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
-
Defining the methodological approach for wastewater-based epidemiological studies—Surveillance of SARS-CoV-2
This article has 7 authors:Reviewed by Rapid Reviews Infectious Diseases, ScreenIT
-
In Silico Design of siRNAs Targeting Existing and Future Respiratory Viruses with VirusSi
This article has 2 authors:Reviewed by ScreenIT
-
Climate & BCG: Effects on COVID-19 Death Growth Rates
This article has 2 authors:Reviewed by ScreenIT
-
Meta-analysis of virus-induced host gene expression reveals unique signatures of immune dysregulation induced by SARS-CoV-2
This article has 5 authors:Reviewed by ScreenIT
-
Stability of SARS-CoV-2 in different environmental conditions
This article has 8 authors:Reviewed by ScreenIT
-
Extending the range of symptoms in a Bayesian Network for the Predictive Diagnosis of COVID-19
This article has 2 authors:Reviewed by ScreenIT
-
Development of CpG-adjuvanted stable prefusion SARS-CoV-2 spike antigen as a subunit vaccine against COVID-19
This article has 15 authors:Reviewed by ScreenIT
-
Substrate specificity of SARS-CoV-2 nsp10-nsp16 methyltransferase
This article has 6 authors:Reviewed by ScreenIT
-
Performance of a Point of Care Test for Detecting IgM and IgG Antibodies Against SARS-CoV-2 and Seroprevalence in Blood Donors and Health Care Workers in Panama
This article has 44 authors:Reviewed by ScreenIT
-
The importance of non-pharmaceutical interventions during the COVID-19 vaccine rollout
This article has 3 authors:Reviewed by ScreenIT