Company Datasets for valuable business insights!

Discover new business prospects, identify investment opportunities, track competitor performance, and streamline your sales efforts with comprehensive Company Datasets.

These datasets are sourced from top industry providers, ensuring you have access to high-quality information:

• Owler: Gain valuable business insights and competitive intelligence. -AngelList: Receive fresh startup data transformed into actionable insights. -CrunchBase: Access clean, parsed, and ready-to-use business data from private and public companies. -Craft.co: Make data-informed business decisions with Craft.co's company datasets. -Product Hunt: Harness the Product Hunt dataset, a leader in curating the best new products.

We provide fresh and ready-to-use company data, eliminating the need for complex scraping and parsing. Our data includes crucial details such as:

• Company name;
• Size;
• Founding date;
• Location;
• Industry;
• Revenue;
• Employee count;
• Competitors.

You can choose your preferred data delivery method, including various storage options, delivery frequency, and input/output formats.

Receive datasets in CSV, JSON, and other formats, with storage options like AWS S3 and Google Cloud Storage. Opt for one-time, monthly, quarterly, or bi-annual data delivery.

With Oxylabs Datasets, you can count on:

• Fresh and accurate data collected and parsed by our expert web scraping team.
• Time and resource savings, allowing you to focus on data analysis and achieving your business goals.
• A customized approach tailored to your specific business needs.
• Legal compliance in line with GDPR and CCPA standards, thanks to our membership in the Ethical Web Data Collection Initiative.

Pricing Options:

Standard Datasets: choose from various ready-to-use datasets with standardized data schemas, priced from $1,000/month.

Custom Datasets: Tailor datasets from any public web domain to your unique business needs. Contact our sales team for custom pricing.

Experience a seamless journey with Oxylabs:

• Understanding your data needs: We work closely to understand your business nature and daily operations, defining your unique data requirements.
• Developing a customized solution: Our experts create a custom framework to extract public data using our in-house web scraping infrastructure.
• Delivering data sample: We provide a sample for your feedback on data quality and the entire delivery process.
• Continuous data delivery: We continuously collect public data and deliver custom datasets per the agreed frequency.

Unlock the power of data with Oxylabs' Company Datasets and supercharge your business insights today!

Context

A fictional dataset for exploratory data analysis (EDA) and to test simple prediction models.

This toy dataset features 150000 rows and 6 columns.

Columns

Note: All data is fictional. The data has been generated so that their distributions are convenient for statistical analysis.

Number: A simple index number for each row

City: The location of a person (Dallas, New York City, Los Angeles, Mountain View, Boston, Washington D.C., San Diego and Austin)

Gender: Gender of a person (Male or Female)

Age: The age of a person (Ranging from 25 to 65 years)

Income: Annual income of a person (Ranging from -674 to 177175)

Illness: Is the person Ill? (Yes or No)

Acknowledgements

Stock photo by Mika Baumeister on Unsplash.

This book is written for statisticians, data analysts, programmers, researchers, teachers, students, professionals, and general consumers on how to perform different types of statistical data analysis for research purposes using the R programming language. R is an open-source software and object-oriented programming language with a development environment (IDE) called RStudio for computing statistics and graphical displays through data manipulation, modelling, and calculation. R packages and supported libraries provides a wide range of functions for programming and analyzing of data. Unlike many of the existing statistical softwares, R has the added benefit of allowing the users to write more efficient codes by using command-line scripting and vectors. It has several built-in functions and libraries that are extensible and allows the users to define their own (customized) functions on how they expect the program to behave while handling the data, which can also be stored in the simple object system.For all intents and purposes, this book serves as both textbook and manual for R statistics particularly in academic research, data analytics, and computer programming targeted to help inform and guide the work of the R users or statisticians. It provides information about different types of statistical data analysis and methods, and the best scenarios for use of each case in R. It gives a hands-on step-by-step practical guide on how to identify and conduct the different parametric and non-parametric procedures. This includes a description of the different conditions or assumptions that are necessary for performing the various statistical methods or tests, and how to understand the results of the methods. The book also covers the different data formats and sources, and how to test for reliability and validity of the available datasets. Different research experiments, case scenarios and examples are explained in this book. It is the first book to provide a comprehensive description and step-by-step practical hands-on guide to carrying out the different types of statistical analysis in R particularly for research purposes with examples. Ranging from how to import and store datasets in R as Objects, how to code and call the methods or functions for manipulating the datasets or objects, factorization, and vectorization, to better reasoning, interpretation, and storage of the results for future use, and graphical visualizations and representations. Thus, congruence of Statistics and Computer programming for Research.

