---
license: cc-by-4.0
language:
  - es
tags:
  - synthetic
  - synthetic-data
  - personas
  - synthetic-personas
  - persona
  - synthetic-users
  - user-simulation
  - agent-evaluation
  - spain
  - stratasynth
pretty_name: StrataSynth Personas Spain
size_categories:
  - 1K<n<10K
task_categories:
  - text-generation
configs:
  - config_name: default
    data_files:
      - split: train
        path: personas_es.jsonl
---

# StrataSynth Personas Spain

**1,250 synthetic people from Spain, written in Spanish, each with a biography, a way of speaking, hobbies, skills and goals, and a measured personality.**

1,250 people · 27 fields per person · 17 autonomous communities

Part of the [StrataSynth Personas collection](https://huggingface.co/collections/StrataSynth/stratasynth-personas-6ab96c1be53faa6c4c596031): eight countries, 10,000 people. All of them together: [StrataSynth/stratasynth-personas-8-countries](https://huggingface.co/datasets/StrataSynth/stratasynth-personas-8-countries).

## Why this dataset

- **A real population, not a stereotype.** Age, region, living arrangements and family structure follow the country's real adult population. Children's ages fit their parents' ages, and names follow real name frequencies by generation.
- **Written in Spanish.** Biographies, daily routines, speaking styles, hobbies, skills and goals are written natively, not translated.
- **Personality you can filter on.** Big Five (0-1) and adult attachment for every person. More extraverted people have more social hobbies; more open people have more cultural and learning-oriented ones.
- **Ready to become a user.** A biography plus a speaking style is what a user simulator needs: drop one into a prompt and you have a customer, a patient or a respondent from Spain.

## The population

![Age by gender](img/es-age.png)

![Employment status](img/es-employment.png)

![Household type by age](img/es-household.png)

- **Ages** 18-85, median 50.
- **Employment:** 56 % employed, 18 % retired, 8 % at home, 8 % unemployed, 8 % studying.
- **Home:** 50 % own their home, 31 % rent, 18 % live in the family home.
- **Living with parents:** 19 % of adults.

## What people use it for

- **Testing chatbots and agents** built for Spain with realistic local users before going live.
- **Fairness and bias checks** across age, gender and region.
- **Seeding synthetic data** in Spanish: "write the question this person would ask".
- **Synthetic respondents** to pre-test surveys, products and messaging.

## Example

```json
{
  "id": "es-00002",
  "name": "María Dolores Pascual Martínez",
  "country": "ES",
  "language": "es",
  "age": 50,
  "gender": "female",
  "region": "Principado de Asturias",
  "city_size": "large",
  "education": "secondary",
  "occupation": "cajera de supermercado",
  "last_occupation": null,
  "income_level": "low",
  "household_type": "empty_nester",
  "marital_status": "partnered",
  "children": 2,
  "children_ages": [
    24,
    26
  ],
  "housing": "rented",
  "employment_status": "employed",
  "lives_with_parents": false,
  "daily_routine": "María Dolores se levanta a las seis y media, antes que nadie, para tener la casa en orden antes de entrar a turno en el supermercado. Después de fichar, pasa casi siempre por casa de su madre a preparar la comida y…",
  "persona": "María Dolores Pascual Martínez tiene cincuenta años y lleva media vida doblando toallas que nadie le agradece. Es cajera de supermercado en una gran ciudad asturiana, cuida de su madre enferma y de una casa que sostiene…",
  "speaking_style": "Habla con frases medianas, cálidas y con mucha carga emocional contenida; usa muletillas como «mujer», «ya te digo» o «no sé yo» cuando no quiere comprometerse. Su tono es cercano y familiar, pero se vuelve cortante…",
  "hobbies_and_interests": [
    "Cocinar para la familia",
    "Ver series con su pareja",
    "Quedar con amigas del barrio",
    "Pasear por la ciudad",
    "Visitar a sus hijos"
  ],
  "skills": [
    "Manejo de caja y cobros",
    "Atención al cliente",
    "Organización del hogar",
    "Cuidado de su madre",
    "Cocina casera"
  ],
  "goals": [
    "Cuidar de su madre sin descuidar su propia salud.",
    "Mantener el contacto con sus hijos y verlos más a menudo.",
    "Encontrar tiempo para sí misma y descansar mejor."
  ],
  "consumer": {
    "shopping_style": "habitual",
    "price_sensitivity": "high",
    "brand_loyalty": "medium",
    "sustainability_concern": "low",
    "tech_adoption": "mainstream"
  },
  "personality": {
    "big_five": {
      "O": 0.4,
      "C": 0.67,
      "E": 0.46,
      "A": 0.7,
      "N": 0.84
    },
    "attachment": {
      "anxiety": 0.62,
      "avoidance": 0.23
    }
  }
}
```

## Fields

| Field | Type | Description |
|---|---|---|
| `id` | string | Country code + number, e.g. `es-00001` |
| `name` | string | Full name. Two surnames in Spain, Mexico and Brazil, as is customary there |
| `country` | string | `ES`, `MX`, `US`, `GB`, `DE`, `FR`, `IT`, `BR` |
| `language` | string | Language of all text fields: `es`, `en`, `de`, `fr`, `it`, `pt` |
| `age` | int | 18 to 85 |
| `gender` | string | `male`, `female`, `non_binary` |
| `region` | string | Region within the country (Brazil: macro-region) |
| `city_size` | string | `rural`, `small`, `medium`, `large`, `metro` |
| `education` | string | `secondary`, `vocational`, `university`, `postgrad` |
| `occupation` | string \| null | Current occupation, in the person's language. `null` if not working |
| `last_occupation` | string \| null | Last occupation of people who are retired, unemployed, studying or at home |
| `income_level` | string | `low`, `medium`, `high`, relative to the country |
| `household_type` | string | `single`, `couple`, `family_young`, `family_teen`, `empty_nester` |
| `marital_status` | string | `single`, `partnered`, `married`, `divorced`, `widowed` |
| `children` | int | Number of children |
| `children_ages` | list[int] | Ages of the children |
| `housing` | string | `owned`, `rented`, `family_home` |
| `employment_status` | string | `employed`, `unemployed`, `student`, `retired`, `homemaker`, `other` |
| `lives_with_parents` | bool \| null | Whether the person lives with their parents (not provided for Brazil) |
| `daily_routine` | string | A typical day, in the person's language |
| `persona` | string | Biography, in the person's language |
| `speaking_style` | string | How the person talks: tone, length, vocabulary, habits, in the person's language |
| `hobbies_and_interests` | list[string] | Five hobbies and interests |
| `skills` | list[string] | Three to six skills |
| `goals` | list[string] | One to three goals for the coming years |
| `consumer` | object | `shopping_style`, `price_sensitivity`, `brand_loyalty`, `sustainability_concern`, `tech_adoption` |
| `personality.big_five` | object | `O` openness, `C` conscientiousness, `E` extraversion, `A` agreeableness, `N` neuroticism, 0 to 1 |
| `personality.attachment` | object | Adult attachment `anxiety` and `avoidance`, 0 to 1 |

## Load it

```python
from datasets import load_dataset
people = load_dataset("StrataSynth/stratasynth-personas-spain", split="train")
```

## Want more?

These people come from StrataSynth's Humans Engine. If you need people for a specific segment, or want to interview them, write to **hello@stratasynth.com**.

## Notes

- Every person is fully synthetic. They are not records of real people or of any institution, and any resemblance to a real person is coincidental.
- Names are drawn from real name frequencies, so a synthetic person may share a name with real people.

## License

CC BY 4.0. Please credit **StrataSynth** when you use it.