Resources
Useful links, etc
Featured
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I guess everybody, even the smartest people who ever lived, have days when they feel dumb — really, really dumb. Oct. 1, 1861, was that kind of day for Charles Darwin.
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How small changes to a paper can help to smooth the review process.
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The Awesomest 7-Year Postdoc or: How I Learned to Stop Worrying and Love the Tenure-Track Faculty Life
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This paper presents a set of good computing practices that every researcher can adopt, regardless of their current level of computational skill.
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Learning the command line, data management, and other important CS skills that you may not have learned yet.
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Blog post describing good poster design principles.
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A introductory book to statistical analysis using R.
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A introductory book to statistical analysis using Python.
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This repository is a collection of modules that are combined into 1-5 day workshops on computational topics for the childhood cancer research community.
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An introduction to bash scripting.
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A textbook and accompanying codebase on data visualization.
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This workshop teaches data management and analysis for genomics research.
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Interesting statistical anomalies and correlations.
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Free silhouette images of animals, plants, etc.
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Tool for identifying gendered language in job ads and letters of ref.
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A collection of R packages for easy statistics and models.
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This book focuses on content intrinsically related to the infrastructure surrounding data analysis in R, but does not delve into the data analysis itself.
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Best practices for the analysis of high-throughput sequencing data from gene expression (RNA-seq) studies.
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Ten simple rules for attending your first conference.
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Recommended textbook on data mining, statistics, and predictive modeling.
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A guide to making visualizations that accurately reflect the data, tell a story, and look professional.
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Workshop materials from the Childhood Cancer Data Lab.
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A guide to revising/improving your scientific writing.
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Efficient Reading of Papers in Science and Technology.
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Tutorials by the Harvard Chang bioinformatics core.
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A collection of R code snippets and instructions featuring up-to-date best practices for coding in R
Enabling scientists to understand and analyze their own experimental data by providing instruction and training in bioinformatics software, databases, analyses techniques, and emerging technologies.