Datasets:

electricsheepafrica
/

africa-synth-agriculture-and-food-security-dataset-all

@@ -1,330 +1,145 @@
 ---
 language:
 - en
-license: cc-by-4.0
-size_categories:
-- 100K<n<1M
 task_categories:
-- tabular-regression
 - tabular-classification
-pretty_name: African Agriculture & Food Security Synthetic Dataset
 tags:
-- agriculture
-- food-security
-- africa
-- synthetic-data
-- smallholder-farming
-- climate
-- yields
-- synthetic
-data_type: synthetic
 ---
-> ⚠️ **Synthetic dataset** — Parameterized from published SSA literature, not real observations. Not suitable for empirical analysis or policy inference.
-# African Agriculture & Food Security Synthetic Dataset v2.0
-## Dataset Summary
-This dataset contains **1,000,000 synthetic household records** for African agriculture and food security research. Every variable is grounded in peer-reviewed literature, making it ideal for algorithm development, methods research, and education without privacy concerns.
-**Key Features:**
-- 🌍 **African-specific**: Patterns from Sub-Saharan Africa
-- 📚 **Literature-grounded**: 23 peer-reviewed sources
-- 🔒 **100% synthetic**: No real households
-- ✅ **Validated**: 88.9% validation success rate
-- 🔄 **Reproducible**: Fixed random seed (42)
-## Dataset Details
-### Dataset Description
-**Size**: 1,000,000 rows × 12 variables
-**Format**: CSV (162 MB) + Parquet (64 MB)
-**License**: CC-BY-4.0
-**Created**: November 2025
-**Version**: 2.0
-### Variables
-| Variable | Type | Description | Source |
-|----------|------|-------------|--------|
-| `agro_ecological_zone` | categorical | AEZ classification (5 zones) | FAO (2003) |
-| `region_type` | categorical | Settlement type (rural/peri-urban/urban) | Tacoli (2003) |
-| `farm_size_ha` | continuous | Farm size in hectares | Lowder et al. (2016) |
-| `soil_quality_index` | continuous | Soil quality (0-100 scale) | Vagen et al. (2016) |
-| `rainfall_mm_annual` | continuous | Annual rainfall (mm) | Sheffield et al. (2014) |
-| `household_size` | count | Number of household members | DHS (2018-2020) |
-| `market_distance_km` | continuous | Distance to nearest market (km) | Minten et al. (2013) |
-| `livestock_tlu` | continuous | Livestock in Tropical Livestock Units | Robinson et al. (2011) |
-| `extension_access` | binary | Agricultural extension access (yes/no) | Davis et al. (2010) |
-| `fertilizer_use_kg_ha` | continuous | Fertilizer application rate (kg/ha) | Sheahan & Barrett (2017) |
-| `rainfall_mm_season` | continuous | Growing season rainfall (mm) | Ramirez-Villegas & Thornton (2015) |
-| `maize_yield_kg_ha` | continuous | Maize grain yield (kg/ha) | FAO Stat |
-### Dataset Structure
-```python
-import pandas as pd
-# Load data (Parquet recommended for speed)
-df = pd.read_parquet('synthetic_data.parquet')
-# Shape
-print(df.shape)  # (1000000, 12)
-# Preview
-print(df.head())
-```
-**Example row:**
-```
-agro_ecological_zone: semi_arid
-region_type: rural_accessible
-farm_size_ha: 1.79
-soil_quality_index: 22.5
-rainfall_mm_annual: 520
-household_size: 5
-market_distance_km: 8.1
-livestock_tlu: 13.6
-extension_access: no
-fertilizer_use_kg_ha: 0.0
-rainfall_mm_season: 314
-maize_yield_kg_ha: 1064
 ```
-## Uses
-### Direct Use
-✅ **Permitted:**
-- Machine learning algorithm development & testing
-- Statistical methods research
-- Educational tutorials & training
-- Reproducible research examples
-- Capacity building in data-scarce environments
-- Policy simulation (with appropriate caveats)
-❌ **Prohibited:**
-- Real-world policy targeting
-- Actual resource allocation decisions
-- Surveillance or monitoring
-- Any use that could harm vulnerable populations
-### Out-of-Scope Use
-- **NOT for real targeting**: Never use for selecting actual households for programs
-- **Context-specific**: Designed for Sub-Saharan African contexts only
-- **Validate before deployment**: Always test on real data before production use
-## Dataset Creation
-### Curation Rationale
-African agriculture research faces a **data paradox**: high-stakes decisions require robust evidence, but household-level data is scarce, restricted, or privacy-sensitive. This synthetic dataset enables:
-1. **Algorithm development** without waiting for data access
-2. **Reproducible research** with shareable datasets
-3. **Capacity building** in data science for agriculture
-4. **Privacy preservation** while maintaining realistic patterns
-### Source Data
-#### Data Collection
-This is **100% synthetic data** - no real households were included. All parameters derived from:
-- **Peer-reviewed publications** (primary source)
-- **FAO statistical databases**
-- **DHS (Demographic & Health Surveys)**
-- **LSMS-ISA (Living Standards Measurement Study)**
-- **Climate databases** (FEWS NET, Sheffield et al.)
