IntroductionUK Power Network maintains the 132kV voltage level network and below. An important part of the distribution network is the stepping down of voltage as it is moved towards the household; this is achieved using transformers. Transformers have a maximum rating for the utilisation of these assets based upon protection, overcurrent, switch gear, etc. This dataset contains the Grid Substation Transformers, also known as Bulk Supply Points, that typically step-down voltage from 132kV to 33kV (occasionally down to 66 or more rarely 20-25). These transformers can be viewed on the single line diagrams in our Long-Term Development Statements (LTDS) and the underlying data is then found in the LTDS tables.Care is taken to protect the private affairs of companies connected to the 33kV network, resulting in the redaction of certain transformers. Where redacted, we provide monthly statistics to continue to add value where possible. Where monthly statistics exist but half-hourly is absent, this data has been redacted.This dataset provides monthly statistics data across these named transformers from 2021 through to the previous month across our license areas. The data are aligned with the same naming convention as the LTDS for improved interoperability.To find half-hourly current and power flow data for a transformer, use the ‘tx_id’ that can be cross referenced in the Grid Transformers Half Hourly Dataset.If you want to download all this data, it is perhaps more convenient from our public sharepoint: Open Data Portal Library - Grid Transformers - All Documents (sharepoint.com)This dataset is part of a larger endeavour to share more operational data on UK Power Networks assets. Please visit our Network Operational Data Dashboard for more operational datasets.Methodological ApproachThe dataset is not derived, it is the measurements from our network stored in our historian.The measurement devices are taken from current transformers attached to the cable at the circuit breaker, and power is derived combining this with the data from voltage transformers physically attached to the busbar. The historian stores datasets based on a report-by-exception process, such that a certain deviation from the present value must be reached before logging a point measurement to the historian. We extract the data following a 30-min time weighted averaging method to get half-hourly values. Where there are no measurements logged in the period, the data provided is blank; due to the report-by-exception process, it may be appropriate to forward fill this data for shorter gaps.We developed a data redactions process to protect the privacy or companies according to the Utilities Act 2000 section 105.1.b, which requires UK Power Networks to not disclose information relating to the affairs of a business. For this reason, where the demand of a private customer is derivable from our data and that data is not already public information (e.g., data provided via Elexon on the Balancing Mechanism), we redact the half-hourly time series, and provide only the monthly averages. This redaction process considers the correlation of all the data, of only corresponding periods where the customer is active, the first order difference of all the data, and the first order difference of only corresponding periods where the customer is active. Should any of these four tests have a high linear correlation, the data is deemed redacted. This process is not simply applied to only the circuit of the customer, but of the surrounding circuits that would also reveal the signal of that customer.The directionality of the data is not consistent within this dataset. Where directionality was ascertainable, we arrange the power data in the direction of the LTDS "from node" to the LTDS "to node". Measurements of current do not indicate directionality and are instead positive regardless of direction. In some circumstances, the polarity can be negative, and depends on the data commissioner's decision on what the operators in the control room might find most helpful in ensuring reliable and secure network operation.Quality Control StatementThe data is provided "as is". In the design and delivery process adopted by the DSO, customer feedback and guidance is considered at each phase of the project. One of the earliest steers was that raw data was preferable. This means that we do not perform prior quality control screening to our raw network data. The result of this decision is that network rearrangements and other periods of non-intact running of the network are present throughout the dataset, which has the potential to misconstrue the true utilisation of the network, which is determined regulatorily by considering only by in-tact running arrangements. Therefore, taking the maximum or minimum of these transformers are not a reliable method of correctly ascertaining the true utilisation. This does have the intended added benefit of giving a realistic view of how the network was operated. The critical feedback was that our customers have a desire to understand what would have been the impact to them under real operational conditions. As such, this dataset offers unique insight into that.Assurance StatementCreating this dataset involved a lot of human data imputation. At UK Power Networks, we have differing software to run the network operationally (ADMS) and to plan and study the network (PowerFactory). The measurement devices are intended to primarily inform the network operators of the real time condition of the network, and importantly, the network drawings visible in the LTDS are a planning approach, which differs to the operational. To compile this dataset, we made the union between the two modes of operating manually. A team of data scientists, data engineers, and power system engineers manually identified the LTDS transformer from the single line diagram, identified the line name from LTDS Table 2a/b, then identified the same transformer in ADMS to identify the measurement data tags. This was then manually inputted to a spreadsheet. Any influential customers to that circuit were noted using ADMS and the single line diagrams. From there, a python code is used to perform the triage and compilation of the datasets. There is potential for human error during the manual data processing. These issues can include missing transformers, incorrectly labelled transformers, incorrectly identified measurement data tags, incorrectly interpreted directionality. Whilst care has been taken to minimise the risk of these issues, they may persist in the provided dataset. Any uncertain behaviour observed by using this data should be reported to allow us to correct as fast as possible.Additional informationDefinitions of key terms related to this dataset can be found in the Open Data Portal Glossary.Download dataset information: Metadata (JSON)We would be grateful if you find this dataset useful to submit a “reuse” case study to tell us what you did and how you used it. This enables us to drive our direction and gain better understanding for how we improve our data offering in the future. Click here for more information: Open Data Portal Reuses — UK Power NetworksTo view this data please register and login.

