SkillAtlasSkill 详情

education-data-explorer

Security audit: baseline 52/52 CLEAN

审核状态:已审核Quality 80Security 80

复制安装命令

用 Codex 或 Claude 安装复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它先审查 Skill 页面再帮你安装。

复制前请先查看来源、License 和安全提示。

项目 README

来源文件:README.md

抓取于 2026年8月5日

Awesome GitHub stars License: CC BY-SA 4.0 PRs Welcome Validate catalog OpenSSF Scorecard Security audit: baseline 52/52 CLEAN Rigor coverage Powered by StatsPAI

Auto-Empirical Research Skills (AERS)

📌 文档结构(2026-07-22 起): 本文件是中文默认入口 —— banner + badges + 信任面 + 9 阶段流水线速览 + 76 行合集总表。 每个合集的完整描述、按用途分组、精确数字、验证方法在 docs/CONTENT_ZH.md(扩展正文,总表行内的 → 直接跳转到对应锚点)。

English version: README-en.md · 中文扩展正文:docs/CONTENT_ZH.md · README-zh-CN.md 已弃用(重定向占位)

🌐 语言: English | 简体中文(默认) | 繁體中文 | 日本語 | 한국어


CoPaper.AI Stanford REAP - Center on China's Economy & Institutions

Stanford REAP × CoPaper.AI · 实证研究 AI 工具的学术工业级产品
由斯坦福实证研究方法论团队打造,覆盖从数据清洗到顶刊投稿的完整工作流



实证研究智能体技能大全封面图

🚀 New here? Open the Skill Search → to filter all 1,096 skills by method, stage, language, and license. The 5-minute tour (make quickstart) prints the same picture in your terminal.

🇨🇳 中文用户从本文件开始(流水线速览 + 76 行总表),每个合集的完整描述见 docs/CONTENT_ZH.md。📖 English readers: see README-en.md.


信任面 · Trust surface (rigor stats)

Rigor laneCountWhere
Numeric benchmark tasks — gold values recomputed from real data each run17benchmark/
Behavioral eval scenarios / rubric items37 / 183eval-harness/

Full trust overview: docs/TRUST.md · docs/RIGOR_COVERAGE.md


中文文档结构

中文内容分两级维护,各司其职:

  • 本文件(README.md,GitHub 默认入口):banner、badges、信任面、9 阶段流水线速览、76 行合集总表。
  • docs/CONTENT_ZH.md(扩展正文):每个合集的完整描述(#skill-NN 锚点)、按用途分组、精确数字、2 分钟验证、三层信任、旗舰流水线详解、贡献与引用。总表行内的 → 直接跳到对应锚点。
  • 其他语言:README-en.md · README-zh-TW.md · README-ja.md · README-ko.md

[!NOTE] 维护规则: 改合集总表 → 本文件与 CONTENT_ZH.md 的锚点表两处同步;改合集详情 / 分组 / 数字 → 只改 docs/CONTENT_ZH.md。统计数字(合集数 / skill 数)以 catalog/skills.json 为准,由 make validate 的 readme-stats 检查器守护。

贡献者(Contributors): 提交前请在本地跑通完整门禁 make check(catalog 校验 + 链接 + 单元测试 + eval-harness + benchmark)。详见 CONTRIBUTING.md。

旧版归档: README-zh-CN.md 已弃用,仅作向后兼容的重定向占位。


🚀 从一个 idea 到一篇论文:社科实证研究 · 端到端流水线(全自动、可介入)

AERS 不只是 76 个散装 skill —— 它能陪你走完一篇论文。 从模糊 idea → 选题精炼 → 文献综述 → 数据获取 → 识别策略 → 估计建模 → 稳健性审计 → 出版级表格 / 图形 → 写作与同行评审 → 降 AIGC → 投稿。端到端、全自动、每一步都可被人介入(中间任何一步你都可以接过去手工改方法、补变量、加稳健性,再让流水线自动接上跑)。

9 阶段流水线 · 每一步都覆盖到具体 skill

#阶段关键 skills(点合集名进目录,→ 进完整说明)
1️⃣选题精炼 — Agent 把模糊想法收紧成"可证伪 + 可执行"的研究问题· 25 Diverga · 33 claude-scholar · 05 research-superpower · 11 compound-science
2️⃣文献综述 — 检索 · 筛选 · PRISMA 流程 · 批判性阅读 · 主题分析· 36 literature-review-skill · 24 academic-research-skills · 59 openalex-skill · 68 research-productivity-skills · 53 thematic-analysis
3️⃣数据获取 — 公开数据库 · API · 网页抓取 · 数据清洗· 33 claude-scholar · 68 research-productivity-skills · 32 stata-skill · 57 edgartools
4️⃣识别策略 — DiD / RD / IV / SCM / DML / matching 全覆盖· ⭐ 00 StatsPAI 🔥 · 10 causal-inference-mixtape · 13 MixtapeTools · 51 CausalPy · 63 scientific-agent-skills
5️⃣估计建模 — Python / Stata / R 三栈,900+ 估计器· ⭐ 00.1 Full Empirical · Python · ⭐ 00.2 Full Empirical · Stata · ⭐ 00.3 Full Empirical · R · 40 pyfixest · 39 marginaleffects · 09 awesome-econ-ai
6️⃣稳健性审计 — 复现包检查 · Honest-DiD · R&R 模拟· 41 sewage-econometrics-check · ⭐ 50 AER-skills · 21 AI-research-feedback
7️⃣表格 & 图形 — 期刊出版级排版 · LaTeX 嵌入· ⭐ 00 StatsPAI · 07 AI-Research-SKILLs · 33 claude-scholar · 08 latex-document-skill
8️⃣写作 & 同行评审 — LaTeX / Quarto · 仿审稿人 · 校对· 06 stats-paper-writing · 04 scientific-writer · 22 christopherkenny-skills · 38 academic-proofreader · 56 econ-writing-skill · 16 clo-author
9️⃣降 AIGC & 投稿 — 知网 / 万方 / Turnitin / 23 类 AI 痕迹模式· ⭐ 48 de-AIGC-skills 🇨🇳🇬🇧 · 44 humanizer_academic · 45 deslop · 46 stop-slop · 47 avoid-ai-writing · 49 humanize-chinese

