v3.0: Add notes.csv, accords.csv, new voting format

README.md

@@ -1,5 +1,5 @@
 ---
-license: mit
+license: cc-by-nc-4.0
 task_categories:
   - feature-extraction
   - text-classification
@@ -12,6 +12,7 @@ tags:
   - recommendation-system
   - e-commerce
   - retail
+  - fragrantica
 size_categories:
   - 10<n<100
 configs:
@@ -25,6 +26,12 @@ configs:
   - config_name: perfumers
     data_files: perfumers.csv
     sep: "|"
+  - config_name: notes
+    data_files: notes.csv
+    sep: "|"
+  - config_name: accords
+    data_files: accords.csv
+    sep: "|"
 dataset_info:
   - config_name: fragrances
     features:
@@ -84,6 +91,10 @@ dataset_info:
         dtype: string
       - name: news_ids
         dtype: string
+      - name: reviews_count
+        dtype: int64
+      - name: pros_cons
+        dtype: string
   - config_name: brands
     features:
       - name: id
@@ -130,11 +141,55 @@ dataset_info:
         dtype: int64
       - name: biography
         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
+  - 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
 ---
 
-# FragDB — Fragrance Database (Sample)
+# FragDB v3.0 — Fragrantica Fragrance Database (Sample)
 
-The most comprehensive structured fragrance database available. This is a **free sample** containing 10 fragrances with related brands and perfumers.
+The most comprehensive structured fragrance database available. This is a **free sample** containing 10 fragrances with related brands, perfumers, notes, and accords.
+
+## What's New in v3.0
+
+- **2 new reference files**: `notes.csv` and `accords.csv`
+- **New voting format**: `category:votes:percent` (e.g., `love:5000:45.2`)
+- **New accords format**: `id:percent` (join with accords.csv for colors)
+- **Enhanced notes pyramid**: includes opacity and weight values
+- **Total**: 67 data fields across 5 files
 
 ## Dataset Description
 
@@ -142,18 +197,22 @@ FragDB is a relational database of the fragrance industry containing:
 
 | File | Records | Fields | Description |
 |------|---------|--------|-------------|
-| `fragrances.csv` | 10 | 28 | Iconic fragrances (Chanel, Dior, Tom Ford...) |
-| `brands.csv` | 7 | 10 | Luxury brand profiles |
-| `perfumers.csv` | 15 | 11 | Master perfumer profiles |
+| `fragrances.csv` | 10 | 30 | Iconic fragrances with notes, accords, ratings |
+| `brands.csv` | 10 | 10 | Luxury brand profiles |
+| `perfumers.csv` | 10 | 11 | Master perfumer profiles |
+| `notes.csv` | 10 | 11 | Fragrance notes with Latin names, odor profiles |
+| `accords.csv` | 10 | 5 | Scent accords with display colors |
 
 ### Full Database
 
 | | Sample | Full Database |
 |---|--------|---------------|
-| Fragrances | 10 | **119,000+** |
-| Brands | 7 | **7,200+** |
-| Perfumers | 15 | **2,700+** |
-| Year Range | 1992-2015 | **1533-2026** |
+| Fragrances | 10 | **120,871** |
+| Brands | 10 | **7,296** |
+| Perfumers | 10 | **2,815** |
+| Notes | 10 | **2,448** |
+| Accords | 10 | **92** |
+| **Total Records** | 50 | **133,522** |
 
 Full database available at [fragdb.net](https://fragdb.net)
 
@@ -165,12 +224,11 @@ Full database available at [fragdb.net](https://fragdb.net)
 from datasets import load_dataset
 
