About this dataset

Context

The dataset tabulates the data for the England, AR population pyramid, which represents the England population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for England, AR, is 36.5.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for England, AR, is 25.8.
• Total dependency ratio for England, AR is 62.3.
• Potential support ratio, which is the number of youth (working age population) per elderly, for England, AR is 3.9.

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 for the England population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the England for the selected age group is shown in the following column.
• Population (Female): The female population in the England for the selected age group is shown in the following column.
• Total Population: The total population of the England for the selected age group is shown in the following column.

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 England population by age cohorts (Children: Under 18 years; Working population: 18-64 years; Senior population: 65 years or more). It lists the population in each age cohort group along with its percentage relative to the total population of England. The dataset can be utilized to understand the population distribution across children, working population and senior population for dependency ratio, housing requirements, ageing, migration patterns etc.

Key observations

The largest age group was 18 to 64 years with a poulation of 1,489 (57.98% of the total population). 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 cohorts:

• Under 18 years
• 18 to 64 years
• 65 years and over

Variables / Data Columns

• Age Group: This column displays the age cohort for the England population analysis. Total expected values are 3 groups ( Children, Working Population and Senior Population).
• Population: The population for the age cohort in England is shown in the following column.
• Percent of Total Population: The population as a percent of total population of the England is shown in the following column.

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

[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

This Location Data & Foot traffic dataset available for all countries include enriched raw mobility data and visitation at POIs to answer questions such as:

-How often do people visit a location? (daily, monthly, absolute, and averages). -What type of places do they visit ? (parks, schools, hospitals, etc) -Which social characteristics do people have in a certain POI? - Breakdown by type: residents, workers, visitors. -What's their mobility like enduring night hours & day hours?
-What's the frequency of the visits partition by day of the week and hour of the day?

Extra insights -Visitors´ relative income Level. -Visitors´ preferences as derived by their visits to shopping, parks, sports facilities, churches, among others.

Overview & Key Concepts Each record corresponds to a ping from a mobile device, at a particular moment in time and at a particular latitude and longitude. We procure this data from reliable technology partners, which obtain it through partnerships with location-aware apps. All the process is compliant with applicable privacy laws.

We clean and process these massive datasets with a number of complex, computer-intensive calculations to make them easier to use in different data science and machine learning applications, especially those related to understanding customer behavior.

Featured attributes of the data Device speed: based on the distance between each observation and the previous one, we estimate the speed at which the device is moving. This is particularly useful to differentiate between vehicles, pedestrians, and stationery observations.

Night base of the device: we calculate the approximated location of where the device spends the night, which is usually their home neighborhood.

Day base of the device: we calculate the most common daylight location during weekdays, which is usually their work location.

Income level: we use the night neighborhood of the device, and intersect it with available socioeconomic data, to infer the device’s income level. Depending on the country, and the availability of good census data, this figure ranges from a relative wealth index to a currency-calculated income.

POI visited: we intersect each observation with a number of POI databases, to estimate check-ins to different locations. POI databases can vary significantly, in scope and depth, between countries.

Category of visited POI: for each observation that can be attributable to a POI, we also include a standardized location category (park, hospital, among others). Coverage: Worldwide.

Delivery schemas We can deliver the data in three different formats:

Full dataset: one record per mobile ping. These datasets are very large, and should only be consumed by experienced teams with large computing budgets.

Visitation stream: one record per attributable visit. This dataset is considerably smaller than the full one but retains most of the more valuable elements in the dataset. This helps understand who visited a specific POI, characterize and understand the consumer's behavior.

Audience profiles: one record per mobile device in a given period of time (usually monthly). All the visitation stream is aggregated by category. This is the most condensed version of the dataset and is very useful to quickly understand the types of consumers in a particular area and to create cohorts of users.

