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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Evaluation of Increases in Drug Overdose Mortality Rates in the US by Race and Ethnicity Before and During the COVID-19 Pandemic
This article has 2 authors:Reviewed by ScreenIT
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Modelling COVID-19 Pandemic Dynamics Using Transparent, Interpretable, Parsimonious and Simulatable (TIPS) Machine Learning Models: A Case Study from Systems Thinking and System Identification Perspectives
This article has 2 authors:Reviewed by ScreenIT
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Factors Influencing the Acceptance of COVID-19 Vaccines in a Country with a High Vaccination Rate
This article has 8 authors:Reviewed by ScreenIT
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Examining the unit costs of COVID-19 vaccine delivery in Kenya
This article has 5 authors:Reviewed by ScreenIT
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Homologous and Heterologous Anti-COVID-19 Vaccination Does Not Induce New-Onset Formation of Autoantibodies Typically Accompanying Lupus Erythematodes, Rheumatoid Arthritis, Celiac Disease and Antiphospholipid Syndrome
This article has 8 authors:Reviewed by ScreenIT
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Cross-Border Transmissions of the Delta Substrain AY.29 During Tokyo Olympic and Paralympic Games
This article has 6 authors:Reviewed by ScreenIT
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Interferon pathway lupus risk alleles modulate risk of death from acute COVID-19
This article has 12 authors:Reviewed by ScreenIT
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Using portable air purifiers to reduce airborne transmission of infectious respiratory viruses – a computational fluid dynamics study
This article has 11 authors:Reviewed by ScreenIT
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Predictors of COVID testing among Australian youth: Insights from the Longitudinal Study of Australian Children
This article has 3 authors:Reviewed by ScreenIT
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Simplified Cas13-based assays for the fast identification of SARS-CoV-2 and its variants
This article has 24 authors:Reviewed by ScreenIT