This dataset captures monthly data from HSS' phone system and includes metrics pertaining to Calls Answered, Average Speed of Answer, Abandonment Rate, In-person Assistance. This data supports the City's Performance Measures requirements. In April of 2023 HSS switched to a new phone system - WEBEX (Finess).

Please review this brief video for a better understanding of how these data are created: https://www.youtube.com/watch?v=lvTCjVHxpAU

This data represents police response activity. Each row is a record of a Call for Service (CfS) logged with the Seattle Police Department (SPD) Communications Center. Calls originated from the community and range from in progress or active emergencies to requests for problem solving. Additionally, officers will log calls from their observations of the field.

Previous versions of this data set have withheld approximately 40% of calls. This updated process will release more than 95% of all calls but we will no longer provide latitude and longitude specific location data. In an effort to safeguard the privacy of our community, calls will only be located to the “beat” level. Beats are the most granular unit of management used for patrol deployment. To learn more about patrol deployment, please visit: https://www.seattle.gov/police/about-us/about-policing/precinct-and-patrol-boundaries.

As with any data, certain conditions and qualifications apply:

1) These data are queried from the Data Analytics Platform (DAP), and updated incrementally on a daily basis. A full refresh will occur twice a year and is intended to reconcile minor changes.

2) This data set only contains records of police response. If a call is queued in the system but cleared before an officer can respond, it will not be included.

3) These data contain administrative call types. Use the “Initial” and “Final” call type to identify the calls you wish to include in your analysis.

We invite you to engage these data, ask questions and explore.

This data set includes information on Do Not Call and robocall complaints reported to the Federal Trade Commission. The data set contains information reported by consumers, including the telephone number originating the unwanted call, the date the complaint was created, the time the call was made, the consumer’s city and state locations reported, the subject of the call, and whether the call was a robocall. None of the information about the reported calls is verified.

Statistical data grouped, aggregated and sorted by years and months

In 2022, contacting victims via text was the most common method employed by fraudsters, being used in ******* fraud cases reported to the Federal Trade Commission (FTC) in the United States. Contacting victims via phone call was the second most common method, with ******* reported cases.

The Listening to Young Lives at Work: COVID-19 Phone Survey, First Call, Second Call and Third Call, 2020 is an adapted version of the Round 6 survey with additional questions to directly assess the impact of COVID-19. The survey consists of three phone calls with each of our Young Lives respondents, across both the younger and older cohorts, and in all four study countries (reaching an estimated total of around 11,000 young people).

The Phone Survey will enable Young Lives to inform policy makers on the short-term effects of the COVID-19 pandemic. Subsequently, and together with data collected in further survey rounds, Young Lives will be able to assess the medium and long term implications of the crisis. Further information is available on the Young Lives at Work webpage.

The Listening to Young Lives at Work: COVID-19 Phone Survey, First Call, Second Call and Third Call, 2020 is held at the UK Data Archive under SN 8678 and the Listening to Young Lives at Work: COVID-19 Phone Survey Calls 1-5 Constructed Files, 2020-2021 is held under SN 9070.

Latest edition information:
For the fourth edition (July 2022), region and cluster location variables have been added to the main survey datasets for all four countries, across the three phone surveys. Food security variables have also been added to the Second and Third Call datasets. A small inconsistency in the labelling of the typesite variable (urban/rural) has also been corrected. Additionally, documents related to copyright and survey references have been added, as well as a technical note related to the food security variables.

The Customer Relations Center (CRC) or Tempe 311 is often the first and possibly only contact a resident has with the City. Our goal is to make each interaction as smooth and efficient as possible. To efficiently provide our residents an improved level of customer service, Tempe 311 strives to serve our residents by acting as the central connection to accessible information and government services. Our purpose is realized through our ability to resolve calls with a single point of contact. When we do this, we have met 311’s mission and provided effective customer service. Tempe 311 CRC strives to achieve Single Point of Contact (SPOC) resolution rate greater than or equal to 75% of incoming calls.This page provides data for the 311 First-Call Resolution Rate performance measure.The performance measure dashboard is available at 2.03 311 First-Call Resolution Rate.Additional InformationSource:Contact: Moncayo, KimContact E-Mail: Kim_Moncayo@tempe.govData Source Type: Accela CRM, Excel, Cisco Unified IntelligencePreparation Method: The data from every 311 call is entered into the city's Accela CRM database system. We use that information in conjunction with Cisco Unified Intelligence Center, a separate report is generated to pull out transferred and non 311 callsPublish Frequency: QuarterlyPublish Method: ManualData Dictionary

Introduction

This Thai 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 Thai 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 Thai 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:

Introduction

This Punjabi Call Center Speech Dataset for the Healthcare industry is purpose-built to accelerate the development of Punjabi speech recognition, spoken language understanding, and conversational AI systems. With 30 Hours of unscripted, real-world conversations, it delivers the linguistic and contextual depth needed to build high-performance ASR models for medical and wellness-related customer service.

Created by FutureBeeAI, this dataset empowers voice AI teams, NLP researchers, and data scientists to develop domain-specific models for hospitals, clinics, insurance providers, and telemedicine platforms.

Speech Data

The dataset features 30 Hours of dual-channel call center conversations between native Punjabi speakers. These recordings cover a variety of healthcare support topics, enabling the development of speech technologies that are contextually aware and linguistically rich.

Topic Diversity

The dataset spans inbound and outbound calls, capturing a broad range of healthcare-specific interactions and sentiment types (positive, neutral, negative).

These real-world interactions help build speech models that understand healthcare domain nuances and user intent.

Transcription

Every audio file is accompanied by high-quality, manually created transcriptions in JSON format.

