Access a comprehensive dataset of over 240,000 shoe product listings directly from Amazon UK. This dataset is ideal for researchers, e-commerce analysts, and AI developers looking to explore pricing trends, brand performance, product features, or build training data for retail-focused models.

All data is neatly packaged in a downloadable ZIP archive containing files in JSON format, making it easy to integrate with your preferred analytics or database tools.

🔎 Use Cases:

• Price and discount trend analysis

• Competitor benchmarking

• Product attribute extraction and modeling

• AI/ML training datasets (e.g., shoe recommendation systems)

• Retail assortment planning

🔄 Updates & Delivery:

This dataset is available as a static snapshot, but you can request weekly or monthly updates through the Crawl Feeds dashboard. Upon purchase, the data will be bundled and delivered via a direct download link.

Photos of the soles of 160 pairs of shoes (“Nike Winflo 4” or “Adidas Seeley”; 4 sizes each), with associated measurements and data about shoe wear, surfaces, and wearers. The photos are divided up into 6 zip files.Each pair of shoes was worn for at least 10,000 steps per week over a 6-month period, with multiple measurements of the shoe soles taken initially and during three check-in periods spaced at approximately 5 week intervals.The images are accompanied by 3 CSV files describing the shoes, visit (information collected from surveys along with the shoes), and individual images. The codebooks contain descriptions of the variables in each of the CSV files as well as a more extensive description of the file naming scheme outlined in the README.These files can be used to examine wear pattern development, to verify the presence of randomly acquired characteristics, and to develop algorithms which match photos of shoe soles to the corresponding prints from those soles.

This dataset contains information about the sales of shoes in a particular region. The data includes information on the brand, model, type of shoe, gender, size, color, material, and price.

Column Details

Brand: The brand of the shoe, such as Nike, Adidas, or Reebok.

Model: The specific model name or number of the shoe, such as Air Jordan 1, Ultra Boost 21, or Classic Leather.

Type: The type of shoe, such as running, casual, or skate. This column describes the intended use or function of the shoe.

Gender: The gender the shoe is designed for, such as men, women, or unisex. This column specifies the target demographic for the shoe.

Size: The size of the shoe, using US sizing. This column indicates the length of the shoe in inches or centimeters.

Color: The color of the shoe's exterior. This column describes the predominant color or color combination of the shoe.

Material: The primary material of the shoe, such as leather, mesh, or suede. This column indicates the material that comprises the majority of the shoe's construction.

Price: The price of the shoe, in US dollars. This column specifies the cost of purchasing the shoe.

** The purpose of creating this dataset is solely for educational use, and any commercial use is strictly prohibited and this dataset was large language models generated and not collected from actual data sources.

cover image: https://pin.it/6Eb04Gf

CN: Leather Shoes: Product Inventory data was reported at 14.113 RMB bn in Oct 2015. This records an increase from the previous number of 13.747 RMB bn for Sep 2015. CN: Leather Shoes: Product Inventory data is updated monthly, averaging 10.000 RMB bn from Dec 2001 (Median) to Oct 2015, with 99 observations. The data reached an all-time high of 14.552 RMB bn in Dec 2014 and a record low of 3.625 RMB bn in Dec 2001. CN: Leather Shoes: Product Inventory 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.BIG: Shoes: Leather Shoes.

This is the dataset for a research project called the Virtual Shoe Salon. The Virtual Shoe Salon is a project that focuses on young people, aged 18-24 years, and their sense of self as it is communicated and experienced through their footwear choices. The participants were asked to take photographs of their shoes and write a short narrative response to what the shoes mean to them and how the shoes make them feel as an individual. This dataset includes photographs, along with each individual's written narratives and demographic information.

CN: Leather Shoes: YoY: Total Asset data was reported at 2.620 % in Oct 2015. This records a decrease from the previous number of 4.401 % for Sep 2015. CN: Leather Shoes: YoY: Total Asset data is updated monthly, averaging 13.589 % from Jan 2006 (Median) to Oct 2015, with 89 observations. The data reached an all-time high of 36.548 % in May 2012 and a record low of 2.620 % in Oct 2015. CN: Leather Shoes: YoY: Total Asset 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.BIG: Shoes: Leather Shoes.

