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
-
Analyses of Omicron genomes from India reveal BA.2 as a more transmissible variant
This article has 4 authors:Reviewed by ScreenIT
-
Targeting neutrophils extracellular traps (NETs) reduces multiple organ injury in a COVID-19 mouse model
This article has 16 authors:Reviewed by Rapid Reviews Infectious Diseases, ScreenIT
-
Higher contact among vaccinated can be a mechanism for negative vaccine effectiveness
This article has 4 authors:Reviewed by ScreenIT
-
Health care use attributable to COVID-19: A propensity matched national electronic health records cohort study of 249,390 people in Wales, UK
This article has 10 authors:Reviewed by ScreenIT
-
Anti-SARS-Cov-2 S-RBD IgG Formed after BNT162b2 Vaccination Can Bind C1q and Activate Complement
This article has 5 authors:Reviewed by ScreenIT
-
Targeted genomic sequencing with probe capture for discovery and surveillance of coronaviruses in bats
This article has 26 authors:This article has been curated by 1 group: -
A cocktail containing two synergetic antibodies broadly neutralizes SARS-CoV-2 and its variants including Omicron BA.1 and BA.2
This article has 27 authors:Reviewed by ScreenIT
-
Monthly excess mortality across counties in the United States during the COVID-19 pandemic, March 2020 to February 2022
This article has 11 authors:Reviewed by ScreenIT
-
Safety, tolerability and immunogenicity of Biological E’s CORBEVAX™ vaccine in children and adolescents: A prospective, randomised, double-blind, placebo controlled, phase-2/3 study
This article has 22 authors:Reviewed by ScreenIT
-
Seasonal patterns of SARS-CoV-2 transmission in secondary schools: a modelling study
This article has 6 authors:Reviewed by ScreenIT