electricsheepafrica/africa-gha-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 - gha pretty_name: "Ghana - Requirements and Funding Data" dataset_info: splits: - name: train num_examples: 25 - name: test num_examples: 6 --- # Ghana - Requirements and Funding Data **Publisher:** OCHA Financial Tracking System (FTS) · **Source:** [HDX](https://data.humdata.org/dataset/gha-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. Data was last updated on HDX on 2026-04-06. Geographic scope: **GHA**. *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)** | 32 | | **Columns** | 6 (2 numeric, 4 categorical, 0 datetime) | | **Train split** | 25 rows | | **Test split** | 6 rows | | **Geographic scope** | GHA | | **Publisher** | OCHA Financial Tracking System (FTS) | | **HDX last updated** | 2026-04-06 | --- ## Variables **Geographic** — `countrycode` (GHA), `year` (range 2001.0–2028.0). **Identifier / Metadata** — `name` (Not specified, West Africa 2010, West Africa 2009), `esa_source` (HDX), `esa_processed` (2026-04-06). **Other** — `funding` (range 150.0–20347680.0). --- ## Quick Start ```python from datasets import load_dataset ds = load_dataset("electricsheepafrica/africa-gha-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% | GHA | | `name` | object | 0.0% | Not specified, West Africa 2010, West Africa 2009 | | `year` | int64 | 0.0% | 2001.0 – 2028.0 (mean 2013.8438) | | `funding` | float64 | 3.1% | 150.0 – 20347680.0 (mean 5902209.0) | | `esa_source` | object | 0.0% | HDX | | `esa_processed` | object | 0.0% | 2026-04-06 | --- ## Numeric Summary | Column | Min | Max | Mean | Median | |---|---|---|---|---| | `year` | 2001.0 | 2028.0 | 2013.8438 | 2012.5 | | `funding` | 150.0 | 20347680.0 | 5902209.0 | 4883965.0 | --- ## 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`. 8 column(s) with >80% missing values were removed: `id`, `code`, `typeid`, `typename`, `startdate`, `enddate`.... 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. - Refer to the [original HDX dataset page](https://data.humdata.org/dataset/gha-requirements-and-funding-data) for the publisher's own methodology notes and caveats. --- ## Citation ```bibtex @dataset{hdx_africa_gha_requirements_and_funding_data, title = {Ghana - Requirements and Funding Data}, author = {OCHA Financial Tracking System (FTS)}, year = {2026}, url = {https://data.humdata.org/dataset/gha-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（no-annotation） language_creators: - 采集型（found） language: - 英语（en） license: cc-by-4.0 multilinguality: - 单语言（monolingual） size_categories: - 样本量少于1000（n<1K） source_datasets: - 原始数据集（original） task_categories: - 表格分类（tabular-classification） - 表格回归（tabular-regression） task_ids: [] tags: - 非洲（africa） - 人道主义（humanitarian） - HDX - electric-sheep-africa - 新型冠状病毒肺炎（covid-19） - 资金（funding） - humanitarian-financial-tracking-service-fts - gha pretty_name: "加纳——需求与资金数据" dataset_info: splits: - name: 训练集（train） num_examples: 25 - name: 测试集（test） num_examples: 6 # 加纳——需求与资金数据 **发布方：** 联合国人道主义事务协调厅资金跟踪系统（OCHA Financial Tracking System, FTS） · **来源：** [HDX](https://data.humdata.org/dataset/gha-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。该数据集涵盖流向某一国家的全部人道主义资金，以及专门上报或可与人道主义响应计划中列明的资金需求精准匹配的资金。数据源自联合国人道主义事务协调厅的[资金跟踪服务（Financial Tracking Service, FTS）](https://fts.unocha.org/)，编码格式为utf-8。 本数据集的每一行代表国家级汇总数据。数据最后一次在HDX平台更新的时间为2026-04-06。地理覆盖范围：**GHA（加纳）**。 *本数据集由[Electric Sheep Africa](https://huggingface.co/electricsheepafrica)整理为适用于机器学习的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** | 32 | | **列数** | 6（2个数值型，4个分类型，0个日期型） | | **训练集划分** | 25条数据 | | **测试集划分** | 6条数据 | | **地理覆盖范围** | GHA（加纳） | | **发布方** | 联合国人道主义事务协调厅资金跟踪系统（FTS） | | **HDX最后更新时间** | 2026-04-06 | --- ## 变量说明 **地理类** — `countrycode`（国家代码，取值为GHA）、`year`（年份，范围2001.0–2028.0）。 **标识符/元数据类** — `name`（名称，可选值为未指定、西非2010、西非2009）、`esa_source`（数据来源，取值为HDX）、`esa_processed`（数据处理时间，取值为2026-04-06）。 **其他类** — `funding`（资金额，范围150.0–20347680.0）。 --- ## 快速上手 python from datasets import load_dataset ds = load_dataset("electricsheepafrica/africa-gha-requirements-and-funding-data") train = ds["train"].to_pandas() test = ds["test"].to_pandas() print(train.shape) train.head() --- ## 数据结构 | 列名 | 数据类型 | 缺失率 | 范围/示例值 | |---|---|---|---| | `countrycode` | 字符串（object） | 0.0% | GHA | | `name` | 字符串（object） | 0.0% | 未指定、西非2010、西非2009 | | `year` | 64位整数（int64） | 0.0% | 2001.0 – 2028.0（均值2013.8438） | | `funding` | 64位浮点数（float64） | 3.1% | 150.0 – 20347680.0（均值5902209.0） | | `esa_source` | 字符串（object） | 0.0% | HDX | | `esa_processed` | 字符串（object） | 0.0% | 2026-04-06 | --- ## 数值统计摘要 | 列名 | 最小值 | 最大值 | 均值 | 中位数 | |---|---|---|---|---| | `year` | 2001.0 | 2028.0 | 2013.8438 | 2012.5 | | `funding` | 150.0 | 20347680.0 | 5902209.0 | 4883965.0 | --- ## 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平台下载，并转换为Parquet格式。列名统一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`。移除了8个缺失值占比超过80%的列：`id`、`code`、`typeid`、`typename`、`startdate`、`enddate`……。使用固定随机种子（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。 --- ## 局限性 - 数据源自联合国人道主义事务协调厅资金跟踪系统（FTS），未经过Electric Sheep Africa的独立验证。 - 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。 - 请参阅[原始HDX数据集页面](https://data.humdata.org/dataset/gha-requirements-and-funding-data)获取发布方提供的方法说明与注意事项。 --- ## 引用格式 bibtex @dataset{hdx_africa_gha_requirements_and_funding_data, title = {Ghana - Requirements and Funding Data}, author = {OCHA Financial Tracking System (FTS)}, year = {2026}, url = {https://data.humdata.org/dataset/gha-requirements-and-funding-data}, 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。尼日利亚拉各斯。*