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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Modeling COVID-19 Growing Trends to Reveal the Differences in the Effectiveness of Non-Pharmaceutical Interventions among Countries in the World
This article has 5 authors:Reviewed by ScreenIT
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Epidemiological Investigation and Prevention Control Analysis of the Longitudinal Distribution of COVID-19 in Henan Province, China
This article has 7 authors:Reviewed by ScreenIT
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No lockdown policy for COVID-19 epidemic in Bangladesh: Good, bad or ugly?
This article has 4 authors:Reviewed by ScreenIT
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Long Term Impact on Lung Function of Patients With Moderate and Severe COVID-19. A Prospective Cohort Study
This article has 6 authors:Reviewed by ScreenIT
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Dual attention multiple instance learning with unsupervised complementary loss for COVID-19 screening
This article has 6 authors:Reviewed by ScreenIT
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Mask adherence and rate of COVID-19 across the United States
This article has 6 authors:Reviewed by ScreenIT
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Longitudinal Serological Analysis and Neutralizing Antibody Levels in Coronavirus Disease 2019 Convalescent Patients
This article has 16 authors:Reviewed by ScreenIT
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Detection of SARS-CoV-2 in wastewater in Japan during a COVID-19 outbreak
This article has 5 authors:Reviewed by ScreenIT
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BCG Vaccine Derived Peptides Induce SARS-CoV-2 T Cell Cross-Reactivity
This article has 9 authors:Reviewed by ScreenIT
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SARS-CoV-2 detection and genomic sequencing from hospital surface samples collected at UC Davis
This article has 19 authors:Reviewed by ScreenIT