This statistic gives information on the most popular Pinterest categories according to users in the United States as of February 2017. During the survey, it was found that 37 percent of responding Pinterest users liked the clothing and apparel category on the site.

This dataset provides a comprehensive overview of online sales transactions across different product categories. Each row represents a single transaction with detailed information such as the order ID, date, category, product name, quantity sold, unit price, total price, region, and payment method.

Columns:

• Order ID: Unique identifier for each sales order.
• Date:Date of the sales transaction.
• Category:Broad category of the product sold (e.g., Electronics, Home Appliances, Clothing, Books, Beauty Products, Sports).
• Product Name:Specific name or model of the product sold.
• Quantity:Number of units of the product sold in the transaction.
• Unit Price:Price of one unit of the product.
• Total Price: Total revenue generated from the sales transaction (Quantity * Unit Price).
• Region:Geographic region where the transaction occurred (e.g., North America, Europe, Asia).
• Payment Method: Method used for payment (e.g., Credit Card, PayPal, Debit Card).

Insights:

• 1. Analyze sales trends over time to identify seasonal patterns or growth opportunities.
• 2. Explore the popularity of different product categories across regions.
• 3. Investigate the impact of payment methods on sales volume or revenue.
• 4. Identify top-selling products within each category to optimize inventory and marketing strategies.
• 5. Evaluate the performance of specific products or categories in different regions to tailor marketing campaigns accordingly.

Journals & Ranking

An academic journal or research journal is a periodical publication in which research articles relating to a particular academic discipline is published, according to Wikipedia. Currently, there are more than 25,000 peer-reviewed journals that are indexed in citation index databases such as Scopus and Web of Science. These indexes are ranked on the basis of various metrics such as CiteScore, H-index, etc. The metrics are calculated from yearly citation data of the journal. A lot of efforts are given to make a metric that reflects the journal's quality.

Journal Ranking Dataset

This is a comprehensive dataset on the academic journals coving their metadata information as well as citation, metrics, and ranking information. Detailed data on their subject area is also given in this dataset. The dataset is collected from the following indexing databases: - Scimago Journal Ranking - Scopus - Web of Science Master Journal List

The data is collected by scraping and then it was cleaned, details of which can be found in HERE.

Key Features

• Rank: Overall rank of journal (derived from sorted SJR index).
• Title: Name or title of journal.
• OA: Open Access or not.
• Country: Country of origin.
• SJR-index: A citation index calculated by Scimago.
• CiteScore: A citation index calculated by Scopus.
• H-index: Hirsh index, the largest number h such that at least h articles in that journal were cited at least h times each.
• Best Quartile: Top Q-index or quartile a journal has in any subject area.
• Best Categories: Subject areas with top quartile.
• Best Subject Area: Highest ranking subject area.
• Best Subject Rank: Rank of the highest ranking subject area.
• Total Docs.: Total number of documents of the journal.
• Total Docs. 3y: Total number of documents in the past 3 years.
• Total Refs.: Total number of references of the journal.
• Total Cites 3y: Total number of citations in the past 3 years.
• Citable Docs. 3y: Total number of citable documents in the past 3 years.
• Cites/Doc. 2y: Total number of citations divided by the total number of documents in the past 2 years.
• Refs./Doc.: Total number of references divided by the total number of documents.
• Publisher: Name of the publisher company of the journal.
• Core Collection: Web of Science core collection name.
• Coverage: Starting year of coverage.
• Active: Active or inactive.
• In-Press: Articles in press or not.
• ISO Language Code: Three-letter ISO 639 code for language.
• ASJC Codes: All Science Journal Classification codes for the journal.

