electricsheepafrica/africa-civ-requirements-and-funding-data

---
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
- covid-19
- funding
- humanitarian-financial-tracking-service-fts
- civ
pretty_name: "Côte d'Ivoire - Requirements and Funding Data"
dataset_info:
splits:
- name: train
num_examples: 32
- name: test
num_examples: 8
---

# Côte d'Ivoire - Requirements and Funding Data

**Publisher:** OCHA Financial Tracking System (FTS) · **Source:** [HDX](https://data.humdata.org/dataset/civ-requirements-and-funding-data) · **License:** `cc-by-igo` · **Updated:** 2026-04-06

---

## Abstract

FTS publishes data on humanitarian funding flows as reported by donors and recipient organizations. It presents all humanitarian funding to a country and funding that is specifically reported or that can be specifically mapped against funding requirements stated in humanitarian response plans. The data comes from OCHA's [Financial Tracking Service](https://fts.unocha.org/) and is encoded as utf-8.

Each row in this dataset represents country-level aggregates. Temporal coverage is indicated by the `startdate`, `enddate` column(s). Geographic scope: **CIV**.

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

---

## Dataset Characteristics

| | |
|---|---|
| **Domain** | Humanitarian and development data |
| **Unit of observation** | Country-level aggregates |
| **Rows (total)** | 40 |
| **Columns** | 14 (6 numeric, 6 categorical, 2 datetime) |
| **Train split** | 32 rows |
| **Test split** | 8 rows |
| **Geographic scope** | CIV |
| **Publisher** | OCHA Financial Tracking System (FTS) |
| **HDX last updated** | 2026-04-06 |

---

## Variables

**Geographic** — `countrycode` (CIV), `typeid` (range 110.0–111.0), `typename` (Consolidated appeals process, Regional response plan), `year` (range 2001.0–2028.0).

**Temporal** — `startdate`, `enddate`.

**Outcome / Measurement** — `percentfunded` (range 29.0–375.0).

**Identifier / Metadata** — `id` (range 119.0–380.0), `name` (Not specified, Cote d'Ivoire 2012, West Africa 2011), `code` (CCIV12, CXWAF11, RBENBFACIVGHAGINGNBLBRMLIMRTNERNGASENSLETGO10), `esa_source` (HDX), `esa_processed` (2026-04-07).

**Other** — `requirements` (range 350000.0–160691683.0), `funding` (range 7805.0–101406953.0).

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-civ-requirements-and-funding-data")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()

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

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `countrycode` | object | 0.0% | CIV |
| `id` | float64 | 70.0% | 119.0 – 380.0 (mean 246.1667) |
| `name` | object | 0.0% | Not specified, Cote d'Ivoire 2012, West Africa 2011 |
| `code` | object | 70.0% | CCIV12, CXWAF11, RBENBFACIVGHAGINGNBLBRMLIMRTNERNGASENSLETGO10 |
| `typeid` | float64 | 70.0% | 110.0 – 111.0 (mean 110.5) |
| `typename` | object | 70.0% | Consolidated appeals process, Regional response plan |
| `startdate` | datetime64[ns] | 70.0% | |
| `enddate` | datetime64[ns] | 70.0% | |
| `year` | int64 | 0.0% | 2001.0 – 2028.0 (mean 2012.4) |
| `requirements` | float64 | 70.0% | 350000.0 – 160691683.0 (mean 57042005.0833) |
| `funding` | int64 | 0.0% | 7805.0 – 101406953.0 (mean 19495436.975) |
| `percentfunded` | float64 | 70.0% | 29.0 – 375.0 (mean 78.4167) |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-07 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `id` | 119.0 | 380.0 | 246.1667 | 242.5 |
| `typeid` | 110.0 | 111.0 | 110.5 | 110.5 |
| `year` | 2001.0 | 2028.0 | 2012.4 | 2010.5 |
| `requirements` | 350000.0 | 160691683.0 | 57042005.0833 | 49014079.0 |
| `funding` | 7805.0 | 101406953.0 | 19495436.975 | 12683092.5 |
| `percentfunded` | 29.0 | 375.0 | 78.4167 | 54.5 |

---

## 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`. 2 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 Financial Tracking System (FTS) 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: `id`, `code`, `typeid`, `typename`, `startdate`, `enddate`, `requirements`, `percentfunded`.
- Refer to the [original HDX dataset page](https://data.humdata.org/dataset/civ-requirements-and-funding-data) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_civ_requirements_and_funding_data,
title = {Côte d'Ivoire - Requirements and Funding Data},
author = {OCHA Financial Tracking System (FTS)},
year = {2026},
url = {https://data.humdata.org/dataset/civ-requirements-and-funding-data},
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:
- 已获取（found）
language:
- 英语
license: cc-by-4.0
multilinguality:
- 单语言
size_categories:
- n<1K
source_datasets:
- 原创数据集
task_categories:
- 表格分类
- 表格回归
task_ids: []
tags:
- 非洲
- 人道主义
- HDX（Humanitarian Data Exchange）
- electric-sheep-africa
- COVID-19
- 资金
- 人道主义金融跟踪服务（FTS，Financial Tracking Service）
- CIV
pretty_name: "科特迪瓦——需求与资金数据"
dataset_info:
splits:
- name: train
num_examples: 32
- name: test
num_examples: 8

# 科特迪瓦——需求与资金数据

**发布方**：联合国人道主义事务协调厅（OCHA，Office for the Coordination of Humanitarian Affairs）金融跟踪服务（FTS，Financial Tracking Service） · **来源**：[HDX（Humanitarian Data Exchange）](https://data.humdata.org/dataset/civ-requirements-and-funding-data) · **许可证**：`cc-by-igo` · **更新时间**：2026-04-06

---

## 摘要

FTS发布由捐赠方与受援组织上报的人道主义资金流动数据。该数据集涵盖某国全部人道主义资金，以及已明确上报或可匹配人道主义应对计划中列明的资金需求的专项资金。本数据集源自联合国人道主义事务协调厅（OCHA）的[金融跟踪服务（FTS）](https://fts.unocha.org/)，编码格式为UTF-8。

本数据集每一行代表国家级聚合数据。时间覆盖范围由`startdate`（开始日期）、`enddate`（结束日期）列标注。地理范围：**CIV（科特迪瓦国家代码）**。

*本数据集已由[Electric Sheep Africa](https://huggingface.co/electricsheepafrica)整理为可供机器学习直接使用的Parquet格式。*

---

## 数据集特征

| | |
|---|---|
| **领域** | 人道主义与发展数据 |
| **观测单元** | 国家级聚合数据 |
| **总行数** | 40 |
| **列数** | 14（6个数值型、6个分类型、2个日期时间型） |
| **训练集划分** | 32行 |
| **测试集划分** | 8行 |
| **地理范围** | CIV（科特迪瓦国家代码） |
| **发布方** | 联合国人道主义事务协调厅（OCHA）金融跟踪服务（FTS） |
| **HDX最后更新时间** | 2026-04-06 |

---

## 变量说明

**地理类变量** — `countrycode`（国家代码，值为CIV）、`typeid`（取值范围110.0–111.0）、`typename`（类别为：联合呼吁程序、区域应对计划）、`year`（取值范围2001.0–2028.0）。
**时间类变量** — `startdate`（开始日期）、`enddate`（结束日期）。
**结果/测量类变量** — `percentfunded`（资金到位率，取值范围29.0–375.0）。
**标识符/元数据类变量** — `id`（取值范围119.0–380.0）、`name`（取值示例：未指定、科特迪瓦2012、西非2011）、`code`（取值示例：CCIV12、CXWAF11、RBENBFACIVGHAGINGNBLBRMLIMRTNERNGASENSLETGO10）、`esa_source`（数据来源，值为HDX）、`esa_processed`（数据处理时间，值为2026-04-07）。
**其他变量** — `requirements`（资金需求总额，取值范围350000.0–160691683.0）、`funding`（实际到账资金总额，取值范围7805.0–101406953.0）。

