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Research Interests: Computer Science and AGU
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Report from the National Science Foundation-funded workshop held February 17-18, 2015, at the Westin Arlington Gateway in Arlington, Virginia for Software Infrastructure for Sustained Innovation (SI2) Principal Investigators
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The past decade has witnessed an increasing frequency and intensity of disasters, from extreme weather, drought, and wildfires to hurricanes, floods, and wars. Providing timely disaster response and humanitarian aid to these events is a... more
The past decade has witnessed an increasing frequency and intensity of disasters, from extreme weather, drought, and wildfires to hurricanes, floods, and wars. Providing timely disaster response and humanitarian aid to these events is a critical topic for decision makers and relief experts in order to mitigate impacts and save lives. When a disaster occurs, it is important to acquire first-hand, real-time information about the potentially affected area, its infrastructure, and its people in order to develop situational awareness and plan a response to address the health needs of the affected population. This requires rapid assembly of multi-source geospatial data that need to be organized and visualized in a way to support disaster-relief efforts. In this paper, we introduce a new cyberinfrastructure solution—GeoGraphVis—that is empowered by knowledge graph technology and advanced visualization to enable intelligent decision making and problem solving. There are three innovative fea...
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Document Version History Version Date Modified by Comment V1.0 2010-06-14 2 | GEO BON
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Research Interests: Computer Science and AGU
nvestigators in the ecological sciencesuse a wide variety of protocols to col-lect data on complex topics such asmarine bacterial community functionsand global carbon flux. The resulting het-erogeneous data are stored in auto-nomous... more
nvestigators in the ecological sciencesuse a wide variety of protocols to col-lect data on complex topics such asmarine bacterial community functionsand global carbon flux. The resulting het-erogeneous data are stored in auto-nomous database systems dispersedthroughout the research community.There is growing recognition that thesedata should be networked and preservedfor future studies to reuse in replicatingand validating scientific conclusions,enlarging spatiotemporal scale, and so on.Ideally, these archived data should bestored in a framework that enables rapid,powerful access and discovery.In response to this situation, we at theNational Center for Ecological Analysisand Synthesis (NCEAS) at the Universityof California, Santa Barbara, have devel-oped the modular Metacat framework(short for “metadata catalog”). The system(available from the Knowledge Networkfor Biocomplexity homepage at http://knb.ecoinformatics.org/) incorporatesRDF-like methods for packaging data setsto allow re...
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One of the key value propositions for knowledge graphs and semantic web technologies is fostering semantic interoperability, i.e., integrating data across different themes and domains. But why do we aim at interoperability in the first... more
One of the key value propositions for knowledge graphs and semantic web technologies is fostering semantic interoperability, i.e., integrating data across different themes and domains. But why do we aim at interoperability in the first place? A common answer to this question is that each individual data source only contains partial information about some phenomenon of interest. Consequently, combining multiple diverse datasets provides a more holistic perspective and enables us to answer more complex questions, e.g., those that span between the physical sciences and the social sciences. Interestingly, while these arguments are well established and go by different names, e.g., variety in the realm of big data, we seem less clear about whether the same arguments apply on the level of schemata. Put differently, we want diverse data, but do we also want diverse schemata or a single one to rule them all?
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Knowledge graphs (KGs) are a novel paradigm for the representation, retrieval, and integration of data from highly heterogeneous sources. Within just a few years, KGs and their supporting technologies have become a core component of... more
Knowledge graphs (KGs) are a novel paradigm for the representation, retrieval, and integration of data from highly heterogeneous sources. Within just a few years, KGs and their supporting technologies have become a core component of modern search engines, intelligent personal assistants, business intelligence, and so on. Interestingly, despite large-scale data availability, they have yet to be as successful in the realm of environmental data and environmental intelligence. In this paper, we will explain why spatial data require special treatment, and how and when to semantically lift environmental data to a KG. We will present our KnowWhereGraph that contains a wide range of integrated datasets at the human–environment interface, introduce our application areas, and discuss geospatial enrichment services on top of our graph. Jointly, the graph and services will provide answers to questions such as “what is here,” “what happened here before,” and “how does this region compare to …” f...
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Space and time are useful nexuses for integrating data. For instance, events affect the places in which they occur and the people that participate in them. By capturing the effects that they may have on a place, coupled with authoritative... more
Space and time are useful nexuses for integrating data. For instance, events affect the places in which they occur and the people that participate in them. By capturing the effects that they may have on a place, coupled with authoritative sources on possible causality between types of events, we can model causal relations between events. In this paper we present an ontology design pattern for modeling the causal relations between events, discuss the primary conceptual components, how they may be instantiated, and present overarching examples related to the domain of disaster risk management.
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Research and data management communities in the earth and environmental sciences are increasingly recognizing the value of using formal, controlled vocabularies for annotating datasets, rather than relying on unconstrained keywords.... more
Research and data management communities in the earth and environmental sciences are increasingly recognizing the value of using formal, controlled vocabularies for annotating datasets, rather than relying on unconstrained keywords. However, data managers and users have many potential vocabularies to choose from, that are hosted in multiple ways (https://vocab.nerc.ac.uk/, http://www.ontobee.org/, http://esipfed.github.io/cor/), often with overlapping topical coverage and differences in how they are formally constructed and managed. The community currently needs guidelines for choosing among available vocabularies, to ensure data can be more readily found and interpreted by target stakeholders.The goal of this working session is to outline a set of guidelines to assist repositories and data managers in choosing appropriate vocabularies. Concise guidelines will assist them in ascertaining the quality and utility of a controlled vocabulary, and must be balanced with vocabulary’s own c...
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The scale and magnitude of complex and pressing environmental issues lend urgency to the need for integrative and reproducible analysis and synthesis, facilitated by data-intensive research approaches. However, the recent pace of... more
The scale and magnitude of complex and pressing environmental issues lend urgency to the need for integrative and reproducible analysis and synthesis, facilitated by data-intensive research approaches. However, the recent pace of technological change has been such that appropriate skills to accomplish data-intensive research are lacking among environmental scientists, who more than ever need greater access to training and mentorship in computational skills. Here, we provide a roadmap for raising data competencies of current and next-generation environmental researchers by describing the concepts and skills needed for effectively engaging with the heterogeneous, distributed, and rapidly growing volumes of available data. We articulate five key skills: (1) data management and processing, (2) analysis, (3) software skills for science, (4) visualization, and (5) communication methods for collaboration and dissemination. We provide an overview of the current suite of training initiatives...
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Introduction to session and ECSO (ontology) for annotation of datasets
This Distributed Graduate Seminar (DGS) aims to syn thesize current ecological information on the role that biodiversity in agricultural landscap es (agrobiodiversity) plays in providing ecosystem services. In recent years numerous studie... more
This Distributed Graduate Seminar (DGS) aims to syn thesize current ecological information on the role that biodiversity in agricultural landscap es (agrobiodiversity) plays in providing ecosystem services. In recent years numerous studie aimed at examining the importance of agroecosystems to the conservation of biodiversity have been published. These studies focus on identifying the types of agricultural landscapes, a nd landscape components that support the greatest levels of biodiversity (Dale, Pearson et a l. 1994; Daily, Ehrlich et al. 2001; HornerDevine, Daily et al. 2003; Mayfield and Daily 2005) . The most commonly discussed goal associated with these studies is to determine wheth er and how agricultural landscapes can be managed to support high levels of biodiversity, whi le continuing to allow for profitable agriculture (DeFries, Foley et al. 2004; Ricketts 2 004). Less attention has been given to the role that agrobiodiversity plays in providing ecosystem services or how ...