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
-
Female reproductive tract has low concentration of SARS-CoV2 receptors
This article has 3 authors:Reviewed by ScreenIT
-
Pathogenicity, tissue tropism and potential vertical transmission of SARSr-CoV-2 in Malayan pangolins
This article has 29 authors:Reviewed by ScreenIT
-
Distinct antibody repertoires against endemic human coronaviruses in children and adults
This article has 25 authors:Reviewed by ScreenIT
-
Mathematical modeling explains differential SARS CoV-2 kinetics in lung and nasal passages in remdesivir treated rhesus macaques
This article has 4 authors:Reviewed by ScreenIT
-
Generation of SARS-CoV-2 S1 Spike Glycoprotein Putative Antigenic Epitopes in Vitro by Intracellular Aminopeptidases
This article has 5 authors:Reviewed by ScreenIT
-
CoVA: An Acuity Score for Outpatient Screening that Predicts Coronavirus Disease 2019 Prognosis
This article has 27 authors:Reviewed by ScreenIT
-
Genetic Diversity Among SARS-CoV2 Strains in South America may Impact Performance of Molecular Detection
This article has 9 authors:Reviewed by ScreenIT
-
Blood molecular markers associated with COVID‐19 immunopathology and multi‐organ damage
This article has 27 authors:Reviewed by ScreenIT
-
A systematic review and meta-analysis reveals long and dispersive incubation period of COVID-19
This article has 10 authors:Reviewed by ScreenIT
-
Transcriptomic profiling of disease severity in patients with COVID-19 reveals role of blood clotting and vasculature related genes
This article has 11 authors:Reviewed by ScreenIT