What Google Maps can do for Biomedical Data Dissemination: Examples and a Design Study

Background: Biologists often need to assess whether unfamiliar datasets warrant the time investment required for more detailed exploration. Basing such assessments on brief descriptions provided by data publishers is unwieldy for large datasets that contain insights dependent on specific scientific questions. Alternatively, using complex software systems for a preliminary analysis may be deemed as too time consuming in itself, especially for unfamiliar data types and formats. This may lead to wasted analysis time and discarding of potentially useful data. Results: We present an exploration of design opportunities that the Google Maps interface offers to biomedical data visualization. In particular, we focus on synergies between visualization techniques and Google Maps that facilitate the development of biological visualizations which have both low-overhead and sufficient expressivity to support the exploration of data at multiple scales. The methods we explore rely on displaying pre-rendered visualizations of biological data in browsers, with sparse yet powerful interactions, by using the Google Maps API. We structure our discussion around five visualizations: a gene co-regulation visualization, a heatmap viewer, a genome browser, a protein interaction network, and a planar visualization of white matter in the brain. Feedback from collaborative work with domain experts suggests that our Google Maps visualizations offer multiple, scale-dependent perspectives and can be particularly helpful for unfamiliar datasets due to their accessibility. We also find that users, particularly those less experienced with computer use, are attracted by the familiarity of the Google Maps API. Our five implementations introduce design elements that can benefit visualization developers. Conclusions: We describe a low-overhead approach that lets biologists access readily analyzed views of unfamiliar scientific datasets. We rely on pre-computed visualizations prepared by data experts, accompanied by sparse and intuitive interactions, and distributed via the familiar Google Maps framework. Our contributions are an evaluation demonstrating the validity and opportunities of this approach, a set of design guidelines benefiting those wanting to create such visualizations, and five concrete example visualizations.

Across-scale patterning of plant-soil-water interactions surrounding tree islands in Southern Everglades landscapes

The freshwater Everglades is a complex system containing thousands of tree islands embedded within a marsh-grassland matrix. The tree island-marsh mosaic is shaped and maintained by hydrologic, edaphic and biological mechanisms that interact across multiple scales. Preserving tree islands requires a more integrated understanding of how scale-dependent phenomena interact in the larger freshwater system. The hierarchical patch dynamics paradigm provides a conceptual framework for exploring multi-scale interactions within complex systems. We used a three-tiered approach to examine the spatial variability and patterning of nutrients in relation to site parameters within and between two hydrologically defined Everglades landscapes: the freshwater Marl Prairie and the Ridge and Slough. Results were scale-dependent and complexly interrelated. Total carbon and nitrogen patterning were correlated with organic matter accumulation, driven by hydrologic conditions at the system scale. Total and bioavailable phosphorus were most strongly related to woody plant patterning within landscapes, and were found to be 3 to 11 times more concentrated in tree island soils compared to surrounding marshes. Below canopy resource islands in the slough were elongated in a downstream direction, indicating soil resource directional drift. Combined multi-scale results suggest that hydrology plays a significant role in landscape patterning and also the development and maintenance of tree islands. Once developed, tree islands appear to exert influence over the spatial distribution of nutrients, which can reciprocally affect other ecological processes.

Sociocultural complexities of ecosystem restoration: Remaking identity, landscape and belonging in the Florida Everglades

The Florida Everglades is a highly diverse socionatural landscape that historically spanned much of the south Florida peninsula. Today, the Florida Everglades is an iconic but highly contested conservation landscape. It is the site of one of the world's largest publicly funded ecological restoration programs, estimated to cost over $8 billion (U.S. GAO 2007), and it is home to over two million acres of federally protected lands, including the Big Cypress National Preserve and Everglades National Park. However, local people's values, practices and histories overlap and often conflict with the global and eco-centric values linked to Everglades environmental conservation efforts, sparking environmental conflict. My dissertation research examined the cultural politics of nature associated with two Everglades conservation and ecological restoration projects: 1) the creation and stewardship of the Big Cypress National Preserve, and 2) the Tamiami Trail project at the northern boundary of Everglades National Park. Using multiple research methods including ethnographic fieldwork, archival research, participant observation, surveys and semi-structured interviews, I documented how these two projects have shaped environmental claims-making strategies to Everglades nature on the part of environmental NGOs, the National Park Service and local white outdoorsmen. In particular, I examined the emergence of an oppositional white identity called the Gladesmen Culture. My findings include the following: 1) just as different forms of nature are historically produced, contingent and power-laden, so too are different claims to Everglades nature; 2) identity politics are an integral dimension of Everglades environmental conflicts; and 3) the Big Cypress region's history and contemporary conflicts are shaped by the broader political economy of development in south Florida. My dissertation concluded that identity politics, class and property relations have played a key, although not always obvious, role in shaping Everglades history and environmental claims-making, and that they continue to influence contemporary Everglades environmental conflicts.

