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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Seroprevalence and Correlates of SARS-CoV-2 Antibodies in Health Care Workers in Chicago
This article has 14 authors:Reviewed by ScreenIT
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A Workflow of Integrated Resources to Catalyze Network Pharmacology Driven COVID-19 Research
This article has 19 authors:Reviewed by ScreenIT
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The influence of HLA genotype on the severity of COVID‐19 infection
This article has 8 authors:Reviewed by ScreenIT
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Threshold analyses on combinations of testing, population size, and vaccine coverage for COVID-19 control in a university setting
This article has 4 authors:Reviewed by ScreenIT
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Brazilian model estimation for SARS-CoV-2 peak contagion (BMESPC)
This article has 3 authors:Reviewed by ScreenIT
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Rational Design of SARS-CoV-2 Spike Glycoproteins To Increase Immunogenicity By T Cell Epitope Engineering
This article has 5 authors:Reviewed by ScreenIT
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From people to Panthera : Natural SARS-CoV-2 infection in tigers and lions at the Bronx Zoo
This article has 37 authors:Reviewed by ScreenIT
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REACT-1 round 8 final report: high average prevalence with regional heterogeneity of trends in SARS-CoV-2 infection in the community in England during January 2021
This article has 15 authors:Reviewed by ScreenIT
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Demographic and Hygienic Factors as Predictors of Face Mask Wearing During Covid-19 Pandemic in Malaysia
This article has 3 authors:Reviewed by ScreenIT
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Evaluation of the accuracy, ease of use and limit of detection of novel, rapid, antigen-detecting point-of-care diagnostics for SARS-CoV-2
This article has 26 authors:Reviewed by ScreenIT