 # Load all files
-dataset = load_dataset("FragDB/fragrance-database")
-
-# Access each table
-fragrances = dataset['fragrances']
-brands = dataset['brands']
-perfumers = dataset['perfumers']
+fragrances = load_dataset("FragDBnet/fragrance-database", "fragrances")
+brands = load_dataset("FragDBnet/fragrance-database", "brands")
+perfumers = load_dataset("FragDBnet/fragrance-database", "perfumers")
+notes = load_dataset("FragDBnet/fragrance-database", "notes")
+accords = load_dataset("FragDBnet/fragrance-database", "accords")
 ```
 
 ### Using Pandas
@@ -181,6 +239,8 @@ import pandas as pd
 fragrances = pd.read_csv('fragrances.csv', sep='|')
 brands = pd.read_csv('brands.csv', sep='|')
 perfumers = pd.read_csv('perfumers.csv', sep='|')
+notes = pd.read_csv('notes.csv', sep='|')
+accords = pd.read_csv('accords.csv', sep='|')
 
 # Join fragrances with brands
 fragrances['brand_id'] = fragrances['brand'].str.split(';').str[1]
@@ -191,7 +251,7 @@ print(df[['name', 'name_brand', 'country', 'rating']])
 
 ## Data Structure
 
-### fragrances.csv (28 fields)
+### fragrances.csv (30 fields)
 
 #### Identity & Basic Info
 | Field | Description | Example |
@@ -204,26 +264,26 @@ print(df[['name', 'name_brand', 'country', 'rating']])
 | `gender` | Target gender | `for men`, `for women`, `for women and men` |
 | `collection` | Collection within brand | Text |
 
-#### Composition
+#### Composition (v3.0 Format)
 | Field | Description | Format |
 |-------|-------------|--------|
-| `accords` | Scent accords with strength | `fruity:100:#FC4B29:#000;woody:67:#774414:#FFF` |
-| `notes_pyramid` | Notes by layer | `top(Bergamot,url,img;...)middle(...)base(...)` |
+| `accords` | Scent accords with strength | `a1:100;a2:67;a3:45` (join with accords.csv) |
+| `notes_pyramid` | Notes by layer | `top(name,id,url,opacity,weight;...)middle(...)base(...)` |
 | `perfumers` | Perfumer names and IDs | `Erwin Creed;p1;Olivier Creed;p2` |
 | `description` | Fragrance description | HTML text |
 
-#### Ratings & Votes
+#### Ratings & Votes (v3.0 Format: category:votes:percent)
 | Field | Description | Format |
 |-------|-------------|--------|
 | `rating` | Average rating & vote count | `4.33;24561` |
-| `appreciation` | Love/like/ok/dislike/hate | `love:100;like:42.23;ok:11.85;...` |
-| `price_value` | Price perception votes | `way_overpriced:6658;overpriced:2844;...` |
-| `ownership` | Ownership status | `have_it:52.82;had_it:12.32;want_it:34.86` |
-| `gender_votes` | Gender suitability votes | `female:149;unisex:866;male:7977;...` |
-| `longevity` | Duration votes | `very_weak:784;weak:1459;moderate:5869;...` |
-| `sillage` | Projection votes | `intimate:1816;moderate:8139;strong:4289;...` |
-| `season` | Seasonal suitability | `winter:44.39;spring:97.60;summer:99.48;fall:74.81` |
-| `time_of_day` | Day/night suitability | `day:100.00;night:68.93` |
+| `appreciation` | Love/like/ok/dislike/hate | `love:5000:45.2;like:3000:27.1;...` |
+| `price_value` | Price perception votes | `way_overpriced:6658:30;overpriced:2844:13;...` |
+| `ownership` | Ownership status | `have_it:5282:52.82;had_it:1232:12.32;...` |
+| `gender_votes` | Gender suitability votes | `female:149:2;unisex:866:10;male:7977:88` |
+| `longevity` | Duration votes | `very_weak:784:5;weak:1459:10;moderate:5869:40;...` |
+| `sillage` | Projection votes | `intimate:1816:12;moderate:8139:55;strong:4289:29;...` |
+| `season` | Seasonal suitability | `winter:4439:44.39;spring:9760:97.60;...` |
+| `time_of_day` | Day/night suitability | `day:10000:100;night:6893:68.93` |
 
 #### Related Fragrances
 | Field | Description | Format |
@@ -233,12 +293,37 @@ print(df[['name', 'name_brand', 'country', 'rating']])
 | `reminds_of` | Similar fragrances | Semicolon-separated PIDs |
 | `also_like` | Recommended fragrances | Semicolon-separated PIDs |
 
-#### Media
+#### New in v3.0
 | Field | Description |
 |-------|-------------|
-| `main_photo` | Main bottle photo URL |
-| `info_card` | Social card image URL |
-| `user_photoes` | User-submitted photos (semicolon-separated) |
+| `reviews_count` | Total number of user reviews |
+| `pros_cons` | AI-generated pros/cons summary with vote counts |
+
+### notes.csv (11 fields) — NEW in v3.0
+
+| Field | Description | Example |
+|-------|-------------|---------|
+| `id` | Unique note identifier | `n1` |
+| `name` | Note name | `Lavender` |
+| `url` | Fragrantica note page | URL |
+| `latin_name` | Latin/scientific name | `Lavandula angustifolia` |
+| `other_names` | Alternative names | `English Lavender, True Lavender` |
+| `group` | Note category | `Flowers`, `Woods`, `Citrus` |
+| `odor_profile` | Scent description | `Fresh, herbal, floral...` |
+| `main_icon` | Primary icon image URL | URL |
+| `alt_icons` | Alternative icons | Semicolon-separated URLs |
+| `background` | Background/splash image | URL |
+| `fragrance_count` | Number of fragrances | `12229` |
