| | --- |
| | dataset_info: |
| | features: |
| | - name: id |
| | dtype: string |
| | - name: title |
| | dtype: string |
| | - name: funder |
| | dtype: string |
| | - name: beneficiary |
| | dtype: string |
| | - name: source_id |
| | dtype: string |
| | - name: abstract |
| | dtype: string |
| | - name: funding_scheme |
| | dtype: string |
| | - name: label |
| | dtype: |
| | class_label: |
| | names: |
| | '0': business_rnd_innovation |
| | '1': fellowships_scholarships |
| | '2': institutional_funding |
| | '3': networking_collaborative |
| | '4': other_research_funding |
| | '5': out_of_scope |
| | '6': project_grants_public |
| | '7': research_infrastructure |
| | splits: |
| | - name: train |
| | num_bytes: 3114447 |
| | num_examples: 2458 |
| | download_size: 1692171 |
| | dataset_size: 3114447 |
| | configs: |
| | - config_name: default |
| | data_files: |
| | - split: train |
| | path: data/train-* |
| | --- |
| | |
| | # Grant Classification Dataset |
| |
|
| | This dataset contains research grant documents classified according to a custom categorization of science, technology, and innovation (STI) policy instruments. |
| |
|
| | ## Dataset Description |
| |
|
| | ### Overview |
| |
|
| | The dataset consists of research grants from various funding sources. |
| | Each grant is classified into one of 8 categories according to a taxonomy based on the OECD's categorization of STI policy instruments. |
| |
|
| | ### Data Sources |
| |
|
| | - **Open Sources**: Publicly available grant data from various sources including NIH, Kohesio, CORDIS, and others |
| |
|
| | ### Features |
| |
|
| | - `id`: Unique identifier for the grant |
| | - `title`: Title of the grant |
| | - `abstract`: Abstract or description of the grant |
| | - `funder`: Organization providing the funding |
| | - `funding_scheme`: Type of funding scheme |
| | - `beneficiary`: Organization or individual receiving the funding |
| | - `source`: Origin of the data (Dimensions or Open source) |
| | - `label`: Classification category (target variable) |
| |
|
| | ### Labels |
| |
|
| | The dataset uses the following classification categories: |
| |
|
| | 1. **business_rnd_innovation**: Direct allocation of funding to private firms for R&D and innovation activities with commercial applications |
| | 2. **fellowships_scholarships**: Financial support for individual researchers or higher education students |
| | 3. **institutional_funding**: Core funding for higher education institutions and public research institutes |
| | 4. **networking_collaborative**: Tools to bring together various actors within the innovation system |
| | 5. **other_research_funding**: Alternative funding mechanisms for R&D or higher education |
| | 6. **out_of_scope**: Grants unrelated to research, development, or innovation |
| | 7. **project_grants_public**: Direct funding for specific research projects in public institutions |
| | 8. **research_infrastructure**: Funding for research facilities, equipment, and resources |
| |
|
| | ### Statistics |
| |
|
| | - Total examples: 2386 |
| | - Class distribution: |
| | - business_rnd_innovation: 170 (7.1% of examples) |
| | - fellowships_scholarships: 342 (14.3% of examples) |
| | - institutional_funding: 48 (2.0% of examples) |
| | - networking_collaborative: 200 (8.4% of examples) |
| | - other_research_funding: 34 (1.4% of examples) |
| | - out_of_scope: 298 (12.5% of examples) |
| | - project_grants_public: 1157 (48.5% of examples) |
| | - research_infrastructure: 137 (5.7% of examples) |
| |
|
| | ## Usage |
| |
|
| | ```python |
| | from datasets import load_dataset |
| | |
| | # Load the dataset |
| | dataset = load_dataset("SIRIS-Lab/grant-classification-dataset") |
| | |
| | # Access the data |
| | train_data = dataset["train"] |
| | validation_data = dataset["validation"] |
| | test_data = dataset["test"] |
| | |
| | # Example of accessing a sample |
| | sample = train_data[0] |
| | print(f"Title: {sample['title']}") |
| | print(f"Label: {sample['label']}") |
| | ``` |
