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45 results for “E-commerce”

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zenodo44/100

E-commerce et covid 19 en France. Une application de la théorie du comportement planifié

<p>Cette base de donn&eacute;es est issue d&rsquo;une enqu&ecirc;te quantitative par questionnaire (n= 378, ann&eacute;e 2021). Elle est construite sur la base de la th&eacute;orie du comportement planifi&eacute;. La variable d&eacute;pendante est l&rsquo;achat de biens et/ou de services par internet.</p> <p><em>Contenu de la base de donn&eacute;es</em></p> <p>Le questionnaire comprend les mesures suivantes&nbsp;: 2 variables de segmentation (piratage et type d&rsquo;achat), le comportement de l&rsquo;individu (2 items&nbsp;: fr&eacute;quence et r&eacute;cence), l&rsquo;impact du Covid-19 sur la fr&eacute;quence d&rsquo;achat (1 item), l&rsquo;intention comportementale (2 items dont 1 d&rsquo;identit&eacute; personnelle), les croyances sur les b&eacute;n&eacute;fices attendus (11 items), l&rsquo;attitude (3 items), les croyances sur les freins per&ccedil;us (13 items), la perception de contr&ocirc;le sur le comportement (2 items), les normes descriptives (2 items), les normes injonctives (2 items), 3 variables de signal&eacute;tique (sexe, &acirc;ge et CSP). L&rsquo;administration &eacute;tant r&eacute;alis&eacute;e en ligne, la base de donn&eacute;es comprend &eacute;galement une variable &laquo;&nbsp;temps de saisie&nbsp;&raquo; du questionnaire qui pourra servir &agrave; &eacute;purer la base. Toutes les variables &agrave; &eacute;chelle sont mesur&eacute;es en 6 points.</p>

opencc-by-4.0Jan 2022View details →
zenodo44/100

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&nbsp;of questions annotated&nbsp;by crowdworkers on a set of products&#39; 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>

opencc-by-4.0Nov 2022View details →
zenodo44/100

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&#39;s e-commerce platform. All the <em>.csv&nbsp;</em>files are the initial data which&nbsp;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):&nbsp;<br> &nbsp; &nbsp; 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> &nbsp; &nbsp; node_id int, node_type string, node_atts string (notice that node_atts are 256-dimensional feature vector strings with delimiter &quot;:&quot;)<br> 3. RCDD_train_labels.csv (training labels):<br> &nbsp; &nbsp; item_id int, label int<br> 4. RCDD_test_ids.csv (testing ids):<br> &nbsp; &nbsp; item_id int<br> 5. RCDD_test_labels.csv (testing labels):<br> &nbsp; &nbsp; item_id int, label int&nbsp; &nbsp;&nbsp;</p> <p>Besides that,<em> graph.bin</em> is the format in DGL which is constructed by&nbsp;&nbsp;all&nbsp;<em>*.csv</em>&nbsp;files, and a&nbsp;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>

opencc-by-4.0Jun 2023View details →
zenodo44/100

E-commerce Product Dataset from Mercado Libre Perú

<p>We offer a dataset comprising approximately 1,198,398 unique products sourced from Mercado Libre Per&uacute;. This dataset was collected from the platform&#39;s public API spanning from February 2022 to May 2023.</p> <p>Files description:</p> <ul> <li>ml_db_raw.db : Raw dataset&nbsp;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 :&nbsp;Category Code given by Mercado Libre&nbsp;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>&nbsp;</p>

opencc-by-4.0Oct 2023View details →
zenodo40/100

Catálogo de publicaciones del e-commerce de Alita Cómics

<p>En este dataset se presenta el cat&aacute;logo de publicaciones ofrecidas por la web de Alita C&oacute;mics a principios de abril de 2022. En el mismo se facilitan diversos campos de cada uno de los art&iacute;culos para su an&aacute;lisis.</p>

opencc-by-4.0Apr 2022View details →
zenodo40/100

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.

opencc-zeroJun 2024View details →
zenodo40/100

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.

opencc-zeroJun 2024View details →
zenodo40/100

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>

opencc-by-4.0Aug 2024View details →
zenodo40/100

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 &ldquo;Journal Article dan Conference Proceeding&rdquo; setelahnya menggunakan fitur filter Subject Matter pada Subject &ldquo;Law&rdquo;. Pada setiap tahapan mulai awal hingga akhir&nbsp; 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.&nbsp;</p>

opencc-by-4.0Nov 2022View details →
dryad40/100

National impacts of e-commerce growth: Development of a spatial demand based tool

Open the record for dataset details and reuse information.

publicAug 2022View details →
dryad40/100

Coping with the rise of E-commerce generated home deliveries through innovative last-mile technologies and strategies

Open the record for dataset details and reuse information.

publicApr 2023View details →
zenodo36/100

Data Responden Keputusan Pembelian Pelanggan E-commerce berdasarkan Konten TikTok

