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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Anti‐spike IgG antibody kinetics following the second and third doses of BNT162b2 vaccine in nursing home residents
This article has 10 authors:Reviewed by ScreenIT
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Role of Covid vaccine in determining ICU admission and death due to Covid-19 in Tamil Nadu
This article has 6 authors:Reviewed by ScreenIT
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EMoMiS: A Pipeline for Epitope-based Molecular Mimicry Search in Protein Structures with Applications to SARS-CoV-2
This article has 11 authors:Reviewed by ScreenIT
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Factors Associated With Higher Levels of Grief and Support Needs Among People Bereaved During the Pandemic: Results from a National Online Survey
This article has 10 authors:Reviewed by ScreenIT
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Inequalities in mental and social wellbeing during the COVID-19 pandemic: prospective longitudinal observational study of five UK cohorts
This article has 6 authors:Reviewed by ScreenIT
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Evaluation of a Rapid and Accessible Reverse Transcription-Quantitative PCR Approach for SARS-CoV-2 Variant of Concern Identification
This article has 19 authors:Reviewed by ScreenIT
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Evaluating fomite risk of brown paper bags storing personal protective equipment exposed to SARS-CoV-2: A quasi-experimental study
This article has 7 authors:Reviewed by ScreenIT
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A Bayesian network analysis quantifying risks versus benefits of the Pfizer COVID-19 vaccine in Australia
This article has 8 authors:Reviewed by ScreenIT
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Rapid, high-throughput, cost-effective whole-genome sequencing of SARS-CoV-2 using a condensed library preparation of the Illumina DNA Prep kit
This article has 8 authors:Reviewed by ScreenIT
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Antibody and Memory B-Cell Immunity in a Heterogeneously SARS-CoV-2-Infected and -Vaccinated Population
This article has 24 authors:Reviewed by ScreenIT