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README.md

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 ---
 license: cc-by-nc-4.0
+task_categories:
+  - question-answering
+  - multiple-choice
+language:
+  - en
+tags:
+  - wearable
+  - health
+  - time-series
+  - reasoning
+  - benchmark
+size_categories:
+  - 1K<n<10K
+pretty_name: WearableQA
+configs:
+  - config_name: row
+    default: true
+    data_files:
+      - split: test
+        path: data/row/test-*.parquet
+  - config_name: col
+    data_files:
+      - split: test
+        path: data/col/test-*.parquet
+  - config_name: csv
+    data_files:
+      - split: test
+        path: data/csv/test-*.parquet
+  - config_name: markdown
+    data_files:
+      - split: test
+        path: data/markdown/test-*.parquet
+  - config_name: structured
+    data_files:
+      - split: test
+        path: data/structured/test-*.parquet
 ---
+
+# WearableQA
+
+**A benchmark for health reasoning over real-world wearable data.**
+
+WearableQA comprises **4,084 ten-option multiple-choice questions** built from the wearable time series,
+blood biomarkers, and demographics of **200 real users**, each with up to about 500 days of daily
+measurements. Unlike benchmarks built on synthetic or idealized signals, it preserves authentic
+wearable distributions — device noise, missing days, and inter-individual variability included.
+
+- 📄 Paper: https://arxiv.org/abs/2609.05405
+- 💻 Code: https://github.com/facebookresearch/WearableQA
+
+## Quick start
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("facebook/WearableQA", split="test")
+
+ex = ds[0]
+ex["question"]   # the complete prompt, ready to send to a model
+ex["choices"]    # {"A": ..., ..., "J": ...}
+ex["answer"]     # "A"
+```
+
+The dataset is large (median prompt ~86k characters), so streaming is often convenient:
+
+```python
+ds = load_dataset("facebook/WearableQA", split="test", streaming=True)
+```
+
+## Configurations
+
+The same 4,084 questions in five forms. Ids and answers are identical across all of them — only the
+way the sensor time series is presented changes.
+
+| Config | What it gives you |
+|---|---|
+| **`row`** *(default)* | Prompt with the sensor data as one line per day. This is the released benchmark and the setting the paper reports. |
+| `col` | One block per metric, showing each metric's trajectory together. |
+| `csv` | Dense CSV table, missing values as empty fields. |
+| `markdown` | The same table in markdown. |
+| **`structured`** | No prompt text. Each record carries its own sliced sensor window as **structured values**, so you can build your own prompt. |
+
+```python
+load_dataset("facebook/WearableQA", "markdown", split="test")     # a different serialization
+load_dataset("facebook/WearableQA", "structured", split="test")   # build your own prompts
+```
+
+The representation matters: in our experiments it moved accuracy by several points, and image-based
+renderings of the same data were far worse than any text form.
+
+### Fields — rendered configs (`row`, `col`, `csv`, `markdown`)
+
+| Field | Type | Meaning |
+|---|---|---|
+| `id` | string | Unique question id (`<source>_<user>_<end-date>`) |
+| `question` | string | The complete prompt: instruction, user profile, sensor history, blood panel, cohort percentiles, question stem, and options |
+| `choices` | struct | The ten options, keyed `A`–`J` |
+| `answer` | string | Ground-truth option letter |
+| `category` | string | One of the 16 question types |
+| `reasoning_group` | string | `data` or `health` |
+| `signal` | string | `single` or `cross` |
+| `grounding` | string | `population` or `literature` |
+| `representation` | string | Which serialization this config used |
+
+### Fields — `structured`
+
+Everything above except `question` and `representation`, plus:
+
+| Field | Type | Meaning |
+|---|---|---|
+| `stem` | string | The question text on its own, without the surrounding prompt |
+| `sensor_history` | list of structs | **This question's own window** — one struct per day, with `date` and the 16 metrics (null where the device recorded nothing) |
+| `demographics` | JSON string | Age, sex, BMI, ethnicity |
+| `blood_panel` | JSON string | Up to 17 biomarkers |
+| `cohort_reference` | JSON string | Population percentiles (empty when the question withholds them) |
+| `end_date` | string | Last day of the observation window |
+| `window_size` | int | Length in days of the window the question asks about (28 throughout) |
+
+Each record is self-contained — no joins against a separate user table:
+
+```python
+ds = load_dataset("facebook/WearableQA", "structured", split="test")
+ex = ds[0]
+ex["sensor_history"][0]    # {"date": "2023-10-20", "steps": 27858.0, "rhr": 38.0, ...}
+
+# build whatever prompt you want
+my_prompt = f"{ex['stem']}\n" + "\n".join(
+    f"{d['date']}: steps={d['steps']}, rhr={d['rhr']}" for d in ex["sensor_history"])
+```
+
+## Taxonomy
+
+The 16 question types are organized along two complementary axes:
+
+- **Data vs. health reasoning** — computing over longitudinal measurements (correlations, excursion
+  counts, recovery times, trend shapes) versus interpreting them physiologically (risk assessment,
+  differential diagnosis, prognostic prediction).
+- **Single- vs. cross-signal reasoning** — reasoning within one metric versus integrating several.
+
+| Axis | Split | Count |
+|---|---|---|
+| Reasoning group | data / health | 2,724 / 1,360 |
+| Signal complexity | single / cross | 1,682 / 2,402 |
+| Grounding | population / literature | 3,154 / 930 |
+
+Ground-truth answers are balanced uniformly across options A–J within each reasoning group, so the
+random baseline is **10%**.
+## Citation
+
+```bibtex
+@misc{lee2026wearableqa,
+      title={{WearableQA}: A Benchmark for Health Reasoning over Real-World Wearable Data},
+      author={Ji Soo Lee and Xilun Chen and Pierce Chuang and Ashish Shenoy and Jason Wei and Dohwan Ko and Hyunwoo J. Kim and Benoit Corda},
+      year={2026},
+      eprint={2609.05405},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2609.05405},
+}
+```
+
+## License
+
+The data is licensed under Creative Commons Attribution-Non Commercial 4.0 International
+(CC BY-NC 4.0), and subject to the following additional terms: (i) No re-identification or attempted
+re-identification; (ii) No use in connection with clinical, diagnostic, or treatment decisions;
+(iii) No use in a manner that is discriminatory, harmful, or misleading with respect to
+health-related outcomes; (iv) The Dataset is provided "as is", without warranties of any kind,
+whether express or implied, including without limitation accuracy, completeness, or fitness for a
+particular purpose, and is provided for research and benchmarking purposes only.

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