Standardize Electric Sheep Africa dataset card

README.md

@@ -1,111 +1,144 @@
 ---
 license: cc-by-4.0
+language:
+- en
 task_categories:
 - tabular-classification
 - tabular-regression
-language:
-- en
-tags:
-- climate
-- environment
-- africa
-- synthetic-data
-- sub-saharan-africa
-- climate-vulnerability
-- climate-adaptation
-- climate-risk
-- synthetic
+multilinguality: monolingual
 size_categories:
 - 10K<n<100K
-data_type: synthetic
+tags:
+- "africa"
+- "electric-sheep-africa"
+- "open-data"
+- "metadata-backed"
+- "climate-environment"
+- "csv"
+- "tabular"
+- "text"
+- "climate"
+- "environment"
+- "synthetic-data"
+- "sub-saharan-africa"
+- "climate-vulnerability"
+- "climate-adaptation"
+- "climate-risk"
+- "synthetic"
+pretty_name: "Africa Synth Climate Climate Vulnerability Exposure Niger | Africa (Electric Sheep Africa metadata inventory)"
 ---
 
-> ⚠️ **Synthetic dataset** — Parameterized from published SSA literature, not real observations. Not suitable for empirical analysis or policy inference.
-
-# Climate Vulnerability and Exposure - Africa
-
-A comprehensive synthetic dataset assessing climate vulnerability, exposure indices, and adaptive capacity across Sub-Saharan African countries.
-
-## Dataset Description
-
-This dataset provides detailed records of climate vulnerability assessments across 15 Sub-Saharan African countries. It includes exposure metrics, hazard probabilities, adaptive capacity indicators, and climate policy frameworks. The data enables analysis of climate-nutrition linkages through malnutrition burden stratification.
-
-### Key Statistics
-
-| Metric | Value |
-|--------|-------|
-| Total Records | 14,998 |
-| Countries Covered | 15 |
-| Time Period | 2018-2025 |
-| Files | 3 (high_burden, moderate_burden, low_burden) |
-| Features | 29 |
-
-### Countries Included
-DRC, Ethiopia, Ghana, Kenya, Malawi, Mali, Mozambique, Niger, Nigeria, Rwanda, Senegal, South Africa, Tanzania, Uganda, Zambia
-
-## Column Descriptions
-
-| Column | Type | Description |
-|--------|------|-------------|
-| record_id | string | Unique identifier (CLIM_XXXXXX) |
-| country | string | African country name |
-| year | int | Year of observation (2018-2025) |
-| climate_zone | string | Climate classification |
-| geographic_exposure_index | float | Geographic exposure to climate hazards (0-1) |
-| coastal_proximity | int | Binary coastal proximity (0/1) |
-| elevation_category | string | Elevation classification |
-| temperature_anomaly_c | float | Temperature deviation from baseline (°C) |
-| precipitation_variability_pct | float | Precipitation variability (%) |
-| extreme_heat_days_year | int | Annual extreme heat days |
-| heat_stress_index | float | Heat stress composite index |
-| drought_probability | float | Probability of drought (0-1) |
-| flood_probability | float | Probability of flooding (0-1) |
-| climate_hazard_index | float | Composite hazard index (0-1) |
-| gdp_per_capita_usd | float | GDP per capita (USD) |
-| agriculture_gdp_share_pct | float | Agriculture share of GDP (%) |
-| infrastructure_quality_index | float | Infrastructure quality (0-1) |
-| healthcare_access_index | float | Healthcare access (0-1) |
-| early_warning_coverage_pct | float | Early warning system coverage (%) |
-| social_protection_coverage_pct | float | Social protection coverage (%) |
-| adaptive_capacity_index | float | Overall adaptive capacity (0-1) |
-| climate_policy_strength | float | Climate policy strength (0-1) |
-| ndc_ambition_score | float | NDC ambition score (0-1) |
-| climate_finance_access_million_usd | float | Climate finance accessed (million USD) |
-| vulnerability_index | float | Composite vulnerability index (0-1) |
-| exposure_score | float | Climate exposure score (0-1) |
-| risk_category | string | Risk classification |
-| population_at_risk_millions | float | Population at climate risk (millions) |
-| potential_economic_loss_pct_gdp | float | Potential economic loss (% GDP) |
-
-### Climate Zones
-- tropical_wet, tropical_dry, semi_arid, arid, mediterranean, highland
-
-### Risk Categories
-- Low, Moderate, High, Very_High
-
-## Usage Example
+# Africa Synth Climate Climate Vulnerability Exposure Niger | Africa (Electric Sheep Africa metadata inventory)
+
