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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Predicted Cellular Immunity Population Coverage Gaps for SARS-CoV-2 Subunit Vaccines and Their Augmentation by Compact Peptide Sets
This article has 3 authors:Reviewed by ScreenIT
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The first consecutive 5000 patients with Coronavirus Disease 2019 from Qatar; a nation-wide cohort study
This article has 30 authors:Reviewed by ScreenIT
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Temporal evolution of COVID-19 in the states of India using SIQR Model
This article has 1 author:Reviewed by ScreenIT
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Investigating the effect of national government physical distancing measures on depression and anxiety during the COVID-19 pandemic through meta-analysis and meta-regression
This article has 4 authors:Reviewed by ScreenIT
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Remote Analysis of Respiratory Sounds in Patients With COVID-19: Development of Fast Fourier Transform–Based Computer-Assisted Diagnostic Methods
This article has 8 authors:Reviewed by ScreenIT
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COVID-19 mortality according to civilian records
This article has 6 authors:Reviewed by ScreenIT
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Predicting and interpreting COVID-19 transmission rates from the ensemble of government policies
This article has 5 authors:Reviewed by ScreenIT
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Analysis of single nucleotide polymorphisms between 2019-nCoV genomes and its impact on codon usage
This article has 3 authors:Reviewed by ScreenIT
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COVID-19 infection among healthcare workers in a national healthcare system: The Qatar experience
This article has 7 authors:Reviewed by ScreenIT
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SARS-CoV-2 Genomes From Oklahoma, United States
This article has 10 authors:Reviewed by ScreenIT