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Data Intensive Science: A New Paradigm for Biodiversity Studies. In Press BioScience
"... ABSTRACT: The increasing availability of massive volumes of scientific data requires new synthetic analysis techniques to explore and identify interesting patterns that were otherwise not apparent. For biodiversity studies a “data driven ” approach is necessary due to the complexity of ecological sy ..."
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ABSTRACT: The increasing availability of massive volumes of scientific data requires new synthetic analysis techniques to explore and identify interesting patterns that were otherwise not apparent. For biodiversity studies a “data driven ” approach is necessary due to the complexity of ecological systems, particularly when viewed at large spatial and temporal scales. Data intensive science organizes large volumes of data from multiple sources and fields that are then analyzed using techniques tailored to the discovery of complex patterns in high‐ dimensional data through visualizations, simulations, and various types of model building. By interpreting and analyzing these models, truly novel and surprising patterns that are “born from the data ” can be discovered. These patterns in turn provide valuable insight for concrete hypotheses about the underlying ecological processes that created the observed data. Data intensive science allows scientists to analyze bigger and more complex systems efficiently, and complements more traditional scientific processes of hypothesis generation and experimental testing to refine our understanding of the natural world. 1 Biodiversity research is a branch of ecology that identifies and predicts patterns of organism distribution and abundance, and explains the causes of these patterns. Ecological systems are
The Avian Knowledge Network: A Partnership to Organize, Analyze, and Visualize Bird Observation Data for
- Education, Conservation, Research, and Land Management. Paper presented at the Fourth International Partners in Flight Conference: Tundra to Tropics: Connecting
"... Abstract. The Avian Knowledge Network (AKN) is an international collaboration of academic, nongovernment, and government institutions with the goal of organizing observations of birds into an interoperable format to enhance access, data visualization and exploration, and scientifi c analyses. The A ..."
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Abstract. The Avian Knowledge Network (AKN) is an international collaboration of academic, nongovernment, and government institutions with the goal of organizing observations of birds into an interoperable format to enhance access, data visualization and exploration, and scientifi c analyses. The AKN uses proven cyberinfrastructure and informatics techniques as the foundation of its development. Data are made available via secure and managed pathways. Additionally, data visualization and exploration tools are made available by a broad and diverse community of developers, analysts, and biologists. Through the development of tools and standardized data organization models, new analysis techniques are being developed that explore data fusion and federation techniques that allow the investigation of patterns of bird occurrence across multiple datasets. Finally, the Avian Knowledge Alliance uses the AKN and consists of a distributed network of nodes that provide regional or thematic access to decision support tools and other applications to support research and bird conservation across a variety of spatial scales.
TOOLS FOR HARD BAYESIAN COMPUTATIONS
, 2009
"... Each of the three chapters included here attempts to meet a different computing challenge that presents itself in the context in Bayesian statistics. The first deals with the difficulty of evaluating the computationally-expensive likelihood functions that arise from models that include Gaussian rand ..."
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Each of the three chapters included here attempts to meet a different computing challenge that presents itself in the context in Bayesian statistics. The first deals with the difficulty of evaluating the computationally-expensive likelihood functions that arise from models that include Gaussian random field components. This challenge can be mitigated by introducing sparsity into the covariance matrix in a principled way. Chapter 1 analyzes the properties of estimates, including Bayesian-like estimates, based on this “tapering” strategy. The second challenge is how to design good MCMC samplers. Chapter 2 explores an adaptive Metropolis Hastings sampler, motivating why such adaptation is needed, and demonstrating its efficacy. Chapter 2 concludes by comparing the efficiency of adaptively-tuned Metropolis samplers to three very popular MCMC algorithms, demonstrating that besides having the attractive properties of simplicity and almost unlimited flexibility, adaptively-tuned Metropolis samplers are also extremely efficient. Finally, the third challenge is endowing hierarchical models with the ability to represent conditional mean structures that are complicated, unknown functions of very many covariates. Chapter 3 describes how this is accomplished through the HEBBRU framework, whereby data mining methods are embedded into hierarchical models and fit using an approximate Gibbs sampler.
