Standardize Electric Sheep Africa dataset card

README.md

@@ -1,162 +1,143 @@
 ---
 license: cc-by-4.0
+language:
+- en
 task_categories:
 - tabular-classification
 - tabular-regression
-language:
-- en
-tags:
-- cancer
-- oncology
-- synthetic
-- healthcare
-- sub-saharan-africa
-- multi-country-ssa
-pretty_name: Prostate Cancer - Sub-Saharan Africa
+multilinguality: monolingual
 size_categories:
 - 10K<n<100K
-configs:
-- config_name: low_burden
-  data_files: prostate_cancer_ssa_low_burden.csv
-- config_name: moderate_burden
-  data_files: prostate_cancer_ssa_moderate_burden.csv
-  default: true
-- config_name: high_burden
-  data_files: prostate_cancer_ssa_high_burden.csv
-data_type: synthetic
+tags:
+- "africa"
+- "electric-sheep-africa"
+- "open-data"
+- "metadata-backed"
+- "health"
+- "csv"
+- "tabular"
+- "text"
+- "cancer"
+- "oncology"
+- "synthetic"
+- "healthcare"
+- "sub-saharan-africa"
+- "multi-country-ssa"
+- "clinical"
+pretty_name: "Prostate Cancer - Sub-Saharan Africa | Africa (Electric Sheep Africa metadata inventory)"
 ---
 
-> ⚠️ **Synthetic dataset** — Parameterized from published SSA literature, not real observations. Not suitable for empirical analysis or policy inference.
-
-# Prostate Cancer - Sub-Saharan Africa
+# Prostate Cancer - Sub-Saharan Africa | Africa (Electric Sheep Africa metadata inventory)
 
-## Abstract
+**Size category:** `10K<n<100K` - **Formats:** `csv` - **Sector:** health - *Engineered by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*
 
-This synthetic dataset represents prostate cancer clinical data across sub-saharan africa and is designed to address the significant data gap in cancer research for sub-Saharan Africa. The dataset contains 3,200-4,800 per scenario records per scenario with key epidemiological parameters grounded in GLOBOCAN 2022 estimates, WHO reports, and peer-reviewed literature from the African Cancer Registry Network (AFCRN).
+![size](https://img.shields.io/badge/size-10K%3Cn%3C100K-blue)
+![sector](https://img.shields.io/badge/sector-health-green)
+![downloads](https://img.shields.io/badge/HF_downloads-61-orange)
+![license](https://img.shields.io/badge/license-cc--by--4.0-lightgrey)
 
-The age-standardized incidence rate (ASIR) of Prostate Cancer in the target population is approximately 22.0 per 100,000 population (GLOBOCAN 2022). This dataset provides training data for cancer epidemiology modeling, health systems research, and machine learning applications in oncology.
+## TL;DR
 
-## 1. Introduction
+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.
 
-### 1.1 Problem Statement
-Cancer incidence in sub-Saharan Africa is rising rapidly, with estimated new cases reaching over 1 million annually by 2030. However, the region faces a critical shortage of granular cancer data for research, policy development, and health system planning. Population-based cancer registries cover less than 5% of the African population, creating significant gaps in understanding the true burden of disease.
+## What This Dataset Covers
 
-### 1.2 Data Gap
-- Limited population-based registry data outside major cities
-- Missing survival and outcome data from most facilities
-- Underrepresentation of pediatric and rare cancers
-- Lack of treatment access and outcome metrics
+Health datasets help researchers examine disease burden, service delivery, risk factors, outcomes, and public-health program performance.
 
-### 1.3 Purpose
-This dataset supports:
-- Cancer burden estimation and projection modeling
-- Health system capacity planning
-- Machine learning for risk prediction and triage
-- Epidemiological research on cancer patterns
-- Policy development for cancer control programs
+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. Prostate Cancer - Sub-Saharan Africa Abstract This synthetic dataset represents prostate cancer clinical data across sub-saharan africa and is designed to address the significant data gap in cancer research for sub-Saharan Africa. The dataset contains 3,200-4,800 per scenario records per scenario with key epidemiological… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-synth-cancer-prostate-cancer-ssa-all.
 
-## 2. Methodology
+## Dataset Profile
 
-### 2.1 Target Population
-- **Geographic scope**: Multi-country SSA
-- **Population represented**: Urban and rural populations
-- **Time period**: Variable by data source (2010-2025)
+| Field | Value |
+|---|---|
+| Hugging Face repo | [`electricsheepafrica/africa-synth-cancer-prostate-cancer-ssa-all`](https://huggingface.co/datasets/electricsheepafrica/africa-synth-cancer-prostate-cancer-ssa-all) |
+| Sector | health |
+| Topic tags | cancer, oncology, synthetic, healthcare, sub-saharan-africa, multi-country-ssa |
+| 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 22:48:03+00:00` |
+| Inventory snapshot | `2026-07-16T16:00:34Z` |
 
-### 2.2 Variable Selection
-Variables were selected based on:
-- IARC/WHO cancer registry standards
-- Data availability in African cancer registries
-- Clinical relevance for cancer control
+## How To Read This Dataset
 
-### 2.3 Epidemiological Parameterization
+- 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.
 
