Liquor Authority quarterly list of all active licensees in NYS filtered by Winery and Brewery specific License Types.

The motivation to assemble these historical data was to learn more about wine’s globalization. Some of the world's leading wine economists and historians have contributed to and drawn on this database to examine national wine market developments before, during and in between the 19th century and current waves of globalization. Their initial analyses cover all key wine-producing and wine-consuming countries using a common methodology to explain long-term trends and cycles in national wine production, consumption, and trade. More information about the database, the data sources and the methodology can be found on the Annual Database of Global Wine Markets web page.

This is the data dictionary and use guide for the Napa County Winery Database.

Data for Wine Statistical Releases is derived directly from the Report of Wine Premises Operations Form 5120.17. This form must be filed with TTB 15 days after the close of the period. The Wine Statistical Release report is generated approximately 45 days after the due date.

The database contains geo-spatial (block shape, block length/width ratio, mean and max slope) and management (training system, row spacing and headland size) information of 3686 sample vineyards throughout Italian territory.

Comprehensive dataset containing 341 verified Winery businesses in RS with complete contact information, ratings, reviews, and location data.

Comprehensive dataset containing 87 verified Winery businesses in MK with complete contact information, ratings, reviews, and location data.

Italy and France are historically among the countries that produce the most prestigious wines worldwide. In Europe, these two countries together produce more than half of the wines classified under the Protected Designation of Origin (PDO) label, the strictest quality mark of food and wines in the European Union. Due to their long tradition in wine protection, Italy and France include highly detailed regulatory information in their wine PDO regulatory documents that are usually not available for other countries, such as specific information about the main cultivars that must be used to make each wine product or the related required planting density in the vineyards. However, this information is scattered throughout the documents of each wine production area and has never been extracted and homogenised in a unique dataset. Here, we present the first dataset that characterizes the PDO wines produced in Italy and France at very high detail based on the documents from the official EU geographical indication register. It includes, for each country, a standardized list of the PDO wine names, linked with their specific regulatory requirements, including the wine colour, type, cultivars used and maximum allowed yields. The unprecedent level of detail of this dataset allows for the first time the analysis of more than 5000 traditional wines and their legal and agronomic specifications. This gives insights into the interplay between the European Union quality regulation policy, the wine sector and agronomic practices, enabling researchers and practitioners to analyze wine production in the context of specific regulations or economic scenarios.

THIS IS THE AUTHORITATIVE WINERIES LAYER FOR NAPA COUNTY.This GIS layer contains the locations and attributes (e.g. approved gallons per year production volumes, approved visitation rates, etc) of wineries located within Napa County.This layer contains the locations and attributes for wineries in Napa County and forms the core of the County's "winery database".For a detailed data dictionary and use guide for this layer, please refer to the PDF below:Data Dictionary and Use Guide, Napa Co. Winery Database

Citation Request: This dataset is public available for research. The details are described in [Cortez et al., 2009]. Please include this citation if you plan to use this database:

P. Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Reis. Modeling wine preferences by data mining from physicochemical properties. In Decision Support Systems, Elsevier, 47(4):547-553. ISSN: 0167-9236.

Available at: [@Elsevier] http://dx.doi.org/10.1016/j.dss.2009.05.016 [Pre-press (pdf)] http://www3.dsi.uminho.pt/pcortez/winequality09.pdf [bib] http://www3.dsi.uminho.pt/pcortez/dss09.bib

1. Title: Wine Quality

2. Sources Created by: Paulo Cortez (Univ. Minho), Antonio Cerdeira, Fernando Almeida, Telmo Matos and Jose Reis (CVRVV) @ 2009

3. Past Usage:

   P. Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Reis. Modeling wine preferences by data mining from physicochemical properties. In Decision Support Systems, Elsevier, 47(4):547-553. ISSN: 0167-9236.

   In the above reference, two datasets were created, using red and white wine samples. The inputs include objective tests (e.g. PH values) and the output is based on sensory data (median of at least 3 evaluations made by wine experts). Each expert graded the wine quality between 0 (very bad) and 10 (very excellent). Several data mining methods were applied to model these datasets under a regression approach. The support vector machine model achieved the best results. Several metrics were computed: MAD, confusion matrix for a fixed error tolerance (T), etc. Also, we plot the relative importances of the input variables (as measured by a sensitivity analysis procedure).

