Nemotron-Personas-UK-Extended

A compound AI approach to synthetic personas grounded in real-world distributions

Dataset Overview

Nemotron-Personas-UK-Extended is a commercially-permissive dataset of synthetically-generated personas grounded in real-world demographic, geographic, and personality trait distributions in the United Kingdom, to capture the diversity and richness of the population. This dataset was built in collaboration with Locai Labs, the sovereign AI computer company based in London. It extends Nemotron-Personas-UK, the first UK dataset of its kind aligned with statistics for names, sex, age, ethnicity, religion, marital status, household type, education, employment, occupation, industry, pay, health, language (including Welsh, Gaelic, Scots, Irish and Ulster-Scots), sexual orientation, carer status and location. Unlike the open version, it includes the more sensitive demographic fields and the healthcare and finance personas. The dataset covers all four nations. England and Wales are grounded in Census 2021 from the Office for National Statistics (ONS), Scotland in Scotland's Census 2022 from National Records of Scotland (NRS), and Northern Ireland in Census 2021 from the Northern Ireland Statistics and Research Agency (NISRA). Each census has its own probabilistic graphical model (PGM), and the three are combined into one dataset.

Nemotron-Personas-UK-Extended supports UK model builders in developing Sovereign AI systems that incorporate important region-specific demographics, language, and cultural context. The dataset improves diversity of synthetic data generation, mitigates data and model biases, and helps prevent model collapse (degradation caused by uncurated training on another model's outputs) by reflecting the UK's real geographic and demographic distributions across England and Wales’ 329 local authority districts, Scotland’s 32 council areas, and Northern Ireland’s 11 local government districts. In particular, the dataset is designed to be more representative of underlying demographic distributions along multiple axes, including regional diversity (e.g., major cities, rural areas, and the four nations), ethnicity, religious affiliation, household composition, education levels, and a wide array of occupations, as compared to past persona datasets. As an example, one can produce high-quality multi-turn chat conversation data with real names, ages, and backgrounds that reflect the authentic experience of people across the UK, bringing unique perspectives and regional nuances to that data.

Produced using NeMo Data Designer, an enterprise-grade compound AI system for synthetic data generation, the dataset leverages a Probabilistic Graphical Model (PGM) for each census, along with the Apache-2.0-licensed Google Gemma-4-31B-it model and an ever-expanding set of validators and evaluators built into Data Designer. The dataset was generated using 18 NVIDIA GPUs, hosted across a hybrid set-up of cloud and local compute, using Locai Labs’ own AI computer, the Locai 1.

Nemotron-Personas-UK-Extended is available for use in NeMo Data Designer itself.

This dataset is ready for commercial use.

What is NOT in the dataset

Given the emphasis on personas, the dataset excludes other fields available in NeMo Data Designer, e.g., synthetic addresses.
All data, while mirroring real-world distributions, is completely artificially generated. Any similarity in names or persona descriptions to actual persons, living or dead, is purely coincidental.

Data Developer

Locai Labs & NVIDIA Corporation

Release Date

NGC [NVIDIA: release date] via [NVIDIA: NGC catalog link]
Open version: Hugging Face 20 October 2026 via https://huggingface.co/datasets/nvidia/Nemotron-Personas-UK

Dataset Creation Date
Final generation was completed on Monday 12th October 2026.

License/Terms of Use

This dataset is licensed under the NVIDIA Dataset License Agreement.
The personas are synthetic, built from the public sources below under each source’s license.

Under the Open Government Licence v3.0:

• Source: Office for National Statistics licensed under the Open Government Licence v.3.0 (Census 2021; Annual Survey of Hours and Earnings; Annual Population Survey and Labour Force Survey; Rural Urban Classification 2021; Index of Place Names, July 2024; baby names).
• © Crown copyright. Data supplied by National Records of Scotland (Scotland’s Census 2022; name statistics).
• Source: NISRA : Website: www.nisra.gov.uk (Census 2021; Annual Survey of Hours and Earnings 2024; name statistics).
• Contains OS data © Crown copyright and database right 2024.
• Contains Royal Mail data © Royal Mail copyright and database right 2024.

Under CC BY 4.0:

• Name statistics from the Central Statistics Office, Ireland.
• Big Five trait means: Rentfrow, P. J., Jokela, M., and Lamb, M. E. (2015). Regional personality differences in Great Britain. PLOS ONE 10(3): e0122245. doi:10.1371/journal.pone.0122245
• Names from other Nemotron-Personas datasets (NVIDIA).

Other sources:

• Contains information from Companies House: People with Significant Control data, aggregated name counts only.
• US first names from the Social Security Administration (public domain).

