Standardize Electric Sheep Africa dataset card

README.md

@@ -1,171 +1,150 @@
 ---
 license: cc-by-4.0
+language:
+- en
 task_categories:
 - tabular-classification
 - tabular-regression
-language:
-- en
-tags:
-- synthetic
-- healthcare
-- immunisation
-- vaccination
-- epi
-- zero-dose
-- dropout
-- equity
-- who-unicef
-- wuenic
-- dhs
-- lmic
-- gavi
-pretty_name: Synthetic Childhood Immunisation Coverage & Dropout Dataset (0-23 months)
+multilinguality: monolingual
 size_categories:
 - 10K<n<100K
-configs:
-- config_name: high_coverage
-  data_files: data/immunisation_high_coverage.csv
-- config_name: moderate_coverage
-  data_files: data/immunisation_moderate_coverage.csv
-  default: true
-- config_name: low_coverage
-  data_files: data/immunisation_low_coverage.csv
-data_type: synthetic
+tags:
+- "africa"
+- "electric-sheep-africa"
+- "open-data"
+- "metadata-backed"
+- "culture-language"
+- "csv"
+- "tabular"
+- "text"
+- "synthetic"
+- "healthcare"
+- "immunisation"
+- "vaccination"
+- "epi"
+- "zero-dose"
+- "dropout"
+- "equity"
+- "who-unicef"
+- "wuenic"
+- "dhs"
+- "lmic"
+- "gavi"
+- "literature"
+pretty_name: "Synthetic Childhood Immunisation Coverage & Dropout Dataset (0-23 months) | Africa (Electric Sheep Africa metadata inventory)"
 ---
 
-> ⚠️ **Synthetic dataset** — Parameterized from published SSA literature, not real observations. Not suitable for empirical analysis or policy inference.
-
-# Synthetic Childhood Immunisation Coverage & Dropout Dataset (0–23 months)
-
-## Abstract
-
-This dataset provides **30,000 synthetic records** (10,000 per scenario) of childhood immunisation status for children aged 0-23 months in LMIC settings. Each record contains 28 variables: demographics, socioeconomic determinants (wealth quintile, maternal education, urban/rural, distance to facility), individual vaccine doses (BCG, OPV0-3, Penta1-3, PCV1-3, Rota1-2, IPV1, MCV1-2), and derived indicators (fully immunised, zero-dose, dropout). Coverage and equity gradients are parameterized from WHO/UNICEF WUENIC estimates, Gavi zero-dose analytics, and DHS vaccination equity analyses. Three scenarios (high, moderate, low coverage) capture the spectrum from well-performing programmes to fragile/conflict-affected settings.
-
-## 1. Introduction
-
-Globally, 14.3 million children received no routine vaccines ("zero-dose") in 2022 (WUENIC 2023). Immunisation coverage inequities by wealth, geography, and education remain a central challenge for the Immunization Agenda 2030. Open-access individual-level vaccination datasets from LMICs are scarce—DHS microdata requires registration and is survey-weighted, making it unsuitable for direct ML training.
-
-This synthetic dataset addresses this gap for:
-
-- Training ML models for zero-dose identification and dropout prediction
-- Equity analysis and coverage gap modelling
-- Prototyping immunisation programme dashboards
-- Educational use in vaccinology and public health informatics
+# Synthetic Childhood Immunisation Coverage & Dropout Dataset (0-23 months) | Africa (Electric Sheep Africa metadata inventory)
 
-**This dataset is entirely synthetic. It must not be used for clinical decision-making or programme evaluation.**
+**Size category:** `10K<n<100K` - **Formats:** `csv` - **Sector:** culture_language - *Engineered by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*
 
-## 2. Methodology
+![size](https://img.shields.io/badge/size-10K%3Cn%3C100K-blue)
+![sector](https://img.shields.io/badge/sector-culture_language-green)
+![downloads](https://img.shields.io/badge/HF_downloads-87-orange)
+![license](https://img.shields.io/badge/license-cc--by--4.0-lightgrey)
 
-### 2.1 Vaccine Schedule
+## TL;DR
 
-Based on WHO Expanded Programme on Immunization (EPI) recommendations:
+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.
 
