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
-
Simplified model of the number of Covid-19 patients in the ICU: update April 6, 2020
This article has 7 authors:Reviewed by ScreenIT
-
The epidemiological characteristics of COVID-19 in Libya during the ongoing-armed conflict
This article has 4 authors:Reviewed by ScreenIT
-
S494 O-glycosylation site on the SARS-CoV-2 RBD affects the virus affinity to ACE2 and its infectivity; a molecular dynamics study
This article has 4 authors:Reviewed by ScreenIT
-
Análisis del comportamiento de variables ambientales y sociales como factores de riesgo en la propagación del nuevo coronavirus (SARS-CoV-2): caso de estudio en el Perú
This article has 5 authors:Reviewed by ScreenIT
-
Longitudinal metabolomics of human plasma reveals prognostic markers of COVID-19 disease severity
This article has 13 authors:Reviewed by ScreenIT
-
Antigen production and development of an indirect ELISA based on the nucleocapsid protein to detect human SARS-CoV-2 seroconversion
This article has 20 authors:Reviewed by ScreenIT
-
Predicting COVID-19 Peaks Around the World
This article has 2 authors:Reviewed by ScreenIT
-
No association between circulating levels of testosterone and sex hormone-binding globulin and risk of COVID-19 mortality in UK biobank
This article has 10 authors:Reviewed by ScreenIT
-
Outcomes of mechanically ventilated patients with COVID-19 associated respiratory failure
This article has 18 authors:Reviewed by ScreenIT
-
Longitudinal symptom dynamics of COVID-19 infection
This article has 12 authors:Reviewed by ScreenIT