Standardize Electric Sheep Africa dataset card

README.md

@@ -1,56 +1,137 @@
-# Africa Net ODA received (% of central government expense) Dataset
+---
+license: other
+language:
+- en
+task_categories:
+- tabular-classification
+- tabular-regression
+multilinguality: monolingual
+size_categories:
+- n<1K
+tags:
+- "africa"
+- "electric-sheep-africa"
+- "open-data"
+- "metadata-backed"
+- "governance-security"
+- "csv"
+- "tabular"
+- "text"
+- "government"
+pretty_name: "Africa Net Oda Received Percentage of Central Government Expense | Africa (World Bank)"
+---
 
-## Overview
-This dataset contains net oda received (% of central government expense) data for African countries from the World Bank Aid Effectiveness indicators.
+# Africa Net Oda Received Percentage of Central Government Expense | Africa (World Bank)
 
-## Data Details
-- **Indicator Code**: DT.ODA.ODAT.XP.ZS
-- **Description**: Net ODA received (% of central government expense)
-- **Geographic Coverage**: 42 African countries
-- **Time Period**: 1972-2022
-- **Data Points**: 773 observations
-- **Coverage**: 22.02% of possible country-year combinations
+**Size category:** `n<1K` - **Formats:** `csv` - **Sector:** governance_security - *Engineered by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*
 
-## File Formats
+![size](https://img.shields.io/badge/size-n%3C1K-blue)
+![sector](https://img.shields.io/badge/sector-governance_security-green)
+![downloads](https://img.shields.io/badge/HF_downloads-10-orange)
+![license](https://img.shields.io/badge/license-other-lightgrey)
 
-### Main Dataset (`Africa-Net-Oda-Received-Percentage-of-Central-Government-Expense.csv`)
-- **Rows**: 54 countries
-- **Columns**: 65 years (1960-2024)
-- **Structure**: Countries as rows, years as columns
-- **Missing Value Treatment**: Interpolation → Forward Fill → Backward Fill
-- **Use Case**: Cross-sectional analysis, heatmaps, correlation analysis
+## TL;DR
 
-## Data Quality
+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.
 
-### Coverage Statistics
-- **Total Observations**: 773
-- **Possible Observations**: 3,510
-- **Coverage Rate**: 22.02%
+## What This Dataset Covers
 
-### Countries with No Data
-12 countries have no observations:
-Benin, Comoros, Djibouti, Algeria, Eritrea, Libya, Mauritania, Nigeria, Sierra Leone, South Sudan, Sao Tome and Principe, Chad
+Public datasets help analysts inspect structured evidence, build reproducible workflows, and compare patterns across domains.
 
-## Usage Examples
+Dataset context from the existing Hugging Face card: Africa Net ODA received (% of central government expense) Dataset Overview This dataset contains net oda received (% of central government expense) data for African countries from the World Bank Aid Effectiveness indicators. Data Details Indicator Code: DT.ODA.ODAT.XP.ZS Description: Net ODA received (% of central government expense) Geographic Coverage: 42 African countries Time Period: 1972-2022 Data Points: 773 observations Coverage: 22.02% of possible… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-net-oda-received-percentage-of-central-government-expense.
+
+## Dataset Profile
+
+| Field | Value |
+|---|---|
+| Hugging Face repo | [`electricsheepafrica/africa-net-oda-received-percentage-of-central-government-expense`](https://huggingface.co/datasets/electricsheepafrica/africa-net-oda-received-percentage-of-central-government-expense) |
+| Sector | governance_security |
+| Topic tags | governance_security |
+| Modalities | `tabular`, `text` |
+| Formats | `csv` |
+| Size category | `n<1K` |
+| Countries | Africa-wide or source-defined African coverage |
+| ISO3 coverage | `not declared` |
+| Last modified on HF | `2025-09-01 09:15:57+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
 ```python
-import pandas as pd
+from datasets import load_dataset
+
+ds = load_dataset("electricsheepafrica/africa-net-oda-received-percentage-of-central-government-expense")
+print(ds)
 
-# Load main dataset
-df = pd.read_csv('Africa-Net-Oda-Received-Percentage-of-Central-Government-Expense.csv', index_col=[0, 1])
+split_name = next(iter(ds))
+table = ds[split_name]
+print(table.features)
+print(table[:3])
 ```
 
-### R
-```r
-# Load main dataset
-df <- read.csv('Africa-Net-Oda-Received-Percentage-of-Central-Government-Expense.csv', row.names=c(1,2))
+### 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())
 ```
 
-## Source
-World Bank Open Data - Aid Effectiveness
-- **Original File**: API_DT.ODA.ODAT.XP.ZS_DS2_en_excel_v2_534646.xls
-- **Processed**: 2025-09-01
+## 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, license, language.
+- Do not infer policy meaning from labels alone; confirm definitions, units, and methods in the source material.
+
+## Source And Provenance
+
+- **Source context:** World Bank
+- **Publisher/source attribution:** World Bank open data
+- **License:** Source-specific or other license
+- **Hugging Face URL:** [https://huggingface.co/datasets/electricsheepafrica/africa-net-oda-received-percentage-of-central-government-expense](https://huggingface.co/datasets/electricsheepafrica/africa-net-oda-received-percentage-of-central-government-expense)
+- **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_net_oda_received_percentage_of_central_government_expense_2026,
+  title        = {Africa Net Oda Received Percentage of Central Government Expense | Africa (World Bank)},
+  author       = {World Bank open data},
+  year         = {2026},
+  url          = {https://huggingface.co/datasets/electricsheepafrica/africa-net-oda-received-percentage-of-central-government-expense},
+  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
+  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-net-oda-received-percentage-of-central-government-expense}}
+}
+```
 
 ## License
-This dataset is derived from World Bank Open Data and is available under the Creative Commons Attribution 4.0 International License.
+
+Released under Source-specific or other license.
+
+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`.