---
task_categories:
- question-answering
- text-generation
language:
- es
tags:
- cultural
- heritage
- spanish
- culture
- humanities
- corpus
- nlp
---

# Dataset Introduction

The **ALIA Spanish Cultural and Heritage Retrieval Pairs Corpus** contains tabular instances designed to train and evaluate retrieval-oriented models (e.g., dense retrievers / embedding encoders) using passage-grounded query data produced with a Qwen-style prompting workflow integrated in the ALIA encoders pipeline.

It preserves provenance to the original document and passage while exposing controls such as question `type` and `difficulty` (ranging from `high_school` to `phd` level).

## Dataset Description

### Dataset Summary
- **Data format**: One row per passage-based training/evaluation instance.  
- **Method**: `query` is generated from `passage` using a Qwen-style LLM prompting approach defined in the project scripts.
- **Difficulty scale**: `difficulty` is a categorical label with three levels: `high_school`, `university`, or `phd`.
- **Scope**: Focused on domain-specific cultural heritage and humanities text, and compatible with document segmentation workflows that track document/chunk provenance.

### Dataset Sources
- **Project / organization**: SINAI Research Group (Universidad de Jaén) and ALIA resources.
- **Generation pipeline code**: [ALIA-UJA encoders scripts (triplets)](https://github.com/sinai-uja/ALIA-UJA/tree/main/data/encoders/scripts/triplets).
- **Base corpus**: Derived from [SINAI/ALIA-es-cultural-heritage](https://huggingface.co/datasets/SINAI/ALIA-es-cultural-heritage) heritage corpus integrating 102+ heritage sources.

### Uses
- Training dense retrieval encoders from `query`–`passage` pairs in cultural heritage domains.  
- Building evaluation sets stratified by question `type` and `difficulty` for heritage-focused QA systems.  
- Domain-specific experiments and ablations using `source_id` and document/chunk identifiers in cultural heritage contexts.  

## Dataset Structure

### Data Instances
Each row represents a passage-grounded query instance with identifiers for passage and document provenance.  

Example (schematic):
```json
{
  "id_passage": "Patrimonio_Cultural_Inmaterial_España-PCI_Nacional_4-10",
  "id_passage_query": "Patrimonio_Cultural_Inmaterial_España-PCI_Nacional_4-10_1",
  "id_document": "PCI_Nacional_4",
  "passage": "El Toque Manual de Campanas, declarado en 2019, es un lenguaje sonoro que ha funcionado a lo largo de los siglos como un medio de comunicación, cumpliendo un conjunto de funciones sociales para la comunidad: informar, coordinar, delimitar el territorio y proteger. Los toques de campanas, basados en el ritmo, han sido los encargados de organizar la vida comunitaria, de delimitar el tiempo y el espacio laboral, diario, festivo y de duelo.",
  "character": "Zapotec ritual expert",
  "type": "background",
  "difficulty": "university",
  "query": "¿Qué funciones sociales y comunicativas tiene el Toque Manual de Campanas declarado en 2019 y de qué manera se está protegiendo este patrimonio inmaterial en España?",
  "source_id": "Patrimonio_Cultural_Inmaterial_España"
}
```

### Data Fields (schema)

| Field         | Type   | Description                                                                                      |
| :------------ | :----- | :----------------------------------------------------------------------------------------------- |
| `id_passage`    | string | Identifier for the passage unit (document segment).                                 |
| `id_passage_query`    | string | Unique identifier for the generated query from the passage unit (document segment).                                 |
| `id_document` | string | Identifier of the source document (used to group passages).                                        |
| `passage`     | string | Passage text used as grounding context for `query`.                                              |
| `character`   | string | Character who would ask the question.                                                             |
| `type`        | string | Question type/category defined by the generation pipeline. |
| `difficulty`  | string | Target educational level of the question: one of `high_school`, `university`, or `phd`.          |
| `query`       | string | Question generated/selected from the passage using the Qwen-based prompting methodology.         |
| `source_id`   | string | Provenance identifier (source/domain/corpus) for filtering and auditability.                     |

### Data Statistics
- **Total instances**: 710,519 instances.  
- **Columns**: 9 (`id_passage`, `id_passage_query`, `id_document`, `passage`, `character`, `type`, `difficulty`, `query`, `source_id`).  

**Distribution by Difficulty:**
- `university`: 390,966
- `high_school`: 217,851
- `phd`: 101,678

**Distribution by Type (top types):**
- `background`: 216,728
- `summary`: 158,020
- `acquire_knowledge`: 143,801
- `keywords`: 114,694
- `interpretation`: 49,718
- `procedural`: 9,964
- `requirements`: 4,772
- `eligibility`: 4,064
- `consequences`: 2,450
- `clarification`: 1,752
- `compliance_check`: 1,186
- `yes_or_no`: 1,080
- `deadline`: 288
- And 42 additional types including `schedule`, `methodology`, `description`, `provenance`, `timeline`, `comparison`, `citation`, `iconography`, `materials`, `location`, etc.

