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.

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

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

A guide to revising/improving your scientific writing.

Efficient Reading of Papers in Science and Technology.

A short guide to writing scientific papers.

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.

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.

A step-by-step guide to writing a scientific manuscript.

A 3 pass approach to reading journal articles.

Blog post describing good poster design principles.

A textbook and accompanying codebase on data visualization.

Free silhouette images of animals, plants, etc.

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.

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.

We provide examples and practical tutorials for creating figures that communicate both the cell-level variability and the experimental reproducibility.

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.

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.

This workshop teaches data management and analysis for genomics research.

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.

Workshop materials from the Childhood Cancer Data Lab.

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.

This book aims to guide both beginners and experienced professionals in best practices of single-cell sequencing analysis.

This book will show you how to make use of cutting-edge Bioconductor tools to process, analyze, visualize, and explore scRNA-seq data.

Tutorials from the 2025 epigenomics workshop.

A introductory book to statistical analysis using R.

A introductory book to statistical analysis using Python.

Interesting statistical anomalies and correlations.

A collection of R packages for easy statistics and models.

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

A crash course on how to do Monte Carlo simulations and the basics of running code on a cluster.

Introduction to R coding for data science applications.

Learn to build ML products with Google’s Machine Learning Courses.

A flowchart to help select which statistical should be used in a variety of common scenarios.

The first half of this Guide reviews general principles of statistics, and is not at all specific to GraphPad Prism.

Shows the linear models underlying common parametric and non-parametric tests.

Pollard, Pollard, and Pollard provide guidelines for using statistical methods to analyze the types of experiments reported in cellular and molecular biology journals.

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

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

Ten simple rules for attending your first conference.

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

IDP, roadmap, and other resources for UF graduate students.