metadata
license: cc-by-4.0
language:
- en
task_categories:
- text-generation
- summarization
- feature-extraction
tags:
- research
- papers
- cs
- machine-learning
- nlp
- computer-vision
- ai
size_categories:
- 100K<n<1M
configs:
- config_name: papers
data_files:
- split: train
path: data/papers.jsonl
- split: arxiv
path: data/papers_arxiv.jsonl
- split: conference
path: data/papers_conference.jsonl
- split: journal
path: data/papers_journal.jsonl
- config_name: instruct
data_files:
- split: train
path: data/instruct.jsonl
- config_name: sections
data_files:
- split: train
path: data/sections.jsonl
ResearchScope Papers
Open CS research paper dataset maintained by ResearchScope.
Updated automatically via GitHub Actions.
Quick start
from datasets import load_dataset
ds = load_dataset("kishormorol/researchscope-papers", "papers", split="train")
print(ds[0])
See Usage below for per-source splits, instruction-tuning, and the per-section fine-tuning data.
Stats
- 33,079 papers (raw metadata) — 8,079 arXiv · 20,000 conference · 5,000 journal
- 165,181 instruction-tuning rows
- Sources: arXiv, OpenAlex, ACL Anthology, OpenReview, PMLR, CVF, Semantic Scholar
- Venues: NeurIPS, ICML, ICLR, ACL, EMNLP, CVPR, AAAI, IJCAI, JMLR, TMLR, TACL, TPAMI, NMI and more
Files
| File | Description |
|---|---|
data/papers.jsonl |
Raw paper metadata — title, abstract, authors, venue, year, tags, scores (all sources combined) |
data/papers_arxiv.jsonl |
arXiv / preprint papers only |
data/papers_conference.jsonl |
Conference papers only (NeurIPS, ICML, ICLR, ACL, CVPR, …) |
data/papers_journal.jsonl |
Journal papers only (JMLR, TPAMI, NMI, TACL, …) |
data/instruct.jsonl |
Instruction-tuning pairs — summarize, key contribution, why it matters, plain English |
data/sections.jsonl |
Per-section fine-tuning rows for A* papers — real body text of abstract, introduction, related_work, method, experiments, results, conclusion. Filter by the section field to train a per-section writing agent. |
Usage
from datasets import load_dataset
# All papers (combined)
papers = load_dataset("kishormorol/researchscope-papers", "papers", split="train")
# Just one source — arXiv, conference, or journal papers
arxiv = load_dataset("kishormorol/researchscope-papers", "papers", split="arxiv")
conference = load_dataset("kishormorol/researchscope-papers", "papers", split="conference")
journal = load_dataset("kishormorol/researchscope-papers", "papers", split="journal")
# Instruction tuning
instruct = load_dataset("kishormorol/researchscope-papers", "instruct", split="train")
# Per-section fine-tuning (A* papers) — e.g. train an Introduction-writing agent
sections = load_dataset("kishormorol/researchscope-papers", "sections", split="train")
intros = sections.filter(lambda r: r["section"] == "introduction")
License
Paper metadata is aggregated from open sources. Text content follows the original licenses of each source (arXiv CC0, ACL CC BY, etc.). Dataset schema: CC BY 4.0.