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 minimal model for household effects in epidemics
This article has 7 authors:Reviewed by ScreenIT
-
COVID-19 in patients with hepatobiliary and pancreatic diseases in East London: a single-centre cohort study
This article has 3 authors:Reviewed by ScreenIT
-
Patterns of repeated diagnostic testing for COVID‐19 in relation to patient characteristics and outcomes
This article has 10 authors:Reviewed by ScreenIT
-
Effect of hot zone infection outbreaks on the dynamics of SARS-CoV-2 spread in the community at large
This article has 3 authors:Reviewed by ScreenIT
-
Absence of relevant QT interval prolongation in not critically ill COVID-19 patients
This article has 13 authors:Reviewed by ScreenIT
-
What Is Required to Prevent a Second Major Outbreak of SARS-CoV-2 upon Lifting Quarantine in Wuhan City, China
This article has 16 authors:Reviewed by ScreenIT
-
Temperature and population density influence SARS-CoV-2 transmission in the absence of nonpharmaceutical interventions
This article has 12 authors:Reviewed by ScreenIT
-
Serum lipid profile changes and their clinical diagnostic significance in COVID-19 Mexican Patients
This article has 17 authors:Reviewed by ScreenIT
-
Zebrafish studies on the vaccine candidate to COVID-19, the Spike protein: Production of antibody and adverse reaction
This article has 46 authors:Reviewed by ScreenIT
-
Selecting pharmacies for COVID-19 testing to ensure access
This article has 4 authors:Reviewed by ScreenIT