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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Quantifying the information in noisy epidemic curves
This article has 3 authors:Reviewed by ScreenIT
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Representation of evidence-based clinical practice guideline recommendations on FHIR
This article has 7 authors:Reviewed by ScreenIT
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Isolation of bat sarbecoviruses of SARS-CoV-2 clade, Japan
This article has 10 authors:Reviewed by ScreenIT
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Functional evolution of SARS-COV-2 Spike protein: adaptation on translation and infection via surface charge of spike protein
This article has 2 authors:Reviewed by ScreenIT
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School Reopening Simulations with COVID-19 Agent-based Model for the Philippine Regions
This article has 4 authors:Reviewed by ScreenIT
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Assessment of oxidative stress markers in elderly patients with SARS-CoV-2 infection and potential prognostic implications in the medium and long term
This article has 8 authors:Reviewed by ScreenIT
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A Phase1 Results of a Non-Stabilized Spike-Encoding mRNA Vaccine in Adults
This article has 22 authors:Reviewed by ScreenIT
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A Deterministic Agent-based Model with Antibody Dynamics Information in COVID-19 Epidemic Simulation
This article has 2 authors:Reviewed by ScreenIT
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Elevated Expression of RGS2 May Underlie Reduced Olfaction in COVID-19 Patients
This article has 4 authors:Reviewed by ScreenIT
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Smoking trajectories over the first year of the pandemic in UK middle-aged adults: evidence from the UKHLS COVID-19 study
This article has 1 author:Reviewed by ScreenIT