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66 results for “retail”
Grass-fed beef producers and retailers map
This data package includes two shapefiles and their associated attribute tables. The two files, GFB_producers_2021-02-18.zip and GFB_retailers_2021-02-18.zip, contain all internet-discoverable (at the time of data collection, July-August 2020; with minor edits/additions circa June 2022) grass-fed beef producers and retailers in the Southwest and Southern Plains of the U.S. (Arizona, California, Colorado, Kansas, Nevada, New Mexico, Oklahoma, Texas, Utah), compiled through an internet search. The data were initially collected in August of 2020 using publicly available information from Google search engine and Google map searches with the intention of informing members of the Sustainable Southwest Beef Project (USDA NIFA grant #2019-69012-29853) team about existing grass-fed beef producers and retailers in the study area.
Sample of facial mask N95 and FFP2 pricing on retail webs and time evolution per country
<p>We've gathered - for a Data Science educational project - the pricing of several face mask for breathing protection in a given period of time.</p> <p>Countries : Spain', 'USA', 'France', 'UK', 'Germany', 'Italy', 'Netherlands', 'Australia'</p> <p> </p> <p>'asin' type: STRING "Código de identifícación único de product equivalente de AMAZON"</p> <p>'description' type: STRING 'Texto descriptivo del producto'</p> <p>'dateTime' type: TIMESTAMP 'Cadena de carateres que contiene fecha y hora GMT'</p> <p>'date' Type: DATETIME ' Formato diferente de la misma fecha / hora de captura '</p> <p>'country' type: STRING 'Pais al que pertenece a distribución del producto 'Valores posibles: '</p>
Data set to Conference Paper "The Effect of Queuing Technology on Customer Experience in Physical Retail Environments"
<p>Following an open data policy as supported by the European Union (https://www.openaire.eu/), this is the data set used for the following conference paper: Obermeier, G., Zimmermann, R., & Auinger, A. (2020, July). The Effect of Queuing Technology on Customer Experience in Physical Retail Environments. In <em>International Conference on Human-Computer Interaction</em> (pp. 141-157). Springer, Cham.</p> <p>The present work was conducted within the Innovative Training Network project PERFORM funded by the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No. 765395. The EU Research Executive Agency is not responsible for any use that may be made of the information it contains.</p>
SCALIBUR video 2: From retail food waste to protein, lipids, and chitin
<p>This video is part of a 3 part series explaining the innovative technologies being developed in the SCALIBUR project.</p> <p>The script is as follows: </p> <p>Eyes bigger than your stomach? Hotels and restaurants make a big contribution to the 100 million tonnes of organic waste produced each year in the EU. The SCALIBUR project is developing innovative technologies to convert waste from the food service industry into valuable products. Where we see waste SCALIBUR partners see a resource. Insects like black soldier flies love leftovers, efficiently converting food scraps into a rich biomass. New processes are being developed to extract the valuable materials like proteins, lipids and chitin: raw materials for bioplastics, and food and feed products. These technologies will help cities manage waste in a more sustainable and cost efficient way. And contribute to the creation of a truly circular bio-economy in Europe.</p>
BigMart Retail Sales