Use Case

Developers working on Sovereign AI, training LLMs, seeding user simulations to test and evaluate AI agents, and/or looking to improve diversity of synthetically generated data, mitigate data/model biases, and prevent model collapse.

Data Version

1.0 (20 October 2026)

Intended Use

The Nemotron-Personas-UK-Extended dataset is intended to be used by the community to continue to improve open models and push the state of the art. We welcome feedback from the open-source community and invite developers, researchers, and data enthusiasts to explore the dataset and build upon it.

The Nemotron-Personas-UK-Extended dataset is grounded in distributions of self-reported demographic data in Census 2021 for England and Wales, Scotland's Census 2022, and Census 2021 for Northern Ireland. As such, its primary goal is to support Sovereign AI development by combating missing data and/or potential biases present in model training data today, especially when it comes to existing persona datasets used in synthetic data generation. Despite the improved data diversity and fidelity to the UK's population, we are still limited by data availability and reasonable model complexity. This results in some necessary independence assumptions. The three censuses use different questions and categories, and some, such as qualifications, do not map exactly between nations. These categories were harmonised through mapping to a coarser set of categories for each of the relevant fields; marital status was mapped to a unified 5 category scheme, household type was mapped to 13 categories, qualification categories were mapped to 6. Published census tables also suppress small counts to protect privacy, which prevents the tail of the distribution of census data from being represented. We mitigate this issue through Iterative Proportional Fitting (IPF) of the fine-grained individual census tables to match the broader cross tables which are otherwise suppressed. Sensitive attributes such as ethnicity, religion, disability status, and sexual orientation are synthetic, sampled from published census distributions, and describe no real person. They are included in this version to help developers test and mitigate bias in synthetic data. They must not be used to infer, profile, or make decisions about real individuals or groups. Where age-related signals are derived from public records (for example, Companies House nationality name statistics), only decade-level aggregates are used; no individual dates of birth are retained. We leave further efforts to improve fidelity to future work.

Dataset Details

The dataset contains:

• 1,000,000 records in English with 9 personas per record, for 9,000,000 personas total. The breakdown of records by country is 889,311 for England and Wales, 83,163 for Scotland, and 27,526 for Northern Ireland.
• 35 fields: 9 persona fields, 6 persona-attribute fields (encompassing cultural background, skills and expertise, hobbies and interests, career goals and ambitions, and list versions of the skills and hobbies fields), 14 contextual fields sampled from the PGM including first and last name, 5 personality fields, and a unique identifier.
• 1.1B tokens total, including 492M persona tokens
• Comprehensive coverage across demographic, geographic, and personality trait axes
• 466,865 unique first/last name combinations (27,100 unique first names and 24,929 unique last names)
• A variety of persona types: professional, sports, arts, travel, culinary, healthcare, finance, plus concise and detailed general-purpose personas.
• Natural language persona attributes: cultural background, skills & expertise, goals & ambitions, hobbies & interests
• Sensitive demographic fields not in the open version: ethnicity, religion, country of birth, disability, general health and sexual orientation

Nemotron-Personas-UK-Extended was designed to align with the UK's official demographic and labor statistics (ONS Census 2021, Scotland's Census 2022, and NISRA Census 2021), while extending them into areas important for AI training. In practice, this meant:

• Granular Geographic Distribution: the dataset anchors records in 372 local authorities across England, Wales, Scotland, and Northern Ireland, mirroring the real population density and distribution across the UK.
• Naming Conventions: by leveraging first-name and surname statistics from ONS, NRS, and NISRA, plus Companies House People with Significant Control (PSC) first names and surnames by nationality, the Central Statistics Office of Ireland, US Social Security Administration names, and names from other Nemotron Personas datasets, all of which were classified using Gemma-4-31B by which country the name is likely to be from, the dataset incorporates 27,100 unique first names and 24,929 unique last names, reflecting the linguistic and ethnic diversity of the UK population.
• Standardized Occupational Framework: professional data is aligned with the ONS Standard Occupational Classification 2020 (SOC 2020) and the UK Standard Industrial Classification 2007 (SIC 2007), ensuring that the workforce distribution in the dataset matches official labor market statistics. Weekly pay is drawn from the ONS Annual Survey of Hours and Earnings (ASHE) and Annual Population Survey / Labour Force Survey tables, as well as NISRA ASHE 2024 tables.
• Demographic Alignment: key variables such as age, sex, ethnicity, religion, marital status, household type, education level, economic activity, occupation, industry and long-term health and disability are sampled to reflect the current demographic profile of each nation, providing a statistically representative synthetic population.
• Multi-Census Integration: the England and Wales, Scotland, and Northern Ireland PGMs each follow their own census schema, with counts in proportion to the real adult population share of each nation within the UK (84.3% England, 8.32% Scotland, 4.63% Wales, and 2.75% Northern Ireland).
• Personality Traits: Big Five (OCEAN) T-scores by local area are drawn from Rentfrow, Jokela and Lamb (2015), in particular the S2 table. Northern Ireland, which was excluded from the paper, uses the average of the other nations’ T-scores.
• Sampling: All 1,000,000 generated records are released. Regeneration of the prose was performed on records found to have issues through manual and automatic review.

