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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Cross-sectional study evaluating the seroprevalence of SARS-CoV-2 antibodies among healthcare workers and factors associated with exposure during the first wave of the COVID-19 pandemic in New York
This article has 14 authors:Reviewed by ScreenIT
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Estimating the effect of mobility on SARS-CoV-2 transmission during the first and second wave of the COVID-19 epidemic, Switzerland, March to December 2020
This article has 4 authors:Reviewed by ScreenIT
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Assessment of a Program for SARS-CoV-2 Screening and Environmental Monitoring in an Urban Public School District
This article has 27 authors:Reviewed by ScreenIT
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Modeling SARS-CoV-2 infection and its individual differences with ACE2-expressing human iPS cells
This article has 9 authors:Reviewed by ScreenIT
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3D genomic capture of regulatory immuno-genetic profiles in COVID-19 patients for prognosis of severe COVID disease outcome
This article has 20 authors:Reviewed by ScreenIT
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Adequate knowledge of COVID-19 impacts good practices amongst health profession students in the Philippines
This article has 10 authors:Reviewed by ScreenIT
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Evidence of a dysregulated Vitamin D pathway in SARS-CoV-2 infected patient’s lung cells
This article has 6 authors:Reviewed by ScreenIT
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Inhalable Nanobody (PiN-21) prevents and treats SARS-CoV-2 infections in Syrian hamsters at ultra-low doses
This article has 10 authors:Reviewed by ScreenIT
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Impacts of mild and severe COVID-19 on sick leave
This article has 3 authors:Reviewed by ScreenIT
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Impact of social distancing measures on the daily number of new COVID-19 cases in Côte d’Ivoire: a retrospective cohort study
This article has 7 authors:Reviewed by ScreenIT