<p>The data of respondents&#39; answers for research of Customer&#39;s Purchase Decisions on e-commerce Influenced by TikTok Contents. We remove the private information such as name, age, universities, and gender. We only store the questions and the&nbsp;answers. The data is in the form of a CSV file.&nbsp;</p>

opencc-by-4.0Mar 2022View details →
zenodo36/100

Hasil Survey Pemilihan Platform E-Commerce dengan Metode Weighted Product

<p>Dataset ini merupakan data hasil survey untuk penelitian &quot;Pemilihan Platform E-Commerce dengan Metode Weighted Product&quot;</p>

opencc-by-4.0Sep 2022View details →
zenodo36/100

AI concepts relevant for AI and e-commerce/retail secors

<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'&rsquo;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.&nbsp;</p> <p>These datasets contain AI concepts identified from scientific literature as part of the INAIR Horizon Europe project. It includes keywords for skills and knowledge relevant to AI in the retail and e-commerce sectors. The data supports qualitative and quantitative analyses by providing detailed references to articles discussing each concept. This resource aims to facilitate further research and understanding of the AI-related skills and technologies impacting the retail and e-commerce industries.</p> <p>In our data retrieval process, we used the Scopus API to systematically extract the relevant publications. We collected a diverse range of publications from the database, including conference proceedings, book chapters, and other materials. To ensure the reliability of our analysis, we refined our focus exclusively to peer-reviewed articles, specifically limiting our scope to the categories of articles and reviews.&nbsp;</p> <p>The keywords combinations we used were: &nbsp;</p> <p>TITLE-ABS-KEY ( X AND Y ) AND PUBYEAR &gt; 2017 AND PUBYEAR &lt; 2025</p> <p>Where: X=['sales', 'retail', 'e-commerce'], Y=['AI', 'artificial intelligence']. These keywords were scrutinized within titles, abstracts, author keywords, and &lsquo;topics&rsquo; as delineated by the platform.</p> <ul> <li>`concepts_unique.xlsx` lists unique concepts</li> <li>`concepts_master.xlsx` is&nbsp;a master file referencing all articles mentioning each given concept.</li> </ul> <p>The former file allows for browsing skills and knowledge concepts, while the latter serves as a reference to works addressing each concept and enables quantitative analysis, such as identifying the most frequently mentioned concepts.</p> <p>&nbsp;</p>

opencc-by-4.0Jun 2024View details →
zenodo36/100

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'&rsquo;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.&nbsp;</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>

opencc-by-4.0Jun 2024View details →
zenodo36/100

Dataset - Indonesian People's Readiness for Blockchain Adoption in E-Commerce - 2024

<p>The dataset on Indonesian blockchain adoption was collected through a questionnaire conducted over three months, from April to June 2024, gathering 422 responses. The survey had seven main sections, each designed to collect important data for the study.<br><br>The first section introduced the study&rsquo;s purpose and asked demographic questions. Respondents were also asked about their experience with e-commerce and blockchain technology. The second section explored how ready Indonesians are to adopt blockchain infrastructure for e-commerce retail transactions.<br><br>The final sections focused on key study variables, including Transaction Security [BTS], Transparency [BT], Cost Efficiency [BCE], Transaction Speed [BS], Customer Trust [CT], and E-commerce Adoption [EA]. These variables were used to assess the overall readiness of Indonesians for integrating blockchain into the e-commerce sector.<br><br>This dataset offers valuable insights into the potential challenges and advantages of adopting blockchain technology in Indonesia's e-commerce transactions.</p>

opencc-by-4.0Sep 2024View details →
zenodo36/100

Dataset Questionnaire Driving Repeat Purchases and E-WOM: How Price, Reputation, Hedonic Appeal, and Social Interaction Shape Consumer Behavior in Indonesia's E-Commerce Smartphone Market

<p>The following dataset is a dataset from a study that investigated price advantage, reputation, hedonic effort, and social interaction influence customer satisfaction, which in turn impacts repurchase intention and e-WOM (electronic word-of-mouth).</p>

opencc-by-4.0Nov 2024View details →
zenodo36/100

Automatic Complaints Classification in E-Commerce: A Case Study Using CRISP-DM

<p>The growth of e-commerce has been remarkable in recent years, driven by increasing consumer demand for attention and quick responses. Given the large volume of transactions and complaints accompanying this increase, automating the classification of these complaints can help quickly route them to the appropriate departments. This paper presents a computational approach using the Cross Industry Standard Process for Data Mining (CRISP-DM) methodology to automate the complaints screening process. We categorized 600 real complaints from three e-commerce platforms in Brazil. The learning model was trained progressively, using an initial set of 25 complaints in each category. The classification model obtained an accuracy of 85\% and an average of over 80\% across all relevant metrics, including precision, recall, and F1-Score. The results confirmed the effectiveness of the developed model for automated complaint classification in e-commerce, providing a computational strategy that improves the customer service process and allows for quicker problem resolution.</p>

opencc-by-4.0Nov 2024View details →
zenodo36/100

e-commerce AND human rights

<p>data&nbsp;e-commerce AND human rights</p>

opencc-by-4.0Nov 2022View details →
zenodo36/100

e-commerce AND human rights

<p>Data&nbsp;&nbsp;e-commerce AND human rights</p>

opencc-by-4.0Nov 2022View details →

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Allen Brain Atlas

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allen-brain-atlas
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Last verified 2026-04-30Open record

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abode-home-cage
behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.
Last verified 2026-04-30Open record

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.

dandi-nwb
electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.
Last verified 2026-04-30Open record

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.

ibl
behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.
Last verified 2026-04-29Open record

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.

openneuro
neuroscienceopenPublished datasets are available on demand over the internet.
Last verified 2026-04-29Open record