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Information Research is a freely available, international, scholarly journal, dedicated to making accessible the results of research across a wide range of information-related disciplines.
nodegoat is a web-based data management, network analysis & visualisation environment. Using nodegoat, you can create and manage any number of datasets by use of a graphic user interface. Your own data model autoconfigures the backbone of nodegoat's core functionalities. Within nodegoat you are able to instantly analyse and visualise datasets. nodegoat allows you to enrich data with relational, geographical and temporal attributes. Therefore, the modes of analysis are inherently diachronic and ready-to-use for interactive maps and extensive trailblazing.
The dataset is the release version v2.0.0 of the BHoM to bhOWL converter, which helps convert BHoM data to a knowledge graph in any software BHoM supports. BHoM (The Buildings and Habitats object Model) is collaborative framework that runs within several AEC design software, which helps to represent data in a object oriented database model. OWL (Web Ontology Language) provides a standardized and expressive language for representing knowledge and relationships within a domain. It allows for the creation of ontologies, which are formal descriptions of the concepts and relationships within a domain. Additionally, OWL can support reasoning and inference over ontologies, allowing for automated reasoning about the relationships between different concepts and data elements. This can be particularly useful in identifying inconsistencies or gaps in data, or in suggesting additional data sources or mappings that may be needed to support integration efforts. Our BHoM to bhOWL converter is developed as an extension to BHoM framework, and runs within any software that BHoM supports (eg. Grasshopper 3D, Excel, etc.) The dataset contains also example Grasshopper 3D files that exemplify the process of converting BHoM data to bhOWL.
J. Pelzer, und M. Schulte. Dataset, (2023)Related to: Pelzer, Julia, and Miriam Schulte. "Efficient two-stage modeling of heat plume interactions of geothermal heat pumps in shallow aquifers using convolutional neural networks." Geoenergy Science and Engineering (2024): 212788. doi: 10.1016/j.geoen.2024.212788.
J. Pelzer, und M. Schulte. Dataset, (2023)Related to: Pelzer, Julia, and Miriam Schulte. "Efficient two-stage modeling of heat plume interactions of geothermal heat pumps in shallow aquifers using convolutional neural networks." Geoenergy Science and Engineering (2024): 212788. doi: 10.1016/j.geoen.2024.212788.
J. Pelzer, und M. Schulte. Dataset, (2024)Related to: Miliczek, Pascal. "Rotational Equivariance in Convolutional Neural Networks (CNNs) for Modelling Heat Plumes of Heat Pumps in Groundwater". Bachelor Thesis (2024). doi: 10.18419/opus-15412.
J. Pelzer, und M. Schulte. Software, (2024)Related to: Pelzer, Julia, and Miriam Schulte. "Efficient two-stage modeling of heat plume interactions of geothermal heat pumps in shallow aquifers using convolutional neural networks." Geoenergy Science and Engineering (2024): 212788. doi: 10.1016/j.geoen.2024.212788.
J. Pelzer, und M. Schulte. Software, (2024)Related to: Pelzer, Julia, and Miriam Schulte. "Efficient two-stage modeling of heat plume interactions of geothermal heat pumps in shallow aquifers using convolutional neural networks." Geoenergy Science and Engineering (2024): 212788. doi: 10.1016/j.geoen.2024.212788.
J. Pelzer, und M. Schulte. Dataset, (2024)Related to: Johanna Trick. "Erweiterung eines Convolutional Neural Network auf 3D zur Modellierung von Grundwasserwärmepumpen". Bachelor Thesis (2024). (unpublished).
J. Pelzer, und M. Schulte. Dataset, (2024)Related to: Pelzer, Julia, and Miriam Schulte. "Scalable Machine Learning Approach for Heat Transport in Groundwater". In proceedings of 23rd International Conference of Machine Learning and Applications (2024). In progress.
J. Pelzer, und M. Schulte. Dataset, (2025)Related to: Pelzer, Julia and Verburg, Corné and Heinlein, Alexander and Schulte, Miriam. "Few-Shot Learning by Explicit Physics Integration: An Application to Groundwater Heat Transport". 2025. Submitted.
J. Pelzer, und M. Schulte. Dataset, (2025)Related to: Pelzer, Julia and Verburg, Corné and Heinlein, Alexander and Schulte, Miriam. "Few-Shot Learning by Explicit Physics Integration: An Application to Groundwater Heat Transport". 2025. Submitted.
J. Pelzer, C. Verburg, und M. Schulte. Dataset, (2025)Related to: Pelzer, Julia and Verburg, Corné and Heinlein, Alexander and Schulte, Miriam. "Few-Shot Learning by Explicit Physics Integration: An Application to Groundwater Heat Transport". 2025. Submitted.
J. Pelzer, und M. Schulte. Dataset, (2025)Related to: Pelzer, Julia and Verburg, Corné and Heinlein, Alexander and Schulte, Miriam. "Few-Shot Learning by Explicit Physics Integration: An Application to Groundwater Heat Transport". 2025. Submitted.
J. Pelzer, und M. Schulte. Dataset, (2025)Related to: Pelzer, Julia and Verburg, Corné and Heinlein, Alexander and Schulte, Miriam. "Few-Shot Learning by Explicit Physics Integration: An Application to Groundwater Heat Transport". 2025. Submitted.
