README and Documentation Files

Last updated on 2026-08-24 | Edit this page

Overview

Questions

  • What should a good README contain?
  • How do I document data provenance and processing steps?
  • How should I handle Jupyter notebooks in a reproducible project?

Objectives

  • Write a comprehensive README for your project
  • Document raw data provenance and processing steps
  • Organize notebooks for reproducibility

The README: Your Project’s Front Door

A good README is essential for reproducibility. Someone should be able to read your README and understand:

  1. What the project is about
  2. Who to contact with questions
  3. How to set up their environment
  4. How to reproduce your analysis
  5. What the expected outputs are
  6. How to interpret results

Here’s a template:

MARKDOWN 



# Gene Expression Analysis - Alzheimer's vs. Control

Brief description of the study and research question.

## Project Overview

What scientific question are you answering? What's the scope?

## Data

### Raw Data

- Where does the data come from?
- How much data is there?
- How to obtain it (see data/raw/README.md)

### Processed Data

- What processing steps were applied?
- See data/processed/PROCESSING_LOG.md for details

## Methods

Brief summary of analytical approach:

- Statistical tests used
- Key parameters or thresholds
- Software versions (see environment.yml)

## How to Reproduce

### Setup

1. Install conda
2. Clone repository: `git clone https://...`
3. Create environment: `conda env create -f environment.yml`
4. Activate environment: `conda activate gene-analysis`



### Run Analysis

```bash
make all
```

Expected runtime: ~5 minutes

## Project Structure


gene_analysis/
├── data/
│   ├── raw/            # Original data (READ-ONLY)
│   └── processed/      # Cleaned data
├── src/                # Analysis code
├── results/            # Output figures and tables
├── Makefile            # Automation
└── environment.yml     # Dependencies


## Authors and Contact

- Alice Smith (alice.smith@university.edu)

## License

This work is licensed under CC-BY 4.0 (see LICENSE)

## Citation

Smith et al. "Gene expression changes" *Journal* (2024)
doi: 10.1234/example

Documenting Data Provenance

Each raw data file should have a corresponding README explaining: - What the file contains - When it was generated - Any known issues or limitations - Column definitions and units - Data collection methodology

MARKDOWN 

# data/raw/README.md

## How to Obtain Raw Data

The raw data files are too large to include in this repository.

### samples.csv
- Source: Gene Expression Omnibus (GEO) accession GSE12345
- Download: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE12345
- Size: 450 MB
- Expected MD5 checksum: a1b2c3d4e5f6...

Document processing steps:

MARKDOWN 

# data/processed/PROCESSING_LOG.md

## samples_cleaned.csv

Generated by: src/clean_data.py
Generated: 2024-02-20

### Processing steps:
1. Removed rows with missing values in key columns
2. Converted date strings to datetime format
3. Filtered to samples with coverage > 100x
4. Removed obvious outliers (>3 SD from mean)

Example: Reorganizing a Messy Project

Starting with a messy folder, we reorganize to:

MARKDOWN 

gene_analysis/
├── README.md
├── LICENSE
├── .gitignore
├── environment.yml
├── Makefile
├── data/
│   ├── raw/
│   │   ├── samples.csv (original, read-only)
│   │   └── README.md
│   └── processed/
│       ├── samples_cleaned.csv (generated by clean.R)
│       └── PROCESSING_LOG.md
├── src/
│   ├── clean.R           # One script, clear purpose
│   ├── analyze.R
│   └── visualize.R
└── results/
    ├── figures/
    │   ├── heatmap.pdf
    │   └── volcano.pdf
    └── tables/
        └── de_results.csv

Now it’s clear what’s original data, each script has one job, results are generated not manually edited, and a colleague can clone it and immediately understand what’s going on.

Notebooks: The Exception

If you use Jupyter notebooks, they blur the line between code and exploration. For reproducibility:

  1. Put exploratory notebooks in notebooks/: These are for learning and trying things

  2. Write final analysis as clean scripts in src/: These are for reproducibility

  3. Don’t commit notebook outputs: Add to .gitignore:

    .ipynb_checkpoints/
notebooks/*.ipynb

    Or clean them before committing:

    BASH 

    jupyter nbconvert --clear-output notebooks/*.ipynb

  4. If notebooks are part of your publication: Version them carefully, clear outputs before committing, and make sure they’re reproducible from scratch

A good practice: exploratory notebooks are for your benefit; your audience benefits from clean scripts and generated figures.

Challenge

Exercise: Write a Stub README

Write a README for a project (real or hypothetical) with these sections:

  • Project Overview (2-3 sentences)
  • Data (where it comes from)
  • How to Reproduce (setup and run commands)
  • Project Structure (directory tree)

If you’re unsure about your project structure, discuss with a neighbor.

MARKDOWN 

# Sagehen Climate Trend Analysis

Analysis of 30-year temperature trends in Southern California
using station data from NOAA.

## Data

Raw temperature data from NOAA Climate Data Online.
See `data/raw/README.md` for download instructions.

## How to Reproduce

```bash
git clone https://github.com/mylab/climate-trends.git
cd climate-trends
conda env create -f environment.yml
conda activate climate-trends
make all
```

Results appear in `results/` (~10 minutes).

## Project Structure

```
climate-trends/
├── data/
│   ├── raw/            # NOAA station data (READ-ONLY)
│   └── processed/      # Cleaned temperature data
├── src/                # Analysis scripts
├── results/            # Output figures and tables
├── Makefile
└── environment.yml
```

The README need not be long – clarity and completeness matter more than length.

Key Points
  • A comprehensive README is essential for reproducibility
  • Document data provenance with source URLs, checksums, and download instructions
  • Processing logs explain how raw data becomes processed data
  • Jupyter notebooks should be exploratory; final analysis belongs in clean scripts
  • A clear project structure section helps newcomers navigate your repository