by florian.prasse -

Impulse #3: Nadieh Bremer, WebExpo 2025

This blogpost will be a reflection inspired by Nadieh Bremers’ WebExpo 2025 talk Creating an effective & beautiful data visualisation from scratch with d3.js. Bremer demonstrates how visual interfaces can be designed to convey information clearly and emotionally. She outlines a design process that begins with understanding the data’s story and ends with polishing details such as visual hierarchy, color, and interaction. Her approach emphasizes that visuals should not only communicate facts but also evoke engagement and a sense of discovery. I rewatched the digital documentation of her talk to recap the content of her presentation.

Bremer presents visualization as a communication medium, where design choices directly impact user comprehension and emotional experience. Clarity reduces frustration, while appealing design increases motivation to explore. This perspective positions data visualization as a critical component of user experience, not merely a decorative or aesthetic layer.

Learning about new technologies for data visualization

When I encountered Nadieh Bremers work, I was already familiar with data visualization, but mostly through print media and a little experience with Processing. Designing layouts for magazines or static posters taught me how much data visuals can influence perception and guide a narrative. Around that time we went to WebExpo, I got into JS coding but wasn’t aware of the posibilities to use it for data visualization. Her projects demonstrated what I had been missing in print -> interactivity and adaptivity.

Why adaptive data visualization matters for a good user experience

During my deeper dive into adaptive data visualization literature, I explored a research paper focusing on real-time decision support in complex systems. It argues that static dashboards are no longer enough to support organizations facing rapidly changing data environments. Instead, visualizations must adapt to:

  • Incoming data streams
  • User interactions
  • Context shifts
  • Multivariate complexity

Adaptive systems combine machine learning, real-time processing, and flexible visualization layers to support faster and more informed decision-making. This means that the visualization is not just displaying data, it is interpreting and reacting to it. The paper specifically highlights D3.js as one of the technologies capable of creating these highly flexible and dynamic interfaces. Unlike pre-built dashboards, D3 allows developers to adapt interactions, transitions, and representations directly to user needs and situational changes.

In my earlier blog posts I wrote about affective computing. Combining the gained knowled I came to a conclusion: If a system can visually adapt based not only on the dataset, but also on the emotional state of the user, could generate a better user experience?

Sources:

https://slideslive.com/39043157/creating-an-effective-beautiful-data-visualisation-from-scratch

https://www.researchgate.net/publication/387471439_ADAPTIVE_DATA_VISUALIZATION_TECHNIQUES_FOR_REAL-TIME_DECISION_SUPPORT_IN_COMPLEX_SYSTEMS

by florian.prasse -

WebExpo Talk #1: Nadieh Bremer

Creating an effective & beautiful data visualisation from scratch

The field trip to Prague is over, and I’ve been thinking about the really interesting talk by Nadieh Bremer. Nadieh is a freelance data visualization designer from the Netherlands, and her work focuses on turning raw data into interactive and static visual art. It was fascinating to see how she approaches data, especially since my interest in data visualization started a few years ago during my bachelor’s in graphic and information design. This talk made me think in new ways about the potential of visualizing data, and I’m excited to dive deeper into it.

One of the things that stood out to me the most during the talk was how Nadieh works with D3.js, a JavaScript library for creating (interactive) data visualizations. I was amazed by how quickly she could take raw data—just numbers—and turn them into beautiful, meaningful visualizations. She made it look so easy, and the fact that she could transform the data into something visually stunning in such a short amount of time really caught my attention. I had heard about D3.js before and had been meaning to check it out, but like most people, I never had the time. So, this talk came at the perfect moment for me, and it made me realize just how powerful and useful this tool is for working with data.

As someone who has mainly worked with data in print media, I’ve always focused on static visualizations. Most of the techniques I’ve learned are for creating things like printed charts, posters, or other fixed formats. But seeing how Nadieh used D3.js to create interactive, dynamic visualizations opened up a whole new world for me. The idea that data can be more than just something to look at on paper—that it can be experienced and interacted with—was something I hadn’t fully considered before. With D3.js, the data is not just displayed; it’s alive and engaging. You can hover over elements to get more information, zoom in to explore trends, and see the data change in real-time. This is something you simply can’t do with traditional print media, and I’m excited to explore how I could bring this kind of interactivity to my own work.