-#### Who are the source data producers?
-**Literature sources** (23 peer-reviewed papers):
-- Agricultural economics journals
-- Climate science publications
-- Development economics research
-- Soil science literature
-- Livestock systems research
-Full bibliography in `literature_inventory.csv`
-### Annotations
-#### Annotation process
-Not applicable - this is fully synthetic data generated from statistical distributions.
-#### Who are the annotators?
-N/A - synthetic generation
-### Personal and Sensitive Information
-**None** - This dataset contains NO personal information. All data is synthetically generated.
-## Bias, Risks, and Limitations
-### Known Limitations
-1. **Simplified relationships**: Real agricultural systems are more complex
-2. **Cross-sectional only**: No panel/longitudinal data (yet)
-3. **Missing variables**: Not all relevant variables included (e.g., prices, credit access)
-4. **No spatial coordinates**: Zone-level only, no GPS
-5. **Generalized parameters**: Represents "typical" SSA patterns, not country-specific
-### Biases
-- **Literature bias**: Parameters reflect published research, which may under-represent marginalized populations
-- **Geographic bias**: Primarily East/Southern Africa (where more data exists)
-- **Temporal**: Reflects 2010-2020 period patterns
-### Recommendations
-Users should:
-1. **Always validate** on real data before production deployment
-2. **Acknowledge synthetic nature** in all uses
-3. **Understand limitations** for their specific use case
-4. **Consider biases** in the underlying literature
-5. **Cite properly** (see below)
-## Additional Information
-### Dataset Curators
-Created using the **Synthetic Data Generation Playbook** methodology:
-- Parameter extraction from 23 peer-reviewed sources
-- Literature-grounded distributions
-- African context constraints
-- Validated against benchmarks (88.9% success rate)
-### Licensing Information
-- **Dataset**: Creative Commons Attribution 4.0 (CC-BY-4.0)
-- **Code**: MIT License
-- **Documentation**: CC-BY-4.0
-**Requirements**:
-- ✅ Attribution required
-- ✅ Must acknowledge synthetic nature
-- ✅ Follow acceptable use policy
-- ❌ No warranty provided
-### Citation Information
 ```bibtex
-@dataset{african_agriculture_synthetic_2024,
-  title = {African Agriculture and Food Security Synthetic Dataset v2.0},
-  author = {Electric Sheep Africa},
-  year = {2024},
-  publisher = {HuggingFace},
-  version = {2.0.0},
-  url = {https://huggingface.co/datasets/electricsheepafrica/african-agriculture-synthetic},
-  note = {1 million synthetic household records, 12 variables, 23 literature sources}
 }
 ```
-### Contributions
-Contributions welcome! Please see `CONTRIBUTING.md` for guidelines.
-**Report issues**:
-- Data quality issues
-- Documentation improvements
-- New variable suggestions
-- Validation findings
-## Quality Metrics
-### Validation Results
-**Overall**: 88.9% validation success rate (32/36 tests passed)
-**Passed Tests** ✅:
-- Schema validation (all 12 variables present)
-- Range validation (all variables within bounds)
-- Distribution shapes (skewness, zero-inflation)
-- Key correlations (rainfall-yield, fertilizer-yield)
-- Missing data patterns
-- Logical consistency (seasonal ≤ annual rainfall)
-- Benchmark comparisons
-**Known Issues** ⚠️:
-- Farm size median 1.28 ha vs. expected 1.6 ha (acceptable - reflects peri-urban farms)
-- Some correlations weaker than literature (expected for synthetic data)
-Full validation report in `output/validation_results.json`
-## Technical Specifications
-### Generation Details
-- **Sample size**: 1,000,000 households
-- **Random seed**: 42 (reproducible)
-- **Generation time**: ~47 seconds
-- **Framework**: Python 3.8+, NumPy, Pandas, SciPy
-- **Methodology**: Conditional generation with dependency graphs
-### Distribution Types Used
-- **Normal/Lognormal**: Farm size, market distance
-- **Gamma**: Rainfall, household size
-- **Beta**: Soil quality (0-100 bounded)
-- **Zero-inflated Gamma**: Livestock, fertilizer
-- **Categorical**: AEZ, region type, extension access
-### Conditional Dependencies
-Variables respect realistic dependencies:
-- **Farm size** varies by region type (peri-urban smaller)
-- **Livestock** varies by AEZ, farm size, rainfall
-- **Fertilizer** affected by market distance, extension, AEZ
-- **Yields** driven by rainfall, soil, fertilizer, AEZ
-## Version History
-### v2.0 (November 2025) - Current
-**Changes from v1.0**:
-- ✅ Added 5 new variables (soil quality, extension, fertilizer, seasonal rainfall, region type)
-- ✅ Fixed soil quality scaling (now 0-100)
-- ✅ Fixed seasonal ≤ annual rainfall constraint
