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
-
What pushed Israel out of herd immunity? Modeling COVID-19 spread of Delta and Waning immunity
This article has 2 authors:Reviewed by ScreenIT
-
Role of sleep quality in the acceleration of biological aging and its potential for preventive interaction on air pollution insults: Findings from the UK Biobank cohort
This article has 4 authors:Reviewed by ScreenIT
-
Rapid identification of neutralizing antibodies against SARS-CoV-2 variants by mRNA display
This article has 15 authors:Reviewed by ScreenIT
-
Experiences with opt-in, at-home screening for SARS-CoV-2 at a primary school in Germany: an implementation study
This article has 5 authors:Reviewed by ScreenIT
-
Social-economic drivers overwhelm climate in underlying the COVID-19 early growth rate
This article has 1 author:Reviewed by ScreenIT
-
Emergence of SARS-CoV-2 Resistance with Monoclonal Antibody Therapy
This article has 28 authors:Reviewed by ScreenIT
-
Therapeutic efficacy of an oral nucleoside analog of remdesivir against SARS-CoV-2 pathogenesis in mice
This article has 23 authors:Reviewed by ScreenIT
-
SARS-CoV-2 Infection Fatality Rates in India: Systematic Review, Meta-analysis and Model-based Estimation
This article has 7 authors:Reviewed by ScreenIT
-
Antibody responses to BNT162b2 mRNA vaccine: Infection‐naïve individuals with abdominal obesity warrant attention
This article has 26 authors:Reviewed by ScreenIT
-
High viral SARS-CoV-2 load in placenta of patients with hypertensive disorders after COVID-19 during pregnancy
This article has 10 authors:Reviewed by ScreenIT