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
-
Impact of Relaxing Covid-19 Social Distancing Measures on Rural North Wales: A Simulation Analysis
This article has 2 authors:Reviewed by ScreenIT
-
Transmission dynamics of the COVID-19 epidemic in India and modeling optimal lockdown exit strategies
This article has 15 authors:Reviewed by ScreenIT
-
Pregnancy and Neonatal Outcomes in SARS-CoV-2 Infection: A Systematic Review
This article has 6 authors:Reviewed by ScreenIT
-
COVID-19 pandemic: every day feels like a weekday to most
This article has 5 authors:Reviewed by ScreenIT
-
Anti-spike, Anti-nucleocapsid and Neutralizing Antibodies in SARS-CoV-2 Inpatients and Asymptomatic Individuals
This article has 10 authors:Reviewed by ScreenIT
-
Systematic Review and Meta-Analysis of Sex-Specific COVID-19 Clinical Outcomes
This article has 7 authors:Reviewed by ScreenIT
-
Adjusting Coronavirus Prevalence Estimates for Laboratory Test Kit Error
This article has 2 authors:Reviewed by ScreenIT
-
Bounding the Accuracy of Diagnostic Tests, With Application to COVID-19 Antibody Tests
This article has 1 author:Reviewed by ScreenIT
-
Systems serology detects functionally distinct coronavirus antibody features in children and elderly
This article has 41 authors:Reviewed by ScreenIT
-
Unequal Impact of Structural Health Determinants and Comorbidity on COVID-19 Severity and Lethality in Older Mexican Adults: Considerations Beyond Chronological Aging
This article has 10 authors:Reviewed by ScreenIT