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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Association of Cancer with Risk and Mortality of COVID-19: Results from the UK Biobank
This article has 10 authors:Reviewed by ScreenIT
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Epidemiological characteristics of COVID-19 patients in Samarinda, East Kalimantan, Indonesia
This article has 5 authors:Reviewed by ScreenIT
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Quantitative SARS-CoV-2 Serology in Children With Multisystem Inflammatory Syndrome (MIS-C)
This article has 19 authors:Reviewed by ScreenIT
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Serum-IgG responses to SARS-CoV-2 after mild and severe COVID-19 infection and analysis of IgG non-responders
This article has 14 authors:Reviewed by ScreenIT
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Identifying SARS-CoV-2 Entry Inhibitors through Drug Repurposing Screens of SARS-S and MERS-S Pseudotyped Particles
This article has 15 authors:Reviewed by ScreenIT
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A Highly Immunogenic Measles Virus-based Th1-biased COVID-19 Vaccine
This article has 8 authors:Reviewed by ScreenIT
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ACE2-expressing endothelial cells in aging mouse brain
This article has 4 authors:Reviewed by ScreenIT
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A simple protein-based surrogate neutralization assay for SARS-CoV-2
This article has 23 authors:Reviewed by ScreenIT
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ReCoNet: Multi-level Preprocessing of Chest X-rays for COVID-19 Detection Using Convolutional Neural Networks
This article has 4 authors:Reviewed by ScreenIT
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Bibliometric Analysis of COVID-19 in the Context of Migration Health: A Study Protocol
This article has 5 authors:Reviewed by ScreenIT
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