Sense making is one of the biggest challenges in data analysis faced by both the industry and the research community. It involves understanding the data and uncovering its model, generating a hypothesis, selecting analysis methods, creating novel solutions, designing evaluation, and also critical thinking and learning wherever needed. The research and development for such sense making tasks lags far behind the fast-changing user needs. As a result, sense making is often performed manually and the limited human cognition capability becomes the bottleneck of sense making in data analysis and decision making.

A recent advance in sense making research is the capture, visualization, and analysis of provenance information. Provenance is the history and context of sense making, including the data/analysis used and the users’ critical thinking process. It has been shown that provenance can effectively support many sense making tasks. For instance, provenance can provide an overview of what has been examined and reveal gaps such as unexplored information or solution possibilities. Besides, provenance can support collaborative sense making and communication by sharing the rich context of the sense making process.

Besides data analysis and decision making, provenance has been studied in many other fields, sometimes under different names, for different types of sense making. For example, the Human-Computer Interaction community relies on the analysis of logging to understand user behaviors and intentions; the WWW and database community has been working on data lineage to understand uncertainty and trustworthiness; and finally, reproducible science heavily relies on provenance to improve the reliability and efficiency of scientific research.

For this special issue, we are soliciting papers that describe innovative research, design, system/tools, and viewpoints regarding the collection, analysis, and summarization of provenance information to support the design and evaluation of novel techniques for sense making across different application domains:

Use cases of provenance and logging information, such as:
- Supporting sense making;
- Understanding user sense making activities and/or evaluation of sense making tools;
- Supporting collaborative sense making;
- Providing sense making transparency and reproducibility

Research related to the challenges in capturing the required provenance information, such as:
- The complex provenance information required for different use cases;
- Automatic capture of high-level provenance such as human thinking and reasoning;
- Software architecture for provenance capture for both new and existing systems.

Research related to the analysis and visualization of provenance data, such as:
- Visualization and summarization of provenance information;
- Machine learning and Nature Language Processing techniques that can help analysis of provenance data.

Special Issue on Generative AI for Computer Graphics
截稿日期: 2024-09-05

We invite submissions for a special issue on Generative AI for Computer Graphics, aimed at exploring the cutting-edge advancements at the intersection of artificial intelligence and computer graphics. Generative AI techniques have revolutionized the field, enabling unprecedented creativity and realism in generating images, animations, and 3D models. This special issue seeks to showcase the latest research, methodologies, and applications in this rapidly evolving domain.

We welcome original research contributions addressing various aspects of Generative AI for Computer Graphics, including but not limited to image synthesis, style transfer, 3D shape generation, texture synthesis, animation generation, and procedural content creation. Submissions employing novel neural network architectures, advanced optimization techniques, and innovative applications of generative models in computer graphics are particularly encouraged.

Topics are as below but are not limited to the following areas:
- Generative techniques for realistic texture and material generation
- Style transfer techniques for artistic rendering and image manipulation
- Neural rendering methods for synthesizing photorealistic images and animations
- Procedural generation of landscapes, environments, and virtual worlds
- Interactive generative systems for creative design and content creation
- Applications of generative AI in spatial computing (AR/VR) experiences
- Differentiable rendering and its applications
- Control, editability and explainability of Generative AI methods in related applications
- Physics informed techniques to simulate hair, cloth, fluids, and other phenomenon
- AI assisted sampling, denoising, path-guiding for rendering
- Use of AI for deformation and animation
- Character control and facial animation
- Digital twins, Avatars and other synthetic data generation techniques
- Applications of generative AI to cinematography, layout, image and sequence editing
- Ethical considerations and implications of generative AI in computer graphics 

Special Issue on Critical Data Visualization
截稿日期: 2024-10-31

Critical data visualization generally refers to the practice of examining and representing data with an awareness of the cultural, social, and ethical implications. These methods support consideration of the power structures embedded into visualization designs, inspection of the politics latent to research methods, and reflection on visualization content and context more broadly. Coming to prominence in information visualization from Dörk et al. (2013), the term critical visualization originally described principles for authoring visualizations that expose embedded values and support empowerment for readers. This field has since grown to encompass a range of topics—for instance, integrating considerations of how data and graphical practices might embrace feminist methods. Works in this area strive to challenge existing visualization dogmas and identify overlooked assumptions as a way to push conventional visualization practice toward more inclusive and reflective practices.

Despite growing usage, however, there is a lack of a shared definition of what ‘criticality’ refers to for visualization and how those ideas can be applied in a rigorous and useful manner. While this plural understanding of criticality has afforded a vast array of creative applications, it has precluded critical methods and goals from being broadly understood or adopted. Through this special issue, we aim to both complicate and coalesce the visualization research community’s understanding and definition of critical visualizations, as well as the challenges associated with it. For instance, answering questions like: How to design ‘critical’ visualizations? What are the goals of critical visualization? Or even, what is critical visualization? To address these concerns we invite essays, explorations, and empirical work on topics including:
- Examinations of how critical theory intersects with data visualization (theoretical or empirical)
- Critical methods that have led to new visualization methods, theories, or designs
- Investigations of the relationship between criticality and feminism in visualization
- Reflections on the ethical implications of visualization research
- Challenges to appropriateness of critical theory for visualization research
- Case studies of critical methods applied to visualization (for example, speculative design/auto-ethnography/diffraction)
- New means of critically evaluating visualizations
- Grand challenges for critical visualization. 

Special Issue on Inclusive Data Experiences
截稿日期: 2024-12-30

While the visualization community has made great strides over the last few decades, visualization research has traditionally centered on certain user groups, notably individuals without disabilities, overlooking the diverse capabilities and needs of a broader range of people. The disparities in sensory, cognitive, and motor abilities lead to unequal access to data visualization and the underlying data.

Data has become an integral part of our lives and holds immense potential to influence how we work, live, and engage with the world. Data visualization has proven a powerful tool for gaining and communicating insights, as well as supporting informed decision-making across various domains and contexts. The inequitable access to data and data visualization significantly affects education and employment, as well as health and lifestyle. Recognizing the urgent need to address this significant equity issue, research communities have put increasing efforts towards making data visualization accessible to people with disabilities. We aspire not only to amplify this trend but also to more broadly consider inclusive data experiences. This goes beyond data visualization to understand broader interactions between data and people so that everyone can equitably access and benefit from data.

This special issue aims to foster novel insights and advancements for inclusive data experiences. We invite submissions that examine various needs and challenges, introduce innovative approaches and solutions, present robust methodologies and evaluations, and offer visions and viewpoints, among other things.

Topics of interest include, but not limited to:
- Accessible visualization authoring tools and production methods
- Novel devices for accessible data interaction
- Technologies for inclusive data experiences
- Data physicalization for inclusive data experiences
- Challenges and opportunities of AI for inclusive data experiences
- Accessible multimodal data representations
- Evidence-based guidelines for inclusive data representations
- Data and visualization education for inclusive data representations
- Real-world data needs of people with disabilities and marginalized users
- Accessible personal informatics for people with disabilities and marginalized