Sociocultural Complexities of Ecosystem Restoration: Remaking Identity, Landscape and Belonging in the Florida Everglades

The Florida Everglades is a highly diverse socionatural landscape that historically spanned much of the south Florida peninsula. Today, the Florida Everglades is an iconic but highly contested conservation landscape. It is the site of one of the world’s largest publicly funded ecological restoration programs, estimated to cost over $8 billion (U.S. GAO 2007), and it is home to over two million acres of federally protected lands, including the Big Cypress National Preserve and Everglades National Park. However, local people’s values, practices and histories overlap and often conflict with the global and eco-centric values linked to Everglades environmental conservation efforts, sparking environmental conflict. My dissertation research examined the cultural politics of nature associated with two Everglades conservation and ecological restoration projects: 1) the creation and stewardship of the Big Cypress National Preserve, and 2) the Tamiami Trail project at the northern boundary of Everglades National Park. Using multiple research methods including ethnographic fieldwork, archival research, participant observation, surveys and semi-structured interviews, I documented how these two projects have shaped environmental claims-making strategies to Everglades nature on the part of environmental NGOs, the National Park Service and local white outdoorsmen. In particular, I examined the emergence of an oppositional white identity called the Gladesmen Culture. My findings include the following: 1) just as different forms of nature are historically produced, contingent and power-laden, so too are different claims to Everglades nature; 2) identity politics are an integral dimension of Everglades environmental conflicts; and 3) the Big Cypress region’s history and contemporary conflicts are shaped by the broader political economy of development in south Florida. My dissertation concluded that identity politics, class and property relations have played a key, although not always obvious, role in shaping Everglades history and environmental claims-making, and that they continue to influence contemporary Everglades environmental conflicts.

Exploring the goals, content, and methods of entrepreneurship professors: A multiple case study

Along with the accumulation of evidence supporting the role of entrepreneurship in economic development (Acs & Armington, 2006; Kuratko, 2005, Reynolds, 2007), governments have persisted in encouraging people to become entrepreneurs (Acs & Stough, 2008; Brannback & Carsrud, 2008). These efforts have tried to reproduce the conditions under which entrepreneurship emerges. One of these conditions is to develop entrepreneurial skills among students and scientists (Fan & Foo, 2004). Entrepreneurship education within higher education has experienced a remarkable expansion in the last 20 years (Green, 2008). To develop entrepreneurial skills among students, scholars have proposed different teaching approaches. However, no clear relationship has been demonstrated between entrepreneurship education, learning outcomes, and business creation (Hostager & Decker, 1999). Despite policy makers demands for more accountability from educational institutions (Klimoski, 2007) and entrepreneurship instructors demands for consistency about what should be taught and how (Maidment, 2009), the appropriate content for entrepreneurship programs remains under constant discussion (Solomon, 2007). Entrepreneurship education is still in its infancy, professors propose diverse teaching goals and radically different teaching methods. This represents an obstacle to development of foundational and consistent curricula across the board (Cone, 2008). Entrepreneurship education is in need of a better conceptualization of the learning outcomes pursued in order to develop consistent curriculum. Many schools do not have enough qualified faculty to meet the growing student demand and a consistent curriculum is needed for faculty development. Entrepreneurship instructors and their teaching practices are of interest because they have a role in producing the entrepreneurs needed to grow the economy. This study was designed to understand instructors’ perspectives and actions related to their teaching. The sample studied consisted of eight college and university entrepreneurship instructors. Cases met predetermined criteria of importance followed maximum variation strategies. Results suggest that teaching content were consistent across participants while different teaching goals were identified: some instructors inspire and develop general skills of students while others envision the creation of a real business as the major outcome of their course. A relationship between methods reported by instructors and their disciplinary background, teaching perspective, and entrepreneurial experience was found.

