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
-
Spatial-Temporal Relationship Between Population Mobility and COVID-19 Outbreaks in South Carolina: Time Series Forecasting Analysis
This article has 7 authors:Reviewed by ScreenIT
-
A phase 2 single center open label randomised control trial for convalescent plasma therapy in patients with severe COVID-19
This article has 38 authors:Reviewed by ScreenIT
-
Dynamic data-driven meta-analysis for prioritisation of host genes implicated in COVID-19
This article has 11 authors:Reviewed by ScreenIT
-
Role of high-dose exposure in transmission hot zones as a driver of SARS-CoV-2 dynamics
This article has 3 authors:Reviewed by ScreenIT
-
Hospitalization and mortality associated with SARS-CoV-2 viral clades in COVID-19
This article has 13 authors:Reviewed by ScreenIT
-
Modeling and Short-Term Forecasts of Indicators for COVID-19 Outbreak in 25 Countries at the end of March
This article has 4 authors:Reviewed by ScreenIT
-
Expected impact of COVID-19 outbreak in a major metropolitan area in Brazil
This article has 7 authors:Reviewed by ScreenIT
-
COVID-19 era, Preventive effect of no going out against co-infection of the seasonal influenza virus and SARS-CoV-2
This article has 3 authors:Reviewed by ScreenIT
-
Detection of SARS-CoV-2 in the air in Indian hospitals and houses of COVID-19 patients
This article has 21 authors:Reviewed by ScreenIT
-
Rare loss-of-function variants in type I IFN immunity genes are not associated with severe COVID-19
This article has 52 authors:Reviewed by ScreenIT