five

OECD - Education at a Glance|教育统计数据集|国际比较数据集

收藏
www.oecd.org2024-10-25 收录
教育统计
国际比较
下载链接:
https://www.oecd.org/education/education-at-a-glance/
下载链接
链接失效反馈
资源简介:
该数据集提供了关于教育系统在不同国家和地区的详细统计数据,包括教育支出、教育参与率、教育成果、教师资源等多个方面。数据涵盖了OECD成员国以及部分非成员国。
提供机构:
www.oecd.org
AI搜集汇总
数据集介绍
main_image_url
构建方式
OECD - Education at a Glance数据集的构建基于OECD(经济合作与发展组织)的广泛教育统计数据收集和分析。该数据集整合了来自多个国家和地区的教育系统数据,涵盖了从学前教育到高等教育的各个阶段。数据收集过程严格遵循国际标准,确保数据的准确性和可比性。通过问卷调查、官方统计数据和实地考察等多种方式,OECD确保了数据的全面性和深度。
特点
OECD - Education at a Glance数据集以其全球覆盖和多维度分析著称。该数据集不仅提供了各国教育系统的基本统计数据,还深入分析了教育投入、教育成果、教育公平等多个关键指标。此外,数据集还包含了时间序列数据,允许用户进行跨年度和跨国家的比较分析。这种多层次、多角度的数据结构,使得该数据集成为教育政策制定和学术研究的重要资源。
使用方法
OECD - Education at a Glance数据集适用于多种研究目的和应用场景。政策制定者可以利用该数据集评估和比较不同国家的教育政策效果,从而制定更加科学和有效的教育政策。学术研究人员可以通过该数据集进行跨国和跨时间段的比较研究,探索教育系统的发展趋势和影响因素。此外,教育机构和企业也可以利用该数据集进行市场分析和战略规划,以更好地适应全球教育市场的变化。
背景与挑战
背景概述
OECD - Education at a Glance数据集是由经济合作与发展组织(OECD)发布的一系列年度报告,旨在提供全球教育系统的全面概览。自2002年首次发布以来,该数据集已成为政策制定者、教育研究者和公众了解教育趋势和政策效果的重要工具。通过收集和分析来自多个国家和地区的教育数据,OECD - Education at a Glance揭示了教育投入、教育成果、教育公平性等方面的关键指标,为国际比较和政策优化提供了坚实的基础。
当前挑战
OECD - Education at a Glance数据集在构建过程中面临多重挑战。首先,数据收集的复杂性在于需要从不同国家获取一致且高质量的教育数据,这要求各国在数据报告标准和方法上达成共识。其次,数据分析的挑战在于如何处理不同教育系统和文化背景下的数据差异,以确保比较的准确性和有效性。此外,随着教育政策和技术的快速变化,数据集需要不断更新和扩展,以反映最新的教育趋势和挑战。
发展历史
创建时间与更新
OECD - Education at a Glance数据集首次发布于2002年,此后每年定期更新,以反映全球教育领域的最新动态和发展趋势。
重要里程碑
该数据集的一个重要里程碑是2008年,当时OECD首次引入了国际学生评估项目(PISA)的数据,这一举措极大地丰富了数据集的内容,使其在全球教育政策制定中发挥了关键作用。此外,2015年,OECD将可持续发展目标(SDGs)的相关数据纳入其中,进一步提升了数据集的国际影响力和实用性。
当前发展情况
当前,OECD - Education at a Glance数据集已成为全球教育研究和政策分析的重要资源。它不仅涵盖了教育投入、教育成果、教育公平等多个维度,还通过与PISA和SDGs等项目的结合,提供了更为全面和深入的分析视角。该数据集的持续更新和扩展,为各国政府、教育机构和研究者提供了宝贵的数据支持,推动了全球教育领域的持续进步和创新。
