Context

World champion barista James Hoffmann and Cometeer partnered to conduct a first-of-its-kind coffee taste test. Cometeer shipped 5000 coffee kits across America. Kits contained four different coffees - pre-extracted and flash frozen. Tasters melted and diluted the coffee capsules for a largely identical tasting experience. Tasting and ratings were conducted blind [1]. After survey responses were collected (provided data), some attributes of the coffee were revealed.

In October 2023, World champion barista James Hoffmann and coffee company Cometeer held the "Great American Coffee Taste Test" on YouTube, during which viewers were asked to fill out a survey about 4 coffees they ordered from Cometeer for the tasting. Data blogger Robert McKeon Aloe analyzed the data the following month.

Content

Geography: US

Time-period: 2023

Unit of Analysis: The Great American Coffee Taste Test

Variables

• submission_id = Submission ID
• age = What is your age?
• cups = How many cups of coffee do you typically drink per day?
• where_drink = Where do you typically drink coffee?
• brew = How do you brew coffee at home?
• brew_other = How else do you brew coffee at home?
• purchase = On the go, where do you typically purchase coffee?
• purchase_other = Where else do you purchase coffee?
• favorite = What is your favorite coffee drink?
• favorite_specify = Please specify what your favorite coffee drink is
• additions = Do you usually add anything to your coffee?
• additions_other = What else do you add to your coffee?
• dairy = What kind of dairy do you add?
• sweetener = What kind of sugar or sweetener do you add?
• style = Before today's tasting, which of the following best described what kind of coffee you like?
  -**strength** = How strong do you like your coffee?
• roast_level = What roast level of coffee do you prefer?
• caffeine = How much caffeine do you like in your coffee?
• expertise = Lastly, how would you rate your own coffee expertise?
• coffee_a_bitterness = Coffee A - Bitterness
• coffee_a_acidity = Coffee A - Acidity
• coffee_a_personal_preference = Coffee A - Personal Preference
• coffee_a_notes = Coffee A - Notes
• coffee_b_bitterness = Coffee B - Bitterness
• coffee_b_acidity = Coffee B - Acidity
• coffee_b_personal_preference = Coffee B - Personal Preference
• coffee_b_notes = Coffee B - Notes
• coffee_c_bitterness = Coffee C - Bitterness
• coffee_c_acidity = Coffee C - Acidity
• coffee_c_personal_preference = Coffee C - Personal Preference
• coffee_c_notes = Coffee C - Notes
• coffee_d_bitterness = Coffee D - Bitterness
• coffee_d_acidity = Coffee D - Acidity
• coffee_d_personal_preference = Coffee D - Personal Preference
• coffee_d_notes = Coffee D - Notes
• prefer_abc = Between Coffee A, Coffee B, and Coffee C which did you prefer?
• prefer_ad = Between Coffee A and Coffee D, which did you prefer?
• prefer_overall = Lastly, what was your favorite overall coffee?
• wfh = Do you work from home or in person?
• total_spend = In total, how much money do you typically spend on coffee in a month?
• why_drink = Why do you drink coffee?
• why_drink_other = Other reason for drinking coffee
• taste = Do you like the taste of coffee?
• know_source = Do you know where your coffee comes from?
• most_paid = What is the most you've ever paid for a cup of coffee?
• most_willing = What is the most you'd ever be willing to pay for a cup of coffee?
• value_cafe = Do you feel like you’re getting good value for your money when you buy coffee at a cafe?
• spent_equipment = Approximately how much have you spent on coffee equipment in the past 5 years?
• value_equipment = Do you feel like you’re getting good value for your money when you buy coffee at a cafe?
• gender = Gender
• gender_specify = Gender (please specify)
• education_level = Education Level
• ethnicity_race = Ethnicity/Race
• ethnicity_race_specify = Ethnicity/Race (please specify)
• employment_status = Employment Status
• number_children = Number of Children
• political_affiliation = Political Affiliation

Acknowledgement

Datasource: The data is collected thorugh a survey called The Great American Coffee Taste Test held by James Haffmann

Inspiration: [Great American Coffee...

