electricsheepafrica/africa-who-is-doing-what-and-where-in-sudan-november-2020

---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license: cc-by-4.0
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
- hxl
- operational-presence
- sdn
pretty_name: "Who is doing What and Where in Sudan, November 2020"
dataset_info:
splits:
- name: train
num_examples: 2184
- name: test
num_examples: 546
---

# Who is doing What and Where in Sudan, November 2020

**Publisher:** OCHA Sudan · **Source:** [HDX](https://data.humdata.org/dataset/who-is-doing-what-and-where-in-sudan-november-2020) · **License:** `cc-by` · **Updated:** 2025-04-10

---

## Abstract

The data shows Who is doing What and Where by locality in Sudan

Each row in this dataset represents first-level administrative unit observations. Data was last updated on HDX on 2025-04-10. Geographic scope: **SDN**.

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

---

## Dataset Characteristics

| | |
|---|---|
| **Domain** | Humanitarian and development data |
| **Unit of observation** | First-level administrative unit observations |
| **Rows (total)** | 2,730 |
| **Columns** | 7 (0 numeric, 7 categorical, 0 datetime) |
| **Train split** | 2,184 rows |
| **Test split** | 546 rows |
| **Geographic scope** | SDN |
| **Publisher** | OCHA Sudan |
| **HDX last updated** | 2025-04-10 |

---

## Variables

**Geographic** — `state` (South Darfur, South Kordofan, Kassala), `locality` (Beliel, Ag Geneina, Abyei), `implementing_organization_type` (UN, INGO, NGO).

**Identifier / Metadata** — `implementing_organization_name` (UNICEF, UNHCR, SRCS), `esa_source` (HDX), `esa_processed` (2026-04-19).

**Other** — `sector_intervention` (Protection, FSL, WASH).

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-who-is-doing-what-and-where-in-sudan-november-2020")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()

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

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `state` | object | 0.0% | South Darfur, South Kordofan, Kassala |
| `locality` | object | 0.0% | Beliel, Ag Geneina, Abyei |
| `sector_intervention` | object | 0.0% | Protection, FSL, WASH |
| `implementing_organization_name` | object | 0.0% | UNICEF, UNHCR, SRCS |
| `implementing_organization_type` | object | 0.0% | UN, INGO, NGO |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-19 |

---

## 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`. 135 exact duplicate rows were removed. 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 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/who-is-doing-what-and-where-in-sudan-november-2020) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_who_is_doing_what_and_where_in_sudan_november_2020,
title = {Who is doing What and Where in Sudan, November 2020},
author = {OCHA Sudan},
year = {2025},
url = {https://data.humdata.org/dataset/who-is-doing-what-and-where-in-sudan-november-2020},
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.*

annotations_creators:
- 无注释
language_creators:
- 采集所得
language:
- 英语
license: CC-BY 4.0
multilinguality:
- 单语言
size_categories:
- 1K<n<10K
source_datasets:
- 原始数据集
task_categories:
- 表格分类
- 表格回归
task_ids:
- 无
tags:
- 非洲
- 人道主义
- 人类数据交换平台（Humanitarian Data Exchange, HDX）
- electric-sheep-africa
- hxl
- 业务存在
- SDN
pretty_name: "2020年11月苏丹：谁在何地开展何种工作"
dataset_info:
splits:
- name: 训练集
num_examples: 2184
- name: 测试集
num_examples: 546

# 2020年11月苏丹：谁在何地开展何种工作

**发布方：联合国人道主义事务协调厅（Office for the Coordination of Humanitarian Affairs, OCHA）苏丹办事处** · **来源：[人类数据交换平台（Humanitarian Data Exchange, HDX）](https://data.humdata.org/dataset/who-is-doing-what-and-where-in-sudan-november-2020)** · **许可证：`CC-BY`** · **最后更新：2025-04-10**

---

## 摘要
本数据集展示了苏丹各一级行政单元中，各主体在何地开展何种工作的相关信息。数据集中每一行对应一条一级行政单元的观测记录。该数据最近一次在HDX平台的更新时间为2025年4月10日。地理覆盖范围：**SDN**。
*本数据集已由[Electric Sheep Africa](https://huggingface.co/electricsheepafrica)整理为适配机器学习的Parquet格式。*

