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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Evolution of COVID-19 Pandemic in India
This article has 3 authors:Reviewed by ScreenIT
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Sensitive Detection of SARS-CoV-2–Specific Antibodies in Dried Blood Spot Samples
This article has 16 authors:Reviewed by ScreenIT
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A Genome Epidemiological Study of SARS-CoV-2 Introduction into Japan
This article has 25 authors:Reviewed by ScreenIT
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The effect of exercise and affect regulation skills on mental health during the COVID-19 pandemic: A cross-sectional survey
This article has 8 authors:Reviewed by ScreenIT
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Differential COVID‐19 case positivity in New York City neighborhoods: Socioeconomic factors and mobility
This article has 3 authors:Reviewed by ScreenIT
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Estimating underdiagnosis of COVID-19 with nowcasting and machine learning
This article has 11 authors:Reviewed by ScreenIT
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Data from the COVID-19 epidemic in Florida suggest that younger cohorts have been transmitting their infections to less socially mobile older adults
This article has 1 author:Reviewed by Rapid Reviews Infectious Diseases, ScreenIT
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Role of pharmacist during the COVID-19 pandemic: A scoping review
This article has 3 authors:Reviewed by ScreenIT
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Transmission dynamics of the COVID-19 epidemic in England
This article has 3 authors:Reviewed by ScreenIT
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Exploring Epidemiological Behavior of Novel Coronavirus (COVID-19) Outbreak in Bangladesh
This article has 4 authors:Reviewed by ScreenIT