electricsheepafrica/africa-sudan-flood-hazard-data-for-disaster-risk-assessment-selected-co-9ec1f5bf

Flood: Hazard Data for Disaster Risk Assessment (selected countries) | Africa (Sudan official open data)

295,411 rows - 1 Africa country - 2000-2018 - Repackaged by Electric Sheep Africa

TL;DR

This dataset packages one official CSV resource from Sudan 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: Flood: Hazard Data for Disaster Risk Assessment (selected countries)
• Publisher: ETH Zürich - Weather and Climate Risks
• Resource: nigeria-admin1-flood.csv
• Format: CSV
• License: CC BY 4.0
• Packaging mode: tabular_resource

Geographic coverage

1 Africa country:

Country	Rows	First year	Last year	Name
SDN	295,411	2000	2018	Sudan

Indicators or Resource Contents

• This source file is packaged as a normalized tabular resource.

Schema

Column	Type	Description	Example
source_record_id	string	Stable row identifier for tabular resources.	c17b749d-bc30-47b2-9cdf-77b17e6a94e4:0
country_iso3	category	ISO3 country code.	SDN
country_name	category	Country name.	Sudan
country_name_2	string	Source column.	#country
region_name	string	Source column.	#adm1+name
latitude	float64	Source column.	``
longitude	float64	Source column.	``
aggregation	string	Source column.	``
indicator	string	Source column.	#indicator+name
value	float64	Numeric observation value.	``
source_period_start_year	Int64	First year inferred from source resource metadata.	2000
source_period_end_year	Int64	Last year inferred from source resource metadata.	2018
source_period_label	category	Human-readable period inferred from source resource metadata.	2000-2018
source_provider	category	Publishing organization.	ETH Zürich - Weather and Climate Risks
source_dataset	category	Source package title.	Flood: Hazard Data for Disaster Risk Assessment (selected countries)
source_resource	category	Source resource title.	nigeria-admin1-flood.csv
source_package_id	category	CKAN package UUID.	46c703fe-7ba1-484b-a38c-8c53f0cf00c4
source_resource_id	category	CKAN resource UUID.	c17b749d-bc30-47b2-9cdf-77b17e6a94e4
source_url	category	Original source resource URL.	https://data.humdata.org/dataset/46c703fe-7ba1-484b-a38c-8c53f0cf00c4/re
license_id	category	Source license identifier.	cc-by
retrieved_at	category	UTC retrieval timestamp.	2026-08-19T06:27:19Z

Usage

from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-sudan-flood-hazard-data-for-disaster-risk-assessment-selected-co-9ec1f5bf")
df = ds["train"].to_pandas()
print(df.head())

Filter to one country

sample_country = df[df["country_iso3"] == "SDN"]

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_sudan_flood_hazard_data_for_disaster_risk_assessment_selected_co_9ec1f5bf_2018,
  title        = {Flood: Hazard Data for Disaster Risk Assessment (selected countries) | Africa (Sudan official open data)},
  author       = {ETH Zürich - Weather and Climate Risks},
  year         = {2018},
  url          = {https://data.humdata.org/dataset/climada-flood-dataset},
  publisher    = {HuggingFace Datasets, repackaged by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-sudan-flood-hazard-data-for-disaster-risk-assessment-selected-co-9ec1f5bf}}
}

License

Released under CC BY 4.0.

Original data (c) ETH Zürich - Weather and Climate Risks. 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 2026-08-19 via the Electric Sheep pipeline. Source URL: https://data.humdata.org/dataset/46c703fe-7ba1-484b-a38c-8c53f0cf00c4/resource/c17b749d-bc30-47b2-9cdf-77b17e6a94e4/download/nigeria-admin1-flood.csv