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---
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 |