What I also found really interesting was how data can be art. Nadieh’s visualizations weren’t just about presenting data clearly; they were also about making the data visually appealing and impactful. She showed that data visualization doesn’t have to be cold or purely functional—it can be something beautiful. This idea was a bit of an eye-opener for me, as I’d always thought of data as something to be communicated in a straightforward, no-frills way. But seeing her work made me realize that data can be both informative and artistic, and it’s something I want to try in my own designs.

The talk really showed me the potential of D3.js and how it can take data visualization to a whole new level. It’s not just about making a chart or graph anymore. It’s about telling a story through data, using color, motion, and interactivity to make the information more engaging and easier to understand. This is something that I think would take much longer to achieve using traditional print techniques, and it’s a huge opportunity for people like me who are interested in graphic design and information design.

Overall, I’m really glad I got to experience Nadieh’s talk. It made me realize just how much more there is to data visualization and how powerful tools like D3.js can be for creating engaging, interactive, and even artistic visualizations. I’m excited to start experimenting with D3.js myself and see where it takes me. I’ve learned that data doesn’t have to be static and technical—it can be creative and expressive, even be used in an artistic sense. And that’s a new perspective I learned and will keep in mind as I continue to work with data.

by victoria.bremer -

WebExpo Conference Talk #1 – Data Visualization

As someone who is very interested in visual design, data visualization and interdisciplinary topics, mixing design and science or values and aesthetics, I was really curious about Nadieh Bremers talk „Creating an Effective & Beautiful Data Visualisation from Scratch”. I wasn’t sure what to expect, since I have found that „beautiful data visualization“ often just means clear and structured, but I was more that positively surprised to see how much artistic creativity she was able to incorporate into her visualizations while still maintaining the data to communicate. What I was also surprised by and really broadened my view on the topic was her approach and angle to how she creates her visualizations. I had never heard of the tool she uses (coding it in D3.js) and thought it was so cool to create truly interactive pieces with the actual data in the background instead of using visual tools like Illustrator, which I was more used to when it comes to creatively visualizing data.

What I also thought was a great starting point was her emphasis on storytelling through data. Rather than beginning with tools or templates, she encouraged designers to start with the narrative: what is the data trying to say? This approach really aligns with interaction design principles, where the goal is not just functionality but clarity, emotion, and user connection. Sketching ideas before coding is sort of like prototyping in UX or any other visually creative field, reminding us that visual thinking is critical to problem solving. I really enjoyed that she considered aesthetic and emotional engagement. I feel like many visualizations aim for neutrality or objectivity, but in her case the work also aims to be expressive, and fun. She challenged the idea that beauty is just decoration. Instead, she argued that beauty and clarity are not mutually exclusive, and that well-designed visuals can help users stay curious, linger longer, and feel more connected to the data. This view aligns with interaction design’s attention to emotional and engaging user experiences and human centered design.

As mentioned her use of D3.js was also very interesting for me. By building a data visualization from scratch in a live coding session, she nicely demonstrated what a workflow can look like, which I found really helpful. What made this talk especially valuable was watching her iterative process. Trying something to see what happens, then continuing from there, changing things along the way and making mistakes. Her process reminded me of the iterative prototyping cycles in interaction design: test, tweak, refine. Even a small change in data structure or layout can significantly shift the meaning of a visualization. It was a really eyeopening creative process and a reminder that you don’t need a perfect or exact vision to start and then go through with, but rather develop an idea of what works along the way. This process also showed me how D3 (and coding in general) can empower designers to go beyond their visual tools and create more immersive and interactive experiences while still maintaining the aesthetics.

by desiree.steigerwald -

The Power of Data Visualization: Turning Numbers into Insight

Thinking of my project and the first meeting, the main and big problem is how data must be visualized to help people understand it. It seems so simple but most of the data gets lost in the dark because people don’t know what to do with them. Data visualization is another form of visual art that grabs our interest and keeps our eyes on the message. It’s storytelling with a purpose.

