PUMA publications for /group/researchcode/ToolsMon Mar 30 17:00:01 CEST 2026Applied Sciences31608Ontology-based production simulation with ontologysim122022forschungsdaten metadata ontologie simulation tools 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 Mon Jun 30 14:41:02 CEST 2025Scientific Data1837--WorkflowHub: a registry for computational workflows122025registry forschungsdaten metadata researchObjects tools The rising popularity of computational workflows is driven by the need for repetitive and scalable data processing, sharing of processing know-how, and transparent methods. As both combined records of analysis and descriptions of processing steps, workflows should be reproducible, reusable, adaptable, and available. Workflow sharing presents opportunities to reduce unnecessary reinvention, promote reuse, increase access to best practice analyses for non-experts, and increase productivity. In reality, workflows are scattered and difficult to find, in part due to the diversity of available workflow engines and ecosystems, and because workflow sharing is not yet part of research practice. WorkflowHub provides a unified registry for all computational workflows that links to community repositories, and supports both the workflow lifecycle and making workflows findable, accessible, interoperable, and reusable (FAIR). By interoperating with diverse platforms, services, and external registries, WorkflowHub adds value by supporting workflow sharing, explicitly assigning credit, enhancing FAIRness, and promoting workflows as scholarly artefacts. The registry has a global reach, with hundreds of research organisations involved, and more than 800 workflows registered.Wed Jun 25 09:33:11 CEST 2025E-Science-Tage 2025 Research Data Management: Challenges in a Changing WorldheiBOOKSEfficient Data Curation by Automating Workflows2025myown forschungsdaten metadata curation tools Tue Apr 29 16:45:30 CEST 202502Einführung in Terminologien und Terminologiedienste 2025terminologies forschungsdaten metadata pid tools Fri Dec 06 13:34:57 CET 20242020 IEEE Third International Conference on Artificial Intelligence and Knowledge Engineering (AIKE)12174-178Knowledge Graph Visualization: Challenges, Framework, and Implementation2020visualization tools knowledgegraph A knowledge graph (KG) is a rich resource representing real-world facts. Visualizing a knowledge graph helps humans gain a deep understanding of the facts, leading to new insights and concepts. However, the massive and complex nature of knowledge graphs has brought many longstanding challenges, especially to attract non-expert users. This paper discusses these challenges; we turned them into a generic knowledge-graph visualization framework, namely KGViz, consisting of four dimensions: modularity, intuitive user interface, performance, and access control. Our implementation of KGViz is a high-capacity, extendable, and scalable KG visualizer, which we designed to promotes community contributions.Thu Dec 05 15:34:23 CET 2024The Journal of Supercomputing88946--8966Like a rainbow in the dark: metadata annotation for HPC applications in the age of dark data772021metadata engmeta tools extraction The deluge of dark data is about to happen. Lacking data management capabilities, especially in the field of supercomputing, and missing data documentation (i.e., missing metadata annotation) constitute a major source of dark data. The present work contributes to addressing this challenge by presenting ExtractIng, a generic automated metadata extraction toolkit. Existing metadata information of simulation output files scattered through the file system, can be aggregated, parsed and converted to the EngMeta metadata model. Use cases from computational engineering are considered to demonstrate the viability of ExtractIng. The evaluation results show that the metadata extraction is simulation-code independent in the sense that it can handle data outputs from various fields of science, is easy to integrate into simulation workflows and compatible with a multitude of computational environments.Tue Oct 24 11:32:47 CEST 2023Kadi4Mat - Karlsruhe Data Infrastructure for Materials Science2023forschungsdaten software eln tools Fri Sep 22 09:24:10 CEST 202304{DLR-SC/prov-comics: QS PROV Comics Prototype - Big fixes}2017forschungsdaten provenance metadata software comics tools javascript Thu Sep 14 14:04:57 CEST 2023KDIR227--234Moving towards a General Metadata Extraction Solution for Research Data with State-of-the-Art Methods.2020forschungsdaten metadata harvesting similarity tools Tue Jun 06 13:28:33 CEST 2023EnglandTrends in genetics : TIG033223--234Data Lakes, Clouds, and Commons: A Review of Platforms for Analyzing and Sharing Genomic Data352019forschungsdaten definition tools Data commons collate data with cloud computing infrastructure and commonly used software services, tools, and applications to create biomedical resources for the large-scale management, analysis, harmonization, and sharing of biomedical data. Over the past few years, data commons have been used to analyze, harmonize, and share large-scale genomics datasets. Data ecosystems can be built by interoperating multiple data commons. It can be quite labor