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224 results for “wine”
Data From: Exploring Gelatin-A and Mouse Proline-Rich Protein 5 as Probes for Wine Polyphenols analysis by Quartz Crystal Microbalance with Dissipation Monitoring
<p>Polyphenols are essential in winemaking, affecting the wine's quality, color, astringency, bitterness, and chemical stability. Conventional methods for assessing polyphenolic content are both expensive and time-intensive, underscoring the need for new, efficient techniques.</p> <p>The Quartz Crystal Microbalance with Dissipation Monitoring (QCM-D) sensor is recognized for its speed and reliability as a label-free detection tool. This study applies QCM-D to evaluate Gelatin Type A (Gel-A) from porcine skin and Mouse Proline-Rich Protein 5 (MP5) for polyphenol analysis in red wines without pre-treatment. MP5 notably exhibited a linear dissipation signal response with both total polyphenol and hydroxybenzoic acid concentrations. These findings highlight the potential for creating a stand-alone sensor platform for real-time polyphenol monitoring in winemaking.</p>
Food fraud vulnerability assessment data (on spice/ginger and wine)
<p>The dataset includes the results of food fraud vulnerability assessments (on spice/ginger and wine) of various companies based in China and Europe. The data form part of WP3 (Task 3.2): <em>Implementation of innovations in food authenticity. </em>The data is generated to better understand the food fraud vulnerability within selected food chains. The data is useful for anyone working in the field of food authentication.</p>
Wine trade - Export/Import - Intra-EU/Extra-EU
<p>Import and export between intra-EU and extra-EU states per HS-/CN-code (product ID), reporting and partner country, value in EUR€, and quantity in kg and litres. The dataset is based on data gathered from Eurostat. </p> <p>The dataset underpins the report "Mapping the local-global wine chain from Europe to China: Towards shared standards and benchmarks in wine traceability and authenticity" (Nofima report 10/2021 - Link: https://hdl.handle.net/11250/2734677). </p>
Embrapa Wine Grape Instance Segmentation Dataset – Embrapa WGISD
<p><strong>Embrapa Wine Grape Instance Segmentation Dataset – Embrapa WGISD</strong></p> <p>For a detailed description of this dataset, following the <em>datasheet for the datasets</em> recommendation proposed by <a href="https://arxiv.org/abs/1803.09010">Gebru et al.</a>, check the <strong>README.md</strong> file.</p> <p><strong>Motivation for Dataset Creation</strong></p> <p><em>Why was the dataset created?</em></p> <p>Embrapa WGISD (<em>Wine Grape Instance Segmentation Dataset</em>) was created to provide images and annotation to study <em>object detection and instance segmentation</em> for image-based monitoring and field robotics in viticulture. It provides instances from five different grape varieties taken on field. These instances shows variance in grape pose,<br> illumination and focus, including genetic and phenological variations such as shape, color and compactness.</p> <p><em>What (other) tasks could the dataset be used for?</em></p> <p>Possible uses include relaxations of the instance segmentation problem: classification (Is a grape in the image?), semantic segmentation (What are the "grape pixels" in the image?), and object detection (Where are the grapes in the image?). The WGISD can also be used in grape variety identification.</p> <p> </p>
Primary vine varieties of European wine PDOs
<p>Primary varieties are the traditional vine cultivars of a region that are primarily used for making the wine products of a PDO region. In most cases, they are clearly defined in the legal document that regulate each PDO. We extracted primary varieties by analyzing the product specification files of European wine PDOs.</p>
Long document similarity datasets, Wikipedia excerptions for movies, video games and wine collections