This annual report provides comparative international data on the communications sector. The aim of the report is to benchmark the UK communications sector against a range of comparator countries in order to assess how the UK is performing in an international context. The report compares the availability, take-up and use of services in the UK and 17 comparator countries - France, Germany, Italy, the US, Japan, Australia, Spain, the Netherlands, Sweden, Poland, Singapore, South Korea, Brazil, Russia, India, China and Nigeria, although we focus on a smaller subset of comparator countries for some of our analysis. This report is intended to be used in a number of ways: to benchmark the UK’s communications sector, to learn from market and regulatory developments in other countries, and to provide the context for Ofcom’s regulatory initiatives. It also contributes to the richness of the information we draw upon, better enabling us to understand how our actions and priorities can influence outcomes for citizens and consumers, and for communications markets generally. The sectors covered include television and radio broadcasting; internet on-demand content; telecommunications and (since 2012) the postal market. The final edition of the ICMR was in 2017.

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AI Training Dataset Market Size 2025-2029

The ai training dataset market size is valued to increase by USD 7.33 billion, at a CAGR of 29% from 2024 to 2029. Proliferation and increasing complexity of foundational AI models will drive the ai training dataset market.

Market Insights

North America dominated the market and accounted for a 36% growth during the 2025-2029.
By Service Type - Text segment was valued at USD 742.60 billion in 2023
By Deployment - On-premises segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 479.81 million 
Market Future Opportunities 2024: USD 7334.90 million
CAGR from 2024 to 2029 : 29%

Market Summary

The market is experiencing significant growth as businesses increasingly rely on artificial intelligence (AI) to optimize operations, enhance customer experiences, and drive innovation. The proliferation and increasing complexity of foundational AI models necessitate large, high-quality datasets for effective training and improvement. This shift from data quantity to data quality and curation is a key trend in the market. Navigating data privacy, security, and copyright complexities, however, poses a significant challenge. Businesses must ensure that their datasets are ethically sourced, anonymized, and securely stored to mitigate risks and maintain compliance. For instance, in the supply chain optimization sector, companies use AI models to predict demand, optimize inventory levels, and improve logistics. Access to accurate and up-to-date training datasets is essential for these applications to function efficiently and effectively. Despite these challenges, the benefits of AI and the need for high-quality training datasets continue to drive market growth. The potential applications of AI are vast and varied, from healthcare and finance to manufacturing and transportation. As businesses continue to explore the possibilities of AI, the demand for curated, reliable, and secure training datasets will only increase.

What will be the size of the AI Training Dataset Market during the forecast period?

