Dataset Viewer
Auto-converted to Parquet Duplicate
adm1_state
stringclasses
10 values
adm1_pcode
stringclasses
10 values
adm2_county
stringlengths
3
14
adm2_pcode
stringlengths
6
6
proxy_gam_2022
float64
0.04
0.27
esa_source
stringclasses
1 value
esa_processed
stringdate
2026-04-17 00:00:00
2026-04-17 00:00:00
Lakes
SS04
Yirol East
SS0407
0.134
HDX
2026-04-17
Upper Nile
SS07
Ulang
SS0712
0.175
HDX
2026-04-17
Eastern Equatoria
SS02
Kapoeta North
SS0204
0.113
HDX
2026-04-17
Central Equatoria
SS01
Yei
SS0106
0.109
HDX
2026-04-17
Northern Bahr el Ghazal
SS05
Aweil North
SS0503
0.159
HDX
2026-04-17
Unity
SS06
Guit
SS0602
0.241
HDX
2026-04-17
Western Bahr el Ghazal
SS09
Wau
SS0903
0.099
HDX
2026-04-17
Upper Nile
SS07
Malakal
SS0707
0.177
HDX
2026-04-17
Unity
SS06
Koch
SS0603
0.241
HDX
2026-04-17
Jonglei
SS03
Bor South
SS0303
0.223
HDX
2026-04-17
Unity
SS06
Mayendit
SS0605
0.246
HDX
2026-04-17
Upper Nile
SS07
Baliet
SS0701
0.226
HDX
2026-04-17
Upper Nile
SS07
Luakpiny/Nasir
SS0704
0.175
HDX
2026-04-17
Eastern Equatoria
SS02
Ikotos
SS0202
0.094
HDX
2026-04-17
Lakes
SS04
Rumbek East
SS0404
0.089
HDX
2026-04-17
Western Equatoria
SS10
Nagero
SS1007
0.099
HDX
2026-04-17
Unity
SS06
Pariang
SS0608
0.223
HDX
2026-04-17
Western Equatoria
SS10
Ibba
SS1002
0.043
HDX
2026-04-17
Jonglei
SS03
Fangak
SS0306
0.222
HDX
2026-04-17
Western Bahr el Ghazal
SS09
Jur River
SS0901
0.099
HDX
2026-04-17
Lakes
SS04
Awerial
SS0401
0.134
HDX
2026-04-17
Upper Nile
SS07
Melut
SS0709
0.226
HDX
2026-04-17
Eastern Equatoria
SS02
Torit
SS0208
0.094
HDX
2026-04-17
Upper Nile
SS07
Panyikang
SS0710
0.177
HDX
2026-04-17
Central Equatoria
SS01
Morobo
SS0104
0.109
HDX
2026-04-17
Jonglei
SS03
Canal/Pigi
SS0304
0.196
HDX
2026-04-17
Unity
SS06
Abiemnhom
SS0601
0.223
HDX
2026-04-17
Eastern Equatoria
SS02
Kapoeta East
SS0203
0.113
HDX
2026-04-17
Warrap
SS08
Tonj North
SS0804
0.105
HDX
2026-04-17
Eastern Equatoria
SS02
Budi
SS0201
0.206
HDX
2026-04-17
Northern Bahr el Ghazal
SS05
Aweil South
SS0504
0.159
HDX
2026-04-17
Western Equatoria
SS10
Nzara
SS1008
0.059
HDX
2026-04-17
Warrap
SS08
Twic
SS0806
0.183
HDX
2026-04-17
Unity
SS06
Rubkona
SS0609
0.269
HDX
2026-04-17
Western Equatoria
SS10
Tambura
SS1009
0.099
HDX
2026-04-17
Jonglei
SS03
Ayod
SS0302
0.222
HDX
2026-04-17
Lakes
SS04
Rumbek Centre
SS0403
0.089
HDX
2026-04-17
Unity
SS06
Leer
SS0604
0.246
HDX
2026-04-17
Lakes
SS04
Cueibet
SS0402
0.134
HDX
2026-04-17
Upper Nile
SS07
Fashoda
SS0702
0.155
HDX
2026-04-17
Jonglei
SS03
Uror
SS0311
0.255
HDX
2026-04-17
Unity
SS06
Mayom
SS0606
0.259
HDX
2026-04-17
Western Equatoria
SS10
Maridi
SS1003
0.043
HDX
2026-04-17
Upper Nile
SS07
Renk
SS0711
0.271
HDX
2026-04-17
Eastern Equatoria
SS02
Lafon
SS0206
0.223
HDX
2026-04-17
Lakes
SS04
Yirol West
SS0408
0.134
HDX
2026-04-17
Western Equatoria
SS10
Mvolo
SS1006
0.088
HDX
2026-04-17
Warrap
SS08
Gogrial East
SS0801
0.183
HDX
2026-04-17
Warrap
SS08
Tonj South
SS0805
0.105
HDX
2026-04-17
Western Equatoria
SS10
Ezo
SS1001
0.059
HDX
2026-04-17
Northern Bahr el Ghazal
SS05
Aweil West
SS0505
0.159
HDX
2026-04-17
Lakes
SS04
Rumbek North
SS0405
0.089
HDX
2026-04-17
Central Equatoria
SS01
Kajo-Keji
SS0102
0.109
HDX
2026-04-17
Upper Nile
SS07
Maiwut
SS0706
0.175
HDX
2026-04-17
Jonglei
SS03
Pibor
SS0308
0.185
HDX
2026-04-17
Central Equatoria
SS01
Lainya
SS0103
0.109
HDX
2026-04-17
Jonglei
SS03
Twic East
SS0310
0.207
HDX
2026-04-17
Jonglei
SS03
Nyirol
SS0307
0.196
HDX
2026-04-17
Warrap
SS08
Gogrial West
SS0802
0.183
HDX
2026-04-17
Western Equatoria
SS10
Mundri East
SS1004
0.043
HDX
2026-04-17
Jonglei
SS03
Akobo
SS0301
0.255
HDX
2026-04-17
Upper Nile
SS07
Maban
SS0705
0.226
HDX
2026-04-17

