Abstract Earth system models synthesize the science of interactions amongst multiple biophysical and, increasingly, human processes across a wide range of scales. Ecohydrologic models are a subset of earth system models that focus particularly on the complex interactions between ecosystem processes and the storage and flux of water. Ecohydrologic models often focus at scales where direct observations occur: plots, hillslopes, streams, and watersheds, as well as where land and resource management decisions are implemented. These models complement field‐based and data‐driven science by combining theory, empirical relationships derived from observation and new data to create virtual laboratories. Ecohydrologic models are tools that managers can use to ask “what if” questions and domain scientists can use to explore the implications of new theory or measurements. Recent decades have seen substantial advances in ecohydrologic models, building on both new domain science and advances in software engineering and data availability. The increasing sophistication of ecohydrologic models however, presents a barrier to their widespread use and credibility. Their complexity, often encoding 100s of relationships, means that they are effectively “black boxes,” at least for most users, sometimes even to the teams of researchers that contribute to their design. This opacity complicates the interpretation of model results. For models to effectively advance our understanding of how plants and water interact, we must improve how we visualize not only model outputs, but also the underlying theories that are encoded within the models. In this paper, we outline a framework for increasing the usefulness of ecohydrologic models through better visualization. We outline four complementary approaches, ranging from simple best practices that leverage existing technologies, to ideas that would engage novel software engineering and cutting edge human–computer interface design. Our goal is to open the ecohydrologic model black box in ways that will engage multiple audiences, from novices to model developers, and support learning, new discovery, and environmental problem solving.
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An Empirical Approach to Understanding Data Science and Engineering Education
As data science is an evolving field, existing definitions reflect this uncertainty with overloaded terms and inconsistency. As a result of the field’s fluidity, there is often a mismatch between what data-related programs teach, what employers expect, and the actual tasks data scientists are performing. In addition, the tools available to data scientists are not necessarily the tools being taught; textbooks do not seem to meet curricular needs; and empirical evidence does not seem to support existing program design. Currently, the field appears to be bifurcating into data science (DS) and data engineering (DE), with specific but overlapping roles in the combined data science and engineering (DSE) lifecycle. However, curriculum design has not yet caught up to this evolution. This working group report shows an empirical and data-driven view of the data-related education landscape, and includes several recommendations for both academia and industry that are based on this analysis.
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- PAR ID:
- 10170821
- Date Published:
- Journal Name:
- Proceedings of the Working Group Reports on Innovation and Technology in Computer Science Education
- Page Range / eLocation ID:
- 73 to 87
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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