Standardize Electric Sheep Africa dataset card

README.md

@@ -1,102 +1,146 @@
 ---
 license: cc-by-4.0
+language:
+- en
 task_categories:
 - tabular-classification
 - tabular-regression
-tags:
-- governance
-- anti-corruption
-- accountability
-- sub-saharan-africa
-- synthetic
-- lmic
-- transparency
-- enforcement
-- public-sector
-pretty_name: African Anti-Corruption Enforcement
+multilinguality: monolingual
 size_categories:
 - 10K<n<100K
-configs:
-- config_name: baseline
-  data_files: data/baseline.csv
-- config_name: strengthened_enforcement
-  data_files: data/strengthened_enforcement.csv
-- config_name: weakened_accountability
-  data_files: data/weakened_accountability.csv
-data_type: synthetic
+tags:
+- "africa"
+- "electric-sheep-africa"
+- "open-data"
+- "metadata-backed"
+- "governance-security"
+- "csv"
+- "tabular"
+- "text"
+- "governance"
+- "anti-corruption"
+- "accountability"
+- "sub-saharan-africa"
+- "synthetic"
+- "lmic"
+- "transparency"
+- "enforcement"
+- "public-sector"
+- "corruption"
+pretty_name: "African Anti-Corruption Enforcement | Africa (Electric Sheep Africa metadata inventory)"
 ---
 
-> ⚠️ **Synthetic dataset** — Parameterized from published SSA literature, not real observations. Not suitable for empirical analysis or policy inference.
+# African Anti-Corruption Enforcement | Africa (Electric Sheep Africa metadata inventory)
+
+**Size category:** `10K<n<100K` - **Formats:** `csv` - **Sector:** governance_security - *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-governance_security-green)
+![downloads](https://img.shields.io/badge/HF_downloads-103-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
 
-# African Anti-Corruption Enforcement
+Public datasets help analysts inspect structured evidence, build reproducible workflows, and compare patterns across domains.
 
-Synthetic dataset modelling anti-corruption enforcement outcomes across 12 Sub-Saharan African countries under three policy scenarios. Parameters are anchored to real-world statistics from Transparency International, the EFCC (Nigeria), SIU/Zondo Commission (South Africa), EACC (Kenya), the African Union's Common African Position on Asset Recovery, and the G20 2025 Accountability Report on Whistleblower Protection.
+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 Anti-Corruption Enforcement Synthetic dataset modelling anti-corruption enforcement outcomes across 12 Sub-Saharan African countries under three policy scenarios. Parameters are anchored to real-world statistics from Transparency International, the EFCC (Nigeria), SIU/Zondo Commission (South Africa), EACC (Kenya), the African… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-synth-governance-anti-corruption-enforcement-all.
 