<p>Three corpora in different domains extracted from Wikipedia.</p> <p>For all datasets, the figures and tables have been filtered out, as well as the categories and "see also" sections.</p> <p>The article structure, and particularly the sub-titles and paragraphs are kept in these datasets</p> <p> </p> <p><strong>Wines</strong></p> <p>Wikipedia wines dataset consists of 1635 articles from the wine domain. The extracted dataset consists of a non-trivial mixture of articles, including different wine categories, brands, wineries, grape types, and more. The ground-truth recommendations were crafted by a human sommelier, which annotated 92 source articles with ~10 ground-truth recommendations for each sample. Examples for ground-truth expert-based recommendations are </p> <ul> <li>Dom Pérignon - Moët & Chandon</li> <li>Pinot Meunier - Chardonnay</li> </ul> <p><strong>Movies</strong></p> <p>The Wikipedia movies dataset consists of 100385 articles describing different movies. The movies' articles may consist of text passages describing the plot, cast, production, reception, soundtrack, and more.<br> For this dataset, we have extracted a test set of ground truth annotations for 50 source articles using the "<a href="https://bestsimilar.com/">BestSimilar</a>" database. Each source articles is associated with a list of ${\scriptsize \sim}12$ most similar movies.<br> Examples for ground-truth expert-based recommendations are </p> <ul> <li>Schindler's List - The Pianist</li> <li>Lion King - The Jungle Book</li> </ul> <p><strong>Video games</strong></p> <p>The Wikipedia video games dataset consists of 21,935 articles reviewing video games from all genres and consoles. Each article may consist of a different combination of sections, including summary, gameplay, plot, production, etc. Examples for ground-truth expert-based recommendations are:</p> <ul> <li>Grand Theft Auto - Mafia</li> <li>Burnout Paradise - Forza Horizon 3</li> </ul>
Insights into intraspecific diversity of central carbon metabolites in Saccharomyces cerevisiae during wine fermentation
<p>Supplementary data including the data set used for the " Insights into intraspecific diversity of central carbon metabolites in <em>Saccharomyces cerevisiae</em> during wine fermentation" publication.</p> <p>Abstract:</p> <p><em>Saccharomyces cerevisiae</em>, as the workhorse of alcoholic fermentation, is a major actor in winemaking. In this context, this yeast species performs alcoholic fermentation to convert sugars from the grape must into ethanol and CO2 with outstanding efficiency as it reaches on average 92% of the maximum theoretical yield of conversion. Primary metabolites produced during fermentation have a great importance in wine where they significantly impact wine characteristics. While ethanol content contributes to the overall profile, others metabolites also have significant impacts, even when present in lower concentrations: glycerol, succinate, acetate, ⍺-ketoglutarate, lactate… <em>S. cerevisiae</em> is known for its great genetic diversity and plasticity that is directly related to its living environment, natural or technological and therefore to domestication. This leads to a wide phenotypic diversity of metabolites production. However, the range of metabolic diversity is variable and depends on the pathway considered. With the aim to improve wine quality, the selection, development and use of strains with dedicated metabolites production without genetic modifications can rely on the already existing natural diversity. Here we detail a screening experiment that aims to assess the diversity of primary metabolites production in a set of 51 <em>S. cerevisiae</em> strains from various genetic backgrounds (wine, flor, rum, West African, sake…). To approximate winemaking conditions, we used a synthetic grape must as fermentation medium and measured seven metabolites by HPLC. Results pointed out great yield differences between strains depending on the metabolite considered. Ethanol appeared as the one with the smallest variation among our set of strains, although it was by far the most produced. A clear negative correlation between ethanol and glycerol was observed, confirming glycerol synthesis as a suitable lever to reduce ethanol yield. Genetic groups were linked to specific metabolic yields such as high α-ketoglutarate and low acetate yields for wine strains. This study thus helps to characterise the phenotypic diversity of <em>S. cerevisiae</em> in a wine-like context and comforts the use of natural diversity in the development of new strains. Finally, it provides a detailed data set usable to study diversity of well known (ethanol, glycerol, acetate) or little-known (lactate) primary metabolites production, including in common commercial wine strains.