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
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Spectre of SARS-CoV-2 RNA in the ambient urban waters of Ahmedabad and Guwahati: A tale of two Indian cities
This article has 11 authors:Reviewed by ScreenIT
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Survival of Vaccine-Induced Human Milk SARS-CoV-2 IgG, IgA and SIgA Immunoglobulins across Simulated Human Infant Gastrointestinal Digestion
This article has 9 authors:Reviewed by ScreenIT
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Generation and transmission of interlineage recombinants in the SARS-CoV-2 pandemic
This article has 24 authors:Reviewed by Rapid Reviews Infectious Diseases, ScreenIT
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Increased risk of severe COVID-19 in pregnancy in a multicenter propensity score-matched study
This article has 11 authors:Reviewed by ScreenIT
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SARS-CoV-2 seroprevalence among Vancouver public school staff in British Columbia, Canada: a cross-sectional study
This article has 19 authors:Reviewed by ScreenIT
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Inequities among vulnerable communities during the COVID-19 vaccine rollout
This article has 5 authors:Reviewed by ScreenIT
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A Multicenter, Prospective, Observational, Cohort-Controlled Study of Clinical Outcomes Following Coronavirus Disease 2019 (COVID-19) Convalescent Plasma Therapy in Hospitalized Patients With COVID-19
This article has 19 authors:Reviewed by ScreenIT
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Observed and self-reported COVID-19 health protection behaviours on a university campus and the impact of a single simple intervention
This article has 3 authors:Reviewed by ScreenIT
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Assessment of Simulated Surveillance Testing and Quarantine in a SARS-CoV-2–Vaccinated Population of Students on a University Campus
This article has 10 authors:Reviewed by ScreenIT
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Endonuclease-based genotyping of the RBM as a method to track the emergence or evolution of SARS-CoV-2 variants
This article has 10 authors:Reviewed by ScreenIT