Although Graph Neural Networks (GNNs) have made significant advances in spatio-temporal traffic forecasting, their performance is limited when they rely solely on sensor proximity or road-network topology. This paper presents a spatio-temporal prediction framework, developed to incorporate knowledge in various forms. This framework aims to improve sensor-level, contextual understanding of the environment. A general-purpose knowledge graph (e.g., Wikidata) is used to create semantic subgraphs around traffic sensors and generate knowledge graph embeddings that capture meaningful relationships, such as nearby points of interest, administrative hierarchies, and the functional roles of locations. These embeddings are then fused with conventional traffic sensor graphs to provide additional adjacency matrices informed by semantics. This allows GNNs to learn the semantic context beyond physical connectivity. This study differs from previous research in two key ways. Firstly, rather than proposing a novel GNN architecture, it demonstrates the general impact of external knowledge on prediction accuracy. Secondly, experiments with well-established traffic forecasting approaches show that external knowledge provides additional information that street network data alone cannot convey. The results show that integrating data from general-purpose knowledge graphs and sensor networks through data fusion can enhance the prediction accuracy of traffic forecasting models, and offers a potential pathway toward improved interpretability.

Interdisciplinary research projects aim to answer overarching questions combining research of multiple disciplines. Besides FAIR requirements this requires effective Research Data Management (RDM) to support the synthesis necessary to provide such desired answers and involves integrating data from various disciplines. Research projects benefit from early decision-making on core aspects for organization and management of data. We propose a framework to identify such aspects which require early decisions for successful planning and execution. Following up on our activities and experiences in the interdisciplinary research project CRC 1266 (Scales of Transformation), we propose an approach that focuses on reliability of external repositories and combines it with a project-specific wrapper-like infrastructure consisting of roles, processes and a software system deeply integrated into the research project for FAIR and enriched research data management, presentation and synthesis.

Within the DFG funded, archaeology-focused project Collaborative Research Center 1266 (Scales of Transformation) an information system is implemented including roles, processes, hardware and software infrastructure with the goal to collect data and meta data available, integrate the data for (re)presentation, exploration as well as analysis, and therefore support a project-wide data-driven synthesis. Several challenges arise from the diversity of people coming together and the heterogeneous data environment present over all the disciplines involved. This poster illustrates these challenges, what it takes to bridge the gaps between differing perspectives and briefly proposes approaches involved to successfully address them.

Archaeological excavations play a major role in gaining knowledge about prehistoric landscapes and ways of living. However, archaeological excavations are destructive acts and very resource intensive, so they cannot be performed in every area of interest. Therefore prospection methods have been developed, where feedbacks of e.g. lidar, radar or magnetic sensors are utilized to get an overview of the distribution, extent and complexity of underground structures in larger areas. After automated pre-processing of the sensor data arrays (and images) of these, grid data is provided as an input for exploration, analysis and annotation using geographic information system tools like QGIS. Annotating the images has been a fully manual task, performed by domain scientists. In this work we demonstrate a tool that supports domain scientists through automated annotation prediction. The implementation is integrated in the prevalent scientific workflow using available input and required output formats. The implementation is based on a pre-trained Rotated Retina Net. The manually annotated data of underground house remains from one of three archaeological sites is then used to pre-process and augment a feasible amount of training data for this specific task. One challenge was that global normalization of pixel values in the images did not yield useful results, because of modern infrastructure (such as utility pipes) distorting the magnetic feedback. A separated portion of the annotated data has been used for a quantitative evaluation of model performance. The system has also been applied to two additional, formerly unseen and non-annotated datasets where predicted annotations were found to be valuable for domain scientists. The system's output data can be used in GIS tools to edit annotations by experts, explore the sites, identify promising excavation sites and perform e.g. cluster analysis on house sizes and other features.

Scientific disciplines are generating large volumes of valuable data with the potential to drive future discoveries across various domains. However, the effective management of these research data has now become indispensable and increasingly crucial in all scientific disciplines. Research Data Management (RDM) critically addresses challenges related to data integrity and curation throughout the research data life cycle. While RDM practices and procedures often exhibit substantial variations due to the diverse nature of projects, making it challenging to find overlap between disciplines, there are still similarities regarding expectations, requirements, and familiarity with tools, techniques, and infrastructure. This is due to the evolving dynamics of data generation and analytics in every scientific discipline. In this study, we conducted two comprehensive surveys by focusing on a multidisciplinary research environment involving 15 research projects and formulated the questionnaires to evaluate the RDM prerequisites and workflows for these projects. The surveys are associated with different activities in the research data life cycle, which we illustrate in the concept of the RDM Impact Cycle. We present acquired observations and provide comprehensive insights into full-cycle RDM best practices. Our discussion then emphasizes the influence of RDM on interdisciplinary research paradigms, paving the way for exciting data-driven studies supported by robust RDM practices.

Damage detection in materials and structures plays a critical role in engineering and science applications like structural health monitoring. A particular challenge is presented by micro-scale cracks, which are imperceptible to the naked eye or in images, but may ultimately evolve into larger, potentially dangerous cracks. In this work, we propose spatio-temporal pattern recognition techniques to enable the detection of such imperceptible micro-cracks. In order to make these cracks detectable, we generate seismic waves on the surface area of interest and monitor how cracks interfere with the spatial propagation of the wave over time. On the resulting propagation image series we then apply segmentation techniques using deep encoder-decoder CNNs to predict the location of cracks, which otherwise could not be directly observed. Our solution is evaluated through extensive experiments on highly-realistic finite element simulations, which were developed by domain experts.