🎼 元编排:⭐ 69 Paper-WorkFlow —— 一键串起来

Paper-WorkFlow 是 AERS 的"指挥棒",它把上面 9 个阶段的 skill 串成 一条按键即运行的端到端流水线。 你在 IDE 入口给它一句自然语言:

"开一个新论文项目:空气污染与中国劳动力市场,CS 设计 + 省级面板"

它会自动按顺序调:

  1. ⭐ 00 StatsPAI → sp.csdid(...) 给出 CS-DID 估计草案 + 写出估计方程与识别假设
  2. 33 claude-scholar → 抓变量定义 / 数据源候选 / 相关文献
  3. ⭐ 00 StatsPAI → 真跑 sp.feols(...) + sp.honest_did(...)
  4. 41 sewage-econometrics-check → 10 项复现包审计 + 稳健性体检
  5. ⭐ 00 StatsPAI + 07 AI-Research-SKILLs → 出 Table 1–5 + 期刊级图
  6. 38 academic-proofreader → 通读 + §comment 标"审稿人会挑刺的位置"
  7. 56 econ-writing-skill 起草初稿 + ⭐ 48 de-AIGC-skills 🇨🇳🇬🇧 + 45 deslop 过知网 / Turnitin

任何阶段你都可以手动介入 —— 上一阶段的产物全部落盘(产物-幂等 pipeline),你接过去改方法、补控制、加稳健性,再让流水线自动接下去跑。这就是"全自动 + 可介入"。

🏆 7 个 Stanford REAP × CoPaper.AI 自研 skill —— 是整个流水线的主干

⭐ Skill在流水线里的角色
00 StatsPAI 🔥因果引擎:900+ 函数,sp.causal(...) 一行跑闭环(DID / RD / IV / SCM / DML / matching)
00.1 Full Empirical · Python 📘显式 Python 栈(pandas / statsmodels / linearmodels / pyfixest)
00.2 Full Empirical · Stata 📊显式 Stata 栈(reghdfe / ivreg2 / csdid / sdid / rdrobust)
00.3 Full Empirical · R 📗显式 R 栈(tidyverse / fixest / did / HonestDiD)+ Quarto 渲染
48 de-AIGC-skills 🇨🇳🇬🇧中英双语学术降 AIGC(Turnitin AI / GPTZero / 知网 / 万方)
50 AER-skills 📕Top-5 经济学投稿套件:识别 → 稳健性 → R&R
69 Paper-WorkFlow 🧭元编排器,把上面 9 个阶段串成一键流水线

为什么挑这 7 个?因为它们的行为都被基准钉死了 —— 不是营销口径,是对着已知答案反复跑过验证过的(17 项数值 benchmark + 37 项行为评测 ↗)。

看到这里 —— 完整 76 行合集目录

↴ 直跳到下方 76 行总表(每个合集带 #skill-NN 锚点)。如果你更关心"这些 skill 怎么用"而不是"有哪些 skill",看 📘 中文唯一权威正文 里的「按用途分组」与「旗舰流水线」两节。


🧰 76 个核心 Skills 合集一览(00 → 72,编号连续无空缺)

打开仓库 → 看见整座库。 全部 76 个合集 · 1,096 个 skill,每一个都已 vendor 进本仓库,由 catalog/skills.json 跟踪。⭐ = Stanford REAP × CoPaper.AI 团队自研的 skill;其余为精选、经安全审计的社区作品。

主题图例 — 🚀 全流程与编排器 · 🎯 因果推断与计量经济学 · 📚 文献与研究设计 · ✍️ 写作 / 编辑 / 去 AIGC · 📑 引用 / 复现 / 同行评审 · 🛠️ 数据 / 工具 / 基础设施

