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
-
A Vulnerability Analysis for the Management of and Response to the COVID-19 Epidemic in the Second Most Populous State in Brazil
This article has 5 authors:Reviewed by ScreenIT
-
No Evidence for Reduced Hospital Admissions or Increased Deaths from Stroke or Heart Attack During COVID-19
This article has 8 authors:Reviewed by ScreenIT
-
Clinical Validation of a Sensitive Test for Saliva Collected in Healthcare and Community Settings with Pooling Utility for Severe Acute Respiratory Syndrome Coronavirus 2 Mass Surveillance
This article has 15 authors:Reviewed by ScreenIT
-
Is India heading towards a new high? : An optimistic approach to estimate ending life-cycle and cumulative cases by the end of the major COVID-19 pandemic wave in India and some of its states
This article has 4 authors:Reviewed by ScreenIT
-
Prevalence of SARS-CoV-2 among high-risk populations in Lomé (Togo) in 2020
This article has 26 authors:Reviewed by ScreenIT
-
Estimating the undetected infections in the Covid-19 outbreak by harnessing capture–recapture methods
This article has 4 authors:Reviewed by ScreenIT
-
Role of cytokines and other prophetic variables in the development and progression of disease in patients suffering from COVID-19
This article has 4 authors:Reviewed by ScreenIT
-
A Simple Ventilator Designed To Be Used in Shortage Crises: Construction and Verification Testing
This article has 14 authors:Reviewed by ScreenIT
-
Containing the spread of mumps on college campuses
This article has 6 authors:Reviewed by ScreenIT
-
The Role of Societal Aspects in the Formation of Official COVID-19 Reports: A Data-Driven Analysis
This article has 4 authors:Reviewed by ScreenIT