Rest of the features provide further details on the journal's subject area or category: - Life Sciences: Top level subject area. - Social Sciences: Top level subject area. - Physical Sciences: Top level subject area. - Health Sciences: Top level subject area. - 1000 General: ASJC main category. - 1100 Agricultural and Biological Sciences: ASJC main category. - 1200 Arts and Humanities: ASJC main category. - 1300 Biochemistry, Genetics and Molecular Biology: ASJC main category. - 1400 Business, Management and Accounting: ASJC main category. - 1500 Chemical Engineering: ASJC main category. - 1600 Chemistry: ASJC main category. - 1700 Computer Science: ASJC main category. - 1800 Decision Sciences: ASJC main category. - 1900 Earth and Planetary Sciences: ASJC main category. - 2000 Economics, Econometrics and Finance: ASJC main category. - 2100 Energy: ASJC main category. - 2200 Engineering: ASJC main category. - 2300 Environmental Science: ASJC main category. - 2400 Immunology and Microbiology: ASJC main category. - 2500 Materials Science: ASJC main category. - 2600 Mathematics: ASJC main category. - 2700 Medicine: ASJC main category. - 2800 Neuroscience: ASJC main category. - 2900 Nursing: ASJC main category. - 3000 Pharmacology, Toxicology and Pharmaceutics: ASJC main category. - 3100 Physics and Astronomy: ASJC main category. - 3200 Psychology: ASJC main category. - 3300 Social Sciences: ASJC main category. - 3400 Veterinary: ASJC main category. - 3500 Dentistry: ASJC main category. - 3600 Health Professions: ASJC main category.

In April 2025, the leading category on TikTok Shop in Southeast Asia was beauty and personal care, with a sales share of around ** percent. This was followed by womenswear and underwear, accounting for about ** percent of sales on TikTok Shop in the region.

Foreword

This users dataset is a preview of a much bigger dataset, with lots of related data (product listings of sellers, comments on listed products, etc...).

My Telegram bot will answer your queries and allow you to contact me.

Context

Studying top products requires more than just product listings. You also need to know what sells well and what does not.

Content

This dataset contains product listings as well as products ratings and sales performance, which you would not find in other datasets.

With this, you can finally start to look for correlations and patterns regarding the success of a product and the various components.

Inspiration

• How about trying to validate the established idea of human sensitiveness to price drops ? (discounted price compared to original retail_price)
• You may look for top categories of products so that you know what sells best
• Do bad products sell ? How about the relationship between the quality of a product (ratings) and its success ? Does the price factor into this ?

Collection Methodology

The data comes from the Wish platform. Basically, the products listed in the dataset are those that would appear if you type "summer" in the search field of the platform.

You can browse the Wish website or app to get a feel of the type of information you can get from there and how they are presented. This might give you some ideas and a better understanding.

If you are confused about some columns, you can either look at the column descriptions, browse the Wish website/app, or you can ask in the comments.

The data was scraped with french as settings (hence the presence of some non-ascii latin characters such as « é » and « à ») in the title column.

Features and Columns

The data was scraped in the french localisation (hence some non-ascii latin characters such as « é » and « à ») in the title column.

The title_orig on the other hand contains the original title (the base title) that is displayed by default. When a translation is provided by the seller, it appears in the title column. When the title and title_orig columns are the same, it generally means that the seller did not specify a translation that would be displayed to users with french settings.

A picture is worth a thousand words. In the following screenshot you see some features and how to interpret them.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1488294%2F308810459ae5232399672ba3eef228ef%2Fannotated-search-results-wish-website.jpg?generation=1598785563117062&alt=media" alt="Search results page and columns explained">

According to a survey conducted in Southeast Asia from June to August 2024, around ** percent of respondents reported having purchased beauty items due to recommendations from an influencer or celebrity. In comparison, around ** percent of respondents said they had purchased products in the travel category based on an influencer or celebrity's recommendation.

Products and rating of e-commerce website NewChic.com

Foreword

This dataset is a preview of a bigger dataset.

My Telegram bot will answer your queries and allow you to contact me. Whether you want updated data (2022), english listings or a custom requests, you can reach out through the bot.

Content

Product records and their popularity / interactions with customers.

Data snapshot on early august 2020.

Inspiration

Among other opportunities, you may use this data for ...

