1) Data Introduction • The Call Center Data includes a variety of data related to call center operations, including customer inquiries, call times, agent information, and service types that occur routinely at the call center.

2) Data Utilization (1) Call Center Data has characteristics that: • This dataset provides key indicators and details of call operation, including call ID, customer ID, counselor ID, call start/end time, call length, inquiry type, and call results. (2) Call Center Data can be used to: • Service Quality and Efficiency Analysis: Use call time, call result data to assess the performance of the counselor and the quality of service of the call center. • Analysis of trends by type of customer inquiry: By analyzing inquiry type and frequency data, you can identify key customer needs and trends, and use them to improve service.

If you are in need of emergency shelter space, please call the City of Toronto’s Central Intake line at 416-338-4766 or 1-877-338-3398. This catalogue entry provides two data sets related to calls to Central Intake. Central Intake is a City-operated, 24/7 telephone-based service that offers referrals to emergency shelter and other overnight accommodation, as well as information about other homelessness services. These two data sets provide information about calls received by Central Intake, the outcomes of those calls, and the number of individuals who could not be matched to a shelter space each day. The first, Central Intake Service Queue Data, provides counts of the number of unique individuals who contacted Central Intake to access emergency shelter but were not matched to a shelter space. Generated through Central Intake caseworkers' use of the City's Shelter Management Information System (SMIS), the data are reported as a count for every operational day. The SMIS service queue for Central Intake records when a bed is requested for a caller seeking a shelter space. Those callers who could not be matched to an available space that suits their needs at the time of their call remain in the queue until they can be provided a referral or until the closeout process at the end of the night (i.e. 4:00 a.m.). Service Queue data combines data exported from the Central Intake service queue at 4:00 a.m., with manually coded outcome data based on the review of each individual's SMIS records for the day. SSHA began collecting data on how many people remain unmatched in the service queue over a 24 hour period at the beginning of November 2020. Given the manual nature of the preparation of the data in this data set, this file will be updated on a monthly basis. Data will be reported separately for every operational day in that month. The second data set, Central Intake Call Wrap-Up Codes Data, provides counts of calls answered by Central Intake, classified by the nature of the call. When a call is handled by a caseworker at Central Intake, the caseworker assigns a wrap-up code to the call. This tracking allows for analysis of call trends. Central Intake uses 13 distinct wrap-up codes to code the calls they receive. This data set provides a daily summary of the number of calls received by each call wrap-up code. The data are manually retrieved from the City's call centre database reports. Given the manual nature of the preparation of the data in this data set, this file will be updated on a monthly basis. Data will be reported separately for every operational day in that month. Please note that while the wrap-up codes provide information related to the volume and type of calls answered by Central Intake, the data do not track requests made by unique individuals nor the ultimate outcomes of referrals. Please also note that the previews and Data Features below only show information pertaining to the Central Intake Call Wrap-Up Codes Data dataset.

Total number of calls received by the agency's call center This data is collected to assess and assist residents of NYC. The data is collected by a 3rd party application that goes by the name PRIMA. Data is collected as a meausre of workvolume and customer demand for services offered by the Dept. of Health. Data is collected automatically by call management software. Each record represents a type of service offered to the public by the Dept. of Health. Data is used to assess quality of service offered, collect customer feedback for service improvements and determine size of staff needed to appropriate deliver the service. Data available counts number of calls related to a particular topic. There is no drill down available to disect specific reasons why customers may be calling.

Update Frequency: Daily

For up to date information on service requests please visit, https://city.milwaukee.gov/ucc .

A log of the Unified Call Center's service requests.

From potholes, abandoned vehicles, high weeds on vacant lots, and curbside trash to faulty traffic signals, the City of Milwaukee's Unified Call Center (UCC) makes it easy to submit service requests to solve problems. The UCC also allows you to track your service requests. Each time you complete a service request online, you will be assigned a tracking number that you can use to see when a City of Milwaukee representative expects to investigate or take care of your request.

To download XML and JSON files, click the CSV option below and click the down arrow next to the Download button in the upper right on its page.