</p> <p> </p> <p> </p>
Pesticide metabolites database and occurrence data in wine
<p>Pesticide metabolites database is the overview of potential pesticide metabolites originated from selected parent compounds. These metabolites can be found in various source including JMPR (FAO/WHO) documents, EU pesticide database, EFSA etc. Based on their elemental formula respective ions (protonated / deprotonated molecules, their adducts) originated in ESI source can be derived for ultra-high-performance liquid chromatography coupled with high-resolution mass spectrometry (UHPLC-HRMS).</p> <p>Occurrence data were obtained from determination of pesticide residues and their metabolites in samples of grapevine and wine, using UHPLC-HRMS, with the objective of supporting the possibility of the verification of the method of farming. It documents the identification of pesticide metabolites commonly used in conventional farming and provides a characterization of pesticide degradation during grapevine growth, maturation and during the wine-making process.</p> <p>The dataset is useful for anyone working on authentication of organic fruits and vegetables.</p>
X-ray imaging of 30 year old wine grape wood reveals cumulative impacts of rootstocks on scion secondary growth and harvest index
<p><span></span></p> <p><span>Annual rings from vines in a 30 year old, California rootstock trial were measured to determine the effects of 15 different rootstocks on Chardonnay and Cabernet Sauvignon scions. Viticultural traits measuring vegetative growth, yield, berry quality, and nutrient uptake were measured at the beginning and end of the lifetime of the vineyard.</span></p> <p><span>X-ray Computed Tomography (CT) was used to measure ring widths in 103 vines. Ring width was modeled as a function of ring number using a negative exponential model. Early and late wood ring widths, cambium width, and scion trunk radius were correlated with 27 traits. </span></p> <p><span>Modeling of annual ring width shows that scions alter the width of the first rings but that rootstocks alter the decay thereafter, consistently shortening ring width throughout the lifetime of the vine. The ratio of yield to vegetative growth, juice pH, photosynthetic assimilation and transpiration rates, and stomatal conductance are correlated with scion trunk radius.</span></p> <p><span>Rootstocks modulate secondary growth over years, altering hydraulic conductance, physiology, and agronomic traits. Rootstocks act in similar but distinct ways from climate to modulate ring width, which borrowing techniques from dendrochronology, can be used to monitor both genetic and environmental effects in woody perennial crop species.</span></p>
Growth and metabolome data of Saccharomyces uvarum grown in synthetic wine must with different nitrogen sources
<p><em>Raw data: Metabolome of S. uvarum (Su) and S. cerevisiae (Sc) in wine fermentations in 13 different nitrogen conditions. Concentrations of compounds expressed in mg/L at 60 g/L CO2 sampling point and at the end of fermentation. The volatile compounds are grouped according to the chemical functional group (ethyl esters, acetate esters, higher alcohols, medium chain fatty acids (MCFA) and branched-chain fatty acids (BCFA)). The central carbon metabolites (CCM) and sugars conform the last group. Each value is the mean of three biological replicates. The raw data is reported in a processed form in the manuscript entitled “The growth and metabolome of Saccharomyces uvarum in wine fermentations is strongly influenced by the route of nitrogen assimilation” </em></p>
