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
-
Modeling the Effective Control Strategy for the Transmission Dynamics of Global Pandemic COVID-19
This article has 7 authors:Reviewed by ScreenIT
-
Characteristics of scientific articles on COVID-19 published during the initial 3 months of the pandemic
This article has 2 authors:Reviewed by ScreenIT
-
An interactive tool to forecast US hospital needs in the coronavirus 2019 pandemic
This article has 5 authors:Reviewed by ScreenIT
-
A model-based evaluation of the efficacy of COVID-19 social distancing, testing and hospital triage policies
This article has 2 authors:Reviewed by ScreenIT
-
Psychiatric symptoms related to the COVID-19 pandemic
This article has 5 authors:Reviewed by ScreenIT
-
From community-acquired pneumonia to COVID-19: a deep learning–based method for quantitative analysis of COVID-19 on thick-section CT scans
This article has 13 authors:Reviewed by ScreenIT
-
Monitoring COVID-19 progression: Look at Us Today, See Yourself Tomorrow
This article has 1 author:Reviewed by ScreenIT
-
Clinical characteristics of imported and second-generation COVID-19 cases outside Wuhan, China: A multicenter retrospective study
This article has 15 authors:Reviewed by ScreenIT
-
Evaluating the contributions of strategies to prevent SARS-CoV-2 transmission in the healthcare setting: a modelling study
This article has 4 authors:Reviewed by ScreenIT
-
The association between age, COVID-19 symptoms, and social distancing behavior in the United States
This article has 5 authors:Reviewed by ScreenIT