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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The UK Covid-19 lockdown weakened in April and May 2020: implications for the size of the epidemic and for outcomes had lockdown been earlier
This article has 1 author:Reviewed by ScreenIT
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Snapshot of the evolution and mutation patterns of SARS-CoV-2
This article has 12 authors:Reviewed by ScreenIT
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The sensitivity of respiratory tract specimens for the detection of SARS-CoV-2: A protocol for a living systematic review and meta-analysis
This article has 10 authors:Reviewed by ScreenIT
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Safe contact tracing for COVID-19: A method without privacy breach using functional encryption techniques based-on spatio-temporal trajectory data
This article has 3 authors:Reviewed by ScreenIT
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Characterization of respiratory microbial dysbiosis in hospitalized COVID-19 patients
This article has 33 authors:Reviewed by ScreenIT
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Association of Sickle Cell Trait with Risk and Mortality of COVID-19: Results from the United Kingdom Biobank
This article has 13 authors:Reviewed by ScreenIT
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COVID-19 associated anxiety enhances tinnitus
This article has 10 authors:Reviewed by ScreenIT
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Reduced inflammatory responses to SARS-CoV-2 infection in children presenting to hospital with COVID-19 in China
This article has 10 authors:Reviewed by ScreenIT
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Dynamics of coagulopathy in patients with different COVID-19 severity
This article has 17 authors:Reviewed by ScreenIT
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Laboratory-based surveillance of COVID-19 in the Greater Helsinki area, Finland, February–June 2020
This article has 15 authors:Reviewed by ScreenIT