Transcriptome data of Saccharomyces uvarum grown in synthetic wine must with different nitrogen sources Created Jun 9, 2022 12:06:22 PM, modified Jun 9, 2022 12:07:57 PM
<p>Transcriptome analysis of S. uvarum grown on synthetic wine must with different nitrogen sources: Ammonium, Phenylalanine, Asparagine, or Methionine. This is a dataset for the thesis of Angela Coral (Autumn 2022) , which can be accessed at www.ucc.ie. The work will also be submitted for publication and this will be a supplementary data file.</p>
Wikidata ALPINE-based WINE & SPINE embedding
<p>100-dimensional Wikidata graph embedding obtained using degree-based WINE and SPINE.</p>
Dataset: Vintage Wine Estates, Inc. (VWEWW) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Vintage Wine Estates, Inc. (VWE) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
French wine dataset to mapping the expected harvest value by county
<p>This database is built from open data as described in the paper entitled ‘French wine: Combination of multiple open data sources to mapping the expected harvest value’ (2024).</p> <table> <tbody> <tr> <td> <p>CODE_CULTU</p> </td> <td> <p><strong>Crop code of the graphic land registry database </strong></p> </td> </tr> <tr> <td> <p>CodeCdC</p> </td> <td> <p><strong>Crop code in Multi Perils Crop Insurance specification </strong></p> </td> </tr> <tr> <td> <p>Harvest Value B</p> </td> <td> <p><strong>Harvest value (€/ha organic wine)</strong></p> </td> </tr> <tr> <td> <p>Harvest Value C</p> </td> <td> <p><strong>Harvest value (€/ha no-organic wine)</strong></p> </td> </tr> <tr> <td> <p>IDA</p> </td> <td> <p><strong>ID of geographical areas of INAO</strong></p> </td> </tr> <tr> <td> <p>Insee_Com</p> </td> <td> <p><strong>County code (INSEE)</strong></p> </td> </tr> <tr> <td> <p>Label_CdC</p> </td> <td> <p><strong>Crop label in Multi Perils Crop Insurance specification </strong></p> </td> </tr> <tr> <td> <p>Label_Dpt</p> </td> <td> <p><strong>Department</strong></p> </td> </tr> <tr> <td> <p>Label_Insee_com</p> </td> <td> <p><strong>County</strong></p> </td> </tr> <tr> <td> <p>Label_RA</p> </td> <td> <p><strong>Agricultural Region (AGRESTE)</strong></p> </td> </tr> <tr> <td> <p>Label_appellation</p> </td> <td> <p><strong>Appellation (INAO)</strong></p> </td> </tr> <tr> <td> <p>Label_code3</p> </td> <td> <p><strong>Crop (FADN)</strong></p> </td> </tr> <tr> <td> <p>Label_cvi</p> </td> <td> <p><strong>Wine name (vineyard register of customs services) </strong></p> </td> </tr> <tr> <td> <p>Label_idGeo</p> </td> <td> <p><strong>Geographical ID of Quality Sign (INAO)</strong></p> </td> </tr> <tr> <td> <p>PxBaremAOP</p> </td> <td> <p><strong>Price listed in Multi Perils Crop Insurance specification (€/hl no-organic) </strong></p> </td> </tr> <tr> <td> <p>PxBaremAOPBio</p> </td> <td> <p><strong>Price listed in Multi Perils Crop Insurance specification (€/hl organic) </strong></p> </td> </tr> <tr> <td> <p>RdtMOAOP</p> </td> <td> <p><strong>Harvest wine yield (hl/ha)</strong></p> </td> </tr> <tr> <td> <p>SurfaceModel</p> </td> <td> <p><strong>Surface of wine as fitted by model</strong></p> </td> </tr> <tr> <td> <p>code3</p> </td> <td> <p><strong>Crop code (FADN)</strong></p> </td> </tr> <tr> <td> <p>code_dept</p> </td> <td> <p><strong>Department code</strong></p> </td> </tr> <tr> <td> <p>code_regag</p> </td> <td> <p><strong>Code of Agricultural Region (AGRESTE)</strong></p> </td> </tr> <tr> <td> <p>cvi</p> </td> <td> <p><strong>Wine code (vineyard register of customs services)</strong></p> </td> </tr> <tr> <td> <p>id_appellation</p> </td> <td> <p><strong>Appellation code (INAO)</strong></p> </td> </tr> <tr> <td> <p>id_denomination_geo</p> </td> <td> <p><strong>Geographical ID of Quality Sign (INAO)</strong></p> </td> </tr> </tbody> </table> <p> </p> <p>Find here the relative research paper :</p> <p><a href="https://univ-lemans.hal.science/hal-04627672">https://univ-lemans.hal.science/hal-04627672</a></p> <p>Please find below the list of the sites where used data could be found (lasted view the June 26, 2024).