-All parameters are derived from:
-- GLOBOCAN 2022 (IARC)
-- WHO Cancer Reports
-- African Cancer Registry Network (AFCRN)
-- DHS/MICS survey data
-- Peer-reviewed literature
+## Usage
 
-### 2.4 Scenario Design
-
-| Scenario | Description | Records |
-|----------|-------------|---------|
-| low_burden | Low cancer burden setting | Varies by dataset |
-| moderate_burden | Standard burden setting | Varies by dataset |
-| high_burden | High burden / late presentation | Varies by dataset |
+```python
+from datasets import load_dataset
 
-### 2.5 Generation Process
+ds = load_dataset("electricsheepafrica/africa-synth-cancer-prostate-cancer-ssa-all")
+print(ds)
 
-Generation follows a conditional sampling approach based on directed acyclic graphs (DAGs) representing causal relationships between variables:
+split_name = next(iter(ds))
+table = ds[split_name]
+print(table.features)
+print(table[:3])
+```
 
-1. Sample demographic variables (age, sex, location)
-2. Sample cancer type conditional on demographics
-3. Sample clinical variables (stage, morphology, grade)
-4. Sample treatment and outcome variables
-5. Derive survival times from outcome models
+### Convert To Pandas When Tabular
 
-## 3. Dataset Description
+```python
+from datasets import Dataset
 
-### 3.1 Key Variables
-Prostate cancer clinical data across sub-Saharan Africa
+first_split = ds[next(iter(ds))]
+if isinstance(first_split, Dataset):
+    df = first_split.to_pandas()
+    print(df.head())
+```
 
-### 3.2 Data Quality
-- All categorical distributions validated against published literature
-- Continuous variables modeled with appropriate statistical distributions
-- Survival times based on exponential models with literature-derived parameters
+## Data Quality Notes
 
-## 4. Validation
+- 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.
 
-### 4.1 Prevalence Verification
-All prevalence values are validated against GLOBOCAN 2022 and published registry reports.
+## Source And Provenance
 
-### 4.2 Distribution Quality
-- Age and sex distributions match expected patterns
-- Cancer type frequencies align with regional estimates
+- **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-cancer-prostate-cancer-ssa-all](https://huggingface.co/datasets/electricsheepafrica/africa-synth-cancer-prostate-cancer-ssa-all)
+- **Inventory retrieved at:** `2026-07-16T16:00:34Z`
 
-### 4.3 Clinical Plausibility
-- No biologically impossible combinations
-- Treatment patterns consistent with resource-limited settings
+## Suggested Analyses
 
-## 5. Usage
+- 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.
 
-### 5.1 Loading with HuggingFace
-```python
-from datasets import load_dataset
-ds = load_dataset("electricsheepafrica/prostate-cancer-ssa", "moderate_burden")
-```
+## Citation
 
-### 5.2 Loading from CSV
-```python
-import pandas as pd
-df = pd.read_csv("prostate_cancer_ssa_moderate_burden.csv")
+```bibtex
+@misc{electric_sheep_africa_africa_synth_cancer_prostate_cancer_ssa_all_2026,
+  title        = {Prostate Cancer - Sub-Saharan Africa | Africa (Electric Sheep Africa metadata inventory)},
+  author       = {Public dataset metadata},
+  year         = {2026},
+  url          = {https://huggingface.co/datasets/electricsheepafrica/africa-synth-cancer-prostate-cancer-ssa-all},
+  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
+  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-synth-cancer-prostate-cancer-ssa-all}}
+}
 ```
 
-## 6. Limitations
-
-- **Synthetic data**: Generated from aggregated statistics, not individual patient records
-- **Simplified correlations**: May not capture complex dependencies
-- **Not for clinical use**: Designed for research and ML training only
+## License
 
-## 7. References
+Released under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/).
 
-1. GLOBOCAN 2022. IARC Cancer Observatory.
-2. African Cancer Registry Network (AFCRN).
-3. WHO Cancer Control Reports.
-4. DHS/MICS Survey Data.
+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.
 
-## Citation
+## About Electric Sheep Africa
 
-```bibtex
-@dataset{prostate_cancer_ssa,
-  title={Prostate Cancer - Sub-Saharan Africa},
-  author={Electric Sheep Africa},
-  year={2025},
-  publisher={HuggingFace},
-  dataset_url={https://huggingface.co/datasets/electricsheepafrica/prostate-cancer-ssa}
-}
-```
+Electric Sheep Africa publishes ML-ready African public datasets on Hugging Face.
 
-## License
+---
 
-CC-BY-4.0
+Provenance: metadata-backed README standardized 2026-08-12 by the Electric Sheep Africa README system. Inventory source: `catalog/esa_metadata_inventory/master_metadata.jsonl`.