| --- |
| license: cc-by-4.0 |
| task_categories: |
| - tabular-classification |
| - feature-extraction |
| language: |
| - en |
| tags: |
| - drug-deal |
| - biotech-business |
| - licensing-deals |
| - pharma-m-a |
| - market-intelligence |
| pretty_name: Global Biopharma Licensing & M/A Transactions |
| size_categories: |
| - 1K<n<10K |
| --- |
| 🤝 **Dataset Summary** |
|
|
| Pharmaceutical licensing and deal intelligence records covering 1,000 transactions. Each record captures the deal structure, involved organizations, financial terms, and asset details, sourced from news and company disclosures. |
|
|
| **🚀 Key Features** |
|
|
| - **Deal Structure:** `deal_type` classifies transaction type (license, acquisition, collaboration, etc.) with multilingual labels. |
| - **Financial Terms:** `deal_value` provides structured breakdown of payment types (upfront, milestone) with normalized USD values (~34.5% of records). |
| - **Organizational Mapping:** `principle_org` and `partner_org` identify both sides of each transaction. |
| - **Asset Linkage:** `deal_project` links deals to specific drugs or programs by name and type. |
| - **Territory Coverage:** `territory_included` specifies geographic rights where disclosed (~33.8% of records). |
|
|
| **💻 Quick Start & MCP Integration** |
|
|
| ```python |
| from datasets import load_dataset |
| |
| dataset = load_dataset("your-org/drug-deals", split="train") |
| record = dataset[0] |
| print(record["deal_title"]) |
| # Output: 'GSK exercises option on Anacor\'s novel antibiotic for the treatment of gram-negative infections' |
| print(record["principle_org"]) |
| # Output: ['Anacor Pharmaceuticals'] |
| print(record["deal_value"][0]["value"]) |
| # Output: '15.00' (million dollars, upfront payment) |
| ``` |
|
|
| --- |
