Department of Intelligent Systems Engineering, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, 47408, USA.
School of Information Management, Sun Yat-sen University, Guangzhou, 510006, China.
Sci Data. 2024 Jun 4;11(1):574. doi: 10.1038/s41597-024-03416-8.
Experts from 18 consortia are collaborating on the Human Reference Atlas (HRA) which aims to map the 37 trillion cells in the healthy human body. Information relevant for HRA construction and usage is held by experts, published in scholarly papers, and captured in experimental data. However, these data sources use different metadata schemas and cannot be cross-searched efficiently. This paper documents the compilation of a dataset, named HRAlit, that links the 136 HRA v1.4 digital objects (31 organs with 4,279 anatomical structures, 1,210 cell types, 2,089 biomarkers) to 583,117 experts; 7,103,180 publications; 896,680 funded projects, and 1,816 experimental datasets. The resulting HRAlit has 22 tables with 20,939,937 records including 6 junction tables with 13,170,651 relationships. The HRAlit can be mined to identify leading experts, major papers, funding trends, or alignment with existing ontologies in support of systematic HRA construction and usage.
来自 18 个联盟的专家正在合作开发人类参考图谱 (HRA)，旨在绘制健康人体 37 万亿个细胞的图谱。与 HRA 构建和使用相关的信息由专家掌握，发表在学术论文中，并包含在实验数据中。然而，这些数据源使用不同的元数据模式，无法有效地进行交叉搜索。本文记录了一个名为 HRAlit 的数据集的编制过程，该数据集将 136 个 HRA v1.4 数字对象（31 个器官，4279 个解剖结构，1210 种细胞类型，2089 个生物标志物）与 583117 名专家、7103180 篇出版物、896680 个资助项目和 1816 个实验数据集联系起来。由此产生的 HRAlit 有 22 个表，包含 20939937 条记录，其中包括 6 个连接表，包含 13170651 个关系。可以对 HRAlit 进行挖掘，以识别领先的专家、主要论文、资助趋势或与现有本体的一致性，从而支持系统的 HRA 构建和使用。
