Dataset

This dataset was created by ahmed elmokhraty

Contents

Overview

This issue of the Australian forest and wood products statistics (AFWPS) includes updated 2015-16 data for key domestic indicators of forestry sector activity, including sales and service income and industry valued added. It also includes interim estimates of the volume and value of logs harvested in 2016-17, along with updated 2016-17 trade statistics for wood products and data for housing and other residential commencements.

Key Issues
• Forestry industry value added, or contribution to GDP, increased for the third year in a row, growing by 9 per cent to $8.6 billion in 2015-16. Sales and service income for the forestry sector also grew strongly to $23.7 billion in 2015-16 (up 7 per cent from the previous year). • ABARES estimates that Australia's forestry sector continued to grow strongly in 2016-17, with the total volume and value of logs harvested reaching record levels. Estimated total volume log harvest from native forests and commercial plantations increased by 9 per cent to 32.8 million cubic metres and estimated total value increased by 12 per cent to $2.5 billion. • After four consecutive years of growth in residential construction activity in Australia, dwelling commencements fell by 6 per cent to 219,300 in 2016-17. The decrease in commencements of other residential buildings (including units and house conversions) was greater than the decrease in house commencements. • Australia's trade in wood products has been growing since 2012-13 and reached a record level of $8.6 billion in 2016-17. The value of exports reached a record level of $3.4 billion (up 9 per cent from the previous year), while the value of imports fell to $5.3 billion (down 4 per cent from a record level in the previous year). • China was our largest trading partner in 2016-17 for wood products, accounting for over a quarter of Australia's total wood product imports, nearly half of total wood product exports and the majority of total wood products export growth over the year.

Online Retail Transaction Data

UK Online Retail Sales and Customer Transaction Data

By UCI [source]

About this dataset

Comprehensive Dataset on Online Retail Sales and Customer Data

Welcome to this comprehensive dataset offering a wide array of information related to online retail sales. This data set provides an in-depth look at transactions, product details, and customer information documented by an online retail company based in the UK. The scope of the data spans vastly, from granular details about each product sold to extensive customer data sets from different countries.

This transnational data set is a treasure trove of vital business insights as it meticulously catalogues all the transactions that happened during its span. It houses rich transactional records curated by a renowned non-store online retail company based in the UK known for selling unique all-occasion gifts. A considerable portion of its clientele includes wholesalers; ergo, this dataset can prove instrumental for companies looking for patterns or studying purchasing trends among such businesses.

The available attributes within this dataset offer valuable pieces of information:

• InvoiceNo: This attribute refers to invoice numbers that are six-digit integral numbers uniquely assigned to every transaction logged in this system. Transactions marked with 'c' at the beginning signify cancellations - adding yet another dimension for purchase pattern analysis.

• StockCode: Stock Code corresponds with specific items as they're represented within the inventory system via 5-digit integral numbers; these allow easy identification and distinction between products.

• Description: This refers to product names, giving users qualitative knowledge about what kind of items are being bought and sold frequently.

• Quantity: These figures ascertain the volume of each product per transaction – important figures that can help understand buying trends better.

• InvoiceDate: Invoice Dates detail when each transaction was generated down to precise timestamps – invaluable when conducting time-based trend analysis or segmentation studies.

• UnitPrice: Unit prices represent how much each unit retails at — crucial for revenue calculations or cost-related analyses.

Finally,

• Country: This locational attribute shows where each customer hails from, adding geographical segmentation to your data investigation toolkit.

This dataset was originally collated by Dr Daqing Chen, Director of the Public Analytics group based at the School of Engineering, London South Bank University. His research studies and business cases with this dataset have been published in various papers contributing to establishing a solid theoretical basis for direct, data and digital marketing strategies.

Access to such records can ensure enriching explorations or formulating insightful hypotheses about consumer behavior patterns among wholesalers. Whether it's managing inventory or studying transactional trends over time or spotting cancellation patterns - this dataset is apt for multiple forms of retail analysis

How to use the dataset

1. Sales Analysis:

Sales data forms the backbone of this dataset, and it allows users to delve into various aspects of sales performance. You can use the Quantity and UnitPrice fields to calculate metrics like revenue, and further combine it with InvoiceNo information to understand sales over individual transactions.

2. Product Analysis:

Each product in this dataset comes with its unique identifier (StockCode) and its name (Description). You could analyse which products are most popular based on Quantity sold or look at popularity per transaction by considering both Quantity and InvoiceNo.

