electricsheepeurope/europe-worldbank-education

--- license: cc-by-4.0 language: - en task_categories: - tabular-classification - tabular-regression - time-series-forecasting multilinguality: monolingual size_categories: - 100K<n<1M tags: - tabular - europe - world-bank - education - wdi - development-indicators - time-series - country-level pretty_name: "Education — Europe (World Bank WDI)" --- # Education — Europe (World Bank WDI) 🇪🇺 **144,835 observations** · **44 Europe countries** · **1960–2025** · *Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)*      ## TL;DR This dataset contains **144,835 observations** of `Education` data across **44 Europe countries**, spanning **1960–2025**, covering **164 distinct indicators**. ## About the source The World Bank's **World Development Indicators (WDI)** is the world's most-cited reference for global development data. It compiles officially-recognized international statistics from national accounts, government agencies, and partner organizations (IMF, WHO, FAO, UNESCO, ILO, UN agencies). The WDI covers over 1,400 indicators across 21 topics for 217 economies, with annual updates as new data become available. - **Source:** [World Bank](https://databank.worldbank.org/source/world-development-indicators) - **Publisher:** World Bank Group - **License:** [cc-by-4.0](https://creativecommons.org/licenses/by/4.0/) - **Topic:** Education ## Methodology Data is pulled directly from the World Bank's public API (`api.worldbank.org/v2`) for every indicator tagged to this topic across all countries in the region. Records with no observed value are dropped. Years are normalized to integers; values are coerced to float64. ## Geographic coverage 44 Europe countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `ESP` | 4,561 | 1960 | 2025 | | `GRC` | 4,351 | 1960 | 2025 | | `MLT` | 4,202 | 1960 | 2025 | | `ITA` | 4,173 | 1960 | 2025 | | `HUN` | 4,116 | 1960 | 2025 | | `NOR` | 4,104 | 1960 | 2025 | | `FIN` | 4,097 | 1960 | 2025 | | `SWE` | 4,058 | 1960 | 2025 | | `BGR` | 3,963 | 1960 | 2025 | | `DNK` | 3,912 | 1960 | 2025 | | `PRT` | 3,794 | 1960 | 2025 | | `AUT` | 3,769 | 1960 | 2025 | | `CHE` | 3,752 | 1960 | 2025 | | `ISL` | 3,674 | 1960 | 2025 | | `BEL` | 3,657 | 1960 | 2025 | | ... | _29 more countries_ | | | ## Indicators (sample) - `SE.ADT.1524.LT.FE.ZS` — Literacy rate, youth female (% of females ages 15-24) - `SE.ADT.1524.LT.FM.ZS` — Literacy rate, youth (ages 15-24), gender parity index (GPI) - `SE.ADT.1524.LT.MA.ZS` — Literacy rate, youth male (% of males ages 15-24) - `SE.ADT.1524.LT.ZS` — Literacy rate, youth total (% of people ages 15-24) - `SE.ADT.LITR.FE.ZS` — Literacy rate, adult female (% of females ages 15 and above) - `SE.ADT.LITR.MA.ZS` — Literacy rate, adult male (% of males ages 15 and above) - `SE.ADT.LITR.ZS` — Literacy rate, adult total (% of people ages 15 and above) - `SE.COM.DURS` — Compulsory education, duration (years) - `SE.ENR.PRIM.FM.ZS` — School enrollment, primary (gross), gender parity index (GPI) - `SE.ENR.PRSC.FM.ZS` — School enrollment, primary and secondary (gross), gender parity index (GPI) - _... and 154 more indicators_ ## Schema | Column | Type | Description | Example | |--------|------|-------------|---------| | `indicator_id` | `string` | World Bank WDI indicator code (e.g. `SP.POP.TOTL`) | `SE.ADT.1524.LT.FE.ZS` | | `indicator_name` | `string` | Human-readable indicator name | `Literacy rate, youth female (% of fem…` | | `country_iso2` | `string` | ISO 