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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 33,467 papers (raw metadata) — 8,467 arXiv · 20,000 conference · 5,000 journal 166,887… See the full description on the dataset page: https://huggingface.co/datasets/kishormorol/researchscope-papers.

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ResearchScope Papers

Open CS research paper dataset maintained by ResearchScope.

Updated automatically via GitHub Actions.

Quick start

python
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

  • —33,467 papers (raw metadata) — 8,467 arXiv · 20,000 conference · 5,000 journal
  • —166,887 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

FileDescription
data/papers.jsonlRaw paper metadata — title, abstract, authors, venue, year, tags, scores (all sources combined)
data/papers_arxiv.jsonlarXiv / preprint papers only
data/papers_conference.jsonlConference papers only (NeurIPS, ICML, ICLR, ACL, CVPR, …)
data/papers_journal.jsonlJournal papers only (JMLR, TPAMI, NMI, TACL, …)
data/instruct.jsonlInstruction-tuning pairs — summarize, key contribution, why it matters, plain English
data/sections.jsonlPer-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

python
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.