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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Behavioural responses to Covid-19 health certification: a rapid review
This article has 8 authors:Reviewed by ScreenIT
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Gut microbiota diversity and C-Reactive Protein are predictors of disease severity in COVID-19 patients
This article has 30 authors:Reviewed by ScreenIT
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Estimating epidemiologic dynamics from cross-sectional viral load distributions
This article has 7 authors:Reviewed by ScreenIT
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Antiviral Activity of Influenza A Virus Defective Interfering Particles against SARS-CoV-2 Replication In Vitro through Stimulation of Innate Immunity
This article has 9 authors:Reviewed by ScreenIT
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Identifying optimal COVID-19 testing strategies for schools and businesses: Balancing testing frequency, individual test technology, and cost
This article has 5 authors:Reviewed by ScreenIT
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A novel evidence-based predictor tool for hospitalization and length of stay: insights from COVID-19 patients in New York city
This article has 13 authors:Reviewed by ScreenIT
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Dexamethasone modulates immature neutrophils and interferon programming in severe COVID-19
This article has 16 authors:Reviewed by ScreenIT
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Temporal changes in T cell subsets and expansion of cytotoxic CD4+ T cells in the lungs in severe COVID-19
This article has 19 authors:Reviewed by ScreenIT
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Structural Analysis of the Novel Variants of SARS-CoV-2 and Forecasting in North America
This article has 4 authors:Reviewed by ScreenIT
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Mapping the SARS-CoV-2 spike glycoprotein-derived peptidome presented by HLA class II on dendritic cells
This article has 16 authors:Reviewed by ScreenIT