Olfaction and emotion: The quest for olfactory restoration in two speech communities

ABSTRACT

Olfaction has been described as one of the least studied of the senses. This has certainly proven true in Communication. This project applies Ethnography of Communication and Cultural Discourse Analysis to examine the role that olfaction plays in the cultural meaning systems of two speech communities: residents surrounding the Gowanus Canal and bereaved parents. The cultural hub used to explore the cultural significance of olfaction in both speech communities is feeling. The analyses illustrate how emotions are tied to associated sensations and further become activated through radiants of dwelling, relating and acting. The analysis also shows how olfaction may serve to unify and separate members of a community. The significance of this research encourages stretching the boundaries of communication studies by examining the less studied physical senses and the boundaries of Cultural Discourse Analysis by providing distinctions that others can incorporate when examining feelings.     

Data Visualization as Participatory Research: A Model for Digital Collections to Inspire User-Driven Research

The emerging creative practices of digital collections are increasing in prominence thanks to the access to, and appeal of, data visualizations. Interactive data visualizations, such as a map, timeline, chart, graph, or artist’s rendering of data, enable users to pursue their own research interests within a collection and participate in new areas of research. Use of these data visualizations enhances the scope of the collection and enables a modern and diverse thematic collection to emerge. This article considers the question: Does the addition of data visualizations affect how users participate with a research collection? Impacts on collection development, application of user-centered design, and analysis of users’ exploratory strategies are discussed. Examples of data visualizations used in digital humanities projects and digital collections are presented as case studies.     

Deep teaching in a college STEM classroom

Cultural Studies of Science Education - The retention of underrepresented students remains a significant challenge in the STEM (Science, Technology, Engineering and Math) disciplines. A broad range...     

Digital Module 16: Longitudinal Data Analysis

In this digital ITEMS module, Dr. Jeffrey Harring and Ms. Tessa Johnson introduce the linear mixed effects (LME) model as a flexible general framework for simultaneously modeling continuous repeated measures data with a scientifically defensible function that adequately summarizes both individual change as well as the average response. The module begins with a nontechnical overview of longitudinal data analyses drawing distinctions with cross-sectional analyses in terms of research questions to be addressed. Nuances of longitudinal designs, timing of measurements, and the real possibility of missing data are then discussed. The three interconnected components of the LME model—(1) a model for individual and mean response profiles, (2) a model to characterize the covariation among the time-specific residuals, and (3) a set of models that summarize the extent that individual coefficients vary—are discussed in the context of the set of activities comprising an analysis. Finally, they demonstrate how to estimate the linear mixed effects model within an open-source environment (R). The digital module contains sample R code, diagnostic quiz questions, hands-on activities in R, curated resources, and a glossary.