electricsheepafrica/africa-hdro-data-for-south-sudan
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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 - demographics - development - education - gender - health - indicators - socioeconomics - ssd pretty_name: "South Sudan - Human Development Indicators" dataset_info: splits: - name: train num_examples: 400 - name: test num_examples: 100 --- # South Sudan - Human Development Indicators **Publisher:** UNDP Human Development Reports Office (HDRO) · **Source:** [HDX](https://data.humdata.org/dataset/hdro-data-for-south-sudan) · **License:** `cc-by-igo` · **Updated:** 2026-03-04 --- ## Abstract The aim of the Human Development Report is to stimulate global, regional and national policy-relevant discussions on issues pertinent to human development. Accordingly, the data in the Report require the highest standards of data quality, consistency, international comparability and transparency. The Human Development Report Office (HDRO) fully subscribes to the Principles governing international statistical activities. The HDI was created to emphasize that people and their capabilities should be the ultimate criteria for assessing the development of a country, not economic growth alone. The HDI can also be used to question national policy choices, asking how two countries with the same level of GNI per capita can end up with different human development outcomes. These contrasts can stimulate debate about government policy priorities. The Human Development Index (HDI) is a summary measure of average achievement in key dimensions of human development: a long and healthy life, being knowledgeable and have a decent standard of living. The HDI is the geometric mean of normalized indices for each of the three dimensions. The 2019 Global Multidimensional Poverty Index (MPI) data shed light on the number of people experiencing poverty at regional, national and subnational levels, and reveal inequalities across countries and among the poor themselves.Jointly developed by the United Nations Development Programme (UNDP) and the Oxford Poverty and Human Development Initiative (OPHI) at the University of Oxford, the 2019 global MPI offers data for 101 countries, covering 76 percent of the global population. The MPI provides a comprehensive and in-depth picture of global poverty – in all its dimensions – and monitors progress towards Sustainable Development Goal (SDG) 1 – to end poverty in all its forms. It also provides policymakers with the data to respond to the call of Target 1.2, which is to ‘reduce at least by half the proportion of men, women, and children of all ages living in poverty in all its dimensions according to national definition'. Each row in this dataset represents country-level aggregates. Data was last updated on HDX on 2026-03-04. Geographic scope: **SSD**. *Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).* --- ## Dataset Characteristics | | | |---|---| | **Domain** | Public health | | **Unit of observation** | Country-level aggregates | | **Rows (total)** | 500 | | **Columns** | 10 (2 numeric, 8 categorical, 0 datetime) | | **Train split** | 400 rows | | **Test split** | 100 rows | | **Geographic scope** | SSD | | **Publisher** | UNDP Human Development Reports Office (HDRO) | | **HDX last updated** | 2026-03-04 | --- ## Variables **Geographic** — `country_code` (SSD), `country_name` (South Sudan), `index_id` (GDI, GII, HDI), `index_name` (Gender Development Index, Gender Inequality Index, Human Development Index), `year` (range 1990.0–2023.0). **Outcome / Measurement** — `value` (range 0.048–6591.344). **Identifier / Metadata** — `indicator_id` (abr, le, pop_total), `indicator_name` (Adolescent Birth Rate (births per 1,000 women ages 15-19), Life Expectancy at Birth (years), Population, total (millions)), `esa_source` (HDX), `esa_processed` (2026-04-08). --- ## Quick Start ```python from datasets import load_dataset ds = load_dataset("electricsheepafrica/africa-hdro-data-for-south-sudan") train = ds["train"].to_pandas() test = ds["test"].to_pandas() print(train.shape) train.head() ``` --- ## Schema | Column | Type | Null % | Range / Sample Values | |---|---|---|---| | `country_code` | object | 0.0% | SSD | | `country_name` | object | 0.0% | South Sudan | | `indicator_id` | object | 0.0% | abr, le, pop_total | | `indicator_name` | object | 0.0% | Adolescent Birth Rate (births per 1,000 women ages 15-19), Life Expectancy at Birth (years), Population, total (millions) | | `index_id` | object | 0.0% | GDI, GII, HDI | | `index_name` | object | 0.0% | Gender Development Index, Gender Inequality Index, Human Development Index | | `value` | float64 | 0.0% | 0.048 – 6591.344 (mean 207.6082) | | `year` | int64 | 0.0% | 1990.0 – 2023.0 (mean 2011.626) | | `esa_source` | object | 0.0% | HDX | | `esa_processed` | object | 0.0% | 2026-04-08 | --- ## Numeric Summary | Column | Min | Max | Mean | Median | |---|---|---|---|---| | `value` | 0.048 | 6591.344 | 207.6082 | 36.36 | | `year` | 1990.0 | 2023.0 | 2011.626 | 2014.0 | --- ## 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`. 