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
-
Detection of the SARS-CoV-2 spike protein in saliva with Shrinky-Dink© electrodes
This article has 9 authors:Reviewed by ScreenIT
-
On the role of artificial intelligence in medical imaging of COVID-19
This article has 15 authors:Reviewed by ScreenIT
-
Several forms of SARS-CoV-2 RNA can be detected in wastewaters: Implication for wastewater-based epidemiology and risk assessment
This article has 9 authors:Reviewed by ScreenIT
-
COVID-19 Diagnostic Testing For All - Using Non-Dilutive Saliva Sample Collection, Stabilization and Ambient Transport Devices
This article has 9 authors:Reviewed by ScreenIT
-
Mucin-type O-glycosylation Landscapes of SARS-CoV-2 Spike Proteins
This article has 9 authors:Reviewed by ScreenIT
-
Emergence of multiple SARS-CoV-2 mutations in an immunocompromised host
This article has 17 authors:Reviewed by ScreenIT
-
Epidemiological and clinical characteristics of patients hospitalised with COVID-19 in Kenya: a multicentre cohort study
This article has 16 authors:Reviewed by ScreenIT
-
Time to negative PCR from symptom onset in COVID-19 patients on Hydroxychloroquine and Azithromycin - A real-world experience
This article has 5 authors:Reviewed by ScreenIT
-
THE SEARCH FOR AN ASSOCIATION OF HLA ALLELES AND COVID-19 RELATED MORTALITY IN THE RUSSIAN POPULATION
This article has 5 authors:Reviewed by ScreenIT
-
Structural Variants in SARS-CoV-2 Occur at Template-Switching Hotspots
This article has 10 authors:Reviewed by ScreenIT