Looking for a free Walmart product dataset? The Walmart Products Free Dataset delivers a ready-to-use ecommerce product data CSV containing ~2,100 verified product records from Walmart.com. It includes vital details like product titles, prices, categories, brand info, availability, and descriptions — perfect for data analysis, price comparison, market research, or building machine-learning models.

Key Features

Complete Product Metadata: Each entry includes URL, title, brand, SKU, price, currency, description, availability, delivery method, average rating, total ratings, image links, unique ID, and timestamp.

CSV Format, Ready to Use: Download instantly - no need for scraping, cleaning or formatting.

Good for E-commerce Research & ML: Ideal for product cataloging, price tracking, demand forecasting, recommendation systems, or data-driven projects.

Free & Easy Access: Priced at USD $0.0, making it a great starting point for developers, data analysts or students.

Who Benefits?

• Data analysts & researchers exploring e-commerce trends or product catalog data.
• Developers & data scientists building price-comparison tools, recommendation engines or ML models.
• E-commerce strategists/marketers need product metadata for competitive analysis or market research.
• Students/hobbyists needing a free dataset for learning or demo projects.

Why Use This Dataset Instead of Manual Scraping?

• Time-saving: No need to write scrapers or deal with rate limits.
• Clean, structured data: All records are verified and already formatted in CSV, saving hours of cleaning.
• Risk-free: Avoid Terms-of-Service issues or IP blocks that come with manual scraping.
  Instant access: Free and immediately downloadable.

A little paragraph from one real dataset, with a few little changes to protect students' private information. Permissions are given.

Goals

You are going to help teachers with only the data: 1. Prediction: To tell what makes a brilliant student who can apply for a graduate school, whether abroad or not. 2. Application: To help those who fails to apply for a graduate school with advice in job searching.

Tips

1. Educational data may have subtle structures, hierarchies and heterogeneity are probably involved. Simple regressions can hardly make any difference. Also, you should keep an eye on the collinearity in some indicators collected by teachers who have already forgot statistics.
2. Not all students are free to choose to apply for a graduate school, but some were born with privileges.
3. Some of the students are trying (or planning to try) to apply for a graduate school for years, you should be responsible to give advice accurately under their circumstances

About the Data

Some of the original structure are deleted or censored. For those are left: Basic data like: - ID - class: categorical, initially students were divided into 2 classes, yet teachers suspect that of different classes students may performance significant differently. - gender - race: categorical and censored - GPA: real numbers, float

Some teachers assume that scores of math curriculums can represent one's likelihood perfectly: - Algebra: real numbers, Advanced Algebra - ......

Some assume that background of students can affect their choices and likelihood significantly, which are all censored as: - from1: students' home locations - from2: a probably bad indicator for preference on mathematics - from 3: how did students apply for this university (undergraduate) - from4: a probably bad indicator for family background. 0 with more wealth, 4 with more poverty

The final indicator y: - 0, one fails to apply for the graduate school, who may apply again or search jobs in the future - 1, success, inland - 2, success, abroad

Supply chain analytics is a valuable part of data-driven decision-making in various industries such as manufacturing, retail, healthcare, and logistics. It is the process of collecting, analyzing and interpreting data related to the movement of products and services from suppliers to customers.

Product Review Datasets: Uncover user sentiment

Harness the power of Product Review Datasets to understand user sentiment and insights deeply. These datasets are designed to elevate your brand and product feature analysis, help you evaluate your competitive stance, and assess investment risks.

Data sources:

• Trustpilot: datasets encompassing general consumer reviews and ratings across various businesses, products, and services.

Leave the data collection challenges to us and dive straight into market insights with clean, structured, and actionable data, including:

• Product name;
• Product category;
• Number of ratings;
• Ratings average;
• Review title;
• Review body;

Choose from multiple data delivery options to suit your needs:

1. Receive data in easy-to-read formats like spreadsheets or structured JSON files.
2. Select your preferred data storage solutions, including SFTP, Webhooks, Google Cloud Storage, AWS S3, and Microsoft Azure Storage.
3. Tailor data delivery frequencies, whether on-demand or per your agreed schedule.

Why choose Oxylabs?

1. Fresh and accurate data: Access organized, structured, and comprehensive data collected by our leading web scraping professionals.

2. Time and resource savings: Concentrate on your core business goals while we efficiently handle the data extraction process at an affordable cost.

3. Adaptable solutions: Share your specific data requirements, and we'll craft a customized data collection approach to meet your objectives.