-| Vaccine | Doses | Schedule | Disease Target |
-| --- | --- | --- | --- |
-| BCG | 1 | Birth | Tuberculosis |
-| OPV | 4 (0-3) | Birth, 6, 10, 14 weeks | Poliomyelitis |
-| Penta (DTP-HepB-Hib) | 3 | 6, 10, 14 weeks | Diphtheria, tetanus, pertussis, hepatitis B, Hib |
-| PCV | 3 | 6, 10, 14 weeks | Pneumococcal disease |
-| Rotavirus | 2 | 6, 10 weeks | Rotavirus diarrhoea |
-| IPV | 1 | 14 weeks | Poliomyelitis |
-| Measles (MCV) | 2 | 9, 15 months | Measles |
+## What This Dataset Covers
 
-### 2.2 Equity Determinants
+Public datasets help analysts inspect structured evidence, build reproducible workflows, and compare patterns across domains.
 
-Individual coverage probability is modulated by five equity determinants, each with literature-grounded multipliers:
+Dataset context from the existing Hugging Face card: ⚠️ Synthetic dataset — Parameterized from published SSA literature, not real observations. Not suitable for empirical analysis or policy inference. Synthetic Childhood Immunisation Coverage & Dropout Dataset (0–23 months) Abstract This dataset provides 30,000 synthetic records (10,000 per scenario) of childhood immunisation status for children aged 0-23 months in LMIC settings. Each record contains 28 variables: demographics, socioeconomic determinants (wealth quintile… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-synth-vaccination-childhood-immunisation-all.
 
-| Determinant | Effect | Source |
-| --- | --- | --- |
-| Wealth quintile (1-5) | 0.65x (Q1) to 1.30x (Q5) | Restrepo-Méndez et al., Bull WHO 2016 |
-| Urban/Rural | 1.10x urban, 0.90x rural | DHS pooled estimates |
-| Maternal education | 0.70x (none) to 1.20x (tertiary) | Arsenault et al., Lancet Global Health 2017 |
-| Distance to facility | -1.2% per km | DHS access analyses |
-| Individual random effect | N(1.0, 0.08) | Unobserved heterogeneity |
+## Dataset Profile
 
-### 2.3 Scenario Design
+| Field | Value |
+|---|---|
+| Hugging Face repo | [`electricsheepafrica/africa-synth-vaccination-childhood-immunisation-all`](https://huggingface.co/datasets/electricsheepafrica/africa-synth-vaccination-childhood-immunisation-all) |
+| Sector | culture_language |
+| Topic tags | synthetic, healthcare, immunisation, vaccination, epi, zero-dose, dropout, equity, who-unicef, wuenic, dhs, lmic |
+| Modalities | `tabular`, `text` |
+| Formats | `csv` |
+| Size category | `10K<n<100K` |
+| Countries | Africa-wide or source-defined African coverage |
+| ISO3 coverage | `not declared` |
+| Last modified on HF | `2026-04-14 22:43:44+00:00` |
+| Inventory snapshot | `2026-07-16T16:00:34Z` |
 
-| Scenario | Context | BCG | Penta3 | MCV1 | MCV2 | Zero-dose |
-| --- | --- | --- | --- | --- | --- | --- |
-| High coverage | Well-performing LMIC | 75.5% | 34.8% | 64.1% | 40.1% | 4.8% |
-| Moderate coverage | Average LMIC | 66.5% | 27.8% | 55.4% | 33.4% | 11.6% |
-| Low coverage | Fragile/conflict | 40.5% | 10.7% | 33.1% | 15.8% | 33.9% |
+## How To Read This Dataset
 
-## 3. Dataset Description
+- 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.
 
-### 3.1 Schema
+## Usage
 
-| Column | Type | Description |
-| --- | --- | --- |
-| id | int | Unique identifier |
-| sex | categorical (M/F) | Biological sex |
-| age_months | float | Age in months (0-23.9) |
-| region_type | categorical | Urban or rural |
-| ses_quintile | int (1-5) | Socioeconomic status quintile (1=poorest) |
-| maternal_education | categorical | None, primary, secondary, tertiary |
-| distance_to_facility_km | float | Distance to nearest health facility |
-| bcg, opv0-3, penta1-3, pcv1-3, rota1-2, ipv1, mcv1-2 | binary (0/1) | Vaccine dose received |
-| total_basic_doses | int | Sum of BCG+Penta1-3+OPV1-3+MCV1 (max 8) |
-| fully_immunised | binary | All age-appropriate vaccines received |
-| dropout_penta1_penta3 | binary | Received Penta1 but not Penta3 |
-| dropout_penta1_mcv1 | binary | Received Penta1 but not MCV1 |
-| zero_dose | binary | No vaccines received at all |
-| immunisation_status | categorical | fully_immunised / partially_immunised / zero_dose |
-
-## 4. Validation
-
-### 4.1 Diagnostic Plots
+```python
+from datasets import load_dataset
 
