Caleydo

Caleydo is a research collaboration among Johannes Kepler University Linz, the University of Utah, Graz University of Technology, Harvard University and Harvard Medical School. Samuel Gratzl joined the team during his Phd studies and together they developed a series of awarded publications in the area of information visualization with a focus on biomedical data.

• Category: Research Collaboration
• Website: http://caleydo.org
• Demo: http://caleydo.org
• : https://github.com/Caleydo
• Tags: project, research

Vistories: From Visual Exploration to Storytelling and Back Again

In this publication we propose the CLUE model to reproduce and present visual exploration based on recorded provenance data. The CLUE model is available as a reusable JavaScript library.

• Category: Model and JavaScript library
• Website: http://vistories.org
• Demo: http://vistories.org
• : https://github.com/phovea/phovea_clue
• Tags: publication, reproducibility, provenance, storytelling

Visualization is an important data analysis method that allows scientists to explore a dataset without preconceived questions, and is thus crucial for hypothesis generation. When combined with algorithmic approaches, it bridges the gap between exploration and confirmation. Visualization is also essential in communicating research findings.

datavisyn GmbH

In October 2016 Samuel Gratzl co-founded datavisyn GmbH and became its CTO. Datavisyn develops data visualization solutions for applications in pharmaceutical and biomedical R&D.

• Project Date: founded in October 2016
• Category: Start-Up company
• Website: https://www.datavisyn.io
• Demo: https://www.datavisyn.io
• : https://github.com/datavisyn
• Tags: company, software development, co-founder, start-up

LineUp: Visual Analysis of Multi-Attribute Rankings

LineUp is a visualization technique for multi-attribute rankings. It is implemented as reusable JavaScript library hosted on Github available at lineup.js.org. It won the best paper award out of 152 submissions at IEEE InfoVis 2013.

• Project Date: March 2013
• Category: Visualization Technique and JavaScript library
• Website: https://lineup.js.org
• Demo: https://lineup.js.org
• : http://github.com/datavisyn/lineupjs
• Tags: publication, multi-attribute, library

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. This allows us, for example, to prioritize tasks or to evaluate the performance of products relative to each other. While the visualization of a ranking itself is straightforward, its interpretation is not, because the rank of an item represents only a summary of a potentially complicated relationship between its attributes and those of the other items. It is also common that alternative rankings exist which need to be compared and analyzed to gain insight into how multiple heterogeneous attributes affect the rankings. Advanced visual exploration tools are needed to make this process efficient. In this paper we present a comprehensive analysis of requirements for the visualization of multi-attribute rankings. Based on these considerations, we propose LineUp - a novel and scalable visualization technique that uses bar charts. This interactive technique supports the ranking of items based on multiple heterogeneous attributes with different scales and semantics. It enables users to interactively combine attributes and flexibly refine parameters to explore the effect of changes in the attribute combination. This process can be employed to derive actionable insights as to which attributes of an item need to be modified in order for its rank to change. Additionally, through integration of slope graphs, LineUp can also be used to compare multiple alternative rankings on the same set of items, for example, over time or across different attribute combinations. We evaluate the effectiveness of the proposed multi-attribute visualization technique in a qualitative study. The study shows that users are able to successfully solve complex ranking tasks in a short period of time.

Slack Cleaner, Chart.js Plugins, ...

Samuel Gratzl is also contributing to other open source projects. He took over the orphaned slack_cleaner project. The project aims to easily clean up slack messages. Besides maintaining the existing code base, he created a module version of the project that allows an easy scripting, deletion, and archiving of messages.

In addition, as part of this function at CTO at datavisyn GmbH, he is developing plugins for open source libraries like Chart.js. Among others a Chart.js Box Plot plugin or a Hierarchical Scale plugin integrating a group, expand, and collapse feature for bar charts.

• Category: Open Source Contributions
• : https://github.com/sgratzl/slack_cleaner https://github.com/datavisyn?q=chartjs
• Tags: open source, slack, chart.js, box plot

phovea

Phovea is novel web based client-server visual analysis platform. Samuel Gratzl developed the architecture of the platform and implemented most part of it. A set of demo application are hosted at caleydoapp.org

• Category: Visual Analytics Platform
• Website: http://phovea.caleydo.org
• Demo: http://phovea.caleydo.org
• : https://github.com/phovea/
• Tags: visual analytics, platform, web framework

Target Discovery Platform

The Target Discovery Platform (TDP) was developer by Samuel Gratzl during his PhD as part a research collaboration with Boehringer Ingelheim. The platform and its application like Ordino or dTiles build the foundation for the spin-off company datavisyn.

• Project Date: since December 2015
• Category: Web Platform
• Website: https://www.datavisyn.io/#tdp
• Demo: https://www.datavisyn.io/#tdp
• : https://github.com/datavisyn/tdp_core
• Tags: platform, typescript

Teaching

Besides development new techniques and tools Samuel Gratzl is a lecturer at the Hagenberg University of Applied Sciences since 2016, where he teaches students the fundamentals of information visualization with a focus on web based visualizations using D3.

• Project Date: since 2016
• Category: Teaching
• : https://github.com/sgratzl/d3_tutorial
• Tags: tutorial, D3, lecturer

Visually Guiding Users in Selection, Exploration, and Presentation Tasks

In his PhD thesis Samuel Gratzl describes models, techniques, and tools that supports and guides users in selection, exploration, and presentation tasks encountered during an visual analysis.

• Project Date: April 2017
• Category: PhD thesis
• Tags: thesis, guidance

Making scientific discoveries based on large and heterogeneous datasets is challenging. The continuous improvement of data acquisition technologies makes it possible to collect more and more data. However, not only the amount of data is growing at a fast pace, but also its complexity. Visually analyzing such large, interconnected data collections requires a user to perform a combination of selection, exploration, and presentation tasks. In each of these tasks a user needs guidance in terms of (1) what data subsets are to be investigated from the data collection, (2) how to effectively and efficiently explore selected data subsets, and (3) how to effectively reproduce findings and tell the story of their discovery. On the basis of a unified model called the SPARE model, this thesis makes contributions to all three guidance tasks a user encounters during a visual analysis session: