electricsheepafrica/africa-rwa-requirements-and-funding-data
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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 - covid-19 - funding - humanitarian-financial-tracking-service-fts - rwa pretty_name: "Rwanda - Requirements and Funding Data" dataset_info: splits: - name: train num_examples: 29 - name: test num_examples: 7 --- # Rwanda - Requirements and Funding Data **Publisher:** OCHA Financial Tracking System (FTS) · **Source:** [HDX](https://data.humdata.org/dataset/rwa-requirements-and-funding-data) · **License:** `cc-by-igo` · **Updated:** 2026-04-07 --- ## 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: **RWA**. *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)** | 37 | | **Columns** | 12 (4 numeric, 6 categorical, 2 datetime) | | **Train split** | 29 rows | | **Test split** | 7 rows | | **Geographic scope** | RWA | | **Publisher** | OCHA Financial Tracking System (FTS) | | **HDX last updated** | 2026-04-07 | --- ## Variables **Geographic** — `countrycode` (RWA), `typeid` (range 111.0–111.0), `typename` (Regional response plan), `year` (range 2000.0–2028.0). **Temporal** — `startdate`, `enddate`. **Identifier / Metadata** — `id` (range 224.0–1213.0), `name` (Not specified, Democratic Republic of the Congo Regional Refugee Response Plan 2025, Democratic Republic of the Congo Regional Refugee Response Plan 2024), `code` (RDRC_RRP25, RDRC_RRP24, RDRCRRP23), `esa_source` (HDX), `esa_processed` (2026-04-07). **Other** — `funding` (range 563931.0–62144202.0). --- ## Quick Start ```python from datasets import load_dataset ds = load_dataset("electricsheepafrica/africa-rwa-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% | RWA | | `id` | float64 | 75.7% | 224.0 – 1213.0 (mean 970.8889) | | `name` | object | 0.0% | Not specified, Democratic Republic of the Congo Regional Refugee Response Plan 2025, Democratic Republic of the Congo Regional Refugee Response Plan 2024 | | `code` | object | 75.7% | RDRC_RRP25, RDRC_RRP24, RDRCRRP23 | | `typeid` | float64 | 75.7% | 111.0 – 111.0 (mean 111.0) | | `typename` | object | 75.7% | Regional response plan | | `startdate` | datetime64[ns] | 75.7% | | | `enddate` | datetime64[ns] | 75.7% | | | `year` | int64 | 0.0% | 2000.0 – 2028.0 (mean 2015.8919) | | `funding` | int64 | 0.0% | 563931.0 – 62144202.0 (mean 16181769.0541) | | `esa_source` | object | 0.0% | HDX | | `esa_processed` | object | 0.0% | 2026-04-07 | --- ## Numeric Summary | Column | Min | Max | Mean | Median | |---|---|---|---|---| | `id` | 224.0 | 1213.0 | 970.8889 | 1018.0 | | `typeid` | 111.0 | 111.0 | 111.0 | 111.0 | | `year` | 2000.0 | 2028.0 | 2015.8919 | 2018.0 | | `funding` | 563931.0 | 62144202.0 | 16181769.0541 | 10123427.0 | --- ## 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`. 2 column(s) with >80% missing values were removed: `requirements`, `percentfunded`. 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`. - Refer to the [original HDX dataset page](https://data.humdata.org/dataset/rwa-requirements-and-funding-data) for the publisher's own methodology notes and caveats. --- ## Citation ```bibtex @dataset{hdx_africa_rwa_requirements_and_funding_data, title = {Rwanda - Requirements and Funding Data}, author = {OCHA Financial Tracking System (FTS)}, year = {2026}, url = {https://data.humdata.org/dataset/rwa-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: - 无标注 language_creators: - 采集获取 language: - 英语 license: cc-by-4.0 multilinguality: - 单语言 size_categories: - 样本量小于1000 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 - 电羊非洲（Electric Sheep Africa） - 新冠疫情（COVID-19） - 资金 - 人道主义金融跟踪服务（FTS） - RWA pretty_name: "卢旺达——需求与资金数据" dataset_info: splits: - name: 训练集 num_examples: 29 - name: 测试集 num_examples: 7 --- # 卢旺达——需求与资金数据 **发布方**：联合国人道主义事务协调厅（OCHA）金融跟踪系统（FTS） · **数据源**：[HDX](https://data.humdata.org/dataset/rwa-requirements-and-funding-data) · **许可协议**：`cc-by-igo` · **更新时间**：2026-04-07 --- ## 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，涵盖流向某一国家的全部人道主义资金，以及可与人道主义响应计划中列明的资金需求精准匹配或明确映射的专项资金。