electricsheepafrica/africa-gnb-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 - gnb pretty_name: "Guinea-Bissau - Requirements and Funding Data" dataset_info: splits: - name: train num_examples: 24 - name: test num_examples: 6 --- # Guinea-Bissau - Requirements and Funding Data **Publisher:** OCHA Financial Tracking System (FTS) · **Source:** [HDX](https://data.humdata.org/dataset/gnb-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: **GNB**. *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)** | 31 | | **Columns** | 6 (2 numeric, 4 categorical, 0 datetime) | | **Train split** | 24 rows | | **Test split** | 6 rows | | **Geographic scope** | GNB | | **Publisher** | OCHA Financial Tracking System (FTS) | | **HDX last updated** | 2026-04-06 | --- ## Variables **Geographic** — `countrycode` (GNB), `year` (range 2001.0–2026.0). **Identifier / Metadata** — `name` (Not specified, West Africa 2010, West Africa 2009), `esa_source` (HDX), `esa_processed` (2026-04-07). **Other** — `funding` (range 39764.0–19756103.0). --- ## Quick Start ```python from datasets import load_dataset ds = load_dataset("electricsheepafrica/africa-gnb-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% | GNB | | `name` | object | 0.0% | Not specified, West Africa 2010, West Africa 2009 | | `year` | int64 | 0.0% | 2001.0 – 2026.0 (mean 2012.3226) | | `funding` | int64 | 0.0% | 39764.0 – 19756103.0 (mean 3292126.9032) | | `esa_source` | object | 0.0% | HDX | | `esa_processed` | object | 0.0% | 2026-04-07 | --- ## Numeric Summary | Column | Min | Max | Mean | Median | |---|---|---|---|---| | `year` | 2001.0 | 2026.0 | 2012.3226 | 2010.0 | | `funding` | 39764.0 | 19756103.0 | 3292126.9032 | 2110233.0 | --- ## 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/gnb-requirements-and-funding-data) for the publisher's own methodology notes and caveats. --- ## Citation ```bibtex @dataset{hdx_africa_gnb_requirements_and_funding_data, title = {Guinea-Bissau - Requirements and Funding Data}, author = {OCHA Financial Tracking System (FTS)}, year = {2026}, url = {https://data.humdata.org/dataset/gnb-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（no-annotation） language_creators: - 现有资源采集 language: - en license: cc-by-4.0 multilinguality: - 单语言 size_categories: - 少于1000条 source_datasets: - 原始数据集 task_categories: - 表格分类 - 表格回归 task_ids: [] tags: - 非洲 - 人道主义 - 人道主义数据交换（Humanitarian Data Exchange, HDX） - Electric Sheep Africa - 新型冠状病毒肺炎（COVID-19） - 资金 - 人道主义财务跟踪服务（Humanitarian Financial Tracking Service, FTS） - 几内亚比绍（GNB） pretty_name: "几内亚比绍——需求与资金数据" dataset_info: splits: - name: train num_examples: 24 - name: test num_examples: 6 # 几内亚比绍——需求与资金数据 **发布方**：联合国人道主义事务协调厅（Office for the Coordination of Humanitarian Affairs, OCHA）财务跟踪服务（Financial Tracking Service, FTS） · **来源**：[HDX](https://data.humdata.org/dataset/gnb-requirements-and-funding-data) · **许可协议**：`cc-by-igo` · **最后更新时间**：2026-04-06 --- ## 摘要 财务跟踪服务（Financial Tracking Service, FTS）会发布由捐赠方与受援方组织上报的人道主义资金流动数据，涵盖某一国家的全部人道主义资金，以及在人道主义响应计划中明确上报或可匹配至对应资金需求的专项资金。本数据集的数据源自OCHA的[财务跟踪服务](https://fts.unocha.org/)，编码格式为UTF-8。 本数据集的每一行均代表国家级聚合数据。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：**GNB（几内亚比绍）**。 *本数据集由[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** | 31 | | **列数** | 6（2个数值型、4个分类型、0个日期时间型） | | **训练集划分** | 24条数据 | | **测试集划分** | 6条数据 | | **地理覆盖范围** | GNB（几内亚比绍） | | **发布方** | OCHA财务跟踪服务（FTS） | | **HDX平台最后更新时间** | 2026-04-06 | --- ## 变量说明 **地理类变量** — `countrycode`（国家代码，取值为GNB）、`year`（年份，取值范围2001.0–2026.0）。 **标识符与元数据类变量** — `name`（未指定，示例值：West Africa 2010、West Africa 2009）、`esa_source`（数据来源，取值为HDX）、`esa_processed`（数据处理时间，取值为2026-04-07）。 **其他变量** — `funding`（资金额，取值范围39764.0–19756103.0）。 --- ## 快速上手 python from datasets import load_dataset ds = load_dataset("electricsheepafrica/africa-gnb-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 | 取值范围/示例值 | |---|---|---|---| | `countrycode` | 字符型（object） | 0.0% | 固定值GNB | | `name` | 字符型（object） | 0.0% | 未指定、West Africa 2010、West Africa 2009 | | `year` | 整型（int64） | 0.0% | 2001.0 – 2026.0（均值为2012.3226） | | `funding` | 整型（int64） | 0.0% | 39764.0 – 19756103.0（均值为3292126.9032） | | `esa_source` | 字符型（object） | 0.0% | 固定值HDX | | `esa_processed` | 字符型（object） | 0.0% | 固定值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 | |---|---|---|---|---| | `year` | 2001.0 | 2026.0 | 2012.3226 | 2010.0 | | `funding` | 39764.0 | 19756103.0 | 3292126.9032 | 2110233.0 | --- ## 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。列名被统一转换为小写并标准化为蛇形命名法（snake_case）。常见的缺失值标记（`N/A`、`null`、`none`、`-`、`unknown`、`no data`、`#N/A`）被统一替换为`NaN`。移除了8个缺失值占比超过80%的列：`id`、`code`、`typeid`、`typename`、`startdate`、`enddate`……本数据集以固定随机种子（42）按80/20的比例划分为训练集与测试集，并以Snappy压缩格式保存为Parquet文件。 --- ## 数据局限性 - 本数据集的数据源自OCHA财务跟踪服务（FTS），Electric Sheep Africa未对其进行独立验证。 - 自动化清洗流程无法修正原始数据收集阶段的错报值、定义不一致或抽样偏差问题。 - 如需了解发布方的方法说明与免责条款，请参阅[原始HDX数据集页面](https://data.humdata.org/dataset/gnb-requirements-and-funding-data)。 --- ## 引用格式 bibtex @dataset{hdx_africa_gnb_requirements_and_funding_data, title = {Guinea-Bissau - Requirements and Funding Data}, author = {OCHA Financial Tracking Service (FTS)}, year = {2026}, url = {https://data.humdata.org/dataset/gnb-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。*