The complexity of today's visualization applications demands specific visualization systems tailored for the development of these applications. Frequently, such systems utilize levels of abstraction to improve the application development process, for instance by providing a data flow network editor. Unfortunately, these abstractions result in several issues, which need to be circumvented through an abstraction-centered system design. Often, a high level of abstraction hides low level details, which makes it difficult to directly access the underlying computing platform, which would be important to achieve an optimal performance. Therefore, we propose a layer structure developed for modern and sustainable visualization systems allowing developers to interact with all contained abstraction levels. We refer to this interaction capabilities as usage abstraction levels, since we target application developers with various levels of experience. We formulate the requirements for such a system, derive the desired architecture, and present how the concepts have been exemplary realized within the Inviwo visualization system. Furthermore, we address several specific challenges that arise during the realization of such a layered architecture, such as communication between different computing platforms, performance centered encapsulation, as well as layer-independent development by supporting cross layer documentation and debugging capabilities.

User evaluations have gained increasing importance in visualization research over the past years, as in many cases these evaluations are the only way to support the claims made by visualization researchers. Unfortunately, recent literature reviews show that in comparison to algorithmic performance evaluations, the number of user evaluations is still very low. Reasons for this are the required amount of time to conduct such studies together with the difficulties involved in participant recruitment and result reporting. While it could be shown that the quality of evaluation results and the simplified participant recruitment of crowdsourcing platforms makes this technology a viable alternative to lab experiments when evaluating visualizations, the time for conducting and reporting such evaluations is still very high. In this paper, we propose a software system, which integrates the conduction, the analysis and the reporting of crowdsourced user evaluations directly into the scientific visualization development process. With the proposed system, researchers can conduct and analyze quantitative evaluations on a large scale through an evaluation-centric user interface with only a few mouse clicks. Thus, it becomes possible to perform iterative evaluations during algorithm design, which potentially leads to better results, as compared to the time consuming user evaluations traditionally conducted at the end of the design process. Furthermore, the system is built around a centralized database, which supports an easy reuse of old evaluation designs and the reproduction of old evaluations with new or additional stimuli, which are both driving challenges in scientific visualization research. We will describe the system's design and the considerations made during the design process, and demonstrate the system by conducting three user evaluations, all of which have been published before in the visualization literature.

We present a study of mobility field and temperature dependence for C60 with Kinetic Monte-Carlo simulations. We propose a new scheme to take into account polarization effects in organic materials through atomic induced dipoles on nearby molecules. This leads to an energy correction for the single site energies and to an external reorganization happening after each hopping. The inclusion of polarization allows us to obtain a good agreement with experiments for both mobility field and temperature dependence.

In this paper, we introduce coverage-based opacity estimation to achieve Depth of Field (DoF) effects when visualizing molecular dynamics (MD) data. The proposed algorithm is a novel object-based approach which eliminates many of the shortcomings of state-of-the-art image-based DoF algorithms. Based on observations derived from a physically-correct reference renderer, coverage-based opacity estimation exploits semi-transparency to simulate the blur inherent to DoF effects. It achieves high quality DoF effects, by augmenting each atom with a semi-transparent shell, which has a radius proportional to the distance from the focal plane of the camera. Thus, each shell represents an additional coverage area whose opacity varies radially, based on our observations derived from the results of multi-sampling DoF algorithms. By using the proposed technique, it becomes possible to generate high quality visual results, comparable to those achieved through ground-truth multi-sampling algorithms. At the same time, we obtain a significant speedup which is essential for visualizing MD data as it enables interactive rendering. In this paper, we derive the underlying theory, introduce coverage-based opacity estimation and demonstrate how it can be applied to real world MD data in order to achieve DoF effects. We further analyze the achieved results with respect to performance as well as quality and show that they are comparable to images generated with modern distributed ray tracing engines.

Despite the increasing importance of multimodal volumetric data acquisition and the recent progress in advanced volume illumination, interactive multimodal volume illumination remains an open challenge. As a consequence, the perceptual benefits of advanced volume illumination algorithms cannot be exploited when visualizing multimodal data - a scenario where increased data complexity urges for improved spatial comprehension. The two main factors hindering the application of advanced volumetric illumination models to multimodal data sets are rendering complexity and memory consumption. Solving the volume rendering integral by considering multimodal illumination increases the sampling complexity. At the same time, the increased storage requirements of multimodal data sets forbid to exploit precomputation results, which are often facilitated by advanced volume illumination algorithms to reduce the amount of per-frame computations. In this paper, we propose an interactive volume rendering approach that supports advanced illumination when visualizing multimodal volumetric data sets. The presented approach has been developed with the goal to simplify and minimize per-sample operations, while at the same time reducing the memory requirements. We will show how to exploit illumination-importance metrics, to compress and transform multimodal data sets into an illumination-aware representation, which is accessed during rendering through a novel light-space-based volume rendering algorithm. Both, data transformation and rendering algorithm, are closely intervened by taking compression errors into account during rendering. We describe and analyze the presented approach in detail, and apply it to real-world multimodal data sets from biology, medicine, meteorology and engineering.