1) Data Introduction • The Grocery Sales Database is a retail dataset of relational tables of grocery store sales transactions, customer information, product details, employee records, geographic information, and more across cities and countries.

2) Data Utilization (1) Grocery Sales Database has characteristics that: • The data consists of seven tables, including product categories, city/country information, customer/employee/product details, and sales details, each of which is interconnected by a unique ID. • Sales data are linked to products, customers, employees, and regions, enabling a variety of business analyses, including monthly sales, popular products, customer behavior, and regional performance. (2) Grocery Sales Database can be used to: • Analysis of sales trends and popular products: It can be used to identify trends and derive best-selling products by analyzing sales by monthly and category and sales by product. • Customer Segmentation and Marketing Strategy: Define customer groups based on customer frequency of purchases, total expenditure, and regional information and apply them to developing customized marketing and promotion strategies.

Listing of all purchase orders and contracts issued to procure goods and/or services within City-Parish.

In the City-Parish, a PO/Contract is made up of two components: a header and one or many detail items that comprise the overarching PO/Contract. The header contains information that pertains to the entire PO/Contract. This includes, but is not limited to, the total amount of the PO/Contract, the department requesting the purchase and the vendor providing the goods or services. The detail item(s) contain information that is specific to the individual item ordered or service procured through the PO/Contract. The item/service description, item/service quantity and the cost of the item is located within the PO/Contract details. There may be one or many detail items on an individual PO/Contract. For example, a Purchase Order for a computer equipment may include three items: the computer, the monitor and the base software package.

Both header information and detail item information are included in this dataset in order to provide a comprehensive view of the PO/Contract data. The Record Type field indicates whether the record is a header record (H) or detail item record (D). In the computer purchase example from above, the system would display 4 records – one header record and 3 detail item records.

It should be noted header information will be duplicated on all detail items. No detail item information will be displayed on the header record.

***In October of 2017, the City-Parish switched to a new system used to track PO/Contracts. This data contains all PO/Contracts entered in or after October 2017. For prior year data, please see the Legacy Purchase Order dataset https://data.brla.gov/Government/Legacy-Purchase-Orders/54bn-2sqf

The Product Information Management (PIM) software market, valued at $2214.5 million in 2025, is poised for significant growth over the forecast period (2025-2033). Driven by the increasing need for efficient product data management across diverse sales channels (e-commerce, marketplaces, physical stores), the market is experiencing robust adoption among large enterprises and SMEs. Cloud-based PIM solutions are gaining significant traction due to their scalability, accessibility, and cost-effectiveness, surpassing on-premises deployments in market share. Key trends shaping the market include the integration of Artificial Intelligence (AI) for automated data enrichment and enhanced product discovery, the rising adoption of headless commerce architectures that require robust PIM systems, and a growing focus on omnichannel commerce strategies demanding centralized product information management. While data security and integration complexities pose challenges, the overall market outlook remains optimistic. The competitive landscape is characterized by a mix of established players like Shopify and Magento, alongside niche players catering to specific industry verticals. Geographical expansion, particularly in emerging economies experiencing rapid e-commerce growth, further fuels market expansion. The continued evolution of e-commerce and the demand for personalized customer experiences will solidify the importance of PIM software, resulting in sustained market growth throughout the forecast period. The growth trajectory is expected to be influenced by several factors. The increasing complexity of product catalogs, coupled with the need for consistent and accurate product information across multiple platforms, necessitates the adoption of PIM solutions. Furthermore, the growing importance of data quality for SEO and enhanced customer experience drives market demand. While initial investment costs and the need for skilled personnel to implement and manage PIM systems present some restraint, the long-term benefits of improved operational efficiency, reduced errors, and enhanced customer satisfaction outweigh these considerations. The market will likely witness further consolidation, with larger players acquiring smaller niche players to broaden their product offerings and market reach. The expansion into new geographical regions, particularly within rapidly developing economies in Asia-Pacific, will continue to unlock growth opportunities.

Introduction

The dataset comprises over 12,000 chat conversations, each focusing on specific Retail & E-Commerce related topics. Each conversation provides a detailed interaction between a call center agent and a customer, capturing real-life scenarios and language nuances.