Please note: Due to pandemic call handling modifications, the activity data since March 2020 does not fully represent the agent-handled calls to 311. The actual calls handled are higher. The 311 Call Center Inquiry dataset contains information on all agent-handled calls to the City’s 311 information line, including date, time and topic. Click here for the data dictionary.

About this dataset

Context

The dataset tabulates the Central Falls population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Central Falls. The dataset can be utilized to understand the population distribution of Central Falls by age. For example, using this dataset, we can identify the largest age group in Central Falls.

Key observations

The largest age group in Central Falls, RI was for the group of age 25 to 29 years years with a population of 2,194 (9.76%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Central Falls, RI was the 85 years and over years with a population of 198 (0.88%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Central Falls is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Central Falls total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Central Falls Population by Age. You can refer the same here

Overview

This dataset of medical misinformation was collected and is published by Kempelen Institute of Intelligent Technologies (KInIT). It consists of approx. 317k news articles and blog posts on medical topics published between January 1, 1998 and February 1, 2022 from a total of 207 reliable and unreliable sources. The dataset contains full-texts of the articles, their original source URL and other extracted metadata. If a source has a credibility score available (e.g., from Media Bias/Fact Check), it is also included in the form of annotation. Besides the articles, the dataset contains around 3.5k fact-checks and extracted verified medical claims with their unified veracity ratings published by fact-checking organisations such as Snopes or FullFact. Lastly and most importantly, the dataset contains 573 manually and more than 51k automatically labelled mappings between previously verified claims and the articles; mappings consist of two values: claim presence (i.e., whether a claim is contained in the given article) and article stance (i.e., whether the given article supports or rejects the claim or provides both sides of the argument).

The dataset is primarily intended to be used as a training and evaluation set for machine learning methods for claim presence detection and article stance classification, but it enables a range of other misinformation related tasks, such as misinformation characterisation or analyses of misinformation spreading.

Its novelty and our main contributions lie in (1) focus on medical news article and blog posts as opposed to social media posts or political discussions; (2) providing multiple modalities (beside full-texts of the articles, there are also images and videos), thus enabling research of multimodal approaches; (3) mapping of the articles to the fact-checked claims (with manual as well as predicted labels); (4) providing source credibility labels for 95% of all articles and other potential sources of weak labels that can be mined from the articles' content and metadata.

The dataset is associated with the research paper "Monant Medical Misinformation Dataset: Mapping Articles to Fact-Checked Claims" accepted and presented at ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '22).

The accompanying Github repository provides a small static sample of the dataset and the dataset's descriptive analysis in a form of Jupyter notebooks.

Options to access the dataset

There are two ways how to get access to the dataset:

1. Static dump of the dataset available in the CSV format
2. Continuously updated dataset available via REST API

In order to obtain an access to the dataset (either to full static dump or REST API), please, request the access by following instructions provided below.

References

If you use this dataset in any publication, project, tool or in any other form, please, cite the following papers:

@inproceedings{SrbaMonantPlatform,
  author = {Srba, Ivan and Moro, Robert and Simko, Jakub and Sevcech, Jakub and Chuda, Daniela and Navrat, Pavol and Bielikova, Maria},
  booktitle = {Proceedings of Workshop on Reducing Online Misinformation Exposure (ROME 2019)},
  pages = {1--7},
  title = {Monant: Universal and Extensible Platform for Monitoring, Detection and Mitigation of Antisocial Behavior},
  year = {2019}
}

@inproceedings{SrbaMonantMedicalDataset,
  author = {Srba, Ivan and Pecher, Branislav and Tomlein Matus and Moro, Robert and Stefancova, Elena and Simko, Jakub and Bielikova, Maria},
  booktitle = {Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '22)},
  numpages = {11},
  title = {Monant Medical Misinformation Dataset: Mapping Articles to Fact-Checked Claims},
  year = {2022},
  doi = {10.1145/3477495.3531726},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  url = {https://doi.org/10.1145/3477495.3531726},
}

Dataset creation process

In order to create this dataset (and to continuously obtain new data), we used our research platform Monant. The Monant platform provides so called data providers to extract news articles/blogs from news/blog sites as well as fact-checking articles from fact-checking sites. General parsers (from RSS feeds, Wordpress sites, Google Fact Check Tool, etc.) as well as custom crawler and parsers were implemented (e.g., for fact checking site Snopes.com). All data is stored in the unified format in a central data storage.


