DiagramBank: A Quality-Audited Dataset of Scientific Schematic Diagrams with Multi-Level Document Context
Scientific papers use schematic diagrams to communicate methods, workflows, and system structure, yet existing scientific-figure corpora often mix them with plots, screenshots, and photographs and rarely preserve document context. We introduce DiagramBank, a quality-audited dataset of 57,100 schematic diagrams curated from OpenReview-hosted AI/ML venues. Each record links a diagram image to its paper title, abstract, figure caption, in-text figure-reference spans, venue/year metadata, provenance fields, and filtering labels. DiagramBank is a reusable resource for scientific-document understanding, diagram retrieval, corpus analysis, and future benchmark construction. We describe its extra...
| Authors | Ling Yue, Tingwen Zhang, Jiaying Wang et al. |
|---|---|
| Primary category | cs.IR |
| Published | 28 February 2026 |
| Last updated at source | 27 May 2026 |
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Models and datasets referencing this paper
- ghzlmc/DiagramBankDataset
- zhangt20/DiagramBankDataset
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