Update README.md

README.md

@@ -9,6 +9,8 @@ tags:
 - aircraft-tracking
 - trajectories
 - reinforcement-learning
+- tabular-data
+- time-series
 size_categories:
 - 10K<n<100K
 ---
@@ -16,32 +18,34 @@ size_categories:
 # TartanAviation ADS-B Dataset (19.7K Clean Samples)
 
 ## Dataset Description
+
 **19,714 high-quality ADS-B trajectory datapoints** from aircraft operations, rigorously cleaned and validated. Perfect for machine learning research in aviation, reinforcement learning, and trajectory prediction.
 
 ## Key Features
-- ✅ **19,714 clean samples** - No missing values, no duplicates
-- ✅ **4,946 unique aircraft** - Excellent diversity for ML training
-- ✅ **17 comprehensive features** - Complete aircraft state information
-- ✅ **Realistic aviation values** - Altitude ≤45K ft, Speed ≤600 kts
-- ✅ **Two formats available** - CSV (human-readable) + JSONL (ML-optimized)
+- 19,714 clean samples (no missing data, no duplicates)  
+- 17 comprehensive features including aircraft ID, timestamp components, altitude, speed, heading, geolocation, and metadata  
+- Data period: January to October 2022  
+- Geographic coverage: Ohio airspace (KBTP airport region)  
+- Formats available: CSV (human-readable) and JSONL (ML-optimized)
 
 ## Dataset Structure (17 columns)
-- **Temporal**: year, month, day, hour, minute, second
-- **Aircraft State**: aircraft_id, altitude_ft, ground_speed_kts, heading_deg
-- **Location**: latitude, longitude
-- **Metadata**: aircraft_tail, data_age_sec, range_nm, bearing_deg, altitude_is_gnss
+- Temporal: year, month, day, hour, minute, second  
+- Aircraft State: aircraft_id, altitude_ft, ground_speed_kts, heading_deg  
+- Location: latitude, longitude  
+- Metadata: aircraft_tail, data_age_sec, range_nm, bearing_deg, altitude_is_gnss
 
 ## Use Cases
-- 🚁 **Reinforcement Learning** for air traffic management
-- 📈 **Trajectory Prediction** algorithms
-- 🕸️ **Graph Neural Networks** with aircraft interactions  
-- 📊 **Aviation Analytics** and safety research
-- ⏱️ **Time Series Forecasting** for flight patterns
+- Reinforcement learning for air traffic management  
+- Aircraft trajectory prediction  
+- Graph neural networks modeling  
+- Aviation safety analysis  
+- Time series forecasting for flight dynamics  
 
 ## Quick Start
+
 import pandas as pd
 
-Load the dataset
+Load CSV dataset
 df = pd.read_csv('tartanaviation_adsb_19k_clean.csv')
 print(f"Dataset shape: {df.shape}")
 print(f"Unique aircraft: {df['aircraft_id'].nunique()}")
@@ -49,16 +53,22 @@ print(f"Unique aircraft: {df['aircraft_id'].nunique()}")
 text
 
 ## Dataset Stats
-- **Samples**: 19,714
-- **Features**: 17  
-- **Unique Aircraft**: 4,946
-- **Time Period**: January - October 2022
-- **Region**: Ohio airspace (KBTP airport area)
-- **Data Quality**: 100% complete, validated
+- Samples: 19,714  
+- Features: 17  
+- Unique Aircraft: 4,946  
+- Time Period: January - October 2022  
+- Region: Ohio airspace (KBTP airport area)  
+- Data Quality: 100% complete, validated
 
 ## Citation
+
 @dataset{tartanaviation_adsb_2024,
 title={TartanAviation ADS-B Dataset (19.7K Clean Samples)},
+author={Pathange},
 year={2024},
-publisher={Hugging Face}
-}
+publisher={Hugging Face},
+url={https://huggingface.co/datasets/Pathange/tartanaviation-adsb-19k-clean}
+}
+
+text
+undefined