**Distribution by Source:**
- `Patrimonio_Cultural_Inmaterial_España`: Heritage intangible cultural assets
- `Patrimonio_Castilla_Y_Leon`: Castilla and León heritage resources
- `Wikipedia_Cultura_España`: Spanish cultural Wikipedia
- `Revistas_Culturales_Biblioteca_Virtual_Prensa_Historica`: Cultural journals and historical press
- `Revistas_CSIC`: CSIC institutional journals
- And 91+ additional heritage sources covering regional inventories, archaeological repositories, museums, archives, and cultural institutions

### Example Usage
```python
from datasets import load_dataset

# Replace with your final dataset repo id on Hugging Face
ds = load_dataset("SINAI/ALIA-es-cultural-heritage-pairs", split="train")

print(ds.keys())
print(ds["query"][0])
print(ds["passage"][0][:200])

# Filter by difficulty level
university_level = ds.filter(lambda x: x['difficulty'] == 'university')
phd_level = ds.filter(lambda x: x['difficulty'] == 'phd')

# Filter by question type
background_questions = ds.filter(lambda x: x['type'] == 'background')
heritage_interpretation = ds.filter(lambda x: x['type'] == 'interpretation')
```

## Dataset Creation

### Source Data
The dataset is derived from the [SINAI/ALIA-es-cultural-heritage](https://huggingface.co/datasets/SINAI/ALIA-es-cultural-heritage) heritage corpus, which integrates documentation from 102+ heritage and cultural sources. Documents are segmented into passages, keeping document-level provenance via `id_document` and passage-level provenance via `id_passage`.

### Data Collection and Processing
- Passages are created via document segmentation from heritage and cultural sources and linked to `id_document`/`id_passage` for traceability.
- Questions are produced using a Qwen-driven prompt pipeline in the ALIA encoders scripts, with metadata controls such as `type` and `difficulty`.
- `source_id` supports provenance tracking, source-level audits, and cultural heritage domain attribution.
- The pipeline preserves domain-specific terminology, institutional contexts, and cultural-historical narratives.  

### Annotations
This dataset is primarily built via automatic or LLM-assisted generation rather than mandatory manual annotation, though downstream filtering/review can be applied depending on the pipeline configuration.  

### Personal and Sensitive Information
Because the underlying domain is cultural heritage and humanities, instances may include names of historical figures, cultural entities, institutions, place names, and other entities present in the source material. Most data derives from public institutional repositories and academic sources. Additional filtering may be required depending on deployment constraints and cultural sensitivity considerations.  

## Considerations for Using the Data

### Social Impact of Dataset
This dataset can improve access to domain-specific retrieval and QA capabilities in cultural heritage settings by enabling better model adaptation to specialized heritage narratives, historical discourse, institutional documentation, and cultural terminology. It supports democratization of heritage information and enables AI systems to better understand and provide access to cultural resources.  

### Discussion of Biases
- **Domain bias**: language style and vocabulary are specialized to cultural heritage, humanities, and institutional documentation and may reduce out-of-domain generalization.  
- **Source bias**: the dataset integrates content from institutional heritage repositories and academic journals, which means models may overfit to formal academic and institutional registers over colloquial heritage narratives.
- **Regional and institutional bias**: coverage depends on digitization and publication practices across heritage institutions; some regions or cultural domains may be over-represented relative to others.
- **Historical and editorial bias**: heritage narratives may reflect dominant historiographic, institutional, or curatorial perspectives from the source institutions.
- **Difficulty bias**: the distribution across `high_school`, `university`, and `phd` levels may not be uniform, potentially skewing model performance toward the most represented level.
### Other Known Limitations
- **Leakage risk**: if splitting is done at passage-level rather than document-level, near-duplicate context may appear across splits; splitting by `id_document` is recommended.  
- **LLM artifacts**: synthetic questions may contain stylistic patterns that differ from real heritage researcher or user queries, and may require additional filtering for production use.  
- **Difficulty calibration**: the assignment of `high_school`, `university`, or `phd` difficulty levels is model-driven and may not perfectly align with human heritage expert or educator judgments.
- **Cultural representation**: coverage reflects the breadth of digitized heritage sources; some aspects of cultural heritage (particularly intangible heritage, diaspora heritage, or marginalized cultural traditions) may be under-represented.

## Citation
```bibtex
@misc{ALIA-es-cultural-heritage-pairs,
  title={ALIA Spanish Cultural and Heritage Retrieval Pairs Corpus},
  author={SINAI Research Group},
  year={2026},
  publisher={HuggingFace},
  howpublished={\url{https://huggingface.co/datasets/SINAI/ALIA-es-cultural-heritage-pairs}}
}
```

---

## Funding
This work is funded by the Ministerio para la Transformación Digital y de la Función Pública - Funded by EU – NextGenerationEU within the framework of the project [ALIA](https://alia.gob.es).

## Acknowledgments  
This dataset has been generated thanks to [SCAYLE](https://www.scayle.es/) (Centro de Supercomputación de Castilla y León) which provided the needed computational resources on its CALENDULA supercomputing cluster.

---

**Contact:** [ALIA Project](https://www.alia.gob.es/) - [SINAI Research Group](https://sinai.ujaen.es) - [Universidad de Jaén](https://www.ujaen.es/)

**More Information:** [SINAI Research Group](https://sinai.ujaen.es) | [ALIA-UJA Project](https://github.com/sinai-uja/ALIA-UJA)