Get Key Insights on Market Forecast (PDF) Request Free SampleThe market continues to evolve, with businesses increasingly recognizing the importance of high-quality datasets for developing and refining artificial intelligence models. According to recent studies, the use of AI in various industries is projected to grow by over 40% in the next five years, creating a significant demand for training datasets. This trend is particularly relevant for boardrooms, as companies grapple with compliance requirements, budgeting decisions, and product strategy. Moreover, the importance of data labeling, feature selection, and imbalanced data handling in model performance cannot be overstated. For instance, a mislabeled dataset can lead to biased and inaccurate models, potentially resulting in costly errors. Similarly, effective feature selection algorithms can significantly improve model accuracy and reduce computational resources. Despite these challenges, advances in model compression methods, dataset scalability, and data lineage tracking are helping to address some of the most pressing issues in the market. For example, model compression techniques can reduce the size of models, making them more efficient and easier to deploy. Similarly, data lineage tracking can help ensure data consistency and improve model interpretability. In conclusion, the market is a critical component of the broader AI ecosystem, with significant implications for businesses across industries. By focusing on data quality, effective labeling, and advanced techniques for handling imbalanced data and improving model performance, organizations can stay ahead of the curve and unlock the full potential of AI.

Unpacking the AI Training Dataset Market Landscape

In the realm of artificial intelligence (AI), the significance of high-quality training datasets is indisputable. Businesses harnessing AI technologies invest substantially in acquiring and managing these datasets to ensure model robustness and accuracy. According to recent studies, up to 80% of machine learning projects fail due to insufficient or poor-quality data. Conversely, organizations that effectively manage their training data experience an average ROI improvement of 15% through cost reduction and enhanced model performance.

Distributed computing systems and high-performance computing facilitate the processing of vast datasets, enabling businesses to train models at scale. Data security protocols and privacy preservation techniques are crucial to protect sensitive information within these datasets. Reinforcement learning models and supervised learning models each have their unique applications, with the former demonstrating a 30% faster convergence rate in certain use cases.

Data annot

This data has been taken from LG Inform (http://lginform.local.gov.uk Data Ref ID 1764). It shows financial years 2011/2012 to 2016/2017. Budget - Net current expenditure - children's social care (RA) - This is the estimated budget net expenditure on children's social care services. It is taken from the Revenue Accounts Budget. The data are budget estimates of local authority revenue expenditure. These estimates are on a non International Accounting Standards 19 (IAS19) & Private Finance Initiative (PFI) on an "Off Balance Sheet" basis. Source name: Communities and Local Government Collection name: Budgeted Revenue Accounts Polarity: No polarity Polarity is how sentiment is measured "Sentiment is usually considered to have "poles" positive and negative these are often translated into "good" and "bad" sentiment analysis is considered useful to tell us what is good and bad in our information stream

Sets out the information that will enable the public to assess the value for money and productivity of the services for which we are responsible, holding us more effectively to account. This business plan focusses on the priorities of the Home Office with regard to our public task in cutting crime, reducing immigration and preventing terrorism.

The Beijing Opera Percussion Pattern (BOPP) dataset is a collection of audio examples of percussion patterns played by the percussion ensemble in Beijing Opera (Jingju, 京剧). The percussion ensemble in Jingju plays a set of pre-defined and labeled percussion patterns, which serve many functions. The percussion patterns can be defined as sequences of strokes played by different combinations of the percussion instruments, and the resulting variety of timbres are transmitted using oral syllables as mnemonics. More information on the percussion instruments used in Beijing Opera can be found at http://compmusic.upf.edu/examples-percussion-bo. The dataset presented here was used as the training dataset in the referenced paper. A detailed description of percussion patterns in Jingju can also be found in it. DATASET The dataset is a collection of 133 audio percussion patterns spanning five different pattern classes as described below. The scores for the patterns and additional details about the patterns are at: http://compmusic.upf.edu/bo-perc-patterns Audio Content The audio files are short segments containing one of the above mentioned patterns. The audio is stereo, sampled at 44.1 kHz, and stored as wav files. The segments were chosen from the introductory parts of arias. The recordings of arias are from commercially available releases spanning various artists. The audio and segments were chosen carefully by a musicologist to be representative of the percussion patterns that occur in Jingju. The audio segments contain diverse instrument timbres of percussion instruments (though the same set of instruments are played, there can be slight variations in the individual instruments across different ensembles), recording quality and period of the recording. Though these recordings were chosen from introductions of arias where only percussion ensemble is playing, there are some examples in the dataset where the melodic accompaniment starts before the percussion pattern ends. Annotations Each of the audio patterns has an associated syllable level transcription of the audio pattern. The transcription is obtained from the score for the pattern and is not time aligned to the audio. The transcription is done using a reduced set of five syllables and is sufficient to computationally model the timbres of all the syllables. The annotations are stored as Hidden Markov Model Toolkit (HTK) label files. There is also a single master label file provided for batch processing using HTK (http://htk.eng.cam.ac.uk/). Dataset organization The dataset has wav files and label files. The files are named as

