Conference Information

EuroVis 2027: The Eurographics Conference on Visualization

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Submission Date:
2026-11-23 Due in 12 days
Notification Date:
2027-03-17
Conference Date:
2027-05-31
Location:
Stuttgart, Germany
Years:
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CCF: B   ICORE: B   QUALIS: B1   Viewed: 54141   Tracked: 26   Attend: 3
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Conference Partner Index (CP-I)

79.3 / 100
Ranked #214 of 5,771 conferences · Top 4%

#11 of 151 in Computer Graphics & Multimedia

Academic recognition (35%)
82
Submission selectivity (20%)
71
Editions held (20%)
92
Community attention (10%)
52
Public record completeness (15%)
85

Inputs used: Listed as CCF B, ICORE B, QUALIS B1 · Acceptance rate: 35.7% (mean of 5 editions on file) · Editions on record: 29 · Researchers following it here: 26 · Researchers who opened this page in the past 24 months: 14

Missing from the public record: Best-paper records (+2.3)
Organizers can add these from this page after claiming the conference; scores are recomputed nightly. How to raise this score

Confidence 100% - the share of the score backed by observed data rather than the neutral baseline. How this score is calculated · Browse the ranking · Algorithm version 1.2 · Computed 2026-11-11

Call For Papers

EuroVis 2027 (The Eurographics Conference on Visualization) is a CCF B / ICORE B / QUALIS B1 conference held in Stuttgart, Germany on 2027-05-31. The paper submission deadline is 2026-11-23. Acceptance notifications are sent on 2027-03-17.

Topics We encourage submissions from all areas of visualization and visual analytics. Suggested paper types and topics include, but are not limited to: Algorithm / Technique: novel algorithms and workflows, visual encoding methods, and/or interaction techniques for data transformation, analysis, exploration, explanation, or communication. Algorithms and techniques for handling any data types are welcome, including high-dimensional, multivariate, and multimodal data; time-series and streaming data; spatial and geographic data; hierarchical and network data; uncertainty data; text, image, audio, and video data; simulation data; as well as artificial intelligence or machine learning data and models. Techniques may be specialized for specific devices or form-factors (e.g., mobile or wall-scale visualization) and tailored to diverse contexts of use, including collaborative work, immersive and interactive settings, and domain-specific applications. Application / Design Study: novel use of visualization to address problems in an application domain, including accounts of innovative system design, deployment and impact. We welcome diverse application areas, including the physical, environmental, and earth sciences; health care and life sciences; social sciences; engineering and industry; security and safety; government and public affairs; education, as well as arts, sports, and humanities. We also welcome applications of visualization for enhancing human understanding of ML and AI models in various domains. Systems: new software frameworks, languages, or tools for visualization; systems for large-scale visualization; integrated graphical systems for visual analysis or interactive machine learning; collaborative and web-scale visualization systems. Evaluation & Empirical Research: Empirical studies for comparative evaluation of different visualization approaches (e.g., designs, techniques, tools, systems, and workflows); for observing phenomena in visualization processes, stimulating hypothesis formulation, and collecting data to inform computational models and quality metrics; and for understanding the human factors in visualization processes, including perceptual factors (e.g., visual and nonvisual sensory processes, perception, attention, etc.) and cognitive factors (e.g., memory, learning, reasoning, decision-making, problem-solving, knowledge, emotion, etc.). A diverse range of study methods is welcome, including controlled (e.g., typical laboratory experiments), semi-controlled (e.g., typical crowdsourcing studies), and uncontrolled studies (e.g., small group discussions, think-aloud exercises, field observation, ethnographic studies, etc.), which may be in the form of qualitative or quantitative, cross-sectional or longitudinal research. Theory: mathematical theories and conceptual models of visual encoding, interaction, and/or analysis tasks; implications from theories of perception, cognition, design, and/or aesthetics; methods for automated design or visualization recommendation; and conceptual or design frameworks for integrating visualization with machine learning and artificial intelligence.
Last updated by Admin Agent on

Acceptance Ratio

Average acceptance rate: 40.8% over 11 years (1999–2009).

YearSubmittedAcceptedAccepted(%)
20091434128.7%
20081434531.5%
2007933537.6%
2006984343.9%
20051013736.6%
2004823947.6%
2003623048.4%
2002803138.8%
2001683348.5%
2000662740.9%
1999643046.9%

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