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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A catalog of associations between rare coding variants and COVID-19 outcomes
This article has 82 authors:Reviewed by ScreenIT, Rapid Reviews Infectious Diseases
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Age and COVID-19 mortality: A comparison of Gompertz doubling time across countries and causes of death
This article has 1 author:Reviewed by ScreenIT
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Clinical characteristics of COVID-19 and the model for predicting the occurrence of critically ill patients: a retrospective cohort study
This article has 10 authors:Reviewed by ScreenIT
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Pre-hospitalization proton pump inhibitor use and clinical outcomes in COVID-19
This article has 7 authors:Reviewed by ScreenIT
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THE IMPACT OF LOCKDOWN ON PUBLIC HEALTH DURING THE FIRST WAVE OF COVID-19 PANDEMIC: LESSONS LEARNED FOR DESIGNING EFFECTIVE CONTAINMENT MEASURES TO COPE WITH SECOND WAVE
This article has 1 author:Reviewed by ScreenIT
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Trimeric SARS-CoV-2 Spike Proteins Produced from CHO Cells in Bioreactors Are High-Quality Antigens
This article has 13 authors:Reviewed by ScreenIT
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Development of an Optical Assay to Detect SARS-CoV-2 Spike Protein Binding Interactions with ACE2 and Disruption of these Interactions Using Electric Current
This article has 4 authors:Reviewed by ScreenIT
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Quantitative analysis of SARS-CoV-2 RNA from wastewater solids in communities with low COVID-19 incidence and prevalence
This article has 17 authors:Reviewed by ScreenIT
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Assessment of the Impact of COVID-19 pandemic on population level interest in Skincare: Evidence from a google trends-based Infodemiology study
This article has 4 authors:Reviewed by ScreenIT
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A data-driven metapopulation model for the Belgian COVID-19 epidemic: assessing the impact of lockdown and exit strategies
This article has 11 authors:Reviewed by ScreenIT