The improvement of the human ability to manage data, extract information and gain knowledge from it is of vital importance for a society in the course of digitization. Visualisation is an effective way to enhance the human capabilities to extract and interpret information as also to support human decision making. within the human data interaction group we focus on three research lines:

Human Data Interaction

Due to digitisation, the correct interpretation and communication of data is taking a key role in more and more areas. Data visualisation offers new opportunities to improve the accessibility of data and algorithms – in the exploration as well as presentation scenarios. In addition, new techniques and contexts such as virtual reality and augmented reality or multimodal interactions like audio and physical interactions enables us to interact with data in novel ways. Within this field of research we investigate how the interaction between people and data can be enhanced by new methods and contexts.

Storytelling – Data in context

Though there are various instruments with which we can explore data, techniques to annotate data, to summarize it narratively or to employ storytelling are still relatively underdeveloped. Based on the exponential growth of global data, the ever-increasing availability and the resulting need thereof to use this data, it’s important to research, describe and publish methods that simplify the correct usage of data (understanding, background, assumptions, applicability, implications). There is a need to develop methods to summarize ‘narratives’. A core element thereof are methods to contextualise data.

Data and Society

The amount of available data is ever-increasing. The politics of ‘open data’ promises more transparency and more democracy, especially concerning government and public services data. Currently, working with and understanding this data carries (too) high demands for large parts of the population. As a result of this, the opposite becomes true. Individuals or companies use and/or interpret the data for their own interests. ‘Open data’ can only achieve the intended goal when all individuals are provided with tools that help them to understand data that is interesting/relevant to them. The development of such tools for various audiences in cooperation with the partner institutions is an objective of the knowledge domain.

Nowadays, data can be collected and used in new contexts. It is tempting to assume that existing problems can be solved with this data. This may be partially true, however, “If you only have a hammer (data), everything appears to be a nail (the problem)”. And so, a frequent occurrence is that only certain aspects (those which are covered by data and/or can be solved with data) are tackled. This subsequently leads to a dominance of statistical methods and a focus on the status quo at the expense of systematic approaches. It’s vital to critically review and to safeguard the role of data – and in particular the role of data visualisation – so that qualitative approaches – where needed – may flourish and be incorporated in the visualisation.

2020

2019