license: other license_name: cc-by-4.0-derived-analysis license_link: https://creativecommons.org/licenses/by/4.0/ language:

• en pretty_name: Lunarness Fashion Data Observatory 2026 homepage: https://lunarness.com/pages/fashion-statistics-observatory size_categories:
• n<1K tags:
• fashion
• fashion-week
• ecommerce
• media-impact
• sustainability
• circularity
• tabular configs:
• config_name: default data_files:
  • split: train path: data/fashion_observatory_2026.csv citation: | @dataset{lunarness_fashion_observatory_2026, author = {{Lunarness Editorial Team}}, title = {Lunarness Fashion Data Observatory 2026}, year = {2026}, version = {1.0}, doi = {10.5281/zenodo.22131190}, url = {https://lunarness.com/pages/fashion-statistics-observatory} }

Lunarness Fashion Data Observatory 2026

This package is the machine-readable companion to the Lunarness Fashion Data Observatory 2026. It combines a small set of cited public findings about Fashion Week media attention, historical event attendance, online fashion behavior and textile circularity with clearly labeled arithmetic derivations.

Node.js library and CLI

The zero-dependency Node.js package queries the bundled, versioned release and validates its record structure without scraping a live page.

npm install lunarness-fashion-data
npx lunarness-fashion-data validate
npx lunarness-fashion-data topics
npx lunarness-fashion-data list --topic ecommerce
npx lunarness-fashion-data get eu_textile_consumption
npx lunarness-fashion-data search "Fashion Week" --json

Use it as an ES module:

import {
  getRecord,
  listRecords,
  searchRecords,
  validateDataset,
} from 'lunarness-fashion-data';

const metric = getRecord('eu_textile_consumption');
const ecommerce = listRecords({ topic: 'ecommerce' });
const matches = searchRecords('textile');
const validation = validateDataset();

The package preserves units, periods, source URLs, derivations and caveats. A successful structural validation does not independently verify a third-party source or turn unlike metrics into comparable values.

Python library and CLI

Install the zero-dependency Python distribution from PyPI:

python -m pip install lunarness-fashion-data
lunarness-fashion-data validate
lunarness-fashion-data topics
lunarness-fashion-data list --topic ecommerce
lunarness-fashion-data get eu_textile_consumption
lunarness-fashion-data search "Fashion Week" --json

Use the same query surface from Python:

from lunarness_fashion_data import get_record, list_records, validate_dataset

metric = get_record("eu_textile_consumption")
ecommerce = list_records(topic="ecommerce")
validation = validate_dataset()

The Python wheel bundles the same versioned JSON file as the Node.js package. Tests fail if the packaged copy diverges from the canonical repository file.

The Lunarness page is the canonical editorial presentation. The same versioned package is distributed through the following research and data platforms, with one shared Zenodo DOI:

• Zenodo record
• GitHub repository
• Hugging Face dataset
• Kaggle dataset

Audio companion

The Lunarness Fashion Data Briefing is the official audio companion to this release. Its source-led episodes explain selected Observatory findings without replacing the dataset, source ledger or DOI record.

• Official podcast page
• Open podcast RSS feed
• Spotify listing
• Amazon Music listing
• Apple Podcasts listing
• Podcast Index record

Files

• data/fashion_observatory_2026.csv: flat, row-level release with source and comparability fields.
• data/fashion_observatory_2026.json: compact JSON release used by the web page.
• python_src/lunarness_fashion_data: zero-dependency Python API and CLI package.
• data_dictionary.md: field meanings and interpretation rules.
• croissant.json: MLCommons Croissant-style dataset metadata for machine discovery.
• CITATION.cff: citation metadata for GitHub and scholarly tools.
• .zenodo.json: suggested metadata for a Zenodo deposit or GitHub release integration.
• LICENSE.md: scope of the CC BY 4.0 grant and third-party source notice.

Intended uses

• Referencing individual fashion-market indicators with their units, periods and caveats intact.
• Reproducing the simple sums, shares, ratios and percentage-point differences shown on the canonical page.
• Testing citation-aware retrieval, question answering and data-to-text workflows.
• Teaching why metrics such as MIV, website visits, purchases, consumption and waste cannot be collapsed into a single score.

Out-of-scope uses

• Presenting the historical attendance figures as a current worldwide ranking.
• Treating Media Impact Value as revenue, sales, profit, reach or audited advertising spend.
• Training a model to make environmental claims about individual products.
• Redistributing source downloads or implying that Lunarness owns third-party databases.

Method

1. Use sources with a named publisher, period, geography and measurement description.
2. Record reported values without inventing missing observations.
3. Limit derivations to transparent arithmetic and show the formula in the derivation field.
4. Keep a comparability_note next to values that are easy to overstate.
5. Link every row to the source page that supports it.

Fashion Data Transparency Standard

The repository also hosts the open Lunarness Fashion Data Transparency Standard v1.0. It defines eight testable disclosure requirements for public fashion-data pages: source identity, measurement context, calculation provenance, caveats, machine-readable access, versioning, corrections and evidence integrity.

The public registry distinguishes independent verification from self-assessment. A badge is optional, expires with its registry record and is never evidence by itself. The canonical standard and verification page is lunarness.com/pages/fashion-data-transparency-standard.

Citation

Preferred short citation:

Lunarness Editorial Team (2026). Lunarness Fashion Data Observatory 2026 (Version 1.0). https://doi.org/10.5281/zenodo.22131190

The Zenodo DOI above is the primary archived citation. Distribution copies should retain the same DOI and canonical Lunarness URL instead of minting unrelated identifiers for identical content.

Licensing

Lunarness's original prose, calculations and dataset structure are available under CC BY 4.0. Third-party facts, names and source materials remain subject to their respective source terms. Reusers should cite the canonical Lunarness page and the underlying source for each metric.