Data visualization is the graphical representation of information and data. It uses visual elements like charts, graphs, and maps to provide an accessible way to see and understand trends, outliers, and patterns in data. By presenting data visually, it transforms complex datasets into a more digestible and meaningful format, allowing stakeholders to grasp key insights quickly. 

It’s important to visualize data accurately when you’re doing market research. This is because you can visualize both numerical and categorical data, which makes the insights more impactful and reduces the risk of analysis paralysis.

What is the Goal of Data Visualization? 

The primary goal of data visualization is to communicate data clearly and efficiently. It aims to make complex data more understandable, uncover hidden insights, and facilitate better decision-making. Visualization bridges the gap between raw data and actionable intelligence, helping users to process information faster and make data-driven decisions. 

Why is Data Visualization Important? 

In the world of Big Data, it’s really important to be able to see all that data in a way that makes sense in order of being able to make data-driven decisions. The primary goal of data visualization is to communicate data clearly and efficiently. It provides an accessible way to see and understand trends, outliers, and patterns in data and a way for experts in a specific field to present data to non-technical audiences without confusion.

In today’s data-rich environment, data visualization is crucial for several reasons. It enhances understanding by making complex datasets more accessible through visual representation. This improved clarity supports better decision-making, allowing for quicker and more informed choices by highlighting key data points and trends. Additionally, data visualization aids in communication, effectively conveying information to diverse audiences, including non-technical stakeholders. Lastly, it helps identify patterns and trends that might not be immediately apparent in raw data, enabling organizations to uncover valuable insights.

What Types of Data Visualization Are There? 

There are various types of data visualizations, each suited to different kinds of data and analysis goals: 

  • Chart: Displays information in a graphical form with data along two axes. Types include graphs, diagrams, and maps.
  • Table: Presents figures in rows and columns, useful for detailed data comparison.
  • Graph: A diagram showing relationships between variables, often along two axes.
  • Geospatial: Uses maps to show data relationships with specific locations, employing shapes and colors.
  • Infographic: Combines visuals and text to represent data, often with charts or diagrams.
  • Dashboards: A collection of visualizations for comprehensive data analysis and presentation in one place.
  • Area Map: Shows values over geographical locations, such as choropleths and isopleths.
  • Bar Chart: Uses bars to represent numerical values for easy comparison.

Choosing the Right Data Visualization

Choosing the right data visualization depends on the type of data and the story you want to tell. Factors to consider include the nature of your data (quantitative vs. qualitative), the relationship between data points, and the key message or insight you want to convey. Charts, graphs, and maps serve different purposes and cater to diverse analytical needs, from showing comparisons and trends to highlighting distributions and relationships.

So the bigger purpose of Data visualization is not just about making data look good; it’s about making data more accessible and actionable. By effectively employing various visualization techniques, professionals can transform how data is interpreted and utilized across industries. 

There are a lot of tools to that can help visualization Data like: Google Charts, Tableau, Grafana, Chartist, FusionCharts, Datawrapper, Infogram, and ChartBlocks. In the course of my work with the Risklim team, I will also have to deal with such tools to see how we can work better with the available data. 

Reference

https://www.tableau.com/visualization/what-is-data-visualization#:~:text=Data%20visualization%20is%20the%20graphical,outliers%2C%20and%20patterns%20in%20data. 12.01.25, 21:37

https://www.geeksforgeeks.org/data-visualization-and-its-importance 12.01.25, 21:24

https://www.atlassian.com/data/charts/how-to-choose-data-visualization 12.01.25, 21:57

by cornelia.wysoudil -

#01 Multisensory Data Visualisation

Introduction to Multisensory Data Visualisation

Multisensory data visualization refers to the use of multiple sensory modalities—such as sight, hearing, and touch—to represent complex data sets in more intuitive and accessible ways. While conventional visualization techniques rely on graphs, charts, and maps, these predominantly visual methods can become overwhelming or fail to convey subtle patterns, especially when dealing with high-dimensional or time-sensitive data. Beyond auditory cues (e.g., sonification), incorporating tactile feedback (e.g., haptic vibrations) and other sensory channels has the potential to significantly enhance data interpretation by distributing cognitive load and addressing diverse user needs.