Other-Long-Term-Assets Time Series for British American Tobacco PLC. British American Tobacco p.l.c. provides tobacco and nicotine products to consumers in the Americas, Europe, the Asia-Pacific, the Middle East, Africa, and the United States. It offers vapour, heated, and modern oral nicotine products; combustible cigarettes; and traditional oral products, such as snus and moist snuff. The company provides its products under the Vuse, glo, Velo, Grizzly, Kodiak, Dunhill, Kent, Lucky Strike, Pall Mall, Rothmans, Newport, Natural American Spirit, and Camel brands. The company distributes its products to retail outlets. British American Tobacco p.l.c. was founded in 1902 and is based in London, the United Kingdom.

Total-Long-Term-Liabilities Time Series for Experian PLC. Experian plc, together with its subsidiaries, operates as a data and technology company in North America, Latin America, the United Kingdom, Ireland, Europe, the Middle East, Africa, and the Asia Pacific. It operates through two segments, Business-to-Business and Consumer Services. The company collects, sorts, aggregates, and transforms data from various sources. It also owns, create, and develops analytics, predictive tools, sophisticated software, and platforms; credit risk, fraud prevention, identity management, customer service and engagement, account processing, and account management services; and data analysis, and research and development services. In addition, the company provides credit education, free access to Experian credit reports and scores, and online educational tools. It serves its customers in financial service, direct-to-consumer, health, retail, software and professional services, automotive, insurance, media and technology, telecommunications and utility, and other industries, and government and public sectors. The company was formerly known as Experian Group Limited and changed its name to Experian plc in July 2008. Experian plc was founded in 1826 and is headquartered in Dublin, Ireland.

Some say climate change is the biggest threat of our age while others say it’s a myth based on dodgy science. We are turning some of the data over to you so you can form your own view.

Even more than with other data sets that Kaggle has featured, there’s a huge amount of data cleaning and preparation that goes into putting together a long-time study of climate trends. Early data was collected by technicians using mercury thermometers, where any variation in the visit time impacted measurements. In the 1940s, the construction of airports caused many weather stations to be moved. In the 1980s, there was a move to electronic thermometers that are said to have a cooling bias.

Given this complexity, there are a range of organizations that collate climate trends data. The three most cited land and ocean temperature data sets are NOAA’s MLOST, NASA’s GISTEMP and the UK’s HadCrut.

We have repackaged the data from a newer compilation put together by the Berkeley Earth, which is affiliated with Lawrence Berkeley National Laboratory. The Berkeley Earth Surface Temperature Study combines 1.6 billion temperature reports from 16 pre-existing archives. It is nicely packaged and allows for slicing into interesting subsets (for example by country). They publish the source data and the code for the transformations they applied. They also use methods that allow weather observations from shorter time series to be included, meaning fewer observations need to be thrown away.

In this dataset, we have include several files:

Global Land and Ocean-and-Land Temperatures (GlobalTemperatures.csv):

• Date: starts in 1750 for average land temperature and 1850 for max and min land temperatures and global ocean and land temperatures
• LandAverageTemperature: global average land temperature in celsius
• LandAverageTemperatureUncertainty: the 95% confidence interval around the average
• LandMaxTemperature: global average maximum land temperature in celsius
• LandMaxTemperatureUncertainty: the 95% confidence interval around the maximum land temperature
• LandMinTemperature: global average minimum land temperature in celsius
• LandMinTemperatureUncertainty: the 95% confidence interval around the minimum land temperature
• LandAndOceanAverageTemperature: global average land and ocean temperature in celsius
• LandAndOceanAverageTemperatureUncertainty: the 95% confidence interval around the global average land and ocean temperature

Other files include:

• Global Average Land Temperature by Country (GlobalLandTemperaturesByCountry.csv)
• Global Average Land Temperature by State (GlobalLandTemperaturesByState.csv)
• Global Land Temperatures By Major City (GlobalLandTemperaturesByMajorCity.csv)
• Global Land Temperatures By City (GlobalLandTemperaturesByCity.csv)

The raw data comes from the Berkeley Earth data page.

Abstract

This dataset, a product of the Trade Team - Development Research Group, is part of a larger effort in the group to measure the extent of the brain drain as part of the International Migration and Development Program. It measures international skilled migration for the years 1975-2000.

