| --- |
| pretty_name: "MagBridge-Battery" |
| license: "cc-by-4.0" |
| task_categories: |
| - tabular-regression |
| - tabular-classification |
| - time-series-forecasting |
| tags: |
| - battery |
| - lithium-ion-batteries |
| - battery-health |
| - state-of-health |
| - soh |
| - magnetometry |
| - magnetic-signatures |
| - synthetic-data |
| - anomaly-detection |
| - out-of-distribution |
| - energy-storage |
| - open-data |
| size_categories: |
| - 1K<n<10K |
| language: |
| - en |
| --- |
| |
| # MagBridge-Battery v1.0 |
|
|
| ## Hugging Face mirror note |
|
|
| This Hugging Face repository is a machine-learning-friendly mirror of **MagBridge-Battery v1.0**. The archival dataset record and primary dataset DOI are hosted on Zenodo: https://zenodo.org/records/20260147. |
|
|
| Please cite the **paper first** and the **dataset second** when using this resource: |
|
|
| 1. Paper: https://arxiv.org/abs/2605.20240 — DOI: 10.48550/arXiv.2605.20240 |
| 2. Dataset: https://zenodo.org/records/20260147 — DOI: 10.5281/zenodo.20260147 |
|
|
|
|
| A synthetic magnetic-signature dataset for lithium iron phosphate (LFP) cells, bridging the Mohammadi–Jerschow OSF magnetometry archive with PulseBat electrochemical labels. |
|
|
| - **6,760 samples** at 100 time-steps per sample, 6 signal channels |
| - **Cell-disjoint, leakage-free benchmark split** (`by_cell_primary`) |
| - **Anomaly subtypes**: sensor dropout, calibration drift, temporal warp, periodic interference, low-voltage Regime-B extrapolation |
| - **License**: CC-BY-4.0 (data) / Apache-2.0 (code) |
| - **Schema version**: 1.0 |
|
|
| ## Contents |
|
|
| ``` |
| data/ |
| shard_0000.parquet … shard_0004.parquet # 5 shards × 1352 rows = 6760 |
| metadata.parquet # row-aligned metadata |
| splits/ |
| by_cell_primary.json # USE THIS for benchmark reporting |
| by_record_optimistic_baseline.json # Leaky baseline for contrast only |
| manifest.json # provenance, hashes, config |
| checksums.sha256 # integrity hashes for every file |
| load_example.py # minimal loader |
| dataset_card.md # full dataset card |
| CITATION.cff # machine-readable citation |
| CITING.md # how to cite (paper + dataset) |
| LICENSE # CC-BY-4.0 dataset license + upstream notices |
| LICENSE-CODE # Apache-2.0 code license |
| NOTICE-PULSEBAT # MIT notice from PulseBat upstream |
| README.md # this file |
| ``` |
|
|
|
|
| ## Loading from Hugging Face |
|
|
| After uploading this folder to Hugging Face, users can load the Parquet shards directly: |
|
|
| ```python |
| from datasets import load_dataset |
| |
| # Replace YOUR_USERNAME with the Hugging Face account or organization name. |
| ds = load_dataset("YOUR_USERNAME/MagBridge-Battery", data_files="data/shard_*.parquet", split="train") |
| print(ds) |
| ``` |
|
|
| For the official leakage-safe benchmark split, use `splits/by_cell_primary.json`. The split file contains `train_samples`, `val_samples`, and `test_samples` sample IDs. |
|
|
| ## Quick start |
|
|
| ```bash |
| pip install pandas pyarrow |
| python load_example.py |
| ``` |
|
|
| ```python |
| import pandas as pd, json |
| from pathlib import Path |
| |
| base = Path(".") |
| |
| # 1. Load all shards into one dataframe |
| shards = sorted((base / "data").glob("shard_*.parquet")) |
| df = pd.concat([pd.read_parquet(s) for s in shards], ignore_index=True) |
| |
| # 2. Load the primary (cell-disjoint) split |
| split = json.loads((base / "splits" / "by_cell_primary.json").read_text()) |
| train_ids = set(split["train_samples"]) |
| val_ids = set(split["val_samples"]) |
| test_ids = set(split["test_samples"]) |
| |
| train_df = df[df["sample_id"].isin(train_ids)].reset_index(drop=True) |
| val_df = df[df["sample_id"].isin(val_ids)].reset_index(drop=True) |
| test_df = df[df["sample_id"].isin(test_ids)].reset_index(drop=True) |
| |
| print(f"train={len(train_df)}, val={len(val_df)}, test={len(test_df)}") |
| # Expect: train=4507, val=1074, test=1179 |
| ``` |
|
|
| ## Which split do I use? |
|
|
| **Always `by_cell_primary` for any reported result.** It guarantees: |
|
|
| - Zero physical cells overlap between train, val, and test |
| - Zero (clean parent → anomaly child) pairs are split across train/val/test |
| - Zero sample-ID overlap |
|
|
| `by_record_optimistic_baseline` is intentionally leaky (59 overlapping cells, 292 cross-split parent-child pairs) and is shipped only as a contrast — to demonstrate how much the leakage inflates apparent performance. **Do not report its numbers without explicitly labelling them as the leaky baseline.** |
|
|
| ## Signal channels (per row, length 100) |
|
|
| | Column | Description | Sign | |
| |------------|-----------------------------------------------------------------------------|-----------| |
| | `B_s1Y` | Sensor 1, Y component of magnetic field (nT) | signed | |
| | `B_s1Z` | Sensor 1, Z component | signed | |
| | `B_s2Y` | Sensor 2, Y component | signed | |
