SciVis 2017 (IEEE Scientific Visualization) is an academic conference held in Phoenix, Arizona, USA on 2017-10-01. The paper submission deadline is 2017-03-21. Acceptance notifications are sent on 2017-06-06.
The IEEE Scientific Visualization (SciVis) Conference solicits novel research ideas and innovative applications in all areas of scientific visualization. The scope of the conference, co-located at VIS with the annual IEEE Visual Analytics and IEEE Information Visualization Conferences, includes both fundamental research contributions within scientific visualization, as well as advances towards understanding or solving real world problems, or that impact a particular application in a significant way.
Please note that topics focused on visual analytics, e.g., computational solutions facilitated by visual interfaces to support analysis, might be a better match for the IEEE VAST Conference at IEEE VIS. Similarly, topics which clearly focus on information visualization, e.g., graphical representation of abstract data to aid cognition, might be a better match for the IEEE InfoVis Conference, also at IEEE VIS. Papers chairs reserve the right to move papers between conferences based on its topic and perceived fit.
Topics
Research contributions are welcomed across a range of topics including, but not limited to:
Visualization, rendering, and manipulation of spatial data
Scalar, vector and tensor fields
Multidimensional multi-field, multi-modal, and multivariate data,
Time-varying data
Regular and unstructured grids
Point-based data
Volumetric data
Streaming data
Multi-resolution
Compression.
Visual computing, systems and methodologies
System and toolkit design
Topology-based and geometry-based techniques
Feature extraction and pattern analysis
Uncertainty visualization
View-dependent visualization
PDEs
Glyph-based techniques
Texture based techniques
Illustrative visualization
Integrating spatial and non-spatial data visualization
Applications of visual analytics approaches
Computational steering.
Interaction techniques and devices
User interfaces
Interaction design
Coordinated and multiple views
Data editing for validation
Manipulation and deformation
Multimodal input devices
Haptics for visualization
Mobile and ubiquitous visualization
Visual interaction for data science
Interaction with visualizations in different display environments.
Data Science
Large-scale computing
Storage and data analytics
Distributed, cluster, and grid computing
Scalable data management on and off the cloud
High-performance computing on multi-core, GPUs, FPGA, and embedded devices
Information extraction and knowledge discovery from big data
Petascale visualization
Application of computer vision techniques
Statistical modeling
Data mining, machine learning
Clustering techniques
Reduced-order modeling
Visual steering for data retrieval.
Display techniques
Large and high-res displays
Giga-pixel displays
Wrist displays/wearable displays
Stereo displays
Immersive and virtual environments
Mixed and augmented visualization
Projector-camera systems
Perception and cognition coupled displays
Small displays
Mobile Devices
Foundations
Collaborative and distributed visualization
Visual design and design studies
Mathematical theories for visualization
Scalability issues
Visualization verification
Information theoretic approaches
Perception theory
Color
Texture
Scene and motion perception
Knowledge-assisted visualization
Evaluation
Usability studies and task analysis
Design and user studies
Validation and verification visualization
Statistical techniques
Crowdsourcing
Human computation
Visual computing applications
Mathematics, physical sciences and engineering
Earth, space, and environmental sciences
Flow fields
Terrain visualization
Geographic/geospatial visualization
Molecular, biomedical and medical visualization
Bioinformatics visualization
Software visualization
Business and finance visualization
Social and information sciences
Education
Humanities
For the masses
Multimedia (image/video/music)
Visual computing for emerging applications
Nano-assembly
Live cell imaging
Imaging genetics
Micro-biology
Robotics
Sensor networks
Cybersecurity
Urban science
Computational architecture
Paper Types
Paper Type: Technique
A technique paper describes a new or significantly improved algorithm or technique in sufficient detail so that other researchers can reproduce the results. This technique should ideally be of general application rather than being restricted to a single task or single source of data, and the exposition should be focused on what the technique does, how it does it, when to use it, and what the computational and other costs are.
Paper Type: System
A system paper describes a solution to a problem where the major task is building a large complex software artifact, applying largely known visualization techniques. Here, the focus should be on the design decisions, the implications for software / hardware structure, and comparison with other systems.
Paper Type: Application
An application paper normally starts with an encapsulated description of a problem domain and the questions to be resolved by visualization, then describes the application of visualization to the task, any novel techniques developed, and how the visualization solution answered the questions posed. Techniques related to a single problem are normally application papers, and evaluation is often limited because many application papers are essentially custom software for a specific problem.
Paper Type: Evaluation
An evaluation paper is usually an empirical assessment of how effective a technique or system is when used by humans. As such, these often involve rigorous experimental protocols and statistical analysis, but this is not the only possible form of evaluation. Good evaluation papers go beyond statistical analysis to explain causes, construct models and predict effectiveness of related systems.
Paper Type: Theory
A theory paper describes aspects of the process by which humans construct visualizations to explore data or communicate with other humans. These papers do not usually involve implementation, but contribute by illuminating the role of visualization in data analysis and often by proposing models for improving visualization as a discipline.