The methodology is explained in: "Tendance de long terme des migrations internationals. Analyse à partir des 6 principaux pays recerveurs", Cécily Defoort.

This data set uses the same methodology as used in the Docquier-Marfouk data set on international migration by educational attainment. The authors use data from 6 key receiving countries in the OECD: Australia, Canada, France, Germany, the UK and the US.

It is estimated that the data represent approximately 77 percent of the world’s migrant population.

Bilateral brain drain rates are estimated based observations for every five years, during the period 1975-2000.

Geographic coverage

Australia, Canada, France, Germany, UK and US

Kind of data

Aggregate data [agg]

Mode of data collection

Other [oth]

The data comprise a long-term study of alpine plant community dynamics in the Gunnison National Forest of Colorado. The data comprise annual census data for all plants (including seedlings) in each of 50 2x2m plots, including information on size, reproduction, life stage, and mortality, with all plants identified and geo-located. These data are also made available transformed to provide individual-level estimates of growth, survival, fecundity, and recruitment. The dataset covers several thousand individuals of approximately twenty species, and highlights an apparent pattern of demographic decline. The data also include information on microenvironment / microedaphic variation at 2 m resolution covering the entire research site, including information on temperatures, topography, soil chemistry, soil texture, and other variables. The data also include information on the functional traits of many of the species present at the site, including information on biomass allocation, leaf traits, root traits, seed traits, and floral traits. Full details about this dataset can be found at https://doi.org/10.5285/d850fcd2-b70a-415e-acf4-fc27b38d59c1

The Accented English Speech Dataset provides over 1,000 hours of authentic conversational recordings designed to strengthen ASR systems, conversational AI, and voice applications. Unlike synthetic or scripted datasets, this collection captures real human-to-human and chatbot-guided dialogues, reflecting natural speech flow, spontaneous phrasing, and diverse accents.

Off-the-shelf recordings are available from:

Mexico Colombia Guatemala Costa Rica El Salvador Dominican Republic South Africa

This ensures exposure to Latin American, Caribbean, and African English accents, which are often missing from mainstream corpora.

Beyond these, we support custom collection in any language and any accent worldwide, tailored to specific project requirements.

Audio Specifications

Format: WAV Sample rate: 48kHz Sample size: 16-bit PCM Channel: Mono/Stereo Double-track recording: Available upon request (clean separation of speakers) Data Structure and Metadata Dual-track or single-channel audio depending on project need Metadata includes speaker ID, demographic attributes, accent/region, and context Dialogues include both structured (chatbot/task-based) and free-flow natural conversations

Use Cases

• ASR Training & Benchmarking – Improve transcription across accented English
• Accent Adaptation – Build robust, inclusive systems that work in real-world scenarios
• Multilingual Voice Interfaces – Expand IVR and assistants to support more voices
• Conversational AI – Train chatbots on authentic, unstructured dialogue
• Voice Biometrics – Support research in identity verification and speaker profiling
• Model Fine-Tuning – Enrich foundation models with high-quality speech data

Why It Matters

Mainstream datasets disproportionately focus on U.S. and U.K. English. This dataset fills the gap with diverse accented English coverage, and the ability to collect any language or accent on demand, enabling the creation of fairer, more accurate, and globally deployable AI solutions.

Key Highlights

• 1,000+ hours of accented English speech
• Ready-to-use coverage from Latin America, Caribbean, and Africa
• Authentic dialogues: human-to-human and chatbot-guided
• WAV, 48kHz, 16-bit PCM, mono/stereo, double-track option
• Metadata-rich recordings for advanced AI research
• Custom collection in any language and accent