-- ✅ Improved conditional dependencies
-- ✅ Comprehensive validation framework
-- ✅ 88.9% validation success (up from 83.3%)
-**Stats**:
-- 1,000,000 rows × 12 variables
-- 23 literature sources
-- 162 MB (CSV) / 64 MB (Parquet)
-### v1.0 (November 2025) - Initial
-- 1,000,000 rows × 7 variables
-- 11 literature sources
-- Basic validation
-## Acknowledgments
-**Literature sources**: 23 peer-reviewed publications (see `literature_inventory.csv`)
-**Key references**:
-- Lowder et al. (2016) - Farm size distributions
-- Sheahan & Barrett (2017) - Fertilizer use patterns
-- Vagen et al. (2016) - Soil quality
-- Robinson et al. (2011) - Livestock systems
-- Ramirez-Villegas & Thornton (2015) - Climate impacts
-**Methodology**: Synthetic Data Generation Playbook
 ---
-**Questions?** See full documentation in repository `README.md`
-**Ready to use!** Load with:
-```python
-from datasets import load_dataset
-dataset = load_dataset("electricsheepafrica/african-agriculture-synthetic")
-```

 ---
+license: cc-by-4.0
 language:
 - en
 task_categories:
 - tabular-classification
+- tabular-regression
+multilinguality: monolingual
+size_categories:
+- 1M<n<10M
 tags:
+- "africa"
+- "electric-sheep-africa"
+- "open-data"
+- "metadata-backed"
+- "agriculture-food"
+- "parquet"
+- "tabular"
+- "text"
+- "agriculture"
+- "food-security"
+- "synthetic-data"
+- "smallholder-farming"
+- "climate"
+- "yields"
+- "synthetic"
+- "farming"
+- "food"
+pretty_name: "African Agriculture & Food Security Synthetic Dataset | Africa (Electric Sheep Africa metadata inventory)"
 ---
+# African Agriculture & Food Security Synthetic Dataset | Africa (Electric Sheep Africa metadata inventory)
+**Size category:** `1M<n<10M` - **Formats:** `parquet` - **Sector:** agriculture_food - *Engineered by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*
+![size](https://img.shields.io/badge/size-1M%3Cn%3C10M-blue)
+![sector](https://img.shields.io/badge/sector-agriculture_food-green)
+![downloads](https://img.shields.io/badge/HF_downloads-15-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. African Agriculture & Food Security Synthetic Dataset v2.0 Dataset Summary This dataset contains 1,000,000 synthetic household records for African agriculture and food security research. Every variable is grounded in peer-reviewed literature, making it ideal for algorithm development, methods research, and education without… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-synth-agriculture-and-food-security-dataset-all.
+## Dataset Profile
+| Field | Value |
+|---|---|
+| Hugging Face repo | [`electricsheepafrica/africa-synth-agriculture-and-food-security-dataset-all`](https://huggingface.co/datasets/electricsheepafrica/africa-synth-agriculture-and-food-security-dataset-all) |
+| Sector | agriculture_food |
+| Topic tags | agriculture, food-security, synthetic-data, smallholder-farming, climate, yields, synthetic |
+| Modalities | `tabular`, `text` |
+| Formats | `parquet` |
+| Size category | `1M<n<10M` |
+| Countries | Africa-wide or source-defined African coverage |
+| ISO3 coverage | `not declared` |
+| Last modified on HF | `2026-04-14 22:38:13+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
+from datasets import load_dataset
+ds = load_dataset("electricsheepafrica/africa-synth-agriculture-and-food-security-dataset-all")
+print(ds)
+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: country, upstream_publisher.
+- Do not infer policy meaning from labels alone; confirm definitions, units, and methods in the source material.
+## Source And Provenance
+- **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-agriculture-and-food-security-dataset-all](https://huggingface.co/datasets/electricsheepafrica/africa-synth-agriculture-and-food-security-dataset-all)
+- **Inventory retrieved at:** `2026-07-16T16:00:34Z`
+## 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_agriculture_and_food_security_dataset_all_2026,
+  title        = {African Agriculture & Food Security Synthetic Dataset | Africa (Electric Sheep Africa metadata inventory)},
+  author       = {Public dataset metadata},
+  year         = {2026},
+  url          = {https://huggingface.co/datasets/electricsheepafrica/africa-synth-agriculture-and-food-security-dataset-all},
+  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
+  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-synth-agriculture-and-food-security-dataset-all}}
 }
 ```
+## 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.
 ---
+Provenance: metadata-backed README standardized 2026-08-12 by the Electric Sheep Africa README system. Inventory source: `catalog/esa_metadata_inventory/master_metadata.jsonl`.