Making global data easier to explore

让全球数据探索变得更简单

Every year, entities across the United Nations system compile data to track challenges that affect how we work, learn, stay healthy, and care for our loved ones. These agencies work with some of the highest-integrity data in the world. But the statistics needed to solve big global challenges have lived in separate silos, organized in conflicting formats across, and within, different UN system organizations. Connecting the dots often meant months of painstaking manual work for data analysts before any real analysis could begin.

每年，联合国系统内的各机构都会汇编数据，以追踪影响我们工作、学习、健康以及关爱亲人的各项挑战。这些机构所处理的数据是全球诚信度最高的数据之一。然而，解决重大全球性挑战所需的统计数据长期以来分散在各个孤岛中，在不同联合国机构之间乃至内部，以相互冲突的格式存储。在进行任何实质性分析之前，数据分析师往往需要花费数月时间进行繁琐的手动整理，才能将这些数据串联起来。

To solve this challenge, the UN system is launching the UN System Data Commons—an open-source platform built on Data Commons by Google that unites global statistics into one interconnected resource known as an AI-ready knowledge graph. With support from Google.org to the UN Foundation, the project makes critical data universally accessible, helping everyone from researchers to leaders track global progress in real time.

为了解决这一挑战，联合国系统推出了“联合国系统数据共享平台”（UN System Data Commons）。这是一个基于谷歌 Data Commons 构建的开源平台，它将全球统计数据整合为一个互联互通的资源，即一个“AI 就绪”的知识图谱。在 Google.org 对联合国基金会（UN Foundation）的支持下，该项目实现了关键数据的全球通用访问，帮助从研究人员到决策者的各类用户实时追踪全球进展。

Connected data for complex global efforts

为复杂的全球工作提供互联数据

Many of society’s greatest challenges — from public health to poverty eradication — cannot be solved with a single data source. Effectively tackling these crises requires understanding how different datasets intersect. The UN System Data Commons helps uncover these intersections by unifying siloed datasets, so that they can speak the same language. The platform automatically integrates metrics, timelines, and geographic boundaries into a single interconnected environment. This gives analysts more time to focus on uncovering key trends and designing evidence-based solutions, instead of formatting spreadsheets.

社会面临的许多重大挑战——从公共卫生到消除贫困——都无法仅靠单一数据源解决。有效应对这些危机需要理解不同数据集之间是如何交叉关联的。联合国系统数据共享平台通过统一这些孤立的数据集，使它们能够“使用同一种语言”，从而帮助揭示这些交叉点。该平台自动将指标、时间线和地理边界整合到一个互联的环境中。这使得分析师能够将更多时间专注于发现关键趋势和设计基于证据的解决方案，而不是浪费在格式化电子表格上。

Natural language features for easier exploring

自然语言功能让探索更轻松

The UN System Data Commons uses AI to democratize access to these insights, letting people explore through intuitive, natural-language search. This means anyone, from a nonprofit program manager to a journalist to an international policy analyst, can ask questions in plain language and instantly receive relevant data and interactive visualizations. Users can query the platform directly with questions such as: How does access to clean water in rural areas affect school attendance? How many people gained access to electricity in the last decade? How has life expectancy changed across different regions of the world?

联合国系统数据共享平台利用人工智能普及了对这些洞察的访问，让人们可以通过直观的自然语言搜索进行探索。这意味着无论是公益项目经理、记者还是国际政策分析师，任何人都可以用通俗语言提问，并立即获得相关数据和交互式可视化图表。用户可以直接向平台提出诸如以下的问题：农村地区获得清洁水源如何影响入学率？过去十年中有多少人获得了电力供应？世界不同地区的预期寿命发生了怎样的变化？

If you prefer to browse, the Explore tab makes it easy to filter data by location or themes like health or education. The Blog section also breaks down complex trends into ready-to-read reports, like using UNICEF data to explore what works to reduce child poverty. Most importantly, every dataset is validated with UN system statisticians and technical experts, so every answer stays grounded in trusted, official facts.

如果您更喜欢浏览，可以通过“探索”（Explore）标签轻松按地点或健康、教育等主题筛选数据。“博客”（Blog）板块还将复杂的趋势拆解为易读的报告，例如利用联合国儿童基金会（UNICEF）的数据探讨减少儿童贫困的有效方法。最重要的是，每个数据集都经过联合国系统统计学家和技术专家的验证，确保每一个答案都基于可信的官方事实。

Putting AI to work as agentic research assistants

让 AI 成为智能研究助手

Today’s launch also brings AI assistant capabilities directly to the research workflow. Instead of spending hours manually searching for numbers and assembling spreadsheets, you can prompt an AI assistant to do the heavy lifting. Built on open standards like the Model Context Protocol (MCP), Data Commons makes data AI ready enabling AI agents to autonomously fetch authoritative figures directly from the UN System Data Commons, connect the dots across different domains, and package everything together into ready-to-use charts, graphs, infographics, or written draft reports. Even with grounded, verified data, review the underlying sources before citing critical figures.

今天的发布还将 AI 助手功能直接引入了研究工作流。您无需再花费数小时手动搜索数字和整理电子表格，只需提示 AI 助手即可完成繁重的工作。基于模型上下文协议（MCP）等开放标准，Data Commons 使数据具备了“AI 就绪”的特性，让 AI 智能体能够自动从联合国系统数据共享平台直接获取权威数据，跨领域串联信息，并将所有内容打包成现成的图表、信息图或书面报告草稿。即使数据经过验证且有据可查，在引用关键数据前，仍建议核实底层来源。

More data and new features to come

更多数据与新功能即将推出

Over the coming year, the UN system will continue adding datasets from more UN entities, with a goal of including 80% of UN system statistical datasets by 2027. Explore the data yourself at data.un.org.

在未来一年中，联合国系统将继续增加来自更多联合国机构的数据集，目标是在 2027 年前纳入联合国系统 80% 的统计数据集。欢迎访问 data.un.org 自行探索这些数据。