This data collection consists of behavioural task data for measures of attention and interpretation bias, specifically: emotional Stroop, attention probe (both measuring attention bias) and similarity ratings task and scrambled sentence task (both measuring interpretation bias). Data on the following 6 participant groups are included in the dataset: native UK (n=36), native HK (n=39), UK migrants to HK (short term = 31, long term = 28) and HK migrants to UK (short term = 37, long term = 31). Also included are personal characteristics and questionnaire measures. The way in which we process information in the world around us has a significant effect on our health and well being. For example, some people are more prone than others to notice potential dangers, to remember bad things from the past and assume the worst, when the meaning of an event or comment is uncertain. These tendencies are called negative cognitive biases and can lead to low mood and poor quality of life. They also make people vulnerable to mental illnesses. In contrast, those with positive cognitive biases tend to function well and remain healthy. To date most of this work has been conducted on white, western populations and we do not know whether similar cognitive biases exist in Eastern cultures. This project will examine cognitive biases in Eastern (Hong Kong nationals ) and Western (UK nationals) people to see whether there are any differences between the two. It will also examine what happens to cognitive biases when someone migrates to a different culture. This will tell us whether influences from the society and culture around us have any effect on our cognitive biases. Finally the project will consider how much our own cognitive biases are inherited from our parents. Together these results will tell us whether the known good and bad effects of cognitive biases apply to non Western cultural groups as well, and how much cognitive biases are decided by our genes or our environment. Participants: Local Hong Kong and UK natives; short term and long term migrants in each country, aged 16-65 with no current major physical illness or psychological disorder, who were not receiving psychological therapy or medication for psychological conditions. Sampling procedure: Participants were recruited using circular emails, public flyers and other advertisements in local venues, universities and clubs. Data collection: Participants completed four previously developed and validated cognitive bias tasks (emotional Stroop, attention probe, similarity ratings task and scrambled sentence task) in their native language. They also completed socio-demographic information and questionnaires.

Long-Term-Investments Time Series for Experian PLC. Experian plc, together with its subsidiaries, operates as a data and technology company in North America, Latin America, the United Kingdom, Ireland, Europe, the Middle East, Africa, and the Asia Pacific. It operates through two segments, Business-to-Business and Consumer Services. The company collects, sorts, aggregates, and transforms data from various sources. It also owns, create, and develops analytics, predictive tools, sophisticated software, and platforms; credit risk, fraud prevention, identity management, customer service and engagement, account processing, and account management services; and data analysis, and research and development services. In addition, the company provides credit education, free access to Experian credit reports and scores, and online educational tools. It serves its customers in financial service, direct-to-consumer, health, retail, software and professional services, automotive, insurance, media and technology, telecommunications and utility, and other industries, and government and public sectors. The company was formerly known as Experian Group Limited and changed its name to Experian plc in July 2008. Experian plc was founded in 1826 and is headquartered in Dublin, Ireland.

Spatial data files holding gridded parameter maps of surface soil hydraulic parameters derived from a selection of pedotransfer functions. Modern land surface model simulations capture soil profile water movement through the use of soil hydraulics sub-models, but good hydraulic parameterisations are often lacking - especially in the tropics - and it is this lack that we fill here in the context of South America. Optimal hydraulic parameter values are given for the Brooks and Corey, Campbell, van Genuchten-Mualem and van Genuchten-Burdine soil hydraulic models, which are widely-used hydraulic sub-models in many land surface models (e.g. Joint UK Land Environment Simulator JULES). Full details about this dataset can be found at https://doi.org/10.5285/4078678b-768f-43ff-abba-b87712f648e9

Success.ai’s Company Data Solutions provide businesses with powerful, enterprise-ready B2B company datasets, enabling you to unlock insights on over 28 million verified company profiles. Our solution is ideal for organizations seeking accurate and detailed B2B contact data, whether you’re targeting large enterprises, mid-sized businesses, or small business contact data.

Success.ai offers B2B marketing data across industries and geographies, tailored to fit your specific business needs. With our white-glove service, you’ll receive curated, ready-to-use company datasets without the hassle of managing data platforms yourself. Whether you’re looking for UK B2B data or global datasets, Success.ai ensures a seamless experience with the most accurate and up-to-date information in the market.

Why Choose Success.ai’s Company Data Solution? At Success.ai, we prioritize quality and relevancy. Every company profile is AI-validated for a 99% accuracy rate and manually reviewed to ensure you're accessing actionable and GDPR-compliant data. Our price match guarantee ensures you receive the best deal on the market, while our white-glove service provides personalized assistance in sourcing and delivering the data you need.