</p> <p><a href="https://agreste.agriculture.gouv.fr/agreste-web/methodon/Z.1/!searchurl/listeTypeMethodon/">https://agreste.agriculture.gouv.fr/agreste-web/methodon/Z.1/!searchurl/listeTypeMethodon/</a></p> <p><a href="https://www.casd.eu/source/reseau-dinformation-comptable-agricole/?tab=16">https://www.casd.eu/source/reseau-dinformation-comptable-agricole/?tab=16</a></p> <p><a href="https://www.douane.gouv.fr/la-douane/opendata?f%5B0%5D=categorie_opendata_facet%3A467">https://www.douane.gouv.fr/la-douane/opendata?f%5B0%5D=categorie_opendata_facet%3A467</a></p> <p><a href="http://www.inao.gouv.fr">www.inao.gouv.fr</a></p> <p><a href="https://www.data.gouv.fr/fr/datasets/?q=inao">https://www.data.gouv.fr/fr/datasets/?q=inao</a></p> <p><a href="https://maisons-champagne.com/fr/appellation/aire-geographique/">https://maisons-champagne.com/fr/appellation/aire-geographique/</a></p> <p><a href="https://info.agriculture.gouv.fr/boagri/document_administratif-4b9ef75e-29a7-449d-9e40-7e5253bfd642/telechargement">https://info.agriculture.gouv.fr/boagri/document_administratif-4b9ef75e-29a7-449d-9e40-7e5253bfd642/telechargement</a></p> <p><a href="https://agreste.agriculture.gouv.fr/agreste-web/download/publication/publie/Dos2203/2Pages%20de%20Dossier2022-3_CCAN_ChapitreII.pdf">https://agreste.agriculture.gouv.fr/agreste-web/download/publication/publie/Dos2203/2Pages%20de%20Dossier2022-3_CCAN_ChapitreII.pdf</a></p>
UV mutagenesis conjugated to high throughput screening as a tool to generate new phenotypic diversity in wine yeast
<p><strong>The current global changes, societal and climatic, strongly challenge the wine industry. Multiple methods are applied in the development of new strains for the industry, but many are based on the existing phenotypic and genetic diversities. UV mutagenesis, as an untargeted strategy, has been successfully used for years, with significant examples on wine. Here we developed and validated a UV-mutant generation strategy coupled with a high throughput screening in wine-like conditions. This strategy led to the production of a 502 mutant’s library for which concentrations of eight primary metabolites after fermentation were assessed. This data paper presents the resulting data.</strong></p>
MED-GOLD Indicators for the Wine pilot service over Iberian Peninsula from ERA5 Reanalysis 1979-2020
<p>Indicators of interest for the Wine sector over the Iberian Peninsula using ERA5 Reanalysis data for the period 1979 to 2020:</p> <ol> <li>Growing Season Temperature (GST) [Temp averaged between April and October]</li> <li>Spring Rain (SprR) [ Precip cumulated between 21 apr and 21 Jun],</li> <li>Harvest Rain (HarvestR) [ Precip cumulated between 21 aug and 21 Oct]</li> <li> (SU35) -number of days with temperature higher than 35°C [for April ot October],</li> <li>Warm Spell Duration Index (WSDI) [days with at least 6 consecutive days when the daily temperature maximum exceeds its 90th percentile for April to Oct]</li> </ol> <p>Wine risk indicators, implemented specifically for the MED-GOLD Wine pilot service by ENEA and SOGRAPE VINHOS S.A.:</p> <ol> <li>Sanitary Risk Index= offgts*offsp*(percentile(SprR))+offhart*percentile(HarvestR)+(100-percentile(GST); with offhart=1.; offsp=1; offgts=1.; if percentile (SprR)>= 60; offsp=1.5; if percentile (GST)<= 40; offhart=1.5; if percentile (GST)>=70; offgts=1.5; where the percentile are here computed starting from the distribution over the 1993-2106 hindcast period to be consistent and comparable with the seasonal forecast</li> <li>Heat Risk Index = percentile(GST)+percentile(SU35_AMJJASO) + percentile(WSDI_AMJJASO),where the percentile are here computed srarting from the distribution over the 1993-2106 hindcast period to be consistent and comparable with the seasonal forecast</li> </ol> <p>Datasets computed by ENEA, in collaboration with SOGRAPE VINHOS S.A. in the framework of the European MED-GOLD project, funded from the European Union's Horizon 2020 Research and Innovation programme under Grant agreement No. 776467</p>