点击【→】 跳转到 docs/CONTENT_ZH.md 中该合集的完整描述;点击合集名 直接打开其目录。

#合集一句话详情
⭐ 00StatsPAI 🔥因果引擎 · Agent-native Python DSL:sp.causal(...) 一行跑闭环(DID/RD/IV/SCM/DML,900+ 函数)→
⭐ 00.1Full Empirical · Python 📘显式栈:pandas · statsmodels · linearmodels · pyfixest→
⭐ 00.2Full Empirical · Stata 📊reghdfe · ivreg2 · csdid · sdid · rdrobust 复现包→
⭐ 00.3Full Empirical · R 📗tidyverse · fixest · did · HonestDiD + Quarto 渲染→
01academic-paper-skills大纲 → 手稿写作 + 7 维审稿人模拟→
02research-skills医学影像综述、提案、论文转幻灯片→
03scientific-skills假设生成 + 28 个科学数据库→
04scientific-writer引用管理 + 科学写作→
05research-superpower系统化检索、筛选与引文溯源→
06stats-paper-writing端到端 LaTeX 统计论文写作→
07AI-Research-SKILLs发表级 ML 图表、LaTeX、引文核验→
08latex-document-skill创建 / 编译任意 LaTeX 文档为 PDF→
09awesome-econ-aiPython 面板数据分析(linearmodels)→
10causal-inference-mixtapeDID / IV / RDD / SCM 模板(Cunningham)→
11compound-science面向定量社会科学的贝叶斯估计→
12claude-code-my-workflow提交 → PR → 合并的研究工作流(Emory)→
13MixtapeToolsCunningham 的因果推断工具集与讲义→
14research-starterR 中的 IV / DiD / RDD,含完整诊断→
15social-science-researchR 或 Python 端到端数据分析→
16clo-author多代理数据分析(R / Stata / Python)→
17DAAF安全意识代理框架(32 条 deny rule)→
18stata-accounting来自 126 篇 JAR 论文的实测 Stata 范式→
19vera-economic-intelligence经济情报 / 政策研究情报工作流→
20python-econ-skillDSGE / HANK 与定量经济计算→
21AI-research-feedback用 AI 同行评审生成结构化反馈→
22christopherkenny-skills面向 Quarto(.qmd)的 APSA 风格检查器→
23baygent带护栏的 PyMC / Arviz 贝叶斯工作流→
24academic-research-skills5 审稿人多视角论文评审→
25Diverga研究问题精炼器(抗模式坍缩)→
26scholar统计算法设计与文档→
27my_claude_skills经济学摘要写作指南→
28paper-replicate-agent论文复现代理演示→
29project20XXy可复现手稿 + notebook 项目→
30zirui-song-claude-skillsZirui Song 的研究辅助 Claude 技能集→
31claude-code-skillsPython 面板数据分析→
32stata-skill高性能 Stata C/C++ 插件→
33claude-scholar研究全生命周期:选题 → 综述 → 实验 → 审稿回复→
34research-companion头脑风暴、评估并决策研究方向→
35academic-writing-skills面向投稿场所的工业 AI 文献研究→
36literature-review-skill完整文献综述工作流(中文)→
37IlanStrauss-ai-skillsIlan Strauss 经济学研究 AI 工作流→
38academic-proofreader学术校对→
39marginaleffects预测、斜率与比较(R / Python)→
40pyfixestPython 中的快速固定效应估计→
41sewage-econometrics-check10 项复现包审计→
42ARIS自主「research-in-sleep」代理,端到端→
43research-plugins478 个研究插件:数据可视化、领域、基础设施→
44humanizer_academic为医学/学术手稿去 AI 味(23 类模式)→
45deslop去除 AI 写作痕迹(5 维评分)→
46stop-slop三层 AI 痕迹检测与改写→
47avoid-ai-writing审计 → 改写 → 二次审计 AI 味(留痕)→
⭐ 48de-AIGC-skills 🇨🇳🇬🇧中英双语学术降 AIGC(Turnitin AI / GPTZero / 知网 / 万方)→
49humanize-chinese检测并人性化 AI 生成的中文文本→
⭐ 50AER-skills 📕Top-5 经济学投稿套件:识别 → 稳健性 → R&R→
51CausalPy贝叶斯准实验(PyMC Labs)→
52slr-prisma系统文献综述,PRISMA 2020→
53thematic-analysisBraun & Clarke 六阶段定性主题分析→
54open-science-skills引用一致性、DOI 与论据支撑审计→
55r-skillsR 中用 brms 做贝叶斯推断→
56econ-writing-skill综合 50+ 顶级指南的经济学写作→
57edgartools查询与分析 SEC 文件→
58econstack政策简报(UK GES / AU Treasury)→
59openalex-skill通过 OpenAlex 查询 2.4 亿+ 学术作品→
60superpapers综合性实证研究支持套件→
61research-methods与预注册匹配的验证性检验→
62citation-checker对照 CrossRef / S2 / OpenAlex 核验引用→
63scientific-agent-skillsDoWhy 识别–估计–反驳框架→
64mcp-stata20 个 Stata 因果推断与复现 skill→
65game-theory-paper-writer生成并压力测试博弈论论文→
66empirical-research-skills面向大型面板的 R 性能优化→
67econfin-workflow-toolkit中国公司金融实证工作流,从提案到论文→
68research-productivity-skills论文检索、SSRN、DOI 查询、下载→
⭐ 69Paper-WorkFlow 🧭元编排器,串起整个社会科学论文流水线→
70ssci-polish ✍️SSCI / SCI 英文论文语言润色(语法、可读性、学术语气)→
⭐ 71lit-review-agent-tools 🔍文献综述工具选型 + 一键安装运行(MinerU / PaperQA2 / ASReview / STORM / MCP 服务器)→
⭐ 72Kaggle Research 🧪通过官方 CLI 安全检索 Kaggle 资源、限界下载公开数据并保留审计证据→

想看更详细的描述(主题分类、字段、统计)? 见 docs/CONTENT_ZH.md 中标注 #skill-NN 锚点的同一张表 —— 它是每个合集的完整描述所在的扩展正文。

📈 项目历程

自 2026-04 首次发布以来的主干里程碑(完整提交记录见 Commits 与 CHANGELOG.md):

---
config:
  gitGraph:
    rotateCommitLabel: false
---
gitGraph TB:
   commit id: "2026-04 首次发布"
   branch community
   commit id: "2026-05 首个社区 PR"
   checkout main
   merge community
   commit id: "2026-05 更名 AERS"
   commit id: "2026-06 插件市场"
   commit id: "2026-06 全库路由器"
   commit id: "2026-07 首个 tag" tag: "v2026.07"
   branch kaggle
   commit id: "2026-07 Kaggle 集成"
   checkout main
   merge kaggle
   commit id: "2026-08 de-AIGC 双语"
Star History Chart

Star 增长曲线(非提交数)· 由 scripts/build-star-history.py 从 GitHub API 生成并提交入库

如果 AERS 对你的工作有帮助,请引用它(CITATION.cff)并点个 Star,让更多研究者看到。


AI 是放大器,不是替代品。它替你做最耗时的"搬砖",你保留最核心的"判断"。


CoPaper.AI Stanford REAP

Stanford REAP × CoPaper.AI · 实证研究 AI 工具的学术工业级产品


扫码访问 copaper.ai
扫码访问 copaper.ai
CoPaper.AI 公众号
关注公众号「CoPaper.AI」

内置 20 个方法论 skill · 20 分钟完成实证论文 · 自研 StatsPAI(900+ 函数 / MIT 开源)

数据与 AI

中风险

  • 来源需自行核对维护者身份。
  • 包含脚本或命令调用,安装前请复核。
  • 未检测到明显外部权限要求。
  • 未检测到高风险命令。
  • 扫描发现:2 条。

Codex — Git Clone 安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 克隆仓库:git clone https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills.git
  3. 将 "skills/17-DAAF-Contribution-Community-daaf/dot-claude/skills/education-data-explorer" 文件夹复制到 Codex 的 skills 目录中。
  4. 重启 Codex 让新的 skill 生效。

Codex — 手动复制安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 从源仓库下载 SKILL.md 及相关文件。
  3. 在 Codex 的 skills 目录中创建新文件夹。
  4. 将所有 skill 文件复制到新文件夹中。
  5. 重启 Codex 让新的 skill 生效。

Claude Code — Git Clone 安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 克隆仓库:git clone https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills.git
  3. 将 "skills/17-DAAF-Contribution-Community-daaf/dot-claude/skills/education-data-explorer" 文件夹复制到 Claude Code 的 skills 目录中。
  4. 重启 Claude Code 让新的 skill 生效。

Claude Code — 手动复制安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 从源仓库下载 SKILL.md 及相关文件。
  3. 在 Claude Code 的 skills 目录中创建新文件夹。
  4. 将所有 skill 文件复制到新文件夹中。
  5. 重启 Claude Code 让新的 skill 生效。

Cursor — Git Clone 安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 克隆仓库:git clone https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills.git
  3. 将 "skills/17-DAAF-Contribution-Community-daaf/dot-claude/skills/education-data-explorer" 文件夹复制到 Cursor 的 skills 目录中。
  4. 重启 Cursor 让新的 skill 生效。

Cursor — 手动复制安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 从源仓库下载 SKILL.md 及相关文件。
  3. 在 Cursor 的 skills 目录中创建新文件夹。
  4. 将所有 skill 文件复制到新文件夹中。
  5. 重启 Cursor 让新的 skill 生效。

GitHub Copilot — Git Clone 安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 克隆仓库:git clone https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills.git
  3. 将 "skills/17-DAAF-Contribution-Community-daaf/dot-claude/skills/education-data-explorer" 文件夹复制到 GitHub Copilot 的 skills 目录中。
  4. 重启 GitHub Copilot 让新的 skill 生效。

GitHub Copilot — 手动复制安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 从源仓库下载 SKILL.md 及相关文件。
  3. 在 GitHub Copilot 的 skills 目录中创建新文件夹。
  4. 将所有 skill 文件复制到新文件夹中。
  5. 重启 GitHub Copilot 让新的 skill 生效。

Windsurf — Git Clone 安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 克隆仓库:git clone https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills.git
  3. 将 "skills/17-DAAF-Contribution-Community-daaf/dot-claude/skills/education-data-explorer" 文件夹复制到 Windsurf 的 skills 目录中。
  4. 重启 Windsurf 让新的 skill 生效。

Windsurf — 手动复制安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 从源仓库下载 SKILL.md 及相关文件。
  3. 在 Windsurf 的 skills 目录中创建新文件夹。
  4. 将所有 skill 文件复制到新文件夹中。
  5. 重启 Windsurf 让新的 skill 生效。
查看 SKILL.md 原文
name: education-data-explorer
description: >-
  Discovers education data from Urban Institute Portal: endpoints, variables, year coverage, join keys (CCD, IPEDS, CRDC, Scorecard, SAIPE). Use to map questions to data. Load before education-data-query — discovery here, download there.
metadata:
  audience: research-planner
  domain: data-access

Education Data Explorer

Discovers available education data from the Urban Institute Education Data Portal: endpoints, variables, year coverage, and join keys for schools, districts, and colleges (CCD, IPEDS, CRDC, Scorecard, SAIPE, and more). Use during discovery and scoping phases when identifying what data exists, mapping research questions to endpoints, or resolving variable name discrepancies between documentation and actual field names. Load before education-data-query — this skill covers discovery; education-data-query handles the download.

Discover available education data from the Urban Institute Education Data Portal for research planning and query design.

What is the Education Data Portal?

  • Comprehensive education data from Urban Institute - free and publicly available
  • Three data levels: schools, school-districts, college-university
  • Multiple data sources: CCD, IPEDS, CRDC, College Scorecard, EDFacts, SAIPE, FSA, MEPS, PSEO, etc.
  • Coverage: 1980-2023 depending on source
  • Access: Mirror downloads (parquet/CSV) via education-data-query skill
  • Documentation: https://educationdata.urban.org/documentation/
  • Curation layer, not a direct pass-through: The EDP standardizes data from original federal sources with lowercase variable names, integer-encoded categoricals, and uniform missing value codes (-1, -2, -3)
  • Coverage varies by source: Some sources are fully mirrored; others are partially mirrored with only a subset of variables or datasets available. See individual education-data-source-* skills for source-specific coverage details

Skill Provenance Note: Each *-data-source-* skill includes provenance.skill_last_updated in its frontmatter. When exploring data sources during Stage 2, note the provenance dates of any skills you reference — if more than a few months old, flag this in your findings so the orchestrator can consider re-verifying with data-ingest.

Note: This workflow uses mirror-based file downloads, not paginated API calls. See education-data-query skill for fetch patterns and datasets-reference.md for file paths.

Reference File Structure

FilePurposeWhen to Read
schools-endpoints.mdAll school-level endpoints and variablesResearching K-12 schools
districts-endpoints.mdAll district-level endpoints and variablesResearching school districts
colleges-endpoints.mdAll college-level endpoints and variablesResearching higher education
variable-codes.mdCode values for states, grades, race, etc.Interpreting or filtering data
metadata-api.mdProgrammatic endpoint/variable discoveryDynamic exploration

Decision Trees

What data level do I need?

What entity am I researching?
├─ Individual K-12 schools → schools level
│   └─ See ./references/schools-endpoints.md
├─ School districts / LEAs → school-districts level
│   └─ See ./references/districts-endpoints.md
├─ Colleges / Universities → college-university level
│   └─ See ./references/colleges-endpoints.md
└─ Not sure
    ├─ Need school-specific data (discipline, AP, demographics) → schools
    ├─ Need aggregate district data (finance, poverty) → school-districts
    └─ Need postsecondary data (enrollment, aid, outcomes) → college-university

What topic am I researching?

Research topic?
├─ Enrollment / Demographics
│   ├─ K-12 public schools → CCD enrollment endpoints
│   ├─ Civil rights indicators → CRDC enrollment
│   └─ Colleges → IPEDS enrollment
├─ School Finance
│   ├─ District revenue/expenditure → CCD finance
│   └─ College finance → IPEDS finance
├─ Student Outcomes
│   ├─ K-12 assessments → EDFacts
│   ├─ Graduation rates (K-12) → EDFacts
│   ├─ College completion → IPEDS completions
│   └─ Post-college earnings → College Scorecard
├─ Student Aid / Loans
│   ├─ College financial aid → IPEDS aid
│   ├─ Federal loans/grants → FSA
│   └─ Debt/repayment → College Scorecard
├─ Discipline / Civil Rights
│   └─ K-12 discipline, harassment, restraint → CRDC
├─ Poverty Estimates
│   └─ District-level → SAIPE
└─ Directory / Location
    ├─ K-12 schools → CCD directory
    ├─ Districts → CCD directory
    └─ Colleges → IPEDS directory

How do I find specific variables?

Finding variables?
├─ Know the endpoint → Check reference file for variable list
├─ Know the topic → Use topic index below
├─ Need to search programmatically → See ./references/metadata-api.md
└─ Need code definitions → See ./references/variable-codes.md

Quick Reference: Data Levels

LevelKey SourcesPrimary IDID Format
schoolsCCD, CRDC, EDFacts, MEPS, NHGISncessch12-char string
school-districtsCCD, SAIPE, EDFactsleaid7-char string
college-universityIPEDS, Scorecard, FSA, PSEO, EADAunitid6-digit integer

Quick Reference: Data Sources

SourceLevelDescriptionYears
CCDSchools, DistrictsPublic K-12 directory, enrollment, finance1986-2023
CRDCSchoolsCivil rights indicators, discipline, AP courses2011-2021
EDFactsSchools, DistrictsAssessments, graduation rates2009-2020
IPEDSCollegesEnrollment, completions, finance, institutional data1980-2023
College ScorecardCollegesEarnings, debt, student outcomes1996-2020
SAIPEDistrictsCensus poverty estimates for school-age children1995-2023
FSACollegesFederal student aid, loans, grants, 90/101999-2021
MEPSSchoolsSchool poverty measure2006-2019
NHGISSchoolsCensus geography crosswalks1990, 2000, 2010, 2020

Quick Reference: Common Endpoints

Schools

EndpointDescription
/schools/ccd/directory/{year}/School directory (location, type, enrollment)
/schools/ccd/enrollment/{year}/{grade}/Enrollment by grade
/schools/crdc/discipline/{year}/Discipline incidents
/schools/crdc/ap-ib-enrollment/{year}/race/sex/AP/IB enrollment (requires disaggregation)
/schools/edfacts/assessments/{year}/{grade}/Assessment results

Districts

EndpointDescription
/school-districts/ccd/directory/{year}/District directory
/school-districts/ccd/enrollment/{year}/{grade}/District enrollment
/school-districts/ccd/finance/{year}/Revenue and expenditure
/school-districts/saipe/{year}/Poverty estimates

Colleges

EndpointDescription
/college-university/ipeds/directory/{year}/Institution directory
/college-university/ipeds/admissions-enrollment/{year}/Admissions data
/college-university/ipeds/enrollment-full-time-equivalent/{year}/FTE enrollment
/college-university/ipeds/fall-enrollment/{year}/{level}/Fall enrollment
/college-university/ipeds/graduation-rates/{year}/Graduation rates
/college-university/scorecard/earnings/{year}/Post-college earnings

Exploration Workflow

Follow these steps to identify data for a research question:

  1. Identify data level

    • Schools: individual K-12 school records
    • Districts: school district / LEA records
    • Colleges: postsecondary institution records
  2. Identify relevant data source(s)

    • Use the data sources table above
    • Multiple sources may be needed (e.g., CCD + CRDC)
  3. Check available endpoints

    • Read the appropriate reference file
    • Note endpoint URL pattern and variables
  4. Review variables and filters

    • Check variable lists in reference files
    • Note which variables can be used as filters
  5. Check years available

    • Each endpoint has different year coverage
    • Use metadata API to get exact years
  6. Understand source context (RECOMMENDED)

    • Load the appropriate education-data-source-* skill for deep context
    • Understand data collection methodology and limitations
    • Review variable definitions and coding schemes
  7. Plan query

    • Load education-data-query skill for query construction
    • Or use metadata API to build query programmatically

URL Pattern Structure

All endpoints follow this pattern:

/api/v1/{level}/{source}/{topic}/{year}/[{disaggregation}/]

Examples:

  • /api/v1/schools/ccd/directory/2022/
  • /api/v1/schools/ccd/enrollment/2022/grade-5/
  • /api/v1/schools/ccd/enrollment/2022/grade-5/race/
  • /api/v1/school-districts/ccd/finance/2021/
  • /api/v1/college-university/ipeds/fall-enrollment/2022/undergraduate/

Filtering

Query Parameters

ParameterDescriptionExample
fipsState FIPS code?fips=6 (California)
leaidDistrict ID?leaid=0600001
ncesschSchool ID?ncessch=060000100001
unitidCollege ID?unitid=110635
yearFilter by year?year=2022

Response Format

{
  "count": 12345,
  "next": "https://educationdata.urban.org/api/v1/...?page=2",
  "previous": null,
  "results": [
    {"ncessch": "...", "school_name": "...", ...},
    ...
  ]
}

Cross-Reference to Related Skills

SkillPurposeWhen to Use
education-data-queryDownload data from mirrorsAfter identifying endpoints/variables
education-data-contextInterpret data, understand limitationsAfter retrieving data

Deep-Dive Data Source Skills

For comprehensive understanding of each data source beyond the portal documentation, load the appropriate source-specific skill:

SkillData SourceKey Topics
education-data-source-ccdCommon Core of DataK-12 directory, enrollment, finance, staffing surveys
education-data-source-crdcCivil Rights Data CollectionDiscipline, harassment, course access, civil rights context
education-data-source-saipeSmall Area Income & PovertyDistrict poverty estimates, model methodology
education-data-source-edfactsEDFactsState assessments, graduation rates, accountability
education-data-source-ipedsIPEDSCollege enrollment, graduation, finance, completions
education-data-source-scorecardCollege ScorecardPost-college earnings, debt, repayment
education-data-source-nhgisNHGISCensus geography, demographic crosswalks
education-data-source-fsaFederal Student AidPell, loans, financial responsibility, 90/10
education-data-source-nacuboNACUBOCollege endowment data
education-data-source-nccsNCCSNonprofit data for private colleges
education-data-source-mepsMEPSModel-based school poverty (superior to FRPL)
education-data-source-eadaEADACollege athletics equity data
education-data-source-campus-safetyCampus SafetyCampus crime, Clery Act data
education-data-source-pseoPSEOPost-graduation employment outcomes

When to load source skills:

  • Need deeper understanding of data collection methodology
  • Encountering unexpected values or patterns
  • Planning analysis that requires understanding source limitations
  • Working with less common data elements not covered in this skill

Topic Index

TopicReference FileSection
School directoryschools-endpoints.mdCCD Directory
School enrollmentschools-endpoints.mdCCD Enrollment
Discipline dataschools-endpoints.mdCRDC Discipline
AP/IB coursesschools-endpoints.mdCRDC AP-IB-GT
K-12 assessmentsschools-endpoints.mdEDFacts
District directorydistricts-endpoints.mdCCD Directory
District financedistricts-endpoints.mdCCD Finance
District povertydistricts-endpoints.mdSAIPE
College directorycolleges-endpoints.mdIPEDS Directory
College enrollmentcolleges-endpoints.mdIPEDS Enrollment
College graduationcolleges-endpoints.mdIPEDS Graduation
Financial aidcolleges-endpoints.mdIPEDS Aid, FSA
Post-college earningscolleges-endpoints.mdScorecard
Student debtcolleges-endpoints.mdScorecard, FSA
State FIPS codesvariable-codes.mdState FIPS
Grade codesvariable-codes.mdGrade Codes
Race/ethnicity codesvariable-codes.mdRace Codes
Locale codesvariable-codes.mdUrban-Centric Locale
Programmatic discoverymetadata-api.mdAll

Example: Planning a Research Query

Research question: "What is the relationship between school poverty and AP course offerings in California high schools?"

