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
-
Human genetic factors associated with pneumonia risk, a cue for COVID-19 susceptibility
This article has 15 authors:Reviewed by ScreenIT
-
Performance of a Chest Radiograph AI Diagnostic Tool for COVID-19: A Prospective Observational Study
This article has 17 authors:Reviewed by ScreenIT
-
Comparative Household Secondary Attack Rates associated with B.1.1.7, B.1.351, and P.1 SARS-CoV-2 Variants
This article has 6 authors:Reviewed by ScreenIT
-
This article has 15 authors:
Reviewed by ScreenIT
-
A comparison of four commercially available RNA extraction kits for wastewater surveillance of SARS-CoV-2 in a college population
This article has 6 authors:Reviewed by ScreenIT
-
Patient experience of symptoms and impacts of COVID-19: a qualitative investigation with symptomatic outpatients
This article has 8 authors:Reviewed by ScreenIT
-
Antibody response after first and second-dose of ChAdOx1-nCOV (CovishieldTM®) and BBV-152 (CovaxinTM®) among health care workers in India: The final results of cross-sectional coronavirus vaccine-induced antibody titre (COVAT) study
This article has 7 authors:Reviewed by ScreenIT
-
Immunological Profiling of COVID-19 Patients with Pulmonary Sequelae
This article has 8 authors:Reviewed by ScreenIT
-
Screening of Botanical Drugs against SARS-CoV-2 Entry
This article has 10 authors:Reviewed by ScreenIT
-
Chronic SARS-CoV-2 infection and viral evolution in a hypogammaglobulinaemic individual
This article has 20 authors:Reviewed by ScreenIT