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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Development of a dual-gene loop-mediated isothermal amplification (LAMP) detection assay for SARS-CoV-2: A preliminary study
This article has 4 authors:Reviewed by ScreenIT
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Mathematical modelling to inform New Zealand's COVID‐19 response
This article has 7 authors:Reviewed by ScreenIT
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Nitric oxide gas inhalation to prevent COVID-2019 in healthcare providers
This article has 12 authors:Reviewed by ScreenIT
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Covid-19 Predictions Using a Gauss Model, Based on Data from April 2
This article has 4 authors:Reviewed by ScreenIT
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Association Between BCG Policy is Significantly Confounded by Age and is Unlikely to Alter Infection or Mortality Rates
This article has 1 author:Reviewed by ScreenIT
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A Streamlined Approach to Rapidly Detect SARS-CoV-2 Infection Avoiding RNA Extraction: Workflow Validation
This article has 16 authors:Reviewed by ScreenIT
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Introductions and early spread of SARS-CoV-2 in the New York City area
This article has 35 authors:Reviewed by ScreenIT
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Work-related COVID-19 transmission in six Asian countries/areas: A follow-up study
This article has 5 authors:Reviewed by ScreenIT
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COVID-19 Epidemic Analysis using Machine Learning and Deep Learning Algorithms
This article has 3 authors:Reviewed by ScreenIT
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Angiotensin-Converting Enzyme Inhibitors and Angiotensin II Receptor Blockers are Not Associated with Severe COVID-19 Infection in a Multi-Site UK Acute Hospital Trust
This article has 14 authors:Reviewed by ScreenIT