https://github.com/adamrossnelson/parkselection
Example of reproducible research using Quarto + Jupyter Notebooks
https://github.com/adamrossnelson/parkselection
Last synced: about 1 month ago
JSON representation
Example of reproducible research using Quarto + Jupyter Notebooks
- Host: GitHub
- URL: https://github.com/adamrossnelson/parkselection
- Owner: adamrossnelson
- License: mit
- Created: 2025-04-01T21:39:02.000Z (about 2 months ago)
- Default Branch: main
- Last Pushed: 2025-04-01T23:17:44.000Z (about 2 months ago)
- Last Synced: 2025-04-02T00:20:24.524Z (about 2 months ago)
- Language: Jupyter Notebook
- Size: 5.37 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Wisconsin Lakes Park Selection Analysis
Reproducible data science demonstrating how to create publication-ready documents from Jupyter notebooks using Quarto.
## Project Overview
This repository contains a comprehensive data analysis of Wisconsin lakes, serving as a demonstration of reproducible data science practices. The project showcases how to transform Jupyter notebooks into multiple publication formats (HTML, PDF, and DOCX) while maintaining reproducibility and professional formatting.
## Environmental + Tech Stack
- **Python 3.13** - Core programming language
- **Jupyter** - IDE/Interactive computing environment
- **Quarto** - Publishing system for scientific and technical writing
- **Key Python Libraries**:
- pandas - Data manipulation and analysis
- seaborn/matplotlib - Data visualization
- geopandas - Geospatial data handling
- scikit-learn - Machine learning components
- numpy - Numerical computing
## Repository Structure
```
.
├── ParkSiteSelection.ipynb # Main analysis notebook
├── _quarto.yml # Quarto configuration
├── references.bib # Bibliography file
├── styles.css # Custom CSS styles
├── figures/ # Generated figures
└── original_data/ # Source data files
├── Lakes_Original.csv
└── gigsheet-counties.csv
```
## Reproducibility Features
### Quarto Integration
The project uses Quarto for document generation, supporting:
- Multiple output formats (HTML, PDF, DOCX)
- Automatic table of contents
- Code folding and tools
- Bibliography management
- Custom styling
- Watermarking for draft versions
### Code Organization
- Analysis contained in a single Jupyter notebook for clarity
- Code cells include YAML-style metadata for Quarto processing
- Figures include labeling and cross-referencing
- Data processing steps documented
## Getting Started
1. Clone this repository
2. Install Full Anaconda Distribution [anaconda.com](https://www.anaconda.com/download)
3. Install Quarto from [quarto.org](https://quarto.org)
4. Also install Quarto's tinytex `quarto install tinytex --update-path`
5. Open `ParkSiteSelection.ipynb` in Jupyter
## Rendering Documents
To render the documents in different formats:
```bash
# Preview HTML With
quarto preview ParkSiteSelection.ipynb
# Render HTML
quarto render ParkSiteSelection.ipynb --to html
# Render PDF
quarto render ParkSiteSelection.ipynb --to pdf
# Render Word document
quarto render ParkSiteSelection.ipynb --to docx
```
## Data Sources
- Primary dataset: Wisconsin Department of Natural Resources lake data (16,711 lakes)
- Supporting dataset: Wisconsin county information
## Author
Adam Ross Nelson, JD PhD
University of Wisconsin - Madison
Contact: [email protected]
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## Citation
```bibtex
@misc{nelson2025wisconsin,
author = {Nelson, Adam Ross},
title = {Applied Reproducible Data Science Processes: A demonstration using data from Wisconsin lakes},
year = {2025},
institution = {University of Wisconsin - Madison}
}