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--- |
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dataset_info: |
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features: |
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- name: document_id |
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dtype: string |
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- name: page_number |
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dtype: int32 |
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- name: file_identifier |
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dtype: string |
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- name: image |
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dtype: image |
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- name: text |
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dtype: string |
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- name: alto_xml |
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dtype: string |
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- name: has_image |
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dtype: bool |
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- name: has_alto |
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dtype: bool |
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- name: document_metadata |
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dtype: string |
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- name: has_metadata |
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dtype: bool |
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- name: topic |
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dtype: string |
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- name: year |
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dtype: string |
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- name: reference |
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dtype: string |
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- name: disease_focus |
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dtype: string |
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splits: |
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- name: train |
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num_bytes: 17768943623.127 |
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num_examples: 120903 |
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download_size: 12407753714 |
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dataset_size: 17768943623.127 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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language: |
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- en |
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tags: |
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- ocr |
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pretty_name: A Medical History of British India |
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license: cc0-1.0 |
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--- |
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# A Medical History of British India Dataset |
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## Dataset Description |
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This dataset contains digitized official publications documenting medical research and public health in British India from 1850-1950. The collection represents a crucial period in medical history, capturing the transition from humoral to biochemical medical traditions and documenting major breakthroughs in bacteriology, parasitology, and vaccine development. These documents provide invaluable insights into colonial medical surveillance systems and the evolution of public health policies in British India. |
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### Dataset Summary |
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- **Source**: [National Library of Scotland - A Medical History of British India](https://data.nls.uk/data/digitised-collections/a-medical-history-of-british-india/) |
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- **Time Period**: 1850-1950 |
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- **Format**: Image-text pairs with OCR output |
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- **Processing**: Converted from ALTO XML and JPG images to Hugging Face dataset format |
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- **Contents**: Official medical reports, disease histories, maps, and statistics from British India |
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- **Size**: 117,022 ALTO XML files, 120,903 image files, 22.5 million words |
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- **DOI**: https://doi.org/10.34812/2w0t-3f08 |
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## Dataset Structure |
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### Data Fields |
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Each record in the dataset contains the following fields: |
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- `document_id` (string): Unique identifier for the medical document |
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- `page_number` (int): Sequential page number within the document |
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- `file_identifier` (string): Original file identifier from the NLS dataset |
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- `image` (image): Scanned image of the document page |
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- `text` (string): OCR-extracted text from the page |
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- `alto_xml` (string): Raw ALTO XML containing detailed OCR information |
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- `has_image` (bool): Whether the page has an associated image file |
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- `has_alto` (bool): Whether the page has associated ALTO OCR data |
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- `document_metadata` (string): Full metadata description from inventory |
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- `has_metadata` (bool): Whether metadata is available for this document |
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- `topic` (string): Medical topic or subject of the document |
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- `year` (string): Year of publication (extracted from metadata) |
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- `reference` (string): Document reference code (e.g., "IP/HA.2") |
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- `disease_focus` (string): Primary disease discussed (if applicable) |
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### Disease Categories |
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The dataset includes documents focusing on various diseases prevalent in British India: |
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- Cholera |
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- Leprosy |
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- Plague |
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- Malaria |
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- Beri-beri |
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- Kala-azar (Kála-Azár) |
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- Hookworm |
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- Smallpox |
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- Fever (various types) |
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### Data Statistics |
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- **Total documents**: 468 unique medical reports and publications |
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- **Page distribution**: ~97% of pages have both images and OCR text |
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- **Geographic coverage**: Multiple regions across British India |
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- **Notable medical figures**: Documents work of Sir Ronald Ross and other prominent researchers |
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## Usage |
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### Loading the Dataset |
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```python |
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from datasets import load_dataset |
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# Load the dataset |
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dataset = load_dataset("davanstrien/india-medical-history") |
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# Access the data |
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for example in dataset['train']: |
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print(f"Document: {example['document_id']}") |
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print(f"Topic: {example['topic']}") |
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print(f"Year: {example['year']}") |
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print(f"Disease focus: {example['disease_focus']}") |
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print(f"Text preview: {example['text'][:200]}...") |
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break |
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``` |
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### Filtering by Disease Type |
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```python |
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# Filter documents about cholera |
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cholera_docs = dataset.filter(lambda x: x['disease_focus'] == 'Cholera') |
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print(f"Cholera-related pages: {len(cholera_docs)}") |
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# Filter documents from a specific time period |