| | `B_s2Z` | Sensor 2, Z component | signed | |
| | `B_s1C5` | Sensor 1 OSF channel-5 (originally labelled "Mag" in OSF source) | **signed** | |
| | `B_s2C6` | Sensor 2 OSF channel-6 (originally labelled "Mag" in OSF source) | **signed** | |
| | `time_norm`| Normalized time index in [0, 1], 100 evenly-spaced points | constant grid (same for every sample) | |
|
|
| > **Important — `B_s1C5` and `B_s2C6` are NOT strict magnitudes.** They are the signed channel-5 / channel-6 fields from the OSF source. The OSF archive labels them "Mag" but their values can legitimately be negative (123 rows with negatives in `B_s1C5`, 86 in `B_s2C6`). If you need a non-negative magnitude, compute `sqrt(B_s1Y² + B_s1Z²)` yourself. |
|
|
| > **`time_norm`** is the same vector for every sample. It is included for loader convenience and can be dropped without information loss. The `temporal_warp` anomaly modifies signal values on this fixed grid, not the grid itself. |
| |
| ## Metadata columns |
| |
| `sample_id`, `parent_sample_id`, `cell_id`, `generation_seed`, `bridge_version`, `bridge_config_hash`, `schema_version`, `voltage`, `soc`, `soh`, `chemistry`, `regime`, `nearest_anchor`, `anomaly_flag`, `anomaly_subtype`, `anomaly_origin`, `anomaly_severity`, `second_life_class` |
| |
| See `dataset_card.md` for full semantics. |
| |
| ## Anomaly composition |
| |
| | Subtype | Count | Has parent | Notes | |
| |--------------------------|-------|------------|--------------------------------------------------------| |
| | `none` (clean) | 5,600 | — | Grounded PulseBat-conditioned | |
| | `low_voltage_regime_B` | 560 | no | Extrapolation regime; `soh`, `u_features`, `second_life_class` are NaN by design | |
| | `sensor_dropout` | 150 | yes | Synthetic; parent is a clean sample | |
| | `calibration_drift` | 150 | yes | Synthetic | |
| | `temporal_warp` | 150 | yes | Synthetic; parent-child max-diff median ≈ 13.4 nT | |
| | `periodic_interference` | 150 | yes | Synthetic | |
| |
| Regime-B samples are for **low-voltage / OOD / anomaly-style evaluation** only — not for SOH regression, since SOH is intentionally missing. |
| |
| ## Citation |
| |
| If you use MagBridge-Battery in your work, please cite **both** the paper and |
| the dataset. Cite the paper as your primary reference; include the dataset |
| DOI when the data specifically is what you are using. |
| |
| **Paper (primary citation)** |
| |
| > Gunasekar, S. P. and Rangarajan, P. K. "MagBridge-Battery: A Synthetic |
| > Bridge Dataset for Li-ion Magnetometry and State-of-Health Diagnostics." |
| > arXiv preprint arXiv:2605.20240, 2026. |
| |
| ```bibtex |
| @article{magbridge2026, |
| author = {Gunasekar, Sakthi Prabhu and Rangarajan, Prasanna Kumar}, |
| title = {{MagBridge-Battery}: A Synthetic Bridge Dataset for |
| {Li}-ion Magnetometry and State-of-Health Diagnostics}, |
| journal = {arXiv preprint}, |
| eprint = {2605.20240}, |
| archivePrefix = {arXiv}, |
| year = {2026} |
| } |
| ``` |
| |
| **Dataset (secondary citation)** |
| |
| > Gunasekar, S. P. and Rangarajan, P. K. *MagBridge-Battery v1.0* [Data set]. |
| > Zenodo, 2026. https://doi.org/10.5281/zenodo.20260147 |
| |
| ```bibtex |
| @misc{magbridge_battery_v1_0, |
| author = {Gunasekar, Sakthi Prabhu and Rangarajan, Prasanna Kumar}, |
| title = {{MagBridge-Battery v1.0}}, |
| year = {2026}, |
| publisher = {Zenodo}, |
| doi = {10.5281/zenodo.20260147}, |
| note = {Data set} |
| } |
| ``` |
| |
| See `CITING.md` for full guidance (including upstream-source citations) and |
| `CITATION.cff` for the machine-readable form. The `arXiv:2605.20240` |
| identifier will be added to the Zenodo metadata once the arXiv preprint is |
| live; the dataset DOI above is the final reserved DOI. |
| |
| ## Integrity verification |
| |
| ```bash |
| sha256sum -c checksums.sha256 |
| ``` |
| |
| All files should report `OK`. |
| |
| ## Licenses |
| |
| - **Data** (`data/`, `splits/`, `manifest.json`): CC-BY-4.0. See `LICENSE`. |
| - **Code** (`load_example.py` and other release scripts): Apache-2.0. See `LICENSE-CODE`. |
| - **Upstream attribution**: The `LICENSE` file contains a full upstream-notice section for the OSF magnetometry archive and the PulseBat dataset. The PulseBat upstream MIT license text is reproduced in `NOTICE-PULSEBAT`. |
|
|
| This bundle does not redistribute raw OSF or PulseBat data files. See `LICENSE` for the precise scope of what is and is not licensed under CC-BY-4.0. |
|
|
| ## Provenance |
|
|
| This bundle is derived from two upstream sources (hashes pinned in `manifest.json`): |
|
|
| - **OSF magnetometry archive** (Mohammadi, Jerschow et al.) — `osf_data_hash` in manifest |
| - **PulseBat electrochemical dataset** — `pulsebat_data_hash` in manifest |
|
|
| The bridging procedure, configuration, and code version are recorded in `manifest.json` (`bridge_version`, `bridge_code_commit`, `bridge_config`, `config_hash`). Generation date: 2026-05-16. |
|
|
