kishormorol/researchscope-papers

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

• 34,942 papers (raw metadata) — 9,942 arXiv · 20,000 conference · 5,000 journal
• 174,269 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.