4. Relevant Information:

   The two datasets are related to red and white variants of the Portuguese "Vinho Verde" wine. For more details, consult: http://www.vinhoverde.pt/en/ or the reference [Cortez et al., 2009]. Due to privacy and logistic issues, only physicochemical (inputs) and sensory (the output) variables are available (e.g. there is no data about grape types, wine brand, wine selling price, etc.).

   These datasets can be viewed as classification or regression tasks. The classes are ordered and not balanced (e.g. there are munch more normal wines than excellent or poor ones). Outlier detection algorithms could be used to detect the few excellent or poor wines. Also, we are not sure if all input variables are relevant. So it could be interesting to test feature selection methods.

5. Number of Instances: red wine - 1599; white wine - 4898.

6. Number of Attributes: 11 + output attribute

   Note: several of the attributes may be correlated, thus it makes sense to apply some sort of feature selection.

7. Attribute information:

   For more information, read [Cortez et al., 2009].

   Input variables (based on physicochemical tests): 1 - fixed acidity (tartaric acid - g / dm^3) 2 - volatile acidity (acetic acid - g / dm^3) 3 - citric acid (g / dm^3) 4 - residual sugar (g / dm^3) 5 - chlorides (sodium chloride - g / dm^3 6 - free sulfur dioxide (mg / dm^3) 7 - total sulfur dioxide (mg / dm^3) 8 - density (g / cm^3) 9 - pH 10 - sulphates (potassium sulphate - g / dm3) 11 - alcohol (% by volume) Output variable (based on sensory data): 12 - quality (score between 0 and 10)

8. Missing Attribute Values: None

9. Description of attributes:

   1 - fixed acidity: most acids involved with wine or fixed or nonvolatile (do not evaporate readily)

   2 - volatile acidity: the amount of acetic acid in wine, which at too high of levels can lead to an unpleasant, vinegar taste

   3 - citric acid: found in small quantities, citric acid can add 'freshness' and flavor to wines

   4 - residual sugar: the amount of sugar remaining after fermentation stops, it's rare to find wines with less than 1 gram/liter and wines with greater than 45 grams/liter are considered sweet

   5 - chlorides: the amount of salt in the wine

   6 - free sulfur dioxide: the free form of SO2 exists in equilibrium between molecular SO2 (as a dissolved gas) and bisulfite ion; it prevents microbial growth and the oxidation of wine

   7 - total sulfur dioxide: amount of free and bound forms of S02; in low concentrations, SO2 is mostly undetectable in wine, but at free SO2 concentrations over 50 ppm, SO2 becomes evident in the nose and taste of wine

   8 - density: the density of water is close to that of water depending on the percent alcohol and sugar content

   9 - pH: describes how acidic or basic a wine is on a scale from 0 (very acidic) to 14 (very basic); most wines are between 3-4 on the pH scale

   10 - sulphates: a wine additive which can contribute to sulfur dioxide gas (S02) levels, wich acts as an antimicrobial and antioxidant

   11 - alcohol: the percent alcohol content of the wine

   Output variable (based on sensory data): 12 - quality (score between 0 and 10)

Vineyards along the Mosel in Luxemburg

Dataset

This dataset was created by Nikita Tkachenko

Released under Community Data License Agreement - Permissive - Version 1.0

Contents

Unlock insights on the vintage and rare industry with 25+ years of fine wine data sold at auction and in private markets across all categories including Bordeaux First Growths, Burgundy Grand Crus, Champagne, vintage Port, Italian Super Tuscans, Rhône Valley wines, and emerging wine regions. Tracked producers include: Château Pétrus, Château Le Pin, Domaine de la Romanée-Conti, Screaming Eagle, Harlan Estate, Sassicaia, Ornellaia, Dom Pérignon, Krug, Taylor Fladgate, Fonseca, Penfolds Grange, and hundreds of other prestigious wineries from established and emerging wine regions worldwide.

Yc Winery Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

Heineman Winery is a renowned winery that has been producing fine island wines since 1888. Founded by Gustav Heineman, an immigrant from Baden-Baden, Germany, the winery has been shaped by its rich history and tradition of excellence. With over 136 years of experience, Heineman Winery has perfected the art of crafting sweet and medium wines that delight the taste buds of Ohio residents and beyond.

Winery Exchange Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

Comprehensive dataset containing 576 verified Winery businesses in Georgia with complete contact information, ratings, reviews, and location data.

