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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Aspirin in patients admitted to hospital with COVID-19 (RECOVERY): a randomised, controlled, open-label, platform trial
This article has 36 authors:Reviewed by ScreenIT
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Recognition and inhibition of SARS-CoV-2 by humoral innate immunity pattern recognition molecules
This article has 29 authors:Reviewed by ScreenIT
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ROS/RNS Balancing, Aerobic Fermentation Regulation and Cell Cycle Control – a Complex Early Trait (‘CoV-MAC-TED’) for Combating SARS-CoV-2-Induced Cell Reprogramming
This article has 16 authors:Reviewed by ScreenIT
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Introduction and transmission of SARS-CoV-2 B.1.1.7 in Denmark
This article has 28 authors:Reviewed by ScreenIT
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Dysregulated Neutrophil Phenotype and Function in Hospitalised Non-ICU COVID-19 Pneumonia
This article has 14 authors:Reviewed by ScreenIT
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Seroprevalence of SARS‐CoV‐2 antibodies among rural healthcare workers
This article has 10 authors:Reviewed by ScreenIT
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Targeting of Protein Kinase CK2 Elicits Antiviral Activity on Bovine Coronavirus Infection
This article has 13 authors:Reviewed by ScreenIT
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Sequence of the SARS-CoV-2 Spike Transmembrane Domain Encodes Conformational Dynamics
This article has 4 authors:Reviewed by ScreenIT
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SARS-CoV-2 B.1.1.7 (alpha) and B.1.351 (beta) variants induce pathogenic patterns in K18-hACE2 transgenic mice distinct from early strains
This article has 12 authors:Reviewed by ScreenIT
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Single domain shark VNAR antibodies neutralize SARS‐CoV‐2 infection in vitro
This article has 9 authors:Reviewed by ScreenIT