+
+### accords.csv (5 fields) — NEW in v3.0
+
+| Field | Description | Example |
+|-------|-------------|---------|
+| `id` | Unique accord identifier | `a1` |
+| `name` | Accord name | `woody` |
+| `bar_color` | Display bar color (hex) | `#774414` |
+| `font_color` | Text color (hex) | `#FFFFFF` |
+| `fragrance_count` | Number of fragrances | `45892` |
 
 ### brands.csv (10 fields)
 
@@ -271,26 +356,73 @@ print(df[['name', 'name_brand', 'country', 'rating']])
 | `perfumes_count` | Number of fragrances | `538` |
 | `biography` | Biography | HTML text |
 
-## Sample Fragrances
+## Parsing Examples (v3.0)
 
-The sample includes these iconic fragrances:
+### Parse v3.0 voting format
+```python
+def parse_votes(votes_str):
+    """Parse v3.0 voting format: category:votes:percent"""
+    result = {}
+    for item in votes_str.split(';'):
+        parts = item.split(':')
+        if len(parts) >= 3:
+            result[parts[0]] = {
+                'votes': int(parts[1]),
+                'percent': float(parts[2])
+            }
+    return result
+
+longevity = parse_votes(row['longevity'])
+# {'very_weak': {'votes': 784, 'percent': 5.0}, 'weak': {...}, ...}
+```
+
+### Parse v3.0 accords format with join
+```python
+def parse_accords(accords_str, accords_df):
+    """Parse v3.0 accords format: id:percent and join with reference"""
+    result = []
+    for item in accords_str.split(';'):
+        accord_id, percent = item.split(':')
+        accord_info = accords_df[accords_df['id'] == accord_id].iloc[0]
+        result.append({
+            'name': accord_info['name'],
+            'percent': int(percent),
+            'bar_color': accord_info['bar_color'],
+            'font_color': accord_info['font_color']
+        })
+    return result
+```
+
+### Parse notes pyramid with opacity/weight
+```python
+import re
 
-- Coco Mademoiselle (Chanel)
-- Sauvage (Dior)
-- Black Orchid (Tom Ford)
-- Alien (Mugler)
-- La Vie Est Belle (Lancôme)
-- Black Opium (Yves Saint Laurent)
-- Light Blue (Dolce & Gabbana)
-- And more...
+def parse_notes_pyramid(pyramid_str):
+    """Parse v3.0 notes pyramid with opacity and weight"""
+    result = {'top': [], 'middle': [], 'base': []}
+    for layer in ['top', 'middle', 'base']:
+        match = re.search(rf'{layer}\(([^)]+)\)', pyramid_str)
+        if match:
+            for note in match.group(1).split(';'):
+                parts = note.split(',')
+                result[layer].append({
+                    'name': parts[0],
+                    'id': parts[1] if len(parts) > 1 else None,
+                    'url': parts[2] if len(parts) > 2 else None,
+                    'opacity': float(parts[3]) if len(parts) > 3 else None,
+                    'weight': float(parts[4]) if len(parts) > 4 else None
+                })
+    return result
+```
 
 ## Use Cases
 
 - **Recommendation Systems** — Build "if you like X, try Y" engines using accords, notes, and also_like data
 - **Market Analysis** — Analyze trends by brand, country, year, or perfumer
-- **NLP** — Process 9.4M words of descriptions in the full database
+- **NLP** — Process descriptions, odor profiles, and pros/cons data
 - **Collection Apps** — Build fragrance tracking and discovery apps
 - **E-commerce** — Enrich product catalogs with detailed fragrance data
+- **Data Visualization** — Create accord charts with actual display colors from accords.csv
 
 ## File Format
 
@@ -298,37 +430,6 @@ The sample includes these iconic fragrances:
 - **Encoding**: UTF-8
 - **Quote Character**: `"` (double quote)
 
-## Parsing Examples
-
-### Parse brand field
-```python
-brand_name, brand_id = row['brand'].split(';')
-# brand_name = "Chanel", brand_id = "b91"
-```
-
-### Parse rating field
-```python
-rating_avg, rating_votes = row['rating'].split(';')
-# rating_avg = "4.33", rating_votes = "24561"
-```
-
-### Parse accords field
-```python
-accords = []
-for accord in row['accords'].split(';'):
-    name, strength, bg_color, text_color = accord.split(':')
-    accords.append({'name': name, 'strength': int(strength)})
-```
-
-### Parse notes pyramid
-```python
-import re
-layers = re.findall(r'(top|middle|base)\(([^)]*)\)', row['notes_pyramid'])
-for layer_name, notes_str in layers:
-    notes = [n.split(',')[0] for n in notes_str.split(';')]
-    print(f"{layer_name}: {notes}")
-```
-
 ## Links
 
 - **Full Database**: [fragdb.net](https://fragdb.net)
@@ -337,7 +438,7 @@ for layer_name, notes_str in layers:
 
 ## License
 
-This sample is released under the **MIT License**. Free for personal and commercial use.
+This sample is released under the **CC BY-NC 4.0 License**. Free for non-commercial use with attribution.
 
 The full database requires a commercial license — see [fragdb.net](https://fragdb.net) for details.
 
@@ -345,10 +446,11 @@ The full database requires a commercial license — see [fragdb.net](https://fra
 
