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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Massively parallel interrogation of protein fragment secretability using SECRiFY reveals features influencing secretory system transit
This article has 15 authors:Reviewed by ScreenIT
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Estimating SARS-CoV-2 variant strain infectiousness in Japan as of March 28, 2021
This article has 3 authors:Reviewed by ScreenIT
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SARS-CoV-2 detection with CRISPR diagnostics
This article has 16 authors:Reviewed by ScreenIT
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Early experiences of rehabilitation for patients post-COVID to improve fatigue, breathlessness exercise capacity and cognition
This article has 5 authors:Reviewed by ScreenIT
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The impact of headache disorders on COVID-19 survival: a world population-based analysis
This article has 4 authors:Reviewed by ScreenIT
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Impact of COVID-19 on healthcare workers at a cancer care centre
This article has 4 authors:Reviewed by ScreenIT
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Preliminary estimation of the basic reproduction number of novel coronavirus (2019-nCoV) in China, from 2019 to 2020: A data-driven analysis in the early phase of the outbreak
This article has 11 authors:Reviewed by ScreenIT
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Inhibiting SARS-CoV-2 infection in vitro by suppressing its receptor, angiotensin-converting enzyme 2, via aryl-hydrocarbon receptor signal
This article has 9 authors:Reviewed by ScreenIT
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CD47 as a potential biomarker for the early diagnosis of severe COVID-19
This article has 11 authors:Reviewed by ScreenIT
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Gender differences in the determinants of willingness to get the COVID-19 vaccine among the working-age population in Japan
This article has 10 authors:Reviewed by ScreenIT