3. Customer Segmentation:

If you associated specific business logic onto the transactions (such as calculating total amounts), then you could use standard machine learning methods or even RFM (Recency, Frequency, Monetary) segmentation techniques combining it with 'CustomerID' for your customer base to understand customer behavior better. Concatenating invoice numbers (which stand for separate transactions) per client will give insights about your clients as well.

4. Geographical Analysis:

The Country column enables analysts to study purchase patterns across different geographical locations.

Practical applications

Understand what products sell best where - It can help drive tailored marketing strategies. Anomalies detection – Identify unusual behaviors that might lead frau...

Asos E-Commerce Dataset - 30,845 products, text classification dataset

Using web scraping, we collected information on over 30,845 clothing items from the Asos website. The dataset can be applied in E-commerce analytics in the fashion industry. The dataset is similar to SheIn E-Commerce Dataset.

💴 For Commercial Usage: To discuss your requirements, learn about the price and buy the dataset, leave a request on our website to buy the dataset

Dataset Info

For each item, we extracted:

• url - link to the item on the website
• name - item's name
• size - sizes available on the website
• category - product's category
• price - item's price
• color - item's color
• SKU - unique identifier of the item
• date - date of web scraping; for all items - March 11, 2023
• description - additional description, including product's brand, composition, and care instructions, in JSON format
• images - photographs from the item description

🧩 This is just an example of the data. Leave a request here to learn more

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keywords: web scraping dataset, dataset marketplace, web scraping data, e-commerce dataset, e-commerce marketplace, e-commerce marketplace scraping dataset, e-commerce sales dataset, ecommerce clothing site, e-commerce user behavior dataset, e-commerce text dataset, e-commerce product dataset, text dataset, ratings, product recommendation, text classification, text mining dataset, text data

This is a case study for the company to improve sales

Business Goal
Date: 2023/09/15
Dataset: Sales quantity of a certain brand from January to December 2022 and from January to September 2023.

Please describe what you observe (no specific presentation format required). Among your observations, identify at least three valuable insights and explain why you consider them valuable.
If more resources were available to you (including time, information, etc.), what would you need, and what more could you achieve?

Metadata of the file Data Period: January 2022 - September 2023 Data Fields: - item - store_id - sales of each month

Metadata of the file Data Period: January 2022 - September 2023 Data Fields: - item - store_id - sales of each month

Sample question & answer 1. Product insights: identify the product sales analysis, such as BCG matrix 2. Store insights: identify the sales performance of the sales 3. Supply chain insights: identify the demand 4. Time series forecasting: identify tread, seasonality

Feel free to leave comments on the discussion. I'd appreciate your upvote if you find my dataset useful! 😀

Gross domestic product is the market value of goods and services produced by labor and property in the United States. The U.S. Bureau of Economic Analysis estimates GDP for each quarter and releases new statistics every month. Quarterly GDP data are seasonally adjusted at annual rates.

This dataset contains Sales of Oil & Gas Products by Product and Emirate for- 2015-2020. Data from Federal Competitiveness and Statistics Authority. Follow datasource.kapsarc.org for timely data to advance energy economics research.

Statistics illustrates consumption, production, prices, and trade of Stationery product in Israel from 2007 to 2024.

Gross National Product in Slovenia increased to 66968 EUR Million in 2024 from 63090 EUR Million in 2023. This dataset provides - Slovenia Gross National Product - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Statistics illustrates consumption, production, prices, and trade of Stationery product in Latin America and the Caribbean from Jan 2019 to Oct 2025.

Release Date: 2015-06-16...Table Name.Manufacturing: Subject Series: Industry-Product Analysis: Detailed Miscellaneous Receipts by Industry: 2012....ReleaseSchedule.Data are scheduled for release in June 2015.....Universe.The Universe includes all establishments classified in manufacturing sectors 31-33 with one or more paid employee at any time during the year.....GeographyCoverage.Data are shown at the U.S. level.....IndustryCoverage.Data are shown at the six-digit North American Industry Classification System (NAICS) level and seven-digit product class level based on NAICS.....Data ItemsandOtherIdentifyingRecords.This file contains data on:..Products shipments value ($1,000). .......Sort Order.Data are presented in ascending NAICS code sequence by miscellaneous receipts.......FTP Download..Download the entire EC1231SX3 table at: https://www2.census.gov/econ2012/EC/sector31/EC1231SX3.zip.....ContactInformation. U.S. Census Bureau, Economy Wide Statistics Division. Data User Outreach and Education Staff . Washington, DC 20233-6900. Tel: (800) 242-2184 . Tel: (301) 763-5154. ewd.outreach@census.gov. ..For information on economic census geographies, including changes for 2012, see the economic census Help Center..Data based on the 2012 Economic Census. For information on confidentiality protection, sampling error, nonsampling error, and definitions, see Methodology. Data in this file represent those available when this file was created; data may not be available for all NAICS industries..Symbols:D - Withheld to avoid disclosing data for individual companies; data are included in higher level totalsN - Not available or not comparableFor a complete list of all economic programs symbols, see the Symbols Glossary.Source: U.S. Census Bureau, 2012 Economic Census.Note: The data in this file are based on the 2012 Economic Census. To maintain confidentiality, the U.S. Census Bureau suppresses data to protect the identity of any business or individual. The census results in this file contain nonsampling error. Data users who create their own estimates using data from this file should cite the U.S. Census Bureau as the source of the original data only. For the full technical documentation, see Methodology link in above headnote.

