SkillAtlasSkill 详情

education-data-source-eada

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:1 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-source-eada" 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-source-eada" 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-source-eada" 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-source-eada" 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-source-eada" 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-source-eada
description: >-
  EADA — college athletics gender equity (~2,000+ institutions, 2002-2021). Participation, coaching, salaries, expenses, revenues, athletic aid by gender. Not Title IX compliance data. No sector column; join IPEDS on unitid for institution type.
metadata:
  audience: any-agent
  domain: data-source
  skill-authored: "2026-02-09"
  skill-last-updated: "2026-02-09"

EADA Data Source Reference

Equity in Athletics Disclosure Act (EADA) data for college athletics gender equity analysis covering ~2,000+ institutions (2002-2021). Use when analyzing athletic participation, coaching staff, salaries, expenses, revenues, or athletic aid by gender at colleges/universities, or understanding Title IX context in athletics. EADA is NOT Title IX compliance data. Note: no sector column; join to IPEDS on unitid to filter by institution type.

The EADA provides the only standardized, publicly available dataset on college athletics participation, coaching, finances, and athletic aid by gender across ~2,000+ postsecondary institutions, enabling gender equity analysis in intercollegiate athletics.

CRITICAL: Value Encoding

EADA data from the Education Data Portal uses integer codes for categorical variables. Original EADA web tools use string labels; the Portal converts these to integers. Always verify codes against the codebook (see Truth Hierarchy below).

Contextath_classification_codeMissing values
Portal (integers)1 = NCAA DI FBS-1, -2, -3
Original EADAString labelsBlank / N/A

Note: There is no sector column in EADA Portal data. To filter by sector, join with IPEDS directory data on unitid.

See ./references/variable-definitions.md for complete encoding tables.

What is EADA?

  • Collector: U.S. Department of Education (Office of Postsecondary Education)
  • Coverage: ~2,000+ coeducational postsecondary institutions with intercollegiate athletics
  • Mandate: Institutions participating in Title IV aid with athletic programs must report
  • Frequency: Annual (data publicly available by October 15 each year)
  • Available years: 2002–2021 (Portal mirror)
  • Primary identifier: unitid (6-digit IPEDS institution ID)
  • Content: Athletic participation, coaching staff, salaries, expenses, revenues, and athletic aid — all reported by gender
  • Available through: Education Data Portal mirrors

Reference File Structure

FilePurposeWhen to Read
title-ix-context.mdLegal framework, gender equity requirementsUnderstanding policy context
data-elements.mdParticipation, coaches, salaries, expenses, revenuesIdentifying available variables
sport-level-data.mdData available by individual sportSport-specific analysis
variable-definitions.mdKey variables, codes, special valuesInterpreting specific data elements
limitations.mdData quality issues, comparability, self-reporting caveatsAssessing data reliability
fetch-patterns.mdMirror URLs and fetch code patternsFetching data

Decision Trees

What analysis am I conducting?

Research question?
├─ Gender equity overview → Start with participation + aid ratios
│   └─ See ./references/data-elements.md
├─ Coaching disparities → Coach counts + salaries by gender
│   └─ See ./references/data-elements.md (Coaching section)
├─ Financial investment → Expenses + revenues by team gender
│   └─ See ./references/data-elements.md (Financial section)
├─ Sport-specific analysis → Individual sport data
│   └─ See ./references/sport-level-data.md
├─ Title IX compliance assessment → CAUTION: EADA ≠ compliance data
│   └─ See ./references/limitations.md (Critical)
└─ Trend analysis → Year-over-year comparisons
    └─ See ./references/fetch-patterns.md

What variables do I need?

Variable categories?
├─ Participation counts
│   ├─ Unduplicated by gender → `undup_athpartic_men`, `undup_athpartic_women`
│   ├─ Duplicated (sport-level sum) → `athpartic_men`, `athpartic_women`
│   ├─ Coed teams → `athpartic_coed_men`, `athpartic_coed_women`
│   └─ By sport → See ./references/sport-level-data.md
├─ Coaching
│   ├─ Head coaches → `men_fthdcoach_*`, `women_fthdcoach_*` variables
│   ├─ Assistant coaches → `men_ftascoach_*`, `women_ftascoach_*` variables
│   └─ Salaries → `hdcoach_salary_*`, `ascoach_salary_*` variables
├─ Financial
│   ├─ Expenses → `ath_exp_*` variables
│   ├─ Revenues → `ath_rev_*` variables
│   └─ Athletic aid → `ath_stuaid_*` variables
└─ Detailed definitions → See ./references/variable-definitions.md

How do I interpret the data?

