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-Specific Antibody Prevalence and Symptoms in a Local Austrian Population
This article has 7 authors:Reviewed by ScreenIT
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Case fatality rate of novel coronavirus disease 2019 in China
This article has 4 authors:Reviewed by ScreenIT
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Indirect effects of the COVID-19 pandemic on paediatric healthcare use and severe disease: a retrospective national cohort study
This article has 18 authors:Reviewed by ScreenIT
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Recombinant Fc-fusion vaccine of RBD induced protection against SARS-CoV-2 in non-human primate and mice
This article has 28 authors:Reviewed by ScreenIT
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How high and long will the COVID-19 wave be? A data-driven approach to model and predict the COVID-19 epidemic and the required capacity for the German health system
This article has 2 authors:Reviewed by ScreenIT
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Evaluating the different control policies for COVID-19 between mainland China and European countries by a mathematical model in the confirmed cases
This article has 6 authors:Reviewed by ScreenIT
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Implementing building-level SARS-CoV-2 wastewater surveillance on a university campus
This article has 19 authors:Reviewed by ScreenIT
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Superspreading of airborne pathogens in a heterogeneous world
This article has 3 authors:Reviewed by ScreenIT
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Lower Respiratory Tract Myeloid Cells Harbor SARS-Cov-2 and Display an Inflammatory Phenotype
This article has 16 authors:Reviewed by ScreenIT
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The COVID-19 Healthcare Personnel Study (CHPS): Overview, Methods, and Preliminary Findings
This article has 12 authors:Reviewed by ScreenIT