Metadata

Each conversation and speaker includes detailed metadata to support fine-tuned training and analysis.

Usage and Applications

This dataset can be used across a range of healthcare and voice AI use cases:

Records from operating a customer call center or service center providing services to the public. Services may address a wide variety of topics such as understanding agency mission-specific functions or how to resolve technical difficulties with external-facing systems or programs. Includes:rn- incoming requests and responsesrn- trouble tickets and tracking logs rn- recordings of call center phone conversations with customers used for quality control and customer service trainingrn- system data, including customer ticket numbers and visit tracking rn- evaluations and feedback about customer servicesrn- information about customer services, such as “Frequently Asked Questions” (FAQs) and user guidesrn- reports generated from customer management datarn- complaints and commendation records; customer feedback and satisfaction surveys, including survey instruments, data, background materials, and reports.

In 2023, the ******** of contact center workers in the United States stated they agreed artificial intelligence (AI) had ******** customer service when it came to customer information tasks during their workday. ** percent agreed that AI had made their work easier.

Introduction

This Japanese Call Center Speech Dataset for the Telecom industry is purpose-built to accelerate the development of speech recognition, spoken language understanding, and conversational AI systems tailored for Japanese-speaking telecom customers. Featuring over 40 hours of real-world, unscripted audio, it delivers authentic customer-agent interactions across key telecom support scenarios to help train robust ASR models.

Curated by FutureBeeAI, this dataset empowers voice AI engineers, telecom automation teams, and NLP researchers to build high-accuracy, production-ready models for telecom-specific use cases.

Speech Data

The dataset contains 40 hours of dual-channel call center recordings between native Japanese speakers. Captured in realistic customer support settings, these conversations span a wide range of telecom topics from network complaints to billing issues, offering a strong foundation for training and evaluating telecom voice AI solutions.

Topic Diversity

This speech corpus includes both inbound and outbound calls with varied conversational outcomes like positive, negative, and neutral ensuring broad scenario coverage for telecom AI development.

This variety helps train telecom-specific models to manage real-world customer interactions and understand context-specific voice patterns.

Transcription

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

These transcriptions are production-ready, allowing for faster development of ASR and conversational AI systems in the Telecom domain.

Metadata

Rich metadata is available for each participant and conversation:

State of Palestine (West Bank and Gaza) Avg Number of Telephone Calls data was reported at 69,848,994.000 Unit th in 2017. This records a decrease from the previous number of 96,706,960.000 Unit th for 2016. State of Palestine (West Bank and Gaza) Avg Number of Telephone Calls data is updated yearly, averaging 94,717,618.000 Unit th from Dec 2004 (Median) to 2017, with 13 observations. The data reached an all-time high of 103,248,599.000 Unit th in 2013 and a record low of 33,905,287.000 Unit th in 2004. State of Palestine (West Bank and Gaza) Avg Number of Telephone Calls data remains active status in CEIC and is reported by Palestinian Central Bureau of Statistics. The data is categorized under Global Database’s State of Palestine (West Bank and Gaza) – Table PS.TB003: Number of Phone Subscribers and Calls.

The number of spam text messages received by Americans has been growing in the past few years, reaching on average **** phone text messages per month in 2020, up from *** calls in 2015. The number of spam calls also increased from 2015 to 2019, but saw a slight decline in 2020 when it went from **** calls per month in 2019 to ** calls per month in 2020.

311 Call Center Statistics

Financial overview and grant giving statistics of A Call to Conscience

This data set contains statistical data about phone numbers on the Registry, telemarketers and sellers accessing phone numbers on the Registry, and complaints consumers submit to the FTC about telemarketers allegedly violating the Do Not Call rules for Fiscal Year 2013. Statistical data on Do Not Call (DNC) complaints is based on unverified complaints reported by consumers, not on a consumer survey.

Financial overview and grant giving statistics of R D Call Scholarship Fund

Sign Up for a free trial: https://rampedup.io/sign-up-%2F-log-in - 7 Days and 50 Credits to test our quality and accuracy.

These are the fields available within the RampedUp Global dataset.

CONTACT DATA: Personal Email Address - We manage over 115 million personal email addresses Professional Email - We manage over 200 million professional email addresses Home Address - We manage over 20 million home addresses Mobile Phones - 65 million direct lines to decision makers Social Profiles - Individual Facebook, Twitter, and LinkedIn Local Address - We manage 65M locations for local office mailers, event-based marketing or face-to-face sales calls.

JOB DATA: Job Title - Standardized titles for ease of use and selection Company Name - The Contact's current employer Job Function - The Company Department associated with the job role Title Level - The Level in the Company associated with the job role Job Start Date - Identify people new to their role as a potential buyer

EMPLOYER DATA: Websites - Company Website, Root Domain, or Full Domain Addresses - Standardized Address, City, Region, Postal Code, and Country Phone - E164 phone with country code Social Profiles - LinkedIn, CrunchBase, Facebook, and Twitter

FIRMOGRAPHIC DATA: Industry - 420 classifications for categorizing the company’s main field of business Sector - 20 classifications for categorizing company industries 4 Digit SIC Code - 239 classifications and their definitions 6 Digit NAICS - 452 classifications and their definitions Revenue - Estimated revenue and bands from 1M to over 1B Employee Size - Exact employee count and bands Email Open Scores - Aggregated data at the domain level showing relationships between email opens and corporate domains. IP Address -Company level IP Addresses associated to Domains from a DNS lookup

CONSUMER DATA: Education - Alma Mater, Degree, Graduation Date Skills - Accumulated Skills associated with work experience
Interests - Known interests of contact Connections - Number of social connections. Followers - Number of social followers

Download our data dictionary: https://rampedup.io/our-data