electricsheepafrica/africa-south-sudan-schools-and-enrolment-data-2015-sssams

---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license: other
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- tabular-classification
- tabular-regression
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- education
- education-facilities-schools
- facilities-infrastructure
- gender-and-age-disaggregated-data-gadd
- ssd
pretty_name: "South Sudan : Schools and Enrollment data"
dataset_info:
splits:
- name: train
num_examples: 5106
- name: test
num_examples: 1276
---

# South Sudan : Schools and Enrollment data

**Publisher:** OCHA South Sudan · **Source:** [HDX](https://data.humdata.org/dataset/south-sudan-schools-and-enrolment-data-2015-sssams) · **License:** `other-pd-nr` · **Updated:** 2025-07-22

---

## Abstract

List of public schools & Enrolment data by class and gender (South Sudan Schools’ Attendance Monitoring System (SSSAMS)), developed as part of the Girls’ Education South Sudan (GESS) programme by Ministry of Education Science and Technology Project, for the Republic of South Sudan.

Each row in this dataset represents first-level administrative unit observations. Data was last updated on HDX on 2025-07-22. Geographic scope: **SSD**.

*Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).*

---

## Dataset Characteristics

| | |
|---|---|
| **Domain** | Education |
| **Unit of observation** | First-level administrative unit observations |
| **Rows (total)** | 6,383 |
| **Columns** | 75 (64 numeric, 11 categorical, 0 datetime) |
| **Train split** | 5,106 rows |
| **Test split** | 1,276 rows |
| **Geographic scope** | SSD |
| **Publisher** | OCHA South Sudan |
| **HDX last updated** | 2025-07-22 |

---

## Variables

**Geographic** — `state` (JUB, YEI, IMA), `county` (Juba County, Aweil East County, Twic County), `payam` (Munuki, Yambio Town, Northern Bari), `type` (Primary, Secondary, PRE).

**Outcome / Measurement** — `total`.

**Identifier / Metadata** — `sid` (range 2840.0–13933.0), `sref` (BRM, MEN, MHP), `esa_source` (HDX), `esa_processed`.

**Other** — `g3` (Jubek, Yei River, Imatong), `g2` (Juba County, Aweil East County, Twic County), `g1` (Munuki, Yambio Town, Northern Bari), `geo` (range 28.0–28.0), `school` (Bari Primary [BRM], Maluil Deng Primary School [MEN], Makuach Primary School [MHP]) and 61 others.

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-south-sudan-schools-and-enrolment-data-2015-sssams")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()

