electricsheepafrica/africa-sudan-deep-extracts-related-to-the-sudan-2023-situational-analys-b0f02175

DEEP extracts related to the Sudan 2023 Situational Analyses | Africa (Sudan official open data)

141 rows - 1 Africa country - 2023 - Repackaged by Electric Sheep Africa

TL;DR

This dataset packages one official XLSX 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: DEEP extracts related to the Sudan 2023 Situational Analyses
• Publisher: iMMAP Inc.
• Resource: SDN_DEEP_extracts_2023-12_Context_immap_dfs.xlsx
• Format: XLSX
• License: CC BY 4.0
• Packaging mode: indicator_long

Geographic coverage

1 Africa country:

Country	Rows	First year	Last year	Name
SDN	141	2023	2023	Sudan

Indicators or Resource Contents

• deep-extracts-related-to-the-sudan-2023-situational-analyses-source-id-ad3ba758 - DEEP extracts related to the Sudan 2023 Situational Analyses - source id
• deep-extracts-related-to-the-sudan-2023-situational-analyses-page-count-8ba92821 - DEEP extracts related to the Sudan 2023 Situational Analyses - page count
• deep-extracts-related-to-the-sudan-2023-situational-analyses-entry-id-88c85539 - DEEP extracts related to the Sudan 2023 Situational Analyses - entry id
• deep-extracts-related-to-the-sudan-2023-situational-analyses-812a4f2a - DEEP extracts related to the Sudan 2023 Situational Analyses

Schema

Column	Type	Description	Example
indicator_id	string	Stable indicator identifier.	deep-extracts-related-to-the-sudan-2023-situational-analyses-source-id-a
indicator_name	string	Human-readable indicator name.	DEEP extracts related to the Sudan 2023 Situational Analyses - source id
country_iso3	string	ISO3 country code.	SDN
source_sheet	string	Workbook sheet name, when the source is a spreadsheet.	Entries
country_name	string	Country name.	Sudan
date	string	Observation date.	2023-11-30
year	Int64	Observation year.	2023
value	float64	Numeric observation value.	129762.0
unit	string	Measurement unit, when available.	source_units_unspecified
dimension_date_of_source_publication	string	Source dimension.	07/12/2023
dimension_date_imported	string	Source dimension.	08/12/2023
dimension_verification_status	string	Source dimension.	Controlled
dimension_source_title	string	Source dimension.	Sudan Humanitarian Update (7 December 2023)
dimension_source_url	string	Source dimension.	https://reliefweb.int/attachments/b45ed432-0643-4ad7-ad45-64dfa9e31e5a/S
dimension_authoring_organizations_type	string	Source dimension.	UN Agencies
dimension_author	string	Source dimension.	UN Office for the Coordination of Humanitarian Affairs
dimension_publisher	string	Source dimension.	Reliefweb
dimension_source_priority	string	Source dimension.	Low
dimension_source_entry_id	string	Source dimension.	129762-798413
dimension_modified_excerpt	string	Source dimension.	In North Darfur, inter-communal clashes erupted between Zagawah, Al Tanh
dimension_original_excerpt	string	Source dimension.	``
dimension_operational_environment_dimension	string	Source dimension.	Context, Conflict, Displacement, Casualties
dimension_operational_environment_subdimension	string	Source dimension.	Socio Cultural, Conflict Events , Characteristics, Push Factors, Dead
dimension_sectoral_information_subrow	string	Source dimension.	On System & Services, On People
dimension_sectoral_information_column	string	Source dimension.	Cross, Livelihoods
dimension_sectoral_information_subcolumns	string	Source dimension.	``
dimension_reliability	string	Source dimension.	Usually Reliable
dimension_severity	string	Source dimension.	``
dimension_information_date_from	string	Source dimension.	07-12-2023
dimension_affected_groups_level_1	string	Source dimension.	Affected
dimension_affected_groups_level_2	string	Source dimension.	Displaced
dimension_affected_groups_level_3	string	Source dimension.	IDP
dimension_demographic_groups	string	Source dimension.	``
dimension_specific_needs_groups	string	Source dimension.	``
dimension_country	string	Source dimension.	Sudan
dimension_country_code	string	Source dimension.	Sudan
dimension_state	string	Source dimension.	North Darfur
dimension_state_code	string	Source dimension.	North Darfur
dimension_district	string	Source dimension.	Al Fasher
dimension_district_code	string	Source dimension.	Al Fasher
source_period_start_year	Int64	First year inferred from source resource metadata.	2023
source_period_end_year	Int64	Last year inferred from source resource metadata.	2023
source_period_label	category	Human-readable period inferred from source resource metadata.	2023
source_provider	category	Publishing organization.	iMMAP Inc.
source_dataset	category	Source package title.	DEEP extracts related to the Sudan 2023 Situational Analyses
source_resource	category	Source resource title.	SDN_DEEP_extracts_2023-12_Context_immap_dfs.xlsx
source_package_id	category	CKAN package UUID.	69996c8f-966c-42c3-9c41-af0165a70aa0
source_resource_id	category	CKAN resource UUID.	70fe0235-a77f-4bee-b575-aa06ace5e414
source_url	category	Original source resource URL.	https://data.humdata.org/dataset/69996c8f-966c-42c3-9c41-af0165a70aa0/re
license_id	category	Source license identifier.	cc-by
retrieved_at	category	UTC retrieval timestamp.	2026-08-19T06:27:19Z
dimension_source	string	Source dimension.	``
dimension_title	string	Source dimension.	``

Usage

from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-sudan-deep-extracts-related-to-the-sudan-2023-situational-analys-b0f02175")
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_deep_extracts_related_to_the_sudan_2023_situational_analys_b0f02175_2023,
  title        = {DEEP extracts related to the Sudan 2023 Situational Analyses | Africa (Sudan official open data)},
  author       = {iMMAP Inc.},
  year         = {2023},
  url          = {https://data.humdata.org/dataset/deep-extracts-sudan-2023-situational-analyses},
  publisher    = {HuggingFace Datasets, repackaged by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-sudan-deep-extracts-related-to-the-sudan-2023-situational-analys-b0f02175}}
}

License

Released under CC BY 4.0.

Original data (c) iMMAP Inc.. 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

---

Provenance: ingested 2026-08-19 via the Electric Sheep pipeline. Source URL: https://data.humdata.org/dataset/69996c8f-966c-42c3-9c41-af0165a70aa0/resource/70fe0235-a77f-4bee-b575-aa06ace5e414/download/sdn_deep_extracts_2023-12_context_immap_dfs.xlsx