📊 Kaggle Datasets Scraper

🚀 Surface every public dataset on Kaggle in seconds. Filter by keyword, file format, license, sort order, and size. No API key, no registration, no manual CSV wrangling.

Kaggle hosts more than 400,000 public datasets contributed by data scientists, ML researchers, and academic groups, ranging from a 16 KB CSV of medical insurance costs to half-gigabyte historical stock dumps and image corpora used in published competitions. Each dataset has a rich metadata footprint that matters in practice: number of downloads, votes, view counts, the kernelCount of public Kaggle notebooks that consume it, the license, the file format, an automated usabilityRating for schema clarity, and a long-form Markdown description. This Actor turns that metadata layer into clean dataset rows you can sort, filter, and pipe into downstream tools.

This Actor is built for ML engineers picking training corpora, data scientists benchmarking model results against community baselines, AI researchers tracking which Kaggle datasets are gaining notebook traction, and academic teams sourcing reproducible inputs for coursework or thesis work. It is a pure HTTP scraper against Kaggle's public dataset endpoints, so runs are fast and cheap. It does not download the actual dataset files - only the metadata layer that helps you decide which ones to pull next. Output is plain JSON, ready to feed into BigQuery, a Postgres staging table, a notebook, or a Make / Zapier workflow.

🎯 Target Audience and Primary Use Cases

Audience	Use Case
🤖 ML engineers	Source training corpora and benchmark datasets by file format, license, and size
📈 Data scientists	Track which datasets are trending, surfacing newly hot competitions and corpora
🎓 AI researchers	Build reproducible bibliographies of community datasets used in papers
🏫 Academic teams	Pull dataset metadata for coursework, dissertations, and lit reviews
🧪 Product builders	Validate that public training data exists for a given vertical before committing engineering

📋 What the Kaggle Datasets Scraper does

• 🔎 Search by keyword. Free-text query against dataset titles, descriptions, and tags. Pass finance, nlp, medical imaging, etc., or leave blank to browse without a keyword.
• 🗂️ Filter by file format. CSV, JSON, SQLite, BigQuery, or all formats. Useful when your downstream tooling only accepts one shape.
• ⚖️ Filter by license. Restrict to Creative Commons, GPL, Open Database License, Other, or all licenses.
• 🏷️ Filter by tag. Pass any Kaggle tag slug (classification, nlp, finance, health) to scope the run to a topic, technique, or domain.
• 📐 Filter by size. Set minSize / maxSize in bytes to keep the result set within memory or storage limits for downstream tools.
• 🥇 Sort the way Kaggle does. Hottest, most votes, recently updated, most active, recently published.
• 📜 Optional full description enrichment. When enabled, each record is enriched with the dataset's long-form Markdown description, full tag list, and version history. Disable for faster runs when you only need card-level fields.

Each output record represents one public Kaggle dataset. Alongside the title, owner, and URL, the row includes the canonical ref (owner/slug), license, total bytes, current version number, usability rating, downloads, views, votes, the count of public notebooks (kernels) that reference the dataset, the topic count, last-updated timestamp, an array of tag slugs, a compact version history, and (optionally) the full Markdown description.

💡 Why it matters: Kaggle is one of the largest public catalogues of curated ML data on the web, and it sits behind a JS-heavy UI that is hard to crawl. A clean metadata feed lets you make data-sourcing decisions at the speed of SQL.

⚠️ Good to know: the Kaggle public API tolerates direct calls but rate-limits aggressively under sustained load. For runs above a few thousand datasets, enable Apify Residential proxy in the input.

🚀 How to use

1. 🆔 Create a free account. Create a free account w/ $5 credit.
2. 🔎 Open the Actor. Find the Kaggle Datasets Scraper on Apify Store.
3. 📝 Fill the input form. Set a keyword and pick the filters that matter (file type, license, tag, size, sort).
4. ▶️ Run. Click Start. The log streams listing pages and how many datasets have been collected.
5. ⬇️ Export. Download as JSON, CSV, Excel, or stream into a Make / Zapier / n8n workflow.

⏱️ Total time to first row: under a minute for most filter combinations.

🔗 Recommended Actors

• 🤗 Hugging Face Model Scraper - public model catalogue with download counts and licenses
• 📚 Semantic Scholar Scraper - peer-reviewed papers with citation metadata
• 🧬 medRxiv Scraper - medical preprints for biomedical AI training corpora
• 🏛️ FRED Economic Data Scraper - public economic time series for finance and macro models
• 🏥 ClinicalTrials Scraper - structured clinical trial registry data

💡 Pro Tip: browse the complete ParseForge collection for more public-data scrapers built with the same conventions.

Disclaimer: This Actor is an independent project and is not affiliated with, endorsed by, or sponsored by Kaggle or Google LLC. It only reads public dataset metadata. You are responsible for complying with applicable laws, Kaggle's terms of service, and the per-dataset licenses when using the data downstream.

🆘 Need Help?

If you hit a bug, have questions about setup, or need a scraper we haven't built yet, open our contact form or write to parseforge@protonmail.com. We also take on paid custom data projects.

For faster answers, join our Discord. It's the best place to get support and suggest new actors.