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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Persistence of anti-SARS-CoV-2 antibodies: immunoassay heterogeneity and implications for serosurveillance
This article has 16 authors:Reviewed by ScreenIT
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Tracking cytosine depletion in SARS-CoV-2
This article has 3 authors:Reviewed by ScreenIT
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Social and Clinical Determinants of COVID-19 Outcomes: Modeling Real-World Data from a Pandemic Epicenter
This article has 18 authors:Reviewed by ScreenIT
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Identification of druggable host targets needed for SARS-CoV-2 infection by combined pharmacological evaluation and cellular network directed prioritization both in vitro and in vivo
This article has 26 authors:Reviewed by ScreenIT
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Isolation and characterization of SARS-CoV-2 from the first US COVID-19 patient
This article has 34 authors:Reviewed by ScreenIT
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Tocilizumab Effect in COVID-19 Hospitalized Patients: A Systematic Review and Meta-Analysis of Randomized Control Trials
This article has 10 authors:Reviewed by ScreenIT
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Female-male differences in COVID vaccine adverse events have precedence in seasonal flu shots: a potential link to sex-associated baseline gene expression patterns
This article has 15 authors:Reviewed by ScreenIT
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When can we stop wearing masks? Agent-based modeling to identify when vaccine coverage makes nonpharmaceutical interventions for reducing SARS-CoV-2 infections redundant in indoor gatherings
This article has 2 authors:Reviewed by ScreenIT
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A Single-Cell Atlas of Lymphocyte Adaptive Immune Repertoires and Transcriptomes Reveals Age-Related Differences in Convalescent COVID-19 Patients
This article has 12 authors:Reviewed by ScreenIT
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Tiled-ClickSeq for targeted sequencing of complete coronavirus genomes with simultaneous capture of RNA recombination and minority variants
This article has 24 authors:This article has been curated by 1 group: