electricsheepasia/asia-health-facilities-turkmenistan-healthsites

Turkmenistan 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: TKM.

Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).

Dataset Characteristics

Variables

Geographic — x (range 52.5672–65.2142), y (range 36.5942–42.328), osm_type (way, node), loc_amenity (pharmacy, clinic, hospital), meta_speciality (general, infectious_diseases, paediatrics) and 1 others.

Temporal — changeset_timestamp.

Identifier / Metadata — osm_id (range 157540762.0–13184117314.0), loc_name (Dermanhana, Saglyk Öýi, Saglyk Merkezi), changeset_id (range 18393233.0–172662893.0), meta_id (045da03df74f4c6896c5025dcc9c7c8d, c12a116fae2b4aeab98bddee1a20f7cc, d2dff6d42ed045e7a7dce0f9a3eedd20), esa_source (HDX) and 1 others.

Other — completeness (range 6.25–37.5), meta_healthcare (hospital, clinic, pharmacy), addr_street (Görogly (2009) köçesi, Kardiomerkeze gidýän (1970) köçesi, O Annaýew (2255) köçesi), changeset_version (range 1.0–17.0).

Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/asia-health-facilities-turkmenistan-healthsites")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

print(train.shape)
train.head()

Schema

Numeric Summary

Curation

Raw data was downloaded from HDX via the CKAN API and converted to Parquet. Column names were lowercased and standardised to snakecase. Common missing-value markers (`N/A`, `null`, `none`, `-`, `unknown`, `no data`, `#N/A`) were unified to `NaN`. 20 column(s) with >80% missing values were removed: `metaoperator, geoboundsurl, metaoperatortype, contactphone`, `statusoperationalstatus`, `accesshours`.... 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, loc_name, meta_speciality, addr_street, addr_city.
• —Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

Citation

@dataset{hdx_asia_health_facilities_turkmenistan_healthsites,
  title     = {Turkmenistan Healthsites},
  author    = {Global Healthsites Mapping Project},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/turkmenistan-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.