Ethical considerations

The dataset was collected and is published for research purposes only. We collected only publicly available content of news/blog articles. The dataset contains identities of authors of the articles if they were stated in the original source; we left this information, since the presence of an author's name can be a strong credibility indicator. However, we anonymised the identities of the authors of discussion posts included in the dataset.

The main identified ethical issue related to the presented dataset lies in the risk of mislabelling of an article as supporting a false fact-checked claim and, to a lesser extent, in mislabelling an article as not containing a false claim or not supporting it when it actually does. To minimise these risks, we developed a labelling methodology and require an agreement of at least two independent annotators to assign a claim presence or article stance label to an article. It is also worth noting that we do not label an article as a whole as false or true. Nevertheless, we provide partial article-claim pair veracities based on the combination of claim presence and article stance labels.

As to the veracity labels of the fact-checked claims and the credibility (reliability) labels of the articles' sources, we take these from the fact-checking sites and external listings such as Media Bias/Fact Check as they are and refer to their methodologies for more details on how they were established.

Lastly, the dataset also contains automatically predicted labels of claim presence and article stance using our baselines described in the next section. These methods have their limitations and work with certain accuracy as reported in this paper. This should be taken into account when interpreting them.


Reporting mistakes in the dataset
The mean to report considerable mistakes in raw collected data or in manual annotations is by creating a new issue in the accompanying Github repository. Alternately, general enquiries or requests can be sent at info [at] kinit.sk.

Dataset structure

Raw data

At first, the dataset contains so called raw data (i.e., data extracted by the Web monitoring module of Monant platform and stored in exactly the same form as they appear at the original websites). Raw data consist of articles from news sites and blogs (e.g. naturalnews.com), discussions attached to such articles, fact-checking articles from fact-checking portals (e.g. snopes.com). In addition, the dataset contains feedback (number of likes, shares, comments) provided by user on social network Facebook which is regularly extracted for all news/blogs articles.

Raw data are contained in these CSV files (and corresponding REST API endpoints):

• sources.csv
• articles.csv
• article_media.csv
• article_authors.csv
• discussion_posts.csv
• discussion_post_authors.csv
• fact_checking_articles.csv
• fact_checking_article_media.csv
• claims.csv
• feedback_facebook.csv

Note: Personal information about discussion posts' authors (name, website, gravatar) are anonymised.

Annotations

Secondly, the dataset contains so called annotations. Entity annotations describe the individual raw data entities (e.g., article, source). Relation annotations describe relation between two of such entities.

Each annotation is described by the following attributes:

1. category of annotation (`annotation_category`). Possible values: label (annotation corresponds to ground truth, determined by human experts) and prediction (annotation was created by means of AI method).
2. type of annotation (`annotation_type_id`). Example values: Source reliability (binary), Claim presence. The list of possible values can be obtained from enumeration in annotation_types.csv.
3. method which created annotation (`method_id`). Example values: Expert-based source reliability evaluation, Fact-checking article to claim transformation method. The list of possible values can be obtained from enumeration methods.csv.
4. its value (`value`). The value is stored in JSON format and its structure differs according to particular annotation type.

At the same time, annotations are associated with a particular object identified by:

1. entity type (parameter entity_type in case of entity annotations, or source_entity_type and target_entity_type in case of relation annotations). Possible values: sources, articles, fact-checking-articles.
2. entity id (parameter entity_id in case of entity annotations, or source_entity_id and target_entity_id in case of relation

Context

The idea is that others can run simple regression and classification models on this dataset.

Content

This dataset contains anonymous data from a call center, and the metrics obtained regarding customer service agents.

Introduction

Welcome to the US English Call Center Speech Dataset for the Retail domain designed to enhance the development of call center speech recognition models specifically for the Retail industry. This dataset is meticulously curated to support advanced speech recognition, natural language processing, conversational AI, and generative voice AI algorithms.

Speech Data

This training dataset comprises 30 hours of call center audio recordings covering various topics and scenarios related to the Retail domain, designed to build robust and accurate customer service speech technology.