Statistics illustrates consumption, production, prices, and trade of Stationery product in Bhutan from 2007 to 2024.

This presentation highlights Statistics Canada products, such as Canadian Business Patterns, Canada Food Stats, Taxfiler Data, Employment Dynamics (ED), and Sub-Provincial Employment Dynamics (SPED). (Note: Data associated with this presentation is available on the DLI FTP site under folder 1873-211.)

Firm-product data provide information for various research questions in international trade or innovation economics. However, working with these data require harmonizing product classifications consistently over time to avoid internal validity issues. Harmonization is required because classification systems like the EU classifications Combined Nomenclature (CN) for goods or the Prodcom for the production of manufactured goods undergo several changes. We have addressed this problem and developed an approach to harmonize product codes. This approach tracks product codes from 1995 to 2022 for CN and 2001 to 2021 for Prodcom. Additional years can be conveniently added. We provide the harmonized product codes for CN and Prodcom in the selected period's last (or first) year. Our approach is summarized in an open-source R package so that researchers can consistently track product codes for their selected period. We demonstrate the importance of harmonization using the micro-level trade data for Croatia as a case study. Our approach facilitates working with firm-product data, allowing the analysis of important research questions.

China Industrial Enterprise: Product Sales Ratio data was reported at 98.100 % in 2018. This stayed constant from the previous number of 98.100 % for 2017. China Industrial Enterprise: Product Sales Ratio data is updated yearly, averaging 98.010 % from Dec 1999 (Median) to 2018, with 20 observations. The data reached an all-time high of 98.180 % in 2006 and a record low of 97.150 % in 1999. China Industrial Enterprise: Product Sales Ratio data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Industrial Sector – Table CN.BF: Industrial Financial Data.

The Product Data Management (PDM) Software market has emerged as a critical component in modern enterprises, facilitating the efficient management of product-related data throughout the entire lifecycle. This software suite serves industries ranging from manufacturing to retail, allowing organizations to create, sto

🔍 TSMPD-US-Public v1.0 – Small Merchant Product Dataset (Public Sample)

This dataset provides a public sample of structured product listings from 355,722 verified small U.S.-based merchants, containing:

~3.2 million product records

Text fields only (vendor, title, description, tags, category, last_updated)

No images or variant (SKU) data

It is designed for LLM research, product grounding, semantic commerce, and agent training.

🔐 Looking for the full dataset?

The Partner/Reserve version includes:

All products per merchant (11.9M+ total)

Product variants (67M SKUs)

Product images (54M URLs)

Store domains and product URLs

Dataset watermark for traceability

📬 To request access: email jim@tokuhn.com

This extended version is offered under a commercial or research license to ensure fair and traceable use in LLM applications.

Bureau of Labor Statistics Industrialized Countries - Import Price Index by Origin (NAICS): Beverage and Tobacco Product Manufacturing for Industrialized Countries was 96.30000 Index 2010=100 in August of 2025, according to the United States Federal Reserve. Historically, Bureau of Labor Statistics Industrialized Countries - Import Price Index by Origin (NAICS): Beverage and Tobacco Product Manufacturing for Industrialized Countries reached a record high of 109.50000 in July of 2018 and a record low of 96.30000 in August of 2025. Trading Economics provides the current actual value, an historical data chart and related indicators for Bureau of Labor Statistics Industrialized Countries - Import Price Index by Origin (NAICS): Beverage and Tobacco Product Manufacturing for Industrialized Countries - last updated from the United States Federal Reserve on November of 2025.

View monthly updates and historical trends for Canada Leather and Allied Product Manufacturing Goods or Work in Process. Source: Statistics Canada. Track …