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
-
IgM anti-ACE2 autoantibodies in severe COVID-19 activate complement and perturb vascular endothelial function
This article has 38 authors:Reviewed by ScreenIT
-
Prediction of confirmed cases of and deaths caused by COVID-19 in Chile through time series techniques: A comparative study
This article has 4 authors:Reviewed by ScreenIT
-
Forecasting the spread of COVID19 in Hungary
This article has 3 authors:Reviewed by ScreenIT
-
The digestive system is a potential route of 2019-nCov infection: a bioinformatics analysis based on single-cell transcriptomes
This article has 12 authors:Reviewed by ScreenIT
-
ON THE UNCERTAINTY ABOUT HERD IMMUNITY LEVELS REQUIRED TO STOP COVID-19 EPIDEMICS
This article has 1 author:Reviewed by ScreenIT
-
Distribution of SARS-CoV-2 RNA signal in a home with COVID-19 positive occupants
This article has 6 authors:Reviewed by ScreenIT
-
Performance Characteristics of the Vidas SARS-CoV-2 IgM and IgG Serological Assays
This article has 15 authors:Reviewed by ScreenIT
-
Determinants of persistent post-COVID-19 symptoms: value of a novel COVID-19 symptom score
This article has 16 authors:Reviewed by ScreenIT
-
Are countries’ precautionary actions against COVID-19 effective? An assessment study of 175 countries worldwide
This article has 5 authors:Reviewed by ScreenIT
-
Metaviromic identification of genetic hotspots of coronavirus pathogenicity using machine learning
This article has 2 authors:Reviewed by ScreenIT