---
license: mit
task_categories:
- feature-extraction
tags:
- biology
---
# SequenceDistanceDataset (SDD)  
**A Cross-Domain Benchmark for Sequence Similarity Analysis**  

## Overview  
A comprehensive benchmarking dataset for evaluating distance metrics in two domains:  
- 🧬 **Biological Sequences** (Proteins from UniProt/UniRef)  
- 🗺️ **Movement Trajectories** (GPS data from 3 cities)  

Designed to support research in similarity search, metric learning, and cross-domain analysis.  

---

## Key Features  
✅ **Precomputed Distance Matrices**  
- Eliminates computation overhead for direct benchmarking  
- Includes both training and test sets  

✅ **Curated Data Splits**  
- Standardized `train`/`query`/`base` splits for retrieval tasks  
- Reproducible evaluation protocols  

✅ **Diverse Metrics**  
| Domain          | Metrics                              |
|-----------------|--------------------------------------|
| **Proteins**    | Edit Distance (ED), Needleman-Wunsch (NW) |
| **Trajectories**| DTW, Hausdorff, Fréchet, EDR, EDwP  |

✅ **Real-World Scale**  
- Contains large mobility datasets (e.g., Porto taxi trajectories)  
- Represents diverse geographical contexts (Beijing, Chengdu, Portugal)  

---

## Dataset Structure  

### Protein Domain  
```plaintext
Protein/
├── {UniProt, UniRef}/
│   ├── train_list.txt
│   ├── query_list.txt
│   ├── base_list.txt
│   └── {ED, NW}/  
│       ├── {train/test}_distance_matrix_result
```

### Trajectory Domain
```plaintext
Trajectory/
├── {Geolife, Porto, Chengdu, TrajCL_Porto}/
│   ├── train/
│   ├── query/
│   ├── base/
│   └── {DTW, Haus, DFD, EDR, EDwP}/  
│       ├── {train/test}_distance_matrix_result
```

## Use Cases
### 🔬 ​​Bioinformatics​​

- Compare protein alignment algorithms
- Study sequence homology detection
### 🚕 ​​Urban Computing​​

- Evaluate trajectory similarity methods
- Analyze mobility pattern variations
### 🤖 ​​Machine Learning​​

- Train distance metric learning models
- Benchmark neural embedding methods