About this dataset

Context

The dataset tabulates the England 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 England. The dataset can be utilized to understand the population distribution of England by age. For example, using this dataset, we can identify the largest age group in England.

Key observations

The largest age group in England, AR was for the group of age 55-59 years with a population of 276 (11.03%), according to the 2021 American Community Survey. At the same time, the smallest age group in England, AR was the 85+ years with a population of 32 (1.28%). 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 England is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of England 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 England Population by Age. You can refer the same here

About this dataset

Context

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

Key observations

The percent distribution of England population by race (across all racial categories recognized by the U.S. Census Bureau): 68.50% are white, 22% are Black or African American, 0.31% are American Indian and Alaska Native, 4.52% are some other race and 4.67% are multiracial.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 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 England
• Population: The population of the racial category (excluding ethnicity) in the England is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of England 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 England Population by Race & Ethnicity. You can refer the same here

WiserBrand's Comprehensive Customer Call Transcription Dataset: Tailored Insights

WiserBrand offers a customizable dataset comprising transcribed customer call records, meticulously tailored to your specific requirements. This extensive dataset includes:

• User ID and Firm Name: Identify and categorize calls by unique user IDs and company names.
• Call Duration: Analyze engagement levels through call lengths.
• Geographical Information: Detailed data on city, state, and country for regional analysis.
• Call Timing: Track peak interaction times with precise timestamps.
• Call Reason and Group: Categorised reasons for calls, helping to identify common customer issues.
• Device and OS Types: Information on the devices and operating systems used for technical support analysis. Transcriptions: Full-text transcriptions of each call, enabling sentiment analysis, keyword extraction, and detailed interaction reviews.

WiserBrand's dataset is essential for companies looking to leverage Consumer Data and B2B Marketing Data to drive their strategic initiatives in the English-speaking markets of the USA, UK, and Australia. By accessing this rich dataset, businesses can uncover trends and insights critical for improving customer engagement and satisfaction.

Cases:

1. Training Speech Recognition (Speech-to-Text) and Speech Synthesis (Text-to-Speech) Models

WiserBrand's Comprehensive Customer Call Transcription Dataset is an excellent resource for training and improving speech recognition models (Speech-to-Text, STT) and speech synthesis systems (Text-to-Speech, TTS). Here’s how this dataset can contribute to these tasks:

Enriching STT Models: The dataset comprises a diverse range of real-world customer service calls, featuring various accents, tones, and terminologies. This makes it highly valuable for training speech-to-text models to better recognize different dialects, regional speech patterns, and industry-specific jargon. It could help improve accuracy in transcribing conversations in customer service, sales, or technical support.

Contextualized Speech Recognition: Given the contextual information (e.g., reasons for calls, call categories, etc.), it can help models differentiate between various types of conversations (technical support vs. sales queries), which would improve the model’s ability to transcribe in a more contextually relevant manner.

Improving TTS Systems: The transcriptions, along with their associated metadata (such as call duration, timing, and call reason), can aid in training Text-to-Speech models that mimic natural conversation patterns, including pauses, tone variation, and proper intonation. This is especially beneficial for developing conversational agents that sound more natural and human-like in their responses.

Noise and Speech Quality Handling: Real-world customer service calls often contain background noise, overlapping speech, and interruptions, which are crucial elements for training speech models to handle real-life scenarios more effectively.

1. Training AI Agents for Replacing Customer Service Representatives WiserBrand’s dataset can be incredibly valuable for businesses looking to develop AI-powered customer support agents that can replace or augment human customer service representatives. Here’s how this dataset supports AI agent training:

Customer Interaction Simulation: The transcriptions provide a comprehensive view of real customer interactions, including common queries, complaints, and support requests. By training AI models on this data, businesses can equip their virtual agents with the ability to understand customer concerns, follow up on issues, and provide meaningful solutions, all while mimicking human-like conversational flow.

Sentiment Analysis and Emotional Intelligence: The full-text transcriptions, along with associated call metadata (e.g., reason for the call, call duration, and geographical data), allow for sentiment analysis, enabling AI agents to gauge the emotional tone of customers. This helps the agents respond appropriately, whether it’s providing reassurance during frustrating technical issues or offering solutions in a polite, empathetic manner. Such capabilities are essential for improving customer satisfaction in automated systems.

Customizable Dialogue Systems: The dataset allows for categorizing and identifying recurring call patterns and issues. This means AI agents can be trained to recognize the types of queries that come up frequently, allowing them to automate routine tasks such as order inquiries, account management, or technical troubleshooting without needing human intervention.

Improving Multilingual and Cross-Regional Support: Given that the dataset includes geographical information (e.g., city, state, and country), AI agents can be trained to recognize region-specific slang, phrases, and cultural nuances, which is particularly valuable for multinational companies operating in diverse markets (e.g., the USA, UK, and Australia...

[THIS DATASET HAS BEEN WITHDRAWN]. The leaf phenology product presented here shows the amplitude of annual cycles observed in MODIS (Moderate Resolution Imaging Spectroradiometer) normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) 16-day time-series of 2000 to 2013 for Meso- and South America. The values given represent a conservative measure of the amplitude after the annual cycle was identified and tested for significance by means of the Fourier Transform. The amplitude was derived for four sets of vegtation indices (VI) time-series based on the MODIS VI products (500m MOD13A1; 1000m MOD13A2). The amplitude value can be interpreted as the degree in which the life cycles of individual leaves of plants observed within a pixel are synchronised. In other words, given the local variation in environment and climate and the diversity of species leaf life cycle strategies, an image pixel will represent vegetation communities behaving between two extremes: * well synchronized, where the leaf bud burst and senescence of the individual plants within the pixel occurs near simultaneously, yielding a high amplitude value. Often this matches with an area of low species diversity (e.g. arable land) or with areas where the growth of all plants is controlled by the same driver (e.g. precipitation). * poorly synchronized, where the leaf bud burst and senescence of individual plants within a pixel occurs at different times of the year, yielding a low amplitude value. Often this matches with an area of high species diversity and/or where several drivers could be controlling growth. Full details about this dataset can be found at https://doi.org/10.5285/36795e9d-2380-465c-947b-3c9ae26f92d0

About this dataset

Context

The dataset tabulates the Non-Hispanic population of England by race. It includes the distribution of the Non-Hispanic population of England across various race categories as identified by the Census Bureau. The dataset can be utilized to understand the Non-Hispanic population distribution of England across relevant racial categories.

Key observations

Of the Non-Hispanic population in England, the largest racial group is White alone with a population of 1,759 (71.74% of the total Non-Hispanic population).

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 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 (for Non-Hispanic) for the England
• Population: The population of the racial category (for Non-Hispanic) in the England is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of England total Non-Hispanic 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 England Population by Race & Ethnicity. You can refer the same here

A Century of Sex Education Discourse: Ngram Frequencies (US & UK, 1922-2022)

1. Dataset Overview

This dataset provides historical linguistic frequency data related to sex education discourse in British English and American English from 1922 to 2022. Frequencies were extracted from the Google Ngram Viewer (English-GB and English-US corpora, 2019 version) for terms systematically categorized into four distinct conceptual groups. This dataset aims to support research into the evolution of public discourse, pedagogical approaches, and cultural attitudes surrounding sex education over the past century.

2. Creator / Author(s)

• Creator: Shah Mohammad Rizvi
• Affiliation: N/A
• Contact: smri29.ml@gmail.com

3. Date Information

• Date Created: June 30, 2025
• Last Updated: July 2, 2025

4. Data Source

The data in this dataset was extracted from the Google Ngram Viewer.

• Corpora Version: 2019 (English-GB, English-US)
• URL: https://books.google.com/ngrams

5. Data Collection Methodology

Ngram frequency data was programmatically extracted from the Google Ngram Viewer by accessing generated HTML pages, which contain embedded JSON data. A custom Python script was used to parse the HTML, extract the time-series frequency data for specific terms, and consolidate it into a structured CSV format. Ngram Viewer smoothing was uniformly set to 3 for all queries to mitigate year-to-year fluctuations.

6. Coverage

• Time Period: 1922 - 2022 (inclusive)
• Languages/Corpora:
  • British English (Google Ngram's English (GB) corpus)
  • American English (Google Ngram's English (US) corpus)

7. Term Groups and Specific Terms Queried

Terms were carefully selected and grouped to analyze different facets of sex education discourse. Each group's terms were queried individually or as grouped queries where indicated (e.g., using (All) quantifier in Ngram Viewer).

group01_Primary Discourse Terms (Foundational Concepts)

These terms represent the central, overarching, and foundational concepts that define or are core to the public conversation surrounding sex education.

• sex education
• reproductive health
• sexual health
• contraception
• abstinence
• consent
• STD
• STI

group02_Biological & Reproductive Terms

This group includes vocabulary related to human anatomy, physiological processes, and biological aspects often discussed in the context of sex education.

• puberty
• menstruation
• vagina
• penis
• reproduction
• sperm
• ovulation

group03_Evolving Discourse Terms

These terms reflect contemporary understandings, progressive approaches, inclusivity, and specific modern public health concerns that have gained significant prominence in later decades of the discourse.

• LGBTQ
• gender identity
• sexual orientation
• body autonomy
• safe sex
• HIV prevention
• AIDS education

group04_Historical Terms

This group contains vocabulary that was more prevalent in earlier periods, reflecting older approaches, euphemisms, or terms whose primary usage or connotations have significantly shifted over the past century.

• venereal disease
• chastity
• morality
• family planning
• the pill
• prophylactic

8. File Structure

The dataset is organized as follows:

• sex_education_final_combined_dataset.csv: This file contains all Ngram frequency data for both British and American English, encompassing all terms from all four groups, consolidated into a single DataFrame.
• Sex_ED_UK/: Directory containing individual CSV files for each term group relevant to the British English corpus.
  • group01_Primary Discourse Terms.csv
  • group02_Biological & Reproductive Terms.csv
  • group03_Evolving Discourse Terms.csv
  • group04_Historical Terms.csv
• Sex_ED_USA/: Directory containing individual CSV files for each term group relevant to the American English corpus.
  • group01_Primary Discourse Terms.csv
  • group02_Biological & Reproductive Terms.csv
  • group03_Evolving Discourse Terms.csv
  • group04_Historical Terms.csv
• README.md: This metadata file.

9. Column Descriptions

All CSV files (individual and combined) share the following columns:

• Year: Integer - The year of publication of the texts from which the Ngram frequencies were calculated (ranging from 1922 to 2022).
• Term: String - The specific Ngram term or phrase for which the frequency is provided.
• Frequency: Float - The relative frequency of the Term in the Corpus for that Year. This is a proportion of the total number of Ngrams for that year.
• Corpus: String - The Google Ngram corpus from which the data was extracted (British English or American English).
• TermGroup: String - The conceptu...

As global political preeminence gradually shifted from the United Kingdom to the United States, so did the capacity to culturally influence the rest of the world. In this work, we analyze how the world-wide varieties of written English are evolving. We study both the spatial and temporal variations of vocabulary and spelling of English using a large corpus of geolocated tweets and the Google Books datasets corresponding to books published in the US and the UK. The advantage of our approach is that we can address both standard written language (Google Books) and the more colloquial forms of microblogging messages (Twitter). We find that American English is the dominant form of English outside the UK and that its influence is felt even within the UK borders. Finally, we analyze how this trend has evolved over time and the impact that some cultural events have had in shaping it.

Data from geophysical surveys in many South American and Caribbean countries carried out by the British Geological Survey for different agencies. The surveys range from regional gravity and airborne magnetic mapping to targetted surveys for mineral and water. Individual surveys do not yet have metadata entries: this entry describes a notional database that represents all geophysical surveys carried out within the region.

This record is for Approval for Access product AfA159. This dataset contains details of currently permitted waste carriers, brokers and dealers. Lower tier registrations are indefinite however upper tier need renewing every 3 years. Historical details are not included. Live data can be downloaded from the electronic Public Register: https://environment.data.gov.uk/public-register/view/search-waste-carriers-brokers Carrier: A person who transports controlled waste in the course of a business or otherwise with a view to profit. Broker: Waste brokers are people who make arrangements, on behalf of others, to recover or dispose of waste, regardless of whether or not they handle the waste themselves. Dealer: Waste dealers are people who buy then sell wastes, regardless of whether or not they handle the waste themselves. Exempt activities: People who do not need to register because of a specific exemption in the regulations; - the operator of certain vessels and vehicles where the activity of waste carriage is for the purpose of a specified marine operation and the activity requires a marine licence or can be carried out under a marine exemption - any lower tier carrier who does not normally and regularly transport controlled waste - until after 2013, the existing exemption for carriers who only transport their own waste (unless it is construction and demolition waste) will remain in place. Excluded persons: People who are excluded from the requirement to register. These include: - Any person who carries controlled wastes but not as part of their business or otherwise for profit - Ferry operators carrying vehicles that are carrying waste - Any person carrying waste between different places belonging to the same premises. - Any person carrying waste by air or sea, from a place in Great Britain to any place outside Great Britain - Any person carrying waste from a country outside of Great Britain to the first point of arrival Waste Carriers, Dealers and Brokers are a combined dataset. Operators shift between categories frequently, and so separate datasets could be misleading. Extracting a single type would be extremely time consuming and cost-prohibitive. Attribution statement: © Environment Agency copyright and/or database right 2022. All rights reserved. Special Conditions: 1. You may use the Information for your internal or personal purposes and may only sublicense others to use it if you do so under a written licence which includes the terms of these conditions and the agreement and in particular may not allow any period of use longer than the period licensed to you. 2. The period of permitted use is one year. 3. We have restricted use of the Information as a result of legal restrictions placed upon us to protect the rights or confidentialities of others. If you contact us in writing (this includes email) we will, as far as confidentiality rules allow, provide you with details including, if available, how you might seek permission from a third party to extend your use rights. 4. This condition does not apply if use is limited to use that is authorised by any statute or use that does not require a licence from us

