New techniques for the scientific visualization of three-dimensional multi-variate and vector fields

Crawfis, Roger

doi:10.2172/195760

Cited by 2 publications

(2 citation statements)

References 85 publications

(27 reference statements)

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“…Spherical vortex models [100] can be extended by swirl and tilt to obtain phenomena that occur in nature [89]; although the modified version is no longer a solution to Navier-Stokes equations, it provides flow characteristics that help test certain visualization methods. An often used data model is the "tornado" dataset 9 that is defined by an analytic expression and lends itself to testing interactive texture-based visualization methods [21,123].…”

Section: Vector Field Visualizationmentioning

confidence: 99%

“…Repositories such as SNAP, 19 KONECT, 20 the UFL Sparse Matrix Collection, 21 the DIMACS Implementation Challenges, 22 and the Graph 500 List 23 are opportunistic collections of benchmark data. Many experiments in the graph literature have made use of the same two collections, 24 referred to as the Rome graphs and the AT&T graphs.…”

Section: Network and Graph Visualizationmentioning

confidence: 99%

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Generative Data Models for Validation and Evaluation of Visualization Techniques

Schulz

Nocaj

El‐Assady

et al. 2016

Proceedings of the Sixth Workshop on Beyond Time and Errors on Novel Evaluation Methods for Visualization

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We argue that there is a need for substantially more research on the use of generative data models in the validation and evaluation of visualization techniques. For example, user studies will require the display of representative and unconfounded visual stimuli, while algorithms will need functional coverage and assessable benchmarks. However, data is often collected in a semi-automatic fashion or entirely hand-picked, which obscures the view of generality, impairs availability, and potentially violates privacy. There are some sub-domains of visualization that use synthetic data in the sense of generative data models, whereas others work with real-world-based data sets and simulations. Depending on the visualization domain, many generative data models are "side projects" as part of an ad-hoc validation of a techniques paper and thus neither reusable nor general-purpose. We review existing work on popular data collections and generative data models in visualization to discuss the opportunities and consequences for technique validation, evaluation, and experiment design. We distill handling and future directions, and discuss how we can engineer generative data models and how visualization research could benefit from more and better use of generative data models.

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Section: Vector Field Visualizationmentioning

confidence: 99%

Section: Network and Graph Visualizationmentioning

confidence: 99%