Abstract
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.
Highlights
Since the 1990s, Cooperative Threat Reduction (CTR) programs of the United States and other countries have been implemented as high return-on-investment approaches for reducing the threat of infectious diseases and epidemics at modest cost (Smithson, 2016)
We provide some context on Threat Reduction Networks (TRNs) and scientific networks in general; review related research in ontologies and multidimensional networks; describe our ontology and database development process in detail; and provide a basic example of how this approach can be used to study one particular aspect of a TRN, namely how attendance at a series of workshops built up a network of researchers over time
Sci-Net is our term for the overall framework we have developed for analyzing scientific social networks
Summary
Since the 1990s, Cooperative Threat Reduction (CTR) programs of the United States and other countries have been implemented as high return-on-investment approaches for reducing the threat of infectious diseases and epidemics at modest cost (Smithson, 2016). These programs seek to build international networks of infectious disease laboratories, professionals, and scientists in order. Collaborations help developing countries build capacity and connections to the international scientific community (Owusu-Nimo and Boshoff, 2017) This growth in collaboration creates an increasingly large and wellconnected scientific network across the globe (Leydesdorff et al, 2013). While coauthorship data provide documentation of this increasingly connected network, connectivity is driven by many other activities, including both formal and informal meetings as well as conferences and workshops that bring dispersed research communities together to meet face-to-face (Sonnenwald, 2007; Storme et al, 2017)
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