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## Understand what each series means
- Read `DATA_DICTIONARY.md` for plain-language definitions grouped by category.
- Read `DATA_CATALOG.md` for endpoint, unit, quality notes, and materialization status.
- Download from the `dataset-latest` GitHub Release for the complete generated dataset. A Git clone contains collector code, configuration, and docs, not the generated dataset.
## Download the parquet training archive (recommended)
This archive is the recommended way for model training.
It includes only parquet series plus documentation files.
```bash
curl -L -o resonance-data-train-parquet.zip \
https://github.com/JrVillabona/resonance-data/releases/download/dataset-latest/resonance-data-train-parquet.zip
unzip -o resonance-data-train-parquet.zip
```
Optional integrity verification:
```bash
curl -L -o resonance-data-train-parquet.zip.sha256 \
https://github.com/JrVillabona/resonance-data/releases/download/dataset-latest/resonance-data-train-parquet.zip.sha256
shasum -a 256 -c resonance-data-train-parquet.zip.sha256
```
Optional manifest inspection:
```bash
curl -L -o resonance-data-train-parquet.manifest.json \
https://github.com/JrVillabona/resonance-data/releases/download/dataset-latest/resonance-data-train-parquet.manifest.json
cat resonance-data-train-parquet.manifest.json
```
## Download the full archive (csv + parquet + meta)
Use this only if you need raw csv files or metadata sidecars.
```bash
curl -L -o resonance-data-latest.zip \
https://github.com/JrVillabona/resonance-data/releases/download/dataset-latest/resonance-data-latest.zip
unzip -o resonance-data-latest.zip
```
The full archive also publishes `resonance-data-latest.manifest.json` and `resonance-data-latest.zip.sha256`.
## Load from Hugging Face
Researchers can load the full long-format Parquet table directly from Hugging Face:
```python
from datasets import load_dataset
ds = load_dataset("JrVillabona/resonance-data", split="train")
```
Or with Pandas/Polars through the Hub filesystem:
```python
import pandas as pd
df = pd.read_parquet("hf://datasets/JrVillabona/resonance-data/data/train.parquet")
```
Direct browser or CLI download:
- `https://huggingface.co/datasets/JrVillabona/resonance-data`
- `https://huggingface.co/datasets/JrVillabona/resonance-data/resolve/main/data/train.parquet`
```bash
curl -L -o resonance-train.parquet \
https://huggingface.co/datasets/JrVillabona/resonance-data/resolve/main/data/train.parquet
```
Long-format columns:
- `symbol`
- `date`
- `open`, `high`, `low`, `close`, `volume`
- `source`, `ticker`, `category`, `data_type`, `frequency`, `description`
## Load one series
```python
import pandas as pd
df = pd.read_parquet("data/BTC_daily.parquet")
```
## Verify CSV and Parquet parity
```python
import pandas as pd
csv_df = pd.read_csv("data/BTC_daily.csv", parse_dates=["date"])
pq_df = pd.read_parquet("data/BTC_daily.parquet")
assert len(csv_df) == len(pq_df)
assert csv_df["date"].min() == pq_df["date"].min()
assert csv_df["date"].max() == pq_df["date"].max()
```
## Align mixed frequencies
- Daily: crypto prices, yields, commodities
- Weekly: selected FRED series
- Monthly: CPI, M2, labor metrics
Recommended merge strategy:
1. Build a daily index from min/max dates.
2. Left-join all series.
3. Forward-fill lower-frequency series after release date.
4. Keep publication lags in feature engineering logic.
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