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docs_1880s = dataset.filter(lambda x: x['year'] and '188' in x['year']) |
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print(f"Documents from 1880s: {len(docs_1880s)}") |
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``` |
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### Working with Medical Topics |
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```python |
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# Group documents by disease focus |
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from collections import defaultdict |
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disease_groups = defaultdict(list) |
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for example in dataset['train']: |
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if example['disease_focus']: |
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disease_groups[example['disease_focus']].append(example['document_id']) |
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# Display disease distribution |
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for disease, docs in disease_groups.items(): |
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unique_docs = len(set(docs)) |
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print(f"{disease}: {unique_docs} documents") |
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``` |
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### Extracting Geographic Information |
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```python |
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# Search for location-specific medical reports |
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def extract_locations(text): |
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# Common location patterns in colonial documents |
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locations = ['Bengal', 'Bombay', 'Madras', 'Punjab', 'Assam', 'Burma'] |
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found = [] |
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for loc in locations: |
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if loc.lower() in text.lower(): |
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found.append(loc) |
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return found |
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# Find documents mentioning specific regions |
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for example in dataset['train'][:100]: |
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if example['text']: |
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locations = extract_locations(example['text']) |
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if locations: |
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print(f"Document {example['document_id']}: {', '.join(locations)}") |
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``` |
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## Dataset Creation |
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### Source Data |
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The original data comes from the National Library of Scotland's comprehensive digitization project of official medical publications from British India. These documents include: |
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- Annual medical reports from various provinces |
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- Special investigations into disease outbreaks |
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- Statistical compilations of mortality and morbidity |
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- Research papers on tropical diseases |
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- Maps showing disease distribution |
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### Historical Significance |
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This collection documents: |
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- The transition from traditional humoral medicine to modern biochemical approaches |
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- Development of colonial medical surveillance systems |
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- Breakthroughs in understanding tropical diseases |
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- Early epidemiological studies and disease mapping |
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- The work of pioneering medical researchers in British India |
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### Processing Pipeline |
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1. **Original Format**: The source data consists of: |
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- High-resolution JPG scans of medical documents |
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- ALTO XML files containing OCR output with cleaned-up text |
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- METS metadata files with page ordering information |
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- Inventory CSV with detailed document metadata |
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2. **Conversion Process**: Using the custom `convert_india_papers.py` script: |
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- Parsed METS XML files to maintain correct page ordering |
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- Extracted medical metadata including disease focus, year, and topic |
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- Paired image files with their corresponding ALTO XML |
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- Preserved all structural and descriptive metadata |
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- Added specialized fields for medical history research |
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## Considerations for Using this Data |
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### Historical and Cultural Context |
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These documents represent colonial-era medical perspectives and should be understood within their historical context: |
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- Terminology reflects period-specific medical understanding |
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- Documents may contain colonial-era biases and perspectives |
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- Geographic names and administrative divisions are from the British colonial period |
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- Medical theories and treatments described may be outdated |
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### OCR Quality |
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The OCR quality is generally high due to NLS's cleaning efforts, but varies based on: |
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- Original document preservation state |
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- Complexity of medical terminology |
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- Presence of statistical tables and charts |
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- Mixed languages (English with local terms) |
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### Research Applications |
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This dataset is particularly valuable for: |
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- **Medical History Research**: Studying the evolution of tropical medicine and public health |
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- **Digital Humanities**: Analyzing colonial medical discourse and knowledge production |
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- **Epidemiological Studies**: Understanding historical disease patterns and outbreaks |
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- **OCR Development**: Training models on historical medical texts |
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- **Colonial Studies**: Examining medical aspects of colonial administration |
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- **Public Health History**: Tracing the development of health surveillance systems |
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## Additional Information |
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### Licensing |
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This dataset is in the **public domain** and free of known copyright restrictions. Users should comply with the National Library of Scotland's terms of use when utilizing this dataset. |
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### Citation |
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If you use this dataset, please cite both the original source and this Hugging Face dataset: |
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```bibtex |
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@misc{nls_india_medical, |
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title={A Medical History of British India}, |
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author={National Library of Scotland}, |
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year={2019}, |
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publisher={National Library of Scotland}, |
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doi={10.34812/2w0t-3f08}, |
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howpublished={\url{https://data.nls.uk/data/digitised-collections/a-medical-history-of-british-india/}}, |
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} |
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``` |
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### Acknowledgments |
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Dataset conversion for Hugging Face format done by [davanstrien](https://huggingface.co/davanstrien) |