metadata
license: cc-by-nc-4.0
task_categories:
- feature-extraction
- text-classification
language:
- en
- de
- es
- fr
- cs
- it
- ru
- pl
- pt
- el
- zh
- ja
- nl
- sr
- ro
- ar
- uk
- mn
- ko
- tr
- sv
- he
- hu
tags:
- fragrance
- perfume
- cosmetics
- recommendation-system
- e-commerce
- retail
- fragrantica
- parfumo
- cross-database
- record-linkage
- cross-walk
- multilingual
size_categories:
- 10<n<100
configs:
- config_name: fragrances
data_files: fragrances.csv
default: true
sep: '|'
- config_name: brands
data_files: brands.csv
sep: '|'
- config_name: perfumers
data_files: perfumers.csv
sep: '|'
- config_name: notes
data_files: notes.csv
sep: '|'
- config_name: accords
data_files: accords.csv
sep: '|'
- config_name: translations
data_files: translations.csv
sep: '|'
- config_name: parfumo_perfumes
data_files: parfumo/perfumes.csv
sep: '|'
- config_name: parfumo_brands
data_files: parfumo/brands.csv
sep: '|'
- config_name: parfumo_perfumers
data_files: parfumo/perfumers.csv
sep: '|'
- config_name: parfumo_notes
data_files: parfumo/notes.csv
sep: '|'
- config_name: parfumo_notes_categories
data_files: parfumo/notes_categories.csv
sep: '|'
- config_name: parfumo_accords
data_files: parfumo/accords.csv
sep: '|'
- config_name: cross_matched_pairs
data_files: cross/matched_pairs_sample.csv
sep: '|'
- config_name: cross_brand_matches
data_files: cross/brand_matches_sample.csv
sep: '|'
- config_name: cross_field_equivalence_map
data_files: cross/field_equivalence_map.csv
sep: ','
dataset_info:
- config_name: fragrances
features:
- name: pid
dtype: int64
- name: url
dtype: string
- name: brand
dtype: string
- name: name
dtype: string
- name: year
dtype: int64
- name: gender
dtype: string
- name: collection
dtype: string
- name: main_photo
dtype: string
- name: info_card
dtype: string
- name: user_photoes
dtype: string
- name: video_url
dtype: string
- name: accords
dtype: string
- name: notes_pyramid
dtype: string
- name: perfumers
dtype: string
- name: description
dtype: string
- name: rating
dtype: string
- name: reviews_count
dtype: int64
- name: appreciation
dtype: string
- name: price_value
dtype: string
- name: gender_votes
dtype: string
- name: longevity
dtype: string
- name: sillage
dtype: string
- name: season
dtype: string
- name: time_of_day
dtype: string
- name: pros_cons
dtype: string
- name: by_designer
dtype: string
- name: in_collection
dtype: string
- name: reminds_of
dtype: string
- name: also_like
dtype: string
- name: news_ids
dtype: string
- config_name: brands
features:
- name: id
dtype: string
- name: name
dtype: string
- name: url
dtype: string
- name: logo_url
dtype: string
- name: country
dtype: string
- name: main_activity
dtype: string
- name: website
dtype: string
- name: parent_company
dtype: string
- name: description
dtype: string
- name: brand_count
dtype: int64
- name: country_de
dtype: string
- name: country_es
dtype: string
- name: country_fr
dtype: string
- name: country_cs
dtype: string
- name: country_it
dtype: string
- name: country_ru
dtype: string
- name: country_pl
dtype: string
- name: country_pt
dtype: string
- name: country_el
dtype: string
- name: country_zh
dtype: string
- name: country_ja
dtype: string
- name: country_nl
dtype: string
- name: country_sr
dtype: string
- name: country_ro
dtype: string
- name: country_ar
dtype: string
- name: country_uk
dtype: string
- name: country_mn
dtype: string
- name: country_ko
dtype: string
- name: country_tr
dtype: string
- name: country_sv
dtype: string
- name: country_he
dtype: string
