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
-
Infectiousness of places: The impact of human settlement and activity space in the transmission of COVID-19
This article has 11 authors:Reviewed by ScreenIT
-
Short-Term Instantaneous Prophylaxis and Efficient Treatment Against SARS-CoV-2 in hACE2 Mice Conferred by an Intranasal Nanobody (Nb22)
This article has 19 authors:Reviewed by ScreenIT
-
Durability analysis of the highly effective BNT162b2 vaccine against COVID-19
This article has 14 authors:Reviewed by ScreenIT
-
Effectiveness of the mRNA BNT162b2 vaccine six months after vaccination: findings from a large Israeli HMO
This article has 7 authors:Reviewed by ScreenIT
-
Suicide and self-harm in low- and middle- income countries during the COVID-19 pandemic: A systematic review
This article has 13 authors:Reviewed by ScreenIT
-
Descriptive Epidemiology of COVID-19 Deaths during the First Wave of Pandemic in India: A Single-center Experience
This article has 5 authors:Reviewed by ScreenIT
-
SARS-CoV-2 Circulation in the School Setting: A Systematic Review and Meta-Analysis
This article has 10 authors:Reviewed by ScreenIT
-
Detection of SARS-CoV-2 contamination in the operating room and birthing room setting: a cross-sectional study
This article has 14 authors:Reviewed by ScreenIT
-
Immunogenicity and Safety of the Inactivated SARS-CoV-2 Vaccine (BBIBP-CorV) in Patients with Malignancy
This article has 10 authors:Reviewed by ScreenIT
-
A Delay Differential Equation approach to model the COVID-19 pandemic
This article has 3 authors:Reviewed by ScreenIT