PUMA publications for /group/researchcodeWed Apr 01 13:20:10 CEST 202603Typology of Data Uses: An output of the FORCE11 Data Usage Typologies Working Group 2026forschungsdaten metadata metrics statistics dataUsage Mon Mar 30 17:00:01 CEST 2026Applied Sciences31608Ontology-based production simulation with ontologysim122022forschungsdaten metadata ontologie simulation tools Mon Mar 30 16:58:47 CEST 2026J Inform Technol Construction (ITcon)1208--1243A review of the application of ontologies and semantic web for building information modelling and digital twins based construction management302025metadata digitalTwin ontologie building semanticWeb Mon Mar 30 16:10:11 CEST 2026Journal of intelligent manufacturing63669--3693A review and classification of manufacturing ontologies362025metadata ontologie produktion Mon Mar 30 13:52:01 CEST 2026IEEE Transactions on Big Data52224--2243Digital twin data management: a comprehensive review112025forschungsdaten metadata digitalTwin Mon Mar 30 13:07:08 CEST 2026IEEE Transactions on Knowledge and Data EngineeringOntology embedding: a survey of methods, applications and resources2025overview ontologie embedding Mon Mar 30 13:00:38 CEST 2026Semantic Web Journalx1--45Large language models for ontology engineering: a systematic literature review202025metadata ontologie engineering llm Mon Mar 30 09:53:02 CEST 2026Materials \& Design112603PMD Core Ontology: Achieving semantic interoperability in materials science2372024metadata ontologie materialwissenschaften Wed Feb 11 15:20:32 CET 2026{Atmospheric transmission models for FIFI-LS: altitude 35kft, 39deg O3}2026test Wed Feb 11 11:15:41 CET 2026JoDaKISS - Journal of Data- and Knowledge-integrated Simulation Science03micro-XRCT data sets of a stone mastic asphalt drill core before and after a uniaxial compression test (sample 2): sample 2-112025JoDaKISS Wed Feb 11 08:40:58 CET 2026Journal of Open Source Software021188326conmat: generate synthetic contact matrices for a given age-stratified population112026JOSS Tue Feb 10 18:25:33 CET 2026JoDaKISS - Journal of Data- and Knowledge-integrated Simulation Science07BayesValidRox 2.0.012025JoDaKISS Tue Feb 10 18:04:16 CET 2026JoDaKISS - Journal of Data- and Knowledge-integrated Simulation Science02Replication Data for: Data-Parallel PU-RBF Interpolation22026JoDaKISS Fri Jan 09 15:11:34 CET 2026SoftwareIntegration Scripts for DFG Classification for Dataverse-based Repositories2025myown These scripts integrate the DFG classification from the TIB ontology into Dataverse as an external vocabulary according to the Dataverse Guidelines. get_dfg_class.js: Displays entries from DFG classification in Topic Classification field once user has entered at least three characters. get_dfg_class_with_suggestions.js: Displays entries from DFG classification in Topic Classification field once user has entered at least three characters. Additionally, a button is provided that, when clicked, suggests relevant DFG categories based on the description text. cvocConf_dfg.json (Dataverse-Config-file. Integrates a Javascript file. Path to respective js file on server has to be set.) Please note that when using the TIB Terminology API, you must include a custom HTTP header named 'caller' in every request. This header identifies your institution or organization when accessing the API. In these scripts, the caller header is commented out as shown in the example: headers: { "accept": "application/json", //"caller": "DARUS" }Before using the scripts, uncomment the caller line and replace "DARUS" with the name of your own institution or organization. To activate the feature use: curl -X PUT --upload-file cvocConf_dfg.json http://localhost:8080/api/admin/settings/:CVocConf. If other CVOC functionalities are to be used, they would need to be integrated into the json file. Fri Jan 09 15:10:13 CET 2026DatasetTraining dataset for AI-supported subject indexing of research data with DFG classification2025myown This is a structured JSON dataset for training and evaluating AI models for automated subject indexing of research data and linking of research data and publications. It was created for the project DA-FDM in order to train a vector based model to give automated suggestions for the topic classification of research datasets in DaRUS. In the context of this project, the DFG-classification was integrated as a controlled vocabulary in DaRUS for the Topic Classification field. DFG classes were added manually to datasets from DaRUS that were uploaded prior to the integration.This dataset includes classification tags (DFG, GND, Wikidata), publication links, respective open-access information, and, if the publication is open-access, the respective full texts for datasets from DaRUS as well as TUdatalib. Mon Oct 13 09:04:04 CEST 2025Scientific Data081503Lightweight Distributed Provenance Model for Complex Real--world Environments92022provenance metadata profile prov 17Provenance is information describing the lineage of an object, such as a dataset or biological material. Since these objects can be passed between