electricsheepafrica/africa-world-bank-science-and-technology-indicators-for-somalia

---
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
- economics
- hxl
- indicators
- som
pretty_name: "Somalia - Science and Technology"
dataset_info:
splits:
- name: train
num_examples: 24
- name: test
num_examples: 6
---

# Somalia - Science and Technology

**Publisher:** World Bank Group · **Source:** [HDX](https://data.humdata.org/dataset/world-bank-science-and-technology-indicators-for-somalia) · **License:** `cc-by` · **Updated:** 2025-08-28

---

## Abstract

Contains data from the World Bank's [data portal](http://data.worldbank.org/). There is also a [consolidated country dataset](https://data.humdata.org/dataset/world-bank-combined-indicators-for-somalia) on HDX.

Technological innovation, often fueled by governments, drives industrial growth and helps raise living standards. Data here aims to shed light on countries technology base: research and development, scientific and technical journal articles, high-technology exports, royalty and license fees, and patents and trademarks. Sources include the UNESCO Institute for Statistics, the U.S. National Science Board, the UN Statistics Division, the International Monetary Fund, and the World Intellectual Property Organization.

Each row in this dataset represents country-level aggregates. Data was last updated on HDX on 2025-08-28. Geographic scope: **SOM**.

*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** | 8 (2 numeric, 6 categorical, 0 datetime) |
| **Train split** | 24 rows |
| **Test split** | 6 rows |
| **Geographic scope** | SOM |
| **Publisher** | World Bank Group |
| **HDX last updated** | 2025-08-28 |

---

## Variables

**Geographic** — `country_name` (Somalia, #country+name), `country_iso3` (SOM, #country+code), `year` (range 1984.0–2022.0).

**Outcome / Measurement** — `value` (range 0.0–106.49).

**Identifier / Metadata** — `indicator_name` (Scientific and technical journal articles, Patent applications, nonresidents, #indicator+name), `indicator_code` (IP.JRN.ARTC.SC, IP.PAT.NRES, #indicator+code), `esa_source` (HDX), `esa_processed` (2026-04-08).

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-world-bank-science-and-technology-indicators-for-somalia")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()

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

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `country_name` | object | 0.0% | Somalia, #country+name |
| `country_iso3` | object | 0.0% | SOM, #country+code |
| `year` | float64 | 3.2% | 1984.0 – 2022.0 (mean 2006.5333) |
| `indicator_name` | object | 0.0% | Scientific and technical journal articles, Patent applications, nonresidents, #indicator+name |
| `indicator_code` | object | 0.0% | IP.JRN.ARTC.SC, IP.PAT.NRES, #indicator+code |
| `value` | float64 | 3.2% | 0.0 – 106.49 (mean 9.7373) |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-08 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `year` | 1984.0 | 2022.0 | 2006.5333 | 2007.5 |
| `value` | 0.0 | 106.49 | 9.7373 | 2.32 |

---

## 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 World Bank Group 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/world-bank-science-and-technology-indicators-for-somalia) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_world_bank_science_and_technology_indicators_for_somalia,
title = {Somalia - Science and Technology},
author = {World Bank Group},
year = {2025},
url = {https://data.humdata.org/dataset/world-bank-science-and-technology-indicators-for-somalia},
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:
- 现成采集
language:
- 英语
license: CC BY 4.0
multilinguality:
- 单语言
size_categories:
- 样本量<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
- 经济学
- HXL
- 指标
- SOM
pretty_name: "索马里——科学与技术"
dataset_info:
splits:
- name: train
num_examples: 24
- name: test
num_examples: 6

---

# 索马里——科学与技术数据集

**发布方：** 世界银行集团 · **来源：** [HDX](https://data.humdata.org/dataset/world-bank-science-and-technology-indicators-for-somalia) · **许可证：** `CC BY` · **最后更新时间：** 2025-08-28

---

## 摘要

本数据集数据源自世界银行[官方数据门户](http://data.worldbank.org/)。HDX平台上另有一份[索马里综合国家数据集](https://data.humdata.org/dataset/world-bank-combined-indicators-for-somalia)。

