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Scientific Papers - Raw Full Text
~57 million scientific papers with full text, extracted from multiple large-scale academic paper collections. This dataset provides raw full text suitable for pre-training, fine-tuning, or building search indices over scientific literature.
Subsets
| Subset | Papers | Size | Source |
|---|---|---|---|
| papers-2 | ~18.5M | ~358 GB | S2ORC papers collection (untitled subset) |
| papers-3 | ~27.4M | ~198 GB | S2ORC scientific-papers collection |
| pes2o | ~8.2M | ~106 GB | Pes2oX-fulltext (Semantic Scholar, Apache 2.0) |
| corex | ~2.9M | ~25 GB | CORE repository (core.ac.uk, Apache 2.0) |
| Total | ~57M | ~687 GB |
Schema (9 columns)
| Column | Type | Description |
|---|---|---|
paper_id |
string | Unique identifier (source-specific) |
title |
string | Paper title (when available) |
authors |
string | Author names (semicolon-separated) |
year |
string | Publication year (when available) |
venue |
string | Publication venue (when available) |
doi |
string | DOI (when available) |
abstract |
string | Paper abstract (when available) |
raw_fulltext |
string | Complete paper text |
text_length |
int64 | Character count of full text |
Usage
from datasets import load_dataset
# Load a specific subset
ds = load_dataset("scientifi-papers/scientific-papers", "corex")
# Load papers-3 (largest)
ds = load_dataset("scientifi-papers/scientific-papers", "papers-3")
# Access full text
paper = ds['train'][0]
print(f"Title: {paper['title']}")
print(f"Text length: {paper['text_length']} chars")
print(paper['raw_fulltext'][:500])
Sources
- papers-2 / papers-3: Extracted from the Semantic Scholar Open Research Corpus (S2ORC) via GROBID PDF parsing
- pes2o: Allen AI's Pes2oX-fulltext dataset (cleaned S2ORC full text)
- corex: CORE repository (core.ac.uk) - 2018 full-text dump of open-access research papers
License
CC-BY-4.0
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