Update README.md

docs: add complete dataset card with YAML metadata, columns, usage & license

README.md

@@ -1,19 +1,33 @@
-# Strava Master & Weekly Summary Dataset
+---
+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 Strava export processed on **2025-05-04**  
-Provides per-activity master table and weekly aggregations with intensity metrics.
+> **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 |
-|------|------|-------------|
-| `strava_master_enhanced.parquet` |  ~N  | 1 row = 1 activity, cleaned & enriched |
-| `weekly_sport.parquet` |  ~N  | Weekly totals by sport (Run/Ride/Swim …) |
-| `weekly_category.parquet` |  ~N  | Weekly totals by intensity zone |
+|------|-----:|-------------|
+| `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 |
 
-*(rows will auto-update, no needに手入力可か削除でもOK)*
+*(Parquet → compact, schema-aware, loadable via `datasets.load_dataset`)*  
 
 ---
 
@@ -21,71 +35,123 @@ Provides per-activity master table and weekly aggregations with intensity metric
 
 | Column | `dtype` | Description |
 |--------|---------|-------------|
-| `activity_id` | `int64` | Unique Strava activity ID |
-| `name` | `string` | Activity title on Strava |
-| `sport` | `category` | Sport type (Run / Ride / Swim / Walk / … ) |
-| `date` | `datetime64[ns]` | Local start time |
-| `distance_km` | `float32` | Distance (metres → **km**) |
-| `elapsed_hr` | `float32` | Elapsed time (sec → **h**; incl. stops) |
-| `moving_hr` | `float32` | Moving time (sec → **h**) |
-| `elevation_gain_m` | `float32` | Positive elevation gain (m) |
-| `elevation_loss_m` | `float32` | Negative elevation (descent, 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 |
+| `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); bike-runs mixed |
+| `average_cadence` | `float32` | Avg cadence (rpm) |
 | `max_cadence` | `int16` | Max cadence (rpm) |
-| `intensity_level` | `float32` | Avg HR ÷ **LTHR (165 bpm)** → 0.50–1.10 |
-| `training_category` | `category` | HR zone label: `Z1-2`, `Z3`, `Z4`, `Z5`, `NoHR` |
-| `trimp` | `float32` | Banister TRIMP = `moving_hr × intensity_level × 50` |
-| `pace_min_per_km` | `float32` | Pace (min km-¹); *NaN* for non-run sports |
-| `commute` | `boolean` | Marked as commute on Strava |
-| `gear` | `string` | Bike / Shoes used (if set) |
+| `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 temperature (°C) |
-| `relative_effort` | `Int32` | Strava Relative Effort score |
-| `filename` | `string` | Original FIT/GPX filename (metadata only) |
-| `gpx_path` | `string | None` | Path to GPX in `routes/` (not included if `None`) |
-
-> **Note**  
-> * All numeric time/ distance columns are converted to SI units (hours & kilometres) for easy aggregation.  
-> * Empty sensor data (e.g. HR-less activities) are stored as **`NaN`** so they don’t skew means.  
-> * `training_category` bins: Z1-2 < 0.79, Z3 < 0.89, Z4 < 0.95, Z5 ≥ 0.95 of LTHR.  
-> * `gpx_path` points to a GeoJSON-converted route if the original GPX was processed; raw GPS files are **not** in the repo for privacy.
+| `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. **Header translation** : JP CSV → EN headers (custom script)  
-2. **Unit conversion** : distance m→km, time s→h  
-3. **Intensity & TRIMP** : LTHR = 165 bpm, zones Z1-2/3/4/5  
-4. **Weekly aggregation** via pandas `groupby`  
+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)
 
-Full code is available in `notebooks/` and `make_dataset.py` inside the GitHub repo.
+Code & notebooks live in the companion GitHub repo:  
+<https://github.com/Benj-samurai/Strava-Dataset-PRJ>
 
 ---
 
-## Usage example
+## Usage ✨
 
 ```python
 from datasets import load_dataset
-ds = load_dataset("Benj-samurai/strava_dataset",
-                  data_files="strava_master_enhanced.parquet")
+
+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()
-print(df.head())
+
+# 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}}}
+}
 ```
 
-## License
+## Changelog
 
-Data © <Yuki Asai> 2025 — released under CC BY-NC 4.0.
-Commercial use forbidden. Remove GPS data before public redistribution.
+| Date       | Version | Notes                    |
+|------------|---------|--------------------------|
+| 2025-05-06 | v1.0    | Initial public release   |
 
-## Privacy notes
+---
 
-- **Raw FIT/GPX files are _not_ included.**
-- **No exact home coordinates**  
-  All activity start points are shifted by ≥ 200 m to obscure the true home location.
+### 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！