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
-
Acrylamide fragment inhibitors that induce unprecedented conformational distortions in enterovirus 71 3C and SARS-CoV-2 main protease
This article has 12 authors:Reviewed by ScreenIT
-
Evaluation of Serological SARS-CoV-2 Lateral Flow Assays for Rapid Point-of-Care Testing
This article has 22 authors:Reviewed by ScreenIT
-
SARS-CoV-2 Spike protein co-opts VEGF-A/Neuropilin-1 receptor signaling to induce analgesia
This article has 14 authors:Reviewed by ScreenIT
-
Mask-wearing and control of SARS-CoV-2 transmission in the USA: a cross-sectional study
This article has 13 authors:Reviewed by ScreenIT
-
Maternal respiratory SARS-CoV-2 infection in pregnancy is associated with a robust inflammatory response at the maternal-fetal interface
This article has 31 authors:Reviewed by ScreenIT
-
Emergence and spread of a SARS-CoV-2 lineage A variant (A.23.1) with altered spike protein in Uganda
This article has 11 authors:Reviewed by ScreenIT
-
Probability that an infection like Covid-19 stops without reaching herd immunity, calculated with a stochastic agent-based model
This article has 1 author:Reviewed by ScreenIT
-
Super spreader cohorts and covid-19
This article has 1 author:Reviewed by ScreenIT
-
Aeromedical retrieval diagnostic trends during a period of Coronavirus 2019 lockdown
This article has 8 authors:Reviewed by ScreenIT
-
Phosphoregulation of Phase Separation by the SARS-CoV-2 N Protein Suggests a Biophysical Basis for its Dual Functions
This article has 8 authors:Reviewed by ScreenIT