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
-
Protein Posttranslational Signatures Identified in COVID-19 Patient Plasma
This article has 5 authors:Reviewed by ScreenIT
-
Enrichment analysis on regulatory subspaces: A novel direction for the superior description of cellular responses to SARS-CoV-2
This article has 3 authors:Reviewed by ScreenIT
-
Distinguishing COVID-19 infection and vaccination history by T cell reactivity
This article has 17 authors:Reviewed by ScreenIT
-
QTQTN motif upstream of the furin-cleavage site plays a key role in SARS-CoV-2 infection and pathogenesis
This article has 18 authors:Reviewed by ScreenIT
-
Transcriptomic responses of the human kidney to acute injury at single cell resolution
This article has 25 authors:Reviewed by ScreenIT
-
A Single Dose of COVID-19 mRNA Vaccine Induces Airway Immunity in COVID-19 Convalescent Patients
This article has 18 authors:Reviewed by ScreenIT
-
The impact of shielding during the COVID-19 pandemic on mental health: evidence from the English Longitudinal Study of Ageing
This article has 2 authors:Reviewed by ScreenIT
-
White Matter β-Amyloid Precursor Protein Immunoreactivity in Autopsied Subjects With and Without COVID-19
This article has 12 authors:Reviewed by ScreenIT
-
Relationship between monomer packing, receptor binding domain pocket status, and pH, in the spike trimer of SARS-CoV-2 variants
This article has 1 author:Reviewed by ScreenIT
-
SARS-CoV-2 Spike protein activates TMEM16F-mediated platelet pro-coagulant activity
This article has 13 authors:Reviewed by ScreenIT