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--- |
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dataset_info: |
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features: |
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- name: id |
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dtype: string |
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- name: title |
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dtype: string |
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- name: funder |
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dtype: string |
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- name: beneficiary |
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dtype: string |
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- name: source_id |
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dtype: string |
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- name: abstract |
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dtype: string |
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- name: funding_scheme |
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dtype: string |
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- name: label |
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dtype: |
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class_label: |
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names: |
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'0': business_rnd_innovation |
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'1': fellowships_scholarships |
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'2': institutional_funding |
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'3': networking_collaborative |
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'4': other_research_funding |
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'5': out_of_scope |
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'6': project_grants_public |
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'7': research_infrastructure |
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splits: |
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- name: train |
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num_bytes: 3045058 |
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num_examples: 2386 |
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download_size: 1650227 |
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dataset_size: 3045058 |
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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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--- |
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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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``` |
