Improve dataset card: Add task categories, paper link, project page, tags, and sample usage

This PR enhances the dataset card by:
- Adding `task_categories: ['time-series-forecasting']` and comprehensive `tags` to the metadata for better discoverability, including `time-series`, `self-supervised-learning`, `representation-learning`, `time-series-classification`, and `time-series-regression`.
- Updating the initial reference to include a direct link to the paper on Hugging Face Papers: [https://huggingface.co/papers/2510.22655](https://huggingface.co/papers/2510.22655).
- Adding a link to the official NeurIPS 2025 project page.
- Including a "Datasets Included" section to clearly list the nine datasets and their associated tasks.
- Adding a "Sample Usage" section with code snippets from the GitHub repository's "Quickstart" guide, demonstrating how to use the dataset for pre-training and testing.

These updates provide users with richer context and clearer guidance on how to use this valuable resource.

README.md

@@ -1,8 +1,57 @@
 ---
 language:
 - en
+task_categories:
+- time-series-forecasting
+tags:
+- time-series
+- self-supervised-learning
+- representation-learning
+- time-series-classification
+- time-series-regression
 ---
 
-This dataset repository contains the **preprocessed data** used in our NeurIPS 2025 paper *Learning Without Augmenting*. It includes all nine datasets across five time-series tasks in different ready-to-use format. For training code, loaders, and preprocessing scripts, please see our GitHub repository:
-👉 [https://github.com/eth-siplab/Learning-with-FrameProjections](https://github.com/eth-siplab/Learning-with-FrameProjections)
+This dataset repository contains the **preprocessed data** used in our NeurIPS 2025 paper [Learning Without Augmenting: Unsupervised Time Series Representation Learning via Frame Projections](https://huggingface.co/papers/2510.22655).
 
+**Paper:** [Learning Without Augmenting: Unsupervised Time Series Representation Learning via Frame Projections](https://huggingface.co/papers/2510.22655)
+**Project Page:** [https://neurips.cc/virtual/2025/poster/118514](https://neurips.cc/virtual/2025/poster/118514)
+**GitHub Repository:** [https://github.com/eth-siplab/Learning-with-FrameProjections](https://github.com/eth-siplab/Learning-with-FrameProjections)
+
+### Datasets Included
+This repository includes all nine datasets across five time-series tasks in different ready-to-use formats, as used in the paper:
+*   **Heart rate estimation:** IEEE SPC12, IEEE SPC22, DaLiA
+*   **Activity recognition:** HHAR, USC
+*   **Cardiovascular disease classification:** CPSC2018, Chapman
+*   **Step counting:** Clemson
+*   **Sleep staging:** Sleep-EDF
+
+### Sample Usage
+
+This dataset contains the preprocessed data that can be used with the associated code from the [Learning-with-FrameProjections GitHub repository](https://github.com/eth-siplab/Learning-with-FrameProjections). Here are the quickstart commands for pre-training and testing:
+
+**Pre-training + testing (our method)**
+
+```bash
+python main.py \
+  --framework isoalign \
+  --backbone resnet \
+  --dataset ieee_small \
+  --n_epoch 256 \
+  --batch_size 1024 \
+  --lr 1e-3 \
+  --lr_cls 0.03 \
+  --cuda 0 \
+  --cases subject_large
+```
+
+**Supervised baseline**
+
+```bash
+python main_supervised_baseline.py \
+  --dataset ieee_small \
+  --backbone resnet \
+  --block 8 \
+  --lr 5e-4 \
+  --n_epoch 999 \
+  --cuda 0
+```