Update README

README.md

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+---
+license: other
+license_name: public-domain
+task_categories:
+- tabular-classification
+- tabular-regression
+tags:
+- executive-compensation
+- sec-edgar
+- esg
+- governance
+- quant
+- finance
+- proxy-statements
+- united-states
+pretty_name: SEC EDGAR Executive Compensation 2015-Present
+size_categories:
+- 100K<n<1M
+---
+
+# SEC EDGAR Executive Compensation 2015–Present
+
+500K+ structured executive compensation records for S&P 500 + Russell 2000 companies — parsed from SEC EDGAR DEF 14A proxy filings, 2015–present.
+CEO/CFO pay, stock awards, bonuses, non-equity incentives, and total compensation, linked by CIK, ticker, and fiscal year.
+The most comprehensive open dataset for executive pay research, ESG governance scoring, and quant finance.
+
+| 📊 Records | 📅 Coverage | 🏷️ License | 🔄 Updated |
+|-----------|-------------|-----------|-----------|
+| 500K+ compensation records | 2015–present | Public Domain | Annual |
+
+**This repo contains a free 1,000-row sample.**
+Full dataset (CSV + Parquet) → **[claritystorm.com/datasets/sec-edgar-exec-comp](https://claritystorm.com/datasets/sec-edgar-exec-comp)**
+
+---
+
+## Quick Start
+
+```python
+from datasets import load_dataset
+import pandas as pd
+
+# Load the 1,000-row sample
+ds = load_dataset("claritystorm/sec-edgar-exec-comp")
+df = ds["train"].to_pandas()
+
+# CEO compensation trend by fiscal year
+ceo = df[df["is_ceo"] == True]
+print(ceo.groupby("fiscal_year")["total_compensation"].mean().round(0))
+
+# Top 10 highest-paid CEOs in latest year
+latest_year = ceo["fiscal_year"].max()
+top_ceos = (ceo[ceo["fiscal_year"] == latest_year]
+            [["company_name", "executive_name", "total_compensation"]]
+            .sort_values("total_compensation", ascending=False)
+            .head(10))
+print(top_ceos)
+
+# Pay mix: stock awards vs. salary
+df["stock_pct"] = df["stock_awards"] / df["total_compensation"].replace(0, pd.NA)
+print(df.groupby("fiscal_year")["stock_pct"].mean().round(3))
+```
+
+## Use Cases
+
+- **ESG governance scoring** — CEO-to-median-worker pay ratios, pay-for-performance alignment, and board compensation governance
+- **Quant factor research** — executive pay as an alpha signal; overpaid vs. underpaid CEO portfolios
+- **Corporate governance AI** — train models to flag anomalous compensation structures or governance red flags
+- **Pay equity research** — compare total compensation across sectors, company sizes, and fiscal years
+- **Proxy advisor analytics** — replicate and extend institutional proxy voting research at scale
+- **Financial NLP** — link to DEF 14A proxy text for compensation discussion and analysis (CD&A) NLP
+
+## Schema (selected fields)
+
+| Field | Type | Description |
+|-------|------|-------------|
+| cik | string | SEC Central Index Key (unique company identifier) |
+| ticker | string | Stock ticker symbol |
+| company_name | string | Company name as filed with SEC |
+| fiscal_year | int | Fiscal year of the compensation disclosure |
+| date_filed | string | Proxy filing date (YYYY-MM-DD) |
+| executive_name | string | Executive's full name (normalized) |
+| title | string | Executive's title/position |
+| is_ceo | bool | True if title indicates Chief Executive Officer |
+| is_cfo | bool | True if title indicates Chief Financial Officer |
+| salary | float | Base salary ($) |
+| bonus | float | Discretionary bonus ($) |
+| stock_awards | float | Grant-date fair value of stock awards ($) |
+| option_awards | float | Grant-date fair value of option awards ($) |
+| non_equity_incentive | float | Non-equity incentive plan compensation ($) |
+| total_compensation | float | Total reported compensation as filed ($) |
+| total_comp_computed | float | ClarityStorm computed total (sum of components) |
+| sector | string | Industry sector (where available) |
+
+## ⬇️ Get the Full Dataset
+
+| Tier | Price | Includes |
+|------|-------|----------|
+| Sample | Free | 1,000 rows, Public Domain (this repo) |
+| Complete | $149 | Full 500K+ rows, CSV + Parquet, commercial license |
+| Annual | $299/yr | Complete + annual updates (new proxy season each year) |
+
+👉 **[Purchase at claritystorm.com/datasets/sec-edgar-exec-comp](https://claritystorm.com/datasets/sec-edgar-exec-comp)**
+
+## Source
+
+U.S. Securities and Exchange Commission (SEC), EDGAR — DEF 14A Proxy Statements.
+SEC EDGAR data is a US federal government work in the **public domain** (17 U.S.C. 105).
+Executive compensation for Named Executive Officers (NEOs) is required by SEC Regulation S-K Item 402.
+
+Processed and structured by [ClarityStorm Data](https://claritystorm.com).