3166-1 alpha-2 country code | `AL` | | `country_iso3` | `string` | ISO 3166-1 alpha-3 country code (preferred join key) | `ALB` | | `country_name` | `string` | English country name as published by the World Bank | `Albania` | | `year` | `int64` | Year of observation (CE) | `2023` | | `value` | `float64` | Observed indicator value | `99.2099990844727` | | `unit` | `string` | Unit of measurement (often empty) | `` | | `obs_status` | `string` | World Bank observation-status flag (e.g. preliminary, estimated) | `` | ## Data quality & caveats - Records with null observations are filtered out at ingest. - Indicator coverage varies by country and year; not every (indicator × country × year) combination is present. - Some countries appear under multiple aggregate groupings in WB; this dataset is filtered to **individual sovereign states only**. ## Usage ```python from datasets import load_dataset ds = load_dataset("electricsheepeurope/europe-worldbank-education") df = ds["train"].to_pandas() print(df.head()) ``` ### Filter to one country ```python germany = df[df["country_iso3"] == "DEU"] ``` ### Time-series for a single indicator ```python sample = (df[df["indicator_id"] == "SE.ADT.1524.LT.FE.ZS"] .sort_values("year")) sample.plot(x="year", y="value", title="SE.ADT.1524.LT.FE.ZS") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator_id"] == "SE.ADT.1524.LT.FE.ZS"] .pivot_table(index="year", columns="country_iso3", values="value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{europe_worldbank_education_2025, title = {Education — Europe (World Bank WDI)}, author = {World Bank Group}, year = {2025}, url = {https://databank.worldbank.org/source/world-development-indicators}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe}, howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-worldbank-education}} } ``` ## License Released under [cc-by-4.0](https://creativecommons.org/licenses/by/4.0/). Original data © World Bank Group. When using this dataset, please cite both the original source above and the Electric Sheep Europe repackaging. ## About Electric Sheep Electric Sheep Europe is part of the Electric Sheep mission: a unified, ML-ready data layer for Europe on HuggingFace. We pull data from authoritative open sources, normalize the schemas, package as Parquet, and publish with consistent dataset cards so researchers and developers can use `load_dataset()` to start working in seconds. Browse the full collection: [huggingface.co/electricsheepeurope](https://huggingface.co/electricsheepeurope) --- _Provenance: ingested 2026-05-24 via the Electric Sheep pipeline. Source URL: https://databank.worldbank.org/source/world-development-indicators_This dataset contains 144,835 observations of Education data across 44 Europe countries, spanning 1960–2025, covering 164 distinct indicators. The data is sourced from the World Banks World Development Indicators (WDI), the worlds most-cited reference for global development data. It includes various education-related indicators such as literacy rates, school enrollment rates, compulsory education duration, and gender parity indices. The dataset schema includes indicator_id, indicator_name, country_iso2, country_iso3, country_name, year, value, unit, and obs_status columns. Records with null observations are filtered out, and coverage varies by country and year. This dataset is designed for machine learning tasks including tabular classification, regression, and time-series forecasting.