技术创新通常由政府推动，是工业增长的核心驱动力，亦有助于提升生活水平。本数据集旨在阐明一国的科技基础水平，涵盖研发投入、科技期刊论文数量、高技术产品出口额、特许权使用费与许可费、专利与商标注册量等指标。数据来源包括联合国教科文组织统计研究所、美国国家科学委员会、联合国统计司、国际货币基金组织以及世界知识产权组织。

本数据集每一行均代表国家级别的汇总统计数据。HDX平台上的本数据集最后更新时间为2025-08-28。地理覆盖范围：**SOM（索马里）**。

*本数据集经[Electric Sheep Africa](https://huggingface.co/electricsheepafrica)整理为适配机器学习的Parquet（列式存储格式）格式。*

---

## 数据集特征

| 项 | 详情 |
|---|---|
| **领域** | 人道主义与发展数据 |
| **观测单元** | 国家级汇总数据 |
| **总样本行数** | 31 |
| **列数** | 8列（2个数值型、6个分类型、0个日期时间型） |
| **训练集样本量** | 24行 |
| **测试集样本量** | 6行 |
| **地理覆盖范围** | SOM（索马里） |
| **发布方** | 世界银行集团 |
| **HDX平台最后更新时间** | 2025-08-28 |

---

## 变量说明

**地理类变量**：`country_name`（国家名称，#country+name）、`country_iso3`（国家ISO3代码，SOM，#country+code）、`year`（年份，取值范围1984.0–2022.0）。

**结果/测量类变量**：`value`（指标数值，取值范围0.0–106.49）。

**标识符/元数据类变量**：`indicator_name`（指标名称，如科技期刊论文、非居民专利申请，#indicator+name）、`indicator_code`（指标代码，如IP.JRN.ARTC.SC、IP.PAT.NRES，#indicator+code）、`esa_source`（数据来源，HDX）、`esa_processed`（数据处理时间，2026-04-08）。

---

## 快速入门

python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-world-bank-science-and-technology-indicators-for-somalia")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()

print(train.shape)
train.head()

---

## 数据集结构

| 列名 | 数据类型 | 缺失率 | 取值范围/示例值 |
|---|---|---|---|
| `country_name` | 字符串型 | 0.0% | 索马里，#country+name |
| `country_iso3` | 字符串型 | 0.0% | SOM，#country+code |
| `year` | 浮点型 | 3.2% | 1984.0 – 2022.0（均值2006.5333） |
| `indicator_name` | 字符串型 | 0.0% | 科技期刊论文、非居民专利申请等，#indicator+name |
| `indicator_code` | 字符串型 | 0.0% | IP.JRN.ARTC.SC、IP.PAT.NRES等，#indicator+code |
| `value` | 浮点型 | 3.2% | 0.0 – 106.49（均值9.7373） |
| `esa_source` | 字符串型 | 0.0% | HDX |
| `esa_processed` | 字符串型 | 0.0% | 2026-04-08 |

---

## 数值统计摘要

| 列名 | 最小值 | 最大值 | 均值 | 中位数 |
|---|---|---|---|---|
| `year` | 1984.0 | 2022.0 | 2006.5333 | 2007.5 |
| `value` | 0.0 | 106.49 | 9.7373 | 2.32 |

---

## 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（列式存储）格式。所有列名均转换为小写，并统一为蛇形命名规范。常见的缺失值标记（如`N/A`、`null`、`none`、`-`、`unknown`、`no data`、`#N/A`）均被统一替换为`NaN`。基于解析成功率（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80:20的比例划分为训练集与测试集，采用固定随机种子（42）进行划分，并以Snappy压缩的Parquet格式存储。

---

## 局限性说明

1. 本数据集源自世界银行集团，尚未经Electric Sheep Africa（ESA）独立验证。
2. 自动化数据清洗无法修正原始数据集中的错报值、定义不一致问题或抽样偏差。
3. 如需了解发布方的方法说明与注意事项，请参阅[HDX平台原始数据集页面](https://data.humdata.org/dataset/world-bank-science-and-technology-indicators-for-somalia)。

---

## 引用

bibtex
@dataset{hdx_africa_world_bank_science_and_technology_indicators_for_somalia,
title = {Somalia - Science and Technology},
author = {World Bank Group},
year = {2025},
url = {https://data.humdata.org/dataset/world-bank-science-and-technology-indicators-for-somalia},
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提供商，总部位于尼日利亚拉各斯。*