Topic Diversity

The chat dataset covers a wide range of conversations on Retail & E-Commerce topics, ensuring that the dataset is comprehensive and relevant for training and fine-tuning models for various Retail & E-Commerce use cases. It offers diversity in terms of conversation topics, chat types, and outcomes, including both inbound and outbound chats with positive, neutral, and negative outcomes.

Language Variety & Nuances

The conversations in this dataset capture the diverse language styles and expressions prevalent in Punjabi Retail & E-Commerce interactions. This diversity ensures the dataset accurately represents the language used by Punjabi speakers in Retail & E-Commerce contexts.

The dataset encompasses a wide array of language elements, including:

This linguistic authenticity ensures that the dataset equips researchers and developers with a comprehensive understanding of the intricate language patterns, cultural references, and communication styles inherent to Punjabi Retail & E-Commerce interactions.

Conversational Flow and Interaction Types

The dataset includes a broad range of conversations, from simple inquiries to detailed discussions, capturing the dynamic nature of Retail & E-Commerce customer-agent interactions.

Each of these conversations contains various aspects of conversation flow like:

The global Product Information Management (PIM) Systems market is projected to grow from USD XXX million in 2025 to USD XXX million by 2033, at a CAGR of XX%. The market is driven by the increasing need for centralized and accurate product information across multiple channels, the growing adoption of e-commerce, and the need for improved customer experience. Large enterprises are expected to hold a significant share of the market due to their complex product portfolios and need for efficient PIM systems. Cloud-based PIM systems are gaining popularity due to their flexibility, scalability, and cost-effectiveness. North America is expected to be the largest regional market, followed by Europe and Asia Pacific. The United States is the major contributor to the North American market, due to the presence of a large number of e-commerce businesses and the high adoption of PIM systems by large enterprises. The Asia Pacific market is expected to witness significant growth due to the increasing adoption of e-commerce in the region. Key players in the market include Plytix, Sales Layer, Pimberly, Akeneo, 1WorldSync, PIMworks, Salsify, Syndigo, Image Relay, Catsy, Ergonode PIM, Kontainer, Acquia, Dynamicweb, BetterCommerce, Pattern e-commerce, UNBXD, Quable PIM, Creative Force, OneTimePIM, Stibo Systems, Pimcore, censhare, Brandquad, Talkoot, WisePorter, and RetailOps.

Provide information including: ID, product name, international barcode, manufacturer name, manufacturer address, test results, update date and other field data.

***Please note, the City-Parish has implemented a new financial and procurement system. Effective 9/14/2017 all purchase order processing was halted in the legacy system. This dataset was comprised of data from this legacy system. Our new system went online on 10/2/2017. We are working towards providing the purchase orders from the new system and hope to have it available by the end of October.

Listing of all purchase orders issued to procure goods and/or services within City-Parish.

In the City-Parish Purchasing system, a purchase order (PO) is made up of two components: a header and one or many detail items that comprise the overarching PO. The header contains information that pertains to the entire PO. This includes, but is not limited to, the total amount of the PO, the department requesting the purchase and the vendor providing the goods or services. The detail item(s) contain information that is specific to the individual item ordered or service procured through the Purchase Order. The item/service description, item/service quantity and the cost of the item is located within the Purchase Order details. There may be one or many detail items on an individual Purchase Order. For example, a Purchase Order for a computer equipment may include three items: the computer, the monitor and the base software package.

Both header information and detail item information are included in this dataset in order to provide a comprehensive view of the purchase order data. The Record Type field indicates whether the record is a header record (H) or detail item record (D). In the computer purchase example from above, the system would display 4 records – one header record and 3 detail item records.

It should be noted header information will be duplicated on all detail items. No detail item information will be displayed on the header record.

In order to expand the sales market of military products and enhance the breadth of promotion, the Production and Manufacturing Center of the Armament Administration of the Ministry of National Defense provides the center's product catalog for publication. The scope of the announcement is mainly based on the content contained in the product catalog, indicating that the project includes production plants, product items, Introduction, performance details, illustrations, new military products and other products, etc.

