Handphone Users Survey - Use of Smartphones for Phone Calls since 2012

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).

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

Norfolk Cares is the single point of contact for visitors, residents and businesses to request a City service, report a concern, seek information about Norfolk, or follow-up on a previous request. This dataset provides statistics about incoming calls to the Norfolk Cares Center. The dataset is grouped in 15-minute increments (showing only active call-taking hours) and includes statistics such as the number of incoming calls, abandoned calls, time to answer calls, call duration, and active all takers. This dataset will be updated daily.

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.

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.

Analysis of ‘Do Not Call (DNC) Reported Calls Data 4/6/18 - 4/12/18’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/aa701dbd-d3d9-4316-8e11-0e49c32b078a on 12 February 2022.

--- Dataset description provided by original source is as follows ---

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.

--- Original source retains full ownership of the source dataset ---

311 Customer Service connects citizens with specially-trained customer service representatives ready to assist and facilitate City services including animals, potholes, solid waste collection, property maintenance, downed trees, etc. See more Online Service Requests at www.sanantonio.gov/311. NOTE: Data represents the past 365 days of the last upload date.

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:

Analysis of ‘Do Not Call (DNC) Reported Calls Data 12/14/18 - 12/20/18’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/d0fc1d53-5a49-4162-8436-836cae414b6f on 12 February 2022.

--- Dataset description provided by original source is as follows ---

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.

--- Original source retains full ownership of the source dataset ---

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:

Introduction

This French 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 French-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 French 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:

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:

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.

glaiveai/glaive-function-calling-v2 dataset hosted on Hugging Face and contributed by the HF Datasets community

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 Malay 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 Malay-speaking telecom customers. Featuring over 30 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 30 hours of dual-channel call center recordings between native Malay 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:

PURPOSE Private dataset used to update the following datasets: Law Enforcement Dispatched Calls for Service: Real-Time Law Enforcement Dispatched Calls for Service: Closed

BACKGROUND Logic based on rules developed in conjunction with DEM (Michelle Geddes), POL (Jason Cunningham), and MTA and documented here.

UPDATE PROCESS To update this mapping, edit the Google Spreadsheet here, then download as a CSV and replace the data here in the portal.

NOTES 1. Rules are defined by matching the first characters of a call type (rule_type='prefix'), last characters of a call type (rule_type='suffix'), or the exact call type (rule_type='exact'). 2. Rules are applied in the following order: (1) prefix, (2) suffix, (3) exact. In case of conflict, each rule type supercedes the previous one. 3. Calls not captured by any rule will not have their geographic location suppressed in either dataset. 4. Take care to ensure that no 'prefix' rules conflict with each other, no 'suffix' rules conflict either. An example of a potential conflict: You add a rule stating that all calls beginning with 261B should only be suppressed in the real-time dataset. This would conflict with the existing rule that all 261 calls should be suppressed in both datasets. To resolve this conflict, you would need to specify the behavior for all calls that are exactly '261' as well as for each call beginning with 261A-261Z.

All 311 Service Requests from 2010 to present. This information is automatically updated daily.

Click here to download data from 2011 - https://data.cityofnewyork.us/dataset/311-Service-Requests-From-2011/fpz8-jqf4

Click here to download data from 2012 - https://data.cityofnewyork.us/dataset/311-Service-Requests-From-2012/as38-8eb5

Click here to download data from 2013 - https://data.cityofnewyork.us/dataset/311-Service-Requests-From-2013/hybb-af8n

Click here to download data from 2014 - https://data.cityofnewyork.us/dataset/311-Service-Requests-From-2014/vtzg-7562

Click here to download data from 2015 - https://data.cityofnewyork.us/dataset/311-Service-Requests-From-2015/57g5-etyj

Briefing materials for the presentation to the Standing Committee on Industry, Science and Technology on the subject of Study on fraud calls in Canada