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We are perpetually present in our environment, experiencing it with our senses. Scientific data describes the same environment quantitatively. Our goal is to use scientific and artistic methods to combine these environmental expressions and personal experience through the creation of glyphs visually abstracted from and associated with forms in nature in the representation of climate data. The use of these glyphs removes the distinctions between scientific data and sensory experience, to allow a fuller intuitive association between the two, creating an embodied experience and increasing awareness of the climate effects and changes all around us. 

Marine macroalgae in the Gulf of Mexico is an important potential source for biofuel. However, identifying locations with the correct biogeochemical and hydrodynamic conditions for cultivation on a large enough scale to meet the needs of the U.S. private energy sector is impossible from purely observational studies. Large-scale, HPC modeling of earth systems processes enables researchers to study complex physical relationships with high fidelity. Here, we present novel visualization techniques showing the results of a global run of the E3SM’s MPAS-Ocean model data with biogeochemistry extensions to improve ongoing research in macroalgae cultivation. 

As scientific data continues to grow in size, complexity, and density, the representation scope of three-dimensional spaces, data sampling methods, and transfer functions have improved in parallel, allowing visualization practitioners to produce richer multidimensional encodings. Glyphs, in particular, have become an essential encoding tool due to their versatile applications in co-located multi-variate volumetric datasets. While prior work has been conducted investigating the perceptual attributes of computationally-generated three-dimensional glyph-forms for scientific visualization, their affective and expressive qualities have yet to be examined. Further, our prior work has demonstrated the benefits of artist hand-created glyph forms in contrast to commonly-used synthetic forms in increasing visual diversity, discrimination, and expressive association in complex environmental datasets. In order to begin to address this gap, we establish preliminary groundwork for an affective design space for hand-created glyph forms, produce a novel set of glyphforms based on this design space, describe a non-verbal method for discovering affective classifications of glyph-forms adopted from current affect theory, and report the results of two studies that explore how these three-dimensional forms produce consistent affective responses across assorted study cohorts. 

As scientific data continues to grow in size, complexity, and density, the representation scope of three-dimensional spaces, data sampling methods, and transfer functions have improved in parallel, allowing visualization practitioners to produce richer multidimensional encodings. Glyphs, in particular, have become an essential encoding tool due to their versatile applications in co-located multivariate volumetric datasets. While prior work has been conducted investigating the perceptual attributes of computationally-generated three-dimensional glyph-forms for scientific visualization, their affective and expressive qualities have yet to be examined. Further, our prior work has demonstrated the benefits of artist hand-created glyph forms in contrast to commonly-used synthetic forms in increasing visual diversity, discrimination, and expressive association in complex environmental datasets. In order to begin to address this gap, we establish preliminary groundwork for an affective design space for hand-created glyph forms, produce a novel set of glyph forms based on this design space, describe a non-verbal method for discovering affective classifications of glyph-forms adopted from current affect theory, and report the results of two studies that explore how these three-dimensional forms produce consistent affective responses across assorted study cohorts. 

As computing capacity increases and data grows in both size and complexity, we are capable of understanding our surroundings with increas- ing nuance. Visualizing this often-multivariate environmental data presents complex visual scenes to be navigated, parsed, analyzed, and communicated. We draw from both the natural world and artistic color theory to present 1) a new color system, designed to establish an af- fective connection between big environmental data and its original source material, 2) a tool for extracting these workable palettes from natural imagery, and 3) a selection of pre-made linear colormaps and discrete color sets drawn from natural environments.