19606 Global import shipment records of Sport Shoes with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

389684 Global import shipment records of Shoes with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

We present ShoeVibe, a dataset of footstep-induced floor vibrations from 9 people, each walks with 8 different types of shoes including barefoot. Floor vibrations induced by human footsteps contain rich information, such as a person’s identity and gait, characterized by walking speed, balance, symmetry, and so on, enabling personalized health monitoring in smart buildings. However, footstep-induced structural vibrations not only depend on human walking patterns but also on a person’s shoes as the footstep force transmits from the foot to the floor. This co-dependency leads to difficulty in identifying the owner of the footsteps when multiple people share the same space and each person has multiple pairs of footwear. To address this challenge, the ShoeVibe dataset aims to study the effect of shoes on floor vibrations induced by human walking.

Please cite this dataset as:

Yiwen Dong, Haochen Sun, Ruizhi Wang, and Hae Young Noh. ShoeVibe: A Human-Induced Floor Vibration Dataset with 8 Different Shoe Types. Zenodo. 2024. https://zenodo.org/records/14575148

The associated paper published using this dataset is:

Yiwen Dong, Haochen Sun, Ruizhi Wang, and Hae Young Noh "Robust person identification across various shoe types using footstep-induced structural vibrations", Proc. SPIE 12949, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2024, 129491B (9 May 2024); https://doi.org/10.1117/12.3010554

605 Global import shipment records of Nike Shoes with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

Images of shoe prints from 160 pairs of shoes (“Nike Winflo 4” or “Adidas Seeley”; 4 sizes each) made using fingerprint powder on clear adhesive film (contact paper), with associated measurements and data about shoe wear, surfaces, and wearers.Each pair of shoes was worn for at least 10,000 steps per week over a 6-month period, with multiple measurements of the shoe soles taken initially and during three check-in periods spaced at approximately 5 week intervals. The images are accompanied by 3 CSV files describing the shoes, visit (information collected from surveys along with the shoes), and individual images. The codebooks contain descriptions of the variables in each of the CSV files as well as a more extensive description of the file naming scheme outlined in the README. These files can be used to examine wear pattern development, to look for the presence of identifying characteristics among shoes with similar features, and to develop algorithms for matching shoes based on individualizing characteristics. The film and powder prints are likely to contain the most detail of any of the collection methods used in this project and are the most suitable for research on randomly acquired characteristics.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

ABSTRACT: Objective: this article aims to analyze how footwear companies can innovate frugally in convergence with the principles of sustainability. Theoretical framework: this objective was achieved based on studies that observe the relationship between sustainability and frugal innovation, with an emphasis on bricolage strategies, from a perspective of applicability in the footwear industry, as it is a sector with high environmental impact. Method: the research uses design science research, and made use of a field diary, for greater depth of understanding of the footwear environment, as well as secondary analysis of documents on the internet and transcribed interviews with the seven experts. Results: the study managed to propose two action artifacts, one of them a frugal and sustainable sneaker artifact, which reuses production material to produce cheap, quality footwear, that would otherwise be discarded at a financial loss, and, as a second action artifact, it realized the need for collaborative and constant action between universities and companies in the search for frugal and sustainable solutions, which can benefit both companies, through profitable results, and the university, through longitudinal research and production of articles. Conclusions: the footwear industry yearns for low-cost (frugal) innovations that are at the same time sustainable, and demonstrates capacity and interest in meeting social and political demands for sustainable actions; in order to achieve this, greater proximity to the university presents itself as a collaboration that can produce benefits for both, with side effects of positive impact on environmental initiatives, society, and government policies. Keywords: bricolage; frugal sneakers; university and business collaboration; artifacts; footwear industry.

China Export: Leather Shoes data was reported at 9.314 USD bn in 2018. This records an increase from the previous number of 9.301 USD bn for 2017. China Export: Leather Shoes data is updated yearly, averaging 4.369 USD bn from Dec 1984 (Median) to 2018, with 35 observations. The data reached an all-time high of 13.750 USD bn in 2014 and a record low of 39.360 USD mn in 1985. China Export: Leather Shoes data remains active status in CEIC and is reported by General Administration of Customs. The data is categorized under Global Database’s China – Table CN.JA: Trade: Annual.

Explore detailed Shoes import data of Olem Shoe Corp in the USA—product details, price, quantity, origin countries, and US ports.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

Customs records of are available for SHOES COMFORTABLE INTERNATIONAL CO. Learn about its Importer, supply capabilities and the countries to which it supplies goods

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.