article_release/README.md

Article-Release Dataset Downloads

The finalized article-release dataset is hosted directly in this Hugging Face repository.

Primary Download — Hugging Face

File	Format	View on Hugging Face	Direct download
ALL_benchmark_W60.parquet	Apache Parquet	View file	Download
ALL_benchmark_W60.xlsx	Microsoft Excel	View file	Download

The Parquet file is recommended for programmatic analysis. The Excel file is provided for convenient inspection and use in spreadsheet software.

Backup Download — Google Drive

If the Hugging Face preview or download is temporarily unavailable, the same article-release files can be downloaded from the following public Google Drive backup folder:

Open the Google Drive backup folder

The backup folder contains:

ALL_benchmark_W60.parquet
ALL_benchmark_W60.xlsx

The Google Drive folder is configured as:

Anyone with the link → Viewer

No access request should normally be required.

Dataset Description

Both files contain the same finalized article-release benchmark dataset in different formats.

The dataset contains:

• 14,398 rows
• 107 columns
• data from six strawberry cold-chain shipments;
• resampled multi-sensor temperature measurements;
• engineered W60 features;
• current risk-stage labels;
• future severe-risk prediction targets;
• explanation-consistency cause flags;
• data-quality, confidence, and audit-related fields.

Recommended Format

Parquet

Use ALL_benchmark_W60.parquet for:

• Python or R analysis;
• machine-learning experiments;
• preservation of data types;
• efficient loading and storage.

Excel

Use ALL_benchmark_W60.xlsx for:

• manual inspection;
• spreadsheet-based review;
• convenient viewing of columns and values.

Loading with Python

Parquet

from huggingface_hub import hf_hub_download
import pandas as pd

repo_id = "NifferLi/Cold-Chain-Transportation-Strawberry"

path = hf_hub_download(
    repo_id=repo_id,
    filename="article_release/ALL_benchmark_W60.parquet",
    repo_type="dataset"
)

df = pd.read_parquet(path)

print(df.shape)
print(df.head())

Excel

from huggingface_hub import hf_hub_download
import pandas as pd

repo_id = "NifferLi/Cold-Chain-Transportation-Strawberry"

path = hf_hub_download(
    repo_id=repo_id,
    filename="article_release/ALL_benchmark_W60.xlsx",
    repo_type="dataset"
)

df = pd.read_excel(path)

print(df.shape)
print(df.head())

Loading Files Downloaded from Google Drive

If the files were downloaded from the Google Drive backup folder, load them directly from the local directory:

import pandas as pd

df_parquet = pd.read_parquet("ALL_benchmark_W60.parquet")
df_excel = pd.read_excel("ALL_benchmark_W60.xlsx")

print(df_parquet.shape)
print(df_excel.shape)

Availability Note

Hugging Face is the primary hosting and documentation platform for this dataset.

The public Google Drive folder is maintained as a backup mirror to ensure continuous access if the Hugging Face file preview, content-delivery service, or direct download is temporarily unavailable.

Both locations provide the same finalized article-release files.

Citation

When using this dataset, please cite the associated article:

Li, H., Uygun, Ö., Yu, X., Zhou, Y., Chang, X., & Chen, C.-H.
A Human-Centric Edge-Oriented Decision Support System for Cold Chain Transportation:
Early Warning, Trigger-Time Explanation, and Prescriptive Action Ranking.
Advanced Engineering Informatics, forthcoming.

The DOI and final bibliographic details will be added once available.

The dataset repository may also be cited as:

@dataset{li_coldchain_transportation_strawberry_advei,
  author    = {Li, Hu},
  title     = {Cold-Chain Transportation Strawberry Dataset for ADVEI Article Release},
  publisher = {Hugging Face},
  year      = {2026},
  note      = {Processed dataset for the accepted Advanced Engineering Informatics article}
}

Contact

For questions about the dataset, file contents, or download access, please open a discussion in the Hugging Face dataset repository.