+**Size category:** `10K<n<100K` - **Formats:** `csv` - **Sector:** climate_environment - *Engineered by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*
+
+![size](https://img.shields.io/badge/size-10K%3Cn%3C100K-blue)
+![sector](https://img.shields.io/badge/sector-climate_environment-green)
+![downloads](https://img.shields.io/badge/HF_downloads-40-orange)
+![license](https://img.shields.io/badge/license-cc--by--4.0-lightgrey)
+
+## 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: ⚠️ Synthetic dataset — Parameterized from published SSA literature, not real observations. Not suitable for empirical analysis or policy inference. Climate Vulnerability and Exposure - Africa A comprehensive synthetic dataset assessing climate vulnerability, exposure indices, and adaptive capacity across Sub-Saharan African countries. Dataset Description This dataset provides detailed records of climate vulnerability assessments across 15 Sub-Saharan African countries.… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-synth-climate-climate-vulnerability-exposure-niger.
+
+## Dataset Profile
+
+| Field | Value |
+|---|---|
+| Hugging Face repo | [`electricsheepafrica/africa-synth-climate-climate-vulnerability-exposure-niger`](https://huggingface.co/datasets/electricsheepafrica/africa-synth-climate-climate-vulnerability-exposure-niger) |
+| Sector | climate_environment |
+| Topic tags | climate, environment, synthetic-data, sub-saharan-africa, climate-vulnerability, climate-adaptation, climate-risk, synthetic |
+| Modalities | `tabular`, `text` |
+| Formats | `csv` |
+| Size category | `10K<n<100K` |
+| Countries | Niger |
+| ISO3 coverage | `NER` |
+| Last modified on HF | `2026-04-14 22:52:45+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
 
 ```python
-import pandas as pd
+from datasets import load_dataset
 
-# Load high burden dataset
-df = pd.read_csv('climate_vulnerability_exposure_africa_high_burden.csv')
+ds = load_dataset("electricsheepafrica/africa-synth-climate-climate-vulnerability-exposure-niger")
+print(ds)
 
-# Analyze vulnerability by country
-vulnerability = df.groupby('country')['vulnerability_index'].mean()
-print(vulnerability.sort_values(ascending=False))
+split_name = next(iter(ds))
+table = ds[split_name]
+print(table.features)
+print(table[:3])
+```
+
+### Convert To Pandas When Tabular
+
+```python
+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: upstream_publisher.
+- Do not infer policy meaning from labels alone; confirm definitions, units, and methods in the source material.
+
+## Source And Provenance
 
-# Filter very high risk areas
-very_high_risk = df[df['risk_category'] == 'Very_High']
+- **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-climate-climate-vulnerability-exposure-niger](https://huggingface.co/datasets/electricsheepafrica/africa-synth-climate-climate-vulnerability-exposure-niger)
+- **Inventory retrieved at:** `2026-07-16T16:00:34Z`
 
-# Correlation analysis
-correlation = df[['adaptive_capacity_index', 'vulnerability_index']].corr()
+## 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
+
+```bibtex
+@misc{electric_sheep_africa_africa_synth_climate_climate_vulnerability_exposure_niger_2026,
+  title        = {Africa Synth Climate Climate Vulnerability Exposure Niger | Africa (Electric Sheep Africa metadata inventory)},
+  author       = {Public dataset metadata},
+  year         = {2026},
+  url          = {https://huggingface.co/datasets/electricsheepafrica/africa-synth-climate-climate-vulnerability-exposure-niger},
+  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
+  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-synth-climate-climate-vulnerability-exposure-niger}}
+}
 ```
 
-## Research Sources
+## License
+
+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.
+
+---
 
-This synthetic dataset is inspired by and aligned with data from:
-- [ND-GAIN Country Index](https://gain.nd.edu/our-work/country-index/)
-- [INFORM Risk Index](https://drmkc.jrc.ec.europa.eu/inform-index)
-- [World Bank Climate Change Knowledge Portal](https://climateknowledgeportal.worldbank.org/)
-- [IPCC Climate Reports](https://www.ipcc.ch/)
+Provenance: metadata-backed README standardized 2026-08-12 by the Electric Sheep Africa README system. Inventory source: `catalog/esa_metadata_inventory/master_metadata.jsonl`.