- name: country_hu
dtype: string
- name: main_activity_de
dtype: string
- name: main_activity_es
dtype: string
- name: main_activity_fr
dtype: string
- name: main_activity_cs
dtype: string
- name: main_activity_it
dtype: string
- name: main_activity_ru
dtype: string
- name: main_activity_pl
dtype: string
- name: main_activity_pt
dtype: string
- name: main_activity_el
dtype: string
- name: main_activity_zh
dtype: string
- name: main_activity_ja
dtype: string
- name: main_activity_nl
dtype: string
- name: main_activity_sr
dtype: string
- name: main_activity_ro
dtype: string
- name: main_activity_ar
dtype: string
- name: main_activity_uk
dtype: string
- name: main_activity_mn
dtype: string
- name: main_activity_ko
dtype: string
- name: main_activity_tr
dtype: string
- name: main_activity_sv
dtype: string
- name: main_activity_he
dtype: string
- name: main_activity_hu
dtype: string
- config_name: perfumers
features:
- name: id
dtype: string
- name: name
dtype: string
- name: url
dtype: string
- name: photo_url
dtype: string
- name: status
dtype: string
- name: company
dtype: string
- name: also_worked
dtype: string
- name: education
dtype: string
- name: web
dtype: string
- name: perfumes_count
dtype: int64
- name: biography
dtype: string
- name: status_de
dtype: string
- name: status_es
dtype: string
- name: status_fr
dtype: string
- name: status_cs
dtype: string
- name: status_it
dtype: string
- name: status_ru
dtype: string
- name: status_pl
dtype: string
- name: status_pt
dtype: string
- name: status_el
dtype: string
- name: status_zh
dtype: string
- name: status_ja
dtype: string
- name: status_nl
dtype: string
- name: status_sr
dtype: string
- name: status_ro
dtype: string
- name: status_ar
dtype: string
- name: status_uk
dtype: string
- name: status_mn
dtype: string
- name: status_ko
dtype: string
- name: status_tr
dtype: string
- name: status_sv
dtype: string
- name: status_he
dtype: string
- name: status_hu
dtype: string
- name: perfumer_name_ru
dtype: string
- name: perfumer_name_uk
dtype: string
- name: perfumer_name_ja
dtype: string
- name: perfumer_name_zh
dtype: string
- name: perfumer_name_ko
dtype: string
- name: perfumer_name_ar
dtype: string
- config_name: notes
features:
- name: id
dtype: string
- name: name
dtype: string
- name: url
dtype: string
- name: latin_name
dtype: string
- name: other_names
dtype: string
- name: group
dtype: string
- name: odor_profile
dtype: string
- name: main_icon
dtype: string
- name: alt_icons
dtype: string
- name: background
dtype: string
- name: fragrance_count
dtype: int64
- name: note_name_de
dtype: string
- name: note_name_es
dtype: string
- name: note_name_fr
dtype: string
- name: note_name_cs
dtype: string
- name: note_name_it
dtype: string
- name: note_name_ru
dtype: string
- name: note_name_pl
dtype: string
- name: note_name_pt
dtype: string
- name: note_name_el
dtype: string
- name: note_name_zh
dtype: string
- name: note_name_ja
dtype: string
- name: note_name_nl
dtype: string
- name: note_name_sr
dtype: string
- name: note_name_ro
dtype: string
- name: note_name_ar
dtype: string
- name: note_name_uk
dtype: string
- name: note_name_mn
dtype: string
- name: note_name_ko
dtype: string
- name: note_name_tr
dtype: string
- name: note_name_sv
dtype: string
- name: note_name_he
dtype: string
- name: note_name_hu
dtype: string
- name: note_group_de
dtype: string
- name: note_group_es
dtype: string
- name: note_group_fr
dtype: string
- name: note_group_cs
dtype: string
- name: note_group_it
dtype: string
- name: note_group_ru