RESEARCH opportunities - to find trends of products that do not age - top products - user tastes - study the segmentation of user tastes and link that to the population of visitors

BUSINESS opportunities

• Finding top products to sell

  • => optimise your stock by picking styles people want

• Finding top categories of products

  • which niches might sell the most products ? That way you may focus your investments and not waste funds on dead categories.

User Feedback Dataset from the Top 15 Downloaded Mobile Applications

This dataset comprises user feedback data collected from 15 globally acclaimed mobile applications, spanning diverse categories. The included applications are among the most downloaded worldwide, providing a rich and varied source for analysis. The dataset is particularly suitable for Natural Language Processing (NLP) applications, such as text classification and topic modeling.

List of Included Applications:

• TikTok
• Instagram
• Facebook
• WhatsApp
• Telegram
• Zoom
• Snapchat
• Facebook Messenger
• Capcut
• Spotify
• YouTube
• HBO Max
• Cash App
• Subway Surfers
• Roblox

Data Columns and Descriptions:

• review_id: Unique identifiers for each user feedback/application review.
• content: User-generated feedback/review in text format.
• score: Rating or star given by the user.
• TU_count: Number of likes/thumbs up (TU) received for the review.
• app_id: Unique identifier for each application.
• app_name: Name of the application.
• RC_ver: Version of the app when the review was created (RC).

Terms of Use:

This dataset is open access for scientific research and non-commercial purposes. Users are required to acknowledge the authors' work and, in the case of scientific publication, cite the most appropriate reference:

1.Paper

M. H. Asnawi, A. A. Pravitasari, T. Herawan, and T. Hendrawati, "The Combination of Contextualized Topic Model and MPNet for User Feedback Topic Modeling," in IEEE Access, vol. 11, pp. 130272-130286, 2023, doi: https://doi.org/10.1109/ACCESS.2023.3332644

2.Dataset

Asnawi, M. H., Pravitasari, A. A., Herawan, T., & hendrawati, T. (2023). User Feedback Dataset from the Top 15 Downloaded Mobile Applications [Data set]. In The Combination of Contextualized Topic Model and MPNet for User Feedback Topic Modeling (1.0.0, Vol. 11, pp. 130272–130286). Zenodo. https://doi.org/10.5281/zenodo.10204232

Researchers and analysts are encouraged to explore this dataset for insights into user sentiments, preferences, and trends across these top mobile applications. If you have any questions or need further information, feel free to contact the dataset authors.

The dataset contains counts for the Top Five inpatient diagnosis groups based on Major Diagnostic Categories (MDCs) from the Patient Discharge Data (PDD) for each California hospital. Each MDC corresponds to a major organ system (e.g., Respiratory System, Circulatory System, Digestive System) rather than a specific disease (e.g., cancer, sepsis). The MDCs are also generally associated with a particular medical specialty. Therefore, the MDCs can be used to help identify what types of health care specialists are needed at each facility. For instance, a facility with “Circulatory System, Disease and Disorders” as one of their Top Five MDC diagnosis groups is more likely to have a greater need for cardiac specialists. The data will be updated on an annual basis.

Top five product categories imported by Brazil between January and November 2025, including HS codes, import value in USD, share percentage, and total quantity.

Dataset Card for Dataset Name

  Dataset Summary

This dataset is a subset of Kaggle's Google Landmark Recognition 2021 competition with only the categories with more than 500 images. https://www.kaggle.com/competitions/landmark-recognition-2021/data The dataset consists of a total of 45579 224x224 color images in 51 categories.

  Languages

English

  Dataset Structure





  Data Fields

landmark_id: Int - Numeric identifier of the category category :… See the full description on the dataset page: https://huggingface.co/datasets/pemujo/GLDv2_Top_51_Categories.

Social buzz is a social media platform that help you to interact with other people. In this you can reacts you people's post with many way including liking it. In this i found out the top 5 categories that are most searched/used by people in this platform.

