VIS Paper Submission Keywords

At VIS 2019, the VEC and the V-I-S Steering Committees adopted a proposal by the reVISe committee to replace the current PCS keywords with a new set of keywords to improve the reviewing process. The motivation for this change, as well as a preliminary brief overview of the new keywords can be found in the reVISe Amended Proposal.



  • Starting in 2020 IEEE VIS will use a new set of keywords in the submission process
  • Keywords are meant to support matching of papers to reviewers
  • Authors should use keywords differently than in previous years:
    • Authors should check keywords to indicate expertise required to review their submitted paper.
    • They should NOT check all keywords necessary to describe the content of their submitted paper.
  • Reviewers use the keywords to rate their expertise, as in the years before.

Keyword Use

Keywords in the paper submission and review system (PCS) are meant to help match papers to the most appropriate reviewers. Without going into the technical detail, there is an algorithm that tries to suggest reviewers for papers based on the expertise reviewers provided for keywords and based on the keywords selected for each paper. Below we give advice on how keywords should be used by authors and reviewers to ensure the best matches.

For authors

When submitting a paper in the submission system, the corresponding author will have to select keywords to describe the expertise required to review their paper. Note that this is a change from the keyword usage in previous years where authors were asked to describe the content of their paper. Instead now, authors should imagine that the list of keywords is preceded by the following phrase:

“A reviewer judging my work should have expertise related to…”

Authors can choose any number of keywords to define required expertise. Keywords can also be combined to describe a specific required expertise (see examples below).

Authors can also provide additional keywords in using the available text fields. These additional keywords cannot be taken into account to recommend a paper automatically to potential reviewers with the selected expertise but they might be used during manual assignments and will be used to collect data for future iterations on the keyword set.

For junior authors we strongly suggest consulting with their advisor about the most appropriate keywords to choose.

For reviewers

A reviewer should imagine preceding each keyword by the phrase:

“I have the following level of expertise related to…”

and then for each keyword select an expertise rating among four options:

  • None: I have not published on or read enough papers related to the keyword to feel comfortable giving advice as a reviewer.
  • Limited: I have limited experience and knowledge related to the keyword. I can give some advice as a reviewer but not about specific details.
  • Competent: I feel competent to give input as a reviewer on at least some topics covered by this keyword. I understand and can discuss topics related to this keyword.
  • Expert: I am an expert in topics covered by this keyword. I can provide guidance, troubleshoot and answer questions related to this keyword or contexts where it is used.

Reviewers can also specify additional expertise keywords in provided text fields. These additional keywords cannot be taken into account by the paper-matching algorithm and, therefore, will not be used to automatically recommend papers to a reviewer but paper chairs and PC members might take them into account for manual assignments from papers to reviewers and they will be used to collect data on future iterations of the keyword set.


Following is the complete list of keywords with a description for each keyword:

Data Types and Their Use in Visualization and Visual Analytics

  • Geospatial Data (Geospatial): data with geospatial (lat/lon) locations or trajectories
  • Graph/Network and Tree Data (Network): data with network (node/link) or tree/hierarchy structure
  • High-dimensional Data (HD): data with a large number of dimension columns (features/attributes) that requires extra effort to process
  • Data Models (Models): the structure of statistical and simulation models, model results and outputs, and the parameter spaces of model inputs as for example in machine learning
  • Scalar Field Data (Scalar): spatial/volume data with one or more scalar variables
  • Image and Video Data (ImageVideo): imagery data in the form of stills or video
  • Tabular Data (Tabular): tables of row/column data with a moderate number of columns that are directly represented
  • Temporal Data (Time): data that has a temporal component (e.g. time series, time-oriented data, events, time-varying data, trajectories over time…)
  • Text/Document Data (Text): data in the form of text or documents
  • Vector and Tensor Field Data (Vector_Tensor): spatial data containing vector and tensor fields
  • Other Data (OtherData): a data type that does not reasonably fit into any other category
  • DataType Agnostic (NAData): no special expertise on data types is required for my paper