All organisations hold information about the core of their business. Forestry England holds information on trees and forests. We use this information to help us run our business and make decisions. The role of the Forest Inventory (the Sub-compartment Database (SCDB) and the stock maps) is to be our authoritative data source, giving us information for recording, monitoring, analysis and reporting. Through this it supports decision-making on the whole of the FE estate. Information from the Inventory is used by FE, wider government, industry and the public for economic, environmental and social forest-related decision-making. Furthermore, it supports forest-related national policy development and government initiatives, and helps us meet our national and international forest-related reporting responsibilities. Information on our current forest resource, and the future expansion and availability of wood products from our forests, is vital for planners both in and outside FE. It is used when looking at the development of processing industries, regional infrastructure, the effect upon communities of our actions, and to prepare and monitor government policies. The Inventory (SCDB and stock maps), with ‘Future Forest Structure’ and the ‘rollback’ functionality of Forester, will help provide a definitive measure of trends in extent, structure, composition, health, status, use, and management of all FE land holdings. We require this to meet national and international commitments, to report on the sustainable management of forests as well as to help us through the process of business and Forest Design Planning. As well as helping with the above, the SCDB helps us address detailed requests from industry, government, non-government organisations and the public for information on our estate. FE's growing national and international responsibilities and the requirements for monitoring and reporting on a range of forest statistics have highlighted the technical challenges we face in providing consistent, national level data. A well kept and managed SCDB and GIS (Geographical Information System - Forester) will provide the best solution for this and assist countries in evidence-based policy making. Looking ahead at international reporting commitments; one example of an area where requirements look set to increase will be reporting on our work to combat climate change and how our estate contributes to carbon sequestration. We have put in place processes to ensure that at least the basics of our inventory are covered: The inventory of forests; The land-uses; The land we own ( Deeds); The roads we manage. We depend on others to allow us to manage the forests and to provide us with funds and in doing so we need to be seen to be responsible and accountable for our actions. A foundation of achieving this is good record keeping. A subcompartment should be recognisable on the ground. It will be similar enough in land use, species or habitat composition, yield class, age, condition, thinning history etc. to be treated as a single unit. They will generally be contiguous in nature and will not be split by roads, rivers, open space etc. Distinct boundaries are required, and these will often change as crops are felled, thinned, replanted and resurveyed. In some parts of the country foresters used historical and topographical features to delineate subcompartment boundaries, such as hedges, walls and escarpments. In other areas no account of the history and topography of the site was taken, with field boundaries, hedges, walls, streams etc. being subsumed into the sub-compartment. Also, these features may or may not appear on the OS backdrop, again this was dependent on the staff involved and what they felt was relevant to the map. The main point is that, as managers we may find such obvious features in the middle of a subcompartment when nothing is indicated on the stock map, while the same thing would be indicated elsewhere. Attributes; FOREST Cost centre Nos. COMPTMENT Compartment Nos. SUBCOMPT Sub-compartment letter BLOCK Block nos. CULTCODE Cultivation Code CULTIVATN Cultivation PRIHABCODE Primary Habitat Code PRIHABITAT Primary Habitat PRILANDUSE Land Use of primary component PRISPECIES Primary component tree species PRI_PLYEAR prim. component year planted PRIPCTAREA Prim. component %Area of sub-compartment SECHABCODE Secondary Habitat Code SECHABITAT Secondary Habitat SECLANDUSE Land Use of secondary component SECSPECIES Secondary component tree species SEC_PLYEAR Secondary component year planted SECPCTAREA Secondary component %Area of sub-compartment TERLANDUSE Land Use of tertiary component TERSPECIES Tertiary component tree species TER_PLYEAR Tertiary component year planted TERPCTAREA Tertiary component %Area of sub-compartment TERHABITAT Tertiary Habitat TERHABCODE Tertiary Habitat Code. Any maps produced using this data should contain the following Forestry Commission acknowledgement: “Contains, or is based on, information supplied by the Forestry Commission. © Crown copyright and database right 2025 Ordnance Survey AC0000814847”. Attribution statement: © Forestry Commission copyright and/or database right 2025. All rights reserved. Contains OS data © Crown copyright and database right 2025.

About the Dataset:

Amazon is one of the biggest online retailers in the UK. With this dataset, you can get an in-depth idea of what products sell best, which SEO titles generate the most sales, the best price range for a product in a given category, and much more.

It took a lot of time and energy to prepare this original dataset, so don't forget to hit the upvote button! 😊💝

Similar Datasets:

Amazon Canada Products

Amazon India Products

Amazon USA Products

Interesting Task Ideas:

1. Uncover trending product categories and their sales performance.
2. Analyze customer ratings to find top-rated products.
3. Train a product title generator that can generate sales-worthy product titles based on products with the most sales.
4. Gain insight into the best price for any given product based on sales data and competition.
5. Identify which niches are the easiest to make sales in.
6. Gain insights into the general spending habits of online shoppers.
7. Use as a seed dataset for practicing database management and performance optimization.
8. Train (or learn to train) an AI-based search model to recommend Amazon products.

Checkout my other datasets

USA Unemployment Rates by Demographics & Race

USA Hispanic-White Wage Gap Dataset

Median and Avg Hourly Wages in the USA

Health Insurance Coverage in the USA

Black-White Wage Gap in the USA Dataset

Photo by Canonicalized

Our Point of Interest (POI) data supports various location intelligence projects and facilitates the development of precise mapping and navigation tools, location analysis, address validation, and much more. Gain access to highly accurate, clean, and globally scaled POI data featuring over 164 million verified locations across 220 countries. We have been providing this data to companies worldwide for 30 years.

