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 |
public-domain | 6 datasets | 1 MB |
| Ember | OptimalSolution/databank-ember |
CC-BY-4.0 | 19 datasets | 27 MB |
| FIGARO | OptimalSolution/databank-figaro |
CC-BY-4.0 | 27 datasets | 862 MB |
| Natural Earth | 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.
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
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.