Interpretation question?
├─ What counts as "participation"?
│   └─ See ./references/variable-definitions.md
├─ Why don't participation ratios match enrollment?
│   └─ See ./references/limitations.md
├─ Is this institution Title IX compliant?
│   └─ CANNOT determine from EADA data alone
│       └─ See ./references/limitations.md (Critical)
├─ Why are some values missing or zero?
│   └─ See ./references/limitations.md
└─ How do I compare across institutions?
    └─ See ./references/limitations.md (Comparability section)

Quick Reference: Key Metrics

Participation Equity Indicators

MetricCalculationInterpretation
Female participation ratioundup_athpartic_women / (undup_athpartic_men + undup_athpartic_women)Compare to female enrollment ratio
Participation gapFemale enrollment % - Female participation %Positive = underrepresentation
Opportunities per studentundup_athpartic_total / enrollment_totalAthletic opportunity rate

Financial Equity Indicators

MetricCalculationNotes
Aid ratioath_stuaid_women / (ath_stuaid_men + ath_stuaid_women)Should approximate participation ratio
Per-participant expenseath_opexp_perpart_men, ath_opexp_perpart_womenPre-calculated per-participant operating expense
Recruiting investmentrecruitexp_men, recruitexp_womenIndicator of program investment

Coaching Equity Indicators

MetricFocusVariables
Female coaches of women's teams% femalewomen_fthdcoach_fem, women_pthdcoach_fem
Salary equityAvg salary comparisonhdcoach_salary_men, hdcoach_salary_women

Key Identifiers

IDFormatLevelExampleNotes
unitid6-digit integerInstitution110635Same as IPEDS; primary join key
opeidStringInstitution"00123400"OPE ID (may be null for early years)
year4-digit integerReporting year2021Fiscal year ending
fipsIntegerState6 (California)Federal FIPS code
inst_nameStringInstitution"University of..."Institution name

Common Filters

FilterVariableExample Values
Institutionunitid6-digit IPEDS ID
Yearyear2002–2021
StatefipsInteger FIPS code (e.g., 6 = California)
Athletic Divisionath_classification_codeInteger codes 1–20 (see below)

Note: There is no sector column in the EADA Portal data. To filter by institutional sector, join with IPEDS directory data on unitid.

Athletic Classification Codes

CodeDivisionCodeDivision
1NCAA Division I FBS12NJCAA Division I
2NCAA Division I FCS13NJCAA Division II
3NCAA Division I (no football)14NJCAA Division III
4NCAA Division II (with football)15NCCAA Division I
5NCAA Division II (no football)16NCCAA Division II
6NCAA Division III (with football)17CCCAA
7NCAA Division III (no football)18Independent
8Other (check ath_classification_other)19NWAC
9NAIA Division I20USCAA
10NAIA Division II
11NAIA Division III

Note: Code 1 was historically labeled "NCAA Division I-A" and code 2 "NCAA Division I-AA" in earlier years. The ath_classification_name string column reflects the label used at the time of reporting.

Missing Data Codes

CodeMeaningWhen Used
-1Missing/not reportedData not submitted by institution
-2Not applicableItem doesn't apply (e.g., no men's team)
-3SuppressedData suppressed for privacy

Data Availability

TopicYears AvailableUpdate Frequency
Institution-level2002–2021Annual
Sport-level2002–2021Annual
Coaching details2002–2021Annual
Financial data2002–2021Annual

Note: Some columns (e.g., num_sports, aggregated totals with _all suffix) are null for earlier years (2002) and were added in later reporting cycles. The opeid column is null for 2002.

Example Research Questions

QuestionKey VariablesReference
Are women underrepresented in athletics?undup_athpartic_*, enrollment_*data-elements.md
How much do institutions invest in women's sports?ath_exp_*, ath_rev_*data-elements.md
Are coaches of women's teams paid fairly?hdcoach_salary_*variable-definitions.md
Which sports have most female participants?Sport-level datasport-level-data.md
Has participation equity improved over time?Multi-year trendfetch-patterns.md

Data Access

Datasets for EADA are available via the Education Data Portal mirror system. All data fetching uses fetch_from_mirrors() from fetch-patterns.md, with mirrors defined in mirrors.yaml and canonical paths in datasets-reference.md.