  1. Data level: Schools (individual school records)

  2. Data sources needed:

    • CRDC for AP course data
    • MEPS or CCD for poverty measure
  3. Endpoints:

    • /schools/crdc/ap-ib-enrollment/{year}/race/sex/ - AP enrollment (requires disaggregation)
    • /schools/meps/{year}/ - School poverty measure
  4. Key variables:

    • ncessch - school identifier (for joining)
    • fips=6 - California filter
    • AP enrollment variables from CRDC
    • Poverty measure from MEPS
  5. Years: Check overlap (CRDC: 2011-2021, MEPS: 2006-2019)

  6. Next step: Load education-data-query skill to construct the actual API calls

Common Pitfalls

  • Year coverage varies: Always check years available for each endpoint
  • Different ID formats: ncessch (12-char), leaid (7-char), unitid (6-digit)
  • Disaggregation in URL: Grade, race, sex are often URL path components, not query params
  • Missing data codes: -1, -2, -3 have specific meanings (see variable-codes.md)

Pre-Query Validation

CRITICAL: Variable Name Discrepancies

The Education Data Portal API often uses different variable names than documentation suggests. Always fetch a sample first:

# Test query to verify actual column names
response = requests.get(
    "https://educationdata.urban.org/api/v1/college-university/ipeds/directory/2023/"
)
data = response.json()
print("Actual columns:", list(data['results'][0].keys()))

Known discrepancies:

DocumentedActual API FieldEndpoint
inst_levelinstitution_levelIPEDS Directory
applicants_totalnumber_appliedIPEDS Admissions
admissions_totalnumber_admittedIPEDS Admissions
grad_rate_150pctcompletion_rate_150pctIPEDS Graduation Rates
school_povertymeps_poverty_pctMEPS
population_5_17_povertyest_population_5_17_povertySAIPE

See the relevant education-data-source-* skill for comprehensive variable mappings per source.

Metadata API Limitations

The metadata API has undocumented limitations:

  • ?section=schools works to filter by data level
  • ?source=ipeds does NOT work - filter client-side instead
  • Response field names differ: source is actually class_name, source_name is actually label

Data Source Details

Quick summaries below. For comprehensive documentation including methodology, variable definitions, data quality issues, and historical changes, load the corresponding education-data-source-* skill.

CCD (Common Core of Data)

Coverage: All public elementary and secondary schools and districts in the U.S.

TopicSchoolsDistricts
DirectoryYesYes
EnrollmentYes (by grade, race, sex)Yes (by grade, race, sex)
FinanceNoYes (revenue, expenditure)

Key Variables:

  • ncessch: 12-character NCES school ID
  • leaid: 7-character NCES district ID
  • enrollment: Total enrollment count
  • free_or_reduced_price_lunch: FRPL-eligible students (poverty proxy)
  • charter: Charter school indicator
  • urban_centric_locale: Urban/suburban/town/rural classification

Deep dive: Load education-data-source-ccd for survey components, data collection process, variable coding, state variations, and historical changes (e.g., 2006 locale code revision, 2010 race category changes).

CRDC (Civil Rights Data Collection)

Coverage: Biennial survey of public schools (2011, 2013, 2015, 2017, 2020, 2021)

Topics:

  • Discipline (suspensions, expulsions, arrests)
  • Chronic absenteeism
  • Harassment and bullying
  • Restraint and seclusion
  • Advanced courses (AP, IB, gifted)
  • Course offerings
  • Teacher qualifications
  • Retention
  • COVID impacts (2020 only)

Key Feature: Disaggregation by race, sex, disability, and LEP status

Deep dive: Load education-data-source-crdc for civil rights legal context (Title VI, IX, Section 504), collection methodology, underreporting issues, and year-to-year changes.

EDFacts

Coverage: State assessment and accountability data

Topics:

  • Assessment proficiency rates (reading, math)
  • Graduation rates (4-year adjusted cohort)

Key Feature: Data available by special populations (disability, economically disadvantaged, LEP, homeless, migrant, foster care)

CRITICAL: State assessment scores CANNOT be compared across states (different tests, cut scores).

Deep dive: Load education-data-source-edfacts for ESSA/NCLB accountability context, why cross-state comparison is invalid, ACGR methodology, and subgroup reporting rules.

