The castor bean plant (Ricinus communis) is a member of the family Euphorbiaceae. In spite of its common name, the castor plant is not a true bean (i.e., leguminous plants belonging to the family, Fabaceae). Ricinus communis is native to tropical Africa, but because the plant was recognized for its production of oil with many desirable properties, it has been introduced and cultivated in warm temperate regions throughout the world (Armstrong 1999 and Brown 2005). Castor bean plants have also been valued by gardeners as an ornamental plant and, historically, as a natural rodenticide. Today, escaped plants grow like weeds throughout much of the southwestern United States, and castor seeds are even widely available to the public for order through the Internet. In this study, multiple loci of chloroplast noncoding sequence data and a few nuclear noncoding regions were examined to identify DNA polymorphisms present among representatives from a geographically diverse panel of Ricinus communis cultivated varieties. The primary objectives for this research were (1) to successfully cultivate castor plants and extract sufficient yields of high quality DNA from an assortment of castor cultivated varieties, (2) to use PCR and sequencing to screen available universal oligos against a small panel …

Knowledge discovery from large and complex collections of today's scientific datasets is a challenging task. With the ability to measure and simulate more processes at increasingly finer spatial and temporal scales, the increasing number of data dimensions and data objects is presenting tremendous challenges for data analysis and effective data exploration methods and tools. Researchers are overwhelmed with data and standard tools are often insufficient to enable effective data analysis and knowledge discovery. The main objective of this thesis is to provide important new capabilities to accelerate scientific knowledge discovery form large, complex, and multivariate scientific data. The research covered in this thesis addresses these scientific challenges using a combination of scientific visualization, information visualization, automated data analysis, and other enabling technologies, such as efficient data management. The effectiveness of the proposed analysis methods is demonstrated via applications in two distinct scientific research fields, namely developmental biology and high-energy physics.Advances in microscopy, image analysis, and embryo registration enable for the first time measurement of gene expression at cellular resolution for entire organisms. Analysis of high-dimensional spatial gene expression datasets is a challenging task. By integrating data clustering and visualization, analysis of complex, time-varying, spatial gene expression patterns and their formation …

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Much of the previous work in the large data visualization area has solely focused on handling the scale of the data. This task is clearly a great challenge and necessary, but it is not sufficient. Applying standard visualization techniques to large scale data sets often creates complicated pictures where meaningful trends are lost. A second challenge, then, is to also provide algorithms that simplify what an analyst must understand, using either visual or quantitative means. This challenge can be summarized as improving the legibility or reducing the complexity of massive data sets. Fully meeting both of these challenges is the work of many, many PhD dissertations. In this dissertation, we describe some new techniques to address both the scale and legibility challenges, in hope of contributing to the larger solution. In addition to our assumption of simultaneously addressing both scale and legibility, we add an additional requirement that the solutions considered fit well within an interoperable framework for diverse algorithms, because a large suite of algorithms is often necessary to fully understand complex data sets. For scale, we present a general architecture for handling large data, as well as details of a contract-based system for integrating advanced optimizations into a …