README.md

---
language:
- en
- gd
- la
- fr
- hi
- sa
- pt
- nl
tags:
- cultural-heritage
- information-retrieval
- RAG
- OCR
- multimodal
- historical-documents
- digital-humanities
license: cc-by-4.0
size_categories:
- 100B<n<1T
---

# NLS-CH-Multimodal: A Large-Scale Multi-Modal Cultural Heritage Corpus


## Dataset Summary

NLS-CH-Multimodal is a large-scale multimodal corpus derived from the **National Library of Scotland (NLS)** digital collections, 
comprising over 512,000 files and exceeding 1 TB in size. The dataset is designed to support Information Retrieval (IR), 
Retrieval-Augmented Generation (RAG), and analysis of large language model (LLM) behaviour on historical data.

The corpus integrates multiple aligned modalities, including OCR/HTR-derived text, high-resolution document images, page-level layout annotations (ALTO XML), 
and structured metadata in unified CSV/JSON formats. It spans over five centuries (15th–21st century) and covers domains such as social history, political records, 
and cartographic collections.

Unlike the original NLS digital archive—designed primarily for human browsing—this dataset restructures materials into machine-readable, 
IR-ready representations. In addition, it includes a reproducible evaluation benchmark consisting of queries and relevance judgements, 
enabling systematic experimentation that is not supported by the original archive.

---

## Key Contributions
- A large-scale multimodal cultural heritage corpus (>512K files, >1TB)
- An automated data acquisition pipeline for harvesting metadata, transcriptions, images, and PDFs from the National Library of Scotland collections.
- A structured machine-readable organization of archival resources, linking collection metadata with associated digital objects and transcriptions.
- An IR- and RAG-ready dataset that enables direct integration into retrieval and generation pipelines.
- A human-reviewed evaluation benchmark with queries and relevance judgements
- A machine-readable transformation of heterogeneous archival collections

## Dataset Structure

The corpus is organised into two primary directory trees:

### `data/collections/`

Each NLS source collection has its own subdirectory organised by temporal
period, containing:

- `doc_id.txt` — full OCR/HTR transcript
- `doc_id.pdf` — composite scanned PDF
- `collection_name.csv` — structured metadata (title, author, year, URL, etc.)
- `description.txt` — collection-level summary

### `data/collections/`

Contains three thematic E-Resources sub-collections:

- `Africa_And_New_Imperialism/` — Colonial-era records and court documents
- `Indiaraj/` — India-related archival materials
- `Slavery/` — Slavery and abolition records

Documents are organised as:
`year/document_title/doc_id.txt` and `.pdf`
Documents with missing year metadata are stored under `Unknown/`.

---

## Data Statistics

The corpus exceeds one terabyte in size and comprises over **512,000 files**,
spanning the **15th to the 21st century**. It includes more than **42,000**
OCR-derived plain text documents, **215,567** high-resolution document images,
**215,567** page-level OCR representations encoded in ALTO XML, and over
**11,000** CSV document-level metadata files, alongside rich metadata expressed
in CSV and METS formats. The collection covers broad thematic domains including
social history, political records, and extensive cartographic material.


**Temporal range:** 15th – 21st century
**Primary languages:** English, Scottish Gaelic, Latin, French, Dutch, Portuguese
**Thematic domains:** Social history, political records, cartographic material

---

## Intended Use and Supported Tasks

### 🔍 Information Retrieval

- Benchmarking sparse and dense retrieval on historical corpora
- Cross-era and cross-collection retrieval experiments

### 🤖 Retrieval-Augmented Generation (RAG)

- End-to-end evaluation of RAG pipelines
- Retrieval grounding for historically complex queries

### 📊 LLM Evaluation

- Robustness to OCR noise and archaic language
- Behaviour on multilingual and historically situated inputs

### 🏛️ Digital Humanities

- Longitudinal language analysis
- Named entity recognition and linking
- Document layout and structure analysis

---

## Who Can Use This Dataset?


| User Group                     | Use Case                                                          |
| ------------------------------ | ----------------------------------------------------------------- |
| IR researchers                 | Benchmarking retrieval models on long-tail, historical queries    |
| NLP / LLM researchers          | Evaluating models on archaic language and OCR-noisy text          |
| RAG engineers                  | Testing retrieval pipelines over heterogeneous multimodal corpora |
| Digital humanities scholars    | Longitudinal and cross-cultural document analysis                 |
| Cultural heritage institutions | Reproducible baseline for heritage digitisation projects          |


---

## Evaluation Setup

A reproducible evaluation benchmark is included for IR and RAG experiments.

