In the “DataGenie” project, the team is developing a functional prototype for AI-powered data quality analysis of product master data. A chatbot gathers domain-specific contextual knowledge from data owners and employees in the business departments. This enables large language models (LLMs) to account for company-specific rules, detect errors more accurately and improve positive predictive value.
The MECC@Solingen project is developing a modular digital twin for data-driven urban development. Building on preliminary work and the existing infrastructure of the city of Solingen, data sources, sensors, and systems are being interconnected. This creates an open, scalable data infrastructure that supports administration, planning, and operations and enables the flexible integration of future smart city applications. The focus is on the areas of municipal depots, urban intersections, and smart structural inspections.
Geo Data Space Germany is a national geodata space developed by the data analytics institute (dai) and Fraunhofer ISST. It consolidates and harmonizes all relevant geodata—from 3D building data and road networks to cadastral information—into a unified model. This data serves as a foundational infrastructure to support domain-specific data spaces, such as those for the energy sector or disaster management.
In the BoostEDIC M&L project, we are supporting the establishment of a common European data infrastructure for the mobility and logistics sector. The aim of the four-year project is to further develop the European Mobility Data Space (EMDS) and establish a European Digital Infrastructure Consortium (EDIC). We are developing a knowledge hub as a central knowledge platform and drawing up recommendations for interoperable data spaces that enable cross-border data exchange throughout Europe.
The NGDI project is developing the technological and organizational foundations for the next generation of European data spaces. The goal is to enable interoperability between domain-specific data spaces, thereby promoting innovative, cross-industry business models. With the help of semantic web technologies, large language models, and distributed persistent identifier systems, barriers to entry for companies are lowered, enabling sovereign, secure data usage across sector boundaries.
The project optimizes the onboarding of SME as data providers in the Mobility Data Space (MDS) by establishing a data trustee. The focus is on SME in logistics, especially those involved in first- and middle-mile logistics. Standardized interfaces, clear minimum requirements, and a secure data trustee model reduce technical and organizational hurdles and pave the way for data-driven applications.
On behalf of the German Energy Agency (dena), the test field consortium, led by Fraunhofer IEE, is designing and implementing the two defined use cases for energy in a data space. In addition to industry dialogue and knowledge transfer, the test field is a component of the overall project being carried out by dena on behalf of the US Department of Energy (DOE). The focus is on operational data exchange, which is essential for the smart integration of decentralized annexes such as photovoltaics, heat pumps, and charging infrastructures for electric cars. The goal is to network, process, and make available high-quality energy data across sectors in order to drive forward the energy transition.
The aim of the DEPLOYTOUR project is to establish a European Tourism Data Space (ETDS). By providing a trustworthy data space architecture based on guidelines and rules, the exchange of data between actors in the tourism sector is to be promoted in order to create sustainable and innovative developments. Due to the dependence of the tourism sector on other sectors, the ETDS is intended to function as a comprehensive, interoperable data space.
The project aims to further develop existing approaches to data sharing and to promote the intersectoral exchange of geodata. To this end, an optimized data trust model is being developed that makes geodata available for different domains.
As direct business relationships evolve into value creation networks, complex ecosystems with a large number of dynamically changing participants emerge. The participants in the ecosystems have different technical and business dependencies on each other, which, combined with the dynamics, result in a high level of complexity. In this context, data spaces, i.e. multi-sided technological platforms, enable the cross-organizational sharing of data.
The “FDOOne - FAIR Digital Object One” project is all about networking data spaces according to a basic standard that fulfills the FAIR principles. The acronym FAIR stands for Findable, Accessible, Interoperable and Reusable. This approach aims to bring together the highly fragmented digital space of all digital objects. In order to achieve this goal, three project priorities have been agreed: 1. the networking of data spaces, focusing on the transfer of data into secure, trustworthy artificial intelligence (AI) applications, 2. strengthening trust in AI, which primarily includes reproducibility, 3. strengthening the AI ecosystem, in particular by defining and improving framework conditions for companies.
In the “MDSxNRW” project, we are developing and testing an intelligent recommendation engine that recommends a suitable connector for organizations to participate in the Mobility Data Space (MDS). This recommendation is based on company-specific information that is requested in a questionnaire. Based on the user information, detailed instructions for connecting to the MDS are then provided and options for automated connection to the MDS are recommended.
The Opt-In project brings data sovereignty to the world of the "smart home". The focus is on the requirements and needs of citizens to make free decisions about their data. Individuals are empowered to retain control over their data and at the same time encouraged to enable innovative services with their data.
The goal of the project "BuildingTrust" is to develop a modular data trustee for building data that motivates users to provide, maintain and anonymously share (personal) data. The goal is to optimize and accelerate sustainable building through a self-determined data trustee that generates added value for data givers and data takers and creates trust for sharing data.
In the "ScaleDTM" project, we are developing the technical building blocks for realizing decentralized data trustee models. The goal of this two-year individual project is to create a competitive, scalable, and easily extensible data fiduciary architecture to promote collaborative data processing and value creation. Here, we consider not only the exchange of data, but also the fiduciary execution of code as a service provided by the data trustee.
Fraunhofer Institute for Software and Systems Engineering