South Sudan: Nutrition GAM rates | Africa (original)

Size category: n<1K - Formats: parquet - Sector: health - Engineered by Electric Sheep Africa

size sector downloads license

TL;DR

This dataset is part of the Electric Sheep Africa catalog on Hugging Face. It is indexed for African data discovery with standardized metadata, loading guidance, provenance notes, and analyst-oriented context.

What This Dataset Covers

Health datasets help researchers examine disease burden, service delivery, risk factors, outcomes, and public-health program performance.

Dataset context from the existing Hugging Face card: South Sudan: Nutrition GAM rates Publisher: UNICEF South Sudan · Source: HDX · License: cc-by · Updated: 2025-05-05 Abstract South Sudan nutrition GAM rates from the Nutrition Cluster as of December 2022. Each row in this dataset represents first-level administrative unit observations. Data was last updated on HDX on 2025-05-05. Geographic scope: SSD. Curated into ML-ready Parquet format by Electric Sheep Africa. Dataset Characteristics… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-south-sudan-nutrition-gam-rates.

Dataset Profile

Field Value
Hugging Face repo electricsheepafrica/africa-south-sudan-nutrition-gam-rates
Sector health
Topic tags humanitarian, hdx, electric-sheep-africa, global-acute-malnutrition-gam, nutrition, ssd
Modalities text
Formats parquet
Size category n<1K
Countries South Sudan
ISO3 coverage SSD
Last modified on HF 2026-04-20 08:05:52+00:00
Inventory snapshot 2026-07-16T16:00:34Z

How To Read This Dataset

  • Start from the repository files and the dataset viewer when available.
  • Treat the README context as a fast orientation layer; confirm variable definitions and units in the data files before modeling.
  • Use explicit country columns when present. When geography is only implied by the title or source metadata, document that assumption in downstream analysis.
  • Preserve missing values until you have a defensible imputation rule.

Usage

from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-south-sudan-nutrition-gam-rates")
print(ds)

split_name = next(iter(ds))
table = ds[split_name]
print(table.features)
print(table[:3])

Convert To Pandas When Tabular

from datasets import Dataset

first_split = ds[next(iter(ds))]
if isinstance(first_split, Dataset):
    df = first_split.to_pandas()
    print(df.head())

Data Quality Notes

  • This card was standardized from the Electric Sheep Africa Hugging Face metadata inventory.
  • Exact schema, row counts, and source files should be inspected in the repository data files.
  • Metadata gaps from the inventory: upstream_publisher.
  • Do not infer policy meaning from labels alone; confirm definitions, units, and methods in the source material.

Source And Provenance

Suggested Analyses

  • Inspect schema and missingness before modeling.
  • Profile variables by geography, time, and subgroup columns where present.
  • Join with other Electric Sheep Africa datasets using explicit country, year, and indicator fields when available.
  • Build reproducible notebooks that cite both the original source context and the Electric Sheep Africa Hugging Face repo.

Citation

@misc{electric_sheep_africa_africa_south_sudan_nutrition_gam_rates_2026,
  title        = {South Sudan: Nutrition GAM rates | Africa (original)},
  author       = {original},
  year         = {2026},
  url          = {https://huggingface.co/datasets/electricsheepafrica/africa-south-sudan-nutrition-gam-rates},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-south-sudan-nutrition-gam-rates}}
}

License

Released under CC BY 4.0.

Original source rights remain with the original publisher or data provider. Electric Sheep Africa engineering standardizes discovery metadata, documentation, and usage guidance for analysis on Hugging Face.

About Electric Sheep Africa

Electric Sheep Africa publishes ML-ready African public datasets on Hugging Face.


Provenance: metadata-backed README standardized 2026-08-12 by the Electric Sheep Africa README system. Inventory source: catalog/esa_metadata_inventory/master_metadata.jsonl.

Downloads last month
37

Collections including electricsheepafrica/africa-south-sudan-nutrition-gam-rates