dataset-finder-guide

name	dataset-finder-guide
description	Search and download research datasets from Kaggle, HuggingFace, and repos
metadata	{"openclaw":{"emoji":"🗄️","category":"tools","subcategory":"scraping","keywords":["dataset","Kaggle","data download","HuggingFace","data repository","open data"],"source":"wentor-research-plugins"}}

Dataset Finder Guide

Search, evaluate, and download research datasets from major repositories including Kaggle, Hugging Face, Google Dataset Search, Zenodo, UCI Machine Learning Repository, and domain-specific archives. This skill helps researchers locate the right data for their experiments efficiently.

Overview

Finding suitable datasets is often one of the most time-consuming phases of empirical research. Datasets are scattered across dozens of platforms, each with different APIs, licensing terms, download mechanisms, and metadata standards. A single research project might require datasets from Kaggle for benchmarking, Hugging Face for NLP tasks, Zenodo for supplementary materials from published papers, and government open data portals for demographic or economic variables.

This skill provides a unified approach to dataset discovery: formulating search queries, evaluating dataset quality and suitability, understanding licensing implications, and efficiently downloading and organizing data. It covers both general-purpose repositories and domain-specific archives that researchers in various fields need.

The emphasis is on reproducibility -- every dataset used in research should be citable, versioned, and documented. This skill includes patterns for recording dataset provenance, creating data cards, and managing dataset versions across experiments.

Dataset Repositories

General-Purpose Repositories

Repository	Strengths	API	Citation Support
Kaggle	ML benchmarks, competitions, community kernels	REST + CLI	DOI via dataset cards
Hugging Face Datasets	NLP, CV, audio; streaming support	Python library	Built-in citation
Zenodo	Any research data, DOI minting, EU-funded	REST API	Automatic DOI
Google Dataset Search	Meta-search across repositories	Web only	Links to source
UCI ML Repository	Classic ML benchmarks	Direct download	BibTeX provided
Figshare	Figures, datasets, media, preprints	REST API	DOI per item
Dryad	Ecology, biology, environmental science	REST API	DOI per dataset
ICPSR	Social science survey data	Restricted API	Persistent IDs
Harvard Dataverse	Multi-discipline, institutional	REST API	DOI per dataset

Domain-Specific Archives

Domain	Repository	Notable Datasets
Genomics	NCBI GEO, ENA	Gene expression, sequencing data
Astronomy	NASA archives, SDSS	Sky surveys, spectral data
Economics	FRED, World Bank, IMF	Time series, macro indicators
Climate	NOAA, CMIP6	Temperature, precipitation records
Linguistics	LDC, CLARIN	Corpora, treebanks
Medical	PhysioNet, MIMIC	Clinical records, ECG/EEG
Chemistry	PubChem, ChEMBL	Molecular structures, bioassays

Searching for Datasets

Kaggle CLI

# Install and configure
pip install kaggle
# Place kaggle.json in ~/.kaggle/

# Search datasets
kaggle datasets list -s "sentiment analysis" --sort-by votes
kaggle datasets list -s "medical imaging" --file-type csv --min-size 100MB

# Get dataset details
kaggle datasets metadata -d stanford/imdb-review-dataset

# Download dataset
kaggle datasets download -d stanford/imdb-review-dataset -p ./data/
unzip ./data/imdb-review-dataset.zip -d ./data/imdb/

# Download competition data
kaggle competitions download -c titanic -p ./data/

Hugging Face Datasets

from datasets import load_dataset, list_datasets

# Search for datasets by task
from huggingface_hub import HfApi
api = HfApi()
datasets = api.list_datasets(
    search="scientific papers",
    sort="downloads",
    direction=-1,
    limit=20
)
for ds in datasets:
    print(f"{ds.id}: {ds.downloads} downloads")

# Load a dataset (with streaming for large datasets)
dataset = load_dataset("scientific_papers", "arxiv", streaming=True)

# Inspect structure
print(dataset["train"].features)
print(f"Number of examples: {dataset['train'].num_rows}")

# Load specific split and subset
validation = load_dataset(
    "scientific_papers", "arxiv",
    split="validation[:1000]"
)