4. Legal compliance: Partner with a trusted leader in ethical data collection. Oxylabs is a founding member of the Ethical Web Data Collection Initiative, aligning with GDPR and CCPA standards.

Pricing Options:

Standard Datasets: choose from various ready-to-use datasets with standardized data schemas, priced from $1,000/month.

Custom Datasets: Tailor datasets from any public web domain to your unique business needs. Contact our sales team for custom pricing.

Experience a seamless journey with Oxylabs:

• Understanding your data needs: We work closely to understand your business nature and daily operations, defining your unique data requirements.
• Developing a customized solution: Our experts create a custom framework to extract public data using our in-house web scraping infrastructure.
• Delivering data sample: We provide a sample for your feedback on data quality and the entire delivery process.
• Continuous data delivery: We continuously collect public data and deliver custom datasets per the agreed frequency.

Join the ranks of satisfied customers who appreciate our meticulous attention to detail and personalized support. Experience the power of Product Review Datasets today to uncover valuable insights and enhance decision-making.

These four labeled data sets are targeted at ordinal quantification. The goal of quantification is not to predict the label of each individual instance, but the distribution of labels in unlabeled sets of data.

With the scripts provided, you can extract CSV files from the UCI machine learning repository and from OpenML. The ordinal class labels stem from a binning of a continuous regression label.

We complement this data set with the indices of data items that appear in each sample of our evaluation. Hence, you can precisely replicate our samples by drawing the specified data items. The indices stem from two evaluation protocols that are well suited for ordinal quantification. To this end, each row in the files app_val_indices.csv, app_tst_indices.csv, app-oq_val_indices.csv, and app-oq_tst_indices.csv represents one sample.

Our first protocol is the artificial prevalence protocol (APP), where all possible distributions of labels are drawn with an equal probability. The second protocol, APP-OQ, is a variant thereof, where only the smoothest 20% of all APP samples are considered. This variant is targeted at ordinal quantification tasks, where classes are ordered and a similarity of neighboring classes can be assumed.

Usage

You can extract four CSV files through the provided script extract-oq.jl, which is conveniently wrapped in a Makefile. The Project.toml and Manifest.toml specify the Julia package dependencies, similar to a requirements file in Python.

Preliminaries: You have to have a working Julia installation. We have used Julia v1.6.5 in our experiments.

Data Extraction: In your terminal, you can call either

make

(recommended), or

julia --project="." --eval "using Pkg; Pkg.instantiate()"
julia --project="." extract-oq.jl

Outcome: The first row in each CSV file is the header. The first column, named "class_label", is the ordinal class.

Further Reading

Implementation of our experiments: https://github.com/mirkobunse/regularized-oq

Each R script replicates all of the example code from one chapter from the book. All required data for each script are also uploaded, as are all data used in the practice problems at the end of each chapter. The data are drawn from a wide array of sources, so please cite the original work if you ever use any of these data sets for research purposes.

Explore the world of data visualization with this Power BI dataset containing HR Analytics and Sales Analytics datasets. Gain insights, create impactful reports, and craft engaging dashboards using real-world data from HR and sales domains. Sharpen your Power BI skills and uncover valuable data-driven insights with this powerful dataset. Happy analyzing!

This dataset contains data from overground walking trials of 166 subjects with several trials per subject (approximately 2900 trials total).

DATA ORIGINS & LICENSE INFORMATION

The data comes from four existing open datasets collected by others:

Schreiber & Moissenet, A multimodal dataset of human gait at different walking speeds established on injury-free adult participants

article: https://www.nature.com/articles/s41597-019-0124-4

dataset: https://figshare.com/articles/dataset/A_multimodal_dataset_of_human_gait_at_different_walking_speeds/7734767

Fukuchi et al., A public dataset of overground and treadmill walking kinematics and kinetics in healthy individuals

article: https://peerj.com/articles/4640/

dataset: https://figshare.com/articles/dataset/A_public_data_set_of_overground_and_treadmill_walking_kinematics_and_kinetics_of_healthy_individuals/5722711

Horst et al., A public dataset of overground walking kinetics and full-body kinematics in healthy adult individuals

article: https://www.nature.com/articles/s41598-019-38748-8

dataset: https://data.mendeley.com/datasets/svx74xcrjr/3

Camargo et al., A comprehensive, open-source dataset of lower limb biomechanics in multiple conditions of stairs, ramps, and level-ground ambulation and transitions

article: https://www.sciencedirect.com/science/article/pii/S0021929021001007

dataset (3 links): https://data.mendeley.com/datasets/fcgm3chfff/1 https://data.mendeley.com/datasets/k9kvm5tn3f/1 https://data.mendeley.com/datasets/jj3r5f9pnf/1

In this dataset, those datasets are referred to as the Schreiber, Fukuchi, Horst, and Camargo datasets, respectively. The Schreiber, Fukuchi, Horst, and Camargo datasets are licensed under the CC BY 4.0 license (https://creativecommons.org/licenses/by/4.0/).

