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README.md

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+---
+license: cc-by-4.0
+task_categories:
+  - time-series-forecasting
+  - tabular-classification
+tags:
+  - bioprocess
+  - manufacturing
+  - anomaly-detection
+  - fault-detection
+  - batch-monitoring
+  - pharma
+pretty_name: Golden Batch Sentinel Data
+size_categories:
+  - 1M<n<10M
+---
+
+# Golden Batch Sentinel Data
+
+Benchmark datasets for process monitoring and fault detection in batch manufacturing.
+
+## Datasets
+
+### IndPenSim (Industrial Penicillin Simulation)
+
+A 100,000L fermentation simulation with 100 batches and rich multivariate signals.
+
+- **Source**: [Mendeley Data](https://data.mendeley.com/datasets/pdnjz7zz5x/2)
+- **Paper**: [Modern day monitoring and control challenges...](https://doi.org/10.1016/j.compchemeng.2018.05.019)
+- **Batches**: 100 (90 normal, 10 faulty)
+- **Variables**: 37 process variables (Raman spectra excluded for efficiency)
+- **Time resolution**: 0.2 hours
+
+**Files:**
+- `indpensim/batches.parquet` - Main batch data
+- `indpensim/statistics.parquet` - Batch statistics and fault labels
+
+### Tennessee Eastman Process (TEP)
+
+The most common benchmark for fault detection in multivariate industrial processes.
+
+- **Source**: [Harvard Dataverse](https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/6C3JR1)
+- **Fault types**: 20 different fault scenarios
+- **Variables**: 52 (41 measured + 11 manipulated)
+
+**Files:**
+- `tep/fault_free_train.parquet` - Normal operation (training)
+- `tep/fault_free_test.parquet` - Normal operation (testing)
+- `tep/faulty_train.parquet` - Faulty operation (training, all 20 faults)
+- `tep/faulty_test.parquet` - Faulty operation (testing, all 20 faults)
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+# Load IndPenSim
+indpensim = load_dataset("foundation-models/golden-batch-sentinel-data", data_dir="indpensim")
+
+# Load TEP
+tep = load_dataset("foundation-models/golden-batch-sentinel-data", data_dir="tep")
+
+# Or load specific files
+import pandas as pd
+from huggingface_hub import hf_hub_download
+
+path = hf_hub_download(
+    repo_id="foundation-models/golden-batch-sentinel-data",
+    filename="indpensim/batches.parquet",
+    repo_type="dataset"
+)
+df = pd.read_parquet(path)
+```
+
+## License
+
+The original datasets are provided under their respective licenses:
+- IndPenSim: CC BY 4.0
+- TEP: Public domain
+
+This compilation is provided under CC BY 4.0.
+
+## Citation
+
+If you use this data, please cite the original papers:
+
+```bibtex
+@article{goldrick2019modern,
+  title={Modern day monitoring and control challenges outlined on an industrial-scale benchmark fermentation process},
+  author={Goldrick, Stephen and others},
+  journal={Computers \& Chemical Engineering},
+  year={2019}
+}
+
+@article{downs1993plant,
+  title={A plant-wide industrial process control problem},
+  author={Downs, James J and Vogel, Ernest F},
+  journal={Computers \& Chemical Engineering},
+  year={1993}
+}
+```