Topic Diversity

This dataset offers a diverse range of conversation topics, call types, and outcomes, including both inbound and outbound calls with positive, neutral, and negative outcomes.

This extensive coverage ensures the dataset includes realistic call center scenarios, which is essential for developing effective customer support speech recognition models.

Transcription

To facilitate your workflow, the dataset includes manual verbatim transcriptions of each call center audio file in JSON format. These transcriptions feature:

These ready-to-use transcriptions accelerate the development of the Retail domain call center conversational AI and ASR models for the US English language.

Metadata

The dataset provides comprehensive metadata for each conversation and participant:

This metadata is a powerful tool for understanding and characterizing the data, enabling informed decision-making in the development of US English call center speech recognition models.

Usage and Applications

This dataset can be used for various applications in the fields of speech recognition, natural language processing, and conversational AI, specifically tailored to the Retail domain. Potential use cases include:

Secure and Ethical Collection

Updates and Customization

Understanding the importance of diverse environments for robust ASR models, our call center voice dataset is regularly updated with new audio data captured in various real-world conditions.

License

This Retail domain call center audio dataset is created by FutureBeeAI and is available for commercial use.

About this dataset

Context

The dataset presents median household incomes for various household sizes in Central City, CO, as reported by the U.S. Census Bureau. The dataset highlights the variation in median household income with the size of the family unit, offering valuable insights into economic trends and disparities within different household sizes, aiding in data analysis and decision-making.

Key observations

• Of the 7 household sizes (1 person to 7-or-more person households) reported by the census bureau, Central City did not include 4, 5, 6, or 7-person households. Across the different household sizes in Central City the mean income is $71,554, and the standard deviation is $20,566. The coefficient of variation (CV) is 28.74%. This high CV indicates high relative variability, suggesting that the incomes vary significantly across different sizes of households.
• In the most recent year, 2021, The smallest household size for which the bureau reported a median household income was 1-person households, with an income of $57,915. It then further increased to $95,210 for 3-person households, the largest household size for which the bureau reported a median household income.

https://i.neilsberg.com/ch/central-city-co-median-household-income-by-household-size.jpeg" alt="Central City, CO median household income, by household size (in 2022 inflation-adjusted dollars)">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Household Sizes:

• 1-person households
• 2-person households
• 3-person households
• 4-person households
• 5-person households
• 6-person households
• 7-or-more-person households

Variables / Data Columns

• Household Size: This column showcases 7 household sizes ranging from 1-person households to 7-or-more-person households (As mentioned above).
• Median Household Income: Median household income, in 2022 inflation-adjusted dollars for the specific household size.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Central City median household income. You can refer the same here

About this dataset

Context

The dataset presents the distribution of median household income among distinct age brackets of householders in Central Point. Based on the latest 2019-2023 5-Year Estimates from the American Community Survey, it displays how income varies among householders of different ages in Central Point. It showcases how household incomes typically rise as the head of the household gets older. The dataset can be utilized to gain insights into age-based household income trends and explore the variations in incomes across households.

Key observations: Insights from 2023

In terms of income distribution across age cohorts, in Central Point, householders within the 45 to 64 years age group have the highest median household income at $94,335, followed by those in the 25 to 44 years age group with an income of $91,018. Meanwhile householders within the 65 years and over age group report the second lowest median household income of $55,593. Notably, householders within the under 25 years age group, had the lowest median household income at $45,735.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Age groups classifications include:

• Under 25 years
• 25 to 44 years
• 45 to 64 years
• 65 years and over

Variables / Data Columns

• Age Of The Head Of Household: This column presents the age of the head of household
• Median Household Income: Median household income, in 2023 inflation-adjusted dollars for the specific age group

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Central Point median household income by age. You can refer the same here

About this dataset

Context

The dataset tabulates the Central Point population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Central Point. The dataset can be utilized to understand the population distribution of Central Point by age. For example, using this dataset, we can identify the largest age group in Central Point.

