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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SARS-CoV-2 Detection in Istanbul Wastewater Treatment Plant Sludges
This article has 6 authors:Reviewed by ScreenIT
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Contextual factors and the COVID ‐19 outbreak rate across U.S. counties in its initial phase
This article has 2 authors:Reviewed by ScreenIT
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Country-level determinants of the severity of the first global wave of the COVID-19 pandemic: an ecological study
This article has 9 authors:Reviewed by ScreenIT
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The impact of containment measures and air temperature on mitigating COVID-19 transmission: non-classical SEIR modeling and analysis
This article has 8 authors:Reviewed by ScreenIT
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Understanding the psychological impact of the COVID-19 pandemic and containment measures: An empirical model of stress
This article has 4 authors:Reviewed by ScreenIT
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Factors linked to changes in mental health outcomes among Brazilians in quarantine due to COVID-19
This article has 3 authors:Reviewed by ScreenIT
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Chest MRI of patients with COVID-19
This article has 15 authors:Reviewed by ScreenIT
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The Evaluation of Deep Neural Networks and X-Ray as a Practical Alternative for Diagnosis and Management of COVID-19
This article has 5 authors:Reviewed by ScreenIT
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Olfactory and Gustatory Dysfunction as an Early Identifier of COVID‐19 in Adults and Children: An International Multicenter Study
This article has 29 authors:Reviewed by ScreenIT
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SARS pandemic exposure impaired early childhood development: A lesson for COVID-19
This article has 9 authors:Reviewed by ScreenIT