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
-
Missed Opportunities of Flu Vaccination in Italian Target Categories: Insights from the Online EPICOVID 19 Survey
This article has 16 authors:Reviewed by ScreenIT
-
The role of air conditioning in the diffusion of Sars-CoV-2 in indoor environments: A first computational fluid dynamic model, based on investigations performed at the Vatican State Children's hospital
This article has 6 authors:Reviewed by ScreenIT
-
Disease control across urban–rural gradients
This article has 8 authors:Reviewed by ScreenIT
-
Real-Time Monitoring of Covid-19 in Scotland
This article has 6 authors:Reviewed by ScreenIT
-
A model to forecast regional demand for COVID-19 related hospital beds
This article has 11 authors:Reviewed by ScreenIT
-
Human embryonic stem cell-derived cardiomyocytes express SARS-CoV-2 host entry proteins: screen to identify inhibitors of infection
This article has 14 authors:Reviewed by ScreenIT
-
Diabetes-related acute metabolic emergencies in COVID-19 patients: a systematic review and meta-analysis
This article has 7 authors:Reviewed by ScreenIT
-
Transfer Learning for COVID-19 Pneumonia Detection and Classification in Chest X-ray Images
This article has 5 authors:Reviewed by ScreenIT
-
Biometric covariates and outcome in COVID-19 patients: are we looking close enough?
This article has 5 authors:Reviewed by ScreenIT
-
The mobility gap: estimating mobility thresholds required to control SARS-CoV-2 in Canada
This article has 11 authors:Reviewed by ScreenIT