---
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.