README.md

---
tags:
- synthetic-data
- storytelling
- environmental-data
- cultural-change
- culinary
- time-series
- simulated
license: other
language:
- en
pretty_name: A Future Without French Fries — Synthetic Dataset Collection
---

# A Future Without French Fries — Synthetic Dataset Collection

### Inspired by the short story *“A Future Without French Fries”* from **Uri Kartoun’s** book *“A Future Without: 50 Short Stories of What May Not Be”*

[Click to check out the book on Amazon](https://us.amazon.com/Future-Without-Short-Stories-What/dp/B0D9CLS953/)

<img src="fryless_world.jpeg" alt="Fryless World" width="600">

---

## Source code

The full generator source code is available under a paid commercial license from DBbun LLC. To purchase access, email: contact@dbbun.com

[https://github.com/DBbun/fryless_world](https://github.com/DBbun/fryless_world)

---

## Overview
This dataset collection transforms a speculative narrative into structured synthetic data.  

It models the **global cultural, economic, and environmental consequences** of a fictional 2029 event:  
> *the disappearance of all knowledge of how to make French fries.*

The datasets capture how societies, restaurants, and ecosystems adapt to the loss of one of humanity’s most iconic foods, evolving into a story of innovation, sustainability, and healthier living.

Five dataset scales are provided: **`tiny`**, **`small`**, **`medium`**, **`large`**, and **`xl`**, each generated from the same simulation model with different magnitudes.

---

## Dataset Structure

Each folder (`tiny/`, `small/`, `medium/`, `large/`, `xl/`) contains four files:

| File | Format | Description |
|------|---------|-------------|
| `fryless_timeseries.csv` | CSV | Monthly per-location indicators for 2029 capturing agricultural, behavioral, and culinary transitions. |
| `fryless_innovations.csv` | CSV | Catalog of “replacement fry” experiments — ingredient × method × seasoning — with adoption, health, and environmental scores. |
| `fryless_events.csv` | CSV | Timeline of shock and recovery events by actor type (`System`, `FastFoodChain`, `HighEndRestaurant`, `Household`). |
| `fryless_datadict.json` | JSON | Machine-readable data dictionary and dataset summary. |

Approximate scales:

| Profile | Locations | Months | Time-series rows | Innovation rows (max) | Purpose |
|----------|------------|--------|------------------|------------------------|----------|
| `tiny` | 25 | 12 | ~300 | ≤0.3M | Quick demo / tests |
| `small` | 120 | 12 | ~1.4K | ≤1.5M | Exploratory analysis |
| `medium` | 500 | 12 | ~6K | ≤6M | ML prototyping |
| `large` | 2,000 | 12 | ~24K | ≤20M | Scalability benchmarking |
| `xl` | 10,000 | 12 | ~120K | ≤60M | Big-data stress tests |

---

## Variable Highlights

**Time-Series Variables**
- `potato_acreage_kha` — Thousand hectares of potato cultivation  
- `biodiversity_idx` — Biodiversity proxy (0–1)  
- `sustainable_menu_share` — Portion of menu items tagged sustainable  
- `public_interest_nutrition` — Population-level attention to nutrition (0–1)  
- `nutrition_index` — Aggregate dietary quality  
- `ff_sales_index`, `highend_sales_index`, `household_cooking_index` — Relative demand indices  
- `monthly_innovations` — Count of new “fry-alternative” ideas per month  

**Innovation Variables**
- `ingredient`, `method`, `seasoning` — Composition of each experiment  
- `adoption_score`, `health_score`, `env_score` — Modeled 0–1 adoption and impact metrics  

**Event Variables**
- `actor_type` — `System`, `FastFoodChain`, `HighEndRestaurant`, or `Household`  
- `event` — `ShockResponse` or `MenuInnovation`  
- `intensity` — 0.1–1.3 relative impact

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## What You Can Do With These Datasets

These datasets are designed for **creativity, experimentation, and education**. Users can:

### Research & Analysis
- Model **innovation diffusion** using the `fryless_innovations.csv` file.  
- Study **environmental rebound effects** (e.g., biodiversity vs. potato acreage).  
- Explore **time-series forecasting** of culinary or agricultural trends.  
- Analyze **behavioral shifts** in public nutrition awareness and sustainability.

### Machine Learning & Simulation
- Train regression, classification, or forecasting models on synthetic but coherent data.  
- Benchmark **scalability and streaming pipelines** with large versions (`large`, `xl`).  
- Build generative or causal inference demos using fictional-yet-realistic signals.

### Creative & Educational Uses
- Teach storytelling through data — how fiction can drive structured datasets.  
- Visualize global change narratives through dashboards or animation.  
- Use as a **sandbox** for teaching data cleaning, EDA, or model evaluation.

### Interdisciplinary Projects
- Combine narrative art, social science, and data engineering to explore the intersection of **speculative fiction and synthetic data**.  
- Compare simulated outcomes under alternative “what-if” policies (e.g., rapid vs. slow recovery).

All data are **synthetic** — no personal or real-world records are used.

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## Data-Generation Model

1. **Shock Simulation** — January 2029: “fry memory” disappears globally.  
2. **Recovery Dynamics** — Agriculture, biodiversity, and consumer behavior evolve monthly.  
3. **Innovation Diffusion** — New “fry alternatives” spread by public curiosity and sustainability trends.  
4. **Event Tracking** — Each actor type reacts differently over time.  
5. **Stochastic Realism** — Poisson, lognormal, and normal noise ensure variability across regions.

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## Acknowledgment

This dataset reimagines speculative fiction as data — illustrating how narrative imagination can generate structured, analyzable worlds.

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