dtype: string
- name: note_group_pl
dtype: string
- name: note_group_pt
dtype: string
- name: note_group_el
dtype: string
- name: note_group_zh
dtype: string
- name: note_group_ja
dtype: string
- name: note_group_nl
dtype: string
- name: note_group_sr
dtype: string
- name: note_group_ro
dtype: string
- name: note_group_ar
dtype: string
- name: note_group_uk
dtype: string
- name: note_group_mn
dtype: string
- name: note_group_ko
dtype: string
- name: note_group_tr
dtype: string
- name: note_group_sv
dtype: string
- name: note_group_he
dtype: string
- name: note_group_hu
dtype: string
- config_name: accords
features:
- name: id
dtype: string
- name: name
dtype: string
- name: bar_color
dtype: string
- name: font_color
dtype: string
- name: fragrance_count
dtype: int64
- name: name_de
dtype: string
- name: name_es
dtype: string
- name: name_fr
dtype: string
- name: name_cs
dtype: string
- name: name_it
dtype: string
- name: name_ru
dtype: string
- name: name_pl
dtype: string
- name: name_pt
dtype: string
- name: name_el
dtype: string
- name: name_zh
dtype: string
- name: name_ja
dtype: string
- name: name_nl
dtype: string
- name: name_sr
dtype: string
- name: name_ro
dtype: string
- name: name_ar
dtype: string
- name: name_uk
dtype: string
- name: name_mn
dtype: string
- name: name_ko
dtype: string
- name: name_tr
dtype: string
- name: name_sv
dtype: string
- name: name_he
dtype: string
- name: name_hu
dtype: string
- config_name: translations
features:
- name: id
dtype: string
- name: section
dtype: string
- name: en
dtype: string
- name: de
dtype: string
- name: es
dtype: string
- name: fr
dtype: string
- name: cs
dtype: string
- name: it
dtype: string
- name: ru
dtype: string
- name: pl
dtype: string
- name: pt
dtype: string
- name: el
dtype: string
- name: zh
dtype: string
- name: ja
dtype: string
- name: nl
dtype: string
- name: sr
dtype: string
- name: ro
dtype: string
- name: ar
dtype: string
- name: uk
dtype: string
- name: mn
dtype: string
- name: ko
dtype: string
- name: tr
dtype: string
- name: sv
dtype: string
- name: he
dtype: string
- name: hu
dtype: string
FragDB v5.5 — Fragrance Databases (Fragrantica core + Parfumo bundle preview, Multilingual Sample)
The most comprehensive structured fragrance databases available. This is a free sample of the FragDB bundle:
- Fragrantica (132,124 perfumes, 23 languages) — primary; 10-row CSV samples at root
- Parfumo (219,963 perfumes, English-only) — also available; 10-row CSV samples in
parfumo/ - Cross-database connection layer — 80,968 F↔P matched pairs + schema-level equivalence; structural samples in
cross/
Full datasets + cross-walk: fragdb.net (Cross-Source bundle tier $400+).
What's New in v5.5
- Parfumo database mention — second fragrance database (219,963 perfumes) now visible in the sample:
parfumo/— 6 CSV files with 10 fully-populated P perfumes + FK closurecross/— 8 cross-database artifacts + README (matched_pairs head-30, brand_matches head-30, field_equivalence_map full 206 rows, 5 *_overlap.json)- Full Parfumo + cross-walk: fragdb.net
- Fragrantica data updated from v5.4 → v5.5 (parser run 260601):
- Fragrances: 130,949 → 132,124 (+1,175)
- Brands: 7,815 → 7,881 (+66)
- Perfumers: 2,968 → 2,988 (+20)
- Notes: 2,522 → 2,533 (+11)
- YAML configs added for P + cross sample data (loadable via
load_dataset(..., "parfumo_perfumes")) - Tags added: parfumo, cross-database, record-linkage, cross-walk
Schema unchanged from v5.4
All F column counts identical (30/54/42/55/27/25) — existing scripts work without modification.
