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.

From Data to Viz leads you to the most appropriate graph for your data. It links to the code to build it and lists common caveats you should avoid.

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.

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

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.

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.

A short guide to writing scientific papers.

A quick, concentrated guides for mastering some of the professional challenges research scientists face in their careers.

An annotated summary paragraph provided by Nature.

Nature guide to writing.

This website consists of five sections and a checklist you can print. In each section, you will find a number of cards you can flip through to learn about using plain language in your work. When you are done with the final section, you can print a certificate of completion.

Color-coded example of how to write an abstract.

Claus Wilke’s guide to getting 5 faculty into a room at the same time to listen to your annual research update.

For when you really need a high quality image of your latex equation and don’t want to screenshot it.

When you can’t remember what the latex name is for a symbol, detexify has your back. It lets you draw the symbol and will show close matches.

An easy way to create beautiful latex tables.

Build figures, presentations, and illustrations with 2,000+ science and medical art visuals. This collection of high-quality, scientifically accurate vectors, icons, and brushes is freely available within the public domain.

Chenxin Li’s guide to data visualization. Friends don’t let friends make certain types of data visualization - What are they and why are they bad.

Slides from Dan Larremore and Sam Way’s workshop on data visualization.

Tips and cheatsheets by and for MatPlotLib.

A whole lot of color palettes.