Add README.md

README.md

@@ -1,112 +1,203 @@
 ---
+annotations_creators:
+- no-annotation
+language_creators:
+- found
+language:
+- en
+license: cc-by-4.0
+multilinguality:
+- monolingual
+size_categories:
+- n<1K
+source_datasets:
+- original
+task_categories:
+- tabular-classification
+- other
+task_ids: []
+tags:
+- africa
+- humanitarian
+- hdx
+- electric-sheep-africa
+- aid-worker-security
+- aid-workers
+- conflict-violence
+- nga
+pretty_name: "Nigeria - Aid Worker Security Database"
 dataset_info:
-  features:
-  - name: incident_id
-    dtype: int64
-  - name: year
-    dtype: int64
-  - name: month
-    dtype: int64
-  - name: day
-    dtype: float64
-  - name: country_code
-    dtype: string
-  - name: country
-    dtype: string
-  - name: region
-    dtype: string
-  - name: district
-    dtype: string
-  - name: city
-    dtype: string
-  - name: un
-    dtype: int64
-  - name: ingo
-    dtype: int64
-  - name: icrc
-    dtype: int64
-  - name: nrcs_and_ifrc
-    dtype: int64
-  - name: nngo
-    dtype: int64
-  - name: other
-    dtype: int64
-  - name: nationals_killed
-    dtype: int64
-  - name: nationals_wounded
-    dtype: int64
-  - name: nationals_kidnapped
-    dtype: int64
-  - name: nationals_detained
-    dtype: int64
-  - name: total_nationals
-    dtype: int64
-  - name: internationals_killed
-    dtype: int64
-  - name: internationals_wounded
-    dtype: int64
-  - name: internationals_kidnapped
-    dtype: int64
-  - name: internationals_detained
-    dtype: int64
-  - name: total_internationals
-    dtype: int64
-  - name: total_killed
-    dtype: int64
-  - name: total_wounded
-    dtype: int64
-  - name: total_kidnapped
-    dtype: int64
-  - name: total_detained
-    dtype: int64
-  - name: total_affected
-    dtype: int64
-  - name: gender_male
-    dtype: int64
-  - name: gender_female
-    dtype: int64
-  - name: gender_unknown
-    dtype: int64
-  - name: means_of_attack
-    dtype: string
-  - name: attack_context
-    dtype: string
-  - name: location
-    dtype: string
-  - name: latitude
-    dtype: float64
-  - name: longitude
-    dtype: float64
-  - name: motive
-    dtype: string
-  - name: actor_type
-    dtype: string
-  - name: actor_name
-    dtype: string
-  - name: details
-    dtype: string
-  - name: verified
-    dtype: string
-  - name: source
-    dtype: string
-  - name: esa_source
-    dtype: string
-  - name: esa_processed
-    dtype: string
   splits:
-  - name: train
-    num_bytes: 50015
-    num_examples: 79
-  - name: test
-    num_bytes: 12807
-    num_examples: 20
-  download_size: 58984
-  dataset_size: 62822
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: test
-    path: data/test-*
+    - name: train
+      num_examples: 79
+    - name: test
+      num_examples: 19
 ---
+
+# Nigeria - Aid Worker Security Database
+
+**Publisher:** Humanitarian Outcomes · **Source:** [HDX](https://data.humdata.org/dataset/aid-worker-security-database-nga) · **License:** `cc-by` · **Updated:** 2026-04-10
+
+---
+
+## Abstract
+
+This dataset shows aid worker security incidents in Nigeria. Annually, the data for the previous year undergoes a verification process. Data for the current year is provisional. For incident descriptions, please download data directly from [www.aidworkersecurity.org](www.aidworkersecurity.org)
+
+Each row in this dataset represents discrete events or incidents. Data was last updated on HDX on 2026-04-10. Geographic scope: **NGA**.
