Update dataset card

README.md

@@ -24,12 +24,13 @@ configs:
 # GPU Price Tracker
 
 A continuously-updated dataset of **cross-cloud GPU rental pricing**
-covering 12+ public cloud providers (AWS, GCP, Azure, Lambda Labs,
+covering 13 public cloud providers (AWS, GCP, Azure, Lambda Labs,
 RunPod, Vast.ai, DataCrunch, Cudo Compute, TensorDock, Vultr, Oracle,
-Nebius, CloudRift). Snapshots are collected twice daily by scraping
-provider pricing surfaces via the
-[`gpuhunt`](https://github.com/dstackai/gpuhunt) library and published
-as Hive-partitioned Parquet files (`prices/dt=YYYY-MM-DD/*.parquet`).
+Nebius, CloudRift): 3M+ listing observations, 70+ GPU types, collected
+twice daily since January 2026 by scraping provider pricing surfaces via
+the [`gpuhunt`](https://github.com/dstackai/gpuhunt) library and
+published as Hive-partitioned Parquet files
+(`prices/dt=YYYY-MM-DD/*.parquet`).
 
 The dataset is intended for:
 
@@ -38,8 +39,8 @@ The dataset is intended for:
 - **Practitioners** comparing GPU rental costs across providers for
   capacity planning, procurement, and ML-training cost estimation.
 
-A full dashboard view is at [the hosted Streamlit app](https://github.com/alex-hubbard/gpu_price_tracker)
-(see the GitHub README for the deploy URL).
+Explore it interactively at the hosted dashboard:
+**<https://gpu-price-trends.streamlit.app/>**.
 
 ## Quick start
 
@@ -52,7 +53,10 @@ print(ds[0])
 #  'instance_type': 'p4d.24xlarge', 'gpu_type': 'A100', 'gpu_count': 8,
 #  'gpu_memory_gb': 40, 'vcpus': 96, 'ram_gb': 1152.0,
 #  'region': 'us-east-1', 'price_per_hour': 32.7726, 'is_spot': False,
-#  'available': True, 'availability_zone': None}
+#  'available': True, 'availability_zone': None, 'quality': 'ok',
+#  'region_canonical': 'us-east-virginia', 'country': 'US',
+#  'region_lat': 38.13, 'region_lon': -78.45,
+#  'region_group': 'North America East'}
 ```
 
 Or with DuckDB directly (no `datasets` install required):
@@ -70,7 +74,7 @@ FROM read_parquet('hf://datasets/afhubbard/gpu-prices/prices/**/*.parquet',
 WHERE timestamp = (SELECT MAX(timestamp) FROM read_parquet(
                        'hf://datasets/afhubbard/gpu-prices/prices/**/*.parquet',
                        hive_partitioning = true))
-  AND gpu_count > 0
+  AND quality = 'ok'
 GROUP BY gpu_type
 ORDER BY avg_price_per_gpu_hour
 LIMIT 10
@@ -94,10 +98,21 @@ LIMIT 10
 | `is_spot` | bool | Spot/preemptible flag (semantics vary; see methodology) |
 | `available` | bool (nullable) | Listed and offerable at scrape time |
 | `availability_zone` | string (nullable) | Zone within the region, where applicable |
+| `quality` | string | `ok`, `cpu_only`, `unknown_gpu`, or `missing_memory` — filter to `'ok'` for most analyses |
+| `region_canonical` | string (nullable) | Canonicalized region name (cross-cloud comparable) |
+| `country` | string (nullable) | ISO country code of the region |
+| `region_lat`, `region_lon` | double (nullable) | Approximate region coordinates |
+| `region_group` | string (nullable) | Coarse geographic bucket (e.g. `North America East`) |
 
 Compute `price_per_gpu_hour = price_per_hour / gpu_count` for fair
 cross-SKU comparison.
 
+The `quality` and region columns were added in schema v1.1 (July 2026);
+all earlier snapshot files were upgraded in place, so every file in the
+tree has the same schema. Each Parquet file also embeds provenance
+metadata (`schema_version`, `row_count`, `quality_summary`, and
+`backfilled = true` on upgraded files).
+
 ## Collection cadence
 
 Twice daily (~09:00 and 21:00 UTC) via a GitHub Actions cron. Files
@@ -106,13 +121,15 @@ are append-only — each run produces a new immutable Parquet file under
 
 ## Limitations (read before modeling)
 
-- **Region strings are raw** — not canonicalized across providers.
-  Use a separate lookup if doing cross-cloud regional comparisons.
+- **Region canonicalization is best-effort** — `region_canonical` and
+  friends come from a hand-maintained lookup and are NULL for raw
+  regions not yet mapped; fall back with
+  `COALESCE(region_canonical, region)`.
 - **Spot semantics differ** by provider (AWS auction vs. Vast.ai P2P,
   etc.). See the methodology document.
 - **No customer telemetry** — the data is supply/listing prices only.
-- **CPU/Unknown rows** — a non-trivial fraction of upstream rows have
-  `gpu_count = 0` or `gpu_type = 'Unknown'`. Filter these out for
+- **Noisy rows are tagged, not dropped** — historical snapshots contain
+  `cpu_only` and `unknown_gpu` rows. Filter to `quality = 'ok'` for
   most analyses.
 - **12-hour cadence** — too coarse for intraday auction analyses.