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 characteristics of discharged patients infected with SARS‐CoV‐2 on the Qinghai Plateau
This article has 12 authors:Reviewed by ScreenIT
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Evaluation of SARS-CoV-2 serology assays reveals a range of test performance
This article has 72 authors:Reviewed by ScreenIT
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Liver Chemistries in Patients with Severe or Non-severe COVID-19: A Meta-Analysis
This article has 8 authors:Reviewed by ScreenIT
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Hypertension and Renin–Angiotensin–Aldosterone System Inhibitors in Patients with Covid-19
This article has 7 authors:Reviewed by ScreenIT
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Tapestry: A Single-Round Smart Pooling Technique for COVID-19 Testing
This article has 28 authors:Reviewed by ScreenIT
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Pooled RNA sample reverse transcriptase real time PCR assay for SARS CoV-2 infection: A reliable, faster and economical method
This article has 10 authors:Reviewed by ScreenIT
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Characterizing COVID-19 case detection utilizing influenza surveillance data in the United States, January-March, 2020
This article has 4 authors:Reviewed by ScreenIT
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A new design of an adaptive model of infectious diseases based on artificial intelligence approach: monitoring and forecasting of COVID-19 epidemic cases
This article has 4 authors:Reviewed by ScreenIT
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The role of spatial structure in the infection spread models: population density map of England example
This article has 2 authors:Reviewed by ScreenIT
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Estimation of the infection fatality rate and the total number of SARS-CoV-2 infections
This article has 3 authors:Reviewed by ScreenIT