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
-
Will a natural collective immunity of Ukrainians restrain new COVID-19 waves?
This article has 1 author:Reviewed by ScreenIT
-
Determinants of COVID-19 Vaccine Engagement in Algeria: A Population-Based Study With Systematic Review of Studies From Arab Countries of the MENA Region
This article has 17 authors:Reviewed by ScreenIT
-
Evaluation of the antibody response and adverse reactions of the BNT162b2 vaccine of participants with prior COVID-19 infection in Japan
This article has 9 authors:Reviewed by ScreenIT
-
Lung Epithelial Regulation of BCL2 Related Protein A1 (BCL2A1) by Coronaviruses (SARS-CoV) and Type I Interferon Signaling
This article has 1 author:Reviewed by ScreenIT
-
Antibody Responses to BNT162b2 Vaccination in Japan: Monitoring Vaccine Efficacy by Measuring IgG Antibodies against the Receptor-Binding Domain of SARS-CoV-2
This article has 20 authors:Reviewed by ScreenIT
-
Is the Increased Transmissibility of SARS-CoV-2 Variants Driven by within or Outside-Host Processes?
This article has 3 authors:Reviewed by ScreenIT
-
Development of a prognostic model of COVID-19 severity: a population-based cohort study in Iceland
This article has 22 authors:Reviewed by ScreenIT
-
Longitudinal Analysis of Antibody Responses to the mRNA BNT162b2 Vaccine in Patients Undergoing Maintenance Hemodialysis: A 6-Month Follow-Up
This article has 21 authors:Reviewed by ScreenIT
-
Change in age distribution of COVID-19 deaths with the introduction of COVID-19 vaccination
This article has 8 authors:Reviewed by ScreenIT
-
Longitudinal ventilatory ratio monitoring for COVID-19: its potential in predicting severity and assessing treatment response
This article has 12 authors:Reviewed by ScreenIT