 ```bibtex
 @dataset{fragdb2026,
-  title={FragDB Fragrance Database},
+  title={FragDB Fragrantica Fragrance Database},
   author={FragDB},
   year={2026},
+  version={3.0},
   url={https://fragdb.net},
-  note={Sample dataset}
+  note={Sample dataset with 5 files, 67 fields}
 }
 ```

accords.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fab7b73d5da8832c0b978ae09dfbe0e7e9b285d69b40f925914cc432a1bdb6bb
+size 371

brands.csv

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:d45e66fc9e47d5d7c32b2b024bfa182dc2207315ed2b6a1822d65cb37ec124eb
-size 17542
+oid sha256:160518d03bc64c57c2b9f635804064713fb5378e020f9f6663239a100e8c5328
+size 25798

fragrance-database.py

@@ -0,0 +1,144 @@
+"""FragDB Fragrance Database - Custom loading script for pipe-delimited CSV files."""
+
+import csv
+import datasets
+
+_DESCRIPTION = """
+FragDB is the most comprehensive structured fragrance database available.
+This sample contains 10 fragrances with related brands and perfumers.
+"""
+
+_HOMEPAGE = "https://fragdb.net"
+_LICENSE = "MIT"
+
+_URLS = {
+    "fragrances": "fragrances.csv",
+    "brands": "brands.csv",
+    "perfumers": "perfumers.csv",
+}
+
+
+class FragranceDatabase(datasets.GeneratorBasedBuilder):
+    """FragDB Fragrance Database."""
+
+    VERSION = datasets.Version("2.0.0")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="fragrances", version=VERSION, description="Fragrance data"),
+        datasets.BuilderConfig(name="brands", version=VERSION, description="Brand data"),
+        datasets.BuilderConfig(name="perfumers", version=VERSION, description="Perfumer data"),
+        datasets.BuilderConfig(name="all", version=VERSION, description="All tables"),
+    ]
+
+    DEFAULT_CONFIG_NAME = "all"
+
+    def _info(self):
+        if self.config.name == "fragrances":
+            features = datasets.Features({
+                "pid": datasets.Value("int64"),
+                "url": datasets.Value("string"),
+                "brand": datasets.Value("string"),
+                "name": datasets.Value("string"),
+                "year": datasets.Value("int64"),
+                "gender": datasets.Value("string"),
+                "collection": datasets.Value("string"),
+                "main_photo": datasets.Value("string"),
+                "info_card": datasets.Value("string"),
+                "user_photoes": datasets.Value("string"),
+                "accords": datasets.Value("string"),
+                "notes_pyramid": datasets.Value("string"),
+                "perfumers": datasets.Value("string"),
+                "description": datasets.Value("string"),
+                "rating": datasets.Value("string"),
+                "appreciation": datasets.Value("string"),
+                "price_value": datasets.Value("string"),
+                "ownership": datasets.Value("string"),
+                "gender_votes": datasets.Value("string"),
+                "longevity": datasets.Value("string"),
+                "sillage": datasets.Value("string"),
+                "season": datasets.Value("string"),
+                "time_of_day": datasets.Value("string"),
+                "by_designer": datasets.Value("string"),
+                "in_collection": datasets.Value("string"),
+                "reminds_of": datasets.Value("string"),
+                "also_like": datasets.Value("string"),
+                "news_ids": datasets.Value("string"),
+            })
+        elif self.config.name == "brands":
+            features = datasets.Features({
+                "id": datasets.Value("string"),
+                "name": datasets.Value("string"),
+                "url": datasets.Value("string"),
+                "logo_url": datasets.Value("string"),