---

## 快速上手

python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-civ-requirements-and-funding-data")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()

print(train.shape)
train.head()

---

## 数据模式

| 列名 | 数据类型 | 缺失率 | 取值范围/示例值 |
|---|---|---|---|
| `countrycode` | 字符串型（object） | 0.0% | CIV |
| `id` | 浮点型（float64） | 70.0% | 119.0 – 380.0（均值246.1667） |
| `name` | 字符串型（object） | 0.0% | 未指定、科特迪瓦2012、西非2011 |
| `code` | 字符串型（object） | 70.0% | CCIV12、CXWAF11、RBENBFACIVGHAGINGNBLBRMLIMRTNERNGASENSLETGO10 |
| `typeid` | 浮点型（float64） | 70.0% | 110.0 – 111.0（均值110.5） |
| `typename` | 字符串型（object） | 70.0% | 联合呼吁程序、区域应对计划 |
| `startdate` | 日期时间型（datetime64[ns]） | 70.0% | 无 |
| `enddate` | 日期时间型（datetime64[ns]） | 70.0% | 无 |
| `year` | 整型（int64） | 0.0% | 2001.0 – 2028.0（均值2012.4） |
| `requirements` | 浮点型（float64） | 70.0% | 350000.0 – 160691683.0（均值57042005.0833） |
| `funding` | 整型（int64） | 0.0% | 7805.0 – 101406953.0（均值19495436.975） |
| `percentfunded` | 浮点型（float64） | 70.0% | 29.0 – 375.0（均值78.4167） |
| `esa_source` | 字符串型（object） | 0.0% | HDX |
| `esa_processed` | 字符串型（object） | 0.0% | 2026-04-07 |

---

## 数值型变量统计摘要

| 列名 | 最小值 | 最大值 | 均值 | 中位数 |
|---|---|---|---|---|
| `id` | 119.0 | 380.0 | 246.1667 | 242.5 |
| `typeid` | 110.0 | 111.0 | 110.5 | 110.5 |
| `year` | 2001.0 | 2028.0 | 2012.4 | 2010.5 |
| `requirements` | 350000.0 | 160691683.0 | 57042005.0833 | 49014079.0 |
| `funding` | 7805.0 | 101406953.0 | 19495436.975 | 12683092.5 |
| `percentfunded` | 29.0 | 375.0 | 78.4167 | 54.5 |

---

## 数据整理流程

原始数据通过CKAN应用程序编程接口（CKAN API）从HDX下载，并转换为Parquet格式。列名全部转换为小写，并标准化为蛇形命名法（snake_case）。将常见缺失值标记（`N/A`、`null`、`none`、`-`、`unknown`、`no data`、`#N/A`）统一替换为`NaN`。根据解析成功率（阈值>85%），将2列从字符串类型转换为数值型或日期时间型。本数据集使用固定随机种子（42）按80/20比例划分为训练集与测试集，并保存为Snappy压缩的Parquet格式。

---

## 局限性说明

- 本数据集源自联合国人道主义事务协调厅（OCHA）金融跟踪服务（FTS），未由Electric Sheep Africa进行独立验证。
- 自动化清洗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。
- 以下列的缺失率超过20%，在建模过程中需谨慎使用：`id`、`code`、`typeid`、`typename`、`startdate`、`enddate`、`requirements`、`percentfunded`。
- 如需了解发布方的方法说明与注意事项，请参阅[原始HDX数据集页面](https://data.humdata.org/dataset/civ-requirements-and-funding-data)。

---

## 引用格式

bibtex
@dataset{hdx_africa_civ_requirements_and_funding_data,
title = {"科特迪瓦——需求与资金数据"},
author = {联合国人道主义事务协调厅（OCHA）金融跟踪服务（FTS）},
year = {2026},
url = {https://data.humdata.org/dataset/civ-requirements-and-funding-data},
note = {由Electric Sheep Africa重新打包以适配机器学习需求（https://huggingface.co/electricsheepafrica)}
}

---

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