Add dataset metadata, paper links, and sample usage

README.md

@@ -1,5 +1,62 @@
-Link to the project: https://pageguide.github.io/
+---
+license: mit
+task_categories:
+- question-answering
+---
 
-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 artifacts for **PageGuide**, a browser extension to assist users in navigating a webpage and locating information.
+
+- **Project Page:** 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:** https://github.com/tin-xai/pageguide
+
+---
+
+## Dataset Subsets
+
+The PageGuide project releases several datasets for evaluation and research:
+
+### 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 as shown below:
+
+### User Study Logs
+```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()
+```