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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Association between 2019-nCoV transmission and N95 respirator use
This article has 3 authors:Reviewed by ScreenIT
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Structure and immune recognition of the porcine epidemic diarrhea virus spike protein
This article has 7 authors:Reviewed by ScreenIT
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Epidemic Situation of Novel Coronavirus Pneumonia in China mainland
This article has 3 authors:Reviewed by ScreenIT
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Simulating and forecasting the cumulative confirmed cases of SARS-CoV-2 in China by Boltzmann function-based regression analyses
This article has 5 authors:Reviewed by ScreenIT
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Longitudinal characteristics of lymphocyte responses and cytokine profiles in the peripheral blood of SARS-CoV-2 infected patients
This article has 50 authors:Reviewed by ScreenIT
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Estimation of the final size of the COVID-19 epidemic
This article has 1 author:Reviewed by ScreenIT
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Evaluating new evidence in the early dynamics of the novel coronavirus COVID-19 outbreak in Wuhan, China with real time domestic traffic and potential asymptomatic transmissions
This article has 1 author:Reviewed by ScreenIT
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When will the battle against novel coronavirus end in Wuhan: A SEIR modeling analysis
This article has 6 authors:Reviewed by ScreenIT
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Study on SARS-CoV-2 transmission and the effects of control measures in China
This article has 3 authors:Reviewed by ScreenIT
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Single-cell Analysis of ACE2 Expression in Human Kidneys and Bladders Reveals a Potential Route of 2019-nCoV Infection
This article has 16 authors:Reviewed by ScreenIT