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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Mapping social distancing measures to the reproduction number for COVID-19
This article has 5 authors:Reviewed by ScreenIT
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A fair efficacy formula for assessing the effectiveness of contact tracing applications
This article has 1 author:Reviewed by ScreenIT
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Low COVID-19 Mortality in Old Age Homes in Western India: An empirical study
This article has 5 authors:Reviewed by ScreenIT
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Temperature dependence of COVID-19 transmission
This article has 1 author:Reviewed by ScreenIT
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Beliefs associated with Intentions of Non-Physician Healthcare Workers to Receive the COVID-19 Vaccine in Ontario, Canada
This article has 7 authors:Reviewed by ScreenIT
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Multi-site co-mutations and 5’UTR CpG immunity escape drive the evolution of SARS-CoV-2
This article has 8 authors:Reviewed by ScreenIT
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Optimal diagnostic test allocation strategy during the COVID‐19 pandemic and beyond
This article has 6 authors:Reviewed by ScreenIT
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Gene Expression Meta-Analysis Reveals Interferon-induced Genes
This article has 2 authors:Reviewed by ScreenIT
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Insights from Genomes and Genetic Epidemiology of SARS-CoV-2 isolates from the state of Andhra Pradesh
This article has 25 authors:Reviewed by ScreenIT
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In-depth analysis of laboratory parameters reveals the interplay between sex, age, and systemic inflammation in individuals with COVID-19
This article has 21 authors:Reviewed by ScreenIT