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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The impact of digital contact tracing on the SARS-CoV-2 pandemic—a comprehensive modelling study
This article has 15 authors:Reviewed by ScreenIT
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Impact of COVID-19 pandemic on community medication dispensing: a national cohort analysis in Wales, UK
This article has 14 authors:Reviewed by ScreenIT
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Knowledge, Attitude, and Practices Toward SARS-COV-2 Infection in the United Arab Emirates Population: An Online Community-Based Cross-Sectional Survey
This article has 4 authors:Reviewed by ScreenIT
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Safety and immunogenicity of S-Trimer (SCB-2019), a protein subunit vaccine candidate for COVID-19 in healthy adults: a phase 1, randomised, double-blind, placebo-controlled trial
This article has 11 authors:Reviewed by ScreenIT
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Quantifying the effect of quarantine control in Covid-19 infectious spread using machine learning
This article has 2 authors:Reviewed by ScreenIT
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Salivette, a relevant saliva sampling device for SARS-CoV-2 detection
This article has 10 authors:Reviewed by ScreenIT
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Optimal piecewise constant vaccination and lockdown policies for COVID-19
This article has 4 authors:Reviewed by ScreenIT
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COVID-19 in Iran: A Deeper Look Into The Future
This article has 9 authors:Reviewed by ScreenIT
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The age distribution of mortality from novel coronavirus disease (COVID-19) suggests no large difference of susceptibility by age
This article has 3 authors:Reviewed by ScreenIT
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A parsimonious approach for spatial transmission and heterogeneity in the COVID-19 propagation
This article has 5 authors:Reviewed by ScreenIT