### Query Sets

| File | Description | Count |
|---|---|---|
| `evaluation/queries.jsonl` | Original factoid query set (Q001–Q044) | 44 |
| `evaluation/queries_expanded.jsonl` | Expanded factoid query set (Q051+) | 40 |
| `evaluation/queries_all_full.json` | Original queries with expected answers and OCR quality scores | 44 |
| `evaluation/queries_expanded_full.json` | Expanded queries with expected answers | 40 |
| **Total** | | **84 queries** |

### Relevance Judgements (Qrels)

| File | Description | Count |
|---|---|---|
| `evaluation/qrels.tsv` | Original relevance judgements | 44 |
| `evaluation/qrels_expanded.tsv` | Expanded relevance judgements | 40 |
| `evaluation/qrels_verified.tsv` | Human-verified relevance judgements | 44 |

All qrels follow the standard TREC format: `query-id  corpus-id  score`

### Query Schema

Each query record in the `.jsonl` files follows this structure:

```json
{
  "_id": "Q001",
  "text": "Query text here",
  "metadata": {
    "type": "factoid",
    "difficulty": "easy | medium | hard",
    "status": "verified"
  }
}
```

The full `.json` files additionally include expected answers and OCR quality metadata:

```json
{
  "query_id": "Q001",
  "query": "Query text here",
  "query_type": "factoid",
  "expected_answer": "Ground truth answer",
  "difficulty": "medium",
  "doc_id": "22361639",
  "ocr_quality_tier": "high",
  "ocr_quality": 0.985,
  "verified_by": ["Jakub", "yash"],
  "status": "verified"
}
```

---

## Data Collection Methodology

Data was acquired from three NLS platforms:
1. **NLS Digital Gallery** — OCR transcripts derived from XML/HTM streams
2. **Licensed E-Resources** — JWT-authenticated downloads via Burp Suite and Postman analysis
3. **NLS Data Foundry** — Bulk text, image, layout (ALTO XML), spatial data (GeoJSON, KML)

Collection was performed via automated pipelines combining metadata extraction, document retrieval, and validation.

This approach was necessary due to the absence of a unified bulk-access API across all collections.

---

## Known Gaps and Limitations

- **Post-2000 coverage is sparse** — the corpus is primarily pre-21st century
- **Single relevance per query** — does not yet support multi-document relevance
- **OCR noise:** historical spelling and scanning artefacts preserved.
- **Sensitive content:** Period-specific language reflecting historical
  social norms is present; standard LLM safety filters may flag this content

---

## Citation

If you use this dataset, please cite:

```bibtex
@dataset{nls_ch_multimodal_2025,
  author    = {Dhakade, Yash and {NeuraSearch Laboratory}},
  title     = {NLS-CH-Multimodal: A Large-Scale Multi-Modal Cultural
               Heritage Corpus for Information Retrieval},
  year      = {2025},
  publisher = {HuggingFace},
  url       = {https://huggingface.co/datasets/NeuraSearchLab/NLS-CH-Multimodal}
}
```

---

## Licence and Attribution

This dataset is derived from materials provided by the **National Library of Scotland (NLS)** and associated third-party sources.

A **sample version** of the dataset is publicly available via This HF repository, while the **full dataset (exceeding 1 TB)** is available upon reasonable request
for research purposes. The **dataset structure, metadata, and evaluation benchmark** (including queries and relevance judgements) are released under 
the **Creative Commons Attribution 4.0 (CC BY 4.0)** license. However, the **underlying source materials** (documents, images, and OCR transcripts) remain subject
to the original licensing terms and usage policies of the NLS and any third-party providers.

Users are responsible for complying with the applicable terms of use for each underlying collection and for providing appropriate attribution to 
the **National Library of Scotland** and relevant sources.