Knowledge discovery using multiple sources of biological data

The primary aim of this dissertation is to develop data mining tools for knowledge discovery in biomedical data when multiple (homogeneous or heterogeneous) sources of data are available. The central hypothesis is that, when information from multiple sources of data are used appropriately and effectively, knowledge discovery can be better achieved than what is possible from only a single source. ^ Recent advances in high-throughput technology have enabled biomedical researchers to generate large volumes of diverse types of data on a genome-wide scale. These data include DNA sequences, gene expression measurements, and much more; they provide the motivation for building analysis tools to elucidate the modular organization of the cell. The challenges include efficiently and accurately extracting information from the multiple data sources; representing the information effectively, developing analytical tools, and interpreting the results in the context of the domain. ^ The first part considers the application of feature-level integration to design classifiers that discriminate between soil types. The machine learning tools, SVM and KNN, were used to successfully distinguish between several soil samples. ^ The second part considers clustering using multiple heterogeneous data sources. The resulting Multi-Source Clustering (MSC) algorithm was shown to have a better performance than clustering methods that use only a single data source or a simple feature-level integration of heterogeneous data sources. ^ The third part proposes a new approach to effectively incorporate incomplete data into clustering analysis. Adapted from K-means algorithm, the Generalized Constrained Clustering (GCC) algorithm makes use of incomplete data in the form of constraints to perform exploratory analysis. Novel approaches for extracting constraints were proposed. For sufficiently large constraint sets, the GCC algorithm outperformed the MSC algorithm. ^ The last part considers the problem of providing a theme-specific environment for mining multi-source biomedical data. The database called PlasmoTFBM, focusing on gene regulation of Plasmodium falciparum, contains diverse information and has a simple interface to allow biologists to explore the data. It provided a framework for comparing different analytical tools for predicting regulatory elements and for designing useful data mining tools. ^ The conclusion is that the experiments reported in this dissertation strongly support the central hypothesis.^

An investigation of multiple pathways of developmental intervention change

Convergence among treatment, prevention, and developmental intervention approaches has led to the recognition of the need for evaluation models and research designs that employ a full range of evaluation information to provide an empirical basis for enhancing the efficiency, efficacy, and effectiveness of prevention and positive development interventions. This study reports an investigation of a positive youth development program using an Outcome Mediation Cascade (OMC) evaluation model, an integrated model for evaluating the empirical intersection between intervention and developmental processes. The Changing Lives Program (CLP) is a community supported positive youth development intervention implemented in a practice setting as a selective/indicated program for multi-ethnic, multi-problem at risk youth in urban alternative high schools. This study used a Relational Data Analysis integration of quantitative and qualitative data analysis strategies, including the use of both fixed and free response measures and a structural equation modeling approach, to construct and evaluate the hypothesized OMC model. Findings indicated that the hypothesized model fit the data (χ2 (7) = 6.991, p = .43; RMSEA = .00; CFI = 1.00; WRMR = .459). Findings also provided preliminary evidence consistent with the hypothesis that in addition to having effects on targeted positive outcomes, PYD interventions are likely to have progressive cascading effects on untargeted problem outcomes that operate through effects on positive outcomes. Furthermore, the general pattern of findings suggested the need to use methods capable of capturing both quantitative and qualitative change in order to increase the likelihood of identifying more complete theory informed empirically supported models of developmental intervention change processes.

Monitoring the Accuracy of Multiple Occupancy Forecasts

Corporate executives closely monitor the accuracy of their hotels' occupancy fore- casts since important decisions are based upon these predictions. This study lists the criteria for selecting an appropriate error measure. It discusses several evaluation methods focusing on statistical significance tests and demonstrates the use of two adequate evaluation methods: Mincer- Zamowitz's efficiency test and Wilcoxon's Non-Parametric Matched-Pairs Signed- Ranks test.