We have modified the datasets by analyzing the data with musculoskeletal simulations & analysis software (OpenSim). In this dataset, we publish modified data as well as some of the original data.

STRUCTURE OF THE DATASET The dataset contains two kinds of text files: those starting with "predictors_" and those starting with "response_".

Predictors comprise 12 text files, each describing the input (predictor) variables we used to train artifical neural networks to predict knee joint loading peaks. Responses similarly comprise 12 text files, each describing the response (outcome) variables that we trained and evaluated the network on. The file names are of the form "predictors_X" for predictors and "response_X" for responses, where X describes which response (outcome) variable is predicted with them. X can be: - loading_response_both: the maximum of the first peak of stance for the sum of the loading of the medial and lateral compartments - loading_response_lateral: the maximum of the first peak of stance for the loading of the lateral compartment - loading_response_medial: the maximum of the first peak of stance for the loading of the medial compartment - terminal_extension_both: the maximum of the second peak of stance for the sum of the loading of the medial and lateral compartments - terminal_extension_lateral: the maximum of the second peak of stance for the loading of the lateral compartment - terminal_extension_medial: the maximum of the second peak of stance for the loading of the medial compartment - max_peak_both: the maximum of the entire stance phase for the sum of the loading of the medial and lateral compartments - max_peak_lateral: the maximum of the entire stance phase for the loading of the lateral compartment - max_peak_medial: the maximum of the entire stance phase for the loading of the medial compartment - MFR_common: the medial force ratio for the entire stance phase - MFR_LR: the medial force ratio for the first peak of stance - MFR_TE: the medial force ratio for the second peak of stance

The predictor text files are organized as comma-separated values. Each row corresponds to one walking trial. A single subject typically has several trials. The column labels are DATASET_INDEX,SUBJECT_INDEX,KNEE_ADDUCTION,MASS,HEIGHT,BMI,WALKING_SPEED,HEEL_STRIKE_VELOCITY,AGE,GENDER.

DATASET_INDEX describes which original dataset the trial is from, where {1=Schreiber, 2=Fukuchi, 3=Horst, 4=Camargo}

SUBJECT_INDEX is the index of the subject in the original dataset. If you use this column, you will have to rewrite these to avoid duplicates (e.g., several datasets probably have subject "3").

KNEE_ADDUCTION is the knee adduction-abduction angle (positive for adduction, negative for abduction) of the subject in static pose, estimated from motion capture markers.

MASS is the mass of the subject in kilograms

HEIGHT is the height of the subject in millimeters

BMI is the body mass index of the subject

WALKING_SPEED is the mean walking speed of the subject during the trial

HEEL_STRIKE_VELOCITY is the mean of the velocities of the subject's pelvis markers at the instant of heel strike

AGE is the age of the subject in years

GENDER is an integer/boolean where {1=male, 0=female}

The response text files contain one floating-point value per row, describing the knee joint contact force peak for the trial in newtons (or the medial force ratio). Each row corresponds to one walking trial. The rows in predictor and response text files match each other (e.g., row 7 describes the same trial in both predictors_max_peak_medial.txt and response_max_peak_medial.txt).

See our journal article "Prediction of Knee Joint Compartmental Loading Maxima Utilizing Simple Subject Characteristics and Neural Networks" (https://doi.org/10.1007/s10439-023-03278-y) for more information.

Questions & other contacts: jere.lavikainen@uef.fi

The Google Reviews & Ratings Dataset provides businesses with structured insights into customer sentiment, satisfaction, and trends based on reviews from Google. Unlike broad review datasets, this product is location-specific—businesses provide the locations they want to track, and we retrieve as much historical data as possible, with daily updates moving forward.

This dataset enables businesses to monitor brand reputation, analyze consumer feedback, and enhance decision-making with real-world insights. For deeper analysis, optional AI-driven sentiment analysis and review summaries are available on a weekly, monthly, or yearly basis.

