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
-
Quantifying the Effects of Social Distancing on the Spread of COVID-19
This article has 2 authors:Reviewed by ScreenIT
-
Elevated levels of IL-6 and CRP predict the need for mechanical ventilation in COVID-19
This article has 8 authors:Reviewed by ScreenIT
-
Does COVID-19 Testing Create More Cases? An Empirical Evidence on the Importance of Mass Testing During a Pandemic
This article has 1 author:Reviewed by ScreenIT
-
Nasopharyngeal and serological anti SARS-CoV-2 IgG/IgA responses in COVID-19 patients
This article has 11 authors:Reviewed by ScreenIT
-
Spatially resolved simulations of the spread of COVID-19 in three European countries
This article has 6 authors:Reviewed by ScreenIT
-
Impact of a Public Policy Restricting Staff Mobility Between Nursing Homes in Ontario, Canada During the COVID-19 Pandemic
This article has 8 authors:Reviewed by ScreenIT
-
COVID-19 Growth Rate Decreases with Social Capital
This article has 2 authors:Reviewed by ScreenIT
-
A global omics data sharing and analytics marketplace: Case study of a rapid data COVID-19 pandemic response platform
This article has 4 authors:Reviewed by ScreenIT
-
Ultrametric diffusion equation on energy landscape to model disease spread in hierarchic socially clustered population
This article has 1 author:Reviewed by ScreenIT
-
Impact of the COVID-19 pandemic on developmental care practices for infants born preterm
This article has 6 authors:Reviewed by ScreenIT