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Datasets:
electricsheepafrica
/
online-pharmacy-regulation

Tasks:
Tabular Classification
Tabular Regression
Modalities:
Tabular
Text
Formats:
csv
Languages:
English
Size:
10K - 100K
Tags:
healthcare
medicine-quality
online-pharmacy
e-pharmacy
social-media
darknet
Libraries:
Datasets
pandas
Polars
License:
Dataset card Data Studio Files
xet
 Community
online-pharmacy-regulation
File size: 2,296 Bytes
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---
license: cc-by-4.0
task_categories:
  - tabular-classification
  - tabular-regression
language:
  - en
tags:
  - healthcare
  - medicine-quality
  - online-pharmacy
  - e-pharmacy
  - social-media
  - darknet
  - falsified
  - consumer-safety
  - sub-saharan-africa
  - lmic
pretty_name: "Online Pharmacy & E-Pharmacy Regulation (SF Prevalence, Platform Type, Consumer Safety)"
size_categories:
  - 10K<n<100K
configs:
  - config_name: licensed_e_pharmacy
    data_files: data/epharmacy_licensed_e_pharmacy.csv
  - config_name: social_media_marketplace
    data_files: data/epharmacy_social_media_marketplace.csv
    default: true
  - config_name: rogue_website_darknet
    data_files: data/epharmacy_rogue_website_darknet.csv
---

# Online Pharmacy & E-Pharmacy Regulation Dataset

## Abstract

**30,000 simulated online medicine purchase observations** (10,000 per scenario) across three e-pharmacy platform types. Variables include platform, product category, seller licensing, prescription verification, SF classification, price, payment method, and consumer demographics. Three scenarios: licensed e-pharmacy (6% SF), social media marketplace (65%), rogue website/darknet (89%).

**This dataset is entirely simulated. It must not be used for regulatory or enforcement decisions.**

## Validation

<p align="center">
  <img src="validation_report.png" alt="Validation Report" width="100%">
</p>

## Usage

```python
from datasets import load_dataset
dataset = load_dataset("electricsheepafrica/online-pharmacy-regulation", "social_media_marketplace")
df = dataset["train"].to_pandas()
print(df.groupby('platform_name')['quality_test_result'].apply(lambda x: (x=='fail').mean()))
```

## References

1. WHO (2024). SF medicines sold online.
2. NABP (2023). 95% of online pharmacies non-compliant.
3. Interpol Operation Pangea. Illicit online pharmacy crackdowns.
4. LegitScript. 30,000+ rogue pharmacy websites.

## Citation

```bibtex
@dataset{esa_epharmacy_2025,
  title   = {Online Pharmacy and E-Pharmacy Regulation Dataset},
  author  = {{Electric Sheep Africa}},
  year    = {2025},
  publisher = {Hugging Face},
  url     = {https://huggingface.co/datasets/electricsheepafrica/online-pharmacy-regulation}
}
```

## License

[CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)