electricsheepafrica/africa-mhealth-pharmacy-security

African mHealth and Pharmacy Security Dataset | Africa (original)

Size category: 10K<n<100K - Formats: parquet, optimized-parquet - Sector: governance_security - Engineered by Electric Sheep Africa

TL;DR

This dataset is part of the Electric Sheep Africa catalog on Hugging Face. It is indexed for African data discovery with standardized metadata, loading guidance, provenance notes, and analyst-oriented context.

What This Dataset Covers

Public datasets help analysts inspect structured evidence, build reproducible workflows, and compare patterns across domains.

Dataset context from the existing Hugging Face card: African mHealth and Pharmacy Security Dataset Dataset Description Synthetic dataset of security incidents in mobile health and pharmacy systems across Africa, paired with secure baseline records. mHealth apps for medication adherence, e-pharmacy platforms, and telepharmacy are booming in Africa but operate with minimal security oversight. Research Gap Addressed mHealth security research focuses on FDA-regulated apps in regulated markets. African mHealth… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-mhealth-pharmacy-security.

Dataset Profile

Field	Value
Hugging Face repo	electricsheepafrica/africa-mhealth-pharmacy-security
Sector	governance_security
Topic tags	cybersecurity, healthcare, synthetic, mhealth
Modalities	tabular, text
Formats	parquet, optimized-parquet
Size category	10K<n<100K
Countries	Africa-wide or source-defined African coverage
ISO3 coverage	not declared
Last modified on HF	2026-05-22 23:10:29+00:00
Inventory snapshot	2026-07-16T16:00:34Z

How To Read This Dataset

• Start from the repository files and the dataset viewer when available.
• Treat the README context as a fast orientation layer; confirm variable definitions and units in the data files before modeling.
• Use explicit country columns when present. When geography is only implied by the title or source metadata, document that assumption in downstream analysis.
• Preserve missing values until you have a defensible imputation rule.

Usage

from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-mhealth-pharmacy-security")
print(ds)

split_name = next(iter(ds))
table = ds[split_name]
print(table.features)
print(table[:3])

Convert To Pandas When Tabular

from datasets import Dataset

first_split = ds[next(iter(ds))]
if isinstance(first_split, Dataset):
    df = first_split.to_pandas()
    print(df.head())

Data Quality Notes

• This card was standardized from the Electric Sheep Africa Hugging Face metadata inventory.
• Exact schema, row counts, and source files should be inspected in the repository data files.
• Metadata gaps from the inventory: country, upstream_publisher.
• Do not infer policy meaning from labels alone; confirm definitions, units, and methods in the source material.

Source And Provenance

• Source context: original
• Publisher/source attribution: original
• License: CC BY 4.0
• Hugging Face URL: https://huggingface.co/datasets/electricsheepafrica/africa-mhealth-pharmacy-security
• Inventory retrieved at: 2026-07-16T16:00:34Z

Suggested Analyses

• Inspect schema and missingness before modeling.
• Profile variables by geography, time, and subgroup columns where present.
• Join with other Electric Sheep Africa datasets using explicit country, year, and indicator fields when available.
• Build reproducible notebooks that cite both the original source context and the Electric Sheep Africa Hugging Face repo.

Citation

@misc{electric_sheep_africa_africa_mhealth_pharmacy_security_2026,
  title        = {African mHealth and Pharmacy Security Dataset | Africa (original)},
  author       = {original},
  year         = {2026},
  url          = {https://huggingface.co/datasets/electricsheepafrica/africa-mhealth-pharmacy-security},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-mhealth-pharmacy-security}}
}

License

Released under CC BY 4.0.

Original source rights remain with the original publisher or data provider. Electric Sheep Africa engineering standardizes discovery metadata, documentation, and usage guidance for analysis on Hugging Face.

About Electric Sheep Africa

Electric Sheep Africa publishes ML-ready African public datasets on Hugging Face.

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Provenance: metadata-backed README standardized 2026-08-12 by the Electric Sheep Africa README system. Inventory source: catalog/esa_metadata_inventory/master_metadata.jsonl.