| --- |
| 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']}") |
| ``` |
