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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Advance of Novel Coronavirus Registration Clinical Trial
This article has 3 authors:Reviewed by ScreenIT
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How to improve adherence with quarantine: rapid review of the evidence
This article has 6 authors:Reviewed by ScreenIT
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Machine Learning the Phenomenology of COVID-19 From Early Infection Dynamics
This article has 1 author:Reviewed by ScreenIT
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Maximum entropy method for estimating the reproduction number: An investigation for COVID-19 in China and the United States
This article has 1 author:Reviewed by ScreenIT
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COVID-19: Forecasting short term hospital needs in France
This article has 3 authors:Reviewed by ScreenIT
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Evaluation of recombinant nucleocapsid and spike proteins for serological diagnosis of novel coronavirus disease 2019 (COVID-19)
This article has 18 authors:Reviewed by ScreenIT
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Association between Prolonged Intermittent Renal Replacement Therapy and All-Cause Mortality in COVID-19 Patients Undergoing Invasive Mechanical Ventilation: A Retrospective Cohort Study
This article has 12 authors:Reviewed by ScreenIT
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Epidemiology of Seasonal Coronaviruses: Establishing the Context for the Emergence of Coronavirus Disease 2019
This article has 6 authors:Reviewed by ScreenIT
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Roles of meteorological conditions in COVID-19 transmission on a worldwide scale
This article has 9 authors:Reviewed by ScreenIT
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Pandemic dynamics of COVID-19 using epidemic stage, instantaneous reproductive number and pathogen genome identity (GENI) score: modeling molecular epidemiology
This article has 2 authors:Reviewed by ScreenIT