Matching Items (4)
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- All Subjects: digital humanities
- All Subjects: Social networks--Mathematical models.
- Creators: Laubichler, Manfred
Description
Influenza has shown its potential to affect and even kill millions of people within an extremely short time frame, yet studies and surveys show that the general public is not well educated about the facts about influenza, including prevention and treatment. For this reason, public perception of influenza is extremely skewed, with people generally not taking the disease as seriously as they should given its severity. To investigate the inconsistencies between action and awareness of best available knowledge regarding influenza, this study conducted literature review and a survey of university students about their knowledge, perceptions, and action taken in relationship to influenza. Due to their dense living quarters, constant daily interactions, and mindset that they are "immune" to fairly common diseases like influenza, university students are a representative sample of urban populations. According to the World Health Organization (WHO), 54% of the world's population lived in cities as of 2014 (Urban population growth). Between 2015 and 2020, the global urban population is expected to grow 1.84% per year, 1.63% between 2020 and 2025, and 1.44% between 2025 and 2030 (Urban population growth). Similar projections estimate that by 2017, an overwhelming majority of the world's population, even in less developed countries, will be living in cities (Urban population growth). Results of this study suggest possible reasons for the large gap between best available knowledge and the perceptions and actions of individuals on the other hand. This may lead to better-oriented influenza education initiatives, more effective prevention and treatment plans, and generally raise excitement and awareness surrounding public health and scientific communication.
ContributorsGur-Arie, Rachel Ellen Haviva (Author) / Maienschein, Jane (Thesis director) / Laubichler, Manfred (Committee member) / Creath, Richard (Committee member) / Barrett, The Honors College (Contributor) / School of Life Sciences (Contributor)
Created2014-12
Description
How is knowledge created at the intersections between basic science, biotechnology, and industry? Gene drives are an interesting example, as they combine a long-standing interest with a recent technological breakthrough and a new set of commercial applications. Gene drives are genes engineered such that they are preferentially inherited at a frequency greater than the typical Mendelian fifty percent ratio. During the historical and conceptual evolution of gene drives beginning in the 1960s, there have been many innovations and publications. Along with that, gene drive science developed considerable public attention, explosion of new scientists, and variation in the way the topic is discussed. It is now time to look at this new organization of science using a systematic approach to characterize the system that has enabled knowledge to grow in this scientific field. This project breaks new ground in how knowledge advances in genetic engineering science, and how scientists understand what a “gene drive” is through analysis of language, communities, and other social factors. In effect, this research will advance multiple fields and enable a deeper understanding of knowledge and complexity. This project documents patterns of publication, collaborative relationships, linguistic variation, innovation, and knowledge expansion. The results of computational analysis provide an in-depth and complete characterization of the structure, dynamics, and evolution of scientific knowledge found in the gene drive technology. Further, time series analysis of the multiple layers of discourse enabled a diachronic connective mapping of collaborative relationships and tracked linguistic variation and change, highlighting where ambiguous language may appear, improving and creating more cohesive scientific language.
Overall, depicting the structure, dynamics, and evolution of scientific knowledge during a novel eruption of scientific complexity can shed light on the factors that can lead to: (1) improved scientific communication, (2) reduction of scientific progress, (3) new knowledge, and (4) novel collaborative relationships. Therefore, characterizing the current technological, methodological, and social contexts that can influence scientific knowledge.
ContributorsOToole, Cody Lane (Author) / Laubichler, Manfred (Thesis advisor) / Collins, James P (Committee member) / Simeone, Michael (Committee member) / Evans, James (Committee member) / Arizona State University (Publisher)
Created2021
Description
Analysis of social networks has the potential to provide insights into wide range of applications. As datasets continue to grow, a key challenge is the lack of a widely applicable algorithmic framework for detection of statistically anomalous networks and network properties. Unlike traditional signal processing, where models of truth or empirical verification and background data exist and are often well defined, these features are commonly lacking in social and other networks. Here, a novel algorithmic framework for statistical signal processing for graphs is presented. The framework is based on the analysis of spectral properties of the residuals matrix. The framework is applied to the detection of innovation patterns in publication networks, leveraging well-studied empirical knowledge from the history of science. Both the framework itself and the application constitute novel contributions, while advancing algorithmic and mathematical techniques for graph-based data and understanding of the patterns of emergence of novel scientific research. Results indicate the efficacy of the approach and highlight a number of fruitful future directions.
ContributorsBliss, Nadya Travinin (Author) / Laubichler, Manfred (Thesis advisor) / Castillo-Chavez, Carlos (Thesis advisor) / Papandreou-Suppappola, Antonia (Committee member) / Arizona State University (Publisher)
Created2015
Description
Computational tools in the digital humanities often either work on the macro-scale, enabling researchers to analyze huge amounts of data, or on the micro-scale, supporting scholars in the interpretation and analysis of individual documents. The proposed research system that was developed in the context of this dissertation ("Quadriga System") works to bridge these two extremes by offering tools to support close reading and interpretation of texts, while at the same time providing a means for collaboration and data collection that could lead to analyses based on big datasets. In the field of history of science, researchers usually use unstructured data such as texts or images. To computationally analyze such data, it first has to be transformed into a machine-understandable format. The Quadriga System is based on the idea to represent texts as graphs of contextualized triples (or quadruples). Those graphs (or networks) can then be mathematically analyzed and visualized. This dissertation describes two projects that use the Quadriga System for the analysis and exploration of texts and the creation of social networks. Furthermore, a model for digital humanities education is proposed that brings together students from the humanities and computer science in order to develop user-oriented, innovative tools, methods, and infrastructures.
ContributorsDamerow, Julia (Author) / Laubichler, Manfred (Thesis advisor) / Maienschein, Jane (Thesis advisor) / Creath, Richard (Committee member) / Ellison, Karin (Committee member) / Hooper, Wallace (Committee member) / Renn, Jürgen (Committee member) / Arizona State University (Publisher)
Created2014