65911 Global import shipment records of Wine with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

Unlock the power of data with our comprehensive Total Wine Alcohol Products Dataset. Featuring detailed information on a wide range of wines, spirits, and beers, this dataset is perfect for data analysis, market research, and enhancing your product database.

Access in-depth product details, reviews, ratings, and more.

Download now to explore extensive alcohol product insights and trends.

Data Set Information

This data set contains records related to red and white variants of the Portuguese Vinho Verde wine. It contains information from 1599 red wine samples and 4898 white wine samples. Input variables in the data set consist of the type of wine (either red or white wine) and metrics from objective tests (e.g. acidity levels, PH values, ABV, etc.), while the target/output variable is a numerical score based on sensory data—median of at least 3 evaluations made by wine experts. Each expert graded the wine quality between 0 (very bad) and 10 (very excellent). Due to privacy and logistic issues, there is no data about grape types, wine brand, and wine selling price.

This data set is a combined version of the two separate files (distinct red and white wine data sets) originally shared in the UCI Machine Learning Repository.

The following are some existing data sets on Kaggle from the same source (with notable differences from this data set): - Red Wine Quality (contains red wine data only) - Wine Quality (combination of red and white wine data but with some values randomly removed) - Wine Quality (red and white wine data not combined)

Contents

Input variables:

1 - type of wine: type of wine (categorical: 'red', 'white')

(continuous variables based on physicochemical tests)

2 - fixed acidity: The acids that naturally occur in the grapes used to ferment the wine and carry over into the wine. They mostly consist of tartaric, malic, citric or succinic acid that mostly originate from the grapes used to ferment the wine. They also do not evaporate easily. (g / dm^3)

3 - volatile acidity: Acids that evaporate at low temperatures—mainly acetic acid which can lead to an unpleasant, vinegar-like taste at very high levels. (g / dm^3)

4 - citric acid: Citric acid is used as an acid supplement which boosts the acidity of the wine. It's typically found in small quantities and can add 'freshness' and flavor to wines. (g / dm^3)

5 - residual sugar: The amount of sugar remaining after fermentation stops. It's rare to find wines with less than 1 gram/liter. Wines residual sugar level greater than 45 grams/liter are considered sweet. On the other end of the spectrum, a wine that does not taste sweet is considered as dry. (g / dm^3)

6 - chlorides: The amount of chloride salts (sodium chloride) present in the wine. (g / dm^3)

7 - free sulfur dioxide: The free form of SO2 exists in equilibrium between molecular SO2 (as a dissolved gas) and bisulfite ion; it prevents microbial growth and the oxidation of wine. All else constant, the higher the free sulfur dioxide content, the stronger the preservative effect. (mg / dm^3)

8 - total sulfur dioxide: The amount of free and bound forms of S02; in low concentrations, SO2 is mostly undetectable in wine, but at free SO2 concentrations over 50 ppm, SO2 becomes evident in the nose and taste of wine. (mg / dm^3)

9 - density: The density of wine juice depending on the percent alcohol and sugar content; it's typically similar but higher than that of water (wine is 'thicker'). (g / cm^3)

10 - pH: A measure of the acidity of wine; most wines are between 3-4 on the pH scale. The lower the pH, the more acidic the wine is; the higher the pH, the less acidic the wine. (The pH scale technically is a logarithmic scale that measures the concentration of free hydrogen ions floating around in your wine. Each point of the pH scale is a factor of 10. This means a wine with a pH of 3 is 10 times more acidic than a wine with a pH of 4)

11 - sulphates: Amount of potassium sulphate as a wine additive which can contribute to sulfur dioxide gas (S02) levels; it acts as an antimicrobial and antioxidant agent.(g / dm3)

12 - alcohol: How much alcohol is contained in a given volume of wine (ABV). Wine generally contains between 5–15% of alcohols. (% by volume)

Output variable:

13 - quality: score between 0 (very bad) and 10 (very excellent) by wine experts

Acknowledgements

Source: P. Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Reis. Modeling wine preferences by data mining from physicochemical properties. In Decision Support Systems, Elsevier, 47(4):547-553, 2009.

Data credit goes to UCI. Visit their website to access the original data set directly: https://archive.ics.uci.edu/ml/datasets/wine+quality

Context

So much about wine making remains elusive—taste is very subjective, making it extremely challenging to predict exactly how consumers will react to a certain bottle of wine. There is no doubt that winemakers, connoisseurs, and scientists have greatly contributed their expertise to ...