-<p align="center">
-  <img src="validation_report.png" alt="Validation Report" width="100%">
-</p>
+ds = load_dataset("electricsheepafrica/africa-synth-vaccination-childhood-immunisation-all")
+print(ds)
 
-## 5. Usage
+split_name = next(iter(ds))
+table = ds[split_name]
+print(table.features)
+print(table[:3])
+```
 
-### 5.1 Loading with HuggingFace `datasets`
+### Convert To Pandas When Tabular
 
 ```python
-from datasets import load_dataset
+from datasets import Dataset
 
-dataset = load_dataset("electricsheepafrica/synthetic-childhood-immunisation-coverage-dropout-WUENIC", "moderate_coverage")
-df = dataset["train"].to_pandas()
+first_split = ds[next(iter(ds))]
+if isinstance(first_split, Dataset):
+    df = first_split.to_pandas()
+    print(df.head())
 ```
 
-### 5.2 Regenerating
+## Data Quality Notes
 
-```bash
-pip install numpy pandas matplotlib
-python generate_dataset.py --all-scenarios --n 10000 --seed 42
-python validate_dataset.py
-```
+- 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.
 
-## 6. Limitations
+## Source And Provenance
 
-- **Synthetic**: Not real programme data. Not for programme evaluation.
-- **No campaign vaccines**: Only routine EPI; does not model supplementary immunisation activities (SIAs).
-- **Cross-sectional**: Single snapshot; does not capture timeliness or catch-up dynamics.
-- **Simplified equity model**: Real equity determinants are more complex and context-specific.
+- **Source context:** Electric Sheep Africa metadata inventory
+- **Publisher/source attribution:** Public dataset metadata
+- **License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)
+- **Hugging Face URL:** [https://huggingface.co/datasets/electricsheepafrica/africa-synth-vaccination-childhood-immunisation-all](https://huggingface.co/datasets/electricsheepafrica/africa-synth-vaccination-childhood-immunisation-all)
+- **Inventory retrieved at:** `2026-07-16T16:00:34Z`
 
-## 7. References
+## Suggested Analyses
 
-1. WHO/UNICEF (2023). WUENIC Estimates of National Immunization Coverage.
-2. Gavi (2023). Zero-dose children: Key data and analytics.
-3. Restrepo-Méndez MC, et al. (2016). Inequalities in full immunization coverage. *Bull WHO*, 94:794-805.
-4. Arsenault C, et al. (2017). Equity in antenatal care quality. *Lancet Global Health*, 5(11):e1079-e1088.
-5. WHO (2022). Immunization Agenda 2030.
-6. DHS Program. Vaccination module, multiple countries 2015-2023.
+- 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
 
 ```bibtex
-@dataset{esa_immunisation_2025,
-  title={Synthetic Childhood Immunisation Coverage and Dropout Dataset},
-  author={Electric Sheep Africa},
-  year={2025},
-  publisher={Hugging Face},
-  url={https://huggingface.co/datasets/electricsheepafrica/synthetic-childhood-immunisation-coverage-dropout-WUENIC}
+@misc{electric_sheep_africa_africa_synth_vaccination_childhood_immunisation_all_2026,
+  title        = {Synthetic Childhood Immunisation Coverage & Dropout Dataset (0-23 months) | Africa (Electric Sheep Africa metadata inventory)},
+  author       = {Public dataset metadata},
+  year         = {2026},
+  url          = {https://huggingface.co/datasets/electricsheepafrica/africa-synth-vaccination-childhood-immunisation-all},
+  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
+  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-synth-vaccination-childhood-immunisation-all}}
 }
 ```
 
 ## License
 
-[CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)
+Released under [CC BY 4.0](https://creativecommons.org/licenses/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.
+
+---
+
+Provenance: metadata-backed README standardized 2026-08-12 by the Electric Sheep Africa README system. Inventory source: `catalog/esa_metadata_inventory/master_metadata.jsonl`.