Our founders and our team have a long track record of developing and publishing cutting-edge visualization methods, and we continue to do research and publish it at datavisyn! Our basic research work is also often funded by public agencies, which we gratefully acknowledge.
Design and Comparative Evaluation of Visualization Onboarding Methods
Comprehending and exploring large and complex data is becoming increasingly important for a diverse population of users in a wide range of application domains. Visualization has proven to be well-suited in supporting this endeavor by tapping into the power of human visual perception.
A Process Model for Dashboard Onboarding
Dashboards are used ubiquitously to gain and present insights into data by means of interactive visualizations. To bridge the gap between non-expert dashboard users and potentially complex datasets and/or visualizations, a variety of onboarding strategies are employed, including videos, narration, and interactive tutorials.
Provectories: Embedding-based Analysis of Interaction Provenance Data
Understanding user behavior patterns and visual analysis strategies is a long-standing challenge. Existing approaches rely largely on time-consuming manualprocesses such as interviews and the analysis of observational data.
Projection Path Explorer: Exploring Visual Patterns in Projected Decision-Making Paths
In problem-solving, a path towards solutions can be viewed as a sequence of decisions. The decisions, made by humans or computers, describe a trajectory through a high-dimensional representation space of the problem.
Taggle: Combining Overview and Details in Tabular Data Visualizations
Most tabular data visualization techniques focus on overviews, yet many practical analysis tasks are concerned with investigating individual items of interest.
TourDino: A Support View for Confirming Patterns in Tabular Data
Seeking relationships and patterns in tabular data is a common data exploration task. To confirm hypotheses that are based on visual patterns observed during exploratory data analysis, users need to be able to quickly compare data subsets, and get further information on the significance of the result and the statistical test applied.
Ordino: visual analysis tool for ranking and exploring genes, cell lines, and tissue samples
Summary: Ordino is a web-based analysis tool for cancer genomics that allows users to flexibly rank, filter and explore genes, cell lines and tissue samples based on pre-loaded data, including The Cancer Genome Atlas, the Cancer Cell Line Encyclopedia and manually uploaded information.
KnowledgePearls: Provenance-Based Visualization Retrieval
Storing analytical provenance generates a knowledge base with a large potential for recalling previous results and guiding users in future analyses.
From Visual Exploration to Storytelling and Back Again
The primary goal of visual data exploration tools is to enable the discovery of new insights. To justify and reproduce insights, the discovery process needs to be documented and communicated.
Domino: Extracting, Comparing, and Manipulating Subsets across Multiple Tabular Datasets
Answering questions about complex issues often requires analysts to take into account information contained in multiple interconnected datasets.
LineUp: Visual Analysis of Multi-Attribute Rankings
Rankings are a popular and universal approach to structuring otherwise unorganized collections of items by computing a rank for each item based on the value of one or more of its attributes.
Austrian Research Promotion Agency (FFG)
The pharmaceutical industry is in a reproducibility crisis. Articles from the science magazines Science and Nature prove that only a part of the results of the current publications on the subject of cancer research are understandable. At the same time, the industry is in an efficiency crisis: only 5 out of 5,000 so-called drug candidates make it to approval. The drop-out rate contributes significantly to the enormous development costs (1-3 billion USD) and duration (up to 10 years). Many of these drop-out candidates could already be recognized in the first phase of drug development: in the drug target discovery phase. In this project, systems specially tailored to biomedical research are being developed with fully integrated provenance tracking, structured validation of research results, cutting-edge visual analytics and domain-specific support. In this way, the drug target discovery phase can be designed much more efficiently and the quality of the drug candidates can be increased.
Self-Explanatory Visual Analytics for Data-Driven Insight Discovery (SEVA)
Fachhochschule St. Pölten, Landsiedl Popper OG, Technische Universität Wien, FH JOANNEUM Gesellschaft mbH
Austrian Research Promotion Agency (FFG)
SEVA aims to help people quickly learn new tools for visual data analysis. The project’s goal is to develop automatically generated onboarding methods for visual analysis systems. Appropriate onboarding methods improve the user experience and the understanding of visual data analysis tools for large and complex data sets. Proof-of-concept prototypes are methodically designed, built, and evaluated along with an iterative, user- and problem-oriented research process.