The 3D visualization for eCommerce market is experiencing robust growth, driven by the increasing demand for immersive online shopping experiences and the need for businesses to enhance product presentation and reduce return rates. The market, estimated at $5 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 25% from 2025 to 2033, reaching approximately $25 billion by 2033. This expansion is fueled by several key trends, including the rising adoption of augmented reality (AR) and virtual reality (VR) technologies, the increasing sophistication of 3D modeling software, and the growing preference for personalized online shopping experiences. The fashion, jewelry, and furniture sectors are significant adopters, leveraging 3D visualization to showcase product details and improve customer engagement, thereby reducing uncertainties associated with online purchases. However, challenges remain, including the high cost of implementation for smaller businesses and the need for skilled professionals to create high-quality 3D models. The market is segmented by application (fashion, jewelry, furniture, automotive, home decoration, others) and type (cloud-based, on-premises), with cloud-based solutions gaining traction due to their scalability and cost-effectiveness. The competitive landscape comprises a mix of established players and emerging startups, each vying for market share through innovative product offerings and strategic partnerships. North America and Europe currently dominate the market, but Asia-Pacific is expected to witness significant growth in the coming years due to its expanding e-commerce sector and increasing consumer adoption of digital technologies. The continued integration of 3D visualization into various eCommerce platforms and the development of user-friendly software are key factors driving future growth. Furthermore, the increasing adoption of 3D visualization in mobile applications, enabling consumers to visualize products within their own spaces, presents a significant opportunity for market expansion. Businesses are also exploring the use of 3D visualization for interactive product catalogs, 360-degree product views, and virtual try-on features, further contributing to the market's growth trajectory. The competitive landscape is likely to witness further consolidation as larger players acquire smaller startups to expand their product portfolios and geographical reach. The focus on improving the accuracy and realism of 3D models, alongside increased accessibility and affordability, will be crucial to the market's continued success.

Two-thirds of Chinese luxury goods consumers conducted product research before making a purchase decision for high-end items, according to a survey released in May 2024. Over 40 percent of survey participants looked for product details like parameters, photos, and prices, rankings of similar products, and the new arrivals of the season. One in ten respondents would be interested in information related to the brand's spokesperson or associated key opinion leaders.

Introduction

This Thai Call Center Speech Dataset for the Retail and E-commerce industry is purpose-built to accelerate the development of speech recognition, spoken language understanding, and conversational AI systems tailored for Thai speakers. Featuring over 30 hours of real-world, unscripted audio, it provides authentic human-to-human customer service conversations vital for training robust ASR models.

Curated by FutureBeeAI, this dataset empowers voice AI developers, data scientists, and language model researchers to build high-accuracy, production-ready models across retail-focused use cases.

Speech Data

The dataset contains 30 hours of dual-channel call center recordings between native Thai speakers. Captured in realistic scenarios, these conversations span diverse retail topics from product inquiries to order cancellations, providing a wide context range for model training and testing.

Topic Diversity

This speech corpus includes both inbound and outbound calls with varied conversational outcomes like positive, negative, and neutral, ensuring real-world scenario coverage.

Such variety enhances your model’s ability to generalize across retail-specific voice interactions.

Transcription

All audio files are accompanied by manually curated, time-coded verbatim transcriptions in JSON format.

These transcriptions are production-ready, making model training faster and more accurate.

Metadata

Rich metadata is available for each participant and conversation:

This granularity supports advanced analytics, dialect filtering, and fine-tuned model evaluation.

Usage and Applications

This dataset is ideal for a range of voice AI and NLP applications:

Tender procedures with product details, carried out by Lombardy PAs via the SINTEL e-Procurement platform of the Lombardy Region

Graph and download economic data for Real Gross Domestic Product: Information (51) in Maryland (MDINFORQGSP) from Q1 2005 to Q1 2025 about information, MD, GSP, private industries, private, real, industry, GDP, and USA.

Comprehensive dataset of 0 Disposable items shops in United Kingdom as of June, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

Comprehensive dataset of 1,405 Research and product developments in Italy as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

Introduction

Welcome to the Filipino Scripted Monologue Speech Dataset for the Retail & E-commerce domain. This dataset is built to accelerate the development of Filipino language speech technologies especially for use in retail-focused automatic speech recognition (ASR), natural language processing (NLP), voicebots, and conversational AI applications.

