electricsheepafrica/africa-guinea-bissau-admissions-for-treatment-of-severe-acute-malnutrition-sam-81605917

Admissions for Treatment of Severe Acute Malnutrition (SAM) | Africa (Guinea-Bissau official open data)

1,555 rows - 1 Africa country - 2020-2021 - Repackaged by Electric Sheep Africa

TL;DR

This dataset packages one official CSV resource from Guinea-Bissau as ML-ready Parquet. The source file is the provenance boundary; all usable indicators or tabular columns from the resource stay together in this repo.

About the source

• Source: Admissions for Treatment of Severe Acute Malnutrition (SAM)
• Publisher: UNICEF Data and Analytics (HQ)
• Resource: DF_SITREP_COVID19.csv
• Format: CSV
• License: CC BY 4.0
• Packaging mode: indicator_long

Geographic coverage

1 Africa country:

Country	Rows	First year	Last year	Name
GNB	1,555	2020	2021	Guinea-Bissau

Indicators or Resource Contents

• admissions-for-treatment-of-severe-acute-malnutrition-sam-obs-value-535b4f36 - Admissions for Treatment of Severe Acute Malnutrition (SAM) - obs value
• admissions-for-treatment-of-severe-acute-malnutrition-sam-target-f801b1d9 - Admissions for Treatment of Severe Acute Malnutrition (SAM) - target

Schema

Column	Type	Description	Example
indicator_id	string	Stable indicator identifier.	admissions-for-treatment-of-severe-acute-malnutrition-sam-obs-value-535b
indicator_name	string	Human-readable indicator name.	Admissions for Treatment of Severe Acute Malnutrition (SAM) - obs value
country_iso3	string	ISO3 country code.	GNB
country_name	string	Country name.	Guinea-Bissau
year	Int64	Observation year.	2020
value	float64	Numeric observation value.	0.0
unit	string	Measurement unit, when available.	source_units_unspecified
dimension_ref_area	string	Source dimension.	AFG
dimension_geographic_area	string	Source dimension.	Afghanistan
dimension_sitrep_indicator	string	Source dimension.	CV-03-04
dimension_situation_report_indicator	string	Source dimension.	Number of children 6-59 months admitted for TREATMENT OF SEVERE ACUTE MA
dimension_hac_pillar	string	Source dimension.	CV-03
dimension_humanitarian_action_for_children_pillar	string	Source dimension.	Continuity of health care for women and children
dimension_unit_measure	string	Source dimension.	PS
dimension_unit_of_measure	string	Source dimension.	Persons
dimension_data_source	string	Source dimension.	UNICEF Situation Report 2
dimension_obs_status	string	Source dimension.	V
dimension_observation_status	string	Source dimension.	Unvalidated value
source_period_start_year	Int64	First year inferred from source resource metadata.	``
source_period_end_year	Int64	Last year inferred from source resource metadata.	``
source_period_label	string	Human-readable period inferred from source resource metadata.	``
source_provider	category	Publishing organization.	UNICEF Data and Analytics (HQ)
source_dataset	category	Source package title.	Admissions for Treatment of Severe Acute Malnutrition (SAM)
source_resource	category	Source resource title.	DF_SITREP_COVID19.csv
source_package_id	category	CKAN package UUID.	32e7d10a-ce65-4b6b-aaae-cc124ca0049a
source_resource_id	category	CKAN resource UUID.	92827be4-f6b3-4453-80cc-4c5c2e5d3d5f
source_url	category	Original source resource URL.	https://data.humdata.org/dataset/32e7d10a-ce65-4b6b-aaae-cc124ca0049a/re
license_id	category	Source license identifier.	cc-by
retrieved_at	category	UTC retrieval timestamp.	2026-08-30T05:38:32Z

Usage

from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-guinea-bissau-admissions-for-treatment-of-severe-acute-malnutrition-sam-81605917")
df = ds["train"].to_pandas()
print(df.head())

Filter to one country

sample_country = df[df["country_iso3"] == "GNB"]

Work with indicators

if "indicator_id" in df.columns:
    print(df["indicator_id"].value_counts().head())
    sample = df.sort_values([c for c in ["indicator_id", "year"] if c in df.columns])

Citation

@misc{electric_sheep_africa_africa_guinea_bissau_admissions_for_treatment_of_severe_acute_malnutrition_sam_8_2021,
  title        = {Admissions for Treatment of Severe Acute Malnutrition (SAM) | Africa (Guinea-Bissau official open data)},
  author       = {UNICEF Data and Analytics (HQ)},
  year         = {2021},
  url          = {https://data.humdata.org/dataset/number-of-admissions-for-treatment-of-severe-acute-malnutrition-sam},
  publisher    = {HuggingFace Datasets, repackaged by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-guinea-bissau-admissions-for-treatment-of-severe-acute-malnutrition-sam-81605917}}
}

License

Released under CC BY 4.0.

Original data (c) UNICEF Data and Analytics (HQ). When using this dataset, please cite both the original source above and the Electric Sheep Africa repackaging.

About Electric Sheep

Electric Sheep Africa is part of the Electric Sheep mission: a unified, ML-ready data layer for Africa on Hugging Face. We pull data from authoritative open sources, normalize the schemas, package as Parquet, and publish with consistent dataset cards so researchers and developers can use load_dataset() to start working in seconds.

Browse the full collection: huggingface.co/electricsheepafrica

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Provenance: ingested 2026-08-30 via the Electric Sheep pipeline. Source URL: https://data.humdata.org/dataset/32e7d10a-ce65-4b6b-aaae-cc124ca0049a/resource/92827be4-f6b3-4453-80cc-4c5c2e5d3d5f/download/df_sitrep_covid19.csv