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
-
Machine Learning for Prediction of Patients on Hemodialysis with an Undetected SARS-CoV-2 Infection
This article has 16 authors:Reviewed by ScreenIT
-
Risk of depression in family caregivers: unintended consequence of COVID-19
This article has 2 authors:Reviewed by ScreenIT
-
Mutation density changes in SARS-CoV-2 are related to the pandemic stage but to a lesser extent in the dominant strain with mutations in spike and RdRp
This article has 4 authors: -
Using in-silica Analysis and Reverse Vaccinology Approach for COVID-19 Vaccine Development
This article has 1 author:Reviewed by ScreenIT
-
Implementation of the COVID-19 Vulnerability Index Across an International Network of Health Care Data Sets: Collaborative External Validation Study
This article has 37 authors:Reviewed by ScreenIT
-
Causes of death and comorbidities in hospitalized patients with COVID-19
This article has 17 authors:Reviewed by ScreenIT
-
Diagnostic accuracy of six commercial SARS-CoV-2 IgG/total antibody assays and identification of SARS-CoV-2 neutralizing antibodies in convalescent sera
This article has 6 authors:Reviewed by ScreenIT
-
Can the protection be among us? Previous viral contacts and prevalent HLA alleles avoiding an even more disseminated COVID-19 pandemic
This article has 4 authors:Reviewed by ScreenIT
-
Community Transmission of Severe Acute Respiratory Syndrome Coronavirus 2 Disproportionately Affects the Latinx Population During Shelter-in-Place in San Francisco
This article has 31 authors:Reviewed by ScreenIT
-
Deregulated cellular circuits driving immunoglobulins and complement consumption associate with the severity of COVID‐19 patients
This article has 24 authors:Reviewed by ScreenIT