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Computational & Statistical Reproducibility
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Resources for Research Rigor & Transparency
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Computational and Statistical Reproducibility
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Computational and Statistical Reproducibility
Beyond Rigor: Appropriate Analysis
Quantify and control reproducibility in high-throughput experiments (Zhao et al., Nature Methods 2020)
A set of computational methods, INTRIGUE, to evaluate and control reproducibility in high-throughput settings
bcbio: Python Toolkit pipelines for fully automated high throughput sequencing analysis
Computing Workflows for Biologists: A Roadmap
Best Practices for Computational Science: Software Infrastructure and Environments for Reproducible and Extensible Research by Victoria Stodden and Shelia Miguez
Principles, Statistical and Computational Tools for Reproducible Data Science
Biostatistics Continuing Education
Certificate in Applied Biostatistics - Online
Power failure: why small sample size undermines the reliability of neuroscience (Button et al., Nature Reviews Neuroscience 2013)
Statistics for Biologists
Degrees of Freedom in Planning, Running, Analyzing, and Reporting Psychological Studies: A Checklist to Avoid p-Hacking by Wicherts et. al. (Frontiers in Psychology)
Know when your numbers are significant
Rein in the four horsemen of irreproducibility
Social, Behavioral, and Economic Sciences Perspectives on Robust and Reliable Science from NSF
Criteria for Biological Reproducibility: What does "n" Mean?
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