Consumer Spending in the United States increased to 16445.70 USD Billion in the second quarter of 2025 from 16345.80 USD Billion in the first quarter of 2025. This dataset provides the latest reported value for - United States Consumer Spending - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The data and programs replicate tables and figures from "Why ‘Buy American’ is a bad idea but politicians still like it", by Larch and Lechthaler. Please see the ReadMe file for additional details.

analyze the consumer expenditure survey (ce) with r the consumer expenditure survey (ce) is the primo data source to understand how americans spend money. participating households keep a running diary about every little purchase over the year. those diaries are then summed up into precise expenditure categories. how else are you gonna know that the average american household spent $34 (±2) on bacon, $826 (±17) on cellular phones, and $13 (±2) on digital e-readers in 2011? an integral component of the market basket calculation in the consumer price index, this survey recently became available as public-use microdata and they're slowly releasing historical files back to 1996. hooray! for a t aste of what's possible with ce data, look at the quick tables listed on their main page - these tables contain approximately a bazillion different expenditure categories broken down by demographic groups. guess what? i just learned that americans living in households with $5,000 to $9,999 of annual income spent an average of $283 (±90) on pets, toys, hobbies, and playground equipment (pdf page 3). you can often get close to your statistic of interest from these web tables. but say you wanted to look at domestic pet expenditure among only households with children between 12 and 17 years old. another one of the thirteen web tables - the consumer unit composition table - shows a few different breakouts of households with kids, but none matching that exact population of interest. the bureau of labor statistics (bls) (the survey's designers) and the census bureau (the survey's administrators) have provided plenty of the major statistics and breakouts for you, but they're not psychic. if you want to comb through this data for specific expenditure categories broken out by a you-defined segment of the united states' population, then let a little r into your life. fun starts now. fair warning: only analyze t he consumer expenditure survey if you are nerd to the core. the microdata ship with two different survey types (interview and diary), each containing five or six quarterly table formats that need to be stacked, merged, and manipulated prior to a methodologically-correct analysis. the scripts in this repository contain examples to prepare 'em all, just be advised that magnificent data like this will never be no-assembly-required. the folks at bls have posted an excellent summary of what's av ailable - read it before anything else. after that, read the getting started guide. don't skim. a few of the descriptions below refer to sas programs provided by the bureau of labor statistics. you'll find these in the C:\My Directory\CES\2011\docs directory after you run the download program. this new github repository contains three scripts: 2010-2011 - download all microdata.R lo op through every year and download every file hosted on the bls's ce ftp site import each of the comma-separated value files into r with read.csv depending on user-settings, save each table as an r data file (.rda) or stat a-readable file (.dta) 2011 fmly intrvw - analysis examples.R load the r data files (.rda) necessary to create the 'fmly' table shown in the ce macros program documentation.doc file construct that 'fmly' table, using five quarters of interviews (q1 2011 thru q1 2012) initiate a replicate-weighted survey design object perform some lovely li'l analysis examples replicate the %mean_variance() macro found in "ce macros.sas" and provide some examples of calculating descriptive statistics using unimputed variables replicate the %compare_groups() macro found in "ce macros.sas" and provide some examples of performing t -tests using unimputed variables create an rsqlite database (to minimize ram usage) containing the five imputed variable files, after identifying which variables were imputed based on pdf page 3 of the user's guide to income imputation initiate a replicate-weighted, database-backed, multiply-imputed survey design object perform a few additional analyses that highlight the modified syntax required for multiply-imputed survey designs replicate the %mean_variance() macro found in "ce macros.sas" and provide some examples of calculating descriptive statistics using imputed variables repl icate the %compare_groups() macro found in "ce macros.sas" and provide some examples of performing t-tests using imputed variables replicate the %proc_reg() and %proc_logistic() macros found in "ce macros.sas" and provide some examples of regressions and logistic regressions using both unimputed and imputed variables replicate integrated mean and se.R match each step in the bls-provided sas program "integr ated mean and se.sas" but with r instead of sas create an rsqlite database when the expenditure table gets too large for older computers to handle in ram export a table "2011 integrated mean and se.csv" that exactly matches the contents of the sas-produced "2011 integrated mean and se.lst" text file click here to view these three scripts for...