Key observations

The largest age group in Central Point, OR was for the group of age 35 to 39 years years with a population of 1,662 (8.66%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Central Point, OR was the 80 to 84 years years with a population of 366 (1.91%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Central Point is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Central Point total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Central Point Population by Age. You can refer the same here

About this dataset

Context

The dataset presents the distribution of median household income among distinct age brackets of householders in Central City. Based on the latest 2019-2023 5-Year Estimates from the American Community Survey, it displays how income varies among householders of different ages in Central City. It showcases how household incomes typically rise as the head of the household gets older. The dataset can be utilized to gain insights into age-based household income trends and explore the variations in incomes across households.

Key observations: Insights from 2023

In terms of income distribution across age cohorts, in Central City, householders within the 25 to 44 years age group have the highest median household income at $115,083, followed by those in the 45 to 64 years age group with an income of $95,417. Meanwhile householders within the under 25 years age group report the second lowest median household income of $76,458. Notably, householders within the 65 years and over age group, had the lowest median household income at $52,500.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Age groups classifications include:

• Under 25 years
• 25 to 44 years
• 45 to 64 years
• 65 years and over

Variables / Data Columns

• Age Of The Head Of Household: This column presents the age of the head of household
• Median Household Income: Median household income, in 2023 inflation-adjusted dollars for the specific age group

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Central City median household income by age. You can refer the same here

About this dataset

Context

The dataset tabulates the population of Central Point by race. It includes the population of Central Point across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of Central Point across relevant racial categories.

Key observations

The percent distribution of Central Point population by race (across all racial categories recognized by the U.S. Census Bureau): 89.52% are white, 0.01% are Black or African American, 0.89% are American Indian and Alaska Native, 0.69% are Asian, 1.49% are Native Hawaiian and other Pacific Islander, 2.21% are some other race and 5.21% are multiracial.

https://i.neilsberg.com/ch/central-point-or-population-by-race.jpeg" alt="Central Point population by race">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the Central Point
• Population: The population of the racial category (excluding ethnicity) in the Central Point is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Central Point total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Central Point Population by Race & Ethnicity. You can refer the same here

Introduction

Welcome to the French Call Center Speech Dataset for the Healthcare domain designed to enhance the development of call center speech recognition models specifically for the Healthcare industry. This dataset is meticulously curated to support advanced speech recognition, natural language processing, conversational AI, and generative voice AI algorithms.

Speech Data

This training dataset comprises 30 Hours of call center audio recordings covering various topics and scenarios related to the Healthcare domain, designed to build robust and accurate customer service speech technology.

Topic Diversity

This dataset offers a diverse range of conversation topics, call types, and outcomes, including both inbound and outbound calls with positive, neutral, and negative outcomes.

This extensive coverage ensures the dataset includes realistic call center scenarios, which is essential for developing effective customer support speech recognition models.

Transcription

To facilitate your workflow, the dataset includes manual verbatim transcriptions of each call center audio file in JSON format. These transcriptions feature:

These ready-to-use transcriptions accelerate the development of the Healthcare domain call center conversational AI and ASR models for the French language.

Metadata

The dataset provides comprehensive metadata for each conversation and participant:

This metadata is a powerful tool for understanding and characterizing the data, enabling informed decision-making in the development of French call center speech recognition models.

Usage and Applications

This dataset can be used for various applications in the fields of speech recognition, natural language processing, and conversational AI, specifically tailored to the Healthcare domain. Potential use cases include:

Introduction

Welcome to the Spanish Call Center Speech Dataset for the Healthcare domain designed to enhance the development of call center speech recognition models specifically for the Healthcare industry. This dataset is meticulously curated to support advanced speech recognition, natural language processing, conversational AI, and generative voice AI algorithms.

Speech Data

This training dataset comprises 30 Hours of call center audio recordings covering various topics and scenarios related to the Healthcare domain, designed to build robust and accurate customer service speech technology.

Topic Diversity

This dataset offers a diverse range of conversation topics, call types, and outcomes, including both inbound and outbound calls with positive, neutral, and negative outcomes.

This extensive coverage ensures the dataset includes realistic call center scenarios, which is essential for developing effective customer support speech recognition models.

