Datasets:

minjiechen
/

MagNet

pretty_name: Princeton MagNet Magnetic Material Data
license: cc-by-4.0
task_categories:
  - tabular-regression
  - time-series-forecasting
tags:
  - magnetics
  - power-electronics
  - material-science
  - core-loss
  - hysteresis
size_categories:
  - 100K<n<1M
configs:
  - config_name: steady-state-scalar
    default: true
    data_files:
      - split: full
        path: releases/magnet-steady-state-training-ready/data/scalar/full/*.parquet
  - config_name: steady-state-sequence
    data_files:
      - split: full
        path: >-
          releases/magnet-steady-state-training-ready/data/sequence/full/*.parquet
  - config_name: transient-sequences
    data_files:
      - split: full
        path: >-
          releases/magnet-transient-training-ready/data/sequences/full/**/*.parquet

Princeton MagNet data

This is the public, static data library for Princeton MagNet. It contains four versioned releases in one dataset repository: steady-state raw and training-ready data, and transient raw and training-ready data. The three training-ready Parquet views are available as named configurations; the raw MATLAB and ZIP files are archival downloads. The dataset is not a leaderboard and contains no challenge scores, participant submissions, or team predictions.

Release	Repository folder	Measurements	Materials
Steady-state raw	`releases/magnet-steady-state-raw`	812,492 Combined; 775,865 Cycle	15
Steady-state training-ready	`releases/magnet-steady-state-training-ready`	775,865	15
Transient raw	`releases/magnet-transient-raw`	122,399 B/H/temperature sequences	15
Transient training-ready	`releases/magnet-transient-training-ready`	122,399	15

The manifest in each folder gives exact file sizes, SHA-256 checksums, source lineage, schema version, and validation status. Read the corresponding release card before using a file; raw and processed releases have different contracts. All releases currently expose one full split rather than an arbitrary benchmark train/test split.

Materials

Steady-state: 3C90, 3C92, 3C94, 3C95, 3E6, 3F4, 77, 78, 79, ML95S, N27, N30, N49, N87, T37.

Transient: 3C90, 3C92, 3C94, 3C95, 3E6, 3F4, 77, 78, FEC007, FEC014, N27, N30, N49, N87, T37.

Use the training-ready data

from datasets import load_dataset

scalar = load_dataset(
    "minjiechen/MagNet", name="steady-state-scalar", split="full"
)
waveforms = load_dataset(
    "minjiechen/MagNet", name="steady-state-sequence", split="full"
)
transient = load_dataset(
    "minjiechen/MagNet", name="transient-sequences", split="full"
)

The two steady-state configurations join one-to-one on measurement_id. Steady-state triangular waveforms retain positive and negative duty fractions; sinusoidal duty is null. The transient release retains variable-length B/H sequences and a 16 MHz sampling contract. Model users should review quality flags, operating ranges, and split-leakage guidance in the individual cards.

To download an archival file, use its exact path from the release manifest with hf_hub_download(repo_id="minjiechen/MagNet", repo_type="dataset", filename="releases/<release-id>/<path>").

Provenance and limitations

The selected T37 steady-state Combined file embeds 2023-09-11 while the Cycle file embeds 2023-08-29. The project owner attests that both derive from the August 29, 2023 processing and approved publication with this documented exception. The original MAT bytes retain both embedded dates; independent Cycle-to-Combined derivation has not been verified. See the steady-state cards and source catalogs for detail.

The ten transient source ZIPs had contributor names and local paths removed from Excel package metadata before publication. Scientific worksheet and CSV content was preserved; published checksums and the sanitization audit are in the transient raw release. This release is not a claim of physical measurement accuracy, safety certification, or universal model performance.

License, citation, and maintainer

All four releases are distributed under Creative Commons Attribution 4.0 International (CC BY 4.0). Each release includes its own LICENSE file.

Please cite Li et al., “How MagNet: Machine Learning Framework for Modeling Power Magnetic Material Characteristics,” IEEE Transactions on Power Electronics, 2023, doi:10.1109/TPEL.2023.3309232.

Maintainer: Princeton Power Electronics Research Lab / Minjie Chen.