Background and Inspiration

During my bachelor and bachelor project, I initially explored and dealt with “traditional” forms of data representation, which led me to examine various approaches to accessibility in design. This exploration was further enriched by the talk “Lessons Learned From Our Accessibility-First Approach to Data Visualisation” by Kent Eisenhuth from the Usability Congress in Graz. There I first consiously encountered signification of data and was instantly intrigued.

Why Consider a Multisensory Approach?

  1. Reduced Cognitive Overload
    Representing data through multiple senses can distribute the processing demands across different sensory channels. For instance, tactile cues (such as haptic vibrations) and auditory cues (such as high or low sounds) can indicate threshold crossings or significant deviations in data, relieving some of the burden placed solely on visual elements.
  2. Enhanced Engagement and Emotional Resonance
    Research indicates that incorporating different sensory modalities—particularly auditory and tactile—may intensify user engagement. Whether through auditory signals highlighting sudden shifts or vibrations indicating key events, users often develop deeper cognitive and emotional connections when more than one sense is involved.
  3. Expanded Accessibility
    For users with visual impairments, sonification and tactile feedback can serve as vital tools for understanding data trends and outliers. Similarly, for users with hearing impairments, strategic use of visual and tactile elements can ensure equal access to critical insights. A truly multisensory system can be configured to accommodate a broad range of abilities.
  4. Detection of Subtle or Transient Patterns
    Time-sensitive or multi-dimensional data (e.g., financial fluctuations, climate patterns, or sensor readings) can be challenging to track visually. By adding non-visual modalities, patterns that might be overlooked in a purely visual chart can become more apparent through changes in pitch, rhythm, or tactile pulses.

Next Steps

My next steps will focus on gathering and analyzing data on how combining visual, auditory, and potentially tactile elements can influence user comprehension, retention, and emotional engagement with complex information. This research will involve reviewing existing literature, examining various sensory-mapping strategies, and identifying critical factors (e.g., cognitive load, accessibility requirements, and user preferences) that shape effective multisensory data representations. Comparative studies and expert interviews may inform which modalities are most beneficial for certain data types or user groups. These insights will guide the theoretical framework for understanding multisensory design principles, culminating in recommendations for inclusive and impactful data visualization practices.

Keywords for my Research

AI generated list of keywords to help me in my research.

  1. Sonification
  2. Tactile Feedback / Haptic Interfaces
  3. Data Accessibility
  4. Inclusive Design
  5. Universal Design
  6. Cognitive Load
  7. Sensory Mapping
  8. Multimodal Interaction
  9. Cross-Modal Perception
  10. User Experience (UX) Testing
  11. Threshold Detection
  12. Emotional Resonance
  13. Accessibility Guidelines (e.g., WCAG)
  14. Alt Text and Descriptive Metadata
  15. Adaptive/Assistive Technologies
  16. Perceptual Illusions in Multisensory Design
  17. Pattern Recognition in Data
  18. Interaction Design Principles
  19. Context-Aware Computing
  20. Sensory Substitution

Literature

T. Hogan and E. Hornecker, “Towards a Design Space for Multisensory Data Representation,” Interacting with Computers, vol. 29, no. 2, pp. 147–167, Mar. 2017, doi: 10.1093/iwc/iww015.

S. Tak and L. Toet, “Towards Interactive Multisensory Data Representations,” in Proceedings of the International Conference on Computer Graphics Theory and Applications and International Conference on Information Visualization Theory and Applications (IVAPP-2013), 2013, pp. 558–561. doi: 10.5220/0004346405580561.

A. Storto, “Using Data Visualisations in a Participatory Approach to Multilingualism: ‘I Feel What You Don’t Feel’,” 2024. doi: 10.2307/jj.20558241.11.