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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The proportion of deaths cases in confirmed patients of COVID-19 is still increasing for cumulative cases reported up to 25 April 2020
This article has 1 author:Reviewed by ScreenIT
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COVID-19 in London, a Case Series Demonstrating Late Improvement in Survivors
This article has 11 authors:Reviewed by ScreenIT
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Molecular Mimicry Map (3M) of SARS-CoV-2: Prediction of potentially immunopathogenic SARS-CoV-2 epitopes via a novel immunoinformatic approach
This article has 2 authors:Reviewed by ScreenIT
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The functions of SARS-CoV-2 neutralizing and infection-enhancing antibodies in vitro and in mice and nonhuman primates
This article has 64 authors:Reviewed by ScreenIT
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Comparative incidence and outcomes of COVID‐19 in kidney or kidney‐pancreas transplant recipients versus kidney or kidney‐pancreas waitlisted patients: A single‐center study
This article has 8 authors:Reviewed by ScreenIT
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Gender disparities in coronavirus disease 2019 clinical trial leadership
This article has 7 authors:Reviewed by ScreenIT
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The role of weather conditions in COVID-19 transmission: A study of a global panel of 1236 regions
This article has 7 authors:Reviewed by ScreenIT
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Building a virtual summer research experience in cancer for high school and early undergraduate students: lessons from the COVID-19 pandemic
This article has 15 authors:Reviewed by ScreenIT
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The Hydroalcoholic Extract of Uncaria tomentosa (Cat’s Claw) Inhibits the Infection of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) In Vitro
This article has 9 authors:Reviewed by ScreenIT
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Rapid host response to an infection with Coronavirus. Study of transcriptional responses with Porcine Epidemic Diarrhea Virus
This article has 4 authors:Reviewed by ScreenIT