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 UNDP Human Development Reports Office (HDRO) 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/hdro-data-for-south-sudan) for the publisher's own methodology notes and caveats. --- ## Citation ```bibtex @dataset{hdx_africa_hdro_data_for_south_sudan, title = {South Sudan - Human Development Indicators}, author = {UNDP Human Development Reports Office (HDRO)}, year = {2026}, url = {https://data.humdata.org/dataset/hdro-data-for-south-sudan}, 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: - 公开抓取 language: - en license: cc-by-4.0 multilinguality: - 单语言 size_categories: - 样本数少于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 - electric-sheep-africa - 人口统计学 - 发展 - 教育 - 性别 - 健康 - 指标 - 社会经济 - ssd pretty_name: "南苏丹——人类发展指标" dataset_info: 数据集划分: - 名称: train 样本数: 400 - 名称: test 样本数: 100 --- # 南苏丹——人类发展指标 **发布方：** 联合国开发计划署人类发展报告办公室（HDRO） · **来源：** [HDX](https://data.humdata.org/dataset/hdro-data-for-south-sudan) · **许可证：** `cc-by-igo` · **最后更新：** 2026-03-04 --- ## 摘要 人类发展报告的宗旨是推动全球、区域及国家层面围绕与人类发展相关的议题开展政策导向性讨论。因此，报告中的数据需达到最高的数据质量、一致性、国际可比性与透明度标准。人类发展报告办公室（HDRO）完全遵循国际统计活动准则。 人类发展指数（Human Development Index, HDI）的设立旨在强调，评估国家发展的终极标准应是人民及其能力，而非仅经济增长。HDI还可用于审视国家政策选择，探讨为何两个人均国民总收入水平相当的国家最终会呈现出不同的人类发展成果。这类对比能够引发关于政府政策优先级的讨论。 人类发展指数（HDI）是对人类发展三大核心维度平均成就的综合衡量：健康长寿、拥有知识以及体面的生活水平。HDI是这三个维度各自标准化指数的几何平均值。 2019年全球多维贫困指数（Global Multidimensional Poverty Index, MPI）数据揭示了区域、国家及次国家层面的贫困人口规模，并展现了国家间以及贫困人口内部的不平等状况。该指数由联合国开发计划署（United Nations Development Programme, UNDP）与牛津大学牛津贫困与人类发展倡议（Oxford Poverty and Human Development Initiative, OPHI）联合开发，2019年全球MPI覆盖101个国家，涵盖全球76%的人口。 MPI全面且深入地展现了全球贫困的各个维度，并可用于监测可持续发展目标（Sustainable Development Goal, SDG）1的进展——即消除一切形式的贫困。同时，它还能为政策制定者提供数据支撑，以响应目标1.2的号召："根据各国定义，将各年龄段男女儿童中生活在一切形式贫困中的比例至少降低一半"。 本数据集的每一行均代表国家层面的汇总数据。数据最后一次在HDX平台更新的时间为2026-03-04。地理覆盖范围：**SSD（南苏丹）**。 *本数据集由[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** | 公共卫生 | | **观测单元** | 国家层面汇总数据 | | **总行数** | 500 | | **列数** | 10（2个数值型，8个分类型，0个日期时间型） | | **训练集划分** | 400行 | | **测试集划分** | 100行 | | **地理覆盖范围** | SSD（南苏丹） | | **发布方** | UNDP人类发展报告办公室（HDRO） | | **HDX最后更新时间** | 2026-03-04 | --- ## 变量 **地理类** — `country_code`（国家代码，SSD）、`country_name`（国家名称，南苏丹）、`index_id`（指数代码，GDI、GII、HDI）、`index_name`（指数名称，性别发展指数、性别不平等指数、人类发展指数）、`year`（年份，范围1990.0–2023.0）。 **结果/测量类** — `value`（指标数值，范围0.048–6591.344）。 **标识符/元数据类** — `indicator_id`（指标代码，abr、le、pop_total）、`indicator_name`（指标名称，青少年生育率（每1000名15-19岁女性的活产数）、出生时预期寿命（年）、总人口（百万））、`esa_source`（数据源：HDX）、`esa_processed`（处理时间：2026-04-08）。 --- ## 快速入门 python from datasets import load_dataset ds = load_dataset("electricsheepafrica/africa-hdro-data-for-south-sudan") 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 | 空值占比 | 取值范围/示例值 | |---|---|---| | `country_code` | 字符串型 | 0.0% | SSD | | `country_name` | 字符串型 | 0.0% | 南苏丹 | | `indicator_id` | 字符串型 | 0.0% | abr、le、pop_total | | `indicator_name` | 字符串型 | 0.0% | 青少年生育率（每1000名15-19岁女性的活产数）、出生时预期寿命（年）、总人口（百万） | | `index_id` | 字符串型 | 0.0% | GDI、GII、HDI | | `index_name` | 字符串型 | 0.0% | 性别发展指数、性别不平等指数、人类发展指数 | | `value` | 浮点型 | 0.0% | 0.048 – 6591.344（均值207.6082） | | `year` | 整型 | 0.0% | 1990.0 – 2023.0（均值2011.626） | | `esa_source` | 字符串型 | 0.0% | HDX | | `esa_processed` | 字符串型 | 0.0% | 2026-04-08 | --- ## 数值型变量汇总 | 列名 | 最小值 | 最大值 | 均值 | 中位数 | |---|---|---|---|---| | `value` | 0.048 | 6591.344 | 207.6082 | 36.36 | | `year` | 1990.0 | 2023.0 | 2011.626 | 2014.0 | --- ## 数据整理 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。常见的缺失值标记（`N/A`、`null`、`none`、`-`、`unknown`、`no data`、`#N/A`）被统一替换为`NaN`。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。 --- ## 局限性 - 数据源自联合国开发计划署人类发展报告办公室（HDRO），未由Electric Sheep Africa（ESA）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。 - 如需查看发布方的方法论说明与免责声明，请参阅[原始HDX数据集页面](https://data.humdata.org/dataset/hdro-data-for-south-sudan)。 --- ## 引用 bibtex @dataset{hdx_africa_hdro_data_for_south_sudan, title = {South Sudan - Human Development Indicators}, author = {UNDP Human Development Reports Office (HDRO)}, year = {2026}, url = {https://data.humdata.org/dataset/hdro-data-for-south-sudan}, note = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)} } --- *[Electric Sheep Africa](https://huggingface.co/electricsheepafrica) — 非洲的机器学习数据集基础设施。尼日利亚拉各斯。*
