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 novel cell culture system modeling the SARS-CoV-2 life cycle
This article has 13 authors:Reviewed by ScreenIT
-
Consideration for the asymptomatic transmission of COVID-19: Systematic Review and Meta-Analysis
This article has 5 authors:Reviewed by ScreenIT
-
At home and online during the early months of the COVID-19 pandemic and the relationship to alcohol consumption in a national sample of U.S. adults
This article has 4 authors:Reviewed by ScreenIT
-
SARS-CoV-2 qRT-PCR Ct value distribution in Japan and possible utility of rapid antigen testing kit
This article has 3 authors:Reviewed by ScreenIT
-
An engineered decoy receptor for SARS-CoV-2 broadly binds protein S sequence variants
This article has 4 authors:Reviewed by ScreenIT
-
Impact and Correlation of Air Quality and Climate Variables with COVID-19 Morbidity and Mortality in Dhaka, Bangladesh
This article has 6 authors:Reviewed by ScreenIT
-
Performance of Saliva Specimens for the Molecular Detection of SARS-CoV-2 in the Community Setting: Does Sample Collection Method Matter?
This article has 13 authors:Reviewed by ScreenIT
-
Clinical Ordering Practices of the SARS-CoV-2 Antibody Test at a Large Academic Medical Center
This article has 4 authors:Reviewed by ScreenIT
-
Analysis of the early COVID-19 epidemic curve in Germany by regression models with change points
This article has 4 authors:Reviewed by ScreenIT
-
High COVID-19 transmission potential associated with re-opening universities can be mitigated with layered interventions
This article has 11 authors:Reviewed by ScreenIT