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
-
Clinical Characteristics of Coronavirus Disease 2019 in Hainan, China
This article has 17 authors:Reviewed by ScreenIT
-
Projected geographic disparities in healthcare worker absenteeism from COVID-19 school closures and the economic feasibility of child care subsidies: a simulation study
This article has 5 authors:Reviewed by ScreenIT
-
Role of meteorological temperature and relative humidity in the January-February 2020 propagation of 2019-nCoV in Wuhan, China
This article has 2 authors:Reviewed by ScreenIT
-
A SARS-CoV-2 protein interaction map reveals targets for drug repurposing
This article has 125 authors: -
Emergence of SARS-CoV-2 through recombination and strong purifying selection
This article has 10 authors:Reviewed by ScreenIT
-
Integrative analyses of SARS-CoV-2 genomes from different geographical locations reveal unique features potentially consequential to host-virus interaction, pathogenesis and clues for novel therapies
This article has 4 authors:Reviewed by ScreenIT
-
Molecular characterization of SARS-CoV-2 in the first COVID-19 cluster in France reveals an amino acid deletion in nsp2 (Asp268del)
This article has 14 authors:Reviewed by ScreenIT
-
Building a COVID-19 vulnerability index
This article has 7 authors:Reviewed by ScreenIT
-
Development and utilization of an intelligent application for aiding COVID-19 diagnosis
This article has 13 authors:Reviewed by ScreenIT
-
COVID-19 Coronavirus Vaccine Design Using Reverse Vaccinology and Machine Learning
This article has 4 authors:Reviewed by ScreenIT