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
-
One Year of SARS-CoV-2: How Much Has the Virus Changed?
This article has 2 authors:Reviewed by ScreenIT
-
SARS-CoV-2 among migrants and forcibly displaced populations: A rapid systematic review
This article has 8 authors:Reviewed by ScreenIT
-
A Next Generation Bivalent Human Ad5 COVID-19 Vaccine Delivering Both Spike and Nucleocapsid Antigens Elicits Th1 Dominant CD4+, CD8+ T-cell and Neutralizing Antibody Responses
This article has 21 authors:Reviewed by ScreenIT
-
Neutralizing and binding antibody kinetics of COVID-19 patients during hospital and convalescent phases
This article has 17 authors:Reviewed by ScreenIT
-
COVID-19 Modelling: The Effects of Social Distancing
This article has 3 authors:Reviewed by ScreenIT
-
Cell-type-resolved quantitative proteomics map of interferon response against SARS-CoV-2
This article has 13 authors:Reviewed by ScreenIT
-
A PCR amplicon-based SARS-CoV-2 replicon for antiviral evaluation
This article has 4 authors:Reviewed by ScreenIT
-
Cardiovascular drugs and COVID‐19 clinical outcomes: A living systematic review and meta‐analysis
This article has 6 authors:Reviewed by ScreenIT
-
Proteoforms of the SARS-CoV-2 nucleocapsid protein are primed to proliferate the virus and attenuate the antibody response
This article has 4 authors:Reviewed by ScreenIT
-
Projecting COVID-19 disease severity in cancer patients using purposefully-designed machine learning
This article has 5 authors:Reviewed by ScreenIT