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
-
Adapting the UK Biobank Brain Imaging Protocol and Analysis Pipeline for the C-MORE Multi-Organ Study of COVID-19 Survivors
This article has 20 authors:Reviewed by ScreenIT
-
Surface and Air Contamination With Severe Acute Respiratory Syndrome Coronavirus 2 From Hospitalized Coronavirus Disease 2019 Patients in Toronto, Canada, March–May 2020
This article has 35 authors:Reviewed by ScreenIT
-
Epidemiology and genetic diversity of SARS-CoV-2 lineages circulating in Africa
This article has 16 authors:Reviewed by ScreenIT
-
Economic impact payment, human mobility and COVID-19 mitigation in the USA
This article has 1 author:Reviewed by ScreenIT
-
Modeling waning and boosting of COVID-19 in Canada with vaccination
This article has 6 authors:Reviewed by ScreenIT
-
Differential interferon-α subtype immune signatures suppress SARS-CoV-2 infection
This article has 28 authors:Reviewed by ScreenIT
-
Evaluation of the Family Liaison Officer role during the COVID-19 pandemic
This article has 3 authors:Reviewed by ScreenIT
-
SARS-CoV-2 infection risk during delivery of childhood vaccination campaigns: a modelling study
This article has 57 authors:Reviewed by ScreenIT
-
Preferences for COVID-19 vaccine distribution strategies in the US: A discrete choice survey
This article has 8 authors:Reviewed by ScreenIT
-
Educational status and COVID-19 related outcomes in India: hospital-based cross-sectional study
This article has 10 authors:Reviewed by ScreenIT