Resources

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.

How small changes to a paper can help to smooth the review process.

The Awesomest 7-Year Postdoc or: How I Learned to Stop Worrying and Love the Tenure-Track Faculty Life

This paper presents a set of good computing practices that every researcher can adopt, regardless of their current level of computational skill.

Learning the command line, data management, and other important CS skills that you may not have learned yet.

Blog post describing good poster design principles.

A introductory book to statistical analysis using R.

A introductory book to statistical analysis using Python.

This repository is a collection of modules that are combined into 1-5 day workshops on computational topics for the childhood cancer research community.

An introduction to bash scripting.

A textbook and accompanying codebase on data visualization.

This workshop teaches data management and analysis for genomics research.

Interesting statistical anomalies and correlations.

Free silhouette images of animals, plants, etc.

Tool for identifying gendered language in job ads and letters of ref.

A collection of R packages for easy statistics and models.

This book focuses on content intrinsically related to the infrastructure surrounding data analysis in R, but does not delve into the data analysis itself.

Best practices for the analysis of high-throughput sequencing data from gene expression (RNA-seq) studies.

Ten simple rules for attending your first conference.

Recommended textbook on data mining, statistics, and predictive modeling.

A guide to making visualizations that accurately reflect the data, tell a story, and look professional.

Workshop materials from the Childhood Cancer Data Lab.

A guide to revising/improving your scientific writing.

Efficient Reading of Papers in Science and Technology.

Tutorials by the Harvard Chang bioinformatics core.

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.