DBbun/Fryless_World_v1.0

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

---

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

---

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

---

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.

---

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.

---

Acknowledgment

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

---