Why Choose Success.ai?

• Best Price Guarantee: We offer industry-leading pricing and beat any competitor.
• Global Reach: Access over 28 million verified company profiles across 195 countries.
• Comprehensive Data: Over 15 data points, including company size, industry, funding, and technologies used.
• Accurate & Verified: AI-validated with a 99% accuracy rate, ensuring high-quality data.
• Real-Time Updates: Stay ahead with continuously updated company information.
• Ethically Sourced Data: Our B2B data is compliant with global privacy laws, ensuring responsible use.
• Dedicated Service: Receive personalized, curated data without the hassle of managing platforms.
• Tailored Solutions: Custom datasets are built to fit your unique business needs and industries.

Our database spans 195 countries and covers 28 million public and private company profiles, with detailed insights into each company’s structure, size, funding history, and key technologies. We provide B2B company data for businesses of all sizes, from small business contact data to large corporations, with extensive coverage in regions such as North America, Europe, Asia-Pacific, and Latin America.

Comprehensive Data Points: Success.ai delivers in-depth information on each company, with over 15 data points, including:

Company Name: Get the full legal name of the company. LinkedIn URL: Direct link to the company's LinkedIn profile. Company Domain: Website URL for more detailed research. Company Description: Overview of the company’s services and products. Company Location: Geographic location down to the city, state, and country. Company Industry: The sector or industry the company operates in. Employee Count: Number of employees to help identify company size. Technologies Used: Insights into key technologies employed by the company, valuable for tech-based outreach. Funding Information: Track total funding and the most recent funding dates for investment opportunities. Maximize Your Sales Potential: With Success.ai’s B2B contact data and company datasets, sales teams can build tailored lists of target accounts, identify decision-makers, and access real-time company intelligence. Our curated datasets ensure you’re always focused on high-value leads—those who are most likely to convert into clients. Whether you’re conducting account-based marketing (ABM), expanding your sales pipeline, or looking to improve your lead generation strategies, Success.ai offers the resources you need to scale your business efficiently.

Tailored for Your Industry: Success.ai serves multiple industries, including technology, healthcare, finance, manufacturing, and more. Our B2B marketing data solutions are particularly valuable for businesses looking to reach professionals in key sectors. You’ll also have access to small business contact data, perfect for reaching new markets or uncovering high-growth startups.

From UK B2B data to contacts across Europe and Asia, our datasets provide global coverage to expand your business reach and identify new markets. With continuous data updates, Success.ai ensures you’re always working with the freshest information.

Key Use Cases:

• Targeted Lead Generation: Build accurate lead lists by filtering data by company size, industry, or location. Target decision-makers in key industries to streamline your B2B sales outreach.
• Account-Based Marketing (ABM): Use B2B company data to personalize marketing campaigns, focusing on high-value accounts and improving conversion rates.
• Investment Research: Track company growth, funding rounds, and employee trends to identify investment opportunities or potential M&A targets.
• Market Research: Enrich your market intelligence initiatives by gain...

Long-Term-Investments Time Series for WPP PLC. WPP plc, a creative transformation company, provides communications, experience, commerce, and technology services in North America, the United Kingdom, Western Continental Europe, the Asia Pacific, Latin America, Africa, the Middle East, and Central and Eastern Europe. The company operates through three segments: Global Integrated Agencies, Public Relations, and Specialist Agencies. It offers marketing strategy, creative ideation, production, commerce, influencer marketing, social media management, and technology implementation services; media strategy, planning, buying and activation, commerce media, data analytics, and consulting services; and media management, public affairs, reputation, risk and crisis management, social media management, and strategic advisory services. The company also provides brand consulting, brand identity, product and service design, and corporate and brand publication services. WPP plc was founded in 1985 and is based in London, the United Kingdom.

