Add README.md

README.md

@@ -1,66 +1,166 @@
 ---
+annotations_creators:
+- no-annotation
+language_creators:
+- found
+language:
+- en
+license: other
+multilinguality:
+- monolingual
+size_categories:
+- n<1K
+source_datasets:
+- original
+task_categories:
+- tabular-classification
+task_ids: []
+tags:
+- africa
+- humanitarian
+- hdx
+- electric-sheep-africa
+- health-facilities
+- hxl
+- sle
+pretty_name: "Sierra Leone Healthsites"
 dataset_info:
-  features:
-  - name: x
-    dtype: float64
-  - name: y
-    dtype: float64
-  - name: osm_id
-    dtype: int64
-  - name: osm_type
-    dtype: string
-  - name: completeness
-    dtype: float64
-  - name: amenity
-    dtype: string
-  - name: healthcare
-    dtype: string
-  - name: name
-    dtype: string
-  - name: operator
-    dtype: string
-  - name: source
-    dtype: string
-  - name: speciality
-    dtype: string
-  - name: opening_hours
-    dtype: string
-  - name: dispensing
-    dtype: string
-  - name: emergency
-    dtype: string
-  - name: water_source
-    dtype: string
-  - name: addr_street
-    dtype: string
-  - name: addr_city
-    dtype: string
-  - name: changeset_id
-    dtype: int64
-  - name: changeset_version
-    dtype: int64
-  - name: changeset_timestamp
-    dtype: timestamp[ns, tz=UTC]
-  - name: uuid
-    dtype: string
-  - name: esa_source
-    dtype: string
-  - name: esa_processed
-    dtype: string
   splits:
-  - name: train
-    num_bytes: 100085
-    num_examples: 444
-  - name: test
-    num_bytes: 25472
-    num_examples: 112
-  download_size: 69135
-  dataset_size: 125557
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: test
-    path: data/test-*
+    - name: train
+      num_examples: 444
+    - name: test
+      num_examples: 111
 ---
+
+# Sierra Leone Healthsites
+
+**Publisher:** Global Healthsites Mapping Project · **Source:** [HDX](https://data.humdata.org/dataset/sierra-leone-healthsites) · **License:** `ODbL` · **Updated:** 2025-10-15
+
+---
+
+## Abstract
+
+This dataset shows the list of operating health facilities. Attributes included: Name,Nature of Facility, Activities, Lat, Long
+
+Each row in this dataset represents tabular records. Data was last updated on HDX on 2025-10-15. Geographic scope: **SLE**.
+
+*Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).*
+
+---
+
+## Dataset Characteristics
+
+| | |
+|---|---|
+| **Domain** | Public health |
+| **Unit of observation** | Tabular records |
+| **Rows (total)** | 556 |
+| **Columns** | 23 (6 numeric, 16 categorical, 0 datetime) |
+| **Train split** | 444 rows |
+| **Test split** | 111 rows |
+| **Geographic scope** | SLE |
+| **Publisher** | Global Healthsites Mapping Project |
+| **HDX last updated** | 2025-10-15 |
+
+---
+
+## Variables
+
+**Geographic** — `x` (range -13.2713–-10.3167), `y` (range 6.9678–9.9737), `osm_type` (node, way), `amenity` (clinic, pharmacy, hospital), `speciality` (clinic, hospital, yes) and 2 others.
+
+**Temporal** — `changeset_timestamp`.
+
+**Identifier / Metadata** — `osm_id` (range 224729043.0–13130524315.0), `name` (Ministry of Health and Sanitation Clinic, Matanal Children's Health Post Hospital Building, MCHP), `source` (Red Cross Field Survey, MSF-CH, MSFsurvey), `water_source`, `changeset_id` (range 19113513.0–172755228.0) and 3 others.
+
+**Other** — `completeness` (range 6.25–40.625), `healthcare` (hospital, pharmacy, clinic), `operator` (Ministry of Health and Sanitation, government, combination), `opening_hours` (24/7, 08:00-17:00, Mo-Fr 08:00-17:00), `dispensing` (yes, no) and 2 others.