Intended Contributions to Visualization and Visual Analytics

General Contributions

  • Algorithms (Algorithm): the design or implementation of data analysis/visualization algorithms
  • Data Abstractions and Types (DataAbstr): the process of reducing a particular body of data to a simplified representation and/or improvements or new uses of datasets/-types
  • Datasets (Datasets): contributing new datasets for benchmarking or understanding techniques / the field itself
  • Deployment (Deployment): deployment of tools/techniques “in the wild”
  • Methodologies (Methodology): methodologies for visualization incl. design, evaluation, processes, collaboration, …
  • Application Motivated Visualization (Application): applying, adapting, or creating novel visualization techniques to address specific challenges presented by real-world applications; incl. design studies
  • Guidelines (Guidelines): deriving or applying guidelines for design and use of visualization & visual analytics techniques
  • Interaction Design (Interaction): the design of interaction techniques and/or interaction design methodologies and practices for any interaction modalities (touch, pen, mouse, speech, proxemics, …)
  • Process/Workflow Design (Workflow): designing, developing, evaluating, or improving data analysis workflows
  • Software Architecture, Toolkit/Library, Language (System): designing/implementing novel platforms/libraries/toolkits for developing or testing
  • Software Prototype (Software): writing or analyzing concrete implementations of tools / systems / applications
  • State-of-the-art Survey (STAR): conducting, structuring, and writing systematic literature reviews
  • Task Abstractions & Application Domains (Domain_Task): the practice of eliciting domain or task abstractions and challenges from specific application domains
  • Taxonomy, Models, Frameworks, Theory (Theory): deriving systematic characterizations of a particular space (e.g. design space, taxonomy of techniques), novel abstractions of concepts, discussions of formalisms
  • Visual Representation Design (VisDesign): designing data visualization / visual representations and/or practices/processes of visualization design
  • Other Contribution (OtherContrib): a contribution type that does not reasonabily fit in any other category

Evaluation Contributions

  • Computational Benchmark Studies (CompBenchmark): design/conducting/analysis of computational benchmark studies that for example compare performance results from running implemented techniques/algorithms
  • Human-Subjects Qualitative Studies (HumanQual): design/conducting/analysis of qualitative empirical studies involving human participants
  • Human-Subjects Quantitative Studies (HumanQuant): design/conducting/analysis of quantitative empirical studies involving human participants

Application Areas for Visualization and Visual Analytics

  • Computing: Software, Networks, Security, Performance Engr., Distr. Systems, Databases (CompSystems): applications to the general computing domain incl. software, networks, security, databases, visualization (Vis4Vis) etc.
  • Life Sciences, Health, Medicine, Biology, Bioinformatics, Genomics (LifeBio): applications to the life sciences: incl. medicine, biology, bioinformatics, genomics, health informatics, or others
  • Machine Learning, Statistics, Modelling, and Simulation Applications (MLStatsModel): applications to machine learning, statistics, modelling, or simulation applications (note: find ML for VIS under “Techniques” below)
  • Physical & Environmental Sciences, Engineering, Mathematics (ScienceEngr): applications to physical & environmental sciences, engineering, or mathematics
  • Social Science, Education, Humanities, Journalism, Intelligence Analysis, Knowledge Work (SocHum): applications to the social sciences, education, and humanities incl. knowledge work such as intelligence analysis
  • Other Application Areas (OtherApp): applications to an application area that does not reasonably fit in any other category
  • Domain Agnostic (NAApp): no special expertise on application areas is required for my paper

Human Factors

  • Collaboration (Collab): collaborative data analysis, collaborative workflows, and theories of collaboration
  • Color (Color): the use of color in visualization
  • Communication/Presentation, Storytelling (Storytelling): using visualization to communicate or present a narrative or story from data
  • Data Analysis, Reasoning, Problem Solving, and Decision Making (AnalyzeDecide): support of analytical reasoning, problem solving, decision-making, analysis workflows, and other related cognitive processes
  • General Public (GenPublic): the design and dissemination of tools for/to the general public / communication to the general public or mass audiences
  • Mixed Initiative Human-Machine Analysis (MixedInit): balancing computational and human effort for data analysis
  • Perception & Cognition (Perception): perception and cognition
  • Personal Visualization, Personal Visual Analytics (PersonalVis): design of interactive visual representations for use in a personal context; analytical reasoning by visual representations for use in a personal context