In Newcastle libraries we are endeavouring to open up as much of our data as possible. We will publish data here on a regular basis. Each file is saved in CSV format and has an accompanying text file detailing what data is contained in each file, who is responsible for it and when it was last updated. If there is any additional data you would like us to release then please contact Luke Burton (luke.burton@newcastle.gov.uk) to discuss. You are under no obligation to do so, but since we know you will make great things with our data we would love for you to tell us about them. Additional information To the extent possible under law, Newcastle Libaries has waived all copyright and related or neighbouring rights to its data published below. This work is published from: United Kingdom. For more information please visit: https://www.newcastle.gov.uk/your-council-and-democracy/open-data-and-access-information/open-data/data-sets/libraries-data-sets

Transcripts of interviews with UK policy advisors on Hong Kong education policy. Recently England has engaged heavily in external policy referencing to drive its educational reforms. Hong Kong has been a major source of such referencing by virtue of its strong performance on international tests of pupil achievement. Using Hong Kong as a case study; the project will analyse external policy referencing, with England as the ‘borrower’ and Hong Kong the ‘lender’. The aim is to cast a light on the role of external policy referencing in the policy making process, and how policy referencing is operationalised in the England context. The study provides an insight into the contemporary patterns of external policy referencing, and its manifestation in the West and East Asia, and examines the evidence used to inform the process. The study will undertake a literature review and interviews with stakeholders in both contexts to address the following research questions: (1) What have been the critical features of the patterns of external policy referencing in England since the 1990s? (2) How have policy makers in England interpreted the sources of success of Hong Kong’s education system, and how does this compare with the views of key stakeholders in Hong Kong?In 2007 the Principal Investigator returned to London after working for 31 years in Faculties / Institutes of Education in Hong Kong and specialising in East Asian education systems. As political parties in England competed to promote their vision of schooling, he was constantly bemused as to the extent to which their plans for reform were based on the claim that what they were proposing was a feature of one or all of the high performing East Asian societies that do well on international tests of pupil achievement e.g. the Programme for International Student Assessment (PISA), and Trends in International Mathematics and Science Study (TIMSS). The 2010 Schools White Paper in England and the ongoing review of the National Curriculum extensively cite practices in Hong Kong to support their policies. Also, agencies now bidding to get contracts to examine the New Baccalaureate have to demonstrate that they will follow the best practices of high performing nations. Some of these claims seem far removed from the reality that the Principal Investigator had experienced both as an academic, and as someone heavily engaged in policy making in Hong Kong. What is more worrying is that these claims are largely unchallenged in England. The claims are accepted partly because people generally have limited knowledge of foreign education systems, and comparative educators have tended to avoid engagement in the public debates relating to ongoing policy making about how schools should be reformed. The purpose of this study is to help address that situation. We plan to focus on how policy makers in England portray features of Hong Kong's education system to promote domestic reforms. We examine the nature of these features in Hong Kong by finding out what the relevant laws or rules are, and by interviewing people who are directly involved with these education features. This will allow us to find out the extent to which the claims made in England are valid and accurate. It will also allow us to contribute to the ongoing debates in comparative education as to the influence of global and local factors on education reform. The UK and Hong Kong team carried out a single-case study of England and Hong Kong because the two societies provide a powerful exemplar of the emerging patterns of policy transfer. For the first part of the project, we examined external policy referencing in England historically and currently, and located this within the broader literature on external policy referencing. In the second part of the project, we reviewed the academic literature on external policy referencing with specific reference to England. We carried out analysis of policy and related documents in England (e.g. key government announcements, speeches, and publications), between 1990 and the present, including authoritative sources and references made within policy documents or by policy makers (e.g. the McKinsey Report 2007, 2010). In the third part of the project, we provided an in-depth understanding of the policy making process. This was the part where the main empirical data collection took place. We undertook semi-structured, in-depth interviews with key policy makers involved in developing and implementing education reforms in England (N=10) and Hong Kong (N=15).

