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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CovidExpress: an interactive portal for intuitive investigation on SARS-CoV-2 related transcriptomes
This article has 11 authors:Reviewed by Review Commons, ScreenIT
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Prediction of the effective reproduction number of COVID-19 in Greece. A machine learning approach using Google mobility data
This article has 8 authors:Reviewed by ScreenIT
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Molecular rationale for SARS-CoV-2 spike circulating mutations able to escape bamlanivimab and etesevimab monoclonal antibodies
This article has 5 authors:Reviewed by ScreenIT
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Patient Characteristics in Cases of Reinfection or Prolonged viral shedding in SARS-CoV-2
This article has 6 authors:Reviewed by ScreenIT
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This article has 3 authors:
Reviewed by ScreenIT
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Current challenges of severe acute respiratory syndrome coronavirus 2 seroprevalence studies among blood donors: A scoping review
This article has 13 authors:Reviewed by ScreenIT
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Global mapping of RNA homodimers in living cells
This article has 6 authors:Reviewed by ScreenIT
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Stabilization of the SARS-CoV-2 Spike Receptor-Binding Domain Using Deep Mutational Scanning and Structure-Based Design
This article has 27 authors:Reviewed by ScreenIT
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SARS-CoV-2 transmission in kindergarten to grade 12 schools in the Vancouver Coastal Health region: a descriptive epidemiologic study
This article has 6 authors:Reviewed by ScreenIT
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Phase 2 Randomized Trial of an AS03 Adjuvanted Plant-Based Virus-Like Particle Vaccine for Covid-19 in Healthy Adults, Older Adults and Adults with Comorbidities
This article has 29 authors:Reviewed by ScreenIT