• Develop mapping and navigation tools and software.
• Identify new areas and locations suitable for business development.
• Analyze the presence of competitors and nearby populations.
• Optimize routes to enhance delivery efficiency.
• Evaluate property values based on nearby infrastructure.
• Support disaster management by identifying high-risk areas.
• Promote your products and services using geotargeting strategies.

Our use cases demonstrate how our data has been beneficial and helped our customers in several key areas: 1. Gaining a Competitive Edge: Utilize point of interest (POI) data to analyze competitors, identify high-opportunity areas, and attract more customers. 2. Enhancing Customer Journeys: Leverage location intelligence to provide personalized, real-time recommendations that boost customer engagement. 3. Optimizing Store Expansion: Select the most profitable locations by analyzing foot traffic, demographics, and competitor insights. 4. Streamlining Deliveries: Improve fulfillment accuracy through address validation, reducing failed shipments and increasing customer satisfaction. 5. Driving Smarter Campaigns: Use geospatial insights to effectively target the right audiences, enhance outreach, and maximize campaign impact.

This dataset gives figures for the number of American and European foulbrood samples taken from apiaries in English counties during 2011. The dataset includes the following fields: County Name; No. of different apiaries with AFB (American Foulbrood); No. of different apiaries with EFB (European Foulbrood); No. of different apiaries with Negative samples; Total no. of samples. Please note: this dataset has been drawn from a live database available via the NBU website. This is a dynamic system, which has been sampled here at a single point in time, but which reflects system amendments/data updates in the 'live' environment. This could cause the data to change over time, as data is updated or amended retrospectively. County data is reflective of the counties stored in BeeBase, and may not necessarily reflect current recognised counties/unitary authorities. Attribution statement: ©Crown Copyright

Background: Non-invasive Cardiovascular imaging (NICI), including cardiovascular magnetic resonance (CMR) imaging provides important information to guide the management of patients with cardiovascular conditions. Current rates of NICI use and potential policy determinants in the United States of America (US) and England remain unexplored.Methods: We compared NICI activity in the US (Medicare fee-for-service, 2011–2015) and England (National Health Service, 2012–2016). We reviewed recommendations related to CMR from Clinical Practice Guidelines, Appropriate Use Criteria (AUC), and Choosing Wisely. We then categorized recommendations according to whether CMR was the only recommended NICI technique (substitutable indications). Reimbursement policies in both settings were systematically collated and reviewed using publicly available information.Results: The 2015 rate of NICI activity in the US was 3.1 times higher than in England (31,055 vs. 9,916 per 100,000 beneficiaries). The proportion of CMR of all NICI was small in both jurisdictions, but nuclear cardiac imaging was more frequent in the US in absolute and relative terms. American and European CPGs were similar, both in terms of number of recommendations and proportions of indications where CMR was not the only recommended NICI technique (substitutable indications). Reimbursement schemes for NICI activity differed for physicians and hospitals between the two settings.Conclusions: Fee-for-service physician compensation in the US for NICI may contribute to higher NICI activity compared to England where physicians are salaried. Reimbursement arrangements for the performance of the test may contribute to the higher proportion of nuclear cardiac imaging out of the total NICI activity. Differences in CPG recommendations appear not to explain the variation in NICI activity between the US and England.

