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Dataset results
361 results for “Commerce”
E-commerce et covid 19 en France. Une application de la théorie du comportement planifié
<p>Cette base de données est issue d’une enquête quantitative par questionnaire (n= 378, année 2021). Elle est construite sur la base de la théorie du comportement planifié. La variable dépendante est l’achat de biens et/ou de services par internet.</p> <p><em>Contenu de la base de données</em></p> <p>Le questionnaire comprend les mesures suivantes : 2 variables de segmentation (piratage et type d’achat), le comportement de l’individu (2 items : fréquence et récence), l’impact du Covid-19 sur la fréquence d’achat (1 item), l’intention comportementale (2 items dont 1 d’identité personnelle), les croyances sur les bénéfices attendus (11 items), l’attitude (3 items), les croyances sur les freins perçus (13 items), la perception de contrôle sur le comportement (2 items), les normes descriptives (2 items), les normes injonctives (2 items), 3 variables de signalétique (sexe, âge et CSP). L’administration étant réalisée en ligne, la base de données comprend également une variable « temps de saisie » du questionnaire qui pourra servir à épurer la base. Toutes les variables à échelle sont mesurées en 6 points.</p>
Dataset of 30 energy customers with flexibility data, and distributed generation, considering residential, small commerce, large commerce, and industrial customers
<p>The dataset has 30 customers: ten residential, ten small commerce, five large commerce, and five industrial customers. The combination of several energy customer types allows the creation of a dataset with different types of consumption profiles, generation, and flexibility, and, therefore, different values of participation in demand response events.</p> <p>The residential profiles of the considered customers use the data available in the Working Group on Intelligent Data Mining and Analysis (IDMA): https://site.ieee.org/pes-iss/data-sets/</p> <p>The values represent a week period using 15 minutes reading periods. All the values are expressed in kWh and the matrixes were created as [customer x time_period].</p> <p> </p> <p>We would be grateful if you could acknowledge the use of this dataset in your publications. Please use the Zenodo publication to cite this work.</p>
E commerce text dataset
<p>This is the classification based E-commerce text dataset for 4 categories - "Electronics", "Household", "Books" and "Clothing & Accessories", which almost cover 80% of any E-commerce website. </p> <p>The dataset is in ".csv" format with two columns - the first column is the class name and the second one is the datapoint of that class. The data point is the product and description from the e-commerce website.</p> <p>The dataset has the following features :</p> <p><strong>Data Set Characteristics: </strong>Multivariate</p> <p><strong>Number of Instances: </strong>50425</p> <p><strong>Number of classes:</strong> 4</p> <p><strong>Area: </strong>Computer science<strong> </strong></p> <p><strong>Attribute Characteristics: </strong>Real</p> <p><strong>Number of Attributes: </strong>1</p> <p><strong>Associated Tasks:</strong> Classification</p> <p><strong>Missing Values? </strong>No</p> <p>The dataset has been scraped from Indian e-commerce platform.</p>
E-Commerce Question Answering Dataset
<p>The <strong>E</strong>-<strong>C</strong>ommerce <strong>Q</strong>uestion <strong>A</strong>nswering <strong>D</strong>ataset (ECQuAD) is a reading comprehension dataset for question answering in brazilian e-commerce platforms. It consists of questions annotated by crowdworkers on a set of products' descriptions. It follows the SQuAD-v2 format, so questions might be unanswerable.</p> <p>This is a development set, for public usage, powered by <a href="https://gobots.ai/en/">GoBots</a>.</p>