Gambling Data America can be vital for you to make connections with the gambler. In fact, this database includes information about who gambles. Again, the types of gambling they participate in, and the money affected. Furthermore, everyone can use our valid contacts to expand their betting business in targeted locations. Also, Gambling Data America provides valuable insights. That can support you optimize your betting activities more. As well as it improves profitability. Hence, that helps to stay competitive in the active gambling market. Moreover, Gambling Data America can greatly benefit your betting activities in various ways. This dataset gives you knowledge of what American gamblers like. As a result, that allows you to decide where and how to bet wisely. In other words, this is a very important thigh for targeted marketing. People can use player details and preferences to customize your marketing. Likewise, this helps you reach the right people with your promotions. Indeed, to make it more effective. America Gambling Data is a huge collection of American people. Furthermore, anyone can take advantage of this phone number data by buying this contact list. Similarly, the data we are providing is real and almost 95% latest and active. This website has an expert team that is working regularly to put all the information together. Also, with their effort and our experience, we are creating the best gambling database for our clients. In addition, America Gambling Data has all kinds of information that you need for your business and this will surely grow your business. However, with this library, you can create your own contact directory. Thus, people can build different professions’ contact lists also different states’ lists. So this is a complete package that can be a great asset for any person trying to do marketing.

The Great American Coffee Taste Test

In October 2023, "world champion barista" James Hoffmann and coffee company Cometeer held the "Great American Coffee Taste Test" on YouTube, during which viewers were asked to fill out a survey about 4 coffees they ordered from Cometeer for the tasting. Data blogger Robert McKeon Aloe analyzed the data the following month.

Do you think participants in this survey are representative of Americans in general?

Data Dictionary

coffee_survey.csv

Introduction

This US English Call Center Speech Dataset for the BFSI (Banking, Financial Services, and Insurance) sector is purpose-built to accelerate the development of speech recognition, spoken language understanding, and conversational AI systems tailored for English-speaking customers. Featuring over 30 hours of real-world, unscripted audio, it offers authentic customer-agent interactions across a range of BFSI services to train robust and domain-aware ASR models.

Curated by FutureBeeAI, this dataset empowers voice AI developers, financial technology teams, and NLP researchers to build high-accuracy, production-ready models across BFSI customer service scenarios.

Speech Data

The dataset contains 30 hours of dual-channel call center recordings between native US English speakers. Captured in realistic financial support settings, these conversations span diverse BFSI topics from loan enquiries and card disputes to insurance claims and investment options, providing deep contextual coverage for model training and evaluation.

Topic Diversity

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

This variety ensures models trained on the dataset are equipped to handle complex financial dialogues with contextual accuracy.

Transcription

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

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

Metadata

Rich metadata is available for each participant and conversation:

Description

The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) contains 7356 files (total size: 24.8 GB). The dataset contains 24 professional actors (12 female, 12 male), vocalizing two lexically-matched statements in a neutral North American accent. Speech includes calm, happy, sad, angry, fearful, surprise, and disgust expressions, and song contains calm, happy, sad, angry, and fearful emotions. Each expression is produced at two levels of emotional intensity (normal, strong), with an additional neutral expression. All conditions are available in three modality formats: Audio-only (16bit, 48kHz .wav), Audio-Video (720p H.264, AAC 48kHz, .mp4), and Video-only (no sound). Note, there are no song files for Actor_18.

The RAVDESS was developed by Dr Steven R. Livingstone, who now leads the Affective Data Science Lab, and Dr Frank A. Russo who leads the SMART Lab.

Citing the RAVDESS

The RAVDESS is released under a Creative Commons Attribution license, so please cite the RAVDESS if it is used in your work in any form. Published academic papers should use the academic paper citation for our PLoS1 paper. Personal works, such as machine learning projects/blog posts, should provide a URL to this Zenodo page, though a reference to our PLoS1 paper would also be appreciated.

