Add README.md

README.md

@@ -1,48 +1,160 @@
 ---
+annotations_creators:
+- no-annotation
+language_creators:
+- found
+language:
+- en
+license: cc-by-4.0
+multilinguality:
+- monolingual
+size_categories:
+- 1K<n<10K
+source_datasets:
+- original
+task_categories:
+- tabular-classification
+- tabular-regression
+task_ids: []
+tags:
+- africa
+- humanitarian
+- hdx
+- electric-sheep-africa
+- asylum-seekers
+- internally-displaced-persons-idp
+- population
+- refugees
+- stateless-persons
+- nga
+pretty_name: "Nigeria - Data on forcibly displaced populations and stateless persons"
 dataset_info:
-  features:
-  - name: year
-    dtype: int64
-  - name: country_of_origin_code
-    dtype: string
-  - name: country_of_asylum_code
-    dtype: string
-  - name: country_of_origin_name
-    dtype: string
-  - name: country_of_asylum_name
-    dtype: string
-  - name: refugees
-    dtype: int64
-  - name: asylum_seekers
-    dtype: int64
-  - name: other_people_in_need_of_international_protection
-    dtype: int64
-  - name: internally_displaced_persons
-    dtype: int64
-  - name: stateless_persons
-    dtype: int64
-  - name: others_of_concern_to_unhcr
-    dtype: int64
-  - name: host_community
-    dtype: int64
-  - name: esa_source
-    dtype: string
-  - name: esa_processed
-    dtype: string
   splits:
-  - name: train
-    num_bytes: 208615
-    num_examples: 1687
-  - name: test
-    num_bytes: 52243
-    num_examples: 422
-  download_size: 38049
-  dataset_size: 260858
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: test
-    path: data/test-*
+    - name: train
+      num_examples: 1687
+    - name: test
+      num_examples: 421
 ---
+
+# Nigeria - Data on forcibly displaced populations and stateless persons
+
+**Publisher:** UNHCR - The UN Refugee Agency · **Source:** [HDX](https://data.humdata.org/dataset/unhcr-population-data-for-nga) · **License:** `cc-by-igo` · **Updated:** 2026-02-25
+
+---
+
+## Abstract
+
+Data collated by UNHCR, containing information about forcibly displaced populations and stateless persons, spanning across more than 70 years of statistical activities.  The data includes the countries / territories of asylum and origin.  Specific resources are available for end-year population totals, demographics, asylum applications, decisions, and solutions availed by refugees and IDPs (resettlement, naturalisation or returns).
+
+Each row in this dataset represents first-level administrative unit observations. Data was last updated on HDX on 2026-02-25. Geographic scope: **NGA**.
+
+*Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).*
+
+---
+
+## Dataset Characteristics
+
+| | |
+|---|---|
+| **Domain** | Demographics and population |
+| **Unit of observation** | First-level administrative unit observations |
+| **Rows (total)** | 2,109 |
+| **Columns** | 14 (8 numeric, 6 categorical, 0 datetime) |
+| **Train split** | 1,687 rows |
+| **Test split** | 421 rows |
+| **Geographic scope** | NGA |
+| **Publisher** | UNHCR - The UN Refugee Agency |
+| **HDX last updated** | 2026-02-25 |
+
+---
+
+## Variables
+
+**Geographic** — `year` (range 1968.0–2025.0), `country_of_origin_code` (NGA), `country_of_asylum_code` (ITA, GBR, SWE), `country_of_origin_name` (Nigeria), `country_of_asylum_name` (Italy, United Kingdom of Great Britain and Northern Ireland, Sweden) and 4 others.
+
+**Identifier / Metadata** — `refugees` (range 0.0–264553.0), `esa_source` (HDX), `esa_processed` (2026-04-04).
+
+**Other** — `other_people_in_need_of_international_protection` (range 0.0–0.0), `others_of_concern_to_unhcr` (range 0.0–70000.0).
+
+---
+
+## Quick Start
+
+```python
+from datasets import load_dataset
+
+ds    = load_dataset("electricsheepafrica/africa-unhcr-population-data-for-nga")
+train = ds["train"].to_pandas()
+test  = ds["test"].to_pandas()
+
+print(train.shape)
+train.head()
+```
+
+---
+
+## Schema
+
+| Column | Type | Null % | Range / Sample Values |
+|---|---|---|---|
+| `year` | int64 | 0.0% | 1968.0 – 2025.0 (mean 2012.3589) |
+| `country_of_origin_code` | object | 0.0% | NGA |
+| `country_of_asylum_code` | object | 0.0% | ITA, GBR, SWE |
+| `country_of_origin_name` | object | 0.0% | Nigeria |
+| `country_of_asylum_name` | object | 0.0% | Italy, United Kingdom of Great Britain and Northern Ireland, Sweden |
+| `refugees` | int64 | 0.0% | 0.0 – 264553.0 (mean 1946.2319) |
+| `asylum_seekers` | int64 | 0.0% | 0.0 – 37967.0 (mean 508.5524) |
+| `other_people_in_need_of_international_protection` | int64 | 0.0% | 0.0 – 0.0 (mean 0.0) |
+| `internally_displaced_persons` | int64 | 0.0% | 0.0 – 3575114.0 (mean 14675.844) |
+| `stateless_persons` | int64 | 0.0% | 0.0 – 0.0 (mean 0.0) |
+| `others_of_concern_to_unhcr` | int64 | 0.0% | 0.0 – 70000.0 (mean 33.9654) |
+| `host_community` | int64 | 0.0% | 0.0 – 0.0 (mean 0.0) |
+| `esa_source` | object | 0.0% | HDX |
+| `esa_processed` | object | 0.0% | 2026-04-04 |
+
+---
+
+## Numeric Summary
+
+| Column | Min | Max | Mean | Median |
+|---|---|---|---|---|
+| `year` | 1968.0 | 2025.0 | 2012.3589 | 2014.0 |
+| `refugees` | 0.0 | 264553.0 | 1946.2319 | 9.0 |
+| `asylum_seekers` | 0.0 | 37967.0 | 508.5524 | 12.0 |
+| `other_people_in_need_of_international_protection` | 0.0 | 0.0 | 0.0 | 0.0 |
+| `internally_displaced_persons` | 0.0 | 3575114.0 | 14675.844 | 0.0 |
+| `stateless_persons` | 0.0 | 0.0 | 0.0 | 0.0 |
+| `others_of_concern_to_unhcr` | 0.0 | 70000.0 | 33.9654 | 0.0 |
+| `host_community` | 0.0 | 0.0 | 0.0 | 0.0 |
+
+---
+
+## 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 UNHCR - The UN Refugee Agency 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/unhcr-population-data-for-nga) for the publisher's own methodology notes and caveats.
+
+---
+
+## Citation
+
+```bibtex
+@dataset{hdx_africa_unhcr_population_data_for_nga,
+  title     = {Nigeria - Data on forcibly displaced populations and stateless persons},
+  author    = {UNHCR - The UN Refugee Agency},
+  year      = {2026},
+  url       = {https://data.humdata.org/dataset/unhcr-population-data-for-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.*