搜集汇总

数据集介绍

构建方式

世界银行世界发展指标（WDI）作为全球发展统计的权威基准，长期为教育领域的跨国比较研究提供基础数据。该数据集的构建直接依托世界银行公共API，针对标记为教育主题的全部指标，系统抓取欧洲区域44个主权国家的观测记录。数据清洗环节剔除缺失数值，将年份统一规范为整数类型，指标值强制转换为64位浮点数，并保留世界银行原始观测状态标志，确保数据可追溯至官方源。整个过程由Electric Sheep Europe管道自动化完成，最终以Parquet格式封装发布，便于机器学习流程直接调用。

使用方法

研究者可通过HuggingFace datasets库以一行代码加载该数据集，并直接转换为Pandas数据框进行探索。典型操作包括按ISO三位国家代码筛选特定国家，或按指标代码提取单指标时间序列并排序绘图，亦可利用透视表将长表转换为国家乘以年份的矩阵，便于横向比较。该数据集适用于表格分类、回归及时间序列预测等任务，使用时需遵循CC-BY-4.0许可，并同时引用世界银行原始来源与Electric Sheep Europe的重新封装工作。

背景与挑战

背景概述

世界银行世界发展指标（WDI）自二十世纪七十年代问世以来，始终是全球发展经济学与比较教育学最为倚重的跨国统计基准之一，其教育主题板块涵盖入学率、识字率、性别均等指数及义务教育年限等关键维度。此数据集由世界银行集团官方发布，经Electric Sheep Europe于2025年重新打包发布，覆盖44个欧洲国家自1960年至2025年的164项教育指标，共计144,835条观测记录，旨在为欧洲区域教育发展的时序分析与跨国比较提供标准化、即取即用的机器学习就绪型数据资源。该数据集延续了WDI在政策评估与实证研究中的广泛影响力，为教育公平、人力资本积累及可持续发展目标监测等议题奠定了坚实的数据基础。

当前挑战

教育发展指标的跨国时序建模本身即面临指标覆盖高度不均衡、观测值缺失机制复杂以及统计口径跨年度变更等固有难题，欧洲区域虽数据基础相对优越，但不同国家在指标采集频率与年份跨度上仍存在显著差异，部分国家在早期年份或特定指标上几乎无可用观测。在构建过程中，如何从世界银行API高效提取并整合164项指标、剔除空值记录、统一年份与数值类型，同时确保仅保留主权国家个体而排除聚合分组，均给数据管道的稳健性与一致性带来相当挑战，而观测状态标记与浮动精度亦对后续建模的噪声控制提出了更高要求。

常用场景

经典使用场景

在比较教育学与教育经济学领域，对各国教育发展水平进行长时段、多指标的量化比较是一项基础而关键的任务。该数据集汇聚了44个欧洲国家自1960年至2025年间164项教育指标的144,835条观测记录，为研究者提供了开展面板数据分析的经典素材。其最典型的使用场景之一，是构建以国家为横截面、以年份为时间维度的面板数据模型，用以考察识字率、入学率、性别均等指数等指标的动态演变，进而揭示欧洲各国教育扩张进程中的收敛与分化模式。

解决学术问题

该数据集有效回应了教育研究中长期存在的若干难题，诸如跨国教育可比性不足、长时段数据缺失以及性别平等进展难以量化等。通过统一整理世界银行公开的标准化指标，它为探究教育扩张与经济增长的关联、评估义务教育年限延长的政策效应、检验性别均等假说等学术议题提供了可靠的经验底座。其意义在于降低了跨国教育研究的门槛，并提升了实证结果的可重复性，为发展经济学与教育政策分析注入了坚实的数据基础。

实际应用

在政策实践层面，该数据集可服务于国际组织、政府部门及智库的监测与评估工作。例如，借助其中的青年识字率与入学性别均等指数，相关机构能够追踪欧洲各国在全民教育目标上的落实进度，识别教育资源配置的薄弱环节。教育援助机构亦可据此制定差异化的资助策略，将资源向指标表现滞后的国家倾斜，从而提升跨国教育合作的精准度与成效。

数据集最近研究

最新研究方向

在欧洲教育发展监测与政策评估领域，该数据集正推动基于长时序面板数据的因果推断研究。依托1960至2025年间44国164项教育指标的14万余条观测，学者得以运用双重差分、合成控制等方法，量化评估义务教育年限改革、性别平等政策对识字率与入学率的动态效应。与此同时，性别鸿沟的收敛路径、教育投入与经济增长的长期协整关系成为计量史学与教育经济学交叉前沿。该数据集亦服务于联合国可持续发展目标（SDG 4）的进展追踪，为欧洲区域教育公平与质量的政策模拟提供可复现的实证基础。