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
-
Large-Scale Multi-omic Analysis of COVID-19 Severity
This article has 29 authors:Reviewed by ScreenIT
-
Increased SARS-CoV-2 Testing Capacity with Pooled Saliva Samples
This article has 17 authors:Reviewed by ScreenIT
-
Electrocardiographic Risk Stratification in COVID-19 Patients
This article has 16 authors:Reviewed by ScreenIT
-
Estimating the Effect and Cost-Effectiveness of Facemasks in Reducing the Spread of the Severe Acute Respiratory Syndrome-Coronavirus 2 (SARS-CoV-2) in Uganda
This article has 16 authors:Reviewed by ScreenIT
-
Simultaneous CD8+ T-Cell Immune Response against SARS-Cov-2 S, M, and N Induced by Endogenously Engineered Extracellular Vesicles in Both Spleen and Lungs
This article has 6 authors:Reviewed by ScreenIT
-
The Use of Psychoactive Substances in the Context of the Covid-19 Pandemic in Brazil
This article has 4 authors:Reviewed by ScreenIT
-
Clinical Suspicion of COVID-19 in Nursing Home Residents: Symptoms and Mortality Risk Factors
This article has 7 authors:Reviewed by ScreenIT
-
The neutralizing antibody, LY-CoV555, protects against SARS-CoV-2 infection in nonhuman primates
This article has 57 authors:Reviewed by ScreenIT
-
OpenABM-Covid19—An agent-based model for non-pharmaceutical interventions against COVID-19 including contact tracing
This article has 26 authors:Reviewed by ScreenIT
-
Country distancing increase reveals the effectiveness of travel restrictions in stopping COVID-19 transmission
This article has 4 authors:Reviewed by ScreenIT