Risk Commodity Dataset (RCDD) from Alibaba's e-commerce platform
<p>This is a risk commodity detection dataset (RCDD) that is based on a real risk detection scenario from Alibaba's e-commerce platform. All the <em>.csv </em>files are the initial data which consists of edges, node features, supervised information as well as IDs of candidate items. More details can see in <em>README.md</em>, we list the type of each column in each file as follows:</p> <p>1. RCDD_edges.csv (edge file): <br> source_node_id int, target_node_id int, source_node_type string, target_node_type string, edge_type string<br> 2. RCDD_nodes.csv (node file):<br> node_id int, node_type string, node_atts string (notice that node_atts are 256-dimensional feature vector strings with delimiter ":")<br> 3. RCDD_train_labels.csv (training labels):<br> item_id int, label int<br> 4. RCDD_test_ids.csv (testing ids):<br> item_id int<br> 5. RCDD_test_labels.csv (testing labels):<br> item_id int, label int </p> <p>Besides that,<em> graph.bin</em> is the format in DGL which is constructed by all <em>*.csv</em> files, and a general method to load this graph as follows:</p> <pre><code class="language-python">from dgl import load_graphs #should install dgl ds,_ = load_graphs("./graph.bin") g = ds[0] print(g)</code></pre> <p>And then you can easily get a large-scale heterogeneous graph with 157,814,864 edges and 13,806,619 nodes, our graph task is node classification: detect risk product.</p>
E-commerce Product Dataset from Mercado Libre Perú
<p>We offer a dataset comprising approximately 1,198,398 unique products sourced from Mercado Libre Perú. This dataset was collected from the platform's public API spanning from February 2022 to May 2023.</p> <p>Files description:</p> <ul> <li>ml_db_raw.db : Raw dataset stored in a SQLite Database</li> <li>ml_db_sample.csv : A sample of only 5 electronic categories</li> <li>test.csv* : 20% of data from ml_db_sample.csv</li> <li>train.csv* : 80% of data from ml_db_sample.csv</li> </ul> <p>* The dataset was divided into training and testing sets using a random stratified technique.</p> <p>Attributes description:</p> <ul> <li>CatX : Category Name for X level</li> <li>CatX_code : Category Code given by Mercado Libre for X level</li> <li>id : Unique product identifier</li> <li>title : Original product title</li> <li>price : Product price</li> <li>currency : Product currency (PEN, USD)</li> <li>link : Product link</li> <li>insert_date : Web scraping date</li> <li>mlp_updated_date : Mercado Libre product update date</li> <li>text : Cleaned product title</li> <li>taxonomy : Category path from general to specific categories</li> </ul> <p> </p>
FIG. 4 in Le morse et le phoque dans les mers du Nord au Moyen Âge: chasse, exploitation, commerce. Une approche par les textes
FIG. 4. — Autel portatif en ivoire de morse (Angleterre ou France septentrionale, XIIe siècle). Photo Benoît Roland, Musée Antoine Vivenel, Compiègne.
FIG. 3 in Le morse et le phoque dans les mers du Nord au Moyen Âge: chasse, exploitation, commerce. Une approche par les textes
FIG. 3. — Pièce d'échecs (tour) en ivoire de morse (Angleterre, France ou Scandinavie, milieu XIIIe siècle). Photo Benoît Roland, Musée Antoine Vivenel, Compiègne.
FIG. 1 in Le morse et le phoque dans les mers du Nord au Moyen Âge: chasse, exploitation, commerce. Une approche par les textes
FIG. 1. — -Localisation des Norðrsetr et des sites archéologiques en rapport avec le morse et/ou le phoque.