<p><strong><em>Nothing ever becomes real till it is experienced.</em></strong></p> <p><strong><em>-John Keats</em></strong></p> <p> </p> <p>While we don't know the context in which John Keats mentioned this, we are sure about its implication in data science. While you would have enjoyed and gained exposure to real world problems in this challenge, here is another opportunity to get your hand dirty with this practice problem.</p> <p>_______________________________________</p> <p><strong>Problem Statement :</strong></p> <p>The data scientists at BigMart have collected 2013 sales data for 1559 products across 10 stores in different cities. Also, certain attributes of each product and store have been defined. The aim is to build a predictive model and find out the sales of each product at a particular store.</p> <p>Using this model, BigMart will try to understand the properties of products and stores which play a key role in increasing sales.</p> <p>Please note that the data may have missing values as some stores might not report all the data due to technical glitches. Hence, it will be required to treat them accordingly.</p> <p>________________________________________</p> <p><strong>Data :</strong></p> <p>We have 14204 samples in data set.</p> <p> </p> <p><strong>Variable Description</strong></p> <ul> <li><strong>Item Identifier</strong>: A code provided for the item of sale</li> <li><strong>Item Weight</strong>: Weight of item</li> <li><strong>Item Fat Content</strong>: A categorical column of how much fat is present in the item: ‘Low Fat’, ‘Regular’, ‘low fat’, ‘LF’, ‘reg’</li> <li><strong>Item Visibility</strong>: Numeric value for how visible the item is</li> <li><strong>Item Type</strong>: What category does the item belong to: ‘Dairy’, ‘Soft Drinks’, ‘Meat’, ‘Fruits and Vegetables’, ‘Household’, ‘Baking Goods’, ‘Snack Foods’, ‘Frozen Foods’, ‘Breakfast’, ’Health and Hygiene’, ‘Hard Drinks’, ‘Canned’, ‘Breads’, ‘Starchy Foods’, ‘Others’, ‘Seafood’.</li> <li><strong>Item MRP</strong>: The MRP price of item</li> <li><strong>Outlet Identifier</strong>: Which outlet was the item sold. This will be categorical column</li> <li><strong>Outlet Establishment Year</strong>: Which year was the outlet established</li> <li><strong>Outlet Size</strong>: A categorical column to explain size of outlet: ‘Medium’, ‘High’, ‘Small’.</li> <li><strong>Outlet Location Type</strong>: A categorical column to describe the location of the outlet: ‘Tier 1’, ‘Tier 2’, ‘Tier 3’</li> <li><strong>Outlet Type</strong>: Categorical column for type of outlet: ‘Supermarket Type1’, ‘Supermarket Type2’, ‘Supermarket Type3’, ‘Grocery Store’</li> <li><strong>Item Outlet Sales</strong>: The number of sales for an item.</li> </ul> <p>_________________________________________</p> <p><strong>Evaluation Metric:</strong></p> <p>We will use the <strong>Root Mean Square Error </strong>value to judge your response</p>
EXPLORATORY SPATIAL ANALYSIS OF "ACCESS" TO PHYSICAL AND DIGITAL RETAIL BANKING CHANNELS IN THE UK
<p>File built in order to explore access to banking channels in the UK (February 2019)</p> <p>The report "Exploratory Spatial Analysis of Access to Physical and Digital Retail Banking Channels in the UK" has been published by Think Forward Initiative in October 2019. You can download the full report from here: <a href="https://www.thinkforwardinitiative.com/research/exploratory-spatial-analysis-of-access-to-physical-and-digital-retail-banking-channels-in-the-uk">https://www.thinkforwardinitiative.com/research/exploratory-spatial-analysis-of-access-to-physical-and-digital-retail-banking-channels-in-the-uk</a></p> <p>Related code: <a href="https://github.com/andrasonea">andrasonea</a>/<strong><a href="https://github.com/andrasonea/TFI_AccessToBanking">TFI_AccessToBanking</a></strong></p> <p> </p>
Dataset: VanEck Retail ETF (RTH) 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: Retail Opportunity Investments Corp. (ROIC) 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: Qurate Retail, Inc. (QRTEA) 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: Qurate Retail, Inc. (QRTEP) 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: Qurate Retail, Inc. (QRTEB) 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.
