Semantic Bridges for Biodiversity Sciences
Understanding the impact of climate change and humans on biodiversity requires the retrieval and integration of heterogeneous data sets for the generation of models that provide insights not possible with a single model. Scientists invest a significant am
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Cyber-ShARE Center of Excellence, University of Texas at El Paso, El Paso, USA {nvillanuevarosales,ddpenninngton,lagarnicachavira}@utep.edu 2 Department of Computer Science, University of Texas at El Paso, El Paso, USA 3 Air Force Research Lab, Information Directorate, Rome, USA [email protected] 4 Department of Geology, University of Texas at El Paso, El Paso, USA
Abstract. Understanding the impact of climate change and humans on biodiversity requires the retrieval and integration of heterogeneous data sets for the generation of models that provide insights not possible with a single model. Scientists invest a significant amount of time collecting and manually preprocessing data for the generation of such models. The Earth Life and Semantic Web (ELSEWeb) project aims to create a semantic-based, open-source cyberinfrastructure to automate the ingestion of data by models. This paper describes the ontologies at the backbone of ELSEWeb that provide semantic bridges between environmental data sources and species distribution models. Keywords: Ontology · Data-to-model integration · Model web · Biodiversity · Climate change
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Introduction
What will happen to native species in national parks under scenarios of climate change? When and where might we expect zoonotic infectious disease to spread? These questions and others can be addressed using species distribution models (SDMs) [1]. SDMs predict where animal or plant species might find suitable habitat given present conditions or under change scenarios. Species might be socially relevant because they are endangered or carry diseases. Conducting “what-if” analyses provides insights of changes in the environment as they occur – or before they occur. Scenario analysis is becoming a key tool for the biodiversity (and other) sciences [2] to understand human impacts on the environment coupled with climate change. SDMs require species occurrence data and environmental data. Species occurrence data contains the location of known occurrences of a species in a given time period. Species occurrence data is mostly available in museums, which have invested in digitization and the development of metadata standards and repositories through the Global Biodiversity Information Facility (GBIF)1. Environmental data is heterogeneous and 1
N. del Rio—Affiliated with the University of Texas at El Paso when producing this work. http://www.gbif.org
© Springer International Publishing Switzerland 2015 M. Arenas et al. (Eds.): ISWC 2015, Part II, LNCS 9367, pp. 310–317, 2015. DOI: 10.1007/978-3-319-25010-6_20
Semantic Bridges for Biodiversity Sciences
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available from multiple sources, e.g., satellite imagery. Scientists spend considerable time deciding what data might be most relevant, where to find it, how to obtain it, and what to do with it since data manipulation may require the use of proprietary tools. In addition, each modelling algorithm has its own constraints, operational requirements, assumptions, parameter and data requirements, usage history, and advocates (or diss
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