Key datasets:

DatasetPathTypeCodebook
Institutional Characteristicseada/colleges_eada_inst_characteristicsSingleeada/codebook_colleges_eada_inst-characteristics

EADA naming note: The data path uses inst_characteristics (underscores) while the codebook path uses inst-characteristics (hyphens). Always use the exact paths from datasets-reference.md.

Truth Hierarchy

When interpreting EADA variable definitions and coded values, apply this priority:

PrioritySourceRationale
1 (highest)Actual data file (parquet)What you observe IS the truth
2Live codebook (.xls via get_codebook_url())Authoritative documentation; may lag
3 (lowest)This skill's reference docsSummarized; convenient but may drift

Use get_codebook_url("eada/codebook_colleges_eada_inst-characteristics") from fetch-patterns.md to construct the codebook download URL.

Filtering

import polars as pl

# Filter by athletic division (NCAA Division I FBS only)
df_d1_fbs = df.filter(pl.col("ath_classification_code") == 1)

# Exclude coded missing values before calculations
df_clean = df.filter(
    (pl.col("undup_athpartic_men") >= 0) &
    (pl.col("undup_athpartic_women") >= 0)
)

# Note: No `sector` column in EADA data. To filter by sector,
# join with IPEDS directory data on unitid first.

Common Pitfalls

PitfallIssueSolution
Including coded missing values-1, -2, -3 treated as real numbers skew totals and ratiosFilter >= 0 on all numeric columns before aggregation
Assuming Title IX complianceEADA data cannot determine Title IX compliance — it is a disclosure tool, not an enforcement mechanismRead ./references/limitations.md; use EADA for descriptive analysis only
Comparing across institutions naivelyDifferent reporting practices, program sizes, and classification levels make raw comparisons misleadingNormalize by enrollment, filter to same classification, and note caveats
Using wrong variable namesPortal variable names differ from EADA source documentation (e.g., undup_athpartic_men not partic_men)Always verify column names against actual data or codebook; see ./references/variable-definitions.md
Self-reported data accuracyInstitutions self-report without independent verification; errors and inconsistencies existCross-check outliers against institution websites or IPEDS data
Ignoring zero valuesZero may mean "no team" or "not reported" depending on contextDistinguish between true zeros and missing data using -1/-2 codes
Assuming sector column existsEADA data has no sector columnJoin with IPEDS directory on unitid to get sector

EADA vs. Title IX Compliance

EADA Data                          Title IX Compliance
──────────────────────────────────────────────────────────
Self-reported                      OCR investigation
Snapshot (Oct 15)                  Continuous obligation
Participation counts only          Participation + interest + ability
No "laundry list" items           13+ treatment areas
Public disclosure                  Enforcement mechanism

Always read: ./references/limitations.md before drawing compliance conclusions.

Key Limitations Summary

  • Self-reported: No independent verification
  • Counting methods: Differ from Title IX counting
  • Not comprehensive: Misses many equity factors
  • Comparability issues: Different reporting practices across institutions

Related Data Sources

SourceRelationshipWhen to Use
education-data-source-ipedsComplementary institution dataJoining enrollment, demographics, finances via unitid
education-data-explorerParent discovery skillFinding available endpoints across all sources
education-data-queryData fetchingDownloading parquet/CSV files from mirrors

Topic Index

TopicReference File
Title IX law./references/title-ix-context.md
Gender equity requirements./references/title-ix-context.md
Three-prong test./references/title-ix-context.md
Participation variables./references/data-elements.md
Coaching variables./references/data-elements.md
Salary variables./references/data-elements.md
Expense variables./references/data-elements.md
Revenue variables./references/data-elements.md
Athletic aid./references/data-elements.md
Sport-specific data./references/sport-level-data.md
Variable definitions./references/variable-definitions.md
Integer encoding tables./references/variable-definitions.md
Data limitations./references/limitations.md
Self-reporting issues./references/limitations.md
EADA vs Title IX./references/limitations.md
Fetch patterns./references/fetch-patterns.md
Mirror URLs./references/fetch-patterns.md

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