A repository for discovery and reuse of higher-order network datasets
Florian Frantzen, Michael T. Schaub
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.
Create a lesson
Related papers
Shifting Research Funding Priorities under Geopolitical Pressure: Evidence from Estonia
Yunfeng Gao, Yang Ding
Geospatial Metadata Improves Discoverability by Connecting Datasets Across Scientific Disciplines
Daniel Ebanks, Devika Jain
Quantifying the impact of clinical-academic collaborations
Mohamad Zeina, Nick McNally, Karl S. Peggs et al.
Testing Our Foundations: Citation Trends, Errors, and Emerging Hallucinations in the Computing Education Literature
Paul Denny, Gweneth Barbre, Musa Blake et al.
Toward non-textual representation of social anthropology: Modeling cultures as knowledge graphs
Manolis Peponakis, Sarantos Kapidakis, Martin Doerr et al.
Geometric Signatures of Conceptual Reorganization: A Counterfactual Embedding Framework for Detecting Scientific Revolutions
Dimitris Ntounis, Ariel Schwartzman, Chris Chafe et al.