There is a paucity of data on research funding levels for male reproductive health (MRH). We investigated the research funding for MRH and infertility by examining publicly accessible webdatabases from the UK and USA government funding agencies. Information on the funding was collected from the UKRI-GTR, the NIHR’s Open Data Summary, and the USA’s NIH RePORT webdatabases. Funded projects between January 2016 and December 2019 were recorded and funding support was divided into three research categories: (i) male-based; (ii) female-based; and (iii) not-specified. Between January 2016 and December 2019, UK agencies awarded a total of £11,767,190 to 18 projects for male-based research and £29,850,945 to 40 projects for female-based research. There was no statistically significant difference in the median funding grant awarded within the male-based and female-based categories (p=0.56, W=392). The USA NIH funded 76 projects totalling $59,257,746 for male-based research and 99 projects totalling $83,272,898 for female-based research Again, there was no statistically significant difference in the median funding grant awarded between the two research categories (p=0.83, W=3834). This is the first study examining funding granted by main government research agencies from the UK and USA for MRH. These results should stimulate further discussion of the challenges of tackling male infertility and reproductive health disorders and formulating appropriate investment strategies. Methods Experimental Design: Publicly accessible UK Research and Innovation (UKRI), National Institute for Health Research (NIHR), and National Institutes of Health (NIH) funding agency databases covering awards from January 2016 to December 2019 were examined (see Supplementary Table 1). Following the inclusion and exclusion criteria outlined within Supplementary Tables 2,3, funding data were collected on research proposals investigating infertility and reproductive health. For simplicity, these are referred to collectively as ‘infertility research’. As the primary focus of this research is on infertility, the data were divided into three main categories: (i) male-based, (ii) female-based, and (iii) not-specified (Supplementary Table 2). The first two groups covered projects whose primary aim, based on the information presented in the research abstracts, timeline summaries and/or impact statements, was male- or female-focussed. “Not-specified” includes research projects that have either not specified a primary focus towards either male or female or have explicitly stated a focus on both. The process was conducted and reviewed by E.G. with C.L.R.B. Total funding for all three groups, funding over time, and comparison with overall funding for a particular agency was examined. Briefly, E.G. retrieved the primary data and produced the first set of data for discussion with C.L.R.B. Both went through the complete list and discussed each study/project and decided whether: (a) it should be included or not, and (b) what category does it fell under (male-, female-, or not-specified). The abstracts, which were almost always available and provided by each research study, were all examined and scrutinised by both E.G. and C.L.R.B together. If there was clear disagreement between E.G. and C.L.R.B, which were very rare, the project would not be included. UK Data Collection: From April 2018 the UK research councils, Innovate UK, and Research England are reported under one organization, the UKRI (2019). The councils independently fund research projects according to their respective visions and missions; however, until 2018/19, their annual funding expenditures were reported under the UKRI’s annual reports and budgets. The UKRI’s Gateway to Research (UKRI-GTR) web database allows users to analyse the information provided on taxpayer-funded research. Relevant search terms such as “male infertility” or “female reproductive health” (see Supplementary Table 2) were applied with appropriate database filters (Supplementary Table 1). The project award relevance was determined by assessing the objectives in project abstracts, timeline summaries, and planned impacts. Supplementary Tables 1, 2 and 3 provide the search filters and the reference criteria for inclusion/exclusion utilized for analysis. The UKRI-GTR provides the total funding granted to the projects within a designated period. Data obtained from the NIHR had minor differences. The NIHR has 6 datasets. The Open Data Summary View dataset was used as it provided details on funded projects, grants, summary abstracts, and project dates. Like the UKRI data, the NIHR excel datasheet had specific search terms and filters applied to sift out irrelevant projects (Supplementary Tables 1-3). The UKRI councils and NIHR report their annual expenditure and budgets for 1st April to 31st March. Thus, the projects will fall under the funding period of when their research activities begin (e.g. if a project’s research activities undergo between May 20th, 2017, to March 20th, 2019, this project will be categorized under the funding period 2017/18). The projects collected would begin their investigations between January 2016 to December 2019, therefore 5 consecutive funding periods were examined (2015/16, 2016/17, 2017/18, 2018/19, and 2019/20). The UK data collection period ran between October 2020 to December 2020. USA Data Collection: The NIH has a research portfolio online operating tools sites (RePORT) providing access to their research activities, such as previously funded research, active research projects, and information on NIH’s annual expenditures. The RePORT-Query database has similar features as the UKRI-GTR and NIHR such as providing information on project abstracts, research impact, start- and end-dates, funding grants, and type of research. Like the UK data collection, appropriate search terms were inputted with the database filters applied and followed the same inclusion-exclusion criteria (Supplementary Tables 1, 2, and 3). The UK and US agencies present data on funded research under different calendar and funding periods because the US’ federal tax policy requires federal bodies to report all funding expenses under a fiscal year (FY). The NIH’s FY follows a calendar period from October 1st to September 30th (e.g., FY2016 comprises funding activity from October 1st, 2015, to September 30th, 2016). Projects running over one calendar period are reported several times under consecutive fiscal years and the funds are divided according to the annual period of the project’s activity. During data collection, 74 projects were found as active with incomplete funding sums as the NIH divides the grants according to the budgeting period of every FY. The NIH are in the process of granting funds for the FY2021, so projects ending in 2020 or 2021 provide a complete funding sum. For the active projects ending after 2021, incomplete funding data is provided. It is assumed the funding will increase in value by the time the research ends in the future, but the final awarded sum is unknown. To remain consistent with the UK data, projects granted funding are totalled as one figure and recorded under the FY the project first began research, whether they are active or completed. Thus US funding is referred to as “Current Total Funding”. When going through the REPORTER database, the NIH present the same research project multiple times for every funded fiscal year with consecutive project reference IDs. Therefore, for simplicity, we only included the first project reference ID. For more information on deciphering NIH's project's IDs, see https://era.nih.gov/files/Deciphering_NIH_Application.pdf. For the USA, the initial data collection period ran between October 2020 to December 2020 but then restarted for a brief period in January 2021 to add up the remaining funding values for some of the active research projects. Data Analysis: The data was divided into three main groups and organized into the funding period or FY the project was first awarded. R-Studio (Version 1.3.1093) was utilized for the data analysis. Box-and-whisker plots are presented with rounded P-values. Kruskal-Wallis and Wilcoxon Rank Sum tests were generated to assess any statistical significance. The data was independently collected and does not assume a normal distribution, so the rank-based, non-parametric tests such as the Kruskal-Wallis and Wilcoxon Rank Sum were used. Research Project Details Included in the Collection Datasets: For both, the UK and USA data, we included the following details:

