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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SARS-CoV-2 RNA elements share human sequence identity and upregulate hyaluronan via NamiRNA-enhancer network
This article has 29 authors:Reviewed by ScreenIT
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Chronic Obstructive Pulmonary Disease Patients Have Increased Levels of Plasma Inflammatory Mediators Reported Upregulated in Severe COVID-19
This article has 9 authors:Reviewed by ScreenIT
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Examining unit costs for COVID-19 case management in Kenya
This article has 7 authors:Reviewed by ScreenIT
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Ruling out SARS-CoV-2 infection using exhaled breath analysis by electronic nose in a public health setting
This article has 14 authors:Reviewed by ScreenIT
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COVID-19: Making the Best out of a Forced Transition to Online Medical Teaching—a Mixed Methods Study
This article has 6 authors:Reviewed by ScreenIT
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Prevalence of SARS‐CoV‐2 infections in a pediatric orthopedic hospital
This article has 6 authors:Reviewed by ScreenIT
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COVID-19 Rapid Antigen Test as Screening Strategy at Points of Entry: Experience in Lazio Region, Central Italy, August–October 2020
This article has 15 authors:Reviewed by ScreenIT
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Design of SARS-CoV-2 hFc-Conjugated Receptor-Binding Domain mRNA Vaccine Delivered via Lipid Nanoparticles
This article has 12 authors:Reviewed by ScreenIT
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Mathematical models for assessing vaccination scenarios in several provinces in Indonesia
This article has 6 authors:Reviewed by ScreenIT
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Modeling SARS-CoV-2 infection in vitro with a human intestine-on-chip device
This article has 13 authors:Reviewed by ScreenIT