Datasets:

electricsheepafrica
/

africa-health-facilities-congo-dem-rep

@@ -1,159 +1,141 @@
 ---
-annotations_creators:
-- no-annotation
-language_creators:
-- found
 language:
 - en
-license: other
-multilinguality:
-- monolingual
-size_categories:
-- 1K<n<10K
-source_datasets:
-- original
 task_categories:
 - tabular-classification
-task_ids: []
 tags:
-- africa
-- humanitarian
-- hdx
-- electric-sheep-africa
-- health-facilities
-- hxl
-- cod
-pretty_name: "Democratic Republic of the Congo Healthsites"
-dataset_info:
-  splits:
-    - name: train
-      num_examples: 3165
-    - name: test
-      num_examples: 791
 ---
-# Democratic Republic of the Congo Healthsites
-**Publisher:** Global Healthsites Mapping Project · **Source:** [HDX](https://data.humdata.org/dataset/democratic-republic-of-the-congo-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: **COD**.
-*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)** | 3,957 |
-| **Columns** | 16 (6 numeric, 9 categorical, 0 datetime) |
-| **Train split** | 3,165 rows |
-| **Test split** | 791 rows |
-| **Geographic scope** | COD |
-| **Publisher** | Global Healthsites Mapping Project |
-| **HDX last updated** | 2025-10-15 |
----
-## Variables
-**Geographic** — `x` (range 12.3431–31.1278), `y` (range -11.7058–3.5055), `osm_type` (node, way), `loc_amenity` (doctors, clinic, hospital).
-**Temporal** — `changeset_timestamp`.
-**Identifier / Metadata** — `osm_id` (range 52173208.0–13180731996.0), `loc_name` (Centre de santé, Centre de Santé, Spital Wamba-Luadi), `changeset_id` (range 5147571.0–172854454.0), `meta_id` (a74c4fad7bc7458f966dc283d470ad9e, 8ff3afb9d489488ea92934818f935c85, 657026b1313845cfbca745f67c9e4e76), `esa_source` (HDX) and 1 others.
-**Other** — `completeness` (range 6.25–37.5), `meta_healthcare` (alternative, hospital, doctor), `geo_bounds_url` (MSFsurvey, OMS-DSNIS, MSF), `addr_street` (Université, Nsanga, Kimayala), `changeset_version` (range 1.0–17.0).
----
-## Quick Start
 ```python
 from datasets import load_dataset
-ds    = load_dataset("electricsheepafrica/africa-health-facilities-congo-dem-rep")
-train = ds["train"].to_pandas()
-test  = ds["test"].to_pandas()
-print(train.shape)
-train.head()
 ```
----
-## Schema
-| Column | Type | Null % | Range / Sample Values |
-|---|---|---|---|
-| `x` | float64 | 21.7% | 12.3431 – 31.1278 (mean 22.9484) |
-| `y` | float64 | 21.7% | -11.7058 – 3.5055 (mean -2.7734) |
-| `osm_id` | int64 | 0.0% | 52173208.0 – 13180731996.0 (mean 6085918002.9078) |
-| `osm_type` | object | 0.0% | node, way |
-| `completeness` | float64 | 0.0% | 6.25 – 37.5 (mean 13.8607) |
-| `loc_amenity` | object | 1.8% | doctors, clinic, hospital |
-| `meta_healthcare` | object | 67.3% | alternative, hospital, doctor |
-| `loc_name` | object | 4.3% | Centre de santé, Centre de Santé, Spital Wamba-Luadi |
-| `geo_bounds_url` | object | 71.6% | MSFsurvey, OMS-DSNIS, MSF |
-| `addr_street` | object | 73.7% | Université, Nsanga, Kimayala |
-| `changeset_id` | int64 | 0.0% | 5147571.0 – 172854454.0 (mean 104638381.4549) |
-| `changeset_version` | int64 | 0.0% | 1.0 – 17.0 (mean 3.2302) |
-| `changeset_timestamp` | datetime64[ns, UTC] | 0.0% |  |
-| `meta_id` | object | 0.0% | a74c4fad7bc7458f966dc283d470ad9e, 8ff3afb9d489488ea92934818f935c85, 657026b1313845cfbca745f67c9e4e76 |
-| `esa_source` | object | 0.0% | HDX |
-| `esa_processed` | object | 0.0% | 2026-04-20 |
----
-## Numeric Summary
-| Column | Min | Max | Mean | Median |
-|---|---|---|---|---|
-| `x` | 12.3431 | 31.1278 | 22.9484 | 27.4834 |
-| `y` | -11.7058 | 3.5055 | -2.7734 | -4.0631 |
-| `osm_id` | 52173208.0 | 13180731996.0 | 6085918002.9078 | 6557833167.0 |
-| `completeness` | 6.25 | 37.5 | 13.8607 | 12.5 |
-| `changeset_id` | 5147571.0 | 172854454.0 | 104638381.4549 | 110787049.0 |
-| `changeset_version` | 1.0 | 17.0 | 3.2302 | 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`. 21 column(s) with >80% missing values were removed: `meta_operator`, `meta_speciality`, `meta_operator_type`, `contact_phone`, `status_operational_status`, `access_hours`.... 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`, `meta_healthcare`, `geo_bounds_url`, `addr_street`.
