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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A decision analytic approach for social distancing policies during early stages of COVID-19 pandemic
This article has 3 authors:Reviewed by ScreenIT
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Protocol for a multicentre randomized controlled trial of normobaric versus hyperbaric oxygen therapy for hypoxemic COVID-19 patients
This article has 25 authors:Reviewed by ScreenIT
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Sequence analysis for SNP detection and phylogenetic reconstruction of SARS-cov-2 isolated from Nigerian COVID-19 cases
This article has 6 authors:Reviewed by ScreenIT
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Unique transcriptional changes in coagulation cascade genes in SARS-CoV-2-infected lung epithelial cells: A potential factor in COVID-19 coagulopathies
This article has 2 authors:Reviewed by ScreenIT
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Hardness of Herd Immunity and Success Probability of Quarantine Measures: A Branching Process Approach
This article has 1 author:Reviewed by ScreenIT
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Qing Fei Pai Du Tang, a Chinese multi-herbal medicine formulated against COVID-19, elevates the plasma levels of IL-1β, IL-18, TNF-α, and IL-8
This article has 7 authors:Reviewed by ScreenIT
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What can trends in hospital deaths from COVID-19 tell us about the progress and peak of the pandemic? pandemic? An analysis of death counts from England announced up to 25 April 2020
This article has 5 authors:Reviewed by ScreenIT
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Ipomoeassin-F inhibits the in vitro biogenesis of the SARS-CoV-2 spike protein and its host cell membrane receptor
This article has 7 authors:Reviewed by ScreenIT
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Universal properties of the dynamics of the Covid-19 pandemic
This article has 2 authors:Reviewed by ScreenIT
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A data-driven method to detect the flattening of the COVID-19 pandemic curve and estimating its ending life-cycle using only the time-series of new cases per day.
This article has 4 authors:Reviewed by ScreenIT