Academic paper citation

Livingstone SR, Russo FA (2018) The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS): A dynamic, multimodal set of facial and vocal expressions in North American English. PLoS ONE 13(5): e0196391. https://doi.org/10.1371/journal.pone.0196391.

Personal use citation

Include a link to this Zenodo page - https://zenodo.org/record/1188976

Commercial Licenses

Commercial licenses for the RAVDESS can be purchased. For more information, please visit our license page of fees, or contact us at ravdess@gmail.com.

Contact Information

If you would like further information about the RAVDESS, to purchase a commercial license, or if you experience any issues downloading files, please contact us at ravdess@gmail.com.

Example Videos

Watch a sample of the RAVDESS speech and song videos.

Emotion Classification Users

If you're interested in using machine learning to classify emotional expressions with the RAVDESS, please see our new RAVDESS Facial Landmark Tracking data set [Zenodo project page].

Construction and Validation

Full details on the construction and perceptual validation of the RAVDESS are described in our PLoS ONE paper - https://doi.org/10.1371/journal.pone.0196391.

The RAVDESS contains 7356 files. Each file was rated 10 times on emotional validity, intensity, and genuineness. Ratings were provided by 247 individuals who were characteristic of untrained adult research participants from North America. A further set of 72 participants provided test-retest data. High levels of emotional validity, interrater reliability, and test-retest intrarater reliability were reported. Validation data is open-access, and can be downloaded along with our paper from PLoS ONE.

Contents

Audio-only files

Audio-only files of all actors (01-24) are available as two separate zip files (~200 MB each):

• Speech file (Audio_Speech_Actors_01-24.zip, 215 MB) contains 1440 files: 60 trials per actor x 24 actors = 1440.
• Song file (Audio_Song_Actors_01-24.zip, 198 MB) contains 1012 files: 44 trials per actor x 23 actors = 1012.

Audio-Visual and Video-only files

Video files are provided as separate zip downloads for each actor (01-24, ~500 MB each), and are split into separate speech and song downloads:

• Speech files (Video_Speech_Actor_01.zip to Video_Speech_Actor_24.zip) collectively contains 2880 files: 60 trials per actor x 2 modalities (AV, VO) x 24 actors = 2880.
• Song files (Video_Song_Actor_01.zip to Video_Song_Actor_24.zip) collectively contains 2024 files: 44 trials per actor x 2 modalities (AV, VO) x 23 actors = 2024.

File Summary

In total, the RAVDESS collection includes 7356 files (2880+2024+1440+1012 files).

File naming convention

Each of the 7356 RAVDESS files has a unique filename. The filename consists of a 7-part numerical identifier (e.g., 02-01-06-01-02-01-12.mp4). These identifiers define the stimulus characteristics:

Filename identifiers

• Modality (01 = full-AV, 02 = video-only, 03 = audio-only).
• Vocal channel (01 = speech, 02 = song).
• Emotion (01 = neutral, 02 = calm, 03 = happy, 04 = sad, 05 = angry, 06 = fearful, 07 = disgust, 08 = surprised).
• Emotional intensity (01 = normal, 02 = strong). NOTE: There is no strong intensity for the 'neutral' emotion.
• Statement (01 = "Kids are talking by the door", 02 = "Dogs are sitting by the door").
• Repetition (01 = 1st repetition, 02 = 2nd repetition).
• Actor (01 to 24. Odd numbered actors are male, even numbered actors are female).

Filename example: 02-01-06-01-02-01-12.mp4

1. Video-only (02)
2. Speech (01)
3. Fearful (06)
4. Normal intensity (01)
5. Statement "dogs" (02)
6. 1st Repetition (01)
7. 12th Actor (12)
8. Female, as the actor ID number is even.

License information

The RAVDESS is released under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License, CC BY-NC-SA 4.0

Commercial licenses for the RAVDESS can also be purchased. For more information, please visit our license fee page, or contact us at ravdess@gmail.com.

Related Data sets

• RAVDESS Facial Landmark Tracking data set [Zenodo project page].