本数据集数据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/)，采用UTF-8编码。 本数据集每一行代表国家级汇总数据。时间覆盖范围由`startdate`（开始日期）、`enddate`（结束日期）列标注。地理覆盖范围：**RWA（卢旺达）**。 *本数据集由[电羊非洲（Electric Sheep Africa）](https://huggingface.co/electricsheepafrica)整理为机器学习可用的Parquet格式。* --- ## 数据集特征 | | | |---|---| | **领域** | 人道主义与发展数据 | | **观测单元** | 国家级汇总数据 | | **总行数** | 37 | | **列数** | 12（4个数值型、6个分类型、2个日期时间型） | | **训练集划分** | 29行 | | **测试集划分** | 7行 | | **地理覆盖范围** | RWA（卢旺达） | | **发布方** | OCHA金融跟踪系统（FTS） | | **HDX平台最后更新时间** | 2026-04-07 | --- ## 变量 **地理类变量**：`countrycode`（国家代码，RWA）、`typeid`（取值范围111.0–111.0）、`typename`（类别名称：区域响应计划）、`year`（年份范围2000.0–2028.0）。 **时间类变量**：`startdate`（开始日期）、`enddate`（结束日期）。 **标识符与元数据变量**：`id`（取值范围224.0–1213.0）、`name`（名称：未指定、2025年刚果民主共和国区域难民响应计划、2024年刚果民主共和国区域难民响应计划）、`code`（编码：RDRC_RRP25、RDRC_RRP24、RDRCRRP23）、`esa_source`（数据来源：HDX）、`esa_processed`（数据处理时间：2026-04-07）。 **其他变量**：`funding`（资金额，取值范围563931.0–62144202.0）。 --- ## 快速入门 python from datasets import load_dataset ds = load_dataset("electricsheepafrica/africa-rwa-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（object） | 0.0% | RWA | | `id` | 浮点型（float64） | 75.7% | 224.0 – 1213.0（均值 970.8889） | | `name` | 字符串（object） | 0.0% | 未指定、2025年刚果民主共和国区域难民响应计划、2024年刚果民主共和国区域难民响应计划 | | `code` | 字符串（object） | 75.7% | RDRC_RRP25、RDRC_RRP24、RDRCRRP23 | | `typeid` | 浮点型（float64） | 75.7% | 111.0 – 111.0（均值 111.0） | | `typename` | 字符串（object） | 75.7% | 区域响应计划 | | `startdate` | 日期时间型（datetime64[ns]） | 75.7% | | | `enddate` | 日期时间型（datetime64[ns]） | 75.7% | | | `year` | 整型（int64） | 0.0% | 2000.0 – 2028.0（均值 2015.8919） | | `funding` | 整型（int64） | 0.0% | 563931.0 – 62144202.0（均值 16181769.0541） | | `esa_source` | 字符串（object） | 0.0% | HDX | | `esa_processed` | 字符串（object） | 0.0% | 2026-04-07 | --- ## 数值型变量统计 | 列名 | 最小值 | 最大值 | 均值 | 中位数 | |---|---|---|---|---| | `id` | 224.0 | 1213.0 | 970.8889 | 1018.0 | | `typeid` | 111.0 | 111.0 | 111.0 | 111.0 | | `year` | 2000.0 | 2028.0 | 2015.8919 | 2018.0 | | `funding` | 563931.0 | 62144202.0 | 16181769.0541 | 10123427.0 | --- ## 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。列名统一转为小写并采用蛇形命名法（snake_case）标准化。常见缺失值标记（`N/A`、`null`、`none`、`-`、`unknown`、`no data`、`#N/A`）被统一替换为`NaN`。删除了2个缺失值占比超过80%的列：`requirements`（资金需求）、`percentfunded`（资金完成率）。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。本数据集以固定随机种子（42）按80/20比例划分为训练集与测试集，并以Snappy压缩的Parquet格式存储。 --- ## 数据集局限性 - 数据源自OCHA金融跟踪系统（FTS），未由电羊非洲（ESA）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、定义不一致或采样偏差问题。 - 以下列缺失值占比超过20%，在建模过程中需谨慎使用：`id`、`code`、`typeid`、`typename`、`startdate`、`enddate`。 - 请参阅[原始HDX数据集页面](https://data.humdata.org/dataset/rwa-requirements-and-funding-data)查看发布方提供的方法说明与注意事项。 --- ## 引用 bibtex @dataset{hdx_africa_rwa_requirements_and_funding_data, title = {卢旺达——需求与资金数据}, author = {OCHA金融跟踪系统（FTS)}, year = {2026}, url = {https://data.humdata.org/dataset/rwa-requirements-and-funding-data}, note = {由电羊非洲（Electric Sheep Africa）重新打包以适配机器学习场景（https://huggingface.co/electricsheepafrica)} } --- *[电羊非洲（Electric Sheep Africa）——非洲机器学习数据集基础设施。尼日利亚拉各斯。]*
