Results 131 to 140 of about 9,669,507 (253)
dynoGP: Deep Gaussian Processes for Dynamic System Identification
This work introduces a novel class of deep models for system identification, dynamical deep Gaussian processes, which combine the strengths of data‐driven methods, such as those based on neural network architectures, with the ability to output a probability distribution for uncertainty representation.
Alessio Benavoli +3 more
wiley +1 more source
This article presents the NFDI‐MatWerk Ontology (MWO), a Basic Formal Ontology‐based framework for interoperable research data management in materials science and engineering (MSE). Covering consortium structures, research data management resources, services, and instruments, MWO enables semantic integration, Findable, Accessible, Interoperable, and ...
Hossein Beygi Nasrabadi +4 more
wiley +1 more source
Probabilistic and deep learning approaches for conductivity-driven nanocomposite classification
To foster greater trust and adoption of machine learning models, particularly neural networks, it is essential to develop approaches that quantify and report epistemic uncertainties alongside random uncertainties, which often affect the accuracy of ...
Wejden Gazehi +2 more
doaj +1 more source
A unified research data management framework for heterogeneous materials data is presented. The system integrates multimodal datasets using ontologies and knowledge graphs, enabling interoperability and FAIR (findable, accessible, interoperable, reusable) data principles. By linking data across scales and workflows, it supports reproducible, Artifitial
Doaa Mohamed +6 more
wiley +1 more source
Towards Defect Phase Diagrams: From Research Data Management to Automated Workflows
A research data management infrastructure is presented for the systematic integration of heterogeneous experimental and simulation data required for defect phase diagrams. The approach combines openBIS with a companion application for large‐object storage, automated metadata extraction, provenance tracking and federated data access, thereby supporting ...
Khalil Rejiba +5 more
wiley +1 more source
Fostering Innovation: Streamlining Magnetocaloric Materials Research by Digitalization
Magnetocaloric cooling (MCE) is an environmentally friendly refrigeration method with great potential. Optimizing MCE materials involves the preparation and screening of large quantities of samples, which in turn generates a large amount of data. A digitalization approach is presented that uses ontologies, knowledge graphs, and digital workflows to ...
Simon Bekemeier +17 more
wiley +1 more source
The PRIMA Thesaurus for Materials Science and Engineering
The PRIMA Thesaurus is a structured vocabulary designed to improve how materials science data is described and shared. Developed with input from multiple experts, it enables clear documentation of research workflows, data exchange, and reuse across platforms.
Rossella Aversa +8 more
wiley +1 more source
BIOTECHNOLOGY AND COMMUNICATION: THE META-TECHNOLOGIES OF INFORMATION
Introduction / Sandra Braman -- Acknowledgments / Pt. I. The technologies of biology and communication -- Ch. 1. The meta- technologies of information / Sandra Braman -- Pt. II. The concept of information -- Ch. 2.
Braman, Sandra, ed.
core
A Lightweight Procedural Layer for Hybrid Experimental–Computational Workflows in Materials Science
We unveil a prototype hybrid‐workflow framework that fuses automatedcomputation with hands‐on experiments. Built atop pyiron, a lightweight, parameterized layer translates procedure descriptions into executable manual steps, syncing instrument settings, human interventions, and data capture in real‐time today.
Steffen Brinckmann +8 more
wiley +1 more source
PASTA‐ELN: Simplifying Research Data Management for Experimental Materials Science
Research data management faces ongoing hurdles as many ELNs remain complex and restrictive. PASTA‐ELN offers an open‐source, cross‐platform solution that prioritizes simplicity, offline access, and user control. Its in tuitive folder structure, modular Python add‐ons, and open formats enable seamless documentation, FAIR data practices, and easy ...
S. Brinckmann, G. Winkens, R. Schwaiger
wiley +1 more source