+                "country": datasets.Value("string"),
+                "main_activity": datasets.Value("string"),
+                "website": datasets.Value("string"),
+                "parent_company": datasets.Value("string"),
+                "description": datasets.Value("string"),
+                "brand_count": datasets.Value("int64"),
+            })
+        elif self.config.name == "perfumers":
+            features = datasets.Features({
+                "id": datasets.Value("string"),
+                "name": datasets.Value("string"),
+                "url": datasets.Value("string"),
+                "photo_url": datasets.Value("string"),
+                "status": datasets.Value("string"),
+                "company": datasets.Value("string"),
+                "also_worked": datasets.Value("string"),
+                "education": datasets.Value("string"),
+                "web": datasets.Value("string"),
+                "perfumes_count": datasets.Value("int64"),
+                "biography": datasets.Value("string"),
+            })
+        else:  # all
+            features = datasets.Features({
+                "table": datasets.Value("string"),
+                "data": datasets.Value("string"),
+            })
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+        )
+
+    def _split_generators(self, dl_manager):
+        if self.config.name == "all":
+            return [
+                datasets.SplitGenerator(
+                    name="fragrances",
+                    gen_kwargs={"filepath": dl_manager.download_and_extract(_URLS["fragrances"]), "table": "fragrances"},
+                ),
+                datasets.SplitGenerator(
+                    name="brands",
+                    gen_kwargs={"filepath": dl_manager.download_and_extract(_URLS["brands"]), "table": "brands"},
+                ),
+                datasets.SplitGenerator(
+                    name="perfumers",
+                    gen_kwargs={"filepath": dl_manager.download_and_extract(_URLS["perfumers"]), "table": "perfumers"},
+                ),
+            ]
+        else:
+            return [
+                datasets.SplitGenerator(
+                    name="train",
+                    gen_kwargs={"filepath": dl_manager.download_and_extract(_URLS[self.config.name]), "table": self.config.name},
+                ),
+            ]
+
+    def _generate_examples(self, filepath, table):
+        with open(filepath, encoding="utf-8") as f:
+            reader = csv.DictReader(f, delimiter="|")
+            for idx, row in enumerate(reader):
+                # Convert numeric fields
+                if table == "fragrances":
+                    row["pid"] = int(row["pid"]) if row.get("pid") else 0
+                    row["year"] = int(row["year"]) if row.get("year") else 0
+                elif table == "brands":
+                    row["brand_count"] = int(row["brand_count"]) if row.get("brand_count") else 0
+                elif table == "perfumers":
+                    row["perfumes_count"] = int(row["perfumes_count"]) if row.get("perfumes_count") else 0
+
+                yield idx, row

fragrances.csv

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:8f469616445108eeb2d3170bcd746b22ef6ddf5de087c1bda78ee590e51c1bcd
-size 67970
+oid sha256:f51d270e428f11b52547a3f997bb22c46806d27ca7cbac348d79015df0e00eff
+size 57398

notes.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9d3ccb57d026ff71467eceff0329e8876f86a141858e487683b05c2137449b1a
+size 4095

perfumers.csv

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:837c51ab0edc1827fe0eebc20a024c81beda191bc0c0bd0dcd8118ae79b33ed2
-size 34779
+oid sha256:1356f510e5d79bee2eb9a6e0cd194b7a331d6c11a47f8dab842b35567e76f251
+size 21689