---
license: other
license_name: mixed-see-index
pretty_name: multimod data bank
viewer: false
---

# multimod data bank

An index of curated source datasets. Each SOURCE is its own
dataset repo, because a dataset card carries one `license:` and
these sources do not share one.

| source | repo | licence | contents | size |
|---|---|---|---|---|
| US Input-Output Accounts (BEA) | [`OptimalSolution/databank-bea-io`](https://huggingface.co/datasets/OptimalSolution/databank-bea-io) | public-domain | 6 datasets | 1 MB |
| Ember | [`OptimalSolution/databank-ember`](https://huggingface.co/datasets/OptimalSolution/databank-ember) | CC-BY-4.0 | 19 datasets | 27 MB |
| FIGARO | [`OptimalSolution/databank-figaro`](https://huggingface.co/datasets/OptimalSolution/databank-figaro) | CC-BY-4.0 | 27 datasets | 862 MB |
| Natural Earth | [`OptimalSolution/databank-basemaps`](https://huggingface.co/datasets/OptimalSolution/databank-basemaps) | public-domain | 2 layers, 4 resolutions | 41 MB |

## Fetching, not browsing

These repos are built to be QUERIED, not downloaded. The files are
parquet and Hugging Face serves HTTP range requests, so a client reads
the footer, picks the row groups it needs and pulls only those column
chunks. Measured on an 11.8 MB partition of a 165.9M-row table:
**0.85 MB fetched, 7% of the file**, for a real slice.

`index.json` is what makes that possible without a directory listing.
It carries every source, dataset and layer with its licence, coverage,
size, column list, partition key, manifest hash and **the relative
path of every file** -- so a URL can be constructed rather than
discovered. It is generated from the manifests and never hand-edited.

```r
library(duckdb); library(jsonlite)
idx <- fromJSON(paste0(hub, "/resolve/main/index.json"),
                simplifyVector = FALSE)
# pick a source and dataset, then one entry from its `files` list
con <- dbConnect(duckdb())
dbExecute(con, "INSTALL httpfs; LOAD httpfs;")
dbGetQuery(con, sql)   # read_parquet(url) over HTTP, no download
```

```python
import duckdb
duckdb.sql("INSTALL httpfs; LOAD httpfs;")
duckdb.sql(f"SELECT industry, sum(value) FROM '{url}' GROUP BY 1").df()
```

The dataset viewer works too, but it is a preview: splits are named
`train` because that is Hugging Face's default for a dataset with no
train/test division, not because any of this is training data.