Update dataset card

README.md

@@ -1,3 +1,108 @@
 ---
-license: mit
+license: cc-by-4.0
+tags:
+- federated-learning
+- predictive-maintenance
+- graph-neural-networks
+- heterogeneous-graphs
+- cmapss
+- batadal
+- pytorch-geometric
+size_categories:
+- 1K<n<10K
 ---
+
+# CAFA-GNN Federated Benchmarks
+
+Three federated heterogeneous-graph datasets built to reproduce and benchmark the
+**CAFA-GNN** framework (*Concept-Aware Federated Agentic GNN for Industrial Predictive
+Maintenance*). Each federation ships as a single `.zip` containing one
+`torch_geometric.data.HeteroData` per client.
+
+| Federation | Source | Clients | Node types | Task | Per-node feature |
+|---|---|---|---|---|---|
+| **cmapss** | NASA C-MAPSS turbofan archive (PHM Society mirror) | **3,000** | sensor (14), engine (1), operator (6) | regression (RUL per engine, capped at 125) | `(W=30, D=14)` |
+| **batadal** | BATADAL water-distribution training set | **2,000** | pump (11), tank (7), sensor (12) | binary classification (anomaly per pump) | `(W=30, D=14)` |
+| **d3_synthetic** | procedurally generated, 3-regime physics model | **2,000** | sensor (60), machine (15), line (3) | regression (RUL per machine, capped at 125) | `(W=30, D=14)` |
+
+7,000 client files total. Five relation types per heterograph in each federation
+(see node-type pairs printed by `print(data.edge_types)` after loading).
+
+## Why a federated benchmark?
+
+Predictive-maintenance research on CMAPSS and BATADAL has historically been done
+centrally — one model trained on the union of all engines / all timestamps.
+CAFA-GNN argues that real-world deployments are intrinsically federated (one
+factory per client) and that clusters of clients share similar degradation
+regimes. This benchmark exposes that structure explicitly: each `.pt` file is a
+self-contained per-factory graph snapshot with its own targets, ready to drop
+into Flower or any other FL simulation framework.
+
+## Files
+
+```
+cmapss_federated.zip       # 3000 × client_*.pt  (≈16 MB)
+batadal_federated.zip      # 2000 × client_*.pt  (≈14 MB)
+synthetic_d3_K100.zip      # 2000 × client_*.pt  (≈27 MB)
+```
+
+## Loading
+
+```python
+import zipfile, pathlib, torch
+from torch_geometric.data import HeteroData
+
+zipfile.ZipFile("cmapss_federated.zip").extractall("cmapss/")
+data: HeteroData = torch.load("cmapss/client_0.pt")
+
+print(data.node_types)      # ['sensor', 'engine', 'operator']
+print(data.edge_types)      # 5 relation triples
+print(data['engine'].x.shape)   # (1, 30, 14)
+print(data.y.shape)             # (1,)  — RUL target
+print(data.cmapss_subset, data.cmapss_engine_id)  # provenance
+```
+
+Each `HeteroData` carries:
+- `x` on every node type, shape `(N_nodes, W=30, D=14)`. Channel 0 is the
+  normalised per-entity signal; channels 1–3 are the operating context
+  (CMAPSS op-settings / BATADAL pressure context / D3 cluster progress);
+  remaining channels are zero-padded so the schema is shape-uniform.
+- `edge_index` for each of 5 relation types per federation.
+- `y` — the regression target (RUL for cmapss / d3, ≤125) or binary tensor
+  (per-pump anomaly for batadal).
+- Provenance attributes: `client_id`, `task`, plus federation-specific
+  metadata (`cmapss_subset`, `cmapss_engine_id`, `cmapss_end_cycle`,
+  `cmapss_dominant_regime`, `batadal_window_start`, `batadal_window_attacked`,
+  or `cluster_id` for D3).
+
+## Reproducibility
+
+Every byte of these archives is produced by a single deterministic script:
+[`scripts/generate_hf_datasets.py`](https://github.com/) in the parent
+CAFA-GNN repository, driven by a fixed master seed (default `42`). CMAPSS and
+BATADAL features are derived from the real public datasets (NASA PHM Society
+S3 + batadal.net); D3 features come from a three-regime physics model
+(vibration-dominant, monotonic drift with end-of-life acceleration, stepwise
+faults). Re-running the script with the same seed yields bit-identical
+HeteroData payloads.
+
+## Upstream licensing
+
+- **CMAPSS** — released by NASA Prognostics Center of Excellence; freely usable
+  for research with attribution.
+- **BATADAL** — released alongside the BATADAL challenge; freely usable for
+  research with attribution to the BATADAL organisers.
+- **D3 Synthetic** — generated by this project; CC-BY-4.0.
+
+When citing this bundle, please also cite the upstream CMAPSS (Saxena et al.
+PHM '08) and BATADAL (Taormina et al. 2018) datasets where applicable.
+
+## Citation
+
+```bibtex
+@inproceedings{cafa-gnn,
+  title  = {CAFA-GNN: A Concept-Aware Federated Agentic Framework for Industrial Predictive Maintenance},
+  author = {...},
+  year   = {2026}
+}
+```