Catálogo de publicaciones del e-commerce de Alita Cómics
<p>En este dataset se presenta el catálogo de publicaciones ofrecidas por la web de Alita Cómics a principios de abril de 2022. En el mismo se facilitan diversos campos de cada uno de los artículos para su análisis.</p>
Dataset: Global X E-commerce ETF (EBIZ) 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: Commerce Bancshares, Inc. (CBSH) 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: SPS Commerce, Inc. (SPSC) 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: Newegg Commerce, Inc. (NEGG) 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: First Trust S-Network Global E-Commerce ETF (ISHP) 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: Heritage Commerce Corp (HTBK) 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 on THE INFLUENCE OF INNOVATION, KNOWLEDGE MANAGEMENT, AND E-COMMERCE ADOPTION ON MSME PERFORMANCE, AND ITS IMPACT ON MSMEs SUSTAINABILITY
<p>Dataset on THE INFLUENCE OF INNOVATION, KNOWLEDGE MANAGEMENT, AND E-COMMERCE ADOPTION ON MSME PERFORMANCE, AND ITS IMPACT ON MSMEs SUSTAINABILITY</p>
Dataset and Data Dictionary for "Enhancing Consumer Satisfaction in Live Commerce: A Study of Middle-Aged Women's Cosmetics Purchases Using TAM, PVT, and SIT Models
<p>This dataset is part of a study investigating the underexplored factors driving middle-aged Chinese women’s purchasing behavior in live commerce, particularly in the context of their decision-making amidst the rapid expansion of e-commerce. The study employs a comprehensive theoretical framework based on the Technology Acceptance Model (TAM), Perceived Value Theory (PVT), and Social Influence Theory (SIT).</p> <p>Data were collected through a structured survey administered to 653 women aged 40 to 59. The dataset captures key variables including ease of use, pricing, consumer trust, platform interactivity, and purchase satisfaction. These variables are essential for understanding the complex relationships that influence purchasing decisions in live-stream shopping environments.</p> <p>The dataset has been analyzed using Structural Equation Modeling (SEM), revealing that factors such as ease of use, perceived value from competitive pricing, consumer trust, and real-time platform interactivity significantly enhance purchase satisfaction. Moreover, the results demonstrate that perceived value moderates these relationships, amplifying their effects under conditions of high perceived value.</p> <p>This dataset provides valuable insights into the psychological and social factors that shape e-commerce behavior, offering implications for optimizing platform design to promote consumer trust and long-term engagement.</p> <p>Keywords: Live commerce, Middle-aged women, Purchasing behavior, Technology Acceptance Model (TAM), Perceived Value Theory (PVT), Social Influence Theory (SIT), Structural Equation Modeling (SEM), Consumer trust, E-commerce.</p>
Crossroads of Commerce: How the Taiwan Strait Propels the Global Economy - Supplemental Material
<p>Supplemental Material to the CSIS Report: <a href="https://features.csis.org/chinapower/china-taiwan-strait-trade"><em>Crossroads of Commerce: How the Taiwan Strait Propels the Global Economy</em></a></p> <ol> <li><em>tws_2022_trade_value_estimates.csv</em> contains estimated trade value in USD for 2022. Denominators for % estimates based on <a href="https://www.cepii.fr/CEPII/en/bdd_modele/bdd_modele_item.asp?id=37">2022 CEPII BACI bilateral trade flows</a><br> <table> <tbody> <tr> <td><strong>Variable</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>ISO</td> <td>ISO3 Country Code</td> </tr> <tr> <td>Economy </td> <td>Country/Region Name</td> </tr> <tr> <td>TWS Imports_bln</td> <td>US$ value (billions) of imports that transit the Taiwan Strait</td> </tr> <tr> <td>TWS Imports_%</td> <td>% of total imports that transit the Taiwan Strait</td> </tr> <tr> <td>TWS Exports_bln</td> <td>US$ value (billions) of exports that transit the Taiwan Strait</td> </tr> <tr> <td>TWS Exports_%</td> <td>% of total exports that transit the Taiwan Strait</td> </tr> <tr> <td>Total TWS Trade_bln</td> <td>US$ value (billions) of trade (imports+exports) that transits the Taiwan Strait</td> </tr> <tr> <td>Total TWS Trade_%</td> <td>% of total trade (imports+exports) that transits the Taiwan Strait</td> </tr> </tbody> </table> </li> <li> <em>ChinaPower_CrossroadsCommerce_TaiwanStrait_methodology.pdf </em>provides and overview of methodology and data sources. </li> <li> <em>ChinaPower_CrossroadsCommerce_TaiwanStrait_factsheet.pdf </em>provides key highlights derived from the data.</li> </ol> <p>For questions about the data, please contact David Peng (dpeng@csis.org) </p> <p> </p>
Dataset Literature Review E-commerce And CSR Strategy
<p>Data tersebut didapatkan dengan menggunakan website lens.org dengan memilih Scholarly Works dengan kunci/keyword E-commerce And CSR Strategy. Yang selanjutnya di filter sebanyak 2 kali pada bagian Document Type “Journal Article dan Conference Proceeding” setelahnya menggunakan fitur filter Subject Matter pada Subject “Law”. Pada setiap tahapan mulai awal hingga akhir semua data tersebut masing-masing di export dengan format CSV dan BIBTEX, dan diambil semua gambar yang muncul pada menu analisys di lens.org pada tahap terakhir. </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
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.