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
-
Regional comparisons of COVID reporting rates, burden, and mortality age-structure using auxiliary data sources
This article has 4 authors:Reviewed by ScreenIT
-
The burden of isolation to the individual: a comparison between isolation for COVID-19 and for other influenza-like illnesses in Japan
This article has 3 authors:Reviewed by ScreenIT
-
Virological characteristics of SARS-CoV-2 vaccine breakthrough infections in health care workers
This article has 17 authors: -
COVID-19 patient serum less potently inhibits ACE2-RBD binding for various SARS-CoV-2 RBD mutants
This article has 21 authors:Reviewed by ScreenIT
-
Screening and vaccination against COVID-19 to minimise school closure: a modelling study
This article has 11 authors:Reviewed by ScreenIT
-
Rapid spread of a SARS-CoV-2 Delta variant with a frameshift deletion in ORF7a
This article has 2 authors:Reviewed by ScreenIT
-
Interpreting Wastewater SARS-CoV-2 Results using Bayesian Analysis
This article has 2 authors:Reviewed by ScreenIT
-
Mutations that adapt SARS-CoV-2 to mustelid hosts do not increase fitness in the human airway
This article has 29 authors:Reviewed by Rapid Reviews Infectious Diseases, ScreenIT
-
Nationwide increases in anti-SARS-CoV-2 IgG antibodies between October 2020 and March 2021 in the unvaccinated Czech population
This article has 11 authors:Reviewed by ScreenIT
-
Diminishing Immune Responses against Variants of Concern in Dialysis Patients 4 Months after SARS-CoV-2 mRNA Vaccination
This article has 19 authors:Reviewed by ScreenIT