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
-
Clinical Characteristics and Prognostic Factors for Intensive Care Unit Admission of Patients With COVID-19: Retrospective Study Using Machine Learning and Natural Language Processing
This article has 4 authors:Reviewed by ScreenIT
-
A Modelling Analysis of Strategies for Relaxing COVID-19 Social Distancing
This article has 3 authors:Reviewed by ScreenIT
-
Differences of clinical and imaging findings in multiple generations of secondary COVID-19 infection in Xi’an, China
This article has 11 authors:Reviewed by ScreenIT
-
SARS-CoV-2 seroprevalence and neutralizing activity in donor and patient blood
This article has 49 authors:Reviewed by ScreenIT
-
Effects of information-induced behavioural changes during the COVID-19 lockdowns: the case of Italy
This article has 2 authors:Reviewed by ScreenIT
-
A Pattern Categorization of CT Findings to Predict Outcome of COVID-19 Pneumonia
This article has 21 authors:Reviewed by ScreenIT
-
Immunochromatographic test for the detection of SARS-CoV-2 in saliva
This article has 9 authors:Reviewed by ScreenIT
-
In silico Proteome analysis of Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)
This article has 3 authors:Reviewed by ScreenIT
-
Eat, Pray, Work: A meta-analysis of COVID-19 Transmission Risk in Common Activities of Work and Leisure
This article has 1 author:Reviewed by ScreenIT
-
Structural basis of SARS-CoV-2 spike protein induced by ACE2
This article has 3 authors:Reviewed by ScreenIT