This study presents a large-scale network dataset, NIH-MPINet, curated from NIH RePORTER and PubMed, characterizing collaboration among multiple Principal Investigators (multi-PIs) on NIH R01-equivalent grants from 2006 to 2023. The network characterizes 30,127 PIs as nodes and their collaborations on 86,743 NIH R01-equivalent grants as edges, spanning 888 recipient organizations and supported by 40 NIH Institutes and Centers. We also curated comprehensive metadata, including node-level features such as PI affiliation, alongside edge-level features comprising grant years, titles, and abstracts. Using these data, we constructed a PI collaboration network and identified 19 communities as well as 20 major research topics. Several collaboration communities showed distinct thematic profiles, such as cardiovascular health, cancer immunotherapy, neuroscience, and microbiome research, while genetics and genomics were broadly represented across communities. By incorporating temporal analysis, we observed shifts in research topics and collaboration patterns over time. Topics like healthcare and outcomes research, cognitive health, and Alzheimer's disease have become more prominent in recent years, whereas molecular and cellular biology has seen a relative decline. Overall, this work provides a high-fidelity, feature-rich resource for advancing statistical learning methods and network analysis-based discoveries in the study of long-term biomedical collaboration.

翻译：本研究提出一个大规模网络数据集NIH-MPINet，该数据集从NIH RePORTER和PubMed中整理，刻画了2006年至2023年间多个首席研究员（multi-PIs）在NIH R01等效资助项目上的合作关系。该网络将30,127位PI作为节点，其合作涉及86,743项NIH R01等效资助项目，作为边进行表征，覆盖888个受资助机构，并得到40个NIH研究所和中心的资助支持。我们还整理了全面的元数据，包括节点级特征（如PI所属机构）以及边级特征（包含资助年份、标题和摘要）。利用这些数据，我们构建了PI合作网络，识别出19个社区和20个主要研究主题。多个合作社区呈现出独特的研究主题分布，例如心血管健康、癌症免疫疗法、神经科学和微生物组研究，而遗传学和基因组学则在各社区中广泛分布。通过纳入时间分析，我们观察到研究主题和合作模式随时间的变化。近年来，医疗保健与结局研究、认知健康以及阿尔茨海默病等主题逐渐凸显，而分子与细胞生物学则呈现相对下降趋势。总体而言，本研究提供了一个高保真、特征丰富的资源，用于推进统计学习方法及基于网络分析的发现，以促进对长期生物医学合作的研究。