IPEDS (Integrated Postsecondary Education Data System)

Coverage: All Title IV-eligible postsecondary institutions

Topics:

  • Institutional characteristics and directory
  • Admissions and enrollment
  • Student charges (tuition, fees, room, board)
  • Financial aid
  • Finance (revenue, expenditure, assets)
  • Graduation rates
  • Completions (degrees awarded by CIP code)
  • Human resources (salaries, faculty)

Key Variables:

  • unitid: 6-digit IPEDS institution ID
  • inst_control: 1=Public, 2=Private nonprofit, 3=Private for-profit
  • institution_level: 1=Less-than-2-year, 2=2-year, 4=4-year (no code 3)
  • hbcu: Historically Black college indicator

Deep dive: Load education-data-source-ipeds for critical graduation rate limitations (first-time full-time only), GASB vs FASB finance accounting, survey components, and identifier changes.

College Scorecard

Coverage: Title IV institutions with outcome data

Topics:

  • Post-college earnings (6 and 10 years after entry)
  • Student debt and repayment
  • Default rates
  • Completion rates by income level

Key Feature: Links education to labor market outcomes

CRITICAL: Only covers Title IV aid recipients (selection bias toward lower-income students).

Deep dive: Load education-data-source-scorecard for earnings methodology (IRS data), population coverage limitations, suppression rules, and field-of-study data.

SAIPE (Small Area Income and Poverty Estimates)

Coverage: Census Bureau poverty estimates for school districts

Key Variables:

  • population_5_17_poverty: Children 5-17 in poverty
  • population_5_17_poverty_pct: Percent in poverty
  • median_household_income: District median income

Deep dive: Load education-data-source-saipe for model-based estimation methodology, confidence intervals (not available at district level), and comparison to other poverty measures.

FSA (Federal Student Aid)

Coverage: Title IV institutions receiving federal aid

Topics:

  • Pell grants
  • Direct loans (subsidized, unsubsidized, PLUS)
  • Campus-based aid (Perkins, work-study)
  • Financial responsibility scores
  • 90/10 revenue (for-profit institutions)

Deep dive: Load education-data-source-fsa for Title IV program details, financial responsibility composite scores, and 90/10 rule compliance.

Additional Data Sources

SourceCoverageDeep Dive Skill
MEPSSchool-level poverty estimates (superior to FRPL for cross-state comparison)education-data-source-meps
NHGISCensus geography crosswalks for schoolseducation-data-source-nhgis
NACUBOCollege endowment dataeducation-data-source-nacubo
NCCSNonprofit data for private colleges (Form 990)education-data-source-nccs
EADACollege athletics equity dataeducation-data-source-eada
Campus SafetyCampus crime statistics (Clery Act)education-data-source-campus-safety
PSEOPost-graduation employment outcomeseducation-data-source-pseo

Joining Data Across Sources

School-Level Joins

Join school data across sources using ncessch:

Source 1Source 2Join KeyUse Case
CCDCRDCncesschEnrollment + discipline
CCDEDFactsncesschDirectory + assessments
CCDMEPSncesschEnrollment + poverty
CRDCMEPSncesschAP courses + poverty

Note: Match on year when joining (years may not align perfectly)

District-Level Joins

Join district data using leaid:

Source 1Source 2Join KeyUse Case
CCD DirectoryCCD FinanceleaidCharacteristics + spending
CCDSAIPEleaidEnrollment + poverty
CCDEDFactsleaidEnrollment + outcomes

College-Level Joins

Join college data using unitid:

Source 1Source 2Join KeyUse Case
IPEDS DirectoryIPEDS FinanceunitidCharacteristics + finance
IPEDSScorecardunitidEnrollment + earnings
IPEDSFSAunitidEnrollment + aid data

Disaggregation Patterns

URL Path Disaggregation

Some disaggregations are part of the URL path:

/schools/ccd/enrollment/{year}/{grade}/           # By grade
/schools/ccd/enrollment/{year}/{grade}/race/      # By grade and race
/schools/ccd/enrollment/{year}/{grade}/race/sex/  # By grade, race, and sex

Query Parameter Disaggregation

Other filters are query parameters:

?fips=6                    # California only
?charter=1                 # Charter schools only
?school_level=3            # High schools only
?urban_centric_locale=11   # Large cities only

Available Disaggregations by Source

SourceGradeRaceSexDisabilityEcon StatusLEP
CCDYesYesYesNoNoNo
CRDCNoYesYesYesNoYes
EDFactsYesYesYesYesYesYes
IPEDSLevelYesYesNoNoNo

Year Coverage Quick Reference

SourceEarliestLatestUpdate Frequency
CCD Directory19862023Annual
CCD Finance19892021Annual (2-year lag)
CRDC20112021Biennial
EDFacts20092020Annual
IPEDS19802023Annual
Scorecard19962020Annual
SAIPE19952023Annual
FSA19992021Annual

Example Research Scenarios

ScenarioData SourcesKey Variables
Charter vs traditional school outcomesCCD directory + EDFacts assessmentscharter, read_test_pct_prof_midpt
College affordability by incomeIPEDS directory + net-price-by-incomeinst_control, income_level, avg_net_price
Discipline disparities by raceCRDC discipline + enrollment (by race)race, oss_one, expulsions_*
Spending and graduation ratesCCD finance + EDFacts grad-ratesexp_current_per_pupil, grad_rate_midpt
School poverty and AP accessCRDC ap-ib-enrollment + MEPSap_enrollment, meps_poverty_pct
College earnings by majorIPEDS completions + Scorecard earningscip_code, earn_median_wne_p10

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