🖼️ SMK Denmark Collection Scraper

🚀 Export SMK Denmark Open Collection data in seconds. Clean records straight from the public source, ready for spreadsheets, BI tools, and warehouses.

🕒 Last updated. 2026-06-05 · 📊 10 fields per record · Public source · No login required

The SMK Denmark Collection Scraper turns the public SMK Denmark Open Collection source into a clean, structured dataset. It walks the public listing, parses each record, and pushes one row per result into an Apify dataset that you can download in seconds.

This actor scrapes only data already visible to anyone visiting the source without logging in. No credentials, no member-only content.

🎯 Target Audience	💡 Primary Use Cases
📊 Analysts	Pull a structured copy of the SMK Denmark Open Collection feed for analysis.
🏢 Business teams	Build market maps and lead lists.
🤖 ML engineers	Build training and benchmark datasets.
📰 Journalists	Verify and cross-reference facts at scale.
👩‍💻 Developers	Avoid writing one-off scrapers.

📋 What the SMK Denmark Collection Scraper does

• Fetches the public SMK Denmark Open Collection listing or API.
• Parses each record into a flat row with normalized field names.
• Surfaces upstream errors as a single record with the error field populated instead of crashing.
• Streams results into an Apify dataset for instant download.

💡 Why it matters. The SMK Denmark Open Collection source presents this data as pages built for humans, not pipelines. This actor flattens it into rows your tools can read without writing custom parsers.

🎬 Full Demo

🚧 Coming soon.

⚙️ Input

Field	Type	Required	Description
query	string	No	Free-text search query.
artist	string	No	Artist name filter.
hasImage	boolean	No	Only works with images.
publicDomain	boolean	No	Only public domain works.
maxItems	integer	No	Free users 10, paid users up to 1,000,000. Prefill 10.

Example 1.

{
  "artist": "Hammershoi",
  "hasImage": true,
  "maxItems": 25
}

Example 2.

{
  "query": "landscape",
  "publicDomain": true,
  "maxItems": 10
}

⚠️ Good to Know. SMK Denmark provides a public open data API. This actor calls it directly.

📊 Output

Each record is a flat object. error is always last.

Field	Type	Description
🖼️ Image imageUrl	text	Field from the SMK Denmark Open Collection record.
📜 Title title	text	Field from the SMK Denmark Open Collection record.
🎨 Artist artist	text	Field from the SMK Denmark Open Collection record.
📅 Year year	text	Field from the SMK Denmark Open Collection record.
🎭 Medium medium	text	Field from the SMK Denmark Open Collection record.
📏 Dimensions dimensions	text	Field from the SMK Denmark Open Collection record.
🏛️ Collection collection	text	Field from the SMK Denmark Open Collection record.
🔗 URL url	text	Field from the SMK Denmark Open Collection record.
🕒 scrapedAt	string	When the row was collected.
❌ error	string	Populated if the upstream response was an error.

Sample record.

{
  "imageUrl": null,
  "title": null,
  "artist": null,
  "year": null,
  "medium": null,
  "dimensions": null,
  "collection": null,
  "url": null,
  "scrapedAt": "2026-06-05T12:00:00.000Z",
  "error": null
}

✨ Why choose this Actor

| 🆓 | Works on the public source without login. | | 🧹 | Clean, normalized field names ready for analytics. | | 🛟 | Errors surfaced as a clean diagnostic record, not a crash. | | 💾 | Push to dataset, instant download in any supported format. |

📈 How it compares to alternatives

Approach	Setup time	Clean fields	Pagination	Error handling
Roll your own fetch	30 min plus	❌	manual	❌
Browser automation	1 hr plus	partial	manual	partial
This Actor	5 sec, no install	✅	✅	✅

🚀 How to use

1. Click Try for free.
2. Fill in the optional filters or accept the prefills.
3. Set maxItems (or leave the prefill of 10).
4. Click Start. Download the dataset in seconds.

💼 Business use cases

📊 Market mapping. Build a structured view of the SMK Denmark Open Collection landscape for strategy work.

📈 Lead generation. Turn the listing into a starting list for outbound research.

🧠 Competitive intelligence. Track changes between runs over time.

🤖 ML and analytics. Use the dataset as a clean input for downstream pipelines.

🔌 Automating SMK Denmark Collection Scraper

• Make and Zapier. Trigger the actor on a schedule, push results to Airtable, Sheets, or Slack.
• Cron schedule. Use the native Apify scheduler.
• Webhooks. Get notified the moment a run finishes.
• Warehouses. Pipe results to BigQuery, Snowflake, or Postgres.

🌟 Beyond business use cases

🎓 Education. Use the dataset as a teaching example in data classes.

🧪 Personal research. Power side projects with structured public data.

🤝 Non-profit and open data. Build public dashboards on top of the dataset.

🧰 Tinkering and prototyping. Spin up a feed in minutes for a new tool.

🤖 Ask an AI assistant about this scraper

Drop this README into ChatGPT, Claude, or any AI assistant and ask it to map your workflow to the actor's inputs.

❓ Frequently Asked Questions

❓ Do I need an account on the source site? No. The actor uses only public pages and endpoints.

❓ Are there rate limits? The actor is conservative by default. If the source returns an error, you will see a clean error record.

❓ Can I schedule runs? Yes, via the Apify scheduler or any external trigger.

❓ What output formats are supported? All formats supported by Apify datasets.

❓ Will fields ever be missing? Yes, when the source page does not include them. They will be null instead of a hard failure.

❓ Will the schema change? The core fields are stable. New optional fields may be added over time.

❓ Is the output deterministic? Order follows the source listing. Re-runs return the same shape but content can change as the site updates.

❓ Can I integrate with my warehouse? Yes, via native Apify integrations.

❓ Can I run this from a script? Yes, via the Apify API or any SDK.

❓ Can I extend the actor? Yes, fork it on Apify or run a custom build.

🔌 Integrate with any app

Apify ships native integrations with Make, Zapier, Slack, Discord, Google Drive, Google Sheets, Gmail, Airbyte, Keboola, Telegram, GitHub, and any REST or webhook endpoint. Pipe results straight into BigQuery, Snowflake, or Postgres.

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