Top Selling Udemy Courses in more than one category , about 15000 course

Dataset Columns - title : course title - image : course image url - url : course url - subtitle : course simple description - avg_rate : the course avg rate - reviews_count : how many reviews on each course - duration : course duration - lecture : how many lectures in the course - Level : Course Level - Instructor : Course Instructor - Discount Price : course price after discount - Original Price : the original course price before discount - flag: the flag on the course like (Best Selling , Hot) - Category : Course category

In 2025, ** percent of respondents in Poland stated that they bought clothing and accessories via internet retailers. The second most popular online shopping category was “Footwear”; ** percent of respondents bought products from this category.

Major product categories Brazil imported from the United States between January and November 2025, ranked by import value and share percentage.

Dataset Title: Setopati News Categories Dataset

Description: This dataset contains news articles scraped from the Setopati website, focusing on a variety of topics related to current affairs and news categories. The data includes three columns: category, link, and news, which provide the classification of each news item, the URL of the news article, and the text content of the article, respectively.

Columns:

category: This column contains the news category, which can include topics such as political, entertainment, social, market, sports, and more.

link: This column contains the URL links to the individual news articles on the Setopati website.

news: This column contains the full text or summary of the news article.

Categories include:

International

Blog

Entertainment

Market

Political

Social

Sports

View

The dataset provides an insightful collection of articles covering diverse topics, making it suitable for various NLP and text analysis projects. You can use it for tasks such as topic classification, text summarization, sentiment analysis, and much more.

Source: Scraped from Setopati, a leading news and political website in Nepal.

Usage: This dataset can be used for educational purposes, research, or to build models that analyze news data. Whether you're exploring sentiment analysis, article categorization, or other text-based analysis, this dataset provides a solid foundation for such tasks.

This dataset offers a comprehensive overview of the top animes of 2024, and is useful for building recommendation systems, visualizing trends in anime popularity and score, predicting scores and popularity, and such.

Contents

The dataset contains 22 features:

• Score: The rating or score assigned to each anime title.
• Popularity: Measure of how popular each anime is among viewers.
• Rank: Ranking of each anime title within the dataset.
• Members: The number of members or viewers associated with each anime.
• Description: A brief overview or summary of the plot and themes of each anime.
• Synonyms: Alternative titles or synonyms used for each anime.
• Japanese Title: Original title of the anime in Japanese.
• English Title: English-translated title of the anime.
• Type: Classification of anime type (e.g., TV series, movie, OVA, etc.).
• Eps: Total number of episodes in each anime series.
• Status: Current status of the anime (e.g., ongoing, completed, etc.).
• Aired: Date range of when the anime was aired.
• Premiered: Date when the anime premiered for the first time.
• Broadcast: Information about the broadcasting platform or channel.
• Producers: Companies or studios involved in producing the anime.
• Licensors: Organizations or companies holding the licensing rights for the anime.
• Studios: Animation studios responsible for producing the anime.
• Source: Original source material for the anime (e.g., manga, novel, original).
• Genres: Categories or genres that the anime belongs to.
• Demographic: Target demographic audience for the anime (e.g., shounen, shoujo, seinen, josei).
• Duration: Duration of each episode or movie.
• Rating: Content rating assigned to each anime (e.g., G, PG, PG-13, R).

Acknowledgements

All of the information in this dataset has been gathered by scraping the MyAnimeList website, and is available under the Creative Commons License.

Cover Photo by: Playground.ai

Leading product categories Brazil imported from China between January and November 2025, including import value, share percentage, and HS codes.

The variety of products that can be purchased online is continuously growing. Among UK consumers the two most popular categories for online purchases are ******** and *****. ** percent and ** percent of consumers respectively chose these answers in our representative online survey. The survey was conducted online among 6,174 respondents in the UK, in 2025. Looking to gain valuable insights about customers of online shops across the globe? Check out our reports about consumers of online shops worldwide. These reports offer the readers a comprehensive overview of customers of eCommerce brands: who they are; what they like; what they think; and how to reach them.

Top product categories exported by Malaysia in 2025 including export value and share percentage.