OpenData Research Tools

A lightweight Python library for searching biomedical databases and returning standardized data.

Philosophy

Focus on search, not management:

• ✅ Search multiple open biomedical databases
• ✅ Return standardized Python dictionaries
• ✅ Optional HTTP caching for performance
• ❌ No data management or storage
• ❌ No opinionated workflows
• ❌ Minimal dependencies (only requests)

Let your application decide how to store, process, and manage the data.

Features

• 10+ Data Sources: PubMed, NCBI Gene, UniProt, ChEMBL, PubChem, Protein Structures, WikiData, Clinical Trials, and more
• Standardized Output: All tools return consistent dictionary formats
• HTTP Caching: Optional caching to reduce API calls and improve performance
• Gene Resolution: Resolve gene symbols across multiple databases
• Zero Configuration: Works out of the box with sensible defaults
• Lightweight: Only one dependency (requests)

Installation

pip install opendata-research-tools

Quick Start

Basic Search

from opendata_research_tools.search import PubMedSearchTool

# Create a search tool instance
tool = PubMedSearchTool(enable_cache=True)

# Search PubMed
results = tool.search(query="cancer immunotherapy", max_results=10)

# Results is a list of dictionaries
for article in results:
    print(f"Title: {article['title']}")
    print(f"PMID: {article['pmid']}")
    print(f"Authors: {article['authors']}")
    print(f"Year: {article['year']}")
    print()

Multiple Data Sources

from opendata_research_tools.search import (
    PubMedSearchTool,
    NCBIGeneSearchTool,
    UniProtSearchTool,
    ChEMBLSearchTool
)

# Search literature
pubmed = PubMedSearchTool()
articles = pubmed.search("JAK2 V617F")

# Search gene databases
gene_tool = NCBIGeneSearchTool()
gene_info = gene_tool.search("JAK2")

# Search protein databases
uniprot = UniProtSearchTool()
protein_info = uniprot.search("JAK2")

# Search for compounds
chembl = ChEMBLSearchTool()
compounds = chembl.search(target="JAK2", activity_type="inhibitor")

# All results are dictionaries - use them however you want!
# Save to database, process with pandas, generate reports, etc.

Custom Caching

from opendata_research_tools.search import PubMedSearchTool

# Disable caching
tool = PubMedSearchTool(enable_cache=False)

# Custom cache directory
tool = PubMedSearchTool(enable_cache=True, cache_dir="./my_cache")

Supported Databases

Return Format

Each tool returns a list of dictionaries with standardized fields. Example for PubMed:

[
    {
        'pmid': '12345678',
        'title': 'Article Title',
        'authors': 'Smith J, Doe J',
        'journal': 'Nature',
        'year': '2023',
        'abstract': 'Article abstract text...',
        'keywords': ['cancer', 'therapy'],
        'doi': '10.1234/example',
        'url': 'https://pubmed.ncbi.nlm.nih.gov/12345678/'
    },
    ...
]

See the API Reference for detailed field descriptions for each data source.

Gene Symbol Resolution

Resolve gene symbols across multiple databases:

from opendata_research_tools.utils import GeneSynonymResolver

resolver = GeneSynonymResolver()
result = resolver.resolve("TP53")

print(f"Canonical Symbol: {result.canonical_symbol}")
print(f"Entrez ID: {result.entrez_id}")
print(f"Aliases: {result.aliases}")
print(f"Organism: {result.organism}")

Documentation

• Quick Start Guide
• API Reference
• Examples

Development

# Clone the repository
git clone https://github.com/xxxxx/opendata-research-tools.git
cd opendata-research-tools

# Install with development dependencies
pip install -e ".[dev]"

# Run tests
pytest

# Run tests with coverage
pytest --cov=opendata_research_tools --cov-report=html

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

MIT License - see LICENSE file for details.

Citation

• Documentation: https://opendata-research-tools.readthedocs.io