With the introduction of the Industry Strategy 2035, Austria has defined several key technology fields, which are considered important to strengthen innovation capacities and long-term economic growth. Despite the strategic importance of those areas, it is currently not clear how those areas are distributed across the national innovation ecosystem, in particular how companies and research institutions interact with each other. Also from a geographical perspective. As a result, policy makers as well as funding organizations face challenges in measuring and funding the defined key technology fields.
An important aspect of supporting decision makers is to ensure correctness of the data. Therefore it is crucial to validate the origin of the used data and also to incorporate well established algorithms in order to pre-process, process and visualize the data.
Following the principles of agile software development, the development of the artifact is conducted as an iterative search process. During the development process, intermediate versions of the system are provided to testers and evaluators. These feedback cycles enable the identification of design and logical improvements in early stages.
The artifact is a user facing dashboards, visualizing relevant data for the defined key technology fields. To allow easy gathering and processing of the raw data, admin facing data pipelines are implemented
The artifact is to be evaluated iteratively. Each development iteration will be reviewed by domain experts, whose feedback will be incorporated within the following development cycles. The evaluation also focuses on key metrics, such as data accuracy, time to insight, usage rate, performance and accessibility. A usability study will be conducted on representative sample of users.
The aim of the artifact is to support decision makers in measuring and funding the defined key technology fields within the national innovation ecosystem.
It is important to provide technical experts with a deep understanding about the data visualization and data processing methods. Additionally it is also crucial to provide policy makers or the economy insights into the underlying data, enabling them to understand the structure and interactions within the data.
The findings of this project will be presented within two Master Theses as well as at the “fteval”-conference.