-## Dataset Summary
+## Dataset Profile
 
-| Property | Value |
+| Field | Value |
 |---|---|
-| Total records | 30,000 (10,000 per scenario) |
-| Countries | 12 SSA nations |
-| Variables | 15 per record |
-| Scenarios | baseline, strengthened_enforcement, weakened_accountability |
-
-## Scenarios
-
-- **baseline** – Calibrated to observed 2023–2025 enforcement statistics. Median prosecution rate ~25%, median asset recovery rate ~7%.
-- **strengthened_enforcement** – Simulates higher agency independence (+18), stronger whistleblower protection (+20), increased prosecution (+45%), doubled asset recovery, and +8 CPI points.
-- **weakened_accountability** – Models institutional erosion: agency independence −15, whistleblower protection −15, prosecution −40%, asset recovery −60%, and −10 CPI points.
-
-## Countries
-
-Nigeria, South Africa, Kenya, Ghana, Tanzania, Uganda, Ethiopia, Senegal, Mozambique, Zambia, Rwanda, Botswana.
-
-## Variables
-
-| Variable | Type | Description |
-|---|---|---|
-| `country` | str | Country name |
-| `scenario` | str | Policy scenario |
-| `corruption_perception_index` | float | TI CPI score (0–100, higher = cleaner) |
-| `cases_investigated` | int | Number of corruption cases under investigation |
-| `cases_prosecuted` | int | Cases forwarded to prosecution |
-| `prosecution_rate` | float | cases_prosecuted / cases_investigated |
-| `convictions` | int | Number of convictions secured |
-| `conviction_rate` | float | convictions / cases_prosecuted |
-| `assets_confiscated_usd_millions` | float | Value of confiscated assets (USD millions) |
-| `asset_recovery_rate` | float | Fraction of estimated stolen assets recovered |
-| `whistleblower_reports` | int | Number of whistleblower submissions |
-| `whistleblower_protection_score` | float | Protection framework quality (0–100) |
-| `agency_independence_score` | float | Anti-corruption agency independence (0–100) |
-| `enforcement_effectiveness_score` | float | Composite effectiveness index (0–100) |
-| `enforcement_class` | str | strong / moderate / weak |
-
-## Research Sources
-
-1. **Transparency International CPI 2025** – SSA average score 32/100; Seychelles (68) highest, Somalia/South Sudan (9) lowest.
-2. **EFCC Nigeria 2024** – 15,724 petitions → 12,928 investigated → 5,083 prosecuted → 4,111 convictions; $214.5M recovered.
-3. **South Africa SIU 2023/24** – 1,919 investigations closed, 583 criminal referrals, R8B saved, ~80% conviction rate in commercial cases.
-4. **Kenya EACC 2023/24** – 5,171 reports, 534 files under probe, 26.7% conviction rate, Ksh2.9B recovered.
-5. **AU Common African Position on Asset Recovery** – Africa loses ~$150B annually through illicit financial flows; asset recovery typically <10%.
-6. **G20 2025 Accountability Report on Whistleblower Protection** – Most SSA countries lack dedicated whistleblower legislation; protection gaps in developing countries.
+| Hugging Face repo | [`electricsheepafrica/africa-synth-governance-anti-corruption-enforcement-all`](https://huggingface.co/datasets/electricsheepafrica/africa-synth-governance-anti-corruption-enforcement-all) |
+| Sector | governance_security |
+| Topic tags | governance, anti-corruption, accountability, sub-saharan-africa, synthetic, lmic, transparency, enforcement, public-sector |
+| Modalities | `tabular`, `text` |
+| Formats | `csv` |
+| Size category | `10K<n<100K` |
+| Countries | Africa-wide or source-defined African coverage |
+| ISO3 coverage | `not declared` |
+| Last modified on HF | `2026-04-14 22:58:07+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/african-anti-corruption-enforcement", "baseline")
-df = ds["train"].to_pandas()
+ds = load_dataset("electricsheepafrica/africa-synth-governance-anti-corruption-enforcement-all")
+print(ds)
+
+split_name = next(iter(ds))
+table = ds[split_name]
+print(table.features)
+print(table[:3])
 ```
 
-## Generation & Validation
+### Convert To Pandas When Tabular
+
+```python
+from datasets import Dataset
 
-```bash
-pip install -r requirements.txt
-python generate_dataset.py --scenario all --n-records 10000
-python validate_dataset.py --data-dir data --plot-dir plots
+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, 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:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)
+- **Hugging Face URL:** [https://huggingface.co/datasets/electricsheepafrica/africa-synth-governance-anti-corruption-enforcement-all](https://huggingface.co/datasets/electricsheepafrica/africa-synth-governance-anti-corruption-enforcement-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_governance_anti_corruption_enforcement_all_2026,
+  title        = {African Anti-Corruption Enforcement | Africa (Electric Sheep Africa metadata inventory)},
+  author       = {Public dataset metadata},
+  year         = {2026},
+  url          = {https://huggingface.co/datasets/electricsheepafrica/africa-synth-governance-anti-corruption-enforcement-all},
+  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
+  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-synth-governance-anti-corruption-enforcement-all}}
+}
 ```
 
 ## License
 
-CC-BY-4.0
+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`.