Update README

README.md

@@ -18,26 +18,53 @@ size_categories:
 
 # OSHA Workplace Injuries & Illness 2016–2023
 
-Every establishment-level workplace injury and illness record submitted to the OSHA
-Injury Tracking Application (ITA) since 2016 — cleaned, deduplicated, and structured
-for analysis. 2.38 million records across 8 survey years covering 600K+ unique
-establishments with NAICS codes, DART/TCIR rates, and detailed injury/illness breakdowns.
+Every establishment-level workplace injury and illness record submitted to the OSHA Injury Tracking Application (ITA) since 2016.
+**2.38 million records** across 8 survey years covering 600K+ unique establishments — with NAICS codes, DART/TCIR rates, and detailed injury/illness breakdowns.
+The only public dataset linking individual establishment safety performance to industry benchmarks.
 
-**This repository contains a 1,000-row sample (Public Domain).**
-Full dataset (CSV + Parquet) available at [claritystorm.com/datasets/osha-injuries](https://claritystorm.com/datasets/osha-injuries).
+| 📊 Records | 📅 Coverage | 🏷️ License | 🔄 Updated |
+|-----------|-------------|-----------|-----------|
+| 2.38M+ records | 2016–2023 (8 years) | Public Domain | Annual |
+
+**This repo contains a free 1,000-row sample.**
+Full dataset (CSV + Parquet) → **[claritystorm.com/datasets/osha-injuries](https://claritystorm.com/datasets/osha-injuries)**
+
+---
 
 ## Quick Start
 
 ```python
 from datasets import load_dataset
+import pandas as pd
+
+# Load the 1,000-row sample
 ds = load_dataset("claritystorm/osha-workplace-injuries")
+df = ds["train"].to_pandas()
 
-import pandas as pd
-df = pd.read_csv("sample_1000.csv")
+# Total workplace deaths by year
 print(df.groupby("survey_year")["total_deaths"].sum())
-print(df.groupby("survey_year")["dart_rate"].mean())
+
+# Industries with highest average DART rate
+print(df.groupby("industry_description")["dart_rate"].mean()
+      .sort_values(ascending=False).head(10))
+
+# Establishments with zero injuries (benchmark group)
+safe_pct = df["no_injuries_illnesses"].mean() * 100
+print(f"Establishments with zero injuries: {safe_pct:.1f}%")
+
+# Size class distribution
+print(df["size_class"].value_counts())
 ```
 
+## Use Cases
+
+- **Workplace safety risk scoring** — predict DART/TCIR rates from NAICS code, size class, and historical performance
+- **ESG & responsible investing** — screen company supply chains and subsidiaries for OSHA safety performance
+- **Insurance underwriting** — establishment-level injury rates for workers' compensation risk modeling
+- **OSHA compliance benchmarking** — compare an establishment's safety record to industry averages
+- **Industry safety trend analysis** — 8-year panel data tracks safety improvements and deterioration by sector
+- **Human capital ML** — injury/illness rates as a feature for company quality and labor conditions scoring
+
 ## Schema (selected fields)
 
 | Field | Type | Description |
@@ -45,33 +72,32 @@ print(df.groupby("survey_year")["dart_rate"].mean())
 | survey_year | int | OSHA reporting year (2016–2023) |
 | estab_name | string | Establishment name |
 | company_name | string | Parent company name |
-| street_address | string | Street address |
-| city | string | City |
 | state | string | US state (2-letter code) |
-| naics_code | string | 6-digit NAICS code (zero-padded) |
+| naics_code | string | 6-digit NAICS code |
 | industry_description | string | Industry description |
 | size_class | string | Establishment size class |
 | annual_average_employees | int | Annual average employee count |
 | total_hours_worked | int | Total hours worked |
-| no_injuries_illnesses | int | 1 if no recordable injuries/illnesses |
 | total_deaths | int | Total workplace deaths |
 | total_dafw_cases | int | Days Away From Work cases |
-| total_djtr_cases | int | Job Transfer or Restriction cases |
-| total_other_cases | int | Other recordable cases |
-| total_dafw_days | int | Days away from work |
-| total_djtr_days | int | Days of job transfer or restriction |
 | total_injuries | int | Total injuries |
-| total_poisonings | int | Poisoning cases |
-| total_hearing_loss | int | Hearing loss cases |
-| total_other_illnesses | int | Other illness cases |
 | total_resp_conditions | int | Respiratory condition cases |
-| total_skin_disorders | int | Skin disorder cases |
 | dart_rate | float | DART rate per 100 FTE (derived) |
 | tcir_rate | float | TCIR rate per 100 FTE (derived) |
 
+## ⬇️ Get the Full Dataset
+
+| Tier | Price | Includes |
+|------|-------|----------|
+| Sample | Free | 1,000 rows, Public Domain (this repo) |
+| Complete | $79 | Full 2.38M+ rows, CSV + Parquet, commercial license |
+| Annual | $149/yr | Complete + annual updates |
+
+👉 **[Purchase at claritystorm.com/datasets/osha-injuries](https://claritystorm.com/datasets/osha-injuries)**
+
 ## Source
 
 US Occupational Safety and Health Administration (OSHA), Injury Tracking Application (ITA).
 OSHA ITA data is a US federal government work in the **public domain** (17 U.S.C. 105).
 
-Processed by [ClarityStorm Data](https://claritystorm.com).
+Processed and structured by [ClarityStorm Data](https://claritystorm.com).