electricsheepasia/asia-who-historical-data-for-tajikistan

Tajikistan - Historical Health Indicators

Publisher: World Health Organization · Source: HDX · License: hdx-other · Updated: 2025-02-07

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Abstract

This dataset contains historical data from WHO's data portal.

Each row in this dataset represents first-level administrative unit observations. Data was last updated on HDX on 2025-02-07. Geographic scope: TJK.

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

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Dataset Characteristics

Domain	Humanitarian and development data
Unit of observation	First-level administrative unit observations
Rows (total)	10,730
Columns	18 (6 numeric, 12 categorical, 0 datetime)
Train split	8,584 rows
Test split	2,146 rows
Geographic scope	TJK
Publisher	World Health Organization
HDX last updated	2025-02-07

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Variables

Geographic — gho_display (Mean BMI (kg/m²) (crude estimate), Adolescent mortality rate (per 1 000 age specific cohort), Alcohol, recorded per capita (15+) consumption (in litres of pure alcohol)), year_display (range 1970.0–2025.0), startyear (range 1970.0–2025.0), endyear (range 1970.0–2025.0), region_code (EUR, #region+code) and 4 others.

Outcome / Measurement — value (No data, No, Yes).

Identifier / Metadata — gho_code (NCD_BMI_MEANC, CHILDMORT10TO19, SA_0000001400_ARCHIVED), dimension_code (SEX_FMLE, SEX_MLE, SEX_BTSX), dimension_name (Female, Male, Both sexes), esa_source, esa_processed.

Other — numeric (range 0.0–189769896.0), low (range 0.0–67187.0), high (range 0.0–190103.0).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/asia-who-historical-data-for-tajikistan")
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%	NCD_BMI_MEANC, CHILDMORT10TO19, SA_0000001400_ARCHIVED
gho_display	object	0.0%	Mean BMI (kg/m²) (crude estimate), Adolescent mortality rate (per 1 000 age specific cohort), Alcohol, recorded per capita (15+) consumption (in litres of pure alcohol)
year_display	float64	0.0%	1970.0 – 2025.0 (mean 2009.4858)
startyear	float64	0.0%	1970.0 – 2025.0 (mean 2009.4744)
endyear	float64	0.0%	1970.0 – 2025.0 (mean 2009.4858)
region_code	object	0.0%	EUR, #region+code
region_display	object	0.0%	Europe, #region+name
country_code	object	0.0%	TJK, #country+code
country_display	object	0.0%	Tajikistan, #country+name
dimension_type	object	20.3%	SEX, WEALTHDECILE, WEALTHQUINTILE
dimension_code	object	20.3%	SEX_FMLE, SEX_MLE, SEX_BTSX
dimension_name	object	21.5%	Female, Male, Both sexes
numeric	float64	25.8%	0.0 – 189769896.0 (mean 25968.5016)
value	object	1.4%	No data, No, Yes
low	float64	44.3%	0.0 – 67187.0 (mean 141.3542)
high	float64	44.3%	0.0 – 190103.0 (mean 332.1176)
esa_source	object	0.0%
esa_processed	object	0.0%

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Numeric Summary

Column	Min	Max	Mean	Median
year_display	1970.0	2025.0	2009.4858	2012.0
startyear	1970.0	2025.0	2009.4744	2012.0
endyear	1970.0	2025.0	2009.4858	2012.0
numeric	0.0	189769896.0	25968.5016	34.0106
low	0.0	67187.0	141.3542	27.5
high	0.0	190103.0	332.1176	44.8

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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 column(s) with >80% missing values were removed: gho_url. 48 exact duplicate rows were removed. 6 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.

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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: dimension_type, dimension_code, dimension_name, numeric, 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_who_historical_data_for_tajikistan,
  title     = {Tajikistan - Historical Health Indicators},
  author    = {World Health Organization},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/who-historical-data-for-tjk},
  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.