+
+*Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).*
+
+---
+
+## Dataset Characteristics
+
+| | |
+|---|---|
+| **Domain** | Conflict and security |
+| **Unit of observation** | Discrete events or incidents |
+| **Rows (total)** | 99 |
+| **Columns** | 46 (30 numeric, 16 categorical, 0 datetime) |
+| **Train split** | 79 rows |
+| **Test split** | 19 rows |
+| **Geographic scope** | NGA |
+| **Publisher** | Humanitarian Outcomes |
+| **HDX last updated** | 2026-04-10 |
+
+---
+
+## Variables
+
+**Geographic** — `year` (range 2004.0–2026.0), `day` (range 1.0–31.0), `country_code` (NG), `country` (Nigeria), `region` (Borno, Zamfara, Federal Capital Territory) and 7 others.
+
+**Temporal** — `month` (range 1.0–12.0).
+
+**Demographic** — `gender_male`, `gender_female`, `gender_unknown`.
+
+**Outcome / Measurement** — `total_nationals` (range 0.0–46.0), `total_internationals` (range 0.0–4.0), `total_killed`, `total_wounded`, `total_kidnapped` and 2 others.
+
+**Identifier / Metadata** — `incident_id` (range 306.0–5809.0), `nationals_kidnapped` (range 0.0–6.0), `internationals_kidnapped` (range 0.0–3.0), `actor_name`, `source` and 2 others.
+
+**Other** — `un` (range 0.0–46.0), `ingo` (range 0.0–10.0), `icrc` (range 0.0–1.0), `nrcs_and_ifrc` (range 0.0–14.0), `nngo` (range 0.0–3.0) and 11 others.
+
+---
+
+## Quick Start
+
+```python
+from datasets import load_dataset
+
+ds    = load_dataset("electricsheepafrica/africa-aid-worker-security-database-nga")
+train = ds["train"].to_pandas()
+test  = ds["test"].to_pandas()
+
+print(train.shape)
+train.head()
+```
+
+---
+
+## Schema
+
+| Column | Type | Null % | Range / Sample Values |
+|---|---|---|---|
+| `incident_id` | int64 | 0.0% | 306.0 – 5809.0 (mean 3699.4141) |
+| `year` | int64 | 0.0% | 2004.0 – 2026.0 (mean 2021.4343) |
+| `month` | int64 | 0.0% | 1.0 – 12.0 (mean 6.5657) |
+| `day` | float64 | 2.0% | 1.0 – 31.0 (mean 15.3608) |
+| `country_code` | object | 0.0% | NG |
+| `country` | object | 0.0% | Nigeria |
+| `region` | object | 1.0% | Borno, Zamfara, Federal Capital Territory |
+| `district` | object | 5.1% | Maiduguri, Mobbar, Monguno |
+| `city` | object | 10.1% | Rann, Monguno, Damasak |
+| `un` | int64 | 0.0% | 0.0 – 46.0 (mean 0.8687) |
+| `ingo` | int64 | 0.0% | 0.0 – 10.0 (mean 1.0) |
+| `icrc` | int64 | 0.0% | 0.0 – 1.0 (mean 0.0101) |
+| `nrcs_and_ifrc` | int64 | 0.0% | 0.0 – 14.0 (mean 0.1818) |
+| `nngo` | int64 | 0.0% | 0.0 – 3.0 (mean 0.4141) |
+| `other` | int64 | 0.0% | 0.0 – 1.0 (mean 0.0101) |
+| `nationals_killed` | int64 | 0.0% | 0.0 – 9.0 (mean 0.7374) |
+| `nationals_wounded` | int64 | 0.0% | 0.0 – 37.0 (mean 0.8485) |
+| `nationals_kidnapped` | int64 | 0.0% | 0.0 – 6.0 (mean 0.7879) |
+| `nationals_detained` | int64 | 0.0% | 0.0 – 1.0 (mean 0.0303) |
+| `total_nationals` | int64 | 0.0% | 0.0 – 46.0 (mean 2.404) |
+| `internationals_killed` | int64 | 0.0% | 0.0 – 1.0 (mean 0.0303) |
+| `internationals_wounded` | int64 | 0.0% | 0.0 – 0.0 (mean 0.0) |
+| `internationals_kidnapped` | int64 | 0.0% | 0.0 – 3.0 (mean 0.0505) |
+| `internationals_detained` | int64 | 0.0% | 0.0 – 0.0 (mean 0.0) |
