Ambrosiano John, Sims Benjamin, Bartlow Andrew W, Rosenberger William, Ressler Mark, Fair Jeanne M
Information Systems and Modeling, Los Alamos National Laboratory, Los Alamos, NM, United States.
Statistical Sciences, Los Alamos National Laboratory, Los Alamos, NM, United States.
Front Res Metr Anal. 2020 Jun 9;5:3. doi: 10.3389/frma.2020.00003. eCollection 2020.
Scientific research communities can be represented as heterogeneous or multidimensional networks encompassing multiple types of entities and relationships. These networks might include researchers, institutions, meetings, and publications, connected by relationships like authorship, employment, and attendance. We describe a method for efficiently and flexibly capturing, storing, and extracting information from multidimensional scientific networks using a graph database. The database structure is based on an ontology that captures allowable types of entities and relationships. This allows us to construct a variety of projections of the underlying multidimensional graph through database queries to answer specific research questions. We demonstrate this process through a study of the U.S. Biological Threat Reduction Program (BTRP), which seeks to develop Threat Reduction Networks to build and strengthen a sustainable international community of biosecurity, biosafety, and biosurveillance experts to address shared biological threat reduction challenges. Networks like these create connectional intelligence among researchers and institutions around the world, and are central to the concept of cooperative threat reduction. Our analysis focuses on a series of seven BTRP genome sequencing training workshops, showing how they created a growing network of participants and countries over time, which is also reflected in coauthorship relationships among attendees. By capturing concept and relationship hierarchies, our ontology-based approach allows us to pose general or specific questions about networks within the same framework. This approach can be applied to other research communities or multidimensional social networks to capture, analyze, and visualize different types of interactions and how they change over time.
科研群体可以被表示为包含多种类型的实体和关系的异构或多维网络。这些网络可能包括研究人员、机构、会议和出版物，它们通过诸如作者身份、雇佣关系和参会等关系相互连接。我们描述了一种使用图形数据库从多维科学网络中高效且灵活地捕获、存储和提取信息的方法。数据库结构基于一个本体，该本体捕获了实体和关系的允许类型。这使我们能够通过数据库查询构建基础多维图的各种投影，以回答特定的研究问题。我们通过对美国生物威胁降低计划（BTRP）的研究来展示这一过程，该计划旨在建立威胁降低网络，以构建和加强一个由生物安全、生物安全和生物监测专家组成的可持续国际社区，以应对共同的生物威胁降低挑战。这样的网络在全球的研究人员和机构之间创造了连接智能，并且是合作性威胁降低概念的核心。我们的分析聚焦于一系列七个BTRP基因组测序培训研讨会，展示了它们如何随着时间推移创建了一个不断扩大的参与者和国家网络，这也反映在参会者之间的共同作者关系中。通过捕获概念和关系层次结构，我们基于本体的方法使我们能够在同一框架内提出关于网络的一般或特定问题。这种方法可以应用于其他研究群体或多维社会网络，以捕获、分析和可视化不同类型的互动以及它们如何随时间变化。