Dataset Highlights

• Location-Specific Reviews – Reviews and ratings for the locations you provide.
• Daily Updates – New reviews and rating changes updated automatically.
• Historical Data Access – Retrieve past reviews where available.
• AI Sentiment Analysis (Optional) – Summarized insights by week, month, or year.
• Competitive Benchmarking – Compare performance across selected locations.

Use Cases

• Franchise & Retail Chains – Monitor brand reputation and performance across locations.
• Hospitality & Restaurants – Track guest sentiment and service trends.
• Healthcare & Medical Facilities – Understand patient feedback for specific locations.
• Real Estate & Property Management – Analyze tenant and customer experiences through reviews.
• Market Research & Consumer Insights – Identify trends and analyze feedback patterns across industries.

Data Updates & Delivery

• Update Frequency: Daily
• Data Format: CSV for easy integration
• Delivery: Secure file transfer (SFTP or cloud storage)

Data Fields Include:

• Business Name
• Location Details
• Star Ratings
• Review Text
• Timestamps
• Reviewer Metadata

Optional Add-Ons:

• AI Sentiment Analysis – Aggregate trends by week, month, or year.
• Custom Location Tracking – Tailor the dataset to fit your specific business needs.

Ideal for

• Marketing Teams – Leverage real-world consumer feedback to optimize brand strategy.
• Business Analysts – Use structured review data to track customer sentiment over time.
• Operations & Customer Experience Teams – Identify service issues and opportunities for improvement.
• Competitive Intelligence – Compare locations and benchmark against industry competitors.

Why Choose This Dataset?

• Accurate & Up-to-Date – Daily updates ensure fresh, reliable data.
• Scalable & Customizable – Track only the locations that matter to you.
• Actionable Insights – AI-driven summaries for quick decision-making.
• Easy Integration – Delivered in a structured format for seamless analysis.

By leveraging Google Reviews & Ratings Data, businesses can gain valuable insights into customer sentiment, enhance reputation management, and stay ahead of the competition.

Market Basket Analysis

Market basket analysis with Apriori algorithm

The retailer wants to target customers with suggestions on itemset that a customer is most likely to purchase .I was given dataset contains data of a retailer; the transaction data provides data around all the transactions that have happened over a period of time. Retailer will use result to grove in his industry and provide for customer suggestions on itemset, we be able increase customer engagement and improve customer experience and identify customer behavior. I will solve this problem with use Association Rules type of unsupervised learning technique that checks for the dependency of one data item on another data item.

Introduction

Association Rule is most used when you are planning to build association in different objects in a set. It works when you are planning to find frequent patterns in a transaction database. It can tell you what items do customers frequently buy together and it allows retailer to identify relationships between the items.

An Example of Association Rules

Assume there are 100 customers, 10 of them bought Computer Mouth, 9 bought Mat for Mouse and 8 bought both of them. - bought Computer Mouth => bought Mat for Mouse - support = P(Mouth & Mat) = 8/100 = 0.08 - confidence = support/P(Mat for Mouse) = 0.08/0.09 = 0.89 - lift = confidence/P(Computer Mouth) = 0.89/0.10 = 8.9 This just simple example. In practice, a rule needs the support of several hundred transactions, before it can be considered statistically significant, and datasets often contain thousands or millions of transactions.

Strategy

• Data Import
• Data Understanding and Exploration
• Transformation of the data – so that is ready to be consumed by the association rules algorithm
• Running association rules
• Exploring the rules generated
• Filtering the generated rules
• Visualization of Rule

Dataset Description

• File name: Assignment-1_Data
• List name: retaildata
• File format: . xlsx
• Number of Row: 522065

• Number of Attributes: 7

  • BillNo: 6-digit number assigned to each transaction. Nominal.
  • Itemname: Product name. Nominal.
  • Quantity: The quantities of each product per transaction. Numeric.
  • Date: The day and time when each transaction was generated. Numeric.
  • Price: Product price. Numeric.
  • CustomerID: 5-digit number assigned to each customer. Nominal.
  • Country: Name of the country where each customer resides. Nominal.

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Libraries in R

First, we need to load required libraries. Shortly I describe all libraries.

