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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Impact of Sampling Type, Frequency, and Scale of the Collection System on SARS-CoV-2 Quantification Fidelity
This article has 8 authors:Reviewed by ScreenIT
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Coronavirus Disease 2019-Associated Mucormycosis in France: A Rare but Deadly Complication
This article has 41 authors:Reviewed by ScreenIT
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Assessment of COVID-19 risk and prevention effectiveness among spectators of mass gathering events
This article has 7 authors:Reviewed by ScreenIT
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If we cannot eliminate them, should we tame them? Mathematics underpinning the dose effect of virus infection and its application on covid-19 virulence evolution
This article has 2 authors:Reviewed by ScreenIT
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Understanding the Impact of Sociocultural Gender on Post-acute Sequelae of COVID-19: a Bayesian Approach
This article has 50 authors:Reviewed by ScreenIT
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COVID-19 and regional differences in the timeliness of hip-fracture surgery: an interrupted time-series analysis
This article has 5 authors:Reviewed by ScreenIT
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Transmission Dynamics of COVID-19 in Ghana and the Impact of Public Health Interventions
This article has 6 authors:Reviewed by ScreenIT
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Giving birth in a pandemic: women’s birth experiences in England during COVID-19
This article has 8 authors:Reviewed by ScreenIT
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Genome Profiling of SARS-CoV-2 in Indonesia, ASEAN and the Neighbouring East Asian Countries: Features, Challenges and Achievements
This article has 12 authors:Reviewed by ScreenIT
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Analysis of feature influence on Covid-19 Death Rate Per Country Using a Novel Orthogonalization Technique
This article has 7 authors:Reviewed by ScreenIT