Datasets:

Sakthigsjhy
/

MagBridge-Battery

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:

Paper: https://arxiv.org/abs/2605.20240 — DOI: 10.48550/arXiv.2605.20240
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

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:

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

pip install pandas pyarrow
python load_example.py

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.

@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

@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

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.