MED-GOLD Indicators for the Wine pilot over Douro Valley based on High Resolution Climate projections
<p>Indicators of interest for the Wine sector over the Douro Valley using high resolution climate projections.</p> <ol> <li>GDD (Growing Degree Days) - summation of daily differences between daily temperature averages and 10 for the period April-October</li> <li>GST (Growing Season Temperature) - average of daily average temperatures for the period April-October</li> <li>SprR (Spring Rain) - Precipitation accumulated between 21st April-to 21st June,</li> <li>HarvR (Harvest Rain)-Precipitation accumulated between 21 August and 21 October</li> <li>SU35 -number of days with temperature higher than 35°C for the period April-October,</li> <li>WSDI (Warm Spell Duration Index) -days with at least 6 consecutive days when the daily temperature maximum exceeds its 90th percentile for the period April-October.</li> </ol> <p>The results are based on an sub-ensemble of five RCMs from the EURO-CORDEX modelling experiment which have been statistically downscaled to 1km x1km horizontal resolution using the PTHRES gridded dataset as the reference dataset. More details can be found in Raül Marcos-Matamoros, (2018). Report on the methodology followed to implement the wine pilot services. Zenodo. https://doi.org/10.5281/zenodo.4543337</p> <p>Datasets computed by National Observatory of Athens, in collaboration with SOGRAPE VINHOS S.A. in the framework of the European MED-GOLD project, funded from the European Union's Horizon 2020 Research and Innovation programme under Grant agreement No.776467</p> <p> </p> <p> </p>
Web scraping UOC, wines from Vinissimus
<p>Dataset with 1320 wines obtained from scraping the <a href="https://www.vinissimus.com/es/">Vinissimus</a> website. The code used to obtain it can be found here: <a href="https://github.com/PepIngla/webScrapingWines">Github</a>.</p> <p>It contains the following fields:</p> <p>Type: String, indicates the type of wine. It can take three values “Vino tinto” or red wine, “Vino blanco” or white wine, “Vinos rosados y rosé” or rosé wine because we have restricted our search to these types.</p> <p>Name: String, contains the name of the wine.</p> <p>Year: Integer, indicates the crop year. If it was not specified on the website its value in the database is -1.</p> <p>Cellar: String, wine producer.</p> <p>Region: String, the region where the wine was produced.</p> <p>Country: String, the country where the wine was produced.</p> <p>Varieties: String, grape varieties contained in the wine. If there is no variety specified on the website, we have set it to "".</p> <p>Eco: String, if the wine has the "eco" label it takes the value "ECO", otherwise, it is nan.</p> <p>Rating: Float, wine rating according to the vinissimus.com customers.</p> <p>Stars: Integer, tells us the number of stars of the wine according to the vinissimus.com customers.</p> <p>Opinions: Integer, number of opinions published on the website about the wine.</p> <p>Likes: Integer, number of likes by the vinissimus.com customers.</p> <p>Parker: String, Parker rating. If it is not available, the value is "".</p> <p>Penin: String, Peñín rating. If it is not available, the value is "".</p> <p>Suckling: String, Suckling rating. If it is not available, the value is "".</p> <p>Tim_atkin: String, Tim Atkin rating. If it is not available, the value is "".</p> <p>Price: String, the price of the wine.</p> <p>Old_price: String, if the wine is on discount, it indicates the old price.</p> <p>Offer: Boolean, True if the wine is on discount, False otherwise.</p> <p>Volume: String, indicates the volume of the bottle.indica el volum de l’ampolla. The default value in case it is not on the website is: “ / bot. 0,75 L “.</p> <p>Image: bytes, an image of the wine bottle.</p>
X-ray imaging of 30 year old wine grape wood reveals cumulative impacts of rootstocks on scion secondary growth and harvest index
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