• arules - Provides the infrastructure for representing, manipulating and analyzing transaction data and patterns (frequent itemsets and association rules).
• arulesViz - Extends package 'arules' with various visualization. techniques for association rules and item-sets. The package also includes several interactive visualizations for rule exploration.
• tidyverse - The tidyverse is an opinionated collection of R packages designed for data science.
• readxl - Read Excel Files in R.
• plyr - Tools for Splitting, Applying and Combining Data.
• ggplot2 - A system for 'declaratively' creating graphics, based on "The Grammar of Graphics". You provide the data, tell 'ggplot2' how to map variables to aesthetics, what graphical primitives to use, and it takes care of the details.
• knitr - Dynamic Report generation in R.
• magrittr- Provides a mechanism for chaining commands with a new forward-pipe operator, %>%. This operator will forward a value, or the result of an expression, into the next function call/expression. There is flexible support for the type of right-hand side expressions.
• dplyr - A fast, consistent tool for working with data frame like objects, both in memory and out of memory.
• tidyverse - This package is designed to make it easy to install and load multiple 'tidyverse' packages in a single step.

https://user-images.githubusercontent.com/91852182/145270210-49c8e1aa-9753-431b-a8d5-99601bc76cb5.png">

Data Pre-processing

Next, we need to upload Assignment-1_Data. xlsx to R to read the dataset.Now we can see our data in R.

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After we will clear our data frame, will remove missing values.

https://user-images.githubusercontent.com/91852182/145270286-05854e1a-2b6c-490e-ab30-9e99e731eacb.png">

To apply Association Rule mining, we need to convert dataframe into transaction data to make all items that are bought together in one invoice will be in ...

1) Data Introduction • The Power BI Sample Data is a financial sample dataset provided for Power BI practice and data visualization exercises that includes a variety of financial metrics and transaction information, including sales, profits, and expenses.

2) Data Utilization (1) Power BI Sample Data has characteristics that: • This dataset consists of numerical and categorical variables such as transaction date, region, product category, sales, profit, and cost, optimized for aggregation, analysis, and visualization. (2) Power BI Sample Data can be used to: • Revenue and Revenue Analysis: Analyze sales and profit data by region, product, and period to understand business performance and trends. • Power BI Dashboard Practice: Utilize a variety of financial metrics and transaction data to design and practice dashboards, reports, visualization charts, and more directly at Power BI.

This dataset offers a focused and invaluable window into user perceptions and experiences with applications listed on the Apple App Store. It is a vital resource for app developers, product managers, market analysts, and anyone seeking to understand the direct voice of the customer in the dynamic mobile app ecosystem.

Dataset Specifications:

• Investment: $45.0
• Status: Published and immediately available.
• Category: Ratings and Reviews Data
• Format: Compressed ZIP archive containing JSON files, ensuring easy integration into your analytical tools and platforms.
• Volume: Comprises 10,000 unique app reviews, providing a robust sample for qualitative and quantitative analysis of user feedback.
• Timeliness: Last crawled: (This field is blank in your provided info, which means its recency is currently unknown. If this were a real product, specifying this would be critical for its value proposition.)

Richness of Detail (11 Comprehensive Fields):

Each record in this dataset provides a detailed breakdown of a single App Store review, enabling multi-dimensional analysis:

1. Review Content:

   • review: The full text of the user's written feedback, crucial for Natural Language Processing (NLP) to extract themes, sentiment, and common keywords.
   • title: The title given to the review by the user, often summarizing their main point.
   • isEdited: A boolean flag indicating whether the review has been edited by the user since its initial submission. This can be important for tracking evolving sentiment or understanding user behavior.

2. Reviewer & Rating Information:

   • username: The public username of the reviewer, allowing for analysis of engagement patterns from specific users (though not personally identifiable).
   • rating: The star rating (typically 1-5) given by the user, providing a quantifiable measure of satisfaction.

3. App & Origin Context:

   • app_name: The name of the application being reviewed.
   • app_id: A unique identifier for the application within the App Store, enabling direct linking to app details or other datasets.
   • country: The country of the App Store storefront where the review was left, allowing for geographic segmentation of feedback.

4. Metadata & Timestamps:

   • _id: A unique identifier for the specific review record in the dataset.
   • crawled_at: The timestamp indicating when this particular review record was collected by the data provider (Crawl Feeds).
   • date: The original date the review was posted by the user on the App Store.

Expanded Use Cases & Analytical Applications:

This dataset is a goldmine for understanding what users truly think and feel about mobile applications. Here's how it can be leveraged:

• Product Development & Improvement:

  • Bug Detection & Prioritization: Analyze negative review text to identify recurring technical issues, crashes, or bugs, allowing developers to prioritize fixes based on user impact.
  • Feature Requests & Roadmap Prioritization: Extract feature suggestions from positive and neutral review text to inform future product roadmap decisions and develop features users actively desire.
  • User Experience (UX) Enhancement: Understand pain points related to app design, navigation, and overall usability by analyzing common complaints in the review field.
  • Version Impact Analysis: If integrated with app version data, track changes in rating and sentiment after new app updates to assess the effectiveness of bug fixes or new features.