Google Dataset Search (Programmatic)

import requests
from bs4 import BeautifulSoup

def search_google_datasets(query, num_results=10):
    """Search Google Dataset Search and extract results."""
    url = f"https://datasetsearch.research.google.com/search"
    params = {"query": query, "docid": ""}
    # Note: Google Dataset Search does not have an official API
    # Use the web interface or alternative approaches
    print(f"Search at: {url}?query={query.replace(' ', '+')}")
    return url

Zenodo API

import requests

def search_zenodo(query, resource_type="dataset", size=10):
    """Search Zenodo for research datasets."""
    url = "https://zenodo.org/api/records"
    params = {
        "q": query,
        "type": resource_type,
        "size": size,
        "sort": "mostrecent",
        "access_right": "open"
    }
    response = requests.get(url, params=params)
    results = response.json()

    for hit in results.get("hits", {}).get("hits", []):
        meta = hit["metadata"]
        print(f"Title: {meta['title']}")
        print(f"DOI: {meta.get('doi', 'N/A')}")
        print(f"License: {meta.get('license', {}).get('id', 'N/A')}")
        print(f"Size: {sum(f['size'] for f in hit.get('files', []))/1e6:.1f} MB")
        print("---")

    return results

Dataset Evaluation Checklist

Before using a dataset in research, verify the following:

Quality Assessment

• Completeness: What percentage of values are missing? Are missing values random or systematic?
• Accuracy: Are values within expected ranges? Are there obvious errors?
• Consistency: Are formats uniform (dates, categories, units)?
• Timeliness: When was the data collected? Is it current enough for your research question?
• Sample size: Is the dataset large enough for your intended analysis?

Licensing and Ethics

License	Commercial Use	Modification	Attribution Required
CC0	Yes	Yes	No
CC-BY 4.0	Yes	Yes	Yes
CC-BY-SA 4.0	Yes	Yes (share-alike)	Yes
CC-BY-NC 4.0	No	Yes	Yes
ODC-ODbL	Yes	Yes (share-alike)	Yes
Custom/Restricted	Varies	Varies	Varies

Reproducibility Documentation

## Data Card

**Dataset**: [Name]
**Source**: [URL]
**Version**: [Version/Date]
**DOI**: [DOI if available]
**License**: [License name]
**Downloaded**: [YYYY-MM-DD]
**Size**: [X rows, Y columns, Z MB]
**Description**: [Brief description]
**Preprocessing**: [Steps applied before use]
**Citation**: [BibTeX entry]

Download and Organization

Project Data Structure

project/
  data/
    raw/              # Original downloaded data (never modify)
      dataset_v1.csv
      README.md       # Data card with provenance
    processed/        # Cleaned and transformed data
      train.csv
      test.csv
    external/         # Third-party reference data
  scripts/
    download_data.py  # Reproducible download script
    preprocess.py     # Data cleaning pipeline

Reproducible Download Script

"""download_data.py - Reproducible dataset download."""
import hashlib
from pathlib import Path
import requests

DATASETS = {
    "main_dataset": {
        "url": "https://zenodo.org/record/12345/files/data.csv",
        "sha256": "abc123...",
        "filename": "raw/main_dataset.csv"
    }
}

DATA_DIR = Path("data")

for name, info in DATASETS.items():
    path = DATA_DIR / info["filename"]
    if path.exists():
        print(f"Already downloaded: {name}")
        continue

    path.parent.mkdir(parents=True, exist_ok=True)
    print(f"Downloading {name}...")
    response = requests.get(info["url"])
    path.write_bytes(response.content)

    # Verify integrity
    sha256 = hashlib.sha256(response.content).hexdigest()
    assert sha256 == info["sha256"], f"Checksum mismatch for {name}"
    print(f"Verified: {name}")

References

• Kaggle API documentation: https://github.com/Kaggle/kaggle-api
• Hugging Face Datasets: https://huggingface.co/docs/datasets
• Zenodo API: https://developers.zenodo.org
• Google Dataset Search: https://datasetsearch.research.google.com
• Gebru et al., "Datasheets for Datasets" (2021): https://arxiv.org/abs/1803.09010