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README.md
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- split: train
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path: data/train-*
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- split: train
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path: data/train-*
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---
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# Grant Classification Dataset
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This dataset contains research grant documents classified according to a custom categorization of science, technology, and innovation (STI) policy instruments.
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## Dataset Description
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### Overview
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The dataset consists of research grants from various funding sources.
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Each grant is classified into one of 8 categories according to a taxonomy based on the OECD's categorization of STI policy instruments.
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### Data Sources
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- **Open Sources**: Publicly available grant data from various sources including NIH, Kohesio, CORDIS, and others
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### Features
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- `id`: Unique identifier for the grant
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- `title`: Title of the grant
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- `abstract`: Abstract or description of the grant
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- `funder`: Organization providing the funding
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- `funding_scheme`: Type of funding scheme
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- `beneficiary`: Organization or individual receiving the funding
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- `source`: Origin of the data (Dimensions or Open source)
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- `label`: Classification category (target variable)
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### Labels
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The dataset uses the following classification categories:
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1. **business_rnd_innovation**: Direct allocation of funding to private firms for R&D and innovation activities with commercial applications
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2. **fellowships_scholarships**: Financial support for individual researchers or higher education students
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3. **institutional_funding**: Core funding for higher education institutions and public research institutes
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4. **networking_collaborative**: Tools to bring together various actors within the innovation system
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5. **other_research_funding**: Alternative funding mechanisms for R&D or higher education
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6. **out_of_scope**: Grants unrelated to research, development, or innovation
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7. **project_grants_public**: Direct funding for specific research projects in public institutions
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8. **research_infrastructure**: Funding for research facilities, equipment, and resources
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### Statistics
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- Total examples: 2386
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- Class distribution:
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- business_rnd_innovation: 170 (7.1% of examples)
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- fellowships_scholarships: 342 (14.3% of examples)
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- institutional_funding: 48 (2.0% of examples)
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- networking_collaborative: 200 (8.4% of examples)
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- other_research_funding: 34 (1.4% of examples)
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- out_of_scope: 298 (12.5% of examples)
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- project_grants_public: 1157 (48.5% of examples)
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- research_infrastructure: 137 (5.7% of examples)
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## Usage
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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("SIRIS-Lab/grant-classification-dataset")
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# Access the data
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train_data = dataset["train"]
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validation_data = dataset["validation"]
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test_data = dataset["test"]
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# Example of accessing a sample
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sample = train_data[0]
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print(f"Title: {sample['title']}")
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print(f"Label: {sample['label']}")
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```
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