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
-
SARS-CoV-2 proteome microarray for global profiling of COVID-19 specific IgG and IgM responses
This article has 10 authors:Reviewed by ScreenIT
-
Coincidence of COVID-19 epidemic and olfactory dysfunction outbreak
This article has 12 authors:Reviewed by ScreenIT
-
A New Predictor of Disease Severity in Patients with COVID-19 in Wuhan, China
This article has 8 authors:Reviewed by ScreenIT
-
Reproducibility and reporting practices in COVID-19 preprint manuscripts
This article has 5 authors:Reviewed by ScreenIT
-
Age-dependent effects in the transmission and control of COVID-19 epidemics
This article has 27 authors:Reviewed by ScreenIT
-
Estimation of SARS-CoV-2 Infection Prevalence in Santa Clara County
This article has 3 authors:Reviewed by ScreenIT
-
Epidemiological Tools that Predict Partial Herd Immunity to SARS Coronavirus 2
This article has 2 authors:Reviewed by ScreenIT
-
Prevalence, Severity and Mortality associated with COPD and Smoking in patients with COVID-19: A Rapid Systematic Review and Meta-Analysis
This article has 9 authors:Reviewed by ScreenIT
-
A Computational Model for Estimating the Progression of COVID-19 Cases in the US West and East Coasts
This article has 3 authors:Reviewed by ScreenIT
-
Automatic Identification of SARS Coronavirus using Compression-Complexity Measures
This article has 2 authors:Reviewed by ScreenIT