These extensions build on a strong foundation of public data, helping create personas that are both statistically grounded and culturally representative, while remaining synthetic, privacy-preserving, and open.

Seed Data

In order to capture the socio-demographic and geographic diversity and complexity of the UK's population, Nemotron-Personas-UK-Extended leveraged:

• Census 2021 for England and Wales, published by the Office for National Statistics (ONS)
• Scotland's Census 2022, published by National Records of Scotland (NRS)
• Census 2021 for Northern Ireland, published by the Northern Ireland Statistics and Research Agency (NISRA)
• All census data from the sources above is from their respective table builders: https://www.ons.gov.uk/datasets/create, https://www.scotlandscensus.gov.uk/webapi/jsf/dataCatalogueExplorer.xhtml, https://build.nisra.gov.uk/en/ which allow a user to download any cross table which is not subject to confidentiality controls.
• Earnings data from the ONS Annual Survey of Hours and Earnings (ASHE) and published Annual Population Survey / Labour Force Survey tables
• First and last name distributions from ONS, NRS, and NISRA name statistics, plus Companies House People with Significant Control (PSC) first names and surnames by nationality, the Central Statistics Office of Ireland, US Social Security Administration names, and names from other Nemotron Personas datasets.
• Big Five personality trait means by local area from Rentfrow, Jokela and Lamb (2015), PLOS ONE
• ONS Census 2021 custom cross-tabulation tables (English proficiency by industry/occupation; religion and main language by country of birth), England and Wales
• NISRA Census 2021 Flexible Table Builder, Northern Ireland (English proficiency by industry/occupation)
• ONS Rural Urban Classification 2021 (holiday-destination weighting)
• ONS Index of Place Names in Great Britain, July 2024 (place-name validation in travel text)
• ONS baby names, Wales tables 1997 to 2005 (Welsh first names)
• Companies House People with Significant Control (Welsh-origin first names and surname consistency; counts only, aggregated)

Schema

The dataset includes 35 fields, as shown below. Researchers will find many contextual fields useful in zeroing in on specific demographics, which is challenging to do with existing datasets. Categorical values use harmonised UK-wide labels rather than each census’ published labels where the field categories differ between countries, otherwise they follow the published labels.

Field & Token Counts

The dataset consists of 1.1B tokens (492M persona tokens) across 1,000,000 records and 35 columns, excluding the globally unique identifier. Note that the data provides comprehensive coverage across England, Wales, Scotland, and Northern Ireland and 372 local authorities (329 local authority districts in England and Wales, 32 council areas in Scotland and 11 local government districts in Northern Ireland). The table below lists, for each column, the number of unique values, the data type, and average and total tokens.

Dataset Description & Quality Assessment

The analysis below provides a breakdown across various axes of the dataset to emphasize the built-in diversity and pattern complexity of the UK demographic data.

Names

Names are provided as dedicated fields (first_name, last_name) and are also infused into persona generation. The dataset contains 27,100 unique first names and 24,929 unique last names, forming 466,865 unique full names, grounded in ONS, NRS, and NISRA name statistics, Companies House People with Significant Control (PSC) first names and surnames by nationality, the Central Statistics Office of Ireland, US Social Security Administration names, and names from other Nemotron Personas datasets. First names are drawn by sex, decade of birth and country of birth; people born abroad or of non-British heritage draw from their birth country's names, filtered to fit their stated religion. Surnames are drawn by country of birth or heritage and conditioned on the first name using Companies House first-name by surname pairs. Every name is held by at least 10 Companies House holders. Names are a proxy, not a census.

Welsh-born Welsh speakers can receive Welsh-origin names. Rare names suppressed in published statistics are not restored; supplementary sources cover them.

Age Distribution

The age distribution of Nemotron-Personas-UK-Extended mirrors the country's real demographic structure. The median age is 48. The adult population is spread evenly from 25 to 64, at 16-17% per ten-year band; 18-24 makes up 10.6%, 65-74 12.6% and 85+ 3.0%. Women are 51.7% overall, rising to 54.9% at 75-84 and 62.8% at 85+.

