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electricsheepasia
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asia-disability-who-data-for-syria

Tasks:
Tabular Classification
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English
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africa
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disability
disease
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metadata 
annotations_creators:
  - no-annotation
language_creators:
  - found
language:
  - en
license: other
multilinguality:
  - monolingual
size_categories:
  - 10K<n<100K
source_datasets:
  - original
task_categories:
  - tabular-classification
task_ids: []
tags:
  - africa
  - humanitarian
  - hdx
  - electric-sheep-africa
  - disability
  - disease
  - environment
  - health
  - indicators
  - malaria
  - maternity
  - mental-health
  - syr
pretty_name: Syrian Arab Republic - Health Indicators
dataset_info:
  splits:
    - name: train
      num_examples: 16152
    - name: test
      num_examples: 4038

Syrian Arab Republic - Health Indicators

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

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: SYR.

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

Dataset Characteristics

Domain Food security and nutrition
Unit of observation First-level administrative unit observations
Rows (total) 20,191
Columns 19 (6 numeric, 13 categorical, 0 datetime)
Train split 16,152 rows
Test split 4,038 rows
Geographic scope SYR
Publisher World Health Organization
HDX last updated 2026-04-15

Variables

Geographic — gho_display (Number of deaths in children aged <5 years, by cause, Deaths per 1 000 live births, Distribution of causes of death among children aged < 5 years (%)), year_display (range 1953.0–2030.0), startyear (range 1953.0–2030.0), endyear (range 1953.0–2030.0), region_code (EMR) and 4 others.

Outcome / Measurement — value.

Identifier / Metadata — gho_code (MORT_100, MORT_200, MORT_300), 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/deaths-per-1-000-live-births, https://www.who.int/data/gho/data/indicators/indicator-details/GHO/distribution-of-causes-of-death-among-children-aged-5-years-%28-%29), numeric (range 0.0–2988141.0), low (range 0.0–171512.268), high (range 0.0–379402.606).

Quick Start 

from datasets import load_dataset

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

print(train.shape)
train.head()

Schema

Column Type Null % Range / Sample Values
gho_code object 0.0% MORT_100, MORT_200, MORT_300
gho_display object 0.0% Number of deaths in children aged <5 years, by cause, Deaths per 1 000 live births, Distribution of causes of death among children aged < 5 years (%)
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/deaths-per-1-000-live-births, https://www.who.int/data/gho/data/indicators/indicator-details/GHO/distribution-of-causes-of-death-among-children-aged-5-years-%28-%29
year_display int64 0.0% 1953.0 – 2030.0 (mean 2008.1873)
startyear int64 0.0% 1953.0 – 2030.0 (mean 2008.1846)
endyear int64 0.0% 1953.0 – 2030.0 (mean 2008.1873)
region_code object 0.0% EMR
region_display object 0.0% Eastern Mediterranean
country_code object 0.0% SYR
country_display object 0.0% Syrian Arab Republic
dimension_type object 11.2% SEX, RESIDENCEAREATYPE, AGEGROUP
dimension_code object 11.2% SEX_BTSX, SEX_FMLE, SEX_MLE
dimension_name object 11.2% Both sexes, Female, Male
numeric float64 11.4% 0.0 – 2988141.0 (mean 4093.6067)
value object 0.1%
low float64 36.5% 0.0 – 171512.268 (mean 1551.6606)
high float64 36.5% 0.0 – 379402.606 (mean 3374.6761)
esa_source object 0.0%
esa_processed object 0.0%

Numeric Summary

Column Min Max Mean Median
year_display 1953.0 2030.0 2008.1873 2010.0
startyear 1953.0 2030.0 2008.1846 2010.0
endyear 1953.0 2030.0 2008.1873 2010.0
numeric 0.0 2988141.0 4093.6067 17.359
low 0.0 171512.268 1551.6606 10.89
high 0.0 379402.606 3374.6761 28.3152

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. 743 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.

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.

Citation 

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

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