Speech Data

This training dataset includes 6,000+ high-quality scripted audio recordings in Filipino, created to reflect real-world scenarios in the Retail & E-commerce sector. These prompts are tailored to improve the accuracy and robustness of customer-facing speech technologies.

Topic Diversity

This dataset includes a comprehensive set of retail-specific topics to ensure wide linguistic coverage for AI training:

Contextual Enrichment

To increase training utility, prompts include contextual data such as:

These additions help your models learn to recognize structured and unstructured retail-related speech.

Transcription

Every audio file is paired with a verbatim transcription, ensuring consistency and alignment for model training.

Metadata

Detailed metadata is included to support filtering, analysis, and model evaluation:

Comprehensive dataset of 210 Research and product developments in Hungary as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

Descrizione del dataset / Dataset description

[ITALIANO]
Il dataset comprende:

• 2 tabelle di esempio, con un numero di righe minimo che consenta di fornire indicazioni sul riempimento di ogni colonna
  • Una per la metadatazione relativa agli oggetti esibilti (.csv)
  • Una per la metadatazione relativa al processo di digitalizzazione (.ods)
• 2 tabelle vuote, da usare per la raccolta di dati in nuovi progetti
  • Una per la metadatazione relativa agli oggetti esibilti (.ods)
  • Una per la metadatazione relativa al processo di digitalizzazione (.ods)

I template delle tabelle sono attualmente disponibili solo in Italiano. Segue una traduzione in inglese, con annessa descrizione di ciascuno dei campi.

[ENGLISH]
The dataset includes:

• 2 sample tables, with a minimum number of rows to give indications on how to fill each column
  • One for metadata related to the exhibited objects (.csv)
  • One for metadata related to the digitisation process (.ods)
• 2 empty tables, to be used for data collection in new projects
  • One for metadata related to the exhibited objects (.ods)
  • One for metadata related to the digitisation process (.ods)

The table templates are currently only available in Italian. Below, each table's field is translated from Italian to English and described.

Italian to English Fields Translation and Description

DATASET DELL’OGGETTO

[Object Dataset - English Translation and Description]

• NR (English translation: ID): Unique identifier for the item, numeric, alphanumeric, alphabetic or descriptive, not repeated within the dataset.

• NR collegato (English translation: Linked ID): Identifier of an item related to the main one in this row (“NR”).

• Relazione (English translation: Relation): Type of relationship linking the main item and the related item (e.g., “part of”, “represents”).

• Sala mostra (English translation: Exhibition Room): Name or code of the exhibition room where the item has been (or is) displayed.

• Didascalia (English translation: Caption): Descriptive text of the item, often containing information relevant to other fields.

• Consistenza (English translation: Quantity / Extent): Quantity or measure of the item (e.g., number of components).

• Tipologia documentaria (English translation: Document Type): Type of object, selected from a controlled vocabulary (e.g., Map, Book, Model, Specimen, etc.).

• Tecnica (English translation: Technique): Technique used to create the item, chosen from a controlled vocabulary (e.g., Engraving, Watercolour, Sculpture, etc.).

• Tipologia riprod. in mostra (English translation: Exhibition Reproduction Type): Type of representation of the item in the exhibition, if it differs from the original format.

• Soggetti (English translation: Subjects): Proper or common names of subjects present or represented, with recommended use of persistent identifiers like VIAF or ULAN.

• Titolo originale (English translation: Original Title): Original title of the work, often provided by the author, with language tag (e.g., @ita).

• Titolo museale (English translation: Exhibition Title): Title assigned by the curator or museum for the exhibition.

• Titolo @en (English translation: Title (English)): English translation of the title, without language tag.

• Data (English translation: Date): Dating of the item, single date or a range of years.

• Scopritore (English translation: Discoverer): Name of the person who discovered or collected the item, with possible ULAN/VIAF ID.

• Autore (English translation: Author): Name of the author of the work or item, with possible ULAN/VIAF ID.