organizations, each organization can document only parts of the objects life cycle. As a result, interconnection of distributed provenance parts forms distributed provenance chains. Dependant on the actual provenance content, complete provenance chains can provide traceability and contribute to reproducibility and FAIRness of research objects. In this paper, we define a lightweight provenance model based on W3C PROV that enables generation of distributed provenance chains in complex, multi-organizational environments. The application of the model is demonstrated with a use case spanning several steps of a real-world research pipeline --- starting with the acquisition of a specimen, its processing and storage, histological examination, and the generation/collection of associated data (images, annotations, clinical data), ending with training an AI model for the detection of tumor in the images. The proposed model has become an open conceptual foundation of the currently developed ISO 23494 standard on provenance for biotechnology domain.Thu Oct 09 14:34:45 CEST 202508KONDA: An LLM-based Tool for Semantic Annotation and Knowledge Graph Creation Using Ontologies for Research Data 2025forschungsdaten metadata AI tools LLM Tue Oct 07 10:47:17 CEST 2025Data Science Journal09Enhancing Multiscale Simulation Data Management with Domain Ontologies and an ELN: Addressing Challenges and Implementing Strategies2025metadata ontologie simulation nfdi4ing multiscale Data plays a fundamental role in advancing knowledge and driving research across all fields. Despite the abundance of data, several challenges still need to be addressed. These challenges include limited accessibility, heterogeneous data, lack of interconnections between associated topics, difficulty retrieving required information, semantic mismatches, and several other challenges. Ontologies provide a structured method to address these challenges effectively. This study addresses two central aspects of ontology research. First, it details the multidisciplinary ontology development process, highlighting the challenges, mitigation strategies, and impacts on domain data management. It then offers guidelines for beginners and individuals with a background in data management on effective engagement in ontology creation. Second, it introduces the Ontology for Multiscale Simulation methods (Onto-MS), constructed by following the guidelines from the first part. The ontology, developed in Web Ontology Language (OWL) using Protégé, integrates with other ontologies, such as the Elementary Multiperspective Material Ontology (EMMO), aligning this research with the Linked Data concept. A custom Python script was used to incorporate the ontology into an Electronic Laboratory Notebook (ELN), enabling the automatic creation of knowledge graphs and systematic data organization conforming to the ontology. This research successfully answers the fundamental questions in interdisciplinary or domain-level ontology development. Onto-MS provides a robust framework for organizing and linking data in multiscale simulations within computational materials science. Furthermore, ontology incorporation into an ELN simplifies its integration into data management practices. While ontology development is ongoing, the current version is functional and continuously refined with new insights and feedback.Tue Sep 16 09:26:11 CEST 2025Catal. Sci. Technol.195699-5713Generating knowledge graphs through text mining of catalysis research related literature142024enrichment NLP forschungsdaten metadata ontologie AI catalysis extraction Structured research data management in catalysis is crucial{,} especially for large amounts of data{,} and should be guided by FAIR principles for easy access and compatibility of data. Ontologies help to organize knowledge in a structured and FAIR way. The increasing numbers of scientific publications call for automated methods to preselect and access the desired knowledge while minimizing the effort to search for relevant publications. While ontology learning can be used to create structured knowledge graphs{,} named entity recognition allows detection and categorization of important information in text. This work combines ontology learning and named entity recognition for automated extraction of key data from publications and organization of the implicit knowledge in a machine- and user-readable knowledge graph and data. CatalysisIE is a pre-trained model for such information extraction for catalysis research. This model is used and extended in this work based on a new data set{,} increasing the precision and recall of the model with regard to the data set. Validation of the presented workflow is presented on two datasets regarding catalysis research. Preformulated SPARQL-queries are provided to show the usability and applicability of the resulting knowledge graph for researchers.Wed Jul 16 14:26:58 CEST 202507FAIR-Prinzipien und Datenschutz | Factsheet 3 - Data Linkage: FAIR und datenschutzkonform 2025Verknüpfung forschungsdaten FAIR rechtlicheFragestellungen Datenschutz