---
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']}")
```