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
-
Dynamic of humoral response to SARS-CoV-2 anti-Nucleocapsid and Spike proteins after CoronaVac vaccination
This article has 7 authors:Reviewed by ScreenIT
-
Demographic Disparities in Clinical Outcomes of COVID-19: Data From a Statewide Cohort in South Carolina
This article has 10 authors:Reviewed by ScreenIT
-
Characterization of the Second Wave of the COVID-19 Pandemic in India: A Google Trends Analysis
This article has 10 authors:Reviewed by ScreenIT
-
Risk Groups for SARS-CoV-2 Infection among Healthcare Workers: Community Versus Hospital Transmission
This article has 5 authors:Reviewed by ScreenIT
-
6′,6′-Difluoro-aristeromycin is a potent inhibitor of MERS-coronavirus replication
This article has 8 authors:Reviewed by ScreenIT
-
Use of Environmental Variables to Predict SARS-CoV-2 Spread in the U.S.
This article has 3 authors:Reviewed by ScreenIT
-
Identification of a novel lineage bat SARS-related coronaviruses that use bat ACE2 receptor
This article has 12 authors:Reviewed by ScreenIT
-
Anti-SARS-CoV-2 IgA and IgG in human milk after vaccination is dependent on vaccine type and previous SARS-CoV-2 exposure: a longitudinal study
This article has 9 authors: -
An aluminum hydroxide:CpG adjuvant enhances protection elicited by a SARS-CoV-2 receptor binding domain vaccine in aged mice
This article has 46 authors:Reviewed by ScreenIT
-
Probenecid inhibits SARS-CoV-2 replication in vivo and in vitro
This article has 9 authors:Reviewed by ScreenIT