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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Weakly Labeled Data Augmentation for Deep Learning: A Study on COVID-19 Detection in Chest X-Rays
This article has 2 authors:Reviewed by ScreenIT
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Asthma and COVID‐19 in children: A systematic review and call for data
This article has 2 authors:Reviewed by ScreenIT
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AKI in Hospitalized Patients with COVID-19
This article has 33 authors:Reviewed by ScreenIT
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Early postmortem brain MRI findings in COVID-19 non-survivors
This article has 13 authors:Reviewed by ScreenIT
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Quantifying the social distancing privilege gap: a longitudinal study of smartphone movement
This article has 5 authors:Reviewed by ScreenIT
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A time-series analysis of testing and COVID-19 outbreaks in Canadian federal prisons to inform prevention and surveillance efforts
This article has 3 authors:Reviewed by ScreenIT
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A seven-day cycle in COVID-19 infection, hospitalization, and mortality rates: Do weekend social interactions kill susceptible people?
This article has 4 authors:Reviewed by ScreenIT
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Who maintains good mental health in a locked-down country? A French nationwide online survey of 11,391 participants
This article has 4 authors:Reviewed by ScreenIT
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Real-time assessment of COVID-19 impact on global surgical case volumes
This article has 9 authors:Reviewed by ScreenIT
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The Heterogeneous Landscape and Early Evolution of Pathogen-Associated CpG Dinucleotides in SARS-CoV-2
This article has 6 authors:Reviewed by ScreenIT