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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Ethnics and economics in COVID-19: Meta-regression of data from countries in the New York metropolitan area
This article has 7 authors:Reviewed by ScreenIT
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Limited specificity of commercially available SARS‐CoV‐2 IgG ELISAs in serum samples of African origin
This article has 20 authors:Reviewed by ScreenIT
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Convalescent COVID-19 patients are susceptible to endothelial dysfunction due to persistent immune activation
This article has 19 authors:Reviewed by ScreenIT
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Public adherence to precautionary measures and preventive guidelines against COVID-19 in Sudan: An application of the Health Belief Model
This article has 3 authors:Reviewed by ScreenIT
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A lesson from COVID-19 on inaccessibility of web-based information for disabled populations worldwide
This article has 9 authors:Reviewed by ScreenIT
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Public participation in crisis policymaking. How 30,000 Dutch citizens advised their government on relaxing COVID-19 lockdown measures
This article has 3 authors:Reviewed by ScreenIT
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A Retrospective Longitudinal Study of COVID-19 as Seen by a Large Urban Hospital in Chicago
This article has 7 authors:Reviewed by ScreenIT
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Automated detection of COVID-19 through convolutional neural network using chest x-ray images
This article has 5 authors:Reviewed by ScreenIT
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Modelling testing and response strategies for COVID-19 outbreaks in remote Australian Aboriginal communities
This article has 6 authors:Reviewed by ScreenIT
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A multiplexed, next generation sequencing platform for high-throughput detection of SARS-CoV-2
This article has 23 authors:Reviewed by ScreenIT