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Practicing reproducible scientific research requires access to appropriate reproducibility methodology and software, as well as open data. Strict reproducibility in complex scientific domains such as environmental science, ecology and medicine, however, is difficult if not impossible. Here, we consider replication as a relaxed but bona fide substitution for strict reproducibility and propose using 3D terrain visualization for replication in environmental science studies that propose causal relationships between one or more driver variables and one or more response variables across complex ecosystem landscapes. We base our contention of the usefulness of visualization for replication on more than ten years observing environmental science modelers who use our 3D terrain visualization software to develop, calibrate, validate, and integrate predictive models. To establish the link between replication and model validation and corroboration, we consider replication as proposed by Munafò, i.e., triangulation. We enumerate features of visualization systems that would enable such triangulation and argue that such systems would render feasible domain-specific, open visualization software for use in replicating environmental science studies. 

Environmental scientists, land managers, and policy actors are increasingly presented with high-stakes high-uncertainty problems stemming from human-ecosystem interactions. These interactions exacerbate already challenging issues associated with environmental policy and natural resource management. To address these problems, scientists and managers frequently use models that produce enormous geospatial and temporal datasets that are constantly modified. To help make sense of this complex and changing data, we are immersed in a co-production effort where software engineers and environmental scientists collaborate on the development of visualization software. We report on this on-going research, and find that visualization is critical not only for communicating science, but integral to many aspects of the science production pipeline and evolving data science field. We also find evidence among our collaborators that this software co-production process helps build legitimacy for the information it produces, with potential implications for generating actionable science for policy and governance. 

Defining characteristics of a problem domain continues to challenge developers of visualization software, even though it is essential for designing both tools and resulting visualizations. Additionally, effectiveness of a visualization software tool often depends on the context of systems and actors within the domain problem. The nested blocks and guidelines model is a useful template for informing design and evaluation criteria for visualization software development because it aligns design to need. [1] Characterizing the outermost block of the nested model—the domain problem—is challenging, mainly due to the nature of contemporary domain problems, which are dynamic and by definition difficult to problematize. We offer here our emerging conceptual model, based on the central question in our research study—what visualization works for whom and in which situation—to characterize the outermost block, the domain problem, of the nested model. [1] We apply examples from a three-year case study of visualization software design and development to demonstrate how the conceptual model might be used to create evaluation criteria affecting design and development of a visualization tool.