发展历程
  • OECD首次发布《Education at a Glance》报告,标志着该数据集的诞生。
    1991年
  • OECD对《Education at a Glance》进行了重大更新,增加了更多国家和地区的教育数据。
    1998年
  • 该数据集首次被应用于全球教育政策分析,成为国际教育比较研究的重要工具。
    2002年
  • OECD在《Education at a Glance》中引入了新的指标体系,涵盖了教育投入、产出和成果的多个维度。
    2008年
  • 该数据集的数据覆盖范围扩展至全球主要经济体,成为全球教育政策制定的重要参考。
    2015年
  • OECD在《Education at a Glance》中增加了关于在线教育和远程学习的最新数据,以应对全球疫情带来的教育挑战。
    2020年
常用场景
经典使用场景
在教育研究领域,OECD - Education at a Glance数据集被广泛用于分析和比较不同国家和地区的教育系统表现。该数据集涵盖了从学前教育到高等教育的多个层次,提供了关于教育支出、学生成绩、教师资源和教育成果的详细数据。研究者利用这些数据进行跨国比较,以识别教育政策的效果和教育系统的优势与不足。
衍生相关工作
基于OECD - Education at a Glance数据集,许多经典研究工作得以展开。例如,学者们通过分析该数据集,发表了关于教育投资回报率、教育不平等和教育技术应用的研究论文。此外,该数据集还催生了多个教育政策模拟模型和预测工具,帮助决策者更好地规划和实施教育改革。
数据集最近研究
最新研究方向
在教育领域,OECD - Education at a Glance数据集的最新研究方向聚焦于全球化背景下教育公平与质量的提升。研究者们通过分析各国教育资源的分配、教育成果的差异以及政策干预的效果,探讨如何在全球化进程中实现教育机会的均等化。此外,该数据集还被用于评估新兴技术在教育中的应用,以及这些技术对教育质量和学生学习成果的影响。这些研究不仅为政策制定者提供了科学依据,也为教育实践者提供了宝贵的参考,推动了全球教育体系的持续改进。
相关研究论文
  • 1
    Education at a Glance 2021: OECD IndicatorsOECD · 2021年
  • 2
    The Impact of COVID-19 on Education: Insights from Education at a Glance 2021OECD · 2021年
  • 3
    Education at a Glance 2020: OECD IndicatorsOECD · 2020年
  • 4
    Education at a Glance 2019: OECD IndicatorsOECD · 2019年
  • 5
    Education at a Glance 2018: OECD IndicatorsOECD · 2018年
以上内容由AI搜集并总结生成
用户留言
有没有相关的论文或文献参考?
这个数据集是基于什么背景创建的?
数据集的作者是谁?
能帮我联系到这个数据集的作者吗?
这个数据集如何下载?
点击留言
数据主题
具身智能
数据集  4098个
机构  8个
大模型
数据集  439个
机构  10个
无人机
数据集  37个
机构  6个
指令微调
数据集  36个
机构  6个
蛋白质结构
数据集  50个
机构  8个
空间智能
数据集  21个
机构  5个
5,000+
优质数据集
54 个
任务类型
进入经典数据集
热门数据集

