Improve dataset card with metadata and sample usage

README.md

@@ -1,5 +1,51 @@
-Link to the project: https://pageguide.github.io/
+---
+license: mit
+task_categories:
+- other
+---
 
-Link to the paper: https://huggingface.co/papers/2604.23772
+# PageGuide Dataset
 
-Link to the code: https://github.com/tin-xai/pageguide
+This repository contains the dataset for **PageGuide**, a browser extension that assists users in navigating webpages and locating information by grounding LLM answers directly in the HTML DOM. 
+
+* **Project Page:** [pageguide.github.io](https://pageguide.github.io/)
+* **Paper:** [PageGuide: Browser extension to assist users in navigating a webpage and locating information](https://huggingface.co/papers/2604.23772)
+* **Code:** [github.com/tin-xai/pageguide](https://github.com/tin-xai/pageguide)
+
+## Dataset Description
+
+The PageGuide evaluation utilizes several distinct datasets representing different tasks:
+
+1. **`pageguide_userstudy`**: Raw interaction logs from the user study — completion times, chat transcripts, correctness labels, paired statistical results, and post-study survey responses.
+2. **`pageguide_find_data`**: Task stimuli for the *Find* condition — 10 real webpages (NASA, Wikipedia, Cleveland Clinic, WWF, Britannica, JMLR) each annotated with up to 2 factual questions, ground-truth answers, and supporting evidence spans.
+3. **`pageguide_guide_data`**: Task stimuli for the *Guide* condition — 7 procedural tasks across 6 platforms (Google Sheets, Google Docs, Google Slides, Coda, TradingView, Scratch), labelled Easy or Medium difficulty.
+4. **`pageguide_hide_data`**: Task stimuli for the *Hide* condition — 37 annotated webpage snapshots (Amazon, Netflix, TechCrunch, Allrecipes, Spotify, Yelp, and more) with `(user_goal, hide_query, difficulty, hidden_elements)` annotations and ground-truth CSS selectors.
+
+## Sample Usage
+
+You can load these datasets using the Hugging Face `datasets` library:
+
+### User Study Data
+```python
+from datasets import load_dataset
+tasks  = load_dataset("ttn0011/pageguide_userstudy", data_files="tasks.csv", split="train").to_pandas()
+paired = load_dataset("ttn0011/pageguide_userstudy", data_files="paired_times.csv", split="train").to_pandas()
+```
+
+### Find Task Data
+```python
+from datasets import load_dataset
+find_tasks = load_dataset("ttn0011/pageguide_find_data", split="train").to_pandas()
+```
+
+### Guide Task Data
+```python
+from datasets import load_dataset
+guide_tasks = load_dataset("ttn0011/pageguide_guide_data", split="train").to_pandas()
+```
+
+### Hide Task Data
+```python
+from datasets import load_dataset
+hide_tasks = load_dataset("ttn0011/pageguide_hide_data", split="train").to_pandas()
+```