A repository for discovery and reuse of higher-order network datasets

2026-08-04

AI Summary

This work addresses the longstanding fragmentation of higher-order network datasets across disparate publications and proprietary platforms, which has hindered their discovery, comparison, and reuse. To overcome this challenge, the authors introduce the Aachen Higher-Order Network Repository (AHORN)—the first structured, traceable, and interoperable centralized repository for such data. AHORN standardizes publicly available higher-order network datasets and provides machine-readable metadata, citation guidelines, format validation, version control, and multi-format export capabilities. By doing so, the platform substantially enhances the centralized management of higher-order network data, facilitates cross-platform reuse, and strengthens scientific reproducibility in the field.

Abstract

Higher-order network datasets are dispersed across publications, institutional archives, and software-specific collections, making them difficult to discover, compare, and reuse. We introduce the Aachen Higher-Order Repository of Networks (AHORN), a curated repository of standardized higher-order network datasets derived from publicly released sources. Each dataset entry links a converted dataset to its source, metadata, citation guidance, conversion code, and version history. The repository supports browsable and machine-readable discovery, revision-specific downloads, format validation, and exports for interoperable reuse. We describe the repository architecture, curation workflow, access tools, and the coverage and limitations of the catalog snapshot analyzed in this article.

Problem

• higher-order networks
• data repository
• dataset discovery
• data reuse
• research data management

Innovation

• higher-order networks
• data repository
• dataset curation
• interoperable reuse
• metadata standardization