Add dataset files

README.md

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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/)

generate_dataset.py

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+#!/usr/bin/env python3
+"""
+Literature-Informed Online Pharmacy & E-Pharmacy Regulation Dataset
+=====================================================================
+
+Each record = ONE online medicine purchase/listing assessed.
+
+Sources (v2.0):
+[1] WHO (2024). SF products increasingly sold online. Rise of
+    unauthorized e-pharmacy sites exacerbates SF problem.
+[2] NABP (2023). 95% of online pharmacies globally do not comply
+    with pharmacy laws and practice standards.
+[3] Interpol Operation Pangea. Annual crackdown on illicit online
+    pharmacies. 2022: 4,000+ websites shut down.
+[4] LegitScript. ~30,000+ rogue pharmacy websites identified.
+    Most sell prescription medicines without prescriptions.
+[5] WHO Africa (2023). E-pharmacy regulation nascent in SSA.
+    Only 5-10 countries have specific e-pharmacy legislation.
+"""
+
+import numpy as np
+import pandas as pd
+import argparse
+import os
+
+PRODUCT_CATEGORIES = [
+    ('erectile_dysfunction', 'lifestyle', 0.15),
+    ('weight_loss', 'lifestyle', 0.10),
+    ('antibiotic', 'prescription', 0.12),
+    ('opioid_analgesic', 'controlled', 0.08),
+    ('benzodiazepine', 'controlled', 0.05),
+    ('antimalarial', 'essential', 0.08),
+    ('ARV_HIV', 'essential', 0.04),
+    ('antihypertensive', 'NCD', 0.06),
+    ('antidiabetic', 'NCD', 0.05),
+    ('contraceptive', 'reproductive', 0.04),
+    ('skin_lightening', 'cosmetic', 0.06),
+    ('COVID_treatment', 'pandemic', 0.03),
+    ('traditional_herbal', 'unregulated', 0.05),
+    ('supplement_vitamin', 'OTC', 0.05),
+    ('cancer_medicine', 'specialty', 0.04),
+]
+
+SCENARIOS = {
+    'licensed_e_pharmacy': {
+        'platform_type': 'licensed_e_pharmacy',
+        'sf_rate': 0.05,
+        'falsified_rate': 0.01,
+        'no_prescription_rate': 0.15,
+        'unregistered_product_rate': 0.08,
+        'regulatory_compliant': 0.85,
+        'pharmacist_available': 0.90,
+        'verified_supplier': 0.88,
+        'delivery_cold_chain': 0.70,
+        'consumer_complaint_rate': 0.03,
+    },
+    'social_media_marketplace': {
+        'platform_type': 'social_media',
+        'sf_rate': 0.35,
+        'falsified_rate': 0.15,
+        'no_prescription_rate': 0.92,
+        'unregistered_product_rate': 0.55,
+        'regulatory_compliant': 0.02,
+        'pharmacist_available': 0.05,
+        'verified_supplier': 0.08,
+        'delivery_cold_chain': 0.05,
+        'consumer_complaint_rate': 0.20,
+    },
+    'rogue_website_darknet': {
+        'platform_type': 'rogue_website',
+        'sf_rate': 0.65,
+        'falsified_rate': 0.40,
+        'no_prescription_rate': 0.99,
+        'unregistered_product_rate': 0.85,
+        'regulatory_compliant': 0.00,
+        'pharmacist_available': 0.01,
+        'verified_supplier': 0.02,
+        'delivery_cold_chain': 0.01,
+        'consumer_complaint_rate': 0.45,
+    },
+}
+
+
+def generate_dataset(n=10000, seed=42, scenario='social_media_marketplace'):
+    rng = np.random.default_rng(seed)
+    sc = SCENARIOS[scenario]
+    records = []
+    n_prod = len(PRODUCT_CATEGORIES)
+
+    for idx in range(n):
+        rec = {'id': idx + 1}
+        rec['platform_type'] = sc['platform_type']
+        rec['seller_id'] = f"EPHARM_{rng.integers(1, 1000):05d}"
+        rec['platform_name'] = rng.choice(
+            ['WhatsApp', 'Facebook', 'Instagram', 'Telegram', 'TikTok',
+             'dedicated_website', 'marketplace_app', 'darknet_market'],
+            p=[0.10, 0.15, 0.10, 0.08, 0.05, 0.25, 0.15, 0.12]
+            if scenario == 'social_media_marketplace'
+            else ([0.02, 0.03, 0.02, 0.01, 0.01, 0.80, 0.10, 0.01]
+                  if scenario == 'licensed_e_pharmacy'
+                  else [0.03, 0.05, 0.03, 0.10, 0.02, 0.30, 0.07, 0.40]))
+
+        prod = PRODUCT_CATEGORIES[rng.choice(n_prod,
+            p=[p[2] for p in PRODUCT_CATEGORIES])]
+        rec['product_name'] = prod[0]
+        rec['product_category'] = prod[1]
+
+        rec['prescription_required_legally'] = 1 if prod[1] in (
+            'prescription', 'controlled', 'essential', 'NCD', 'specialty') else 0
+        rec['prescription_verified'] = 0
+        if rec['prescription_required_legally']:
+            rec['prescription_verified'] = 0 if rng.random() < sc['no_prescription_rate'] else 1