• Market Research & Competitive Intelligence:

  • Competitor Benchmarking: Analyze reviews of competitor apps (if included or combined with similar datasets) to identify their strengths, weaknesses, and user expectations within a specific app category.
  • Market Gap Identification: Discover unmet user needs or features that users desire but are not adequately provided by existing apps.
  • Niche Opportunities: Identify specific use cases or user segments that are underserved based on recurring feedback.

• Marketing & App Store Optimization (ASO):

  • Sentiment Analysis: Perform sentiment analysis on the review and title fields to gauge overall user satisfaction, pinpoint specific positive and negative aspects, and track sentiment shifts over time.
  • Keyword Optimization: Identify frequently used keywords and phrases in reviews to optimize app store listings, improving discoverability and search ranking.
  • Messaging Refinement: Understand how users describe and use the app in their own words, which can inform marketing copy and advertising campaigns.
  • Reputation Management: Monitor rating trends and identify critical reviews quickly to facilitate timely responses and proactive customer engagement.

• Academic & Data Science Research:

  • Natural Language Processing (NLP): The review and title fields are excellent for training and testing NLP models for sentiment analysis, topic modeling, named entity recognition, and text summarization.
  • User Behavior Analysis: Study patterns in rating distribution, isEdited status, and date to understand user engagement and feedback cycles.
  • Cross-Country Comparisons: Analyze country-specific reviews to understand regional differences in app perception, feature preferences, or cultural nuances in feedback.

This App Store Reviews dataset provides a direct, unfiltered conduit to understanding user needs and ultimately driving better app performance and greater user satisfaction. Its structured format and granular detail make it an indispensable asset for data-driven decision-making in the mobile app industry.

IntroductionMachine-assisted topic analysis (MATA) uses artificial intelligence methods to help qualitative researchers analyze large datasets. This is useful for researchers to rapidly update healthcare interventions during changing healthcare contexts, such as a pandemic. We examined the potential to support healthcare interventions by comparing MATA with “human-only” thematic analysis techniques on the same dataset (1,472 user responses from a COVID-19 behavioral intervention).MethodsIn MATA, an unsupervised topic-modeling approach identified latent topics in the text, from which researchers identified broad themes. In human-only codebook analysis, researchers developed an initial codebook based on previous research that was applied to the dataset by the team, who met regularly to discuss and refine the codes. Formal triangulation using a “convergence coding matrix” compared findings between methods, categorizing them as “agreement”, “complementary”, “dissonant”, or “silent”.ResultsHuman analysis took much longer than MATA (147.5 vs. 40 h). Both methods identified key themes about what users found helpful and unhelpful. Formal triangulation showed both sets of findings were highly similar. The formal triangulation showed high similarity between the findings. All MATA codes were classified as in agreement or complementary to the human themes. When findings differed slightly, this was due to human researcher interpretations or nuance from human-only analysis.DiscussionResults produced by MATA were similar to human-only thematic analysis, with substantial time savings. For simple analyses that do not require an in-depth or subtle understanding of the data, MATA is a useful tool that can support qualitative researchers to interpret and analyze large datasets quickly. This approach can support intervention development and implementation, such as enabling rapid optimization during public health emergencies.

Abstract:

In recent years there has been an increased interest in Artificial Intelligence for IT Operations (AIOps). This field utilizes monitoring data from IT systems, big data platforms, and machine learning to automate various operations and maintenance (O&M) tasks for distributed systems.
The major contributions have been materialized in the form of novel algorithms.
Typically, researchers took the challenge of exploring one specific type of observability data sources, such as application logs, metrics, and distributed traces, to create new algorithms.
Nonetheless, due to the low signal-to-noise ratio of monitoring data, there is a consensus that only the analysis of multi-source monitoring data will enable the development of useful algorithms that have better performance.
Unfortunately, existing datasets usually contain only a single source of data, often logs or metrics. This limits the possibilities for greater advances in AIOps research.
Thus, we generated high-quality multi-source data composed of distributed traces, application logs, and metrics from a complex distributed system. This paper provides detailed descriptions of the experiment, statistics of the data, and identifies how such data can be analyzed to support O&M tasks such as anomaly detection, root cause analysis, and remediation.

General Information:

This repository contains the simple scripts for data statistics, and link to the multi-source distributed system dataset.

You may find details of this dataset from the original paper:

Sasho Nedelkoski, Jasmin Bogatinovski, Ajay Kumar Mandapati, Soeren Becker, Jorge Cardoso, Odej Kao, "Multi-Source Distributed System Data for AI-powered Analytics".

