Datasets:

keffdata
/

pwr-smr-2026-01-community

@@ -34,7 +34,24 @@ This "Community Edition" contains **1,518 unique simulation cases** representing
 ---
-## 2. Dataset Structure
 The dataset provides raw output directly from the OpenMC engine to preserve maximum physical fidelity. To protect proprietary methodologies, source XML generation files (`metadata/`) are excluded from this open-access tier.
 For each simulation case, you will find:
@@ -44,7 +61,7 @@ For each simulation case, you will find:
 ---
-## 3. Anomaly Detection Regimes (Machine Learning Guidance)
 This dataset operates in two distinct detectability regimes based on the separability index (Z), where the corrected statistical uncertainty floor is ±56 pcm.
 $$Z = \frac{|\Delta\rho|}{\sigma_{corrected}}$$
@@ -57,7 +74,7 @@ $$Z = \frac{|\Delta\rho|}{\sigma_{corrected}}$$
   * *Approach:* The signal is partially masked by the statistical noise floor. Requires temporal models (LSTMs, Transformers), trend-based analysis, or denoising autoencoders.
 ---
-## 4. How to Use (Python Snippet)
 Due to the large file sizes (90+ GB), we recommend using the `huggingface_hub` library to download specific files or the entire dataset locally.
 ```python
@@ -72,7 +89,7 @@ local_dir = snapshot_download(
 print(f"Dataset successfully downloaded to {local_dir}")
 ```
-## 5. Licensing & Commercial Use
 **Phase 1 Open-Access Framework**
 To accelerate global nuclear AI research, KEFF Data provides this Community Edition dataset under the **Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)** license. It is entirely free for academic, educational, and non-commercial R&D workflows.
@@ -81,7 +98,7 @@ To accelerate global nuclear AI research, KEFF Data provides this Community Edit
 Any commercial entity, corporate R&D division, or national laboratory wishing to utilize this dataset, its underlying methodology, or its outputs for proprietary development, commercial product training, or operational benchmarking must obtain explicit written authorization and a commercial license.
 For enterprise licensing structures and access to our production-tier datasets, contact: **contact@keffdata.com**
-## 6. Citation
 If you use this dataset in your academic research or non-commercial projects, please cite it using the official DOI:
 **DOI:** [10.57967/hf/8914](https://doi.org/10.57967/hf/8914)

 ---
+## 2. Anomaly Classes & Data Distribution
+[cite_start]To ensure robust training for multi-variable deep learning architectures, the 1,518 unique statepoints are distributed across three independent safety-critical operational anomalies and a nominal baseline[cite: 291, 297]:
+| Anomaly Class | Cases (N) | Perturbation Range | Primary Physics Mechanism |
+| :--- | :--- | :--- | :--- |
+| **Control Rod (CR) Misalignment** | 555 | [cite_start]0–200 cm insertion depth  | [cite_start]Control rod worth: negative reactivity insertion  |
+| **Fuel Temperature (Doppler)** | 481 | [cite_start]950–1200 K  | [cite_start]Doppler broadening of U-238 resonance capture  |
+| **Coolant Void Fraction** | 481 | [cite_start]0–35% void fraction  | [cite_start]Under-moderated regime density feedback  |
+| **Nominal Baseline** | 1 | [cite_start]Reference state  | [cite_start]Unperturbed core benchmark ($k_{\text{eff}} = 1.26660$)  |
+### Verification & Validation (V&V) Baselines
+[cite_start]Every simulation case in this community release has been rigorously audited against industrial safety standards to guarantee physical integrity before deployment[cite: 228, 241]:
+* [cite_start]**Source Convergence:** Fundamental-mode spatial distribution verified with a Shannon entropy drift of 0.0419% (Passing industry threshold of < 0.1%)[cite: 66, 300].
+* [cite_start]**Statistical Reliability:** Adjusted for a Lag-1 autocorrelation coefficient of 0.62, raising the conservative industrial noise floor to ±56 pcm[cite: 85, 92, 300].
+* [cite_start]**Code-to-Benchmark Accuracy:** Verified against standard reference benchmarks for fresh PWR fuel pins with a stable code bias of -339.7 pcm, safely within the standard ±1000 pcm acceptance limit[cite: 51, 184, 300].
+## 3. Dataset Structure
 The dataset provides raw output directly from the OpenMC engine to preserve maximum physical fidelity. To protect proprietary methodologies, source XML generation files (`metadata/`) are excluded from this open-access tier.
 For each simulation case, you will find:
 ---
+## 4. Anomaly Detection Regimes (Machine Learning Guidance)
 This dataset operates in two distinct detectability regimes based on the separability index (Z), where the corrected statistical uncertainty floor is ±56 pcm.
 $$Z = \frac{|\Delta\rho|}{\sigma_{corrected}}$$
   * *Approach:* The signal is partially masked by the statistical noise floor. Requires temporal models (LSTMs, Transformers), trend-based analysis, or denoising autoencoders.
 ---
+## 5. How to Use (Python Snippet)
 Due to the large file sizes (90+ GB), we recommend using the `huggingface_hub` library to download specific files or the entire dataset locally.
 ```python
 print(f"Dataset successfully downloaded to {local_dir}")
 ```
+## 6. Licensing & Commercial Use
 **Phase 1 Open-Access Framework**
 To accelerate global nuclear AI research, KEFF Data provides this Community Edition dataset under the **Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)** license. It is entirely free for academic, educational, and non-commercial R&D workflows.
 Any commercial entity, corporate R&D division, or national laboratory wishing to utilize this dataset, its underlying methodology, or its outputs for proprietary development, commercial product training, or operational benchmarking must obtain explicit written authorization and a commercial license.
 For enterprise licensing structures and access to our production-tier datasets, contact: **contact@keffdata.com**
+## 7. Citation
 If you use this dataset in your academic research or non-commercial projects, please cite it using the official DOI:
 **DOI:** [10.57967/hf/8914](https://doi.org/10.57967/hf/8914)