File size: 3,571 Bytes
17d7861 050d415 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 |
---
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: 3045058
num_examples: 2386
download_size: 1650227
dataset_size: 3045058
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']}")
``` |