+
+        rec['product_registered'] = 0 if rng.random() < sc['unregistered_product_rate'] else 1
+        rec['seller_licensed'] = 1 if rng.random() < sc['regulatory_compliant'] else 0
+        rec['pharmacist_consultation'] = 1 if rng.random() < sc['pharmacist_available'] else 0
+        rec['verified_supply_chain'] = 1 if rng.random() < sc['verified_supplier'] else 0
+        rec['cold_chain_maintained'] = 1 if rng.random() < sc['delivery_cold_chain'] else 0
+
+        rec['manufacturer_origin'] = rng.choice(
+            ['India', 'China', 'Europe', 'local_SSA', 'unknown', 'USA'],
+            p=[0.30, 0.20, 0.08, 0.07, 0.30, 0.05]
+            if scenario != 'licensed_e_pharmacy'
+            else [0.35, 0.10, 0.15, 0.15, 0.10, 0.15])
+
+        # SF determination
+        base_sf = sc['sf_rate']
+        if not rec['product_registered']:
+            base_sf *= 1.4
+        if not rec['verified_supply_chain']:
+            base_sf *= 1.3
+        if rec['manufacturer_origin'] == 'unknown':
+            base_sf *= 1.5
+        if prod[1] == 'controlled':
+            base_sf *= 1.3
+        if prod[1] == 'lifestyle':
+            base_sf *= 1.2
+        base_sf = np.clip(base_sf, 0.01, 0.90)
+
+        is_sf = rng.random() < base_sf
+        rec['quality_test_result'] = 'fail' if is_sf else 'pass'
+        rec['sf_classification'] = 'compliant'
+        if is_sf:
+            fals_prob = sc['falsified_rate'] / max(sc['sf_rate'], 0.001)
+            rec['sf_classification'] = 'falsified' if rng.random() < fals_prob else 'substandard'
+
+        rec['API_content_adequate'] = 1
+        if is_sf:
+            rec['API_content_adequate'] = 0 if rng.random() < 0.65 else 1
+
+        rec['price_vs_reference'] = round(np.clip(
+            rng.normal(0.40 if scenario == 'rogue_website_darknet' else
+                       (0.70 if scenario == 'social_media_marketplace' else 0.90), 0.20),
+            0.10, 2.0), 2)
+        rec['payment_method'] = rng.choice(
+            ['mobile_money', 'bank_transfer', 'cash_on_delivery',
+             'cryptocurrency', 'credit_card'],
+            p=[0.35, 0.15, 0.30, 0.05, 0.15]
+            if scenario != 'rogue_website_darknet'
+            else [0.10, 0.10, 0.05, 0.50, 0.25])
+
+        rec['consumer_age_group'] = rng.choice(
+            ['18_24', '25_34', '35_49', '50_plus'],
+            p=[0.25, 0.35, 0.25, 0.15])
+        rec['consumer_education'] = rng.choice(
+            ['primary', 'secondary', 'tertiary'],
+            p=[0.15, 0.40, 0.45] if scenario != 'rogue_website_darknet'
+            else [0.05, 0.30, 0.65])
+        rec['consumer_complaint_filed'] = 1 if rng.random() < sc['consumer_complaint_rate'] else 0
+        rec['adverse_event_reported'] = 0
+        if is_sf:
+            rec['adverse_event_reported'] = 1 if rng.random() < 0.05 else 0
+
+        rec['website_takedown'] = 0
+        if scenario == 'rogue_website_darknet' and rng.random() < 0.03:
+            rec['website_takedown'] = 1
+        elif scenario == 'social_media_marketplace' and rng.random() < 0.01:
+            rec['website_takedown'] = 1
+
+        rec['year'] = rng.choice([2020, 2021, 2022, 2023, 2024],
+                                 p=[0.08, 0.12, 0.18, 0.28, 0.34])
+
+        records.append(rec)
+
+    df = pd.DataFrame(records)
+    sf_rate = df['quality_test_result'].eq('fail').mean() * 100
+    print(f"\n{'='*65}")
+    print(f"Online Pharmacy — {scenario} (n={n}, seed={seed})")
+    print(f"{'='*65}")
+    print(f"  SF rate: {sf_rate:.1f}% (target ~{sc['sf_rate']*100:.0f}%)")
+    print(f"  No prescription: {(1-df['prescription_verified'].mean())*100:.1f}%")
+    print(f"  Licensed seller: {df['seller_licensed'].mean()*100:.1f}%")
+    return df
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--all-scenarios', action='store_true')
+    parser.add_argument('--n', type=int, default=10000)
+    parser.add_argument('--seed', type=int, default=42)
+    args = parser.parse_args()
+    os.makedirs('data', exist_ok=True)
+    if args.all_scenarios:
+        for sc in SCENARIOS:
+            df = generate_dataset(n=args.n, seed=args.seed, scenario=sc)
+            df.to_csv(os.path.join('data', f'epharmacy_{sc}.csv'), index=False)
+            print(f"  -> Saved\n")
+    else:
+        df = generate_dataset(n=args.n, seed=args.seed)
+        df.to_csv(os.path.join('data', 'epharmacy_social_media_marketplace.csv'), index=False)

