How to scrape marine and aviation data from CBS using the Minexa.ai extension

Public statistical data is often the most reliable source available for sector-level research, and CBS publishes some of the most detailed enterprise statistics in Europe. The challenge is not finding the data. It is getting it out of the browser and into a format you can actually work with.

This guide walks through how to extract structured enterprise statistics from CBS, the Dutch national statistics bureau, using the Minexa.ai Chrome extension. No code required. The target page is the dataset at cbs.nl/nl-nl/cijfers/detail/81588NED, which breaks down enterprise counts and employment figures across all SBI 2008 economic sectors, including transport and storage (category H), which covers maritime and aviation activity.

Watch the full tutorial first

The video below covers the complete extraction workflow from opening the extension to exporting the final dataset.

https://www.youtube.com/watch?v=ZW3p3xtHvyo

Why this dataset matters for transport and sector analysis

If you are tracking enterprise activity in maritime shipping, aviation, or logistics, CBS dataset 81588NED gives you quarterly figures broken down by sector, enterprise size class, and SBI branch code. Category H (Vervoer en opslag) alone covers 69,000 enterprises across transport and storage. Extracting this programmatically means you can monitor quarterly shifts, compare sectors over time, or feed the numbers into a broader economic model without manually copying rows from a web table.

Step 1: Open the Minexa.ai extension on the CBS page

Install the Minexa.ai Chrome extension, then navigate to the CBS statistics page. Once the page has fully loaded, open the extension. You will see the Minexa.ai home screen with the option to start a new scraping job.

Click the I'm on the right page button to confirm the current URL is the page you want to scrape.

Step 2: Confirm pagination settings and scraping mode

The extension detects pagination options on the page. For this CBS dataset, the table fits on a single view, so no next-page navigation is needed. Review the detected settings and click Continue.

Next, choose whether to scrape a single list or a list with linked detail pages. For this dataset, select the single list option, then choose the simple scraping scenario on the following screen.

Step 3: Select the data container and create the scraper

After choosing the scraping mode, the extension highlights the full data container on the page automatically. This is the table block holding all sector rows. Minexa identifies the wrapper element, not individual cells, and then discovers all data points within it on its own.

Once you confirm the container, the scraper is created and all extracted columns become visible. You can navigate between data points using the next and previous arrows.

Step 4: Run the job and export your data

Click through to the job summary screen. From here you can optionally connect a Google Sheet or set up a schedule for recurring runs. Then click Run to start extraction.

Once the job finishes, the results appear in a table. Export to Excel or JSON directly from the results screen.

What the extracted data looks like

Each row in the output corresponds to one sector entry from the CBS table. Here are two example records with the key fields:

[
 {
 "economic_activity_category": "H Vervoer en opslag",
 "totaal_bedrijven": "90",
 "bedrijfsgrootte": "69.020",
 "bedrijfstakken_branches_sbi_2008": "69.200",
 "perioden": "53.905",
 "quarter_year": "2026 2e kwartaal*",
 "unique_id": "81588NED-r-11-0",
 "related_id": "81588NED-r-11-1"
 },
 {
 "economic_activity_category": "A-U Alle economische activiteiten",
 "totaal_bedrijven": "1.905",
 "bedrijfsgrootte": "2.413.870",
 "bedrijfstakken_branches_sbi_2008": "2.417.600",
 "perioden": "1.996.955",
 "quarter_year": "2026 2e kwartaal*",
 "unique_id": "81588NED-r-0-0",
 "related_id": "81588NED-r-0-1"
 }
]

The economic_activity_category field gives the SBI 2008 sector label per row. The quarter_year field includes a provisional marker (asterisk) when the reporting period is not yet finalized. The unique_id and related_id fields expose the internal CBS table coordinates, which are useful for row-level referencing if you are joining this data against other CBS exports. The five data_row fields each encode a space-separated string of three DOM cell identifiers describing the row and column intersection within the table structure.

Ready to start collecting CBS statistical data without writing a single line of code? Install the Minexa.ai extension and run your first extraction in under ten minutes.

For more on scraping structured statistical and financial data, see: Scraping tax and accounting data from IDX using the Minexa API.