Retail Sales in the United States increased 0.60 percent in August of 2025 over the previous month. This dataset provides - U.S. December Retail Sales Increased More Than Forecast - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Data Broker Service Market Outlook

The global data broker service market size is projected to grow from USD 250 billion in 2023 to an estimated USD 450 billion by 2032, reflecting a compound annual growth rate (CAGR) of 6.7%. This substantial growth can be attributed to increasing digitalization, the exponential rise of data-driven decision-making across industries, and the growing realization of the value derived from data analytics. As businesses continue to recognize the potential of leveraging consumer, business, financial, and health data, the demand for data brokerage services is poised to expand significantly.

One of the primary growth factors for the data broker service market is the increasing importance of data in driving business strategies and operations. Companies are increasingly relying on consumer and market data to gain insights into market trends, consumer behavior, and competitive landscapes. This surge in data utilization across sectors such as retail, healthcare, and finance is propelling the demand for data brokerage services that can provide accurate and comprehensive data sets. The proliferation of digital platforms and the Internet of Things (IoT) has further amplified the volume of data generated, thus boosting the need for efficient data brokerage services.

Moreover, advancements in artificial intelligence (AI) and machine learning (ML) technologies are significantly contributing to the market's growth. These technologies enable enhanced data analysis, predictive analytics, and real-time decision-making, making data brokerage services more valuable. Businesses are increasingly investing in AI and ML to analyze large datasets more efficiently and extract actionable insights. Data brokers, in turn, are leveraging these technologies to offer more sophisticated and tailored data solutions, thus attracting a broader customer base.

Privacy regulations and data protection laws are also playing a crucial role in shaping the data broker service market. While these regulations pose challenges, they also create opportunities for compliant data brokers to differentiate themselves in the market. Companies are more inclined to partner with data brokers that demonstrate robust data governance practices and adhere to regulatory requirements. This trend is driving the market towards more ethical and transparent data brokerage practices, increasing the trust and credibility of data brokers among businesses and consumers alike.

The regional outlook for the data broker service market highlights North America as a dominant player, primarily due to the high adoption of data-driven strategies among businesses and the presence of major data brokerage firms. Europe follows closely, driven by stringent data protection regulations like GDPR, which necessitate secure and compliant data handling. The Asia Pacific region is expected to witness the fastest growth, fueled by the rapid digital transformation in countries like China and India and the increasing use of data analytics in various industries. Latin America and the Middle East & Africa regions are also showing promising growth, supported by the rising awareness of data's strategic value and increasing investments in data analytics infrastructure.

Data Type Analysis

The data broker service market by data type comprises consumer data, business data, financial data, health data, and other categories. Consumer data is one of the most significant segments within this market. This type of data includes information on consumer behavior, preferences, purchasing patterns, and demographics. Businesses leverage consumer data to tailor their marketing strategies, enhance customer experiences, and drive sales growth. The increasing use of digital platforms for shopping, social interaction, and information consumption is continually generating vast amounts of consumer data, thereby fueling the demand for consumer data brokerage services.

Business data, encompassing company profiles, industry trends, and competitive intelligence, is another vital segment. Organizations require business data to strategize market entry, expansion, and competitive positioning. Data brokers play a crucial role in aggregating and providing actionable business insights that help companies navigate complex market dynamics. The rise of global trade, the need for cross-border business intelligence, and the growing importance of data-driven decision-making in corporate strategies are driving the demand for business data brokerage services.

Financial data is crucial for sectors like banking, fina

Consumer Confidence in the United States decreased to 55 points in October from 55.10 points in September of 2025. This dataset provides the latest reported value for - United States Consumer Sentiment - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

This Dataset highlights and geocodes the estimated shopping mall sales by state. This data provided by the National Research Bureau, 2005, and was collected through http://www.statemaster.com/graph/lif_sho_mal_est_sal-lifestyle-shopping-malls-estimated-sales

This location data product focuses on real-time GPS pings collected across South America. Using the Irys Location API, users can access polygon-based movement patterns from anonymized mobile devices across urban and rural areas.

Events include timestamps, precise geocoordinates, country codes, and device identifiers. Query the dataset using polygon filters and receive structured outputs via API or cloud endpoints. Supported formats include JSON, CSV, and Parquet.