requirements.txt

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+numpy>=1.24
+pandas>=2.0
+matplotlib>=3.7

validate_dataset.py

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+#!/usr/bin/env python3
+"""Validation & Diagnostic Visualization for Online Pharmacy & E-Pharmacy Regulation Dataset."""
+
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import os
+
+SCENARIOS = ['licensed_e_pharmacy', 'social_media_marketplace', 'rogue_website_darknet']
+
+
+def load_scenarios(data_dir='data'):
+    dfs = {}
+    for sc in SCENARIOS:
+        path = os.path.join(data_dir, f'epharmacy_{sc}.csv')
+        if os.path.exists(path):
+            dfs[sc] = pd.read_csv(path)
+    return dfs
+
+
+def make_report(dfs, output='validation_report.png'):
+    fig, axes = plt.subplots(4, 2, figsize=(16, 24))
+    fig.suptitle(
+        'Online Pharmacy & E-Pharmacy Regulation — Validation Report\n'
+        '(Licensed E-Pharmacy → Social Media → Rogue/Darknet)',
+        fontsize=15, fontweight='bold', y=0.99)
+    colors = ['#2ecc71', '#f39c12', '#e74c3c']
+    x = np.arange(len(SCENARIOS))
+    labels = ['Licensed', 'Social Media', 'Rogue/Darknet']
+
+    ax = axes[0, 0]
+    sf = [dfs[sc]['quality_test_result'].eq('fail').mean()*100 for sc in SCENARIOS if sc in dfs]
+    ax.bar(x, sf, color=colors, alpha=0.8)
+    ax.set_xticks(x); ax.set_xticklabels(labels, fontsize=9)
+    for i, v in enumerate(sf):
+        ax.text(i, v+1, f'{v:.0f}%', ha='center', fontsize=10, fontweight='bold')
+    ax.set_ylabel('SF Rate (%)'); ax.set_title('SF Rate by Platform Type')
+
+    ax = axes[0, 1]
+    lic = [dfs[sc]['seller_licensed'].mean()*100 for sc in SCENARIOS if sc in dfs]
+    ax.bar(x, lic, color=colors, alpha=0.8)
+    ax.set_xticks(x); ax.set_xticklabels(labels, fontsize=9)
+    for i, v in enumerate(lic):
+        ax.text(i, v+1, f'{v:.0f}%', ha='center', fontsize=10, fontweight='bold')
+    ax.set_ylabel('Rate (%)'); ax.set_title('Seller Licensed')
+
+    ax = axes[1, 0]
+    df = dfs.get('social_media_marketplace', list(dfs.values())[1])
+    plat = df.groupby('platform_name')['quality_test_result'].apply(
+        lambda x: (x == 'fail').mean()*100).sort_values()
+    ax.barh(range(len(plat)), plat.values, color='#e74c3c', alpha=0.7)
+    ax.set_yticks(range(len(plat)))
+    ax.set_yticklabels([s.replace('_', ' ').title() for s in plat.index], fontsize=7)
+    ax.set_xlabel('SF Rate (%)'); ax.set_title('SF by Platform (Social Media)')
