Update the dataset README.

README.md

@@ -1,67 +1,94 @@
 ---
-license: apache-2.0
 datasets:
-- thermbench
+- ZoneTwelve/Thermal-Heatmap-Source-Localization
 tags:
 - benchmark
 - heatmap
+- physics
 - source-localization
-- synthetic-data
-pretty_name: ThermBench
+- synthetic
+license: apache-2.0
+pretty_name: Thermal Heatmap Source Localization (ThermBench)
 ---
 
-# ThermBench 🔥: Heat Source Localization Benchmark
+# ThermBench 🔥 — Thermal Heatmap Source Localization Benchmark
+
+## 📝 Summary
+**ThermBench** is a physics-inspired **synthetic dataset** designed to evaluate algorithms that infer **hidden thermal sources** from an observed **heat diffusion map**.
 
-## Summary
-**ThermBench** is a synthetic benchmark dataset for evaluating algorithms that infer hidden **heat sources** from a **2D heatmap**.
-Each sample consists of an observed grid (matrix of thermal intensities) and the true underlying source positions and strengths.
-The diffusion rule is based on inverse Manhattan distance:
+Each data sample contains:
+- an **observed heatmap** (matrix of values),
+- and the **ground-truth sources**: `(row, col, intensity)`.
+
+Diffusion follows inverse Manhattan distance:
 
 \[
-H(i,j) \;=\; \sum_{s=1}^K \frac{I_s}{d_{s,(i,j)}+1}
+H(i,j) \;=\; \sum_{s=1}^{K} \frac{I_s}{d(i,j,s)+1}
 \]
 
-where \(I_s\) is the source intensity and \(d\) is the Manhattan distance from source \(s\) to grid cell \((i,j)\).
+where \(d\) is the Manhattan distance to source \(s\).
 
-## Dataset Structure
-- **`input_text`**: Heatmap input following problem format:
-  - First line: grid size `N M`
-  - Second line: number of sources `K`
-  - Then `N` rows of grid heat values
-- **`output_text`**: Ground-truth sources (row, col, intensity)
-- **`level`**: Difficulty tier (`very_easy`, `easy`, `medium`, `hard`, `extreme`)
+---
 
-Example:
+## 📊 Dataset Structure
+
+- **level**: Difficulty tier (`very_easy`, `easy`, `medium`, `hard`, `extreme`)
+- **input_text**: Heatmap formatted as:
+  ```
+  N M
+  K
+  <N rows of values>
+  ```
+- **output_text**: True source positions and intensities in format:
+  ```
+  row col intensity
+  ```
+
+### Example
 ```json
 {
   "level": "easy",
-  "input_text": "5 5\n2\n10 8 6 5 4\n8 10 7 6 5\n...",
+  "input_text": "5 5\n2\n10 8 6 5 4\n8 10 7 6 5\n6 7 10 7 6\n5 6 7 10 8\n4 5 6 8 10",
   "output_text": "1 1 10.0\n5 5 10.0"
 }
 ```
 
-## Difficulty Levels
-- `very_easy`: 3×3 grid, 1 source
-- `easy`: 5×5 grid, 2 sources
-- `medium`: 10×10 grid, 3 sources
-- `hard`: 20×20 grid, 5 sources
-- `extreme`: 30×30 grid, 7 sources
+---
 
-Each level contains **100 samples** (total: 500 entries).
-A **fuzzy variant** (`thermbench-fuzzy`) introduces noise, source jitter, and decay variations to simulate real-world uncertainty.
+## 🚀 Usage
 
-## Usage
 ```python
 from datasets import load_dataset
 
-ds = load_dataset("username/thermbench", split="train")
-print(ds[0])
+dataset = load_dataset(
+    "ZoneTwelve/Thermal-Heatmap-Source-Localization",
+    split="train"
+)
+print(dataset[0])
 ```
 
-## Applications
-- Algorithmic evaluation of source localization
-- Benchmarking robustness against noise
-- Teaching problem-solving with synthetic physics-based data
+---
+
+## 🎚 Difficulty Levels
+
+- **very_easy** → 3×3 grid, 1 source
+- **easy** → 5×5 grid, 2 sources
+- **medium** → 10×10 grid, 3 sources
+- **hard** → 20×20 grid, 5 sources
+- **extreme** → 30×30 grid, 7 sources
+
+Each level contains 100 samples → **500 total**.
+
+A fuzzy extension of ThermBench introduces noise, intensity jitter, and rounding differences to simulate real‑world sensor readings.
+
+---
+
+## 🔧 Intended Applications
+- Benchmarking **inverse problem solvers**
+- Robustness studies for optimization/AI
+- Educational resource for algorithm development
+
+---
 
-## License
-Apache License, Version 2.0
+## 📜 License
+Apache License 2.0 © ZoneTwelve