• Traduttore (English translation: Translator): Name of the person who translated the work, with possible ULAN/VIAF ID.

• Disegnatore (English translation: Illustrator / Draftsman): Person responsible for drawing the item, with possible ULAN/VIAF ID.

• Incisore (English translation: Engraver): Person who engraved the object, with possible ULAN/VIAF ID.

• Editore (English translation: Publisher): Name of the publisher or publishing house, with possible ULAN/VIAF ID.

• Luogo editore (English translation: Publisher Location): Geographical location of the publisher.

• Preparatore museale (English translation: Museum Preparer): Person or institution responsible for the museum preparation of the item, with possible ULAN/VIAF ID.

• Committente (English translation: Commissioner): Name of the person or institution that commissioned the work, with possible ULAN/VIAF ID.

• Tipologia opera parente (English translation: Related Work Type): Type/category of a parent or related work, chosen from a controlled vocabulary.

• Titolo opera parente (English translation: Related Work Title): Title of the related or parent work.

• Volume (English translation: Volume): Volume number or code, if the item is part of a series.

• Collezione (English translation: Collection): Name of the collection to which the item belongs.

• Ente conservatore (English translation: Holding Institution): Name of the institution holding the item.

• Luogo conservazione (English translation: Place of Preservation): Physical place or city where the item is preserved.

• Collocazione / Inventario (English translation: Shelfmark / Inventory Code): Identifier used for cataloguing or inventory at the holding institution.

• Collocazione fisica (English translation: Physical Location): Internal location used for logistical or exhibition needs.

• Regno (English translation: Kingdom): Main biological kingdom classification of a fossil (e.g., Animalia).

• Phylum (English translation: Phylum): Internal taxonomic category identifying structural characteristics (e.g., Chordata).

• Classe (English translation: Class): Taxonomic class defining distinctive traits (e.g., Mammalia).

• Ordine (English translation: Order): Taxonomic order grouping similar families (e.g., Xenarthra).

• Famiglia (English translation: Family): Taxonomic family grouping related genera (e.g., Mylodontidae).

• Genere (English translation: Genus): Taxonomic genus grouping similar species (e.g., Scelidotherium).

• Specie (English translation: Species): Species of the organism (e.g., gladius).

• Taxon_data (English translation: Taxon Data URL): Link to authoritative taxonomy record (e.g., mindat.org).

• Periodo_geologico (English translation: Geological Period): Geological period when the fossil was formed (e.g., Pleistocene).

• Età_specifica (English translation: Specific Geological Age): More precise age within a geological period (e.g., Messinian).

• Formazione_geologica (English translation: Geological Formation): Stratigraphic unit where the fossil was found.

• Ambiente_deposizionale (English translation: Depositional Environment): Type of environment where the fossil was deposited (e.g., deep sea).

• Stato_geografico_raccolta (English translation: Country of Collection): Country or region where the fossil was collected.

• Luogo_raccolta (English translation: Collection Site): Specific locality where the fossil was found (e.g., Lecce).

DATASET DEL PROCESSO DI DIGITALIZZAZIONE

[Digitisation Process Dataset - English Translation and Description]

• NR (English translation: ID): Unique reference number for the item in the dataset.

• OGGETTO (English translation: Object): Description of the item (e.g., manuscript, medal).

• VETRINA (English translation: Display Case): Case or position where the object is exhibited.

• DIDASCALIA (English translation: Caption): Descriptive text accompanying the item in the

Real Gross Domestic Product: Data Processing, Hosting, and Other Information Services (NAICS 518, 519) in Oklahoma was 813.90000 Mil. of Chn. 2009 $ in January of 2023, according to the United States Federal Reserve. Historically, Real Gross Domestic Product: Data Processing, Hosting, and Other Information Services (NAICS 518, 519) in Oklahoma reached a record high of 813.90000 in January of 2023 and a record low of 82.90000 in January of 2000. Trading Economics provides the current actual value, an historical data chart and related indicators for Real Gross Domestic Product: Data Processing, Hosting, and Other Information Services (NAICS 518, 519) in Oklahoma - last updated from the United States Federal Reserve on July of 2025.

