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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Characteristics of lymphocyte subsets and cytokines in peripheral blood of 123 hospitalized patients with 2019 novel coronavirus pneumonia (NCP)
This article has 14 authors:Reviewed by ScreenIT
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Caution on Kidney Dysfunctions of COVID-19 Patients
This article has 24 authors:Reviewed by ScreenIT
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Epidemiological and Clinical Characteristics of 17 Hospitalized Patients with 2019 Novel Coronavirus Infections Outside Wuhan, China
This article has 9 authors:Reviewed by ScreenIT
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Distribution of the COVID-19 epidemic and correlation with population emigration from Wuhan, China
This article has 11 authors:Reviewed by ScreenIT
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Feasibility of controlling COVID-19 outbreaks by isolation of cases and contacts
This article has 22 authors:Reviewed by ScreenIT
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Spatially Explicit Modeling of 2019-nCoV Epidemic Trend Based on Mobile Phone Data in Mainland China
This article has 9 authors:Reviewed by ScreenIT
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Transmission Dynamics of COVID-19 in Malaysia Prior to the Movement Control Order
This article has 2 authors:Reviewed by ScreenIT
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Assessing the Tendency of 2019-nCoV (COVID-19) Outbreak in China
This article has 7 authors:Reviewed by ScreenIT
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Diarrhoea may be underestimated: a missing link in 2019 novel coronavirus
This article has 8 authors:Reviewed by ScreenIT
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The insert sequence in SARS-CoV-2 enhances spike protein cleavage by TMPRSS
This article has 17 authors:Reviewed by ScreenIT