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
-
Integration of viral transcriptome sequencing with structure and sequence motifs predicts novel regulatory elements in SARS-CoV-2
This article has 1 author:Reviewed by ScreenIT
-
Analysis and Prediction of COVID-19 Characteristics Using a Birth-and-Death Model
This article has 1 author:Reviewed by ScreenIT
-
Forecasting COVID-19 and Analyzing the Effect of Government Interventions
This article has 6 authors:Reviewed by ScreenIT
-
An agent based modelling approach to study lockdown efficacy for infectious disease spreads
This article has 3 authors:Reviewed by ScreenIT
-
Knowledge about COVID-19 and beliefs about and use of herbal products during the COVID-19 pandemic: A cross-sectional study in Saudi Arabia
This article has 9 authors:Reviewed by ScreenIT
-
Prevalence and risks of severe events for cancer patients with COVID-19 infection: a systematic review and meta-analysis
This article has 9 authors:Reviewed by ScreenIT
-
Does Biological Therapy Protect against Severe COVID-19?
This article has 14 authors:Reviewed by ScreenIT
-
Seroprevalence of SARS-CoV-2 and Infection Fatality Ratio, Orleans and Jefferson Parishes, Louisiana, USA, May 2020
This article has 8 authors:Reviewed by ScreenIT
-
Using machine learning of clinical data to diagnose COVID-19: a systematic review and meta-analysis
This article has 19 authors:Reviewed by ScreenIT
-
COVID-19 screening using breath-borne volatile organic compounds
This article has 8 authors:Reviewed by ScreenIT