General guidelines and best practices for documenting code:

• A Guide to Reproducible Code in Ecology and Evolution: A guide to write reproducible code for researchers in Ecology and Evolution fields, created by British Ecological Society. Most guidelines can still be applied to other disciplines.  

• Best Practices for Scientific Computing: This article is published in PLoS Biology in 2014 and offers several useful best practices for writing reproducible code.   

Guides for R and Python syntax and styles: 

• The tidyverse style guide: This style guide is created by Hadley Wickham for R programming language. There are two R packages, styler and lintr, that support this style guide.  

• Google’s R style guide: This style guide is a fork from the tidyverse style guide above. Google modified it with its internal R user community.     

• PEP8 style guide for python code: A style guide for writing Python code on Python’s official website.  

• Google’s Python style guides: The style guide created by Google for Python programming language. It is a list of dos and don’ts when programming in Python.  

Tutorials and templates for writing helpful code comments: 

• Code Like a Pro: Comments | How to Write Code Professionally (With Code Examples): An YouTube video shows how to write good comments for code and how to give good variable/function names so the code is self-explanatory.  

• Putting comments in code: the good, the bad and the ugly: An article that discuss documentation comments and clarification comments. 

• Choose an open source license for code: Visit this website to view different types of software license and choose one most appropriate for your code. 

• My easy R script header template: A R script template with header information and instructions for how to use this template.   

• A Python script to create a header for Python scripts: A Python script that can create a header for your own Python scripts. 

• Jim's Computer Science Topics – Commenting Your Code: A professor, H. James de St. Germain, in the School of Computing, University of Utah created these materials for his computer science course students. The programming languages used in examples are Matlab, C, and Actionscript. 

• Writing Comments in Python (Guide): A guide on Real Python about how to write comments for Python code.   

R Markdown is a file format to create interactive documents in R. Such a document can include text,  code chunks and metadata. Other programming languages, such as Python, SQL, D3, can also be documented with R Markdown. 

• R Markdown official site:   The office site for R Markdown, with instructions to get started, and gallery, resources to learn more.   

• RPubs:  A web space to share R Markdown documents, created by RStudio. You can also view other people’s R Markdown documents.   

• R Markdown: The Definitive Guide: A book written by the creator of R Markdown package, Yihui Xie. A good reference when writing an R Markdown document.   

• R Markdown chapter in R for Data Science: An R Markdown chapter in the R for Data Science book, written by Hadley Wickham and Garrett Grolemund. 

• Introduction to R Markdown: A section about R Markdown in Reproducible Research course created by National Bioinformatics Infrastructure Sweden (NBIS).  

• An R and R Markdown Tutorial: A step-by-step instruction to create an R project and an R Markdown document. This is shared on RPubs.  

• Pimp my RMD: a few tips for R Markdown: This is not a guide for how to write an R Markdown document, but some useful tips to improve the appearance of output documents.  

Jupyter Notebook allows you to create interactive documents in Python. You can embed and execute code within Jupyter Notebooks. Other programming languages, such as R and Julia, can be documented with Jupyter Notebooks as well.  

• Jupyter Notebook official site: The official site for Project Jupyter, including documentation, a list of Jupyter community, nbviewer for viewing and sharing Jupyter Notebook and the latest information about Project Jupyter.  

• Jupyter Notebook: An Introduction by Real Python: A brief introduction of Jupyter Notebook, including how to get started, elements in a Jupyter Notebook, how to explore a Notebook and Notebook extensions.  

• Jupyter Notebook Tutorial: The Definitive Guide by Data Camp: A tutorial introduces the history of Jupyter Notebook, and how to install, use and create a Notebook document. In addition, it offers tips, best practices and examples of Jupyter Notebook.   

• A gallery of interesting Jupyter Notebooks: A list of Jupyter Notebook examples in various disciplines on GitHub. 

• Ten simple rules for writing and sharing computational analyses in Jupyter Notebooks: A journal article on PLoS Computational Biology. It provides 10 rules to best sharing data anlaysis in Jupyter Notebooks.  

• Guide for Reproducible Research and Data Science in Jupyter Notebooks: A crowdsourced guidelines and tutorials on GitHub for reproducible research in Jupyter Notebooks.    

Best practices and templates for creating a well-structured ReadMe file:  

• Choose an open source license for code: Visit this website to view different types of software license and choose one most appropriate for your code. 

• How to cite and describe software: Tips to describe and cite software you have used or built your code upon.  

• ReadMe 101: Explain what is a ReadMe file and why you need to create one. Provide several suggestions for a good ReadMe and also a couple templates.   

• Art of ReadMe and examples: Provide some best practices tips for creating a good ReadMe, with several examples of ReadMe and a ReadMe checklist. 

• A good readme template by PurpleBooth: A good ReadMe template for GitHub users. 

• A readme template by scottydocs: Another good ReadMe template on GitHub.

• Computational physics ReadMe template: A ReadMe template on GitLab for researchers work in computational physics field, but could also be expanded to other fields.  

Introduction of version control system, why it is important and best practices for version control. In addition, some resources about Git, GitHub and GitHub Desktop including installation instructions, documentation and tutorials. 

• Source code management: A tutorial created by BitBucket about the importance of source code management, and the benefits and best practices of it.  

• Version control concepts and best practices: Professor Michael Ernst in the Department of Computer Science & Engineering, University of Washington created this as an introduction to version control concepts and best practices tips for versioning. 

• About version control: An introduction to different version control systems on the Git's official site. 

• Comparison of centralized and distributed version control systems:

  • Evolution of version control system

  • Comparison of version control system tools

  • The Wikipedia page that compare multiple version control systems

• The Git official site: The official site of Git. You can download the latest release version of Git and find documentation and other related information here. 

• GitHub Desktop installation and documentation: GitHub Desktop is a graphic user interface (GUI) tool that you can use to interact with GitHub repositories from a local computer.  Here are installation instructions and documentation for how to use GitHub Desktop. 

• Git & GitHub Crash Course For Beginners: An YouTube tutorial shows how to install Git and use command lines to do version control.  

• GitHub Guides: Online guides for using some basic features of GitHub.  

• Happy Git and GitHub for the useR: An online book about how to use Git and GitHub for R users. You will learn how to connect git, GitHub and RStudio.  

• GitHub Learning Lab: Many interactive, hands-on tutorials for GitHub learners.    

• Resources to learn Git:  A few of resources to learn Git.    

• Git and GitHub learning resources: A list of Git and GitHub learning resources in GitHub Documentation.