+
+    ax = axes[1, 1]
+    cat = df.groupby('product_category')['quality_test_result'].apply(
+        lambda x: (x == 'fail').mean()*100).sort_values()
+    ax.barh(range(len(cat)), cat.values, color='#9b59b6', alpha=0.7)
+    ax.set_yticks(range(len(cat)))
+    ax.set_yticklabels([s.replace('_', ' ').title() for s in cat.index], fontsize=8)
+    ax.set_xlabel('SF Rate (%)'); ax.set_title('SF by Product Category')
+
+    ax = axes[2, 0]
+    norx = [(1-dfs[sc]['prescription_verified'].mean())*100 for sc in SCENARIOS if sc in dfs]
+    ax.bar(x, norx, color=colors, alpha=0.8)
+    ax.set_xticks(x); ax.set_xticklabels(labels, fontsize=9)
+    for i, v in enumerate(norx):
+        ax.text(i, v+0.5, f'{v:.0f}%', ha='center', fontsize=10, fontweight='bold')
+    ax.set_ylabel('Rate (%)'); ax.set_title('No Prescription Verified')
+
+    ax = axes[2, 1]
+    price = df['price_vs_reference'].values
+    ax.hist(price, bins=30, color='#3498db', alpha=0.7, edgecolor='white')
+    ax.axvline(x=1.0, color='red', linestyle='--', label='Reference price')
+    ax.set_xlabel('Price vs Reference'); ax.set_title('Price Distribution (Social Media)')
+    ax.legend(fontsize=8)
+
+    ax = axes[3, 0]
+    w = 0.35
+    fals = [dfs[sc]['sf_classification'].eq('falsified').mean()*100 for sc in SCENARIOS if sc in dfs]
+    subs = [dfs[sc]['sf_classification'].eq('substandard').mean()*100 for sc in SCENARIOS if sc in dfs]
+    ax.bar(x - w/2, subs, w, label='Substandard', color='#f39c12', alpha=0.8)
+    ax.bar(x + w/2, fals, w, label='Falsified', color='#e74c3c', alpha=0.8)
+    ax.set_xticks(x); ax.set_xticklabels(labels, fontsize=9)
+    ax.set_ylabel('Rate (%)'); ax.set_title('Substandard vs Falsified'); ax.legend(fontsize=8)
+
+    ax = axes[3, 1]
+    comp = [dfs[sc]['consumer_complaint_filed'].mean()*100 for sc in SCENARIOS if sc in dfs]
+    ax.bar(x, comp, color=colors, alpha=0.8)
+    ax.set_xticks(x); ax.set_xticklabels(labels, fontsize=9)
+    for i, v in enumerate(comp):
+        ax.text(i, v+0.3, f'{v:.1f}%', ha='center', fontsize=10, fontweight='bold')
+    ax.set_ylabel('Rate (%)'); ax.set_title('Consumer Complaints Filed')
+
+    plt.tight_layout(rect=[0, 0, 1, 0.97])
+    plt.savefig(output, dpi=150, bbox_inches='tight')
+    print(f'Saved validation report to {output}')
+    plt.close()
+
+
+if __name__ == '__main__':
+    dfs = load_scenarios()
+    if dfs:
+        make_report(dfs)