Datasets:

electricsheepafrica
/

africa-zimbabwe-gam-mam-and-sam-based-on-muac-measurements

annotations_creators:
  - no-annotation
language_creators:
  - found
language:
  - en
license: cc-by-4.0
multilinguality:
  - monolingual
size_categories:
  - n<1K
source_datasets:
  - original
task_categories:
  - tabular-classification
  - tabular-regression
task_ids: []
tags:
  - africa
  - humanitarian
  - hdx
  - electric-sheep-africa
  - global-acute-malnutrition-gam
  - malnutrition
  - nutrition
  - severe-acute-malnutrition-sam
  - zwe
pretty_name: 'Zimbabwe: GAM, MAM and SAM based on MUAC measurements'
dataset_info:
  splits:
    - name: train
      num_examples: 73
    - name: test
      num_examples: 18

Zimbabwe: GAM, MAM and SAM based on MUAC measurements

Publisher: OCHA Regional Office for Southern and Eastern Africa (ROSEA) · Source: HDX · License: cc-by · Updated: 2025-04-10

Abstract

GAM,MAM and SAM for Zimbabwe based on MUAC measurements

Each row in this dataset represents geolocated point observations. Data was last updated on HDX on 2025-04-10. Geographic scope: ZWE.

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

Dataset Characteristics


Domain	Demographics and population
Unit of observation	Geolocated point observations
Rows (total)	92
Columns	7 (3 numeric, 4 categorical, 0 datetime)
Train split	73 rows
Test split	18 rows
Geographic scope	ZWE
Publisher	OCHA Regional Office for Southern and Eastern Africa (ROSEA)
HDX last updated	2025-04-10

Variables

Geographic — population_group (Children under 5 years).

Identifier / Metadata — esa_source (HDX), esa_processed (2026-04-19).

Other — adm_2 (Bulawayo, Zaka, Victoria Falls), sam (range 0.0–0.047), mam (range 0.0–0.105), gam (range 0.0–0.112).

Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-zimbabwe-gam-mam-and-sam-based-on-muac-measurements")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

print(train.shape)
train.head()

Schema

Column	Type	Null %	Range / Sample Values
`adm_2`	object	0.0%	Bulawayo, Zaka, Victoria Falls
`population_group`	object	0.0%	Children under 5 years
`sam`	float64	0.0%	0.0 – 0.047 (mean 0.0155)
`mam`	float64	0.0%	0.0 – 0.105 (mean 0.0139)
`gam`	float64	0.0%	0.0 – 0.112 (mean 0.0294)
`esa_source`	object	0.0%	HDX
`esa_processed`	object	0.0%	2026-04-19

Numeric Summary

Column	Max	Mean	Median
`sam`	0.047	0.0155	0.013
`mam`	0.105	0.0139	0.009
`gam`	0.112	0.0294	0.025

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. 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 OCHA Regional Office for Southern and Eastern Africa (ROSEA) and has not been independently validated by ESA.
Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

Citation

@dataset{hdx_africa_zimbabwe_gam_mam_and_sam_based_on_muac_measurements,
  title     = {Zimbabwe: GAM, MAM and SAM based on MUAC measurements},
  author    = {OCHA Regional Office for Southern and Eastern Africa (ROSEA)},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/zimbabwe-gam-mam-and-sam-based-on-muac-measurements},
  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

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