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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Machine Learning for Identifying Data-Driven Subphenotypes of Incident Post-Acute SARS-CoV-2 Infection Conditions with Large Scale Electronic Health Records: Findings from the RECOVER Initiative
This article has 19 authors:Reviewed by ScreenIT
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Variant-specific symptoms of COVID-19 among 1,542,510 people in England
This article has 9 authors:Reviewed by ScreenIT
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Understanding Post-Acute Sequelae of SARS-CoV-2 Infection through Data-Driven Analysis with Longitudinal Electronic Health Records: Findings from the RECOVER Initiative
This article has 16 authors:Reviewed by ScreenIT
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Predictions of immunogenicity reveal potent SARS-CoV-2 CD8+ T-cell epitopes
This article has 10 authors:Reviewed by ScreenIT
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A mesoscale agent based modeling framework for flow-mediated infection transmission in indoor occupied spaces
This article has 2 authors:Reviewed by ScreenIT
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Antibody levels following vaccination against SARS-CoV-2: associations with post-vaccination infection and risk factors in two UK longitudinal studies
This article has 37 authors:This article has been curated by 1 group: -
Shared genetic etiology and causality between COVID-19 and venous thromboembolism: evidence from genome-wide cross trait analysis and bi-directional Mendelian randomization study
This article has 7 authors:Reviewed by ScreenIT
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Exploring Barriers and Facilitators to Physical Activity during the COVID-19 Pandemic: A Qualitative Study
This article has 4 authors:Reviewed by ScreenIT
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BNT162b2 induces robust cross-variant SARS-CoV-2 immunity in children
This article has 10 authors:Reviewed by ScreenIT
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Bias-Adjusted Predictions of County-Level Vaccination Coverage from the COVID-19 Trends and Impact Survey
This article has 5 authors:Reviewed by ScreenIT