Research datasets

Research datasets are a catalogue of the original p10k research snapshot, separate from your workspace's private execution datasets. Listing or downloading research content does not upload it, fit a model or change its targets or splits. Catalogue metadata, downloadable content and model qualification are different things: inspect availability, license notices, schema, recorded targets/splits and provenance before using a dataset. Missing source information stays missing.

import os
from pathlib import Path
from welt import Client

with Client(base_url="https://welt.ergodic.dev",
            api_key=os.environ["WELT_API_KEY"]) as client:
    page = client.research_datasets(q="iris", availability="available", limit=10)
    for item in page["items"]:
        print(item["id"], item["name"], item["availability"])

    # Search is discovery; choose this exact reviewed asset deliberately.
    chosen = "asset-d839644b6de36fc5cafdda18c5daeca3"  # Reviewed Iris asset.
    info = client.research_dataset(chosen)
    print(info["license"], info["schema"], info["targets"], info["splits"])
    content = info["content"]
    print(content["media_type"], content["filename"])
    suffix = {"application/vnd.apache.parquet": ".parquet",
              "application/zip": ".zip"}[content["media_type"]]
    from tempfile import mkdtemp
    path = client.download_research_dataset(
        chosen, Path(mkdtemp(prefix="welt-research-")) / ("research" + suffix),
        version="d839644b6de36fc5cafdda18c5daeca3284a5ce8bb6169aff2fcb3fbb4a12021")

import pandas as pd
data = pd.read_parquet(path)
print(data.head())
print(data.shape)
data.info()

Continue with Start to split this exact table, fit a classifier, predict held-out rows and measure their quality. Listing/download itself does not model.

A page preserves next_cursor, total, catalogue_version and facets. Request the next page by passing cursor=page["next_cursor"]; stop when it is None. Filters are q, source, availability and license, with limit from 1 to 50. research_dataset(id) returns full metadata. content=None means there are no eligible downloadable bytes; a blocked_reason explains the recorded boundary. Do not interpret metadata presence or a source retrieval signal as redistribution permission or a model-compatible training recipe.

Downloads request only the service's canonical content endpoint, with an explicit version pinned to its SHA-256. The SDK checks exact byte size and digest, rejects redirects and content encodings, and streams at most 64 KiB per write. The default 64 MiB file limit covers each file in the canonical snapshot; callers may choose max_bytes up to 2 GiB after inspecting the size. This is a per-file safety limit, not permission to fetch arbitrary URLs or an automatic bulk download.

The caller chooses the destination. Catalogue filenames and URLs never choose a local path. A download creates a private temporary file, publishes complete verified bytes atomically, and refuses an existing file or symlink; interrupted, corrupt or cancelled downloads leave no destination. An existing destination raises FileExistsError; choose a new path rather than deleting data as cleanup. Inspect/save info alongside a research experiment to retain its license, version and provenance. No output or dataset belongs in Git by default.

from welt import AsyncClient

async def fetch(dataset_id, path):
    async with AsyncClient(base_url="https://welt.ergodic.dev") as client:
        info = await client.research_dataset(dataset_id)
        return await client.download_research_dataset(
            dataset_id, path, version=info["content"]["version"])

Both clients use the same current workspace-key authorization and typed errors. GET metadata has bounded retries. An interrupted content stream raises a sanitized TransportError; rerun the download into a new destination after checking the catalogue. Partial bytes are never resumed or accepted implicitly.

Inspect content.media_type and its filename hint before opening a download. Ordinary tables are application/vnd.apache.parquet; install the existing parquet extra and use pandas.read_parquet(path). Some source-qualified datasets are application/zip notice-bearing distributions containing exactly dataset.parquet, LICENSE.txt and ATTRIBUTION.txt. Explicitly extract those three members together into a new experiment directory; use fixed local member names, refuse unexpected members or existing destinations, and retain both notice files with the table when sharing. The SDK downloads verified binary bytes and does not silently extract archives or discard notices. The pinned Iris onboarding dataset remains an ordinary Parquet download.

content.version and content.sha256 identify the complete downloaded bytes. For a ZIP these are the archive checksum, while info["provenance"]["canonical_sha256"] identifies the unchanged canonical dataset.parquet inside it. Keep the full detail metadata and distinguish these two checksums when checking the extracted table. Downloadability remains a dataset-specific serving decision; it does not imply that every catalogue entry has eligible content.

Full research files can exceed a serving model's row/feature/class bounds; inspect Client.models() and use an explicitly declared preparation/split recipe before uploading. There is no silent sampling, target inference, scientific benchmark claim or automatic upload here.

SDK 0.6 adds this research surface to the released 0.3 interfaces. Resumable CSV 0.4 and durable batch 0.5 remain separate unreleased candidates; neither is included in this package. Actual endpoint availability follows the independently reviewed service release. See first request and capabilities.