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
-
Pareto-based evaluation of national responses to COVID-19 pandemic shows that saving lives and protecting economy are non-trade-off objectives
This article has 2 authors:Reviewed by ScreenIT
-
Mapping the Global Research and Clinical Trials in COVID-19
This article has 3 authors:Reviewed by ScreenIT
-
Cancer inpatients with COVID-19: A report from the Brazilian National Cancer Institute
This article has 13 authors:Reviewed by ScreenIT
-
The experience of UK patients with bladder cancer during the COVID‐19 pandemic: a survey‐based snapshot
This article has 14 authors:Reviewed by ScreenIT
-
Superspreading in early transmissions of COVID-19 in Indonesia
This article has 7 authors:Reviewed by ScreenIT
-
Predictive model of COVID-19 incidence and socioeconomic description of municipalities in Brazil
This article has 6 authors:Reviewed by ScreenIT
-
Excess mortality and potential undercounting of COVID-19 deaths by demographic group in Ohio
This article has 2 authors:Reviewed by ScreenIT
-
Association of BMI and Obesity with Composite poor outcome in COVID-19 adult patients: A Systematic Review and Meta-Analysis
This article has 7 authors:Reviewed by ScreenIT
-
Early Hemoglobin kinetics in response to ribavirin: Safety lesson learned from Hepatitis C to CoVID-19 therapy
This article has 7 authors:Reviewed by ScreenIT
-
Estimating the state of the COVID-19 epidemic in France using a model with memory
This article has 3 authors:Reviewed by ScreenIT