Update README.md

README.md

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         path: data/normalized/*.parquet
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-# FactoryNet Unified Robot Dataset
-(You can write your dataset description here!)
+# 🏭 FactoryNet: A Unified Multi-Machine Industrial Dataset
+
+## Overview
+FactoryNet is a large-scale, machine-learning-ready foundation dataset for industrial robotics and manufacturing anomaly detection. Historically, industrial datasets have been heavily siloed—every manufacturer and research team uses different column names, units, and structures. 
+
+FactoryNet solves this by forging massive, high-frequency physical datasets from completely different machines into a **single, mathematically unified coordinate system**. By standardizing axes, effort signals, and kinematic feedback, this dataset allows neural networks to learn universal physical relationships across different hardware boundaries.
+
+## 📦 Dataset Composition
+This repository currently contains millions of rows of high-frequency sensor data merged from three distinct open-source industrial datasets:
+
+### 1. UMich CNC Mill Tool Wear Dataset
+* **Machine:** 3-Axis CNC Mill
+* **Task:** Machining wax blocks under varying feedrates and clamp pressures.
+* **Anomalies:** Tool wear (Unworn vs. Worn) and visual inspection failures.
+* **Original Source:** University of Michigan (via Kaggle)
+
+### 2. AURSAD (Automated UR3e Screwdriving Anomaly Dataset)
+* **Machine:** UR3e 6-Axis Collaborative Robot
+* **Task:** Automated screwdriving using an OnRobot Screwdriver.
+* **Anomalies:** Normal operation, damaged screws, missing screws, extra parts, and damaged threads.
+* **Original Source:** Zenodo (Record 4487073)
+
+### 3. voraus-AD (Yu-Cobot Pick-and-Place)
+* **Machine:** Yu-Cobot 6-Axis Collaborative Robot
+* **Task:** Industrial pick-and-place task on a conveyor belt.
+* **Anomalies:** 12 diverse physical anomalies including axis wear (friction/miscommutation), gripping errors, collisions, and added axis weights.
+* **Original Source:** voraus robotik (via Kaggle)
+
+---
+
+## 🏗️ The Unified Schema
+To allow cross-machine learning, all raw variables (which previously had over 300 conflicting names) have been mapped to a standardized `FactoryNet` schema. 
+
+**Standardized Prefix Naming:**
+* `setpoint_*`: The commanded target from the controller (e.g., `setpoint_pos_0`).
+* `feedback_*`: The actual measured state from the sensors (e.g., `feedback_vel_1`).
+* `effort_*`: The physical force/current applied (e.g., `effort_current_2`, `effort_torque_0`).
+* `ctx_*`: Contextual metadata (e.g., `ctx_anomaly_label`, `ctx_busvoltage_0`).
+
+**Standardized Axis Indexing:**
+Regardless of how the original manufacturer numbered their joints (X/Y/Z or 1-6), all axes in this dataset are strictly zero-indexed (`0` through `5`).
+
+---
+
+## ⚙️ Configurations
+This dataset is partitioned into highly compressed Parquet files and is available in two configurations:
+
+1. **`raw`**: The original physical values (Amps, Volts, Radians, etc.) mapped directly into the new schema. Best for physics-informed neural networks or domain-specific thresholding.
+2. **`normalized`**: All continuous physical variables have been independently standardized using a Z-score Scaler (`StandardScaler`) fitted specifically to that machine's domain. Best for immediate deep learning and foundation model training.
+
+## 🚀 How to Use (Python)
+Because this dataset is partitioned using Parquet, you can load the entire multi-gigabyte repository without crashing your local RAM.
+
+```python
+from datasets import load_dataset
+
+# Load the mathematically normalized dataset for AI training
+dataset = load_dataset("your_username/FactoryNet_Unified_Robot_Data", "normalized")
+
+# Convert to a Pandas DataFrame
+df = dataset['train'].to_pandas()
+print(df['machine_type'].value_counts())