Paper Types

EuroVis solicits novel ideas in a broad range of visualization research topics and approaches, including such that address spatial data and related techniques (scientific visualization), as well as visualization of non-spatial data and related techniques (information visualization), and visual analytics. Authors are asked to assign their paper to one of five paper types: algorithm/technique, application/design study, evaluation, theory/model, and system.

We discuss these categories below as a guide to authors and reviewers. While papers can include elements of more than one of these categories, we ask authors to specify the most appropriate single choice in the submission process. Please see the paper Process and Pitfalls in Writing Information Visualization Research Papers by Tamara Munzner for a more detailed discussion on how to write a successful visualization paper that includes an extensive discussion on paper types. While these guidelines were written from the point of view of an InfoVis researcher, they apply more broadly to general visualization papers.


Algorithm/Technique papers introduce novel techniques or algorithms that have not previously appeared in the literature, or that significantly extend known techniques or algorithms, for example by scaling to datasets of much larger size than before or by generalizing a technique to a larger class of uses.

The technique or algorithm description provided in the paper should be complete enough that a competent graduate student in visualization could implement the work, and the authors should create a prototype implementation of the methods. Relevant previous work must be referenced, and the advantage of the new methods over it should be clearly demonstrated. There should be a discussion of the tasks and datasets for which this new method is appropriate, and its limitations. Evaluation through performance benchmarks, informal or formal user studies, or other methods will often serve to strengthen the paper.

Examples include: algorithms for isosurface extraction or rendering; data analysis techniques for visualization (such as transfer function design and interaction); topology and geometry based techniques for data exploration; algorithms for understanding of vector and tensor fields; interaction techniques for visualization; geometric or graphics algorithms for increased scalability of existing techniques; algorithms for layout and navigation of trees, graphs, and networks; browsing and navigation techniques in large information spaces; techniques for visualizing spaces of dozens or hundreds of dimensions. This list is not exhaustive, and we welcome submissions in these and all other areas of visualization and visual analytics.

Application/Design Study

Application/Design Study papers explore the choices made when applying visualization techniques in an application area to make the case that the proposed visual representation is the solution for a particular domain problem. The research contribution of a design study is not typically a new technique or a refinement of one, as in an Algorithm / Technique paper, but rather a well-reasoned justification of how existing techniques can be usefully combined. An implementation is expected, and deployment to target users strengthens the paper. A Design Study should include the requirements analysis for the particular problem, which may require a certain amount of background information about the domain. The visual encoding and interaction mechanisms should be clearly explained and justified in terms of how well they fulfill these requirements with respect to alternative possibilities. The validation of Design Study paper may include case studies documenting insights found by target users in the application domain, formal or informal user studies, or lessons learned by the designers.

Examples include application areas such as the oil and gas industry, medical and biomedical analysis, simulation and fluid flow, mathematical visualization, bioinformatics, databases, finance, and computer-supported cooperative work. We invite submissions on any application area.


Evaluation papers explore the usage of visualization by human users, and typically present an empirical study of visualization techniques or systems. We solicit a wide range of studies and methodology: laboratory and field, quantitative and qualitative, short term and long term. Authors are not necessarily expected to implement the systems used in these studies themselves; the research contribution will be judged on the validity and importance of the experimental results as opposed to the novelty of the systems or techniques under study. The conference committee appreciates the difficulty and importance of designing and performing rigorous experiments, including the definition of appropriate hypotheses, tasks, data sets, selection of subjects, measurement, validation and conclusions. We do suggest that potential authors who have not had formal training in the design of experiments involving human subjects may wish to partner with a colleague from an area such as psychology or human-computer interaction who has experience with designing rigorous experimental protocols and statistical analysis of the resulting data, or from anthropology or human-computer interaction who has experience with ethnographic analysis and qualitative observational methods.

Examples include evaluation metrics for image quality, empirical comparisons of user performance with different visual representations or visualization systems, field studies and usability analyzes of visualization designs, and the identification of and testing of new evaluation metrics and methods.


Theory/Model papers present new interpretations of the foundational theory of visualization. Implementations are usually not relevant for papers in this category. Papers should focus on basic advancement in our understanding of how visualization techniques complement and exploit properties of human vision and cognition.

Examples include the treatment of the rendering integral, visualization taxonomies, task taxonomies, cognitive models, color models, extensions to Bertin's theories of visual encoding, and models to measure the value of visualizations.


System papers focus on the architectural choices made in the design of a visualization infrastructure, framework, or toolkit. A systems paper typically does not introduce new techniques or algorithms in the way that an Algorithm /Technique paper does. It also does not introduce a new design for an application that solves a specific problem, which would be better suited for the Design Study type. An implementation of the system is expected. The paper should include the rationale for significant design decisions, and the lessons learned from building the system and from observing its use. The system should be compared to documented, best-of-breed systems already in use, with specific discussion of how the described system differs from and is, in some significant respects, superior to those systems. For example, the described system may offer substantial advancements in the performance or usability of visualization systems, or novel capabilities. Every effort should be made to compare fairly given external factors such as advances in processor performance, memory sizes, or operating system features. For further suggestions, please review How (and How Not) to Write a Good Systems Paper by Roy Levin and David Redell and Empirical Methods in CS and AI by Toby Walsh.

Examples include visualization toolkit, library, and framework designs.