MultiTalk

MultiTalk数据集是由韩国科学技术院创建,包含超过420小时的2D视频,涵盖20种不同语言,旨在解决多语言环境下3D说话头生成的问题。该数据集通过自动化管道从YouTube收集,每段视频都配有语言标签和伪转录,部分视频还包含伪3D网格顶点。数据集的创建过程包括视频收集、主动说话者验证和正面人脸验证,确保数据质量。MultiTalk数据集的应用领域主要集中在提升多语言3D说话头生成的准确性和表现力,通过引入语言特定风格嵌入,使模型能够捕捉每种语言独特的嘴部运动。

arXiv 收录

Canadian Census

**Overview** The data package provides demographics for Canadian population groups according to multiple location categories: Forward Sortation Areas (FSAs), Census Metropolitan Areas (CMAs) and Census Agglomerations (CAs), Federal Electoral Districts (FEDs), Health Regions (HRs) and provinces. **Description** The data are available through the Canadian Census and the National Household Survey (NHS), separated or combined. The main demographic indicators provided for the population groups, stratified not only by location but also for the majority by demographical and socioeconomic characteristics, are population number, females and males, usual residents and private dwellings. The primary use of the data at the Health Region level is for health surveillance and population health research. Federal and provincial departments of health and human resources, social service agencies, and other types of government agencies use the information to monitor, plan, implement and evaluate programs to improve the health of Canadians and the efficiency of health services. Researchers from various fields use the information to conduct research to improve health. Non-profit health organizations and the media use the health region data to raise awareness about health, an issue of concern to all Canadians. The Census population counts for a particular geographic area representing the number of Canadians whose usual place of residence is in that area, regardless of where they happened to be on Census Day. Also included are any Canadians who were staying in that area on Census Day and who had no usual place of residence elsewhere in Canada, as well as those considered to be 'non-permanent residents'. National Household Survey (NHS) provides demographic data for various levels of geography, including provinces and territories, census metropolitan areas/census agglomerations, census divisions, census subdivisions, census tracts, federal electoral districts and health regions. In order to provide a comprehensive overview of an area, this product presents data from both the NHS and the Census. NHS data topics include immigration and ethnocultural diversity; aboriginal peoples; education and labor; mobility and migration; language of work; income and housing. 2011 Census data topics include population and dwelling counts; age and sex; families, households and marital status; structural type of dwelling and collectives; and language. The data are collected for private dwellings occupied by usual residents. A private dwelling is a dwelling in which a person or a group of persons permanently reside. Information for the National Household Survey does not include information for collective dwellings. Collective dwellings are dwellings used for commercial, institutional or communal purposes, such as a hotel, a hospital or a work camp. **Benefits** - Useful for canada public health stakeholders, for public health specialist or specialized public and other interested parties. for health surveillance and population health research. for monitoring, planning, implementation and evaluation of health-related programs. media agencies may use the health regions data to raise awareness about health, an issue of concern to all canadians. giving the addition of longitude and latitude in some of the datasets the data can be useful to transpose the values into geographical representations. the fields descriptions along with the dataset description are useful for the user to quickly understand the data and the dataset. **License Information** The use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. **Included Datasets** - [Canadian Population and Dwelling by FSA 2011](https://www.johnsnowlabs.com/marketplace/canadian-population-and-dwelling-by-fsa-2011) - This Canadian Census dataset covers data on population, total private dwellings and private dwellings occupied by usual residents by forward sortation area (FSA). It is enriched with the percentage of the population or dwellings versus the total amount as well as the geographical area, province, and latitude and longitude. The whole Canada's population is marked as 100, referring to 100% for the percentages. - [Detailed Canadian Population Statistics by CMAs and CAs 2011](https://www.johnsnowlabs.com/marketplace/detailed-canadian-population-statistics-by-cmas-and-cas-2011) - This dataset covers the population statistics of Canada by Census Metropolitan Areas (CMAs) and Census Agglomerations (CAs). It is categorized also by citizen/immigration status, ethnic origin, religion, mobility, education, language, work, housing, income etc. There is detailed characteristics categorization within these stated categories that are in 5 layers. - [Detailed Canadian Population Statistics by FED 2011](https://www.johnsnowlabs.com/marketplace/detailed-canadian-population-statistics-by-fed-2011) - This dataset covers the population statistics of Canada from 2011 by Federal Electoral District of 2013 Representation Order. It is categorized also by citizen/immigration status, ethnic origin, religion, mobility, education, language, work, housing, income etc. There is detailed characteristics categorization within these stated categories that are in 5 layers. - [Detailed Canadian Population Statistics by Health Region 2011](https://www.johnsnowlabs.com/marketplace/detailed-canadian-population-statistics-by-health-region-2011) - This dataset covers the population statistics of Canada by health region. It is categorized also by citizen/immigration status, ethnic origin, religion, mobility, education, language, work, housing, income etc. There is detailed characteristics categorization within these stated categories that are in 5 layers. - [Detailed Canadian Population Statistics by Province 2011](https://www.johnsnowlabs.com/marketplace/detailed-canadian-population-statistics-by-province-2011) - This dataset covers the population statistics of Canada by provinces and territories. It is categorized also by citizen/immigration status, ethnic origin, religion, mobility, education, language, work, housing, income etc. There is detailed characteristics categorization within these stated categories that are in 5 layers. **Data Engineering Overview** **We deliver high-quality data** - Each dataset goes through 3 levels of quality review - 2 Manual reviews are done by domain experts - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints - Data is normalized into one unified type system - All dates, unites, codes, currencies look the same - All null values are normalized to the same value - All dataset and field names are SQL and Hive compliant - Data and Metadata - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated - Data Updates - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted **Our data is curated and enriched by domain experts** Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts: - Field names, descriptions, and normalized values are chosen by people who actually understand their meaning - Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset - Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations - The data is always kept up to date – even when the source requires manual effort to get updates - Support for data subscribers is provided directly by the domain experts who curated the data sets - Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution. **Need Help?** If you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com).

Databricks 收录

网易云音乐数据集

该数据集包含了网易云音乐平台上的歌手信息、歌曲信息和歌单信息,数据通过爬虫技术获取并整理成CSV格式,用于音乐数据挖掘和推荐系统构建。

github 收录

GME Data

关于2021年GameStop股票活动的数据,包括每日合并的GME短期成交量数据、每日失败交付数据、可借股数、期权链数据以及不同时间框架的开盘/最高/最低/收盘/成交量条形图。

github 收录

OpenPose

OpenPose数据集包含人体姿态估计的相关数据,主要用于训练和评估人体姿态检测算法。数据集包括多视角的图像和视频,标注了人体关键点位置,适用于研究人体姿态识别和动作分析。

github.com 收录