V4ldeLund/faroese-lum-fo

Løgtingsins Umboðsmaður (lum_fo)

Status: Standalone website collection. It is not part of Faroese Dynaword.

About this data

Ombudsman opinions, conclusions, decisions, annual reports, archive metadata, HTML pages and PDFs.

Source: Løgtingsins Umboðsmaður

Size

Measure	Value
Documents	1,108
Characters	24,903,603
Tokens	10,734,008
Possible official documents	700
Tokens in possible official documents	8,511,749

File type	Documents
HTML	581
PDF	527
DOCX	0
CSV	0
plain text	0

The token count uses the AI-Sweden-Models/Llama-3-8B-instruct tokenizer. It counts all collected text, including other languages and repeated website text.

Files

• rows.parquet contains the collected documents.
• parser/ contains the crawler, site settings and Python requirements.

Crawl details

• Crawl started: 2026-08-15
• Crawl completed: 2026-08-15

The crawler used public sitemaps and links from the website. It stayed within the websites and file hosts listed in parser/sites.csv and followed robots.txt.

Text was taken from web pages, text-based PDFs, Word files and CSV files. Scanned PDFs and images were not read with OCR. Very short pages and exact duplicate texts were left out.

Columns

Column	Meaning
source_site, source_homepage	Website name and homepage
source_url, requested_url	Final URL and the URL first requested
title, text, content_type	Document title, extracted text and file type
retrieved_at	When the document was downloaded
text_sha256, text_characters	Text hash and character count
rights_status, rights_basis	Automatic rights label and why it was assigned
extraction_source_url, source_page_url	Extra source links when available

Rights

Some laws, decisions, notices and similar public documents may be outside copyright under section 9 of the Faroese Copyright Act.

likely_official_document only means that the title or URL matched a list of words. It is not a legal classification.

Note for this site

Final opinions and conclusions are the clearest collection on this site. The same text can appear as HTML, PDF and inside an annual report. Case documents can also contain sensitive details.

Known limits

• The crawl may not cover every page or old document.
• The text has not been checked by hand or filtered by language.
• Menus, headers and other repeated website text may remain.
• Text from PDFs may appear in the wrong order.
• Scanned PDFs and text inside images are missing.
• Some pages may contain personal information.

Parser

The crawler used for this website is included in the parser folder. The copy of sites.csv contains only this website.

cd parser
python -m pip install -r requirements.txt
python scrape.py --site lum_fo