11 authors:Reviewed by ScreenIT
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Estimation of risk factors for COVID-19 mortality - preliminary results
This article has 3 authors:Reviewed by ScreenIT
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Clinical and pathological characteristics of 2019 novel coronavirus disease (COVID-19): a systematic reviews
This article has 4 authors:Reviewed by ScreenIT
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Deep Learning Enables Accurate Diagnosis of Novel Coronavirus (COVID-19) With CT Images
This article has 13 authors:Reviewed by ScreenIT