intensive to curate, import, and analyze the data in a data commons. Data lakes provide an alternative to data commons and simply provide access to data, with the data curation and analysis deferred until later and delegated to those that access the data. We review software platforms for managing, analyzing, and sharing genomic data, with an emphasis on data commons, but also cover data ecosystems and data lakes.Thu Apr 27 07:50:40 CEST 2023Briefings in bioinformatics3334--347High performance cellular level agent-based simulation with FLAME for the GPU112010simulations tools Tue Apr 18 07:19:52 CEST 2023Review of Scientific Instruments11113102An open, modular, and flexible micro X-ray computed tomography system for research912020sfb1313 x-ray relatedpublication tools In this paper, a modular and open micro X-ray Computed Tomography (μXRCT) system is presented, which was set up during the last years at the Institute of Applied Mechanics (CE) of the University of Stuttgart and earlier at the Institute of Computational Engineering of Ruhr-University Bochum. The system is characterized by its intrinsic flexibility resulting from the modular and open design on each level and the opportunity to implement advanced experimental in situ setups. On the one hand, the presented work is intended to support researchers interested in setting up an experimental XRCT system for the microstructural characterization of materials. On the other hand, it aims to support scientists confronted with the decision to set up a system on their own or to buy a commercial scanner. In addition to the presentation of the various hardware components and the applied modular software concept, the technical opportunities of the open and modular hard- and software design are demonstrated by implementing a simple and reliable method for the compensation of bad detector pixels to enhance the raw data quality of the projections. A detailed investigation of the performance of the presented system with regard to the achievable spatial resolution is presented. XRCT datasets of three different applications are finally shown and discussed, demonstrating the wide scope of options of the presented system. Sat Feb 11 12:23:47 CET 2023A sustainable infrastructure concept for improved accessibility, reusability, and archival of research software2023forschungsdaten software tools repository Wed Apr 27 17:54:28 CEST 2022E-Science-Tage 2021: Share Your Research Data 267-276The ReSUS Project - Infrastructure for Sharing Research Software2022myown forschungsdaten metadata software tools repository Thu Oct 21 12:10:32 CEST 2021ESWC2021 Poster and Demo TrackTowards Easy Vocabulary Drafts with Neologism 2.02021metadata ontologie tools Shared vocabularies and ontologies are essential for many applications. Although standards and recommendations already cover many areas, adaptations are usually necessary to represent concrete use-cases properly. Domain experts are unfamiliar with ontology engineering, which creates special requirements for needed tool support. Simple sketch applications are usually too imprecise, while comprehensive ontology editors are often too complicated for non-experts. We present Neologism 2.0 - an open-source tool for quick vocabulary creation through domain experts. Its guided vocabulary creation and its collaborative graph editor enable the quick creation of proper vocabularies, even for non-experts, and dramatically reduces the time and effort to draft vocabularies collaboratively. An RDF export allows quick bootstrapping of any other Semantic Web tool.Sat Oct 16 15:00:57 CEST 2021Bausteine ForschungsdatenmanagementOkt.229–40Erfahrungen und Empfehlungen aus der Beratung bei Datenmanagementplänen2020checklisten forschungsdaten dmp tools Thu Apr 29 11:17:28 CEST 2021Journal of Open Law, Technology & Society19-18The FOSSology Project: 10 Years Of License Scanning92018software tools licensing Mon Feb 15 07:13:52 CET 2021Communications in Computer and Information Science11193-205LabTablet: Semantic Metadata Collection on a Multi-domain Laboratory Notebook4782014forschungsdaten metadata ontologie eln tools Tue Feb 09 17:31:47 CET 2021The Journal of SupercomputingLike a rainbow in the dark: metadata annotation for HPC applications in the age of dark data2021forschungsdaten hpc-computing metadata tools automated The deluge of dark data is about to happen. Lacking data management capabilities, especially in the field of supercomputing, and missing data documentation (i.e., missing metadata annotation) constitute a major source of dark data. The present work contributes to addressing this challenge by presenting ExtractIng, a generic automated metadata extraction toolkit. Existing metadata information of simulation output files scattered through the file system, can be aggregated, parsed and converted to the EngMeta metadata model. Use cases from computational engineering are considered to demonstrate the viability of ExtractIng. The evaluation results show that the metadata extraction is simulation-code independent in the sense that it can handle data outputs from various fields of science, is easy to integrate into simulation workflows and compatible with a multitude of computational environments.