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 wastewater monitoring using a novel PCR-based method rapidly captured the Delta-to-Omicron ΒΑ.1 transition patterns in the absence of conventional surveillance evidence
This article has 13 authors:Reviewed by ScreenIT
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Occurrence and transmission potential of asymptomatic and presymptomatic SARS-CoV-2 infections: Update of a living systematic review and meta-analysis
This article has 20 authors:Reviewed by ScreenIT
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Identification of Thresholds on Population Density for Understanding Transmission of COVID‐19
This article has 8 authors:Reviewed by ScreenIT
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Predictive and analysis of COVID-19 cases cumulative total: ARIMA model based on machine learning
This article has 8 authors:Reviewed by ScreenIT
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Limit of Detection for Rapid Antigen Testing of the SARS-CoV-2 Omicron and Delta Variants of Concern Using Live-Virus Culture
This article has 11 authors:Reviewed by ScreenIT
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The Correlation Between Brain Performance Capacity and COVID-19: A Cross-sectional Survey and Canonical Correlation Analysis
This article has 17 authors:Reviewed by ScreenIT
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Investigation of air change rate and aerosol behavior during an outbreak of COVID-19 in a geriatric care facility
This article has 6 authors:Reviewed by ScreenIT
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Acute Upper Airway Disease in Children With the Omicron (B.1.1.529) Variant of SARS-CoV-2—A Report From the US National COVID Cohort Collaborative
This article has 7 authors:Reviewed by ScreenIT
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Evaluation of the systemic and mucosal immune response induced by COVID-19 and the BNT162b2 mRNA vaccine for SARS-CoV-2
This article has 12 authors:Reviewed by ScreenIT
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User acceptability of saliva and gargle samples for identifying COVID-19 positive high-risk workers and household contacts
This article has 10 authors:Reviewed by ScreenIT