Retail sale of alcoholic beverages in physical terms
<p><strong>The data includes retail products of different types of alcoholic beverages in Russia for 2017. Includes a breakdown by region.</strong></p> <p><strong>Here for each type of product you can see the number of sales by region. </strong></p> <p><strong>The data is collected and presented by the Federal service for alcohol market regulation of Russia.</strong></p> <p> </p> <p><strong>The posted information is formed according to the data of the Unified state automated information system. With the exception of beer, beer, cider, Poiret, Mead and alcoholic beverages sold by catering establishments. Information on retail sales in full will be provided from the report for July 2017 (taking into account the data of retail sales in rural settlements)</strong></p>
UNSUPERVISED MACHINE LEARNING AND VECTOR MODELS IN DESIGNING AND OPTIMIZATION OF TELECOM RETAIL CHANNELS
<p>This paper examines the use of unsupervised machine learning and vector models in the design and optimization of retail channels for telecommunications services. Unsupervised machine learning allows you to analyze and identify hidden patterns in large volumes of untagged data, which is especially important in a dynamically changing consumer market. Vector models, in turn, provide high accuracy of demand forecasting and inventory management, contributing to an increase in the efficiency of trading channels. The synergy of these technologies allows companies to improve customer experience, optimize operational processes and increase competitiveness in the market. The main focus of the work is on data processing methods, including correlation analysis, the use of the support vector machine (SVM) method and its adaptation to solve problems related to predicting customer behavior and optimizing logistics processes.</p>
Azole resistance mechanisms and population structure of Aspergillus fumigatus on retail plant products
<p><em>Aspergillus fumigatus </em>is a ubiquitous saprotroph and human-pathogenic fungus that is life-threatening to the immunocompromised. Triazole-resistant <em>A. fumigatus</em><em> </em>was found in patients without prior treatment with azoles, leading researchers to conclude that resistance had developed in agricultural environments where azoles are used against plant pathogens. Previous studies have documented azole-resistant <em>A. fumigatus </em>across agricultural environments, but few have looked at retail plant products. Our objectives were to determine if azole-resistant <em>A. fumigatus </em>is prevalent<em> </em>in retail plant products produced in the United States (U.S.), as well as to identify the resistance mechanism(s) and population genetic structure of these isolates. Five hundred twenty-five isolates were collected from retail plant products and screened for azole resistance. Twenty-four isolates collected from compost, soil, flower bulbs, and raw peanuts were pan-azole resistant. Resistant isolates had the TR<sub>34</sub>/L98H, TR<sub>46</sub>/Y121F/T289A, G448S, and H147Y <em>cyp51A </em>alleles, all known to underly pan-azole resistance, as well as WT alleles, suggesting that non-cyp51A-mechanisms contribute to pan-azole resistance in some isolates. Minimum spanning networks showed two lineages containing isolates with TR alleles or the F46Y/M172V/E427K allele, and discriminant analysis of principle components (DAPC) identified three primary clusters. This is consistent with previous studies detecting three clades of <em>A. fumigatus</em> and identifying pan-azole-resistant isolates with TR alleles in a single clade. We found pan-azole resistance in U.S. retail plant products, particularly compost and flower bulbs, which indicates the risk of exposure to these products for susceptible populations and that highly resistant isolates are likely distributed worldwide on these products.</p>
Job Ads in Retail and E-commerce sectors for INAIR project countries
<p><strong>INAIR</strong> (<a href="https://www.ai4retail.eu/en/">Increasing the Uptake of AI in Retail</a>) is a Coordination and Support Action funded by the European Union'’s Horizon Europe Research and Innovation programme - Grant Agreement No. 101133847. The project aims to contribute to reducing the AI skills gap of European MSMEs in Retail, to let them exploit the potential of AI for greening their businesses, support their competitiveness in the global market and ultimately contribute to reaching the digital decade target of 75%+ European companies adopting AI technologies by 2030. </p> <p>This dataset contains job advertisements from the retail and e-commerce sectors in Cyprus, Germany, Italy, Poland, and Romania, collected as part of the INAIR Horizon Europe project. This resource supports analyses of digital skill requirements and helps identify trends and gaps in the labor market for the retail and e-commerce sectors.</p> <p>Data was collected three times during the period from March to May 2024, resulting in a total of 44,494 job offers.</p>
