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
-
Chest CT Scan of Hospitalized Patients with COVID-19: A Case-Control Study
This article has 5 authors:Reviewed by ScreenIT
-
Non-specific Primers Reveal False-negative Risk in Detection of COVID-19
This article has 1 author:Reviewed by ScreenIT
-
Know your epidemic, know your response: Early perceptions of COVID-19 and self-reported social distancing in the United States
This article has 9 authors:Reviewed by ScreenIT
-
Positive Selection of ORF1ab, ORF3a, and ORF8 Genes Drives the Early Evolutionary Trends of SARS-CoV-2 During the 2020 COVID-19 Pandemic
This article has 6 authors:Reviewed by ScreenIT
-
Human monoclonal antibodies block the binding of SARS-CoV-2 spike protein to angiotensin converting enzyme 2 receptor
This article has 24 authors:Reviewed by ScreenIT
-
Protocol for a prospective, observational, hospital-based multicentre study of nosocomial SARS-CoV-2 transmission: NOSO-COR Project
This article has 9 authors:Reviewed by ScreenIT
-
On Data-Driven Management of the COVID-19 Outbreak in South Africa
This article has 2 authors:Reviewed by ScreenIT
-
Robust Estimation of Infection Fatality Rates during the Early Phase of a Pandemic
This article has 1 author:Reviewed by ScreenIT
-
Purely Data-driven Exploration of COVID-19 Pandemic After Three Months of the Outbreak
This article has 3 authors:Reviewed by ScreenIT
-
Role of RNA Guanine Quadruplexes in Favoring the Dimerization of SARS Unique Domain in Coronaviruses
This article has 9 authors:Reviewed by ScreenIT