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98-Resources.Rmd
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98-Resources.Rmd
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# Resources {#resources}
Here, sits any general useful resources that cover topics across the spectrum of science.
## How-tos
- [JEFworks](https://jef.works/lab/) - has some sections for writing an abstract, reviewing papers and giving a poster presentation.
## Reproducible science
- [Good enough practices in scientific computing](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005510) - article with good computing practices that every researcher can adopt, regardless of their current level of computational skill
- [Improve your workflow for reproducible science](https://www.youtube.com/watch?v=JA-vLsN-sic&feature=youtu.be) - 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](https://mine-cetinkaya-rundel.github.io/improve-repro-workflow-reproducibilitea-2020/).
## Heritability and LD score regression
- Beginner guides to [defining heritability](http://www.nealelab.is/blog/2017/9/13/heritability-101-what-is-heritability) and [estimating heritability](http://www.nealelab.is/blog/2017/9/13/heritability-201-types-of-heritability-and-how-we-estimate-it) - 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](https://www.youtube.com/watch?v=dVrF0l9jMgE&list=PLH2ckGEn9kWEkxWkDLkpzQlj8Y1bGM9ZP&index=4) and [Genetic Correlation](https://www.youtube.com/watch?v=QVPNouAbXsY&list=PLH2ckGEn9kWEkxWkDLkpzQlj8Y1bGM9ZP&index=3) - 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.
## RNA sequencing
- [DIY transcriptomics](https://diytranscriptomics.com/) - 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.
## Statistics and data visualisation
- [Fundamentals of Data Visualization](https://clauswilke.com/dataviz/) - **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](https://www.tmwr.org/software-modeling.html) - nice, short book on using the `tidymodels` packages for model building.
## Books
- [Jeff Leeks book](https://leanpub.com/modernscientist) - free and outlines the core principles behind being a scientist in a open source way.