print(train.shape)
train.head()
```

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `g3` | object | 0.0% | Jubek, Yei River, Imatong |
| `g2` | object | 0.0% | Juba County, Aweil East County, Twic County |
| `g1` | object | 0.0% | Munuki, Yambio Town, Northern Bari |
| `sid` | int64 | 0.0% | 2840.0 – 13933.0 (mean 7853.6491) |
| `sref` | object | 0.0% | BRM, MEN, MHP |
| `geo` | int64 | 0.0% | 28.0 – 28.0 (mean 28.0) |
| `state` | object | 0.0% | JUB, YEI, IMA |
| `county` | object | 0.0% | Juba County, Aweil East County, Twic County |
| `payam` | object | 0.0% | Munuki, Yambio Town, Northern Bari |
| `school` | object | 0.0% | Bari Primary [BRM], Maluil Deng Primary School [MEN], Makuach Primary School [MHP] |
| `type` | object | 0.0% | Primary, Secondary, PRE |
| `p1f` | int64 | 0.0% | 0.0 – 548.0 (mean 29.5186) |
| `p1m` | int64 | 0.0% | 0.0 – 736.0 (mean 38.6453) |
| `p2f` | int64 | 0.0% | 0.0 – 480.0 (mean 20.6129) |
| `p2m` | int64 | 0.0% | 0.0 – 694.0 (mean 26.5798) |
| `p3f` | int64 | 0.0% | 0.0 – 281.0 (mean 18.0855) |
| `p3m` | int64 | 0.0% | 0.0 – 473.0 (mean 23.6583) |
| `p4f` | int64 | 0.0% | 0.0 – 286.0 (mean 16.3848) |
| `p4m` | int64 | 0.0% | 0.0 – 454.0 (mean 21.2627) |
| `p5f` | int64 | 0.0% | 0.0 – 517.0 (mean 19.4448) |
| `p5m` | int64 | 0.0% | 0.0 – 421.0 (mean 19.9848) |
| `p6f` | int64 | 0.0% | 0.0 – 683.0 (mean 16.1062) |
| `p6m` | int64 | 0.0% | 0.0 – 520.0 (mean 15.7768) |
| `p7f` | int64 | 0.0% | 0.0 – 523.0 (mean 11.9633) |
| `p7m` | int64 | 0.0% | 0.0 – 411.0 (mean 12.0456) |
| `p8f` | int64 | 0.0% | 0.0 – 247.0 (mean 5.7667) |
| `p8m` | int64 | 0.0% | 0.0 – 286.0 (mean 6.9463) |
| `pfo` | int64 | 0.0% | 0.0 – 0.0 (mean 0.0) |
| `pmo` | int64 | 0.0% | 0.0 – 0.0 (mean 0.0) |
| `ptf` | int64 | 0.0% | |
| `ptm` | int64 | 0.0% | |
| `totp` | int64 | 0.0% | |
| `s1f` | int64 | 0.0% | |
| `s1m` | int64 | 0.0% | |
| `s2f` | int64 | 0.0% | |
| `s2m` | int64 | 0.0% | |
| `s3f` | int64 | 0.0% | |
| `s3m` | int64 | 0.0% | |
| `s4f` | int64 | 0.0% | |
| `s4m` | int64 | 0.0% | |
| `sfo` | int64 | 0.0% | |
| `smo` | int64 | 0.0% | |
| `stf` | int64 | 0.0% | |
| `stm` | int64 | 0.0% | |
| `tots` | int64 | 0.0% | |
| `othrf` | int64 | 0.0% | |
| `othrm` | int64 | 0.0% | |
| `toto` | int64 | 0.0% | |
| `aes_tf` | int64 | 0.0% | |
| `aes_tm` | int64 | 0.0% | |
| `tot_aes` | int64 | 0.0% | |
| `alp_tf` | int64 | 0.0% | |
| `alp_tm` | int64 | 0.0% | |
| `tot_alp` | int64 | 0.0% | |
| `ecd_tf` | int64 | 0.0% | |
| `ecd_tm` | int64 | 0.0% | |
| `tot_ecd` | int64 | 0.0% | |
| `nur_tf` | int64 | 0.0% | |
| `nur_tm` | int64 | 0.0% | |
| `tot_nur` | int64 | 0.0% | |
| `pre_tf` | int64 | 0.0% | |
| `pre_tm` | int64 | 0.0% | |
| `tot_pre` | int64 | 0.0% | |
| `tti_tf` | int64 | 0.0% | |
| `tti_tm` | int64 | 0.0% | |
| `tot_tti` | int64 | 0.0% | |
| `tvet_tf` | int64 | 0.0% | |
| `tvet_tm` | int64 | 0.0% | |
| `tot_tvet` | int64 | 0.0% | |
| `uni_tf` | int64 | 0.0% | |
| `uni_tm` | int64 | 0.0% | |
| `tot_uni` | int64 | 0.0% | |
| `total` | int64 | 0.0% | |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `sid` | 2840.0 | 13933.0 | 7853.6491 | 7453.0 |
| `geo` | 28.0 | 28.0 | 28.0 | 28.0 |
| `p1f` | 0.0 | 548.0 | 29.5186 | 19.0 |
| `p1m` | 0.0 | 736.0 | 38.6453 | 26.0 |
| `p2f` | 0.0 | 480.0 | 20.6129 | 14.0 |
| `p2m` | 0.0 | 694.0 | 26.5798 | 18.0 |
| `p3f` | 0.0 | 281.0 | 18.0855 | 11.0 |
| `p3m` | 0.0 | 473.0 | 23.6583 | 16.0 |
| `p4f` | 0.0 | 286.0 | 16.3848 | 9.0 |
| `p4m` | 0.0 | 454.0 | 21.2627 | 14.0 |
| `p5f` | 0.0 | 517.0 | 19.4448 | 8.0 |
| `p5m` | 0.0 | 421.0 | 19.9848 | 10.0 |
| `p6f` | 0.0 | 683.0 | 16.1062 | 0.0 |
| `p6m` | 0.0 | 520.0 | 15.7768 | 0.0 |
| `p7f` | 0.0 | 523.0 | 11.9633 | 0.0 |

---

## 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`. 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 OCHA South Sudan and has not been independently validated by ESA.
- Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
- Refer to the [original HDX dataset page](https://data.humdata.org/dataset/south-sudan-schools-and-enrolment-data-2015-sssams) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_south_sudan_schools_and_enrolment_data_2015_sssams,
title = {South Sudan : Schools and Enrollment data},
author = {OCHA South Sudan},
year = {2025},
url = {https://data.humdata.org/dataset/south-sudan-schools-and-enrolment-data-2015-sssams},
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.*