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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Multi-Omic Profiling of Plasma Identify Biomarkers and Pathogenesis of COVID-19 in Children
This article has 17 authors:Reviewed by ScreenIT
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Knowledge, Attitude and Practices towards COVID-19 Guidelines among Students in Bangladesh
This article has 3 authors:Reviewed by ScreenIT
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KLF2 is a therapeutic target for COVID-19 induced endothelial dysfunction
This article has 4 authors:Reviewed by ScreenIT
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Protective population behavior change in outbreaks of emerging infectious disease
This article has 3 authors:Reviewed by ScreenIT
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SARS-CoV-2 B.1.1.7 and B.1.351 spike variants bind human ACE2 with increased affinity
This article has 4 authors:Reviewed by ScreenIT
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Automatic identification of risk factors for SARS-CoV-2 positivity and severe clinical outcomes of COVID-19 using Data Mining and Natural Language Processing
This article has 4 authors:Reviewed by ScreenIT
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Neutrophil-mediated oxidative stress and albumin structural damage predict COVID-19-associated mortality
This article has 20 authors:This article has been curated by 1 group: -
An increase in willingness to vaccinate against COVID-19 in the US between October 2020 and February 2021: longitudinal evidence from the Understanding America Study
This article has 3 authors:Reviewed by ScreenIT
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Reopening California: Seeking robust, non-dominated COVID-19 exit strategies
This article has 8 authors:Reviewed by ScreenIT
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Structure, mechanism and crystallographic fragment screening of the SARS-CoV-2 NSP13 helicase
This article has 13 authors:Reviewed by ScreenIT