electricsheepafrica/africa-south-sudan-south-sudan-operational-presence-536783ab

South Sudan Operational Presence | Africa (OCHA South Sudan)

5,875 rows - 1 Africa country/area - 2024 - source table - Engineered by Electric Sheep Africa

TL;DR

This dataset contains 5,875 rows from OCHA South Sudan, covering South Sudan Operational Presence. It is published as ML-ready Parquet with consistent Hugging Face metadata, source provenance, and analysis-friendly loading examples.

What This Dataset Measures

Official statistics datasets help analysts inspect public data as published by governments, national statistical systems, and regional data portals.

Source-provided context: South Sudan: Who is doing what and where for January _July 2024 (Summary).

How To Read This Dataset

• One row means: one source record from the original tabular resource, with Electric Sheep Africa provenance columns added where available.
• Primary geography column: country_iso3.
• Best time column: year.
• Time coverage basis: year.
• Recommended join keys: country_iso3 where available plus source-specific keys.

Coverage

Dimension	Value
Rows	5,875
Countries/areas	1
First period	2024
Last period	2024
Indicators	0
Columns	23
Source format	XLSX

Geographic Coverage

Top areas shown below, sorted by row count when available:

Area	Rows	First year	Last year	Name
SSD	5,875	2024	2024	South Sudan

Indicators, Variables, Or Resource Contents

• This repo preserves one source tabular resource with its usable columns kept together.

Schema

Column	Type	Description	Example
source_record_id	string	Stable row identifier assigned during Electric Sheep Africa engineering.	06d1db68-17d4-4085-9fc6-af0b0dc8be54:data:0
country_iso3	dictionary<values=string, indices=int8, ordered=0>	ISO3 country or area code.	SSD
country_name	dictionary<values=string, indices=int8, ordered=0>	Country or area name.	South Sudan
source_sheet	string	Source column from the original resource.	data
year	int64	Observation year.	2024
state	string	Source column from the original resource.	Unity
county	string	Source column from the original resource.	Abiemnhom
cluster	string	Source column from the original resource.	Nutrition
name_of_organization	string	Source column from the original resource.	CARE International
acronym	string	Source column from the original resource.	CARE
type	string	Source column from the original resource.	INGO
reporting_month	string	Source column from the original resource.	2024-02-01 00:00:00
source_period_start_year	int64	Start year inferred from source metadata.	2024
source_period_end_year	int64	End year inferred from source metadata.	2024
source_period_label	dictionary<values=string, indices=int8, ordered=0>	Source column from the original resource.	2024
source_provider	dictionary<values=string, indices=int8, ordered=0>	Publishing organization.	OCHA South Sudan
source_dataset	dictionary<values=string, indices=int8, ordered=0>	Source dataset or package title.	South Sudan: Operational Presence
source_resource	dictionary<values=string, indices=int8, ordered=0>	Source resource title, table name, or file name.	ss_20240904_3w_oerational presence_Jan to Jul_2024.xlsx
source_package_id	dictionary<values=string, indices=int8, ordered=0>	Source package identifier.	531fe931-4b87-43e9-ae4c-a3e25a2bd388
source_resource_id	dictionary<values=string, indices=int8, ordered=0>	Source resource identifier.	06d1db68-17d4-4085-9fc6-af0b0dc8be54
source_url	dictionary<values=string, indices=int8, ordered=0>	Original source URL or download URL.	https://data.humdata.org/dataset/531fe931-4b87-43e9-ae4c-a3e25a2bd388...
license_id	dictionary<values=string, indices=int8, ordered=0>	Source license identifier.	cc-by
retrieved_at	dictionary<values=string, indices=int8, ordered=0>	UTC source retrieval timestamp from the Electric Sheep Africa pipeline.	2026-08-10T18:18:23Z

Usage

from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-south-sudan-south-sudan-operational-presence-536783ab")
df = ds["train"].to_pandas()
print(df.head())

Inspect Columns

print(df.info())
print(df.head())

Filter By Geography

if "country_iso3" in df.columns:
    sample = df[df["country_iso3"] == "SSD"]

Time-Series Pattern

if "value" in df.columns and "year" in df.columns:
    trend = df.sort_values("year")

Pivot For Analysis

if {"indicator_id", "year", "value"}.issubset(df.columns):
    matrix = df.pivot_table(index="year", columns="indicator_id", values="value")
    print(matrix.tail())

Data Quality Notes

• Canonical time field: year.
• Missing values are preserved rather than silently imputed.
• Column names are standardized for machine use; source meanings are preserved where known.
• Always confirm source methodology, units, and collection definitions before policy, production, or redistribution-sensitive use.

Source And Provenance

• Source: OCHA South Sudan
• Publisher: OCHA South Sudan
• Portal: https://data.humdata.org
• Resource: ss_20240904_3w_oerational presence_Jan to Jul_2024.xlsx
• License: CC BY 4.0
• Retrieved/generated: 2026-08-10T18:21:25Z
• Hugging Face repo: electricsheepafrica/africa-south-sudan-south-sudan-operational-presence-536783ab

Transformations Applied

• Converted the source table to Parquet for efficient analytics and ML workflows.
• Added or preserved source provenance columns where available.
• Standardized README metadata, dataset loading configuration, schema documentation, and citation format.
• Preserved source-reported values without analytical imputation.

Suggested Analyses

• Profile the distribution of values
• Compare categories or geographies
• Join with complementary public datasets
• Build time-series views and period-over-period comparisons
• Check missingness before modeling
• Use country_iso3 as the safest geography join key when present

Citation

@misc{electric_sheep_africa_africa_south_sudan_south_sudan_operational_presence_536783ab_2024,
  title        = {South Sudan Operational Presence | Africa (OCHA South Sudan)},
  author       = {OCHA South Sudan},
  year         = {2024},
  url          = {https://data.humdata.org/dataset/south-sudan-operational-presence},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-south-sudan-south-sudan-operational-presence-536783ab}}
}

License

Released under CC BY 4.0.

Original data is published by OCHA South Sudan. Electric Sheep Africa engineering standardizes the data for discovery, loading, and analysis on Hugging Face. Cite both the original source and this ML-ready dataset when used.

About Electric Sheep Africa

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

---

Provenance: README standardized 2026-08-12 by the Electric Sheep Africa README system. Source URL: https://data.humdata.org/dataset/south-sudan-operational-presence