Transcription

To facilitate your workflow, the dataset includes manual verbatim transcriptions of each call center audio file in JSON format. These transcriptions feature:

These ready-to-use transcriptions accelerate the development of the Healthcare domain call center conversational AI and ASR models for the Spanish language.

Metadata

The dataset provides comprehensive metadata for each conversation and participant:

This metadata is a powerful tool for understanding and characterizing the data, enabling informed decision-making in the development of Spanish call center speech recognition models.

Usage and Applications

This dataset can be used for various applications in the fields of speech recognition, natural language processing, and conversational AI, specifically tailored to the Healthcare domain. Potential use cases include:

About this dataset

Context

The dataset presents the distribution of median household income among distinct age brackets of householders in Central Falls. Based on the latest 2019-2023 5-Year Estimates from the American Community Survey, it displays how income varies among householders of different ages in Central Falls. It showcases how household incomes typically rise as the head of the household gets older. The dataset can be utilized to gain insights into age-based household income trends and explore the variations in incomes across households.

Key observations: Insights from 2023

In terms of income distribution across age cohorts, in Central Falls, householders within the 25 to 44 years age group have the highest median household income at $56,070, followed by those in the 45 to 64 years age group with an income of $44,265. Meanwhile householders within the under 25 years age group report the second lowest median household income of $33,063. Notably, householders within the 65 years and over age group, had the lowest median household income at $28,574.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Age groups classifications include:

• Under 25 years
• 25 to 44 years
• 45 to 64 years
• 65 years and over

Variables / Data Columns

• Age Of The Head Of Household: This column presents the age of the head of household
• Median Household Income: Median household income, in 2023 inflation-adjusted dollars for the specific age group

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Central Falls median household income by age. You can refer the same here

About this dataset

Context

The dataset tabulates the Central Square population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Central Square. The dataset can be utilized to understand the population distribution of Central Square by age. For example, using this dataset, we can identify the largest age group in Central Square.

Key observations

The largest age group in Central Square, NY was for the group of age 55-59 years with a population of 221 (10.56%), according to the 2021 American Community Survey. At the same time, the smallest age group in Central Square, NY was the 30-34 years with a population of 42 (2.01%). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Central Square is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Central Square total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Central Square Population by Age. You can refer the same here

About this dataset

Context

The dataset presents median household incomes for various household sizes in Central Lake, MI, as reported by the U.S. Census Bureau. The dataset highlights the variation in median household income with the size of the family unit, offering valuable insights into economic trends and disparities within different household sizes, aiding in data analysis and decision-making.

Key observations

• Of the 7 household sizes (1 person to 7-or-more person households) reported by the census bureau, Central Lake did not include 5, 6, or 7-person households. Across the different household sizes in Central Lake the mean income is $55,514, and the standard deviation is $27,489. The coefficient of variation (CV) is 49.52%. This high CV indicates high relative variability, suggesting that the incomes vary significantly across different sizes of households.
• In the most recent year, 2021, The smallest household size for which the bureau reported a median household income was 1-person households, with an income of $30,015. It then further increased to $88,275 for 4-person households, the largest household size for which the bureau reported a median household income.

https://i.neilsberg.com/ch/central-lake-mi-median-household-income-by-household-size.jpeg" alt="Central Lake, MI median household income, by household size (in 2022 inflation-adjusted dollars)">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Household Sizes:

• 1-person households
• 2-person households
• 3-person households
• 4-person households
• 5-person households
• 6-person households
• 7-or-more-person households

Variables / Data Columns

• Household Size: This column showcases 7 household sizes ranging from 1-person households to 7-or-more-person households (As mentioned above).
• Median Household Income: Median household income, in 2022 inflation-adjusted dollars for the specific household size.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Central Lake median household income. You can refer the same here

About this dataset

Context

The dataset tabulates the Central population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Central. The dataset can be utilized to understand the population distribution of Central by age. For example, using this dataset, we can identify the largest age group in Central.

Key observations

The largest age group in Central, LA was for the group of age 35-39 years with a population of 2,578 (8.67%), according to the 2021 American Community Survey. At the same time, the smallest age group in Central, LA was the 85+ years with a population of 334 (1.12%). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Central is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Central total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Central Population by Age. You can refer the same here