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
-
A national cross-sectional survey of public perceptions of the COVID-19 pandemic: Self-reported beliefs, knowledge, and behaviors
This article has 16 authors:Reviewed by ScreenIT
-
An Agent-Based Modeling of COVID-19: Validation, Analysis, and Recommendations
This article has 5 authors:Reviewed by ScreenIT
-
Limited Specificity of Serologic Tests for SARS-CoV-2 Antibody Detection, Benin
This article has 17 authors:Reviewed by ScreenIT
-
SARS-CoV-2 seroprevalence rates of children seeking medical care in Louisiana during the state stay at home order
This article has 27 authors:Reviewed by ScreenIT
-
Risk of Transmission of infection to Healthcare Workers delivering Supportive Care for Coronavirus Pneumonia;A Rapid GRADE Review
This article has 11 authors:Reviewed by ScreenIT
-
SARS-CoV-2 sample-to-answer nucleic acid testing in a tertiary care emergency department: evaluation and utility
This article has 9 authors:Reviewed by ScreenIT
-
Chloroquine for treatment of COVID-19 - a pig in a poke?
This article has 22 authors:Reviewed by ScreenIT
-
Rapid Systematic Review Exploring Historical and Present Day National and International Governance during Pandemics
This article has 3 authors:Reviewed by ScreenIT
-
Joint Detection of Serum IgM/IgG Antibody Is an Important Key to Clinical Diagnosis of SARS-CoV-2 Infection
This article has 4 authors:Reviewed by ScreenIT
-
Atypical presentations of COVID‐19 in care home residents presenting to secondary care: A UK single centre study
This article has 3 authors:Reviewed by ScreenIT