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
-
Validation of a clinical and genetic model for predicting severe COVID-19
This article has 5 authors:Reviewed by ScreenIT
-
Estimating the relative proportions of SARS-CoV-2 haplotypes from wastewater samples
This article has 4 authors:Reviewed by ScreenIT
-
Health and Economic Consequences of Universal Paid Sick Leave Policies During the COVID-19 Pandemic
This article has 5 authors:Reviewed by ScreenIT
-
Application of the Logistic Model to the COVID-19 Pandemic in South Africa and the United States: Correlations and Predictions
This article has 1 author:Reviewed by ScreenIT
-
Characterizing features of outbreak duration for novel SARS-CoV-2 variants of concern
This article has 5 authors:Reviewed by ScreenIT
-
The statistical analysis of daily data associated with different parameters of the New Coronavirus COVID-19 pandemic in Georgia and their monthly interval prediction from September 1, 2021 to December 31, 2021
This article has 3 authors:Reviewed by ScreenIT
-
Assessing the clinical severity of the Omicron variant in the Western Cape Province, South Africa, using the diagnostic PCR proxy marker of RdRp target delay to distinguish between Omicron and Delta infections – a survival analysis
This article has 26 authors:Reviewed by ScreenIT
-
Viral variant-resolved wastewater surveillance of SARS-CoV-2 at national scale
This article has 39 authors:Reviewed by ScreenIT
-
HAT-field: a cheap, robust and quantitative point-of-care serological test for Covid-19
This article has 2 authors:Reviewed by Review Commons, ScreenIT
-
Cutting Edge: T Cell Responses to B.1.1.529 (Omicron) SARS-CoV-2 Variant Induced by COVID-19 Infection and/or mRNA Vaccination Are Largely Preserved
This article has 9 authors:Reviewed by ScreenIT