Add dataset card for HAROOD benchmark

This PR adds a dataset card for the HAROOD benchmark, which includes:
- Links to the paper (KDD 2026) and the official GitHub repository.
- Metadata including task categories and descriptive tags.
- Sample usage code snippets derived from the GitHub README.
- A list of supported OOD generalization algorithms.
- Citation information for the research paper.

README.md

@@ -0,0 +1,77 @@
+---
+task_categories:
+- other
+tags:
+- human-activity-recognition
+- sensor-data
+- time-series
+- out-of-distribution
+---
+
+# HAROOD: A Benchmark for Out-of-distribution Generalization in Sensor-based Human Activity Recognition
+
+[**Paper**](https://huggingface.co/papers/2512.10807) | [**GitHub Repository**](https://github.com/AIFrontierLab/HAROOD)
+
+HAROOD is a modular and reproducible benchmark framework for studying generalization in sensor-based human activity recognition (HAR). It unifies preprocessing pipelines, standardizes four realistic OOD scenarios (cross-person, cross-position, cross-dataset, and cross-time), and implements 16 representative algorithms across CNN and Transformer architectures.
+
+## Key Features
+
+- **6 public HAR datasets** unified under a single framework.
+- **5 realistic OOD scenarios**: cross-person, cross-position, cross-dataset, cross-time, and cross-device.
+- **16 generalization algorithms** spanning Data Manipulation, Representation Learning, and Learning Strategies.
+- **Backbone support**: Includes both CNN and Transformer-based architectures.
+- **Standardized splits**: Provides train/val/test model selection protocols.
+
+## Usage
+
+The benchmark is designed to be modular. Below are examples of how to run experiments using the official implementation:
+
+### Run with a YAML config
+
+```python
+from core import train
+results = train(config='./config/experiment.yaml')
+```
+
+### Run with a Python dict
+
+```python
+from core import train
+config_dict = {
+    'algorithm': 'CORAL',
+    'batch_size': 32,
+}
+results = train(config=config_dict)
+```
+
+### Override parameters
+
+```python
+from core import train
+results = train(
+    config='./config/experiment.yaml',
+    lr=2e-3,
+    max_epoch=200,
+)
+```
+
+## Supported Algorithms
+
+The benchmark implements 16 algorithms across three main categories:
+
+- **Data Manipulation**: Mixup, DDLearn.
+- **Representation Learning**: ERM, DANN, CORAL, MMD, VREx, LAG.
+- **Learning Strategy**: MLDG, RSC, GroupDRO, ANDMask, Fish, Fishr, URM, ERM++.
+
+## Citation
+
+If you use HAROOD in your research, please cite the following paper:
+
+```bibtex
+@inproceedings{lu2026harood,
+  title={HAROOD: A Benchmark for Out-of-distribution Generalization in Sensor-based Human Activity Recognition},
+  author={Lu, Wang and Zhu, Yao and Wang, Jindong},
+  booktitle={The 32rd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)},
+  year={2026}
+}
+```