The effectiveness of direct -instruction and student -centered teaching methods on students' functional understanding of plagiarism

In an attempt to improve students' functional understanding of plagiarism a variety of approaches were tried within the context of a more comprehensive information literacy program. Sessions were taught as a one hour "module" inside a required communications skills class at a small private university. Approaches taken included control sessions (a straightforward PowerPoint presentation of the material), direct instruction sessions (featuring mostly direct lecture but with some seatwork as well), and student-centered sessions (utilizing role playing and group exercises). Students were taught basic content and definitions regarding plagiarism, what circumstances or instances constitute plagiarism, where to go for help in avoiding plagiarism, and what constitutes appropriate paraphrasing and citation. Pre-test and post-test scores determined students' functional understanding primarily by their ability to recognize properly and improperly paraphrased text, content understanding by their combined total score on a multiple choice quiz, and their attitude and conceptual understanding by their ability to recognize circumstances which would constitute plagiarism. While students improved across all methods the study was unable to identify one that performed significantly better than the others. The results supported the need for more education with regard to plagiarism and suggested a need for perhaps more time on task and/or a mixed approach towards conveying the content.

Methods for modeling and analyzing concurrent software

Concurrent software executes multiple threads or processes to achieve high performance. However, concurrency results in a huge number of different system behaviors that are difficult to test and verify. The aim of this dissertation is to develop new methods and tools for modeling and analyzing concurrent software systems at design and code levels. This dissertation consists of several related results. First, a formal model of Mondex, an electronic purse system, is built using Petri nets from user requirements, which is formally verified using model checking. Second, Petri nets models are automatically mined from the event traces generated from scientific workflows. Third, partial order models are automatically extracted from some instrumented concurrent program execution, and potential atomicity violation bugs are automatically verified based on the partial order models using model checking. Our formal specification and verification of Mondex have contributed to the world wide effort in developing a verified software repository. Our method to mine Petri net models automatically from provenance offers a new approach to build scientific workflows. Our dynamic prediction tool, named McPatom, can predict several known bugs in real world systems including one that evades several other existing tools. McPatom is efficient and scalable as it takes advantage of the nature of atomicity violations and considers only a pair of threads and accesses to a single shared variable at one time. However, predictive tools need to consider the tradeoffs between precision and coverage. Based on McPatom, this dissertation presents two methods for improving the coverage and precision of atomicity violation predictions: 1) a post-prediction analysis method to increase coverage while ensuring precision; 2) a follow-up replaying method to further increase coverage. Both methods are implemented in a completely automatic tool.

Evaluating High-Resolution Aerial Photography Acquired by Unmanned Aerial Systems for Use in Mapping Everglades Wetland Plant Associations

Mapping of vegetation patterns over large extents using remote sensing methods requires field sample collections for two different purposes: (1) the establishment of plant association classification systems from samples of relative abundance estimates; and (2) training for supervised image classification and accuracy assessment of satellite data derived maps. One challenge for both procedures is the establishment of confidence in results and the analysis across multiple spatial scales. Continuous data sets that enable cross-scale studies are very time consuming and expensive to acquire and such extensive field sampling can be invasive. The use of high resolution aerial photography (hrAP) offers an alternative to extensive, invasive, field sampling and can provide large volume, spatially continuous, reference information that can meet the challenges of confidence building and multi-scale analysis.

Methods for Modeling and Analyzing Concurrent Software

Concurrent software executes multiple threads or processes to achieve high performance. However, concurrency results in a huge number of different system behaviors that are difficult to test and verify. The aim of this dissertation is to develop new methods and tools for modeling and analyzing concurrent software systems at design and code levels. This dissertation consists of several related results. First, a formal model of Mondex, an electronic purse system, is built using Petri nets from user requirements, which is formally verified using model checking. Second, Petri nets models are automatically mined from the event traces generated from scientific workflows. Third, partial order models are automatically extracted from some instrumented concurrent program execution, and potential atomicity violation bugs are automatically verified based on the partial order models using model checking. Our formal specification and verification of Mondex have contributed to the world wide effort in developing a verified software repository. Our method to mine Petri net models automatically from provenance offers a new approach to build scientific workflows. Our dynamic prediction tool, named McPatom, can predict several known bugs in real world systems including one that evades several other existing tools. McPatom is efficient and scalable as it takes advantage of the nature of atomicity violations and considers only a pair of threads and accesses to a single shared variable at one time. However, predictive tools need to consider the tradeoffs between precision and coverage. Based on McPatom, this dissertation presents two methods for improving the coverage and precision of atomicity violation predictions: 1) a post-prediction analysis method to increase coverage while ensuring precision; 2) a follow-up replaying method to further increase coverage. Both methods are implemented in a completely automatic tool.