With historical backfill and fresh updates every day, the dataset is ideal for retailers, advertisers, city planners, and researchers analyzing behavior and trends across the Americas. It supports use cases like retail site selection, mobility forecasting, and geofencing for public safety.

All data is delivered with a maximum lag of three days and complies with GDPR and CCPA regulations.

We collect, validate, model, and segment raw data signals from over 900+ sources globally to deliver thousands of mobile audience segments. We then combine that data with other public and private data sources to derive interests, intent, and behavioral attributes. Our proprietary algorithms then clean, enrich, unify and aggregate these data sets for use in our products. We have categorized our audience data into consumable categories such as interest, demographics, behavior, geography, etc. Audience Data Categories:Below mentioned data categories include consumer behavioral data and consumer profiles (available for the US and Australia) divided into various data categories. Brand Shoppers:Methodology: This category has been created based on the high intent of users in terms of their visits to Brand outlets in the real world. To create segments containing users with a high-affinity index, we use a precise determination of the number of occurrences at a given time. Place Category Visitors:Methodology: This category has been created based on the high intent of users visiting specific places of interest in the real world. To create segments containing users with a high-affinity index, we use a precise determination of the number of occurrences at a given time. Demographics:This category has been created based on deterministic data that we receive from apps based on the declared gender and age data. Marital Status, Education, Party affiliation, and State residency are available in the US. Geo-Behavioural:This category has been created based on the high intent of users in terms of the frequency of their visits to specific granular places of interest in the real world. To create segments containing users with a high-affinity index, we use a precise determination of the number of occurrences at a given time. Interests:This segment is created based on users' interest in a specific subject while browsing the internet when the visited website category is clearly focused on a specific subject such as cars, cooking, traveling, etc. We use a deterministic model to assign a proper profile and time that information is valid. The recency of data can range from 14 to 30 days, depending on the topic. Intent:Factori receives data from many partners to deliver high-quality pieces of information about users’ shopping intent. We collect data from sources connected to the eCommerce sector and we also receive data connected to online transactions from affiliate networks to deliver the most accurate segments with purchase intentions, such as laptops, mobile phones, or cars. The recency of data can range from 7 to 14 days depending on the product category. Events:This category was created based on the high interest of users in terms of content related to specific global events - sports, culture, and gaming. Among the event segments, we also distinguish categories related to the interest in certain lifestyle choices and behaviors. To create segments containing users with a high-affinity index, we use a precise determination of the number of occurrences at a given time. App Usage:Mobile category is a branch of the taxonomy that is dedicated only to the data that is based on mobile advertising IDs. It is based on the categorization of the mobile apps that the user has installed on the device. Auto Ownership:Consumer Profiles - Available for US and AustraliaThis audience has been created based on users declaring that they own a certain brand of automobile and other automotive attributes via a survey or registration. These audiences are currently available in the USA. Motorcycle Ownership:Consumer Profiles - Available for US and AustraliaThis audience has been created based on users declaring that they own a certain brand of motorcycle and other motorcycle-based attributes via a survey or registration. These audiences are currently available for the USA. Household:Consumer Profiles - Available for the US and AustraliaThis audience has been created based on users' declaring their marital status, parental status, and the overall number of children via a survey or registration. These audiences are currently available in the USA. Financial:Consumer Profiles - Available for the US and Australia this audience has been created based on their behavior in different financial services like property ownership, mortgage, investing behavior, and wealth and declaring their estimated net worth via a survey or registration. Purchase/ Spending Behavior:Consumer Profiles - Available for the US and AustraliaThis audience has been created based on their behavior in different spending behaviors in different business verticals available in the USA. Clusters:Consumer Profiles - Available for the US and AustraliaClusters are groups of consumers who exhibit similar demographic, lifestyle, and media consumption characteristics, empowering marketers to understand the unique attributes that comprise their most profitable consumer segments. Armed with this rich data, data scientists can drive analytics and modeling to power their brand’s unique marketing initiatives. B2B Audiences;Consumer Profiles - Available for US and AustraliaThis audience has been created based on users declaring their employee credentials, designations, and companies they work in, further specifying business verticals, revenue breakdowns, and headquarters locations. Customizable Audiences Data Segment:Brands can choose the appropriate pre-made audience segments or ask our data experts about creating a custom segment that is precisely tailored to your brief in order to reach their target customers and boost the campaign's effectiveness. Location Query Granularity:Minimum area: HEX 8Maximum area: QuadKey 17/City