This data has been taken from LG Inform at http://lginform.local.gov.uk/ data reference ID 31. It shows the percentage of vulnerable people achieving independent living in Plymouth from financial year 2006/2007 to 2010/2011 Percentage of vulnerable people achieving independent living. This is the number of service users (i.e. people who are receiving a Supporting People Service) who have moved on from supported accommodation in a planned way, as a percentage of total service users who have left the service. This was previously reported as NI 141. Source name: Communities and Local Government Collection name: Supporting People Local System (SPLS) Polarity: High is good Polarity is how sentiment is measured "Sentiment is usually considered to have "poles" positive and negative these are often translated into "good" and "bad" sentiment analysis is considered useful to tell us what is good and bad in our information stream

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

The United Kingdom recorded a trade deficit of 5260 GBP Million in July of 2025. This dataset provides - United Kingdom Balance of Trade - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The Influencing Travel Behaviour Team (ITB) provide road safety education, training and publicity to schools, communities, businesses and Leeds residents. We promote sustainable travel throughout Leeds along with helping schools and businesses to develop and implement their travel plans (which promote safe, sustainable and less car dependent patterns of travel). Each year we request mode of travel data from schools in Leeds via a SIMS report or excel spreadsheet. The 10 modes of travel specified in the data collection are: Bus (type not known), Car Share (children travelling together from different households), Car/Van, Cycle, Dedicated School Bus, Other, Public Bus Service, Taxi, Train, Walk (including scooting) This collection forms part of the Statutory duty local authorities have to monitor the success of promoting sustainable travel, and in some cases is linked to a school’s planning obligated travel plan. It is an important part of improving road safety and promoting healthy lifestyles among children in Leeds but since the council declared a climate emergency in March of this year the data is even more valuable. The data helps us understand the environmental context in Leeds and work to effectively limit carbon emissions wherever possible. We strongly encourage all schools to provide the data but not all of them respond to the request and we do not always receive a response for every pupil/student so some school response rates may be low.

Interest Payments on Government Debt in the United Kingdom increased to 8434 GBP Million in August from 7516 GBP Million in July of 2025. This dataset includes a chart with historical data for the United Kingdom Interest Payments On Government Debt.

About this dataset

Context

The dataset presents the distribution of median household income among distinct age brackets of householders in New Britain. 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 New Britain. 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 New Britain, householders within the 45 to 64 years age group have the highest median household income at $69,331, followed by those in the 25 to 44 years age group with an income of $58,454. Meanwhile householders within the under 25 years age group report the second lowest median household income of $44,643. Notably, householders within the 65 years and over age group, had the lowest median household income at $42,475.

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 New Britain median household income by age. You can refer the same here

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 New Britain township by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for New Britain township. The dataset can be utilized to understand the population distribution of New Britain township by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in New Britain township. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for New Britain township.

Key observations

Largest age group (population): Male # 60-64 years (529) | Female # 50-54 years (557). 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

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the New Britain township population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the New Britain township is shown in the following column.
• Population (Female): The female population in the New Britain township is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in New Britain township for each 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 New Britain township Population by Gender. 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 England. 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 England. 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 England, householders within the 45 to 64 years age group have the highest median household income at $62,083, followed by those in the 25 to 44 years age group with an income of $46,694. Meanwhile householders within the 65 years and over age group report the second lowest median household income of $35,125. Notably, householders within the under 25 years age group, had the lowest median household income at $25,000.

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 England median household income by age. You can refer the same here