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---
license: cc-by-4.0
pretty_name: SciLake Fulltext Corpus
configs:
- config_name: default
data_files:
- split: cancer
path: data/cancer-*
- split: neuroscience
path: data/neuroscience-*
- split: energy
path: data/energy-*
- split: transport
path: data/transport-*
- split: general
path: data/general-*
dataset_info:
features:
- name: doi
dtype: string
- name: title
dtype: string
- name: abstract
dtype: string
- name: fulltext_sections
list:
- name: section_content
dtype: string
- name: section_name
dtype: string
- name: section_num
dtype: string
- name: fulltext_additional
list:
- name: section_content
dtype: string
- name: section_name
dtype: string
- name: section_num
dtype: string
splits:
- name: cancer
num_bytes: 31665397
num_examples: 1000
- name: neuroscience
num_bytes: 36997288
num_examples: 1000
- name: energy
num_bytes: 36997288
num_examples: 1000
- name: transport
num_bytes: 42132016
num_examples: 1000
- name: general
num_bytes: 73961824
num_examples: 2000
download_size: 112470719
dataset_size: 221753813
---
# SciLake Fulltext Corpus
The SciLake Fulltext Corpus is a collection of scientific papers parsed and segmented by section, primarily designed for research in the development and evaluation of NLP models. This dataset contains 1,000 full-text papers from various scientific domains, including Neuroscience, Cancer, Transport, and Energy, along with an additional 5,000 random papers from general scientific domains. All papers have been curated with licenses that allow for legal usage, specifically CC-BY and Public Domain.
The dataset provides detailed metadata and full-text sections, offering a robust resource for domain-specific and general scientific research, dataset annotation, model training, and evaluation.
## Corpus Overview
- 1,000 Full-Text Papers Segmented by Section:
- Domain-specific sections: Neuroscience 🧠, Cancer 🦀, Transport 🛻, Energy 🪫.
- Each paper is segmented into sections such as Introduction, Methods, Results, etc.
- 5,000 Random Papers from General Scientific Domains:
- Mix of stratified sampled by MAG level 0 to ensure diversity across multiple domains and disciplines, and random sample.
## Example of Dataset Structure:
```json
{
'doi': DOI,
'title': TITLE,
'description': ABSTRACT,
'fulltext_sections': [
{
'section_name': SECTION_NAME_1,
'section_num': SECTION_NUM_1,
'section_content': SECTION_CONTENT_1,
},
...
],
'fulltext_additional': [
{
'section_name': SECTION_NAME_1,
'section_num': SECTION_NUM_1,
'section_content': SECTION_CONTENT_1,
},
...
]
```
## How to use
```python
from datasets import load_dataset
dataset_ds = load_dataset("SIRIS-Lab/scilake-fulltext-corpus")
```
## Licensing Information
The SciLake Fulltext Corpus is released under the following licenses:
CC-BY (Creative Commons Attribution), licenses have been obtained from the publisher’s landing page, PDFs, metadata in OpenAire, and Unpaywall, filtering fro those with license CC-BY or Public Domain.
## Dataset Acquisition
The papers included in this dataset were sourced through the OpenAIRE index, with random selection to ensure diverse content. The license information was verified by cross-referencing the publisher’s landing pages, metadata from OpenAire, and the Unpaywall database. Papers were retained if they had a CC-BY license or were in the Public Domain.
## Funding
This work was partially funded by a projects under EU’s HORIZON Research and Innovation Programme:
- SciLake (grant agreement No 101058573).
## Contact
For more information, or if you have questions, please contact us at sirislab[at]sirisacademic.com.
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