Note that minors under 18 are excluded from this dataset.

Marital Status by Age Group

The heatmap below displays the fraction of people for each age cohort by marital or civil partnership status, out of Never married, Married or civil partnership, Separated, Divorced or dissolved, and Widowed or surviving partner. Marriage becomes the most common status at 35-44 (53.6%) and peaks at 65-74 (64.4%); divorce peaks at 55-64 (17.0%); widowhood dominates from 85 (59.3%).

All of these considerations are relevant to informing authentic life experiences and personas in the UK context.

Household Type by Age Group

The heatmap below shows, for each age cohort, the type of household personas live in (note that this describes the household, not the persona's own role in it). 18-34-year-olds mostly live in 'other' households (sharers or adults living with parents) or as couples; married couples with dependent children are most common at 35-54 (40.9% at 35-44); from 55, couples whose children have left home dominate; from 65, pensioner families; at 85+, half live alone (50.2%) and 11.3% live in communal establishments.

Education Level by Age Group

The heatmap below captures patterns of educational attainment (highest level of qualification) across age cohorts in the UK. Degree-level qualifications peak at 25-34 (49.0%) and fall with age; 'no qualifications' rises from 8.5% at 25-34 to 54.4% at 85+. Each census's qualifications are mapped to six UK-wide levels; Scottish qualifications use the nearest England and Wales level, with HNC/HND counted as degree level.

Geographic Intricacies of Education Attainment

This slice of the dataset demonstrates how geography informs education and therefore persona descriptions. The choropleth map shows, for each municipality, the share of residents aged 25 and older who hold a degree-level qualification, relative to the national average of 37.1%. The highest areas are Richmond upon Thames (65.9%), Wandsworth (65.4%) and Camden (64.5%), and the lowest are Great Yarmouth (19.0%), Fenland (19.6%) and Boston (20.0%). By region, London is highest (49.3%) and the North East lowest (29.9%).

Occupational Categories

The histogram below reflects the professional distribution of the currently employed personas (584,345, 58.4% of all personas) across major occupation groups. These groups are grounded in the nine major groups of SOC 2020 and are further informed by demographic and geographic distributions. The largest groups are Professional occupations at 21.3%, Associate professional and technical at 12.9%, and Managers, directors, and senior officials at 11.8%. 87% of professionals hold a degree-level qualification, against 16% in elementary occupations and 15% of process, plant and machine operatives, where approximately 25% have no qualifications.

Personas Diversity

The attributes above (and many more) ultimately affect the diversity of the synthetic personas being generated. As an example, the analysis below highlights a multitude of clusters within professional persona descriptions. These clusters are identified by clustering embeddings and reducing dimensionality to 2D.

How to use it

You can download the dataset from NGC or use it directly in Data Designer as follows:
config_builder = DataDesignerConfigBuilder(model_configs=model_configs)

# UK personas in English
config_builder.add_column(
SamplerColumnConfig(
name="person",
sampler_type=SamplerType.PERSON,
params=PersonSamplerParams(
locale="en_GB",
age_range=[18, 95],
),
)
)

Dataset Characterization

Data Collection Method

• Hybrid: Human, Synthetic, Automated

Labeling Method

• Not Applicable

Dataset Format

• All fields are strings excepting age : integer, as well as skills_and_expertise_list, hobbies_and_interests_list : JSON-encoded lists, and the five personality fields are JSON objects (t_score, label, description). The full data structure is provided in the Schema section.

Dataset Quantification

• Record counts: 1,000,000 records (9,000,000 personas)
• Total data storage: 2.1 GB (12 Parquet shards)

Limitations

In Northern Ireland and west-central Scotland, sports personas may reflect the general association between some football clubs and community background. No data source of club support by religion or community is used, and no claim is made about any individual.

Ethical Considerations

NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal teams to ensure this dataset meets requirements for the relevant industry and use case and addresses unforeseen product misuse.
Please report quality, risk, security vulnerabilities or NVIDIA AI Concerns here.

Citation

If you find the data useful, please cite:

@software{nvidia/Nemotron-Personas-UK,
 author = {Stephens, Samuel and Drayson, George and Herr, Nathan and Drayson, James and Drayson, Paul and Jennings, Will and {Kabiri Chimeh}, Mozhgan and Sharabiani, Ashton and Meyer, Yev},
  title = {{Nemotron-Personas-UK}: Synthetic Personas Aligned to Real-World Distributions for the United Kingdom},
  month = {October},
  year = {2026},
  url = {https://huggingface.co/datasets/nvidia/Nemotron-Personas-UK}
}