If you use the data, implementation, or any details of the paper, please cite!

BIBTEX:

_

@inproceedings{nedelkoski2020multi,
 title={Multi-source Distributed System Data for AI-Powered Analytics},
 author={Nedelkoski, Sasho and Bogatinovski, Jasmin and Mandapati, Ajay Kumar and Becker, Soeren and Cardoso, Jorge and Kao, Odej},
 booktitle={European Conference on Service-Oriented and Cloud Computing},
 pages={161--176},
 year={2020},
 organization={Springer}
}

_

The multi-source/multimodal dataset is composed of distributed traces, application logs, and metrics produced from running a complex distributed system (Openstack). In addition, we also provide the workload and fault scripts together with the Rally report which can serve as ground truth. We provide two datasets, which differ on how the workload is executed. The sequential_data is generated via executing workload of sequential user requests. The concurrent_data is generated via executing workload of concurrent user requests.

The raw logs in both datasets contain the same files. If the user wants the logs filetered by time with respect to the two datasets, should refer to the timestamps at the metrics (they provide the time window). In addition, we suggest to use the provided aggregated time ranged logs for both datasets in CSV format.

Important: The logs and the metrics are synchronized with respect time and they are both recorded on CEST (central european standard time). The traces are on UTC (Coordinated Universal Time -2 hours). They should be synchronized if the user develops multimodal methods. Please read the IMPORTANT_experiment_start_end.txt file before working with the data.

Our GitHub repository with the code for the workloads and scripts for basic analysis can be found at: https://github.com/SashoNedelkoski/multi-source-observability-dataset/

Overview This item contains references and test datasets for the Cactus pipeline. Cactus (Chromatin ACcessibility and Transcriptomics Unification Software) is an mRNA-Seq and ATAC-Seq analysis pipeline that aims to provide advanced molecular insights on the conditions under study.

Test datasets The test datasets contain all data needed to run Cactus in each of the 4 supported organisms. This include ATAC-Seq and mRNA-Seq data (.fastq.gz), parameter files (.yml) and design files (*.tsv). They were were created for each species by downloading publicly available datasets with fetchngs (Ewels et al., 2020) and subsampling reads to the minimum required to have enough DAS (Differential Analysis Subsets) for enrichment analysis. Datasets downloaded: - Worm and Humans: GSE98758 - Fly: GSE149339 - Mouse: GSE193393

References One of the goals of Cactus is to make the analysis as simple and fast as possible for the user while providing detailed insights on molecular mechanisms. This is achieved by parsing all needed references for the 4 ENCODE (Dunham et al., 2012; Stamatoyannopoulos et al., 2012; Luo et al., 2020) and modENCODE (THE MODENCODE CONSORTIUM et al., 2010; Gerstein et al., 2010) organisms (human, M. musculus, D. melanogaster and C. elegans). This parsing step was done with a Nextflow pipeline with most tools encapsulated within containers for improved efficiency and reproducibility and to allow the creation of customized references. Genomic sequences and annotations were downloaded from Ensembl (Cunningham et al., 2022). The ENCODE API (Luo et al., 2020) was used to download the CHIP-Seq profiles of 2,714 Transcription Factors (TFs) (Landt et al., 2012; Boyle et al., 2014) and chromatin states in the form of 899 ChromHMM profiles (Boix et al., 2021; van der Velde et al., 2021) and 6 HiHMM profiles (Ho et al., 2014). Slim annotations (cell, organ, development, and system) were parsed and used to create groups of CHIP-Seq profiles that share the same annotations, allowing users to analyze only CHIP-Seq profiles relevant to their study. 2,779 TF motifs were obtained from the Cis-BP database (Lambert et al., 2019). GO terms and KEGG pathways were obtained via the R packages AnnotationHub (Morgan and Shepherd, 2021) and clusterProfiler (Yu et al., 2012; Wu et al., 2021), respectively.

Documentation More information on how to use Cactus and how references and test datasets were created is available on the documentation website: https://github.com/jsalignon/cactus.

These datasets contain reviews from the Goodreads book review website, and a variety of attributes describing the items. Critically, these datasets have multiple levels of user interaction, raging from adding to a shelf, rating, and reading.

Metadata includes

• reviews

• add-to-shelf, read, review actions

• book attributes: title, isbn

• graph of similar books

Basic Statistics:

• Items: 1,561,465

• Users: 808,749

• Interactions: 225,394,930

Jamovi datasets used in Analysis