Stats & Math, Machine Learning, Data Management Methods & Algorithms

  • Data Clustering and Aggregation (ClusterAgg): algorithmic and visualization approaches for aggregating or clustering data
  • Data Management, Processing, Wrangling (DataMgmt): steps for cleaning, processing, and managing data
  • Dimensionality Reduction (DimRed): the use of / techniques for reducing the number of variables under consideration
  • Feature Detection, Extraction, Tracking & Transformation (Features): methods for finding, detecting, mining, extracting, retrieving, transforming, discovering and tracking data, features, patterns, knowledge
  • Large-Scale Data Techniques (BigData): techniques specific to handling large amounts of data
  • Machine Learning Techniques (ML): the use of machine learning in visualization / visual analytics
  • Mathematical Foundations & Numerical Methods (Math): mathematical foundations and numerical methods and their use

Spatial Field Methods & Algorithms

  • Computational Topology-based Techniques (CompTop): computational topology and/or topological abstractions and their use
  • Isosurface Techniques (Isosurfaces): extraction and use of isosurfaces and generalizations
  • Vector, Tensor & Flow Visualization (Flow): techniques for vector fields, tensor flow, tractography, and fluid mechanics
  • Volume Rendering (Volumes): rendering techniques and algorithms for direct visualization of volumetric data

General Visualization Methods

  • Animation and Motion-related Techniques (Motion): methods using animation or other forms for the display of motion
  • Art & Graphic Design (Art): data art, art practice, art-science collaboration, graphic design practice, …
  • Cartography, Maps (Maps): design and use of maps and mapping technology
  • Charts, Diagrams, and Plots (Charts): statistical graphics such as charts, diagrams, or plots (line/bar charts, etc.)
  • Comparison and Similarity (Comparison): methods for visual comparison or determining similarity
  • Computer Graphics Techniques (Graphics): techniques from the field of computer graphics, including raycasting, illustrative / non-photorealistic rendering, etc.
  • Coordinated and Multiple Views (MultiView): linked views, multiple views, coordinated views, coordinated multiple views, or coupled views
  • Image and Signal Processing (ImageProcessing): image and signal processing methods
  • Mobile, AR/VR/Immersive, Specialized Input/Display Hardware (Displays): specialized interaction and display techniques and hardware (mobile, caves, heads-up displays, physicalization, tangibles,… and combinations of devices)
  • Multi-Resolution and Level of Detail Techniques (MultiRes): visualization techniques for showing multiple levels of detail and resolution, including focus+context
  • Specific Computing and Rendering Hardware (Hardware): how to use specific computing or rendering hardware for visualization (CPU/GPU clusters, etc)
  • Uncertainty Visualization (Uncertainty): visually communicating uncertainty (of data, models, algorithmic results, or the visualization process)
  • OtherTopic: a visualization/visual analytics related topic/technique that does not reasonably fit in any category above

Example Papers

The following example papers show how previously published VAST, InfoVis, and SciVis papers can be described using the keywords above.

Conf. Title Citations Paper Type Keyword 1 Keyword 2 Keyword 3 Keyword 4
InfoVis D³ Data-Driven Documents 922 System Software Systems Charts  
InfoVis UpSet: Visualization of Intersecting Sets 328 Technique VisDesign OtherData: Sets Tabular  
InfoVis Narrative Visualization: Telling Stories with Data 252 Theory Storytelling Theory    
SciVis A Systematic Review on the Practice of Evaluating Visualization 101 Empirical Studies HumanQuant HumanQual STAR  
SciVis Fixed-Rate Compressed Floating-Point Arrays 95 Technique Algorithm Systems CompBenchmark DataMgmt
SciVis Contour Boxplots: A Method for Characterizing Uncertainty in Feature Sets from Simulation Ensembles 86 Technique Scalar DataAbstr Isosurfaces Uncertainty
VAST Visual Exploration of Big Spatio-Temporal Urban Data: A Study of New York City Taxi Trips 220 Application / Design Study Geospatial Time Application  
VAST SensePlace2: GeoTwitter analytics support for situational awareness 141 Application / Design Study Text Geospatial SocHum Application
VAST Enterprise Data Analysis and Visualization: An Interview Study 105 Empirical Study HumanQual SocHum