Datasets:

ladybugdb
/

scg-columnar

@@ -1,6 +1,196 @@
-The Scientific Contribution Graph: Automated Literature-based Technological Roadmapping at Scale
-Peter A. Jansen
-https://arxiv.org/abs/2605.15011
-Original dataset:
-https://huggingface.co/buckets/pajansen/scg-explorer-space-data

+---
+pretty_name: Scientific Contribution Graph (SCG)
+language:
+  - en
+license: cc-by-4.0
+task_categories:
+  - graph-ml
+  - text-generation
+  - other
+task_ids:
+  - knowledge-graph-construction
+  - link-prediction
+tags:
+  - scientific-literature
+  - knowledge-graph
+  - technological-roadmapping
+  - prerequisite-prediction
+  - scholarly-documents
+  - nlp
+  - science
+  - s2orc
+  - acl-anthology
+  - parquet
+size_categories:
+  - 1M<n<10M
+source_datasets:
+  - acl-anthology
+  - s2orc
+multilinguality:
+  - monolingual
+configs:
+  - config_name: default
+    data_files:
+      - split: train
+        path: data/**/*.parquet
+---
+# 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](https://huggingface.co/spaces/pajansen/scientific-contribution-graph) and the official API lives in the bucket [`pajansen/scg-explorer-space-data`](https://huggingface.co/buckets/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](https://arxiv.org/abs/2605.15011) (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`](https://huggingface.co/buckets/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](https://creativecommons.org/licenses/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](https://aclanthology.org/) (Feb 2025 snapshot for v1.0 seed) and [Semantic Scholar Open Research Corpus (S2ORC)](https://www.semanticscholar.org/)
+## 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 &lt;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