Cultural Heritage (CH) (In the context of this paper, we consider cultural heritage built tangible cultural heritage, such as buildings or monuments.) is an important source of identity for humankind and needs to be conserved for future generations. Climate change (CC) will morph the environmental landscape, thus leading to climate stress imposed on CH. Experts from different domains, including, but not limited to, material scientists, conservators and managers of cultural heritage collaborate to find out how CC affects CH and how potentially harmful impacts can be mitigated. To find and understand correlations and effects of different factors, researchers collect and analyse vast amounts of data. Still, experts often cannot exchange or make efficient use of data since it often is unstructured, incompatible, or its plain existence is simply unknown. This article introduces means to achieve consent about available knowledge, to exploit synergy effects through the combination of available information and to provide a flexible multisource information platform in collaborative cultural heritage management projects. In the context of the European project HERACLES (HERACLES—HEritage Resilience Against CLimate Events on Site. Further information: http://www.heracles-project.eu/), an application-ontology was developed. The ontology facilitates reuse and integration of data through structuring and representing its semantics. The involvement in the HERACLES project guaranteed end-user driven development, practical results and encompassment of all domains represented in the project.

With the vision “Innovation by experiment” the Bauhaus.MobilityLab started in July 2020 as a living lab in the district Brühl of the city Erfurt, Thuringia, Germany. As a unique project, it is coupling the sectors mobility, logistics and energy into a unified living lab. It allows to design, develop and evaluate innovative services to increase the quality of life in the city. Bauhaus.MobilityLab offers access to live smart city data of different domains and provides a set of powerful artificial intelligence (AI) algorithms for data processing, analytics and forecasting. In contrast to existing platforms, its uniqueness is the available and integrated living lab. It allows directly rolling out new smart city services and to evaluate the impact in the real world. This paper describes the implementation of the technical platform supporting the Bauhaus.MobilityLab, realized according to the DIN SPEC 91357 as an open urban platform. It focuses on data sharing based on the concepts of the International Data Spaces and the integration of AI algorithms. The concepts are presented based on examples in the energy domain.

The energy sector is in a dynamic transition from centralized systems with large fossil power plants to a decentralized system with a high number of renewable energy assets and a rapidly increasing number of additional flexible loads from storage solutions, e-mobility, or power-to-heat applications.To operate the system reliably, demand and supply have to be matched at all times very closely. Thus, the sector is very data and communication intensive and requires advanced ICT solutions to automate processes and deal with the enormous complexity.The Energy Data Space can enable the digitalization of the energy transition by providing an architecture to make data available in order to increase the efficiency in asset and system operation.Data provision and market communication within the system operations of electricity grids is a key use case due to its central role in the sector. Next, the integration of data from the smart meter rollout could as well be built on Data Space technology. Further use cases include predictive maintenance and the energy supply of buildings.Initial research projects have demonstrated the feasibility of basic use cases. On the European level, the Platoon project will provide seven pilot applications by 2024.

Protecting and preserving cultural heritage (CH) in view of global climate change is the main objective of the KERES project. For managing climate impact with proper measurements, including prevention and responsive actions, an ontology has been devised in the course of this project in close cooperation with relevant stakeholders of selected CH assets that serve us as case studies. In particular, the ontology supports modeling specific CH assets with respect to the challenges of climate change. It turns out the main challenge is to subsume the diversity of models and processes specific to individual assets on a proper level of abstraction. Based on the ontology, we succeeded in creating software that assists stakeholders in managing their CH challenges, including an interactive app for suggesting preventative measurements and a web application for creating route cards that are used by emergency service professionals in the case of rescuing cultural assets. We are confident that our methodology of CH assets abstraction and modeling will be applicable to a broader range of CH assets.

Industrial Internet of Things (IIoT) applications are being used more and more frequently. Data collected by various sensors can be used to provide innovative digital services supporting increasing efficiency or cost reduction. The implementation of such applications requires the integration and analysis of heterogeneous data coming from a broad variety of sensors. To support these steps, this paper introduces OPAL, a software toolbox consolidating several software components for the semantically annotated integration and analysis of IoT-data. Data storage is realized in a standardized and INSPIRE-compliant way utilizing the SensorThings API. Supporting a broad variety of use cases, OPAL provides several import adapters to access data sources with various protocols (e.g., the OPC UA protocol, which is often used in industrial environments). In addition, a unified management and execution environment, called PERMA, is introduced to allow the programming language independent integration of algorithms.

Civil protection authorities in Baden-Württemberg, Germany, have long relied on a fragmented landscape of databases and spreadsheets to manage their vehicle and equipment inventories. This heterogeneous data environment prevents cross-organizational analysis, impedes situational awareness during crises, and makes compliance reporting against the state's administrative regulation on disaster control services a labor-intensive manual process. Despite the operational significance of this challenge, no German state currently operates a centralized, IT-supported system for civil protection vehicle management. We present ZIFA (Zentrales Informationssystem für Fahrzeuge und Ausstattungen des Katastrophenschutzes), a GIS-enabled web application developed as a new module within the established Elektronische Lagedarstellung für den Bevölkerungsschutz (ELD-BS) ecosystem. ZIFA provides unified inventory management, map-based visualization, lifecycle management, cost tracking, and automated target/actual comparison across all administrative levels. The system was developed through a participatory co-design process involving the Ministry of the Interior, Digitalisation and Local Government, all four Regional Councils, and selected district authorities and relief organizations. We describe ZIFA's architecture using the five viewpoints of the Reference Model of Open Distributed Processing (RM-ODP) framework and contextualize its functionality through two analytical lenses: the crisis management cycle (prevention, preparedness, response, recovery) and the administrative hierarchy (state, regional, district, local). Implementation is complete and the system is currently undergoing its second testing phase, fully integrated within the ELD-BS environment. We discuss lessons learned from the participatory design process, current limitations, and the transferability of key design elements to other German states and international contexts.