electricsheepasia/asia-disability-who-data-for-turkey

Türkiye - Health Indicators

Publisher: World Health Organization · Source: HDX · License: hdx-other · Updated: 2026-04-15

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Abstract

This dataset contains data from WHO's data portal covering the following categories:

Air pollution, Child mortality, Dementia diagnosis, treatment and care, Environment and health, Food safety, Global Dementia Observatory (GDO), Global Health Estimates: Life expectancy and leading causes of death and disability, Global Information System on Alcohol and Health, Global Patient Safety Observatory, HIV, Health financing, Health systems, Health taxes, Health workforce, Hepatitis, Immunization coverage and vaccine-preventable diseases, Malaria, Maternal and reproductive health, Mental health, Neglected tropical diseases, Noncommunicable diseases, Nutrition, Oral Health, Priority health technologies, Resources for Substance Use Disorders, Road Safety, Sexually Transmitted Infections, Substance use disorders service coverage, Tobacco control, Tuberculosis, Universal health coverage (UHC), SDG Target 3.8, Vaccine-preventable communicable diseases, Violence prevention, Water, sanitation and hygiene (WASH), World Health Statistics.

For links to individual indicator metadata, see resource descriptions.

Each row in this dataset represents first-level administrative unit observations. Data was last updated on HDX on 2026-04-15. Geographic scope: TUR.

Curated into ML-ready Parquet format by Electric Sheep Africa.

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Dataset Characteristics

Domain	Food security and nutrition
Unit of observation	First-level administrative unit observations
Rows (total)	22,669
Columns	19 (6 numeric, 13 categorical, 0 datetime)
Train split	18,135 rows
Test split	4,533 rows
Geographic scope	TUR
Publisher	World Health Organization
HDX last updated	2026-04-15

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Variables

Geographic — gho_display (Number of deaths in children aged <5 years, by cause, Population with primary reliance on fuels and technologies for cooking, by fuel type (in millions), Proportion of population with primary reliance on fuels and technologies for cooking, by fuel type (%)), year_display (range 1953.0–2030.0), startyear (range 1953.0–2030.0), endyear (range 1953.0–2030.0), region_code (EUR) and 4 others.

Outcome / Measurement — value.

Identifier / Metadata — gho_code (MORT_100, PHE_HHAIR_POP_CATEGORY_FUELS, PHE_HHAIR_PROP_POP_CATEGORY_FUELS), dimension_code (SEX_BTSX, SEX_FMLE, SEX_MLE), dimension_name (Both sexes, Female, Male), esa_source, esa_processed.

Other — gho_url (https://www.who.int/data/gho/data/indicators/indicator-details/GHO/number-of-deaths, https://www.who.int/data/gho/data/indicators/indicator-details/GHO/proportion-of-population-with-primary-reliance-on-fuels-and-technologies-for-cooking-by-fuel-type, https://www.who.int/data/gho/data/indicators/indicator-details/GHO/population-with-primary-reliance-on-fuels-and-technologies-for-cooking-by-fuel-type), numeric (range -7.56–176591398000.0), low (range -0.1547–628338.722), high (range 0.0–1082092.871).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/asia-disability-who-data-for-turkey")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

print(train.shape)
train.head()

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Schema

Column	Type	Null %	Range / Sample Values
gho_code	object	0.0%	MORT_100, PHE_HHAIR_POP_CATEGORY_FUELS, PHE_HHAIR_PROP_POP_CATEGORY_FUELS
gho_display	object	0.0%	Number of deaths in children aged <5 years, by cause, Population with primary reliance on fuels and technologies for cooking, by fuel type (in millions), Proportion of population with primary reliance on fuels and technologies for cooking, by fuel type (%)
gho_url	object	0.0%	https://www.who.int/data/gho/data/indicators/indicator-details/GHO/number-of-deaths, https://www.who.int/data/gho/data/indicators/indicator-details/GHO/proportion-of-population-with-primary-reliance-on-fuels-and-technologies-for-cooking-by-fuel-type, https://www.who.int/data/gho/data/indicators/indicator-details/GHO/population-with-primary-reliance-on-fuels-and-technologies-for-cooking-by-fuel-type
year_display	int64	0.0%	1953.0 – 2030.0 (mean 2008.3808)
startyear	int64	0.0%	1953.0 – 2030.0 (mean 2008.3761)
endyear	int64	0.0%	1953.0 – 2030.0 (mean 2008.3808)
region_code	object	0.0%	EUR
region_display	object	0.0%	Europe
country_code	object	0.0%	TUR
country_display	object	0.0%	Türkiye
dimension_type	object	12.2%	SEX, RESIDENCEAREATYPE, AGEGROUP
dimension_code	object	12.2%	SEX_BTSX, SEX_FMLE, SEX_MLE
dimension_name	object	12.4%	Both sexes, Female, Male
numeric	float64	11.8%	-7.56 – 176591398000.0 (mean 13911147.857)
value	object	0.1%
low	float64	39.1%	-0.1547 – 628338.722 (mean 6411.8822)
high	float64	39.1%	0.0 – 1082092.871 (mean 11741.0427)
esa_source	object	0.0%
esa_processed	object	0.0%

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Numeric Summary

Column	Min	Max	Mean	Median
year_display	1953.0	2030.0	2008.3808	2010.0
startyear	1953.0	2030.0	2008.3761	2010.0
endyear	1953.0	2030.0	2008.3808	2010.0
numeric	-7.56	176591398000.0	13911147.857	12.6873
low	-0.1547	628338.722	6411.8822	8.8982
high	0.0	1082092.871	11741.0427	18.0608

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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. 1,179 exact duplicate rows were removed. The dataset was split 80/20 into train and test partitions using a fixed random seed (42) and saved as Snappy-compressed Parquet.

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Limitations

• Data originates from World Health Organization 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: low, high.
• Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

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Citation

@dataset{hdx_asia_disability_who_data_for_turkey,
  title     = {Türkiye - Health Indicators},
  author    = {World Health Organization},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/who-data-for-tur},
  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

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Electric Sheep Africa — Africa's ML dataset infrastructure. Lagos, Nigeria.