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
-
A dynamic model for Covid-19 in Brazil
This article has 2 authors:Reviewed by ScreenIT
-
COVID-19 dynamics across the US: A deep learning study of human mobility and social behavior
This article has 7 authors:Reviewed by ScreenIT
-
Drive-through testing for SARS-CoV-2 in symptomatic health and social care workers and household members: an observational cohort study
This article has 10 authors:Reviewed by ScreenIT
-
COVID-19: Impact on the health and wellbeing of ex-serving personnel (Veterans-CHECK) protocol paper
This article has 10 authors:Reviewed by ScreenIT
-
Food insecurity measurement and prevalence estimates during the COVID-19 pandemic in a repeated cross-sectional survey in Mexico
This article has 4 authors:Reviewed by ScreenIT
-
A simple stochastic theory of extinction shows rapid elimination of a Sars-like pandemic
This article has 1 author:Reviewed by ScreenIT
-
A room, a bar and a classroom: how the coronavirus is spread through the air depends on heavily mask filtration efficiency
This article has 1 author:Reviewed by ScreenIT
-
Estimation of Daily Reproduction Numbers during the COVID-19 Outbreak
This article has 6 authors:Reviewed by ScreenIT
-
Simple quantitative assessment of the outdoor versus indoor airborne transmission of viruses and COVID-19
This article has 4 authors:Reviewed by ScreenIT
-
Healthcare workers hospitalized due to COVID-19 have no higher risk of death than general population. Data from the Spanish SEMI-COVID-19 Registry
This article has 26 authors:Reviewed by ScreenIT