From v5.4 (unchanged in v5.5)
- 23 languages — all labels, note names, accords, countries, statuses translated
- 9 non-Latin scripts for perfumer name transliteration
- translations.csv — vocabulary file (34 entries) for gender and voting labels
- Compact notes pyramid —
note_id,opacity,weight(name/icon via notes.csv JOIN) - Each note name variant has its own ID with translations
- Gender & voting fields use translation IDs for multilingual support
Snapshot freshness
- Fragrantica: refreshed 2026-06-01 (v5.5)
- Parfumo: 1.1 (last data prep 2026-05-26)
- Cross-walk last refreshed: 2026-05-26 (Phase 2B Fellegi-Sunter, quarterly rerun cadence; ~99% precision)
Dataset Description
Fragrantica (primary, at root)
| File | Records | Fields | Description |
|---|---|---|---|
fragrances.csv |
10 | 30 | Top-rated fragrances (v5.5) |
brands.csv |
9 | 54 | Brand profiles + 22 lang translations |
perfumers.csv |
15 | 42 | Perfumer profiles + 22 lang + 9 name translit |
notes.csv |
86 | 55 | Fragrance notes + 22 lang translations |
accords.csv |
32 | 27 | Accords + 22 lang translations |
translations.csv |
34 | 25 | Gender & voting vocabulary (full) |
comments_sample.parquet |
25 | 8 | User reviews preview (parquet, schema unchanged from May) |
news_sample.parquet |
20 | 16 | Editorial articles preview (parquet) |
news_comments_sample.parquet |
20 | 9 | News comments preview (parquet) |
SPEC.md |
— | — | Parquet schema documentation |
Parfumo (in bundle — also available)
| File | Records | Fields | Description |
|---|---|---|---|
parfumo/perfumes.csv |
10 | 34 | Master catalog (10 fully-populated rows) |
parfumo/brands.csv |
6 | 12 | Brand catalog |
parfumo/perfumers.csv |
11 | 11 | Perfumer catalog |
parfumo/notes.csv |
60 | 11 | Notes catalog |
parfumo/notes_categories.csv |
79 | 6 | Hierarchical taxonomy (P-only) |
parfumo/accords.csv |
18 | 4 | Accord catalog |
Cross-database connection layer
| File | Records | Description |
|---|---|---|
cross/matched_pairs_sample.csv |
30 | First 30 of 80,968 F↔P perfume pairs (Phase 2B Fellegi-Sunter, ~99% precision) |
cross/brand_matches_sample.csv |
30 | First 30 of 6,522 brand pairs |
cross/field_equivalence_map.csv |
206 | Full schema-level F↔P column equivalence (same_format / diff_format / partial_overlap / F-only / P-only) |
cross/notes_overlap.json |
— | 1,690 direct + 7,977 species-of stats |
cross/perfumers_overlap.json |
— | 1,669 perfumer pairs + Jaccard |
cross/accord_overlap.json |
— | 15 matched accord pairs |
cross/dictionary_overlap_stats.json |
— | Aggregate dict overlap |
cross/overlap_stats.json |
— | Top-level Jaccard summary |
cross/README.md |
— | Cross-layer schema notes + join recipes |
Loading P + cross data
from datasets import load_dataset
# F (default config, backward-compat)
f = load_dataset("FragDBnet/fragrance-database")
# P configs (non-default)
p_perfumes = load_dataset("FragDBnet/fragrance-database", "parfumo_perfumes")
p_brands = load_dataset("FragDBnet/fragrance-database", "parfumo_brands")
# Cross-walk
matched = load_dataset("FragDBnet/fragrance-database", "cross_matched_pairs")
equiv = load_dataset("FragDBnet/fragrance-database", "cross_field_equivalence_map")
Companion Parquet Datasets — User Reviews, News, and Community Comments
FragDB ships with three Apache Parquet datasets containing 4.9 million rows of user-generated content and editorial coverage — the largest publicly-organized corpus of fragrance reviews and perfumery journalism. Use them for NLP, sentiment analysis, recommendation systems, market research, or training language models on fragrance-specific text.
