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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Identifying Sequential Complication and Mortality Patterns in Diabetes Mellitus: Comparisons of Machine Learning Methodologies
This article has 10 authors:Reviewed by ScreenIT
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Regression Analysis of COVID-19 Spread in India and its Different States
This article has 3 authors:Reviewed by ScreenIT
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Factors associated with decision making on COVID-19 vaccine acceptance among college students in South Carolina
This article has 3 authors:Reviewed by ScreenIT
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Variants in ACE2 and TMPRSS2 genes are not major determinants of COVID-19 severity in UK Biobank subjects
This article has 1 author:Reviewed by ScreenIT
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Controlled Randomized Clinical Trial on Using Ivermectin with Doxycycline for Treating COVID-19 Patients in Baghdad, Iraq
This article has 14 authors:Reviewed by ScreenIT
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Epidemiological and evolutionary considerations of SARS-CoV-2 vaccine dosing regimes
This article has 12 authors:Reviewed by ScreenIT
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Large-scale single-cell analysis reveals critical immune characteristics of COVID-19 patients
This article has 89 authors:Reviewed by ScreenIT
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Individual quarantine versus active monitoring of contacts for the mitigation of COVID-19: a modelling study
This article has 7 authors:Reviewed by ScreenIT
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U.S. county-level characteristics to inform equitable COVID-19 response
This article has 8 authors:Reviewed by ScreenIT
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Bispecific antibody neutralizes circulating SARS-CoV-2 variants, prevents escape and protects mice from disease
This article has 47 authors:Reviewed by ScreenIT