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
-
An outpatient telehealth elective for displaced clinical learners during the COVID-19 pandemic
This article has 7 authors:Reviewed by ScreenIT
-
Dynamics and future of SARS-CoV-2 in the human host
This article has 2 authors:Reviewed by ScreenIT
-
Influence of clinical characteristics and anti-cancer therapy on outcomes from SARS-CoV-2 infection: a systematic review and meta-analysis of 5,678 cancer patients
This article has 12 authors:Reviewed by ScreenIT
-
Allosteric inhibition of the SARS-CoV-2 main protease – insights from mass spectrometry-based assays
This article has 11 authors:Reviewed by ScreenIT
-
A Social Network Model of the COVID-19 Pandemic
This article has 1 author:Reviewed by ScreenIT
-
Performing Qualitative Mask Fit Testing Without a Commercial Kit: Fit Testing Which Can Be Performed at Home and at Work
This article has 5 authors:Reviewed by ScreenIT
-
Performance of Saliva, Oropharyngeal Swabs, and Nasal Swabs for SARS-CoV-2 Molecular Detection: a Systematic Review and Meta-analysis
This article has 5 authors:Reviewed by ScreenIT
-
CpG-adjuvanted stable prefusion SARS-CoV-2 spike protein protected hamsters from SARS-CoV-2 challenge
This article has 20 authors:Reviewed by ScreenIT
-
Age-related immune response heterogeneity to SARS-CoV-2 vaccine BNT162b2
This article has 168 authors:Reviewed by ScreenIT
-
Extraction-free protocol combining proteinase K and heat inactivation for detection of SARS-CoV-2 by RT-qPCR
This article has 9 authors:Reviewed by ScreenIT