electricsheepafrica/africa-data-source-lists-for-south-sudan
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---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license: cc-by-4.0
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- tabular-classification
- tabular-regression
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- baseline-population
- ssd
pretty_name: "Data source lists for South Sudan"
dataset_info:
splits:
- name: train
num_examples: 29
- name: test
num_examples: 7
---
# Data source lists for South Sudan
**Publisher:** Open Crisis (inactive) · **Source:** [HDX](https://data.humdata.org/dataset/data-source-lists-for-south-sudan) · **License:** `cc-by-igo` · **Updated:** 2023-03-03
---
## Abstract
Lists of data sources about South Sudan. Work still in progress.
Each row in this dataset represents tabular records. Data was last updated on HDX on 2023-03-03. Geographic scope: **SSD**.
*Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).*
---
## Dataset Characteristics
| | |
|---|---|
| **Domain** | Demographics and population |
| **Unit of observation** | Tabular records |
| **Rows (total)** | 37 |
| **Columns** | 5 (0 numeric, 5 categorical, 0 datetime) |
| **Train split** | 29 rows |
| **Test split** | 7 rows |
| **Geographic scope** | SSD |
| **Publisher** | Open Crisis (inactive) |
| **HDX last updated** | 2023-03-03 |
---
## Variables
**Identifier / Metadata** — `esa_source` (HDX), `esa_processed` (2026-04-10).
**Other** — `dataset` (Universities in South Sudan, UNHCR refugees in South Sudan, GMRD south sudan), `url` (en.wikipedia.org/wiki/List_of_universities_in_South_Sudan, http://data.geocomm.com/catalog/SU/datalist.html, http://www.icarda.org/docrep/RReports/Poverty_assessment/3_Sudan.pdf), `owner` (UNHCR, UNEP, UNOCHA).
---
## Quick Start
```python
from datasets import load_dataset
ds = load_dataset("electricsheepafrica/africa-data-source-lists-for-south-sudan")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()
print(train.shape)
train.head()
```
---
## Schema
| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `dataset` | object | 13.5% | Universities in South Sudan, UNHCR refugees in South Sudan, GMRD south sudan |
| `url` | object | 2.7% | en.wikipedia.org/wiki/List_of_universities_in_South_Sudan, http://data.geocomm.com/catalog/SU/datalist.html, http://www.icarda.org/docrep/RReports/Poverty_assessment/3_Sudan.pdf |
| `owner` | object | 37.8% | UNHCR, UNEP, UNOCHA |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-10 |
---
## Numeric Summary
| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
_No numeric columns._
---
## Curation
Raw data was downloaded from HDX via the CKAN API and converted to Parquet. Column names were lowercased and standardised to snake_case. Common missing-value markers (`N/A`, `null`, `none`, `-`, `unknown`, `no data`, `#N/A`) were unified to `NaN`. 8 column(s) with >80% missing values were removed: `unnamed_0`, `what`, `api`, `notes`, `unnamed_7`, `unnamed_8`.... The dataset was split 80/20 into train and test partitions using a fixed random seed (42) and saved as Snappy-compressed Parquet.
---
## Limitations
- Data originates from Open Crisis (inactive) and has not been independently validated by ESA.
- Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
- The following columns have >20% missing values and should be treated with caution in modelling: `owner`.
- Refer to the [original HDX dataset page](https://data.humdata.org/dataset/data-source-lists-for-south-sudan) for the publisher's own methodology notes and caveats.
---
## Citation
```bibtex
@dataset{hdx_africa_data_source_lists_for_south_sudan,
title = {Data source lists for South Sudan},
author = {Open Crisis (inactive)},
year = {2023},
url = {https://data.humdata.org/dataset/data-source-lists-for-south-sudan},
note = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}
```
---
*[Electric Sheep Africa](https://huggingface.co/electricsheepafrica) — Africa's ML dataset infrastructure. Lagos, Nigeria.*
提供机构：
electricsheepafrica