This vast repository houses crucial information on international trade transactions, capturing the intricate details of both export and import activities of Zimbabwe. The Export Database contains meticulous records of outbound shipments, offering valuable insights into the products, exporters, and destinations involved in each transaction. On the other hand, the Import Database provides a comprehensive view of inbound shipments, shedding light on the importers, origins, and details of the products acquired. Together, these two databases present a holistic perspective on global trade dynamics, encompassing critical metadata such as dates, product descriptions, quantities, values, and transportation specifics. Whether you are an analyst, researcher, or business professional, this comprehensive database will undoubtedly prove to be an invaluable resource for gaining a deep understanding of international trade patterns and market dynamics. Explore the wealth of information within and unlock new opportunities in the world of trade and commerce.

The Export Database contains information related to export transactions. Each entry in the database represents a specific export event. The metadata fields in this database hold crucial details about the exported products and the transaction itself. The "DATE" field indicates the date of the export. "EXPORTER NAME" refers to the name of the entity or company responsible for exporting the goods. "DESTINATION COUNTRY" indicates the country to which the products are being shipped. The "HS CODE" represents the Harmonized System code, a standardized numerical system used to classify traded products. The "PRODUCT DESCRIPTION" field provides a brief description of the exported item. The "BRAND" field specifies the brand associated with the product. "QUANTITY" indicates the total quantity of the product being exported, and "UNIT OF QUANTITY" represents the measurement unit used for quantity. "SUBITEM QUANTITY" refers to the quantity of a subitem within the main exported product. The "PACKAGES" field indicates the number of packages used for shipment. "GROSS WEIGHT" represents the total weight of the exported products. "SUBITEM FOB VALUE" and "TOTAL FOB VALUE" denote the Free on Board (FOB) value of the subitem and the total FOB value of the export, respectively. "TOTAL CIF VALUE" indicates the total cost, insurance, and freight value. "ITEM NUMBER" is a unique identifier for each product item. "TRANSPORT TYPE" specifies the mode of transportation used for the export. "INCOTERMS" refers to the standardized international trade terms defining the responsibilities of buyers and sellers during transportation. "CUSTOMS" indicates the customs information related to the export. "VARIETY" and "ATTRIBUTES" hold additional details about the product. The "OPERATION TYPE" field indicates the type of export operation, such as direct export or re-export. "MONTH" and "YEAR" represent the month and year when the export occurred.

The Import Database contains information related to import transactions. Each entry in the database represents a specific import event. The metadata fields in this database hold crucial details about the imported products and the transaction itself. The "DATE" field indicates the date of the import. "IMPORTER NAME" refers to the name of the entity or company responsible for importing the goods. "SALES COUNTRY" indicates the country from which the products are being purchased. "ORIGIN COUNTRY" denotes the country where the imported products originate. The "HS CODE" represents the Harmonized System code, a standardized numerical system used to classify traded products. The "PRODUCT DESCRIPTION" field provides a brief description of the imported item. "QUANTITY" indicates the total quantity of the product being imported, and "UNIT OF QUANTITY" represents the measurement unit used for quantity. "SUBITEM QUANTITY" refers to the quantity of a subitem within the main imported product. The "PACKAGES" field indicates the number of packages used for shipment. "GROSS WEIGHT" represents the total weight of the imported products. "TOTAL CIF VALUE" indicates the total cost, insurance, and freight value. "TOTAL FREIGHT VALUE" and "TOTAL INSURANCE VALUE" represent the respective values for freight and insurance. "ITEM FOB VALUE," "SUBITEM FOB VALUE," and "ITEM CIF VALUE" denote the Free on Board (FOB) value of the item, subitem, and the cost, insurance, and freight value of the item, respectively. "ORIGIN PORT" specifies the port from which the products were shipped. "TRANSPORT TYPE" specifies the mode of transportation used for the import. "INCOTERMS" refers to the standardized international trade terms defining the responsibilities of buyers and sellers during transportation. "ITEM NUMBER" is a unique identifier for each product item. "CUSTOMS" indicates the customs information related to the import. "OPERATION TYPE" field indicates the type of import operation, such as direct...