+| `total_internationals` | int64 | 0.0% | 0.0 – 4.0 (mean 0.0808) |
+| `total_killed` | int64 | 0.0% |  |
+| `total_wounded` | int64 | 0.0% |  |
+| `total_kidnapped` | int64 | 0.0% |  |
+| `total_detained` | int64 | 0.0% |  |
+| `total_affected` | int64 | 0.0% |  |
+| `gender_male` | int64 | 0.0% |  |
+| `gender_female` | int64 | 0.0% |  |
+| `gender_unknown` | int64 | 0.0% |  |
+| `means_of_attack` | object | 0.0% | Kidnapping, Shooting, Bodily assault |
+| `attack_context` | object | 0.0% | Ambush, Raid, Individual attack |
+| `location` | object | 0.0% | Road, Office/compound, Public location |
+| `latitude` | float64 | 0.0% |  |
+| `longitude` | float64 | 0.0% |  |
+| `motive` | object | 1.0% | Unknown, Economic, Political |
+| `actor_type` | object | 0.0% | Non-state armed group: Regional, Unknown, Criminal |
+| `actor_name` | object | 0.0% |  |
+| `details` | object | 0.0% |  |
+| `verified` | object | 0.0% |  |
+| `source` | object | 0.0% |  |
+| `esa_source` | object | 0.0% |  |
+| `esa_processed` | object | 0.0% |  |
+
+---
+
+## Numeric Summary
+
+| Column | Min | Max | Mean | Median |
+|---|---|---|---|---|
+| `incident_id` | 306.0 | 5809.0 | 3699.4141 | 3855.0 |
+| `year` | 2004.0 | 2026.0 | 2021.4343 | 2022.0 |
+| `month` | 1.0 | 12.0 | 6.5657 | 7.0 |
+| `day` | 1.0 | 31.0 | 15.3608 | 15.0 |
+| `un` | 0.0 | 46.0 | 0.8687 | 0.0 |
+| `ingo` | 0.0 | 10.0 | 1.0 | 0.0 |
+| `icrc` | 0.0 | 1.0 | 0.0101 | 0.0 |
+| `nrcs_and_ifrc` | 0.0 | 14.0 | 0.1818 | 0.0 |
+| `nngo` | 0.0 | 3.0 | 0.4141 | 0.0 |
+| `other` | 0.0 | 1.0 | 0.0101 | 0.0 |
+| `nationals_killed` | 0.0 | 9.0 | 0.7374 | 0.0 |
+| `nationals_wounded` | 0.0 | 37.0 | 0.8485 | 0.0 |
+| `nationals_kidnapped` | 0.0 | 6.0 | 0.7879 | 0.0 |
+| `nationals_detained` | 0.0 | 1.0 | 0.0303 | 0.0 |
+| `total_nationals` | 0.0 | 46.0 | 2.404 | 1.0 |
+
+---
+
+## Curation
+
+Raw data was downloaded from HDX via the CKAN API and converted to Parquet. Column names were lowercased and standardised to snake_case. Common missing-value markers (`N/A`, `null`, `none`, `-`, `unknown`, `no data`, `#N/A`) were unified to `NaN`. The dataset was split 80/20 into train and test partitions using a fixed random seed (42) and saved as Snappy-compressed Parquet.
+
+---
+
+## Limitations
+
+- Data originates from Humanitarian Outcomes and has not been independently validated by ESA.
+- Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
+- Refer to the [original HDX dataset page](https://data.humdata.org/dataset/aid-worker-security-database-nga) for the publisher's own methodology notes and caveats.
+
+---
+
+## Citation
+
+```bibtex
+@dataset{hdx_africa_aid_worker_security_database_nga,
+  title     = {Nigeria - Aid Worker Security Database},
+  author    = {Humanitarian Outcomes},
+  year      = {2026},
+  url       = {https://data.humdata.org/dataset/aid-worker-security-database-nga},
+  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
+}
+```
+
+---
+
+*[Electric Sheep Africa](https://huggingface.co/electricsheepafrica) — Africa's ML dataset infrastructure. Lagos, Nigeria.*