---

## 数据集特征
| 指标 | 详情 |
|---|---|
| **领域** | 人道主义与发展数据 |
| **观测单元** | 一级行政单元观测样本 |
| **总样本行数** | 2730 |
| **列数** | 7（0个数值型列，7个分类型列，0个日期时间型列） |
| **训练集样本数** | 2184 |
| **测试集样本数** | 546 |
| **地理覆盖范围** | SDN |
| **发布方** | OCHA苏丹办事处 |
| **HDX平台最后更新时间** | 2025-04-10 |

---

## 变量说明
### 地理类变量
`state`（州：南达尔富尔州、南科尔多凡州、卡萨拉州）、`locality`（地区：贝列耶尔、阿杰盖奈、阿卜耶伊）、`implementing_organization_type`（实施机构类型：联合国机构、国际非政府组织、非政府组织）。

### 标识与元数据类变量
`implementing_organization_name`（实施机构名称：联合国儿童基金会、联合国难民署、苏丹红十字会）、`esa_source`（数据来源：HDX）、`esa_processed`（数据处理时间：2026-04-19）。

### 其他类变量
`sector_intervention`（干预领域：保护、粮食安全与生计、水、环境卫生与个人卫生）。

---

## 快速上手示例
python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-who-is-doing-what-and-where-in-sudan-november-2020")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()

print(train.shape)
train.head()

---

## 数据Schema
| 列名 | 数据类型 | 空值占比 | 取值范围/示例值 |
|---|---|---|---|
| `state` | 字符型 | 0.0% | 南达尔富尔州、南科尔多凡州、卡萨拉州 |
| `locality` | 字符型 | 0.0% | 贝列耶尔、阿杰盖奈、阿卜耶伊 |
| `sector_intervention` | 字符型 | 0.0% | 保护、粮食安全与生计、水、环境卫生与个人卫生 |
| `implementing_organization_name` | 字符型 | 0.0% | 联合国儿童基金会、联合国难民署、苏丹红十字会 |
| `implementing_organization_type` | 字符型 | 0.0% | 联合国机构、国际非政府组织、非政府组织 |
| `esa_source` | 字符型 | 0.0% | HDX |
| `esa_processed` | 字符型 | 0.0% | 2026-04-19 |

---

## 数值型变量统计摘要
| 列名 | 最小值 | 最大值 | 均值 | 中位数 |
|---|---|---|---|
_无数值型列。_

---

## 数据整理流程
原始数据通过CKAN API从HDX平台下载，并转换为Parquet格式。列名统一转换为小写并采用蛇形命名法（snake_case）进行标准化。将常见的缺失值标记（`N/A`、`null`、`none`、`-`、`unknown`、`no data`、`#N/A`）统一替换为`NaN`。删除了135条完全重复的样本。采用固定随机种子（42）将数据集按80/20的比例划分为训练集与测试集，并保存为Snappy压缩的Parquet格式文件。

---

## 局限性说明
- 本数据源自OCHA苏丹办事处，未由Electric Sheep Africa进行独立验证。
- 自动化清洗流程无法修正原始数据收集中的错报值、定义不一致或抽样偏差问题。
- 如需了解发布方的方法论说明与注意事项，请参阅[原始HDX数据集页面](https://data.humdata.org/dataset/who-is-doing-what-and-where-in-sudan-november-2020)。

---

## 引用格式
bibtex
@dataset{hdx_africa_who_is_doing_what_and_where_in_sudan_november_2020,
title = {Who is doing What and Where in Sudan, November 2020},
author = {OCHA Sudan},
year = {2025},
url = {https://data.humdata.org/dataset/who-is-doing-what-and-where-in-sudan-november-2020},
note = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

---

*[Electric Sheep Africa](https://huggingface.co/electricsheepafrica) — 非洲机器学习数据集基础设施提供商，尼日利亚拉各斯。*