Exploratory Analysis and Visualization of Complex Data Spaces

The analysis of complex data spaces requires different methodological approaches and varying demands on exploration, visualization, and interpretation. Although suitable analytical methods exist for numerous questions, their application is often hindered by technical barriers to entry, the need for substantial prior methodological knowledge, and limited transparency.

This is particularly relevant for applications that combine data from different sources within a shared analytical context. In such settings, content-related patterns and thematic relationships need to be examined in a structured manner. This calls for an environment that bundles methodological approaches, clarifies their application context, and supports their use across different questions.

The aim of DIVE is to make exploratory analysis and visualization methods accessible within a shared environment and to support their application in the investigation of complex data spaces. The focus is on both the structured provision of methodological approaches and their application-oriented presentation. In this way, complex analytical methods are made usable with low barriers to entry, supporting an exploratory process of gaining insights from datasets.

DIVE bundles exploratory analysis and visualization methods within a shared, modular environment. Interactive dashboards provide different ways of examining complex data spaces and allow these approaches to be combined for a range of research questions. This is based on the integration of different data sources and the use of visual analytics, text analysis, and bibliometric analysis methods.

Depending on the application context, search queries can be created and transferred between database systems, document collections can be structured thematically, funding data can be examined, or datasets can be visualized. The individual methods are not only made available, but are also described in terms of their scientific background, context of use, and limitations.

The modular structure makes it possible to use individual analytical components independently or to connect them along typical workflows. This makes it possible to gain initial insights from datasets and to carry out structuring and in-depth analyses within a coherent framework.

With DIVE, a modular environment has been created in which exploratory analysis and visualization methods are accessible within a shared framework. This allows different datasets to be examined in a structured way and insights to be gained. At the same time, the platform demonstrates how complex methods can be made accessible in an application-oriented manner and situated within a methodologically transparent context.

In the future, DIVE will be expanded to include additional methods, data sources, and analytical components. On this basis, further data-related questions can be explored, existing analytical approaches refined, and methodological relationships made systematically usable.