The project (or study) titles The Project IDs (also referred to as Project Reference or Project Number) The project Start and End Dates The project's Status (identified by the end dates or if explicitly stated in the database) The Funding Organisation (for the UK) and Admin Institute (for the USA) that are funding the research The project Category (i.e. Research Grants or Fellowships) The Amount Granted (for the USA, the funding values were summed up to the most recent awarding date).

Rearranging/Processing Data for Analysis: After the data collection has been completed, the data was processed into a simpler format in Notepad in order to perform the statistical analyses using RStudio. For that, only the essential details were included and organised that the RStudio system would recognise and analyse the information effectively and efficiently. The project Type (male, female or not-specifieded), funding sum for the respective research project Type, and the funding period (UK) / FY (USA) were included. These details were then arranged appropriately to produce box-and-whisker plots with P-values, perform the chosen statistical analysis tests, and produce the data statistics in RStudio. As mentioned earlier, the funding period/fiscal years were added following the timeframes set out by the respective countries.

This data covers the Instagram strategies of five major media outlets: The New York Times, The Guardian, USA Today, The Independent, and The Washington Post. Objectives include examining post volume, engagement metrics, content types, geographic coverage, individual mentions, and hashtag usage. Analysing 9,467 posts from 2023 using statistical and AI techniques, findings show The Washington Post posts most frequently, while The Independent and The Guardian achieve higher average engagement. Hashtags and mentions generally yield lower engagement. Donald Trump is the most mentioned individual, and the United States is the most covered country.

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About this dataset

Context

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

Key observations

The percent distribution of New Britain population by race (across all racial categories recognized by the U.S. Census Bureau): 82.88% are white, 4.57% are Black or African American, 0.35% are American Indian and Alaska Native, 5.78% are Asian, 0.39% are some other race and 6.02% are multiracial.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 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 New Britain
• Population: The population of the racial category (excluding ethnicity) in the New Britain is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of New Britain 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 New Britain Population by Race & Ethnicity. You can refer the same here