2023 Portuguese Energy Tariffs: Retail and Feed-In Tariff Data
<p><strong>Introduction</strong></p> <p>This dataset contains historic data for 20 electric grid consumption and energy injection tariffs in Portugal, during 2023. The data includes both retail tariffs for grid consumption energy and feed-in income tariffs for energy injected back into the grid.</p> <p> </p> <p><strong>Data Overview</strong></p> <p>The dataset comprises the following key elements:</p> <p> 1. Grid Consumption (Retail Tariffs):</p> <ul> <li>Retail tariffs refer to the cost per unit of electricity consumed from the grid.</li> <li>Tariff values are given in euro (€) per kWh and vary across different retailers and hour schemes.</li> </ul> <p> 2. Energy Injection (Feed-in Tariffs):</p> <ul> <li>Feed-in tariffs represent the compensation per unit of electricity generated by solar systems and fed back into the grid.</li> <li>Tariff values are presented in € per kWh and vary across different retailers and conditions.</li> </ul> <p><br><strong>Assumptions</strong></p> <p><br>The retail tariffs in this dataset refer to the consumed energy only and do not include taxes, contracted power costs, and other fees.</p> <p>The dataset starts on Jan 1st, 2023, and ends on Dec 31st, 2023.</p> <p>The dataset uses a 15min time step for each line. The price per hour is applied to each of the four 15 minutes steps.</p> <p><br><strong>Reference data used to create this dataset:</strong></p> <ul> <li>ERSE Regulatory data simulator - https://simulador.precos.erse.pt/eletricidade/</li> <li>2023 Energy Cost Description - https://www.erse.pt/en/activities/market-regulation/tariffs-and-prices-electricity</li> <li>GALP - https://casa.galp.pt/planos-eletricidade-e-gas</li> <li>Eni Plenitude - https://eniplenitude.pt/eletricidade</li> <li>Repsol - https://www.repsol.pt/particulares/casa/eletricidade-gas/</li> <li>EDP - https://www.edp.pt/empresas/energia/tarifarios/</li> </ul> <p> </p> <p>We would be grateful if you could acknowledge the use of this dataset in your publications.</p>
Raw data belonging to paper "Calculating Retail Prices from Demand Response Target Schedules to Operate Domestic Electric Water Heaters"
<p>The Zip file contains the raw data used for drawing conclusions in the paper "Calculating Retail Prices from Demand Response Target Schedules to Operate Domestic Electric Water Heaters" accepted for publication in Energy Informatics 2018.</p> <p>The raw data is the parameters of a sample of 50 domestic electric water heaters (DEWHs)<br> used for evaluating the algorithm in the paper. It is explained in the readme.txt.</p>
From Redirected Navigation to Forced Attention: Uncovering Manipulative and Deceptive Designs in Augmented Reality through Retail Shopping
<p>In the dataset, there are two types of files: one (1) Excel file and ten (10) PDF files.</p> <p>The Excel file contains data analysis and coding.</p> <p>The PDF files are organized by session. <br>Each PDF file contains screenshots of various scenarios and their corresponding discussions. <br>Each PDF has 12 pages, except for Session 8, which was not completed due to technical problems. <br>The files are organized as follows:</p> <p>Page 01 - Scenario 1: Navigation & Attention phase, Grocery Shopper<br>Page 02 - Scenario 2: Navigation & Attention phase, AR User<br>Page 03 - Scenario 3: Interest & Desire phase, Grocery Shopper<br>Page 04 - Scenario 4: Interest & Desire phase, AR User<br>Page 05 - Scenario 5: Action phase, Grocery Shopper<br>Page 06 - Scenario 6: Action phase, AR User<br>Page 07 - Discussion of Scenario 1<br>Page 08 - Discussion of Scenario 2<br>Page 09 - Discussion of Scenario 3<br>Page 10 - Discussion of Scenario 4<br>Page 11 - Discussion of Scenario 5<br>Page 12 - Discussion of Scenario 6</p> <p>The MIRO Board can be found at the following link:<br>https://miro.com/app/board/uXjVM23rRP8=/?share_link_id=82923580188</p>
Food retail in remote Australia
<p>This dataset documents food retail stores that service remote Indigenous communities in Australia. A seed list created by the National Indigenous Australians Agency (NIAA) was extended and validated, including reviews by experts and stakeholders, during 2022. Store location, contact information, management, and ownership/legal registration were identified along with the size of the community the store serves. A final dataset of 233 remote or very remote stores was created.</p>
GeoScan and Remote Geo Smoking Study: Neural and Behavioral Correlates of Smokers' Exposure to Retail Environments
ClinicalTrials.gov study NCT04279483. IPD Sharing: YES. Countries: 1. Publications: 99.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.