ladybugdb/scg-columnar

Dataset Card for the Scientific Contribution Graph (SCG)

Compressed Parquet release: this Hub dataset is a compact Parquet encoding of the Scientific Contribution Graph.
It reduces the on-disk footprint from ~80 GB uncompressed JSON/tree files → ~6 GB Parquet, while preserving the same contributions, prerequisite edges, and metadata as SCG v1.1.

The Scientific Contribution Graph maps how science is built “on the shoulders of giants”: it extracts fine-grained scientific contributions from open-access papers and links each contribution to the precursor contributions it builds on.

This card documents the v1.1 graph in Parquet form. The original full-tree release used by the SCG Explorer and the official API lives in the bucket pajansen/scg-explorer-space-data (~84.4 GB, 655,252 files; ~80 GB uncompressed / ~19 GB tar-compressed). Prefer this Parquet dataset when you want the same graph at ~6 GB.

Paper: The Scientific Contribution Graph: Automated Literature-based Technological Roadmapping at Scale (Findings of EMNLP 2026).

Dataset Details

Dataset Description

Scientific contributions rarely appear in isolation. SCG treats automated technological roadmapping as knowledge-graph construction over full paper text:

• Nodes are individual scientific contributions (name + long-form description + metadata), not whole papers and not short entity spans.
• Edges are prerequisite / precursor relations, each with a natural-language explanation of why one contribution enables another, plus alignment to cited papers.

Extraction is modeled as open-ended sequence-to-sequence generation over full text (on the order of ~43 LLM calls per paper), then cross-paper alignment of prerequisites to contributions in cited work.

Representation	Typical size	Notes
Original v1.1 tree / JSON	~80 GB uncompressed (~19 GB .tar.gz)	Per-paper files in pajansen/scg-explorer-space-data; native API format
This dataset (Parquet)	~6 GB	Columnar, compressed encoding of the same v1.1 graph

Release	Papers	Contributions	Prerequisite edges	Domain
v1.0 (May 2026)	230,454	2,047,426	12,524,458	ACL Anthology seed + S2ORC back-crawl (largely AI/NLP)
v1.1 (Aug 2026, current)	655,195	5,976,913	36,199,762	Broader science via alternating forward/backward crawls

Averages (v1.1):

• ~9 contributions per paper
• ~6 prerequisites per contribution
• ~130 tokens per contribution description
• ~71 tokens per edge explanation
• Prerequisite mix: ~61% external papers, ~34% intra-paper contributions, ~5% tools/URLs
• Linked paper-reference → specific downstream contribution: 29.0% (up from 25.7% in v1.0)

Curated by: Peter A. Jansen (University of Arizona; Allen Institute for Artificial Intelligence)
Language(s): English (source papers are English scholarly text)
License: CC BY 4.0 for the extracted graph annotations and paper. Underlying open-access papers retain their original licenses.

Dataset Sources

• Paper: https://arxiv.org/abs/2605.15011
• DOI: https://doi.org/10.48550/arXiv.2605.15011
• Code / API: https://github.com/cognitiveailab/scientific-contribution-graph
• Live demo: https://huggingface.co/spaces/pajansen/scientific-contribution-graph
• Original (uncompressed) data bucket: https://huggingface.co/buckets/pajansen/scg-explorer-space-data
• Source corpora: ACL Anthology (Feb 2025 snapshot for v1.0 seed) and Semantic Scholar Open Research Corpus (S2ORC)

Uses

Direct Use

• Loading the full SCG on a laptop or modest server without provisioning ~80 GB of JSON
• Technological roadmapping and contribution-level citation / dependency analysis
• Scientific impact assessment at contribution granularity (not only paper-level citations)
• Training or evaluating scientific prerequisite prediction (which existing technologies enable a future contribution)
• Graph mining, visualization, and retrieval over scientific progress
• Input for automated scientific discovery systems that reason about enabling technologies

Out-of-Scope Use

• Treating extracted nodes/edges as gold human annotations or as a complete map of a field (coverage is limited to open-access full text that the crawl reached)
• Using prerequisite edges as legal claims of priority, inventorship, or plagiarism
• Assuming every citation in a paper is a true technological prerequisite (the graph is designed to be finer-grained than raw citations, but alignment is imperfect)
• Deploying rankings or “impact scores” as sole criteria for hiring, funding, or clinical/scientific decisions without human review

Dataset Structure

This repository stores SCG v1.1 as Parquet rather than one JSON document per paper. Content is equivalent to the official release; only the on-disk encoding changes (~80 GB → ~6 GB).

