Datasets:

electricsheepafrica
/

africa-synth-education-learning-materials-nigeria

@@ -1,127 +1,142 @@
 ---
-license: mit
 task_categories:
 - tabular-regression
-tags:
-- nigeria
-- education
-- waec
-- jamb
-- synthetic
-- school-infrastructure-and-resources
 size_categories:
 - 100K<n<1M
-data_type: synthetic
 ---
-> ⚠️ **Synthetic dataset** — Parameterized from published SSA literature, not real observations. Not suitable for empirical analysis or policy inference.
-# Nigeria Education – Learning Materials
-## Dataset Description
-Synthetic School Infrastructure & Resources data for Nigeria education sector.
-**Category**: School Infrastructure & Resources
-**Rows**: 110,000
-**Format**: CSV, Parquet
-**License**: MIT
-**Synthetic**: Yes (generated using reference data from WAEC, JAMB, UBEC, NBS, UNESCO)
-## Dataset Structure
-### Schema
-- **id**: string
-- **date**: string
-- **state**: string
-- **value**: float
-- **category**: string
-### Sample Data
-```
-| id           | date       | state       |   value | category   |
-|:-------------|:-----------|:------------|--------:|:-----------|
-| REC-00510581 | 2023-07-20 | Ondo        |    35.9 | A          |
-| REC-00920667 | 2022-09-03 | Akwa Ibom   |    59.6 | B          |
-| REC-00562943 | 2024-06-09 | Cross River |    87.4 | A          |
-| REC-00287725 | 2023-04-20 | Ogun        |    41.9 | B          |
-| REC-00440019 | 2023-11-02 | Kogi        |    57.7 | C          |
-```
-## Data Generation Methodology
-This dataset was synthetically generated using:
-1. **Reference Sources**:
-   - WAEC (West African Examinations Council) - exam results, pass rates, grade distributions
-   - JAMB (Joint Admissions and Matriculation Board) - UTME scores, subject combinations
-   - UBEC (Universal Basic Education Commission) - enrollment, infrastructure, teacher data
-   - NBS (National Bureau of Statistics) - education surveys, literacy rates
-   - UNESCO - Nigeria education statistics, enrollment ratios
-   - UNICEF - Out-of-school children, gender parity indices
-2. **Domain Constraints**:
-   - WAEC grading system (A1-F9) with official score ranges
-   - JAMB UTME scoring (0-400 points, 4 subjects)
-   - Nigerian curriculum structure (Primary, JSS, SSS)
-   - Academic calendar (3 terms: Sep-Dec, Jan-Apr, May-Jul)
-   - Regional disparities (North-South education gap)
-   - Gender parity indices by region and level
-3. **Quality Assurance**:
-   - Distribution testing (WAEC grade distributions match national patterns)
-   - Correlation validation (attendance-performance, teacher quality-outcomes)
-   - Causal consistency (educational outcome models)
-   - Multi-scale coherence (student → school → state aggregations)
-   - Ethical considerations (representative, unbiased, privacy-preserving)
-See `QUALITY_ASSURANCE.md` in the repository for full methodology.
-## Use Cases
-- **Machine Learning**: Performance prediction, dropout forecasting, admission modeling, resource allocation
-- **Policy Analysis**: Education program evaluation, gender parity assessment, regional disparity studies
-- **Research**: Teacher effectiveness, infrastructure impact, exam performance patterns
-- **Education Planning**: School placement, teacher deployment, budget allocation
-## Limitations
-- **Synthetic data**: While grounded in real distributions from WAEC/JAMB/UBEC, individual records are not real observations
-- **Simplified dynamics**: Some complex interactions (e.g., peer effects, teacher-student matching) are simplified
-- **Temporal scope**: Covers 2022-2025; may not reflect longer-term trends or future policy changes
-- **Spatial resolution**: State/LGA level; does not capture micro-level heterogeneity within localities
-## Citation
-If you use this dataset, please cite:
 ```bibtex
-@dataset{nigeria_education_2025,
-  title = {Nigeria Education – Learning Materials},
-  author = {Electric Sheep Africa},
-  year = {2025},
-  publisher = {Hugging Face},
-  url = {https://huggingface.co/datasets/electricsheepafrica/nigerian_education_learning_materials}
 }
 ```
-## Related Datasets
-This dataset is part of the **Nigeria Education Sector** collection:
-- https://huggingface.co/collections/electricsheepafrica/nigeria-education-sector
-## Contact
-For questions, feedback, or collaboration:
-- **Organization**: Electric Sheep Africa
-- **Collection**: Nigeria Education Sector
-- **Repository**: https://github.com/electricsheepafrica/nigerian-datasets
-## Changelog
-### Version 1.0.0 (October 2025)
-- Initial release
-- 110,000 synthetic records
-- Quality-assured using WAEC/JAMB/UBEC/NBS reference data