Introduction

This US English 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 English 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 US English 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:

Total Vehicle Sales in the United States increased to 16.40 Million in September from 16.10 Million in August of 2025. This dataset provides the latest reported value for - United States Total Vehicle Sales - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Imports in the United States increased to 358.80 USD Billion in July from 338.80 USD Billion in June of 2025. This dataset provides the latest reported value for - United States Imports - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Existing Home Sales in the United States decreased to 4000 Thousand in August from 4010 Thousand in July of 2025. This dataset provides the latest reported value for - United States Existing Home Sales - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Database Monitoring Tool Market Outlook

The global database monitoring tool market size was valued at approximately USD 1.5 billion in 2023 and is projected to reach around USD 3.2 billion by 2032, growing at a compound annual growth rate (CAGR) of 8.5% from 2024 to 2032. The primary growth factors driving this market include the increasing complexity of database systems, the need for enhanced database performance and security, and the rising adoption of advanced monitoring tools for efficient IT operations.

One of the key growth drivers for the database monitoring tool market is the exponential growth in data generation and database complexity. With the proliferation of IoT devices, social media, e-commerce platforms, and enterprise applications, the volume of data generated by organizations has surged dramatically. This has necessitated the need for robust database monitoring tools to ensure optimal performance, minimize downtime, and secure sensitive information. The increasing complexity of database architectures, including multi-cloud deployments and hybrid environments, further underscores the demand for sophisticated monitoring solutions.

Another significant factor contributing to market growth is the rising emphasis on database security. Cybersecurity threats have become increasingly sophisticated, with databases being prime targets for malicious attacks. Database monitoring tools play a crucial role in identifying potential vulnerabilities, detecting anomalies, and ensuring compliance with data protection regulations. As organizations strive to safeguard their data assets, the adoption of advanced monitoring solutions is expected to rise, driving market expansion over the forecast period.

Additionally, the growing adoption of cloud-based database monitoring tools is anticipated to propel market growth. Cloud computing offers several advantages, including scalability, flexibility, and cost-efficiency, which are increasingly being leveraged by enterprises to manage their databases. Cloud-based monitoring solutions provide real-time insights, automated alerts, and predictive analytics, enabling organizations to proactively address performance issues and optimize their database operations. The shift towards cloud-based deployments is likely to further augment the demand for database monitoring tools.

Regionally, North America dominates the database monitoring tool market, driven by the presence of a large number of technology-driven enterprises, high adoption of advanced IT solutions, and stringent data security regulations. Europe follows closely, with significant investments in IT infrastructure and data protection initiatives. The Asia Pacific region is expected to exhibit the highest growth rate during the forecast period, owing to the rapid digital transformation, increasing cloud adoption, and expanding IT sector in countries like China, India, and Japan. Latin America and the Middle East & Africa are also witnessing steady growth, driven by the increasing focus on data management and security.

In the retail industry, the implementation of Transaction Monitoring for Retail is becoming increasingly vital. Retailers are handling vast amounts of transaction data, from point-of-sale systems to online shopping platforms, which necessitates robust monitoring to ensure data integrity and security. Transaction monitoring helps in identifying fraudulent activities, optimizing transaction processes, and enhancing customer experience by ensuring seamless operations. As the retail sector continues to embrace digital transformation, the need for effective transaction monitoring solutions is paramount to manage the growing complexity of retail databases and to maintain competitive advantage.

Component Analysis

The market for database monitoring tools can be segmented into software and services. The software segment encompasses various monitoring solutions that provide real-time insights, analytics, and automated alerts for database performance and security. These solutions are designed to monitor database health, track performance metrics, and detect anomalies, enabling organizations to optimize their database operations. With the increasing complexity of database environments, the demand for advanced software solutions is on the rise, driving the growth of this segment.

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