Datasets:

electricsheepafrica
/

africa-synth-education-teacher-attendance-workload-nigeria

@@ -1,131 +1,141 @@
 ---
-license: mit
-task_categories:
-- tabular-regression
-- tabular-classification
-tags:
-- nigeria
-- education
-- africa
-- synthetic-data
-- teacher-attendance-workload
-- synthetic
 language:
 - en
 size_categories:
 - 100K<n<1M
-pretty_name: Nigeria Education - Teacher Attendance Workload
-data_type: synthetic
 ---
-> ⚠️ **Synthetic dataset** — Parameterized from published SSA literature, not real observations. Not suitable for empirical analysis or policy inference.
-# Nigeria - Teacher Attendance Workload
-## Dataset Description
-Teacher attendance records and teaching workload hours.
-## Dataset Information
-- **Country**: Nigeria
-- **Dataset Name**: teacher_attendance_workload
-- **Total Records**: 100,000
-- **Total Columns**: 7
-- **File Size**: 1.51 MB
-- **Format**: Parquet (full data), CSV (sample)
-- **Generated**: 2025-10-21T23:15:47.181063
-## Schema
-| Column | Data Type | Description |
-|--------|-----------|-------------|
-| `workload_id` | object | Workload Id |
-| `teacher_id` | object | Teacher Id |
-| `date` | datetime64[ns] | Date |
-| `status` | object | Status |
-| `teaching_hours` | float64 | Teaching Hours |
-| `num_classes` | float64 | Num Classes |
-| `country` | object | Country |
-## Sample Data
-The dataset includes a 10,000-row sample in CSV format for quick exploration.
-## Data Quality
-- **Validation Status**: ✅ Passed
-- **Missing Data**: ~2-5% (realistic pattern)
-- **Data Type Enforcement**: Strict
-- **Cross-Dataset Consistency**: Maintained
-## Dependencies
-- `teacher_profiles`
-## Usage Example
-### Python (Pandas)
 ```python
-import pandas as pd
-# Load full dataset
-df = pd.read_parquet('teacher_attendance_workload_full.parquet')
-print(f"Loaded {len(df):,} records")
-print(df.head())
-# Load sample
-df_sample = pd.read_csv('teacher_attendance_workload_sample.csv')
-print(df_sample.describe())
 ```
-### Python (Hugging Face Datasets)
 ```python
-from datasets import load_dataset
-# Load from Hugging Face
-dataset = load_dataset('nigeria-education-teacher_attendance_workload')
-df = dataset['train'].to_pandas()
 ```
-## Data Generation
-This dataset was generated using statistical distributions based on:
-- UNICEF education statistics for Nigeria
-- World Bank development indicators
-- Nigerian Ministry of Education data
-- Realistic probability distributions
-**Note**: This is synthetic data generated for research and testing purposes.
-## Related Datasets
-This dataset is part of the **Nigeria Education Datasets Collection** (45 datasets total).
-Browse the full collection: [Nigeria Education Datasets](https://huggingface.co/collections/nigeria-education)
 ## Citation
 ```bibtex
-@dataset{nigeria_teacher_attendance_workload_2025,
-  title={Nigeria Education Dataset: Teacher Attendance Workload},
-  author={[Your Name/Organization]},
-  year={2025},
-  publisher={Hugging Face},
-  url={https://huggingface.co/datasets/nigeria-education-teacher_attendance_workload}
 }
 ```
 ## License
-[Specify your license]
-## Contact
-[Your contact information]
 ---
-**Part of**: Nigeria Education Datasets Collection
-**Total Datasets**: 45
-**Country**: Nigeria 🇳🇬

 ---
+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"
+- "tabular"
+- "text"
+- "nigeria"
+- "synthetic-data"
+- "teacher-attendance-workload"
+- "synthetic"
+- "teacher"
+pretty_name: "Nigeria Education - Teacher Attendance Workload | Africa (Electric Sheep Africa metadata inventory)"
 ---
+# Nigeria Education - Teacher Attendance Workload | 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-29-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 - Teacher Attendance Workload Dataset Description Teacher attendance records and teaching workload hours. Dataset Information Country: Nigeria Dataset Name: teacher_attendance_workload Total Records: 100,000 Total Columns: 7 File Size: 1.51 MB Format: Parquet (full data), CSV (sample) Generated:… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-synth-education-teacher-attendance-workload-nigeria.
+## Dataset Profile
+| Field | Value |
+|---|---|
+| Hugging Face repo | [`electricsheepafrica/africa-synth-education-teacher-attendance-workload-nigeria`](https://huggingface.co/datasets/electricsheepafrica/africa-synth-education-teacher-attendance-workload-nigeria) |
+| Sector | education |
+| Topic tags | nigeria, education, synthetic-data, teacher-attendance-workload, synthetic |
+| Modalities | `tabular`, `text` |
+| Formats | `parquet` |
+| Size category | `100K<n<1M` |
+| Countries | Nigeria |
+| ISO3 coverage | `NGA` |
+| Last modified on HF | `2026-04-14 22:36:58+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-teacher-attendance-workload-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.
+- 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-teacher-attendance-workload-nigeria](https://huggingface.co/datasets/electricsheepafrica/africa-synth-education-teacher-attendance-workload-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_teacher_attendance_workload_nigeria_2026,
+  title        = {Nigeria Education - Teacher Attendance Workload | Africa (Electric Sheep Africa metadata inventory)},
+  author       = {Public dataset metadata},
+  year         = {2026},
+  url          = {https://huggingface.co/datasets/electricsheepafrica/africa-synth-education-teacher-attendance-workload-nigeria},
+  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
+  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-synth-education-teacher-attendance-workload-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`.