electricsheepafrica/africa-south-sudan-2015-3w-operational-presence

---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license: cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- tabular-classification
- tabular-regression
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- ssd
pretty_name: "SOUTH SUDAN 2015 3W Operational Presence"
dataset_info:
splits:
- name: train
num_examples: 8664
- name: test
num_examples: 2166
---

# SOUTH SUDAN 2015 3W Operational Presence

**Publisher:** OCHA South Sudan · **Source:** [HDX](https://data.humdata.org/dataset/south-sudan-2015-3w-operational-presence) · **License:** `cc-by` · **Updated:** 2023-09-20

---

## Abstract

SOUTH SUDAN : 2015 Operational Presence (3W: Who does What, Where).
These are organisations responding with emergency programs in South Sudan. Information was compiled from monthly 5W submissions to by the clusters.

Each row in this dataset represents time-series observations. Temporal coverage is indicated by the `data_date` column(s). Geographic scope: **SSD**.

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

---

## Dataset Characteristics

| | |
|---|---|
| **Domain** | Humanitarian and development data |
| **Unit of observation** | Time-series observations |
| **Rows (total)** | 10,831 |
| **Columns** | 14 (5 numeric, 8 categorical, 1 datetime) |
| **Train split** | 8,664 rows |
| **Test split** | 2,166 rows |
| **Geographic scope** | SSD |
| **Publisher** | OCHA South Sudan |
| **HDX last updated** | 2023-09-20 |

---

## Variables

**Geographic** — `county_code` (range 7101.0–9401.0), `agency_id` (range 992.0–1335.0), `agency_name` (World Food Programme, International Organization for Migration, World Vision International), `agency_type` (International NGO, National NGO, United Nations), `county_name` (Juba, Bor South, Malakal) and 1 others.

**Temporal** — `data_date`.

**Outcome / Measurement** — `counterid` (range 1.0–10831.0).

**Identifier / Metadata** — `cluster_id` (range 1.0–12.0), `op_oc_id` (range 2.0–2.0), `cluster_name` (Protection, Nutrition, Health), `esa_source` (HDX), `esa_processed` (2026-04-07).

**Other** — `op_oc` (Operational Presence).

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-south-sudan-2015-3w-operational-presence")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()

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

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `counterid` | int64 | 0.0% | 1.0 – 10831.0 (mean 5416.0) |
| `county_code` | int64 | 0.0% | 7101.0 – 9401.0 (mean 7929.5945) |
| `cluster_id` | int64 | 0.0% | 1.0 – 12.0 (mean 7.674) |
| `op_oc_id` | int64 | 0.0% | 2.0 – 2.0 (mean 2.0) |
| `agency_id` | int64 | 0.0% | 992.0 – 1335.0 (mean 1183.5116) |
| `data_date` | datetime64[ns] | 0.0% | |
| `agency_name` | object | 0.0% | World Food Programme, International Organization for Migration, World Vision International |
| `agency_type` | object | 0.0% | International NGO, National NGO, United Nations |
| `cluster_name` | object | 0.0% | Protection, Nutrition, Health |
| `county_name` | object | 0.0% | Juba, Bor South, Malakal |
| `agency_acronym` | object | 0.0% | WFP, IOM, WVI |
| `op_oc` | object | 0.0% | Operational Presence |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-07 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `counterid` | 1.0 | 10831.0 | 5416.0 | 5416.0 |
| `county_code` | 7101.0 | 9401.0 | 7929.5945 | 7308.0 |
| `cluster_id` | 1.0 | 12.0 | 7.674 | 8.0 |
| `op_oc_id` | 2.0 | 2.0 | 2.0 | 2.0 |
| `agency_id` | 992.0 | 1335.0 | 1183.5116 | 1184.0 |

---

## 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`. 1 column(s) were cast from string to numeric or datetime based on parse-success rate (>85% threshold). 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-2015-3w-operational-presence) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_south_sudan_2015_3w_operational_presence,
title = {SOUTH SUDAN 2015 3W Operational Presence},
author = {OCHA South Sudan},
year = {2023},
url = {https://data.humdata.org/dataset/south-sudan-2015-3w-operational-presence},
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.*