+
+---
+
+## Quick Start
+
+```python
+from datasets import load_dataset
+
+ds    = load_dataset("electricsheepafrica/africa-health-facilities-sierra-leone")
+train = ds["train"].to_pandas()
+test  = ds["test"].to_pandas()
+
+print(train.shape)
+train.head()
+```
+
+---
+
+## Schema
+
+| Column | Type | Null % | Range / Sample Values |
+|---|---|---|---|
+| `x` | float64 | 30.6% | -13.2713 – -10.3167 (mean -11.8405) |
+| `y` | float64 | 30.6% | 6.9678 – 9.9737 (mean 8.4376) |
+| `osm_id` | int64 | 0.0% | 224729043.0 – 13130524315.0 (mean 4009847857.0971) |
+| `osm_type` | object | 0.0% | node, way |
+| `completeness` | float64 | 0.0% | 6.25 – 40.625 (mean 18.7837) |
+| `amenity` | object | 2.9% | clinic, pharmacy, hospital |
+| `healthcare` | object | 65.1% | hospital, pharmacy, clinic |
+| `name` | object | 11.9% | Ministry of Health and Sanitation Clinic, Matanal Children's Health Post Hospital Building, MCHP |
+| `operator` | object | 75.4% | Ministry of Health and Sanitation, government, combination |
+| `source` | object | 50.5% | Red Cross Field Survey, MSF-CH, MSFsurvey |
+| `speciality` | object | 74.5% | clinic, hospital, yes |
+| `opening_hours` | object | 76.3% | 24/7, 08:00-17:00, Mo-Fr 08:00-17:00 |
+| `dispensing` | object | 79.0% | yes, no |
+| `emergency` | object | 79.7% | yes, no |
+| `water_source` | object | 78.8% |  |
+| `addr_street` | object | 75.4% |  |
+| `addr_city` | object | 64.9% |  |
+| `changeset_id` | int64 | 0.0% | 19113513.0 – 172755228.0 (mean 104872179.8903) |
+| `changeset_version` | int64 | 0.0% | 1.0 – 10.0 (mean 2.545) |
+| `changeset_timestamp` | datetime64[ns, UTC] | 0.0% |  |
+| `uuid` | object | 0.0% |  |
+| `esa_source` | object | 0.0% |  |
+| `esa_processed` | object | 0.0% |  |
+
+---
+
+## Numeric Summary
+
+| Column | Min | Max | Mean | Median |
+|---|---|---|---|---|
+| `x` | -13.2713 | -10.3167 | -11.8405 | -11.7458 |
+| `y` | 6.9678 | 9.9737 | 8.4376 | 8.4614 |
+| `osm_id` | 224729043.0 | 13130524315.0 | 4009847857.0971 | 4504611402.0 |
+| `completeness` | 6.25 | 40.625 | 18.7837 | 12.5 |
+| `changeset_id` | 19113513.0 | 172755228.0 | 104872179.8903 | 108431838.0 |
+| `changeset_version` | 1.0 | 10.0 | 2.545 | 2.0 |
+
+---
+
+## 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`. 14 column(s) with >80% missing values were removed: `operator_type`, `operational_status`, `beds`, `staff_doctors`, `staff_nurses`, `health_amenity_type`.... 1 column(s) were cast from string to numeric or datetime based on parse-success rate (>85% threshold). 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 Global Healthsites Mapping Project and has not been independently validated by ESA.
+- Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
+- The following columns have >20% missing values and should be treated with caution in modelling: `x`, `y`, `healthcare`, `operator`, `source`, `speciality`, `opening_hours`, `dispensing`....
+- Refer to the [original HDX dataset page](https://data.humdata.org/dataset/sierra-leone-healthsites) for the publisher's own methodology notes and caveats.
+
+---
+
+## Citation
+
+```bibtex
+@dataset{hdx_africa_health_facilities_sierra_leone,
+  title     = {Sierra Leone Healthsites},
+  author    = {Global Healthsites Mapping Project},
+  year      = {2025},
+  url       = {https://data.humdata.org/dataset/sierra-leone-healthsites},
+  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.*