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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Early epidemiological signatures of novel SARS-CoV-2 variants: establishment of B.1.617.2 in England
This article has 21 authors:Reviewed by ScreenIT
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Prediction of severe COVID-19 cases requiring intensive care in Osaka, Japan
This article has 3 authors:Reviewed by ScreenIT
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Effects of immunosuppressive therapy reduction and early post-infection graft function in kidney transplant recipients with COVID-19
This article has 16 authors:Reviewed by ScreenIT
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Temporal Association of Reduced Serum Vitamin D with COVID-19 Infection: Two Single-Institution Case–Control Studies
This article has 4 authors:Reviewed by ScreenIT
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Ambient air pollution and COVID-19 in Delhi, India: a time-series evidence
This article has 1 author:Reviewed by ScreenIT
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Alcov: Estimating Variant of Concern Abundance from SARS-CoV-2 Wastewater Sequencing Data
This article has 6 authors:Reviewed by ScreenIT
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SARS-CoV-2 variants of concern dominate in Lahore, Pakistan in April 2021
This article has 20 authors:Reviewed by ScreenIT
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Temporal trends in the association of social vulnerability and race/ethnicity with county-level COVID-19 incidence and outcomes in the USA: an ecological analysis
This article has 14 authors:Reviewed by ScreenIT
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Inferring SARS-CoV-2 RNA shedding into wastewater relative to the time of infection
This article has 6 authors:Reviewed by ScreenIT
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Self-reported adaptability among postgraduate dental learners and their instructors: Accelerated change induced by COVID-19
This article has 5 authors:Reviewed by ScreenIT