Identifiers

• Papers are keyed by Semantic Scholar corpus IDs.
• Contributions are "{corpus_id}.c{n}" (e.g. 52967399.c1).

Conceptual schema

Each contribution node is a tuple (\langle n_i, d_i, m_i \rangle):

• name — short contribution title
• description — detailed generated summary
• metadata — source paper title, sections mentioning the contribution, etc.

Each prerequisite edge is (\langle c_{\mathrm{pre}}, c_{\mathrm{dep}}, a_j \rangle):

• precursor and dependent contribution IDs
• attributes such as a natural-language justification and the cited paper used for alignment

The official Python API in the GitHub repo still expects the original per-paper tree plus data/metadata/paper_manifest.jsonl. Use this Parquet dump for analysis with datasets, DuckDB, Polars, or Spark; convert back to the tree layout only if you need the demo API unchanged.

Topic distribution (v1.1)

Semantic Scholar topic labels (papers may have multiple topics; percentages sum to >100%):

Topic	Count	%	Topic	Count	%
Medicine	304,898	46.5	Sociology	4,796	0.7
Computer Science	272,796	41.6	Education	3,637	0.6
Biology	177,090	27.0	Political Science	3,108	0.5
Physics	163,066	24.9	Business	2,642	0.4
Mathematics	58,616	8.9	Economics	2,628	0.4
Linguistics	51,830	7.9	Agricultural and Food Sciences	2,615	0.4
Engineering	51,458	7.9	Geography	1,904	0.3
Environmental Science	40,636	6.2	Philosophy	1,900	0.3
Chemistry	35,962	5.5	Law	1,650	0.3
Materials Science	20,198	3.1	History	1,111	0.2
Psychology	13,494	2.1

Topics with <1,000 papers omitted (e.g. Art, Geology).

Data Splits

The graph release itself is a single corpus (no official train/dev/test split of nodes/edges).

The companion scientific prerequisite prediction task uses temporally filtered backtesting (predict precursors of later contributions from earlier graph state). Reported headline result in the paper: 0.48 MAP. Task code/data: task_precursor_prediction/ in the GitHub repository.

Dataset Creation

Curation Rationale

Paper-level citations mix background mentions with true “built upon” links. Sentence-level IE triples are usually too short and decontextualized. SCG sits in between: contribution-level nodes with long descriptions, and explained prerequisite edges.

The Parquet packaging exists so the same 6M-node / 36M-edge graph can be distributed and scanned without an 80 GB checkout.

Source Data

v1.0: full ACL Anthology crawl, then backward crawl through S2ORC along cited open-access papers.

v1.1: alternating forward (impact) and backward (dependency) crawls through S2ORC; reference linking improved by backfilling missing S2 corpus IDs via exact title match, then re-running cross-contribution alignment.

Who produced the source language?

Authors of the underlying scholarly articles. Graph text (names, descriptions, edge explanations) is model-generated from those papers’ full text.

Annotations

Automatically generated; not crowd-annotated. Human evaluation and alignment statistics are in the paper. Residual errors include missed contributions, spurious prerequisites, and failed or coarse paper–contribution alignment (~71% of paper references still not pinned to a specific downstream contribution).

Personal and Sensitive Information

Records are scientific contributions from published open-access papers (titles, IDs, generated technical summaries). Author names may appear as in the source literature. No intent to include private personal data; biomedical papers may discuss clinical topics at the level already present in the publications.

Considerations for Using the Data

Social Impact

Useful for literature review, impact analysis, and discovery systems. Risk of amplifying venue/OA biases or treating generated “roadmaps” as authoritative history of science.

Discussion of Biases

• Open-access and crawl-seed bias (ACL/NLP origin, then expansion)
• English-language scholarly bias
• Field mix in v1.1 is heavy in medicine