Time series data for the statistic Manufacturing, value added (current US$) and country United Kingdom. Indicator Definition:Manufacturing refers to industries belonging to ISIC divisions 15-37. Value added is the net output of a sector after adding up all outputs and subtracting intermediate inputs. It is calculated without making deductions for depreciation of fabricated assets or depletion and degradation of natural resources. The origin of value added is determined by the International Standard Industrial Classification (ISIC), revision 3. Data are in current U.S. dollars.The indicator "Manufacturing, value added (current US$)" stands at 291.80 Billion usd as of 12/31/2024, the highest value since 12/31/2008. Regarding the One-Year-Change of the series, the current value constitutes an increase of 4.53 percent compared to the value the year prior.The 1 year change in percent is 4.53.The 3 year change in percent is 7.36.The 5 year change in percent is 16.33.The 10 year change in percent is 1.17.The Serie's long term average value is 240.24 Billion usd. It's latest available value, on 12/31/2024, is 21.46 percent higher, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 12/31/1993, to it's latest available value, on 12/31/2024, is +77.24%.The Serie's change in percent from it's maximum value, on 12/31/2007, to it's latest available value, on 12/31/2024, is -2.38%.

This data set contains frequency counts of target words in 16 million news and opinion articles from 10 popular news media outlets in the United Kingdom: The Guardian, The Times, The Independent, The Daily Mirror, BBC, Financial Times, Metro, Telegraph, The and The Daily Mail plus a few additional American-based outlets used for comparison reference. The target words are listed in the associated manuscript and are mostly words that denote some type of prejudice, social justice related terms or counterreaction to it. A few additional words are also available since they are used in the manuscript for illustration purposes.

The textual content of news and opinion articles from the outlets listed in Figure 3 of the main manuscript is available in the outlet's online domains and/or public cache repositories such as Google cache (https://webcache.googleusercontent.com), The Internet Wayback Machine (https://archive.org/web/web.php), and Common Crawl (https://commoncrawl.org). We derived relative frequency counts from these sources. Textual content included in our analysis is circumscribed to articles headlines and main body of text of the articles and does not include other article elements such as figure captions.

Targeted textual content was located in HTML raw data using outlet specific xpath expressions. Tokens were lowercased prior to estimating frequency counts. To prevent outlets with sparse text content for a year from distorting aggregate frequency counts, we only include outlet frequency counts from years for which there is at least 1 million words of article content from an outlet.

Yearly frequency usage of a target word in an outlet in any given year was estimated by dividing the total number of occurrences of the target word in all articles of a given year by the number of all words in all articles of that year. This method of estimating frequency accounts for variable volume of total article output over time.

The list of compressed files in this data set is listed next:

-analysisScripts.rar contains the analysis scripts used in the main manuscript

-targetWordsInArticlesCounts.rar contains counts of target words in outlets articles as well as total counts of words in articles

-targetWordsInArticlesCountsGuardianExampleWords contains counts of target words in outlets articles as well as total counts of words in articles for illustrative Figure 1 in main manuscript

Usage Notes

In a small percentage of articles, outlet specific XPath expressions can fail to properly capture the content of the article due to the heterogeneity of HTML elements and CSS styling combinations with which articles text content is arranged in outlets online domains. As a result, the total and target word counts metrics for a small subset of articles are not precise. In a random sample of articles and outlets, manual estimation of target words counts overlapped with the automatically derived counts for over 90% of the articles.

Most of the incorrect frequency counts were minor deviations from the actual counts such as for instance counting the word "Facebook" in an article footnote encouraging article readers to follow the journalist’s Facebook profile and that the XPath expression mistakenly included as the content of the article main text. To conclude, in a data analysis of 16 million articles, we cannot manually check the correctness of frequency counts for every single article and hundred percent accuracy at capturing articles’ content is elusive due to the small number of difficult to detect boundary cases such as incorrect HTML markup syntax in online domains. Overall however, we are confident that our frequency metrics are representative of word prevalence in print news media content (see Figure 1 of main manuscript for supporting evidence).

According to Cognitive Market Research, the global AI Training Dataset Market size will be USD 2962.4 million in 2025. It will expand at a compound annual growth rate (CAGR) of 28.60% from 2025 to 2033.

