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
-
Age significantly influences the sensitivity of SARS-CoV-2 rapid antibody assays
This article has 10 authors:Reviewed by ScreenIT
-
Phylogenetic Analyses of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) B.1.1.7 Lineage Suggest a Single Origin Followed by Multiple Exportation Events Versus Convergent Evolution
This article has 2 authors:Reviewed by ScreenIT
-
A Dual-Antigen Enzyme-Linked Immunosorbent Assay Allows the Assessment of Severe Acute Respiratory Syndrome Coronavirus 2 Antibody Seroprevalence in a Low-Transmission Setting
This article has 24 authors:Reviewed by ScreenIT
-
Covid19 Surveillance in Peru on April using Text Mining
This article has 2 authors:Reviewed by ScreenIT
-
U.S. Field Hospitals: A Study on Public Health Emergency Response to COVID-19
This article has 4 authors:Reviewed by ScreenIT
-
Efficacy Assessment of Newly-designed Filtering Facemasks during the SARS-CoV-2 Pandemic
This article has 11 authors:Reviewed by ScreenIT
-
Cumulative COVID ‐19 incidence, mortality and prognosis in cancer survivors: A population‐based study in Reggio Emilia, Northern Italy
This article has 8 authors:Reviewed by ScreenIT
-
BNT162b vaccines are immunogenic and protect non-human primates against SARS-CoV-2
This article has 74 authors:Reviewed by ScreenIT
-
COVID-19 vaccination intention in the UK: results from the COVID-19 vaccination acceptability study (CoVAccS), a nationally representative cross-sectional survey
This article has 8 authors:Reviewed by ScreenIT
-
Artificial Intelligence-Assisted Loop Mediated Isothermal Amplification (AI-LAMP) for Rapid Detection of SARS-CoV-2
This article has 28 authors:Reviewed by ScreenIT