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
-
Virus strain of a mild COVID-19 patient in Hangzhou represents a new trend in SARS-CoV-2 evolution related to Furin cleavage site
This article has 36 authors:Reviewed by ScreenIT
-
Prolonged presence of SARS-CoV-2 in feces of pediatric patients during the convalescent phase
This article has 8 authors:Reviewed by ScreenIT
-
Prediction of the COVID-19 outbreak in China based on a new stochastic dynamic model
This article has 6 authors:Reviewed by ScreenIT
-
Estimates of the severity of COVID-19 disease
This article has 33 authors:Reviewed by ScreenIT
-
Protocol for a randomized controlled trial testing inhaled nitric oxide therapy in spontaneously breathing patients with COVID-19
This article has 14 authors:Reviewed by ScreenIT
-
Isolation and Contact Tracing Can Tip the Scale to Containment of COVID-19 in Populations With Social Distancing
This article has 3 authors:Reviewed by ScreenIT
-
Protocol of a randomized controlled trial testing inhaled Nitric Oxide in mechanically ventilated patients with severe acute respiratory syndrome in COVID-19 (SARS-CoV-2)
This article has 11 authors:Reviewed by ScreenIT
-
Clinical features of imported cases of coronavirus disease 2019 in Tibetan patients in the Plateau area
This article has 6 authors:Reviewed by ScreenIT
-
Modelling the situation of COVID-19 and effects of different containment strategies in China with dynamic differential equations and parameters estimation
This article has 7 authors:Reviewed by ScreenIT
-
Maternal and Neonatal Outcomes of Pregnant Women With Coronavirus Disease 2019 (COVID-19) Pneumonia: A Case-Control Study
This article has 11 authors:Reviewed by ScreenIT