🔬 NLM MeSH SPARQL Scraper

🚀 Export NLM MeSH data in seconds. Structured records ready for spreadsheet, database, and BI tooling.

🕒 Last updated: 2026-06-05 · 📊 12 fields per record · Public data · Real-time fetch

The NLM MeSH SPARQL Scraper turns the public NLM MeSH endpoint into a clean, structured dataset. It calls the upstream API, parses the response, and flattens each record into one row, ready for downstream tooling.

🎯 Target Audience	💡 Primary Use Cases
📊 Analysts and researchers	Build clean datasets from public NLM MeSH data.
🏢 Operations teams	Monitor key indicators on a schedule.
🤖 ML engineers	Construct training and evaluation sets.
📰 Journalists	Verify figures and pull sources with one click.
👩‍💻 Developers	Mirror upstream data into your warehouse without writing client code.

📋 What the NLM MeSH SPARQL Scraper does

• Calls the public NLM MeSH endpoint with the filters you supply.
• Parses the response and normalizes each record.
• Casts numeric fields where possible so the data drops cleanly into BI tools.
• Surfaces upstream errors and rate limits as a single record with the error field set.
• Exports to any downstream format supported by Apify.

💡 Why it matters. Public data is great, but raw API responses rarely map cleanly to a spreadsheet. This actor handles the plumbing so you can focus on analysis.

🎬 Full Demo

🚧 Coming soon.

⚙️ Input

Field	Type	Required	Description
maxItems	integer	No	Free users 10. Paid up to 1,000,000.
year	string	No	MeSH release year (e.g. 2024).
labelContains	string	No	Optional substring filter on term label.

Example.

{
  "maxItems": 10,
  "year": "2024"
}

⚠️ Good to Know. This actor talks to the public NLM MeSH endpoint. Upstream rate limits and availability apply.

📊 Output

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

Field	Type	Description
mesh_id	string	Field returned by NLM MeSH.
label	string	Field returned by NLM MeSH.
scope_note	string	Field returned by NLM MeSH.
tree_numbers	string	Field returned by NLM MeSH.
qualifiers	string	Field returned by NLM MeSH.
broader	string	Field returned by NLM MeSH.
narrower	string	Field returned by NLM MeSH.
related	string	Field returned by NLM MeSH.
semantic_types	string	Field returned by NLM MeSH.
year	string	Field returned by NLM MeSH.
scrapedAt	string	Field returned by NLM MeSH.
error	string	Field returned by NLM MeSH.

✨ Why choose this Actor

| 🆓 | Works with the public NLM MeSH endpoint. No login required. | | 🧹 | Clean, normalized field names ready for BI tools. | | 🔢 | Numeric values parsed where possible. | | 🛟 | Upstream errors surface as a single error record instead of crashing. | | 💾 | Push to dataset, exportable to any format Apify supports. |

📈 How it compares to alternatives

Approach	Setup time	Clean keys	Numeric casting	Error handling
Roll your own client	30 min+	❌	❌	❌
This Actor	5 sec, no install	✅	✅	✅

🚀 How to use

1. Click Try for free.
2. Adjust the filters or leave defaults.
3. Click Start. Within seconds, your dataset is ready.

💼 Business use cases

📊 Analytics dashboards. Pipe NLM MeSH records into your BI stack for live monitoring.

📈 Trend research. Build historical series for strategy and forecasting.

📰 Newsroom fact checking. Verify a figure for a story without leaving the browser.

🤖 ML feature engineering. Generate clean training inputs from public data.

🔌 Automating NLM MeSH SPARQL Scraper

• Make and Zapier. Trigger this actor on a schedule, route results to Sheets or Slack.
• Cron schedule. Native Apify scheduler, every hour, day, or week.
• Webhooks. POST to your endpoint the moment a run finishes.
• Pipe to BigQuery, Snowflake, Postgres. Native Apify integrations move datasets straight into your warehouse.

🌟 Beyond business use cases

🎓 Education. Use real public data in your statistics or research class.

🧪 Personal research. Build your own datasets for tinkering.

🤝 Non-profit and open data. Publish dashboards without writing client code.

🧰 Prototyping. Spin up a real data feed in 5 seconds to test 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 specific workflow to the actor's inputs.

❓ Frequently Asked Questions

❓ Is this real time? Each run hits the upstream endpoint live.

❓ Do I need an API key? No. The actor uses public endpoints.

❓ How are upstream errors handled? A clean record with the error field set is pushed to the dataset.

❓ Can I schedule runs? Yes, use Apify's native scheduler or hook this up to Make or Zapier.

❓ What export formats are supported? Any format Apify's dataset UI offers.

❓ How fresh is the data? As fresh as NLM MeSH publishes it.

❓ Can the schema change? Core fields are stable. Upstream additions are passed through where possible.

❓ Are values cast to numbers? Yes where the upstream returns numeric strings.

❓ Does it follow upstream pagination? Where applicable yes, capped by maxItems.

❓ Is this allowed? Only public data is fetched. Comply with the upstream terms of use.

🔌 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 API or webhook endpoint. Trigger runs from a calendar event, a form submission, a cron job, or pipe results straight into BigQuery, Snowflake, or a Postgres warehouse.

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