README.md

---
dataset_name: strava_master_dataset
pretty_name: Strava Master Dataset
license: cc-by-nc-4.0
task_categories:
  - time-series-forecasting   # ← ここを修正
tags:
  - running
  - cycling
  - wearable
language:
  - en
---

# Strava Master Dataset ― Benj-samurai

> **Personal multi-sport training log** exported from Strava (JP CSV) and processed into a clean, analysis-ready Parquet table + weekly summaries.  
> Covers **〔開始日 – 終了日〕**, total **〔活動件数〕 activities** across **〔種目数〕 sports**.

---

## Files

| File | Rows | Description |
|------|-----:|-------------|
| `my_strava_dataset/strava_master_enhanced.parquet` | 〔n〕 | Master table — 1 row = 1 activity |
| `my_strava_dataset/weekly_sport.parquet` | 〔m〕 | Weekly totals by *year–week × sport* |
| `my_strava_dataset/weekly_category.parquet` | 〔k〕 | Weekly totals by intensity zone |

*(Parquet → compact, schema-aware, loadable via `datasets.load_dataset`)*  

---

## Column definitions (master)

| Column | `dtype` | Description |
|--------|---------|-------------|
| `activity_id` | `int64` | Strava Activity ID |
| `name` | `string` | Activity title as saved on Strava |
| `sport` | `category` | Sport type (`Run`, `Ride`, `Swim`, `Walk`, …) |
| `date` | `datetime64[ns]` | Local activity start time |
| `distance_km` | `float32` | Distance in **kilometres** (raw m → km) |
| `elapsed_hr` | `float32` | Elapsed time incl. pauses **hours** (raw sec → h) |
| `moving_hr` | `float32` | Moving time (in-motion) **hours** |
| `elevation_gain_m` | `float32` | Total positive elevation gain (m) |
| `elevation_loss_m` | `float32` | Total negative elevation (m) |
| `average_speed_kph` | `float32` | Moving speed (km h⁻¹) |
| `max_speed_kph` | `float32` | Max speed (km h⁻¹) |
| `average_hr` | `float32` | Avg heart-rate (bpm) – *NaN if no sensor* |
| `max_hr` | `int16` | Max heart-rate (bpm) |
| `average_cadence` | `float32` | Avg cadence (rpm) |
| `max_cadence` | `int16` | Max cadence (rpm) |
| `average_power` | `float32` | Avg power (W); bike only |
| `max_power` | `int16` | Peak power (W) |
| `calories_kcal` | `float32` | Calories reported by Strava |
| `training_category` | `category` | HR zone label `Z1-2 / Z3 / Z4 / Z5 / NoHR` |
| `intensity_level` | `float32` | Avg HR ÷ LTHR (165 bpm) |
| `trimp` | `float32` | Banister TRIMP (`moving_hr × intensity_level × 50`) |
| `commute` | `boolean` | Marked as commute on Strava |
| `filename` | `string` | Original FIT/GPX filename (meta only) |
| `gear` | `string` | Bike / shoes used (if set) |
| `weather` | `string` | Weather summary (if available) |
| `temperature_c` | `float32` | Avg temp (°C) |
| `flagged` | `boolean` | Strava flagged activity |
| `year` | `int16` | Calendar year (`date`). fast grouping |
| `month` | `int16` | Calendar month (1–12) |
| `week` | `int16` | ISO week number (1–53) |
| `year_month` | `string` | `"YYYY-MM"` label for plotting |
| `week_start` | `datetime64[ns]` | Monday of ISO week (analysis helper) |
| `sport_weekly_id` | `string` | Composite key `year_week-sport` |
| `distance_ratio` | `float32` | Share of weekly distance (per sport) |
| `pace_min_per_km` | `float32` | Pace (min km⁻¹); NaN for non-run |
| `grade_adjusted_pace` | `float32` | GAP (min km⁻¹); run only |
| `dirt_distance_km` | `float32` | Unpaved distance (km) |
| `total_cycles` | `int32` | Swim strokes / pedal revs where available |
| `route_hash` | `string` | MD5 of polyline (GPS privacy) |
| `gpx_path` | `string \| None` | Optional GeoJSON path file |

> *All numeric distance/time columns are converted to km / hours and stored in
>  low-memory float32/int16 where possible.  
>  Empty sensor data are kept as **`NaN`** so they don’t skew means.*


---

## Processing pipeline

1. **Export**: Strava JP CSV (`activities.csv`)  
2. **Header translation** JP→EN (`translate_headers.py`)  
3. Unit conversion (m→km, s→h) & dtype down-cast (`clean_master.ipynb`)  
4. **Intensity & TRIMP**: LTHR = 165 bpm, Banister formula  
5. Weekly aggregations (`weekly_summary.ipynb`)  
6. Saved as Parquet, tracked via **Git LFS** (compact & diff-friendly)

Code & notebooks live in the companion GitHub repo:  
<https://github.com/Benj-samurai/Strava-Dataset-PRJ>

---

## Usage ✨

```python
from datasets import load_dataset

ds = load_dataset(
    "Benj-samurai/strava_dataset",
    data_files="my_strava_dataset/strava_master_enhanced.parquet",
    streaming=False,    # True = stream without download
)
df = ds["train"].to_pandas()

# quick EDA
weekly_km = (
    df.assign(year_week=df["date"].dt.to_period("W"))
      .groupby(["year_week", "sport"])["distance_km"].sum()
)
print(weekly_km.tail())
```

## Privacy & Personal-use License

- **Raw FIT/GPX files are _not_ included.**
- **Activity start coordinates are jittered ≥ 200 m** to obscure the true home location.
- Released under **CC BY-NC 4.0** – non-commercial use, attribution required.  
  If you wish to use the data commercially, please contact the author first.

---

## Citation

```bibtex
@misc{asai2025strava,
  author       = {Asai, Benj-samurai},
  title        = {Strava Master Dataset},
  year         = {2025},
  howpublished = {\url{https://huggingface.co/datasets/Benj-samurai/strava_dataset}},
  note         = {Version {{\today}}}
}
```

## Changelog

| Date       | Version | Notes                    |
|------------|---------|--------------------------|
| 2025-05-06 | v1.0    | Initial public release   |

---

### 使い方

1. HF Hub ページの **Dataset card** タブ → **Edit** を開く  
2. 上の Markdown を貼り付ける  
3. 〔 〕部分を実際の値に置換して **Commit**  
   *行数* は手元で `len(df)`、週レコードは `len(weekly_sport)` などで確認できます。  

これで “列定義・処理手順・使用例・ライセンス” を網羅したリッチな Dataset Card になります。  
追記やレイアウト調整はお好みでどうぞ！