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
-
Effects of Government Mandated Social Distancing Measures on Cumulative Incidence of COVID-19 in the United States and its Most Populated Cities
This article has 6 authors:Reviewed by ScreenIT
-
Outcomes from COVID-19 across the range of frailty: excess mortality in fitter older people
This article has 7 authors:Reviewed by ScreenIT
-
Rate Estimation and Identification of COVID-19 Infections: Towards Rational Policy Making During Early and Late Stages of Epidemics
This article has 2 authors:Reviewed by ScreenIT
-
SARS-CoV-2 Viral Load on Admission Is Associated With 30-Day Mortality
This article has 10 authors:Reviewed by ScreenIT
-
COVID-19 Pandemic Brings a Sedentary Lifestyle in Young Adults: A Cross-Sectional and Longitudinal Study
This article has 6 authors:Reviewed by ScreenIT
-
Predictors of misconceptions, knowledge, attitudes, and practices of COVID-19 pandemic among a sample of Saudi population
This article has 8 authors:Reviewed by ScreenIT
-
Succumbing to the COVID-19 Pandemic—Healthcare Workers Not Satisfied and Intend to Leave Their Jobs
This article has 7 authors:Reviewed by ScreenIT
-
Determinants of COVID-19 vaccine acceptance in the US
This article has 4 authors:Reviewed by ScreenIT
-
Importations of COVID-19 into African countries and risk of onward spread
This article has 4 authors:Reviewed by ScreenIT
-
COVID-19 detection from chest X-Ray images using Deep Learning and Convolutional Neural Networks
This article has 3 authors:Reviewed by ScreenIT