Project Directory Structure

Project Directory Structure

Data, code, and output files should be organized within a well-structured directory. For example:

Item	Description
0_data/	Raw and manipulated data [^1]
├── external/	Raw data from external sources
├── processed/	Data that has been manipulated
1_code/	Code and scripts for analysis
├── GEE/	Google Earth Engine scripts
│ └── gee_git_clone.sh	Script to clone GEE repository
├── r_scripts/	R scripts for data processing
│ └── r_module.R	Template R script
│ └── r_function.R	Template R function
2_pipeline/	Temporary pipeline files [^1]
3_output/	Final project output files
├── data/	Processed datasets
├── figures/	Generated figures
├── maps/	Generated maps
├── models/	Model outputs
└── tables/	Tables from analysis
4_writing/	Manuscript and reports
├── manuscript/	Drafts of the manuscript
└── reports/	Reports and additional documents
5_presentations/	Presentation materials
├── slides/	Presentation slides (e.g., .pptx)
└── posters/	Conference posters
6_bookdown/	Bookdown documentation and project vignette
├── index.Rmd	Main Bookdown file (project vignette)
├── _bookdown.yml	Bookdown configuration
└── (additional .Rmd)	Chapters/sections of the Bookdown documentation
README.md	Project overview and instructions

Below is an overview of the main folders in a project directory:

0_data

This folder holds all raw and manipulated data used in the project.

• external/

  • Purpose: Stores the original, unprocessed (“raw”) data from external sources (e.g., CSV files, shapefiles, remote sensing data).
  • Typical contents: Files downloaded from a public repository, database exports, or any data provided by collaborators.
  • Tip: Keep these files in their original form for reproducibility and to avoid accidental overwriting.

• processed/

  • Purpose: Contains “analysis-ready” data—data that have been cleaned, transformed, or pre-processed.
  • Typical contents: CSV, RData, shapefiles, etc. that are ready for direct use by scripts in 1_code/.
  • Tip: Think of this as your “staging ground” for cleaned data that can be loaded quickly by your analysis scripts.

1_code

• Purpose: This folder holds all code and scripts for your project—from data cleaning to final analyses.
• Typical contents: R, JavaScript, Python files organized and named according to their step in the project workflow. Note: Consider adding subfolders for other languages or script types (e.g., python_scripts/, bash_scripts/) if needed.

2_pipeline

• Purpose:
  • Houses intermediate files that are not final deliverables but useful for re-running code or debugging.
• Typical contents: checkpoints, logs, temporary files, and partial outputs (e.g., data subsets during model training) to avoid re-generating heavy files from scratch.

Tip: Keeping intermediate files here means you don’t clutter the 0_data or 3_output folders with temporary files.

3_output

Collects the final (or “permanent”) outputs of your analyses—whatever you plan to archive, publish, or share broadly.

• Purpose:Stores complete and polished outputs: cleaned datasets, figures, tables, model results, etc.

• Typical contents:

  • data/: Final data products that might be shared or referenced in publications.
  • figures/: High-quality plots and visualizations for reports, papers, or presentations.
  • maps/: Geographic products (e.g., shapefiles, GIS layers, static map images).
  • models/: Final model objects, predictions, or performance metrics.
  • tables/: Summaries or statistical tables ready for publication.

• Tip: If you were to share your project’s key results, you’d pull them from here.

4_writing

Contains all written materials—manuscripts, reports, and supporting documents.

• manuscript/

  • Purpose: Drafts of the main publication or paper.
  • Typical contents: Word docs, LaTeX files, references

• reports/

  • Purpose: Additional documents outside the primary manuscript (e.g., progress reports, technical docs, stakeholder summaries).
  • Typical contents: PDF or Word documents that serve as “formal communication” to collaborators or funding agencies.

5_presentations

• Purpose: Stores presentation materials used to communicate your project’s work.
• Typical contents: Slide decks, speaker notes, conference posters.

6_bookdown

• Purpose: Contains Bookdown documents. A dedicated space for creating Bookdown-based documentation, tutorials, or a detailed project vignette.

• Typical contents:

  • _bookdown.yml and index.Rmd
  • Chapter .Rmd files: Individual sections of your Bookdown document (e.g., introduction, methodology, results).
  • A compiled "book" (e.g. PDF or HTML)

README.md

The “front door” to your project—an overview and instructions for replication.

• Explains the project’s goals, scope, and context.
• Details how to set up the environment (required libraries, data sources, program languages).
• Guides others (and your future self) on how to run the project steps or locate key files.

Putting It All Together

• 0_data: Raw (external/) and analysis-ready (processed/) data.
• 1_code: Your “toolshed” of scripts in R, Python, GEE, etc.
• 2_pipeline: Intermediate files—logs, partial files, checkpoints, etc.
• 3_output: Final, polished deliverables—data, figures, tables, models.
• 4_writing: Manuscripts, reports, and other written documentation.
• 5_presentations: Slides, posters, and other public-facing materials.
• 6_bookdown: Bookdown documentation for project vignettes, user guides, or tutorials.
• README.md: The roadmap for anyone navigating or replicating the project.

Use a Template to Create a New Repository

A template repository can be found here. It can be used to generate new GitHub repositories with the above directory structure. While the template is designed for R projects, projects in other programming environments should maintain a similar directory structure.

To create a new repository from the template:

1. Navigate to the main page of the template repository in your browser.
2. Look for the “Use this template” button (usually located on the top right side of top GitHub UI bar.
3. Click “Use this template” to open a drop-down menu. Click "Create a new repository" and follow the instructions.

Below is a screenshot showing where to click “Use this template” on GitHub:

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[^1]: Data and pipeline files are generally added to .gitignore to prevent them from being pushed to GitHub.