-- Refer to the [original HDX dataset page](https://data.humdata.org/dataset/democratic-republic-of-the-congo-healthsites) for the publisher's own methodology notes and caveats.
----
 ## Citation
 ```bibtex
-@dataset{hdx_africa_health_facilities_congo_dem_rep,
-  title     = {Democratic Republic of the Congo Healthsites},
-  author    = {Global Healthsites Mapping Project},
-  year      = {2025},
-  url       = {https://data.humdata.org/dataset/democratic-republic-of-the-congo-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.*

 ---
+license: other
 language:
 - en
 task_categories:
 - tabular-classification
+- tabular-regression
+multilinguality: monolingual
+size_categories:
+- 1K<n<10K
 tags:
+- "africa"
+- "electric-sheep-africa"
+- "open-data"
+- "metadata-backed"
+- "health"
+- "parquet"
+- "tabular"
+- "text"
+- "humanitarian"
+- "hdx"
+- "health-facilities"
+- "hxl"
+- "cod"
+pretty_name: "Democratic Republic of the Congo Healthsites | Africa (original)"
 ---
+# Democratic Republic of the Congo Healthsites | Africa (original)
+**Size category:** `1K<n<10K` - **Formats:** `parquet` - **Sector:** health - *Engineered by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*
+![size](https://img.shields.io/badge/size-1K%3Cn%3C10K-blue)
+![sector](https://img.shields.io/badge/sector-health-green)
+![downloads](https://img.shields.io/badge/HF_downloads-7-orange)
+![license](https://img.shields.io/badge/license-other-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
+Health datasets help researchers examine disease burden, service delivery, risk factors, outcomes, and public-health program performance.
+Dataset context from the existing Hugging Face card: Democratic Republic of the Congo Healthsites Publisher: Global Healthsites Mapping Project · Source: HDX · 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: COD. Curated into ML-ready Parquet format by Electric Sheep Africa.… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-health-facilities-congo-dem-rep.
+## Dataset Profile
+| Field | Value |
 |---|---|
+| Hugging Face repo | [`electricsheepafrica/africa-health-facilities-congo-dem-rep`](https://huggingface.co/datasets/electricsheepafrica/africa-health-facilities-congo-dem-rep) |
+| Sector | health |
+| Topic tags | humanitarian, hdx, electric-sheep-africa, health-facilities, hxl, cod |
+| Modalities | `tabular`, `text` |
+| Formats | `parquet` |
+| Size category | `1K<n<10K` |
+| Countries | Democratic Republic of the Congo |
+| ISO3 coverage | `COD` |
+| Last modified on HF | `2026-04-20 11:51:59+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/africa-health-facilities-congo-dem-rep")
+print(ds)
+split_name = next(iter(ds))
+table = ds[split_name]
+print(table.features)
+print(table[:3])
 ```
+### 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())
+```
+## 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: upstream_publisher.
+- Do not infer policy meaning from labels alone; confirm definitions, units, and methods in the source material.
+## Source And Provenance
+- **Source context:** original
+- **Publisher/source attribution:** original
+- **License:** Source-specific or other license
+- **Hugging Face URL:** [https://huggingface.co/datasets/electricsheepafrica/africa-health-facilities-congo-dem-rep](https://huggingface.co/datasets/electricsheepafrica/africa-health-facilities-congo-dem-rep)
+- **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_health_facilities_congo_dem_rep_2026,
+  title        = {Democratic Republic of the Congo Healthsites | Africa (original)},
+  author       = {original},
+  year         = {2026},
+  url          = {https://huggingface.co/datasets/electricsheepafrica/africa-health-facilities-congo-dem-rep},
+  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
+  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-health-facilities-congo-dem-rep}}
 }
 ```
+## 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`.