Summary:The neuro-cocitations dataset is a co-citation hypergraph of highly cited Neuroscience papers, constructed from the OpenAlex Snapshot (2024-09-27; https://developers.openalex.org/download/snapshot-format). Nodes and Hyperedges:Nodes represent highly cited Neuroscience papers. For each Neuroscience subfield, papers were ranked by citation count, and those accounting for the top 10% of cumulative citations were selected as nodes (minimum 100 papers per subfield), yielding 4,267 nodes in total. Hyperedges represent co-citation relationships. For each paper in the OpenAlex corpus (excluding the selected top papers), the subset of top papers it cites was identified. If the same subset appeared in at least 3 citing papers, it was included as a hyperedge, yielding 16,771 hyperedges in total. Each node carries the following attributes: OpenAlex work ID, paper title, publication date, primary topic, subfield, field, domain, and citation count. Basic statistics:- Nodes: 4,267- Hyperedges: 16,771 Source:OpenAlex Snapshot (2024-09-27), https://developers.openalex.org/download/snapshot-format Reference:Kazuki Nakajima, Yuya Sasaki, Takeaki Uno, and Masaki Aida. (2025). Learning Multi-Order Block Structure in Higher-Order Networks. arXiv preprint arXiv:2511.21350.