Ontology-Based Graphs of Research Communities: A Tool for Understanding Threat Reduction Networks
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...
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Frontiers Media S.A.
2020-06-01
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doaj-aaafe204881743919f532f9e92bef0a92021-06-02T18:17:48ZengFrontiers Media S.A.Frontiers in Research Metrics and Analytics2504-05372020-06-01510.3389/frma.2020.00003530255Ontology-Based Graphs of Research Communities: A Tool for Understanding Threat Reduction NetworksJohn Ambrosiano0Benjamin Sims1Andrew W. Bartlow2William Rosenberger3Mark Ressler4Jeanne M. Fair5Information Systems and Modeling, Los Alamos National Laboratory, Los Alamos, NM, United StatesStatistical Sciences, Los Alamos National Laboratory, Los Alamos, NM, United StatesBiosecurity and Public Health, Los Alamos National Laboratory, Los Alamos, NM, United StatesInformation Systems and Modeling, Los Alamos National Laboratory, Los Alamos, NM, United StatesInformation Systems and Modeling, Los Alamos National Laboratory, Los Alamos, NM, United StatesBiosecurity and Public Health, Los Alamos National Laboratory, Los Alamos, NM, United StatesScientific 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.https://www.frontiersin.org/article/10.3389/frma.2020.00003/fullcollaborationconnectional intelligencebioinformaticsglobal healthontologysocial network |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
John Ambrosiano Benjamin Sims Andrew W. Bartlow William Rosenberger Mark Ressler Jeanne M. Fair |
spellingShingle |
John Ambrosiano Benjamin Sims Andrew W. Bartlow William Rosenberger Mark Ressler Jeanne M. Fair Ontology-Based Graphs of Research Communities: A Tool for Understanding Threat Reduction Networks Frontiers in Research Metrics and Analytics collaboration connectional intelligence bioinformatics global health ontology social network |
author_facet |
John Ambrosiano Benjamin Sims Andrew W. Bartlow William Rosenberger Mark Ressler Jeanne M. Fair |
author_sort |
John Ambrosiano |
title |
Ontology-Based Graphs of Research Communities: A Tool for Understanding Threat Reduction Networks |
title_short |
Ontology-Based Graphs of Research Communities: A Tool for Understanding Threat Reduction Networks |
title_full |
Ontology-Based Graphs of Research Communities: A Tool for Understanding Threat Reduction Networks |
title_fullStr |
Ontology-Based Graphs of Research Communities: A Tool for Understanding Threat Reduction Networks |
title_full_unstemmed |
Ontology-Based Graphs of Research Communities: A Tool for Understanding Threat Reduction Networks |
title_sort |
ontology-based graphs of research communities: a tool for understanding threat reduction networks |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Research Metrics and Analytics |
issn |
2504-0537 |
publishDate |
2020-06-01 |
description |
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. |
topic |
collaboration connectional intelligence bioinformatics global health ontology social network |
url |
https://www.frontiersin.org/article/10.3389/frma.2020.00003/full |
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