North America held the major market share for more than 37% of the global revenue with a market size of USD 1096.09 million in 2025 and will grow at a compound annual growth rate (CAGR) of 26.4% from 2025 to 2033.
Europe accounted for a market share of over 29% of the global revenue, with a market size of USD 859.10 million.
APAC held a market share of around 24% of the global revenue with a market size of USD 710.98 million in 2025 and will grow at a compound annual growth rate (CAGR) of 30.6% from 2025 to 2033.
South America has a market share of more than 3.8% of the global revenue, with a market size of USD 112.57 million in 2025 and will grow at a compound annual growth rate (CAGR) of 27.6% from 2025 to 2033.
Middle East had a market share of around 4% of the global revenue and was estimated at a market size of USD 118.50 million in 2025 and will grow at a compound annual growth rate (CAGR) of 27.9% from 2025 to 2033.
Africa had a market share of around 2.20% of the global revenue and was estimated at a market size of USD 65.17 million in 2025 and will grow at a compound annual growth rate (CAGR) of 28.3% from 2025 to 2033.
Data Annotation category is the fastest growing segment of the AI Training Dataset Market

Market Dynamics of AI Training Dataset Market

Key Drivers for AI Training Dataset Market

Government-Led Open Data Initiatives Fueling AI Training Dataset Market Growth

In recent years, Government-initiated open data efforts have strongly driven the development of the AI Training Dataset Market through offering affordable, high-quality datasets that are vital in training sound AI models. For instance, the U.S. government's drive for openness and innovation can be seen through portals such as Data.gov, which provides an enormous collection of datasets from many industries, ranging from healthcare, finance, and transportation. Such datasets are basic building blocks in constructing AI applications and training models using real-world data. In the same way, the platform data.gov.uk, run by the U.K. government, offers ample datasets to aid AI research and development, creating an environment that is supportive of technological growth. By releasing such information into the public domain, governments not only enhance transparency but also encourage innovation in the AI industry, resulting in greater demand for training datasets and helping to drive the market's growth.

India's IndiaAI Datasets Platform Accelerates AI Training Dataset Market Growth

India's upcoming launch of the IndiaAI Datasets Platform in January 2025 is likely to greatly increase the AI Training Dataset Market. The project, which is part of the government's ?10,000 crore IndiaAI Mission, will establish an open-source repository similar to platforms such as HuggingFace to enable developers to create, train, and deploy AI models. The platform will collect datasets from central and state governments and private sector organizations to provide a wide and rich data pool. Through improved access to high-quality, non-personal data, the platform is filling an important requirement for high-quality datasets for training AI models, thus driving innovation and development in the AI industry. This public initiative reflects India's determination to become a global AI hub, offering the infrastructure required to facilitate startups, researchers, and businesses in creating cutting-edge AI solutions. The initiative not only simplifies data access but also creates a model for public-private partnerships in AI development.

Restraint Factor for the AI Training Dataset Market

Data Privacy Regulations Impeding AI Training Dataset Market Growth

Strict data privacy laws are coming up as a major constraint in the AI Training Dataset Market since governments across the globe are establishing legislation to safeguard personal data. In the European Union, explicit consent for using personal data is required under the General Data Protection Regulation (GDPR), reducing the availability of datasets for training AI. Likewise, the data protection regulator in Brazil ordered Meta and others to stop the use of Brazilian personal data in training AI models due to dangers to individuals' funda...

Time series data for the statistic Exports_United_Kingdom_to_the_Marshall_Islands. Indicator Definition:Goods, Value of Exports, Free on board (FOB), US DollarsThe Serie's long term average value is 1.37 Million. It's latest available value, on 1/31/2025, is 10.03 percent lower, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 7/31/2016, to it's latest available value, on 1/31/2025, is +109,998.31%.The Serie's change in percent from it's maximum value, on 4/30/2024, to it's latest available value, on 1/31/2025, is -95.71%.

The Gross Domestic Product per capita in the United Kingdom was last recorded at 47265 US dollars in 2024. The GDP per Capita in the United Kingdom is equivalent to 374 percent of the world's average. This dataset provides the latest reported value for - United Kingdom GDP per capita - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.