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
-
Fast spread of COVID-19 in Europe and the US suggests the necessity of early, strong and comprehensive interventions
This article has 4 authors:Reviewed by ScreenIT
-
Enhanced binding of the N501Y‐mutated SARS‐CoV‐2 spike protein to the human ACE2 receptor: insights from molecular dynamics simulations
This article has 3 authors:Reviewed by ScreenIT
-
Non-alcoholic fatty liver disease (NAFLD) and risk of hospitalization for Covid-19
This article has 9 authors:Reviewed by ScreenIT
-
Dysregulation of brain and choroid plexus cell types in severe COVID-19
This article has 23 authors:Reviewed by ScreenIT
-
Susceptibility of rabbits to SARS-CoV-2
This article has 13 authors:Reviewed by ScreenIT
-
Extended Lifetime of Respiratory Droplets in a Turbulent Vapor Puff and Its Implications on Airborne Disease Transmission
This article has 6 authors:Reviewed by ScreenIT
-
Modeling and Dynamics in Epidemiology, COVID19 with Lockdown and Isolation Effect : Application to Moroccan Case
This article has 1 author:Reviewed by ScreenIT
-
Using RT-PCR Testing to Assess the Effectiveness of Outbreak Control Efforts in São Paulo State, the Pandemic’s Epicenter in Brazil, according to Socioeconomic Vulnerabilities
This article has 8 authors:Reviewed by ScreenIT
-
Hospitalizations, resource use and outcomes of acute pulmonary embolism in Germany during the Covid-19 pandemic
This article has 9 authors:Reviewed by ScreenIT
-
Preliminary evidence on long COVID in children
This article has 7 authors:Reviewed by ScreenIT