Keywords: fragrance reviews · perfume reviews · multilingual UGC corpus · NLP training data · fragrance sentiment · perfumery journalism · perfume recommendation · scent recommendation · review classification · entity linking · knowledge graph · fragrance industry news · perfume articles
comments.parquet — 4.6 Million User Reviews in 23 Languages
The world's largest collection of structured fragrance reviews. Every entry includes the perfume ID (joinable with fragrances.csv), author username, posting date, full review text, avatar URL, and language code.
- 4,643,851 user reviews spanning every major perfume on Fragrantica
- 23 languages — English (1.69M), Russian, Portuguese, Spanish, Korean, Turkish, Japanese, Polish, Italian, Hungarian, Serbian, Swedish, German, Hebrew, Ukrainian, French, Arabic, Greek, Czech, Chinese, Romanian, Mongolian, Dutch
- Coverage: 70.6% of all fragrances have at least one review (93,305 of 132,160 PIDs)
- Deterministic global primary key — stable comment IDs survive re-scrapes
- Zero duplicate rows, zero foreign key orphans against
fragrances.csv.pid - Independent UGC per language — genuine localized content, not machine translation
- 8 fields:
pid,lang,comment_id,author,date,text,avatar_url,gradient_class - PyArrow large_string format — combined corpus exceeds 32-bit string offset limit
Use cases: sentiment analysis · review classification · recommendation systems · perfume similarity from text · language detection benchmark · multilingual NLP training corpus · fragrance market research · author network analysis · trend detection by language
news.parquet — 24,440 Editorial Articles (2008–2026)
Two decades of professional fragrance journalism from Fragrantica's editorial team. Every article includes title, author, full text (plain + HTML), category, related perfumes/brands/perfumers, publication date, and main image.
- 24,440 editorial articles from 2008 to 2026 — complete public archive
- 30+ categories — New Fragrances (34.9%), Fragrance Reviews (22.8%), Niche Perfumery (10.4%), Designer Brands, Interviews, History, Industry News
- Bilingual storage —
text(plain) for NLP,text_html(markup preserved) for rich display - Linked entities —
related_pids[],related_brands[],related_perfumers[]as JSON arrays - 0% orphans over 119,662 PID references
- 63.1% archived legacy, 36.9% modern fully-dated articles
- 16 fields:
nid,title,category,author,url,is_archived,date_unix,description,text,text_html,main_image,article_images,related_pids,related_brands,related_perfumers,comments_count
Use cases: content recommendation · article search engine · perfume knowledge graph · trend analysis · author influence study · entity linking · timeline analysis · industry research · niche perfumery research
news_comments.parquet — 263,798 Threaded Community Comments
Community discussions attached to editorial articles, with threading support for replies. Joinable with news.parquet via nid.
- 263,798 threaded comments across 21,820 articles (89.3% of news articles have ≥1 comment)
- 4.9% reply rate — threaded conversations with reply detection
- 100% populated timestamps
- 9 fields:
nid,comment_id,is_reply,author,date,date_unix,text,avatar_url,gradient
Use cases: community engagement analysis · threaded discussion mining · reply network construction · comment sentiment · author activity profiles
Tier Availability
The parquet datasets ship with all paid tiers except the $200 Core:
| Tier | CSV Core | Parquet Datasets |
|---|---|---|
| $200 One-Time Core | ✅ | ❌ |
| $400 One-Time Full Database | ✅ | ✅ |
| Annual Subscription | ✅ | ✅ (always latest) |
| Lifetime Access | ✅ | ✅ (always latest) |
See https://fragdb.net/#pricing for complete tier comparison.
Quick Start — Parquet
import pyarrow.parquet as pq
import pandas as pd
import json
reviews = pq.read_table('comments.parquet').to_pandas()
fragrances = pd.read_csv('fragrances.csv', sep='|')
reviews_with_meta = reviews.merge(fragrances, on='pid', how='left')
news = pq.read_table('news.parquet').to_pandas()
news['related_pids_list'] = news['related_pids'].apply(json.loads)
news_comments = pq.read_table('news_comments.parquet').to_pandas()
Full schema in SPEC.md.
