8 Resources

Here, sits any general useful resources that cover topics across the spectrum of science.

8.1 How-tos

JEFworks - has some sections for writing an abstract, reviewing papers and giving a poster presentation.

Good enough practices in scientific computing - article with good computing practices that every researcher can adopt, regardless of their current level of computational skill
Improve your workflow for reproducible science - video from a 2-hour workshop on using R, R Markdown, Git, and GitHub to improve reproducibility. This is an excellent primer for new starters on good practices to apply to every research project. Slides from the workshop are available here.

Beginner guides to defining heritability and estimating heritability - these are two excellent blog posts from the Neale lab, which break down the concept of heritability into plain, non-jargon English.
LD Score Regression, Heritability and Partitioning and Genetic Correlation - two approximately 1-hour YouTube videos from a summer school covering LD score regression. This is a great place to start understanding the underlying principles and assumptions of LDSC.

DIY transcriptomics - hybrid course covering best practices for bulk and single cell RNA-sequencing data analysis, with a primary focus the use of lightweight and open-source software and the R/bioconductor environment.

Fundamentals of Data Visualization - excellent book meant as a guide to making visualizations that accurately reflect the data, tell a story, and look professional. Visualisations based on ggplot2. A great read no matter what stage of your career you’re at.
Tidy Modeling with R - nice, short book on using the tidymodels packages for model building.

Jeff Leeks book - free and outlines the core principles behind being a scientist in a open source way.