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
-
A model of endemic coronavirus infections
This article has 1 author:Reviewed by ScreenIT
-
Self-reported vs Directly Observed Face Mask Use in Kenya
This article has 6 authors:Reviewed by ScreenIT
-
Risk factors for community transmission of SARS-CoV-2. A cross-sectional study in 116,678 people
This article has 18 authors:Reviewed by ScreenIT
-
The effect of influenza vaccination on trained immunity: impact on COVID-19
This article has 12 authors:Reviewed by ScreenIT, Rapid Reviews Infectious Diseases
-
Predictors of incident viral symptoms ascertained in the era of COVID-19
This article has 12 authors:Reviewed by ScreenIT
-
The effect of multiple interventions to balance healthcare demand for controlling COVID-19 outbreaks: a modelling study
This article has 8 authors:Reviewed by ScreenIT
-
Flattening the curve and the effect of atypical events on mitigation measures in Mexico: a modeling perspective
This article has 3 authors:Reviewed by ScreenIT
-
A safe protocol to identify low risk patients with COVID-19 pneumonia for outpatient management
This article has 10 authors:Reviewed by ScreenIT
-
Development of a customised data management system for a COVID-19-adapted colorectal cancer pathway
This article has 5 authors:Reviewed by ScreenIT
-
Viable SARS-CoV-2 in the air of a hospital room with COVID-19 patients
This article has 19 authors:Reviewed by ScreenIT