Use Cases
CSV Core (all tiers):
- E-commerce — Enrich product listings with detailed fragrance data, notes, accords
- Mobile Apps — Build fragrance collection managers, scent discovery apps, perfume catalog apps
- Data Analysis — Analyze fragrance industry trends by brand, country, perfumer, year
- Recommendations — Content-based or collaborative filtering systems using accord/note vectors
- Multilingual UIs — Localized perfume catalogs in 23 languages out of the box
- Knowledge Graphs — Brand → Perfumer → Fragrance → Notes → Accords graph construction
- Market Research — Country-of-origin analysis, parent company portfolios, perfumer productivity stats
Parquet Datasets ($400+ tiers):
- NLP & Sentiment Analysis — Train models on 4.6M multilingual fragrance reviews
- Recommender Systems — Hybrid models combining CSV structure with review text similarity
- Language Models — Domain-specific corpus for fragrance/perfumery LLM fine-tuning
- Review Classification — Identify positive/negative reviews, fake review detection
- Trend Detection — News article timeline analysis, emerging fragrance trends
- Author Networks — Identify influential reviewers, perfumery journalists, community leaders
- Content-Based Discovery — "Articles about this perfume" — JOIN news.related_pids with fragrances.pid
- Community Analytics — Reply networks, engagement metrics on editorial content
- Cross-Language Studies — Compare review sentiment across 23 languages for the same fragrance
- Search Engines — Full-text search across reviews, articles, and structured metadata
- Knowledge Extraction — Mine 24K editorial articles for perfume facts, launch dates, perfumer interviews
Full Database
| Sample | Full Database | |
|---|---|---|
| F Fragrances | 10 | 132,124 |
| F Brands | 9 (referenced) | 7,881 |
| F Perfumers | 15 (referenced) | 2,988 |
| F Notes | 86 (referenced) | 2,533 |
| F Accords | 32 (referenced) | 92 |
| F Translations | 34 | 34 |
| F Languages | 23 | 23 |
| F Total Records | ~186 | 145,612 |
| P Perfumes (in bundle) | 10 | 219,963 |
| P Brands | 6 | 14,277 |
| P Perfumers | 11 | 2,472 |
| P Notes | 60 | 12,082 |
| Cross-walk F↔P pairs | 30 | 80,968 |
| Cross-walk brand pairs | 30 | 6,522 |
Quick Start
import pandas as pd
fragrances = pd.read_csv('fragrances.csv', sep='|')
brands = pd.read_csv('brands.csv', sep='|')
notes = pd.read_csv('notes.csv', sep='|')
translations = pd.read_csv('translations.csv', sep='|')
# Join and translate
fragrances['brand_id'] = fragrances['brand'].str.split(';').str[1]
df = fragrances.merge(brands, left_on='brand_id', right_on='id', suffixes=('', '_brand'))
trans = translations.set_index('id')
df['gender_ru'] = df['gender'].map(lambda x: trans.loc[x, 'ru'] if x in trans.index else x)
print(df[['name', 'name_brand', 'country_ru', 'gender_ru']])
File Format
- Format: CSV (pipe
|delimited) - Encoding: UTF-8
- Quote Character:
"(double quote)
Links
- Full Database: fragdb.net
- GitHub: github.com/FragDB/fragrance-database
- Kaggle: kaggle.com/datasets/eriklindqvist/fragdb-fragrance-database
License
This sample is released under the CC BY-NC 4.0 License. Free for non-commercial use with attribution.
Citation
@dataset{fragdb2026,
title={FragDB Fragrantica Fragrance Database},
author={FragDB},
year={2026},
version={5.3},
url={https://fragdb.net},
note={Multilingual dataset with 6 files, 23 languages}
}