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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Depression, Anxiety and Depression-anxiety comorbidity amid COVID-19 Pandemic: An online survey conducted during lockdown in Nepal
This article has 7 authors:Reviewed by ScreenIT
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Impact of ethnicity on outcome of severe COVID-19 infection. Data from an ethnically diverse UK tertiary centre
This article has 5 authors:Reviewed by ScreenIT
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Multiple SARS-CoV-2 Introductions Shaped the Early Outbreak in Central Eastern Europe: Comparing Hungarian Data to a Worldwide Sequence Data-Matrix
This article has 22 authors:Reviewed by ScreenIT
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Mathematical models and deep learning for predicting the number of individuals reported to be infected with SARS-CoV-2
This article has 3 authors:Reviewed by ScreenIT
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Geospatial precision simulations of community confined human interactions during SARS-CoV-2 transmission reveals bimodal intervention outcomes
This article has 18 authors:Reviewed by ScreenIT
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Time-adjusted Analysis Shows Weak Associations Between BCG Vaccination Policy and COVID-19 Disease Progression
This article has 6 authors:Reviewed by ScreenIT
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A role for Biofoundries in rapid development and validation of automated SARS-CoV-2 clinical diagnostics
This article has 9 authors:Reviewed by ScreenIT
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The reproduction number of COVID-19 and its correlation with public health interventions
This article has 3 authors:Reviewed by ScreenIT
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Incorporating and addressing testing bias within estimates of epidemic dynamics for SARS-CoV-2
This article has 3 authors:Reviewed by ScreenIT
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Updated estimates of comorbidities associated with risk for COVID-19 complications based on US data
This article has 3 authors:Reviewed by ScreenIT