 ---
+license: other
+language:
+- en
 task_categories:
+- tabular-classification
 - tabular-regression
+multilinguality: monolingual
 size_categories:
 - 100K<n<1M
+tags:
+- "africa"
+- "electric-sheep-africa"
+- "open-data"
+- "metadata-backed"
+- "education"
+- "parquet"
+- "text"
+- "nigeria"
+- "waec"
+- "jamb"
+- "synthetic"
+- "school-infrastructure-and-resources"
+- "school"
+- "learning"
+pretty_name: "Africa Synth Education Learning Materials Nigeria | Africa (Electric Sheep Africa metadata inventory)"
 ---
+# Africa Synth Education Learning Materials Nigeria | Africa (Electric Sheep Africa metadata inventory)
+**Size category:** `100K<n<1M` - **Formats:** `parquet` - **Sector:** education - *Engineered by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*
+![size](https://img.shields.io/badge/size-100K%3Cn%3C1M-blue)
+![sector](https://img.shields.io/badge/sector-education-green)
+![downloads](https://img.shields.io/badge/HF_downloads-15-orange)
+![license](https://img.shields.io/badge/license-other-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
+Education datasets help researchers study access, participation, attainment, learning systems, staffing, and infrastructure.
+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. Nigeria Education – Learning Materials Dataset Description Synthetic School Infrastructure & Resources data for Nigeria education sector. Category: School Infrastructure & ResourcesRows: 110,000Format: CSV, ParquetLicense: MITSynthetic: Yes (generated using reference data from WAEC, JAMB, UBEC, NBS, UNESCO) Dataset… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-synth-education-learning-materials-nigeria.
+## Dataset Profile
+| Field | Value |
+|---|---|
+| Hugging Face repo | [`electricsheepafrica/africa-synth-education-learning-materials-nigeria`](https://huggingface.co/datasets/electricsheepafrica/africa-synth-education-learning-materials-nigeria) |
+| Sector | education |
+| Topic tags | nigeria, education, waec, jamb, synthetic, school-infrastructure-and-resources |
+| Modalities | `text` |
+| Formats | `parquet` |
+| Size category | `100K<n<1M` |
+| Countries | Nigeria |
+| ISO3 coverage | `NGA` |
+| Last modified on HF | `2026-04-14 22:25:42+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-education-learning-materials-nigeria")
+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: upstream_publisher, language.
+- 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:** mit
+- **Hugging Face URL:** [https://huggingface.co/datasets/electricsheepafrica/africa-synth-education-learning-materials-nigeria](https://huggingface.co/datasets/electricsheepafrica/africa-synth-education-learning-materials-nigeria)
+- **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_education_learning_materials_nigeria_2026,
+  title        = {Africa Synth Education Learning Materials Nigeria | Africa (Electric Sheep Africa metadata inventory)},
+  author       = {Public dataset metadata},
+  year         = {2026},
+  url          = {https://huggingface.co/datasets/electricsheepafrica/africa-synth-education-learning-materials-nigeria},
+  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
+  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-synth-education-learning-materials-nigeria}}
 }
 ```
+## License
+Released under mit.
+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`.