Project Directory Structure

Overview

Questions

• How should I organize my research files?
• What’s the difference between raw data and processed data?
• How do I prevent accidental modification of raw data?
• Why do relative paths matter?

Objectives

• Design a standard project directory structure
• Understand why separating concerns (data/code/results) matters
• Make raw data read-only and tamper-proof
• Use relative paths to ensure code portability

A Common Disaster

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Let’s start with a true story. A biologist has been working on a gene expression analysis for months. Her main analysis folder looks like this:

gene_analysis/
├── data/
│   ├── samples.csv
│   ├── samples_fixed.csv
│   ├── samples_fixed2.csv
│   ├── samples_final.csv
│   ├── samples_backup.csv
│   ├── data.xlsx
│   └── master_v3.csv
├── analysis.R
├── analysis_v2.R
├── analysis_final.R
├── results.csv
├── figure.png
├── figure_v2.png
├── figure_for_paper.png
└── notes.txt

Questions she can’t answer:

• Which data file is the original? Which was modified?
• Which analysis script is the one she actually used?
• What did she change between analysis.R and analysis_final.R?
• Can she hand this to a collaborator and they would know what to do?

This is preventable. The solution is a standardized project structure that clearly separates concerns.

The layout tells you what is safe to delete and what is not.

The Standard Research Project Structure

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Here’s a widely-adopted structure for research projects:

project-name/
├── README.md              # Project overview and how to use it
├── LICENSE                # Legal license for your code
├── data/
│   ├── raw/              # Original, untouched data
│   └── processed/        # Cleaned/transformed versions
├── src/                  # Source code for analysis
│   ├── analysis.py
│   ├── visualization.py
│   └── utilities.py
├── results/              # Output files (figures, tables, summaries)
│   ├── figures/
│   └── tables/
├── notebooks/            # Jupyter notebooks for exploration (optional)
├── Makefile              # Automation commands
├── environment.yml       # conda environment specification
└── .gitignore            # Tell git what to ignore

Core Principle: Separate Concerns

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The key insight is this: Your code, your data, and your results serve different purposes and should live in different places.

Raw Data: Sacred Ground

Raw data is your ground truth. It should:

1. Never be modified: Only in specific, documented, version-controlled ways
2. Be easily readable: In open formats (CSV, JSON, HDF5) not proprietary formats
3. Be annotated: Include metadata about what each column means, units, data collection date
4. Be preserved: Keep the original files, never overwrite them

Processed Data: Tracked and Documented

Processed data is the result of cleaning, filtering, or transforming raw data. Each processed dataset should have a corresponding script that generates it from raw data. You should be able to run python src/clean_data.py and have data/processed/samples_cleaned.csv regenerated identically every time.

Source Code: The Recipe

Each script should have a clear purpose (evident from the filename), document what it does, take inputs and produce outputs explicitly, and be runnable from the command line.

Results: Generated Artifacts

Results should be regeneratable from source code and processed data. Don’t manually edit result files. A colleague can reproduce a result by re-running your code.

Making Raw Data Read-Only

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A critical practice: make your raw data read-only so you can never accidentally modify it.

BASH

chmod a-w data/raw/*

This removes write permissions. Now if you try to edit a raw data file, you’ll get an error. To deliberately modify raw data (rare!), you must explicitly re-enable writing:

BASH

chmod u+w data/raw/file_to_modify.csv
# Make changes
chmod a-w data/raw/file_to_modify.csv

In Git, add to your .gitignore to prevent accidentally committing large raw data files. Instead, document how to obtain raw data in data/raw/README.md with source URLs and MD5 checksums.

Relative Paths: Portability

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Always use relative paths in your code, not absolute paths.

Absolute Path (BAD):

PYTHON

df = pd.read_csv("/home/alice/projects/gene_analysis/data/raw/samples.csv")

If anyone else clones your repository or you move it to a different location, this breaks immediately.

Relative Path (GOOD):

PYTHON

from pathlib import Path
data_file = Path(__file__).parent.parent / "data" / "raw" / "samples.csv"
df = pd.read_csv(data_file)

In R, the here package detects your project root and builds paths relative to it:

R

data_file <- here::here("data", "raw", "samples.csv")
df <- read.csv(data_file)

Never use absolute paths in code you intend to share. Always use paths relative to your project root or the script location.

Challenge

Exercise: Design Your Project Structure

Choose a recent analysis you’ve done or the project you’re about to work on.

1. Sketch your project structure: Draw or write out what files would go in data/, src/, and results/ for your project.
2. Identify raw data: What are the input files that start your analysis? Mark them as read-only.
3. Trace the pipeline: For one key result (a table or figure), write down the path it takes:
   • Raw data file(s) -> Processing script(s) -> Processed data file -> Analysis script(s) -> Result file

Show me the solution

A completed example for a climate data analysis project:

climate-analysis/
├── README.md
├── environment.yml
├── Makefile
├── data/
│   ├── raw/                    # READ-ONLY
│   │   ├── temperature_2023.csv
│   │   └── README.md
│   └── processed/
│       └── temperature_cleaned.csv
├── src/
│   ├── clean_data.py
│   ├── analyze.py
│   └── visualize.py
└── results/
    ├── figures/
    │   └── trend_plot.pdf
    └── tables/
        └── summary_stats.csv

Pipeline trace for trend_plot.pdf:

data/raw/temperature_2023.csv
  -> src/clean_data.py
    -> data/processed/temperature_cleaned.csv
      -> src/analyze.py + src/visualize.py
        -> results/figures/trend_plot.pdf

Verify raw data is read-only:

BASH

$ chmod a-w data/raw/*
$ ls -l data/raw/temperature_2023.csv
-r--r--r-- 1 user user 12345 Mar 15 10:00 temperature_2023.csv

Key Points

• A standard project structure (data/src/results) makes research clear and reproducible
• Raw data is sacred: keep it original, unchanged, and read-only
• Processed data should be generated by scripts, not manually edited
• Results are artifacts that should be regeneratable from code
• Always use relative paths in code so it’s portable
• Separating concerns (data, code, results) prevents confusion and accidents