Results 41 to 50 of about 22,266,474 (251)

Ontology‐Aligned Structuring and Reuse of Multimodal Materials Data and Workflows Toward Automatic Reproduction

Advanced Engineering Materials, EarlyView.
Reproduction of stacking fault energy calculations from literature with a semi‐automated large language model‐assisted extraction procedure: extraction of simulation protocol, atomistic structures, computational parameters, and reported results, ontology alignment, knowledge graph construction and, finally, recomputation forvalidation.
Sepideh Baghaee Ravari, Abril Azocar Guzman, Sarath Menon, Stefan Sandfeld, Tilmann Hickel, Markus Stricker   +5 more
wiley   +1 more source

Provenance-CSL - A Provenance Client Side Library [PDF]

, 2007
Data management is a challenge in both scientific and technical environments. Therefore researchers have developed a special interest in this field. Modern approaches (i.e.
Oster, Marius, Gude, Roland
core  

Requirements for Provenance on the Web [PDF]

, 2012
From where did this tweet originate? Was this quote from the New York Times modified? Daily, we rely on data from the Web, but often it is difficult or impossible to determine where it came from or how it was produced.
Cheney, J, Paul Groth, Gil, Yolanda, Groth, Paul, Yolanda Gil, Miles, Simon, James Cheney, Cheney, James, Simon Miles, Cheney, James; id_orcid, Miles, S, Gil, Y, Groth, Paul T., Groth, P.T.   +13 more
core   +2 more sources

Quantitative Provenance Analysis of the Yangtze and Yellow River Sediments Through Detrital Zircon U‐Pb Geochronology Using an XGBoost Machine Learning Algorithm

Journal of Geophysical Research: Machine Learning and Computation
Over the past two decades, a large number of zircon U‐Pb ages from the Yangtze and Yellow River Basins have been published, yet distinguishing the sources of sediment between these regions remains challenging.
X. T. Huang, Y. L. Guo, P. Wang, S. V. Hohl, X. L. Zhao, Y. L. Li, S. Y. Yang   +6 more
doaj   +1 more source

Data Provenance in Biomedical Research: Scoping Review

Journal of Medical Internet Research, 2023
BackgroundData provenance refers to the origin, processing, and movement of data. Reliable and precise knowledge about data provenance has great potential to improve reproducibility as well as quality in biomedical research and ...
Marco Johns, Thierry Meurers, Felix N Wirth, Anna C Haber, Armin Müller, Mehmed Halilovic, Felix Balzer, Fabian Prasser   +7 more
doaj   +1 more source

DigiChrom: A Domain Ontology for Semantic Representation of Trivalent Chromium Platings and Its Large Language Model‐Based Alignment With Multiple Mid‐Level Ontologies

Advanced Engineering Materials, EarlyView.
Digitalizing electroplating requires both domain knowledge and interoperability. This work introduces PlatOn, a domain ontology for trivalent chromium plating and coating characterization, and a hybrid pipeline that aligns it to a mid‐level reference ontology by combining eight similarity metrics with language model reasoning. Expert‐validated mappings
Janik Harter, Elena Garcia Trelles, Christoph Schweizer, Thomas Seifert, Christoph Baumer, Mahmoud Elkady, Katja Feige, Daniel Grimme, Edgar Hoffmann, Andreas Waibel, Anke Walter   +10 more
wiley   +1 more source

Interactions of the Effects of Provenances and Habitats on the Growth of Scots Pine in Two Provenance Tests in Bosnia and Herzegovina

South-East European Forestry, 2021
This research aims to determine the interaction of the effects of provenance and habitat conditions on provenance tests on the growth of Scots pine on two experimental plots in Bosnia and Herzegovina.
Mirzeta Memišević Hodžić, Dalibor Ballian   +1 more
doaj   +1 more source

OntOMat: Toward Ontology‐Based Product and Process Design Engineering and Optimization Solutions Fueling Circular Value Chains

Advanced Engineering Materials, EarlyView.
The OntOMat ontology establishes a structured framework for polymer matrix fiber reinforced composite materials, integrating manufacturing processes, characterization methods, and multiscale design through the VDI/VDE 3682 formalized process description standard.
Nicolas Christ, Jörg Hohe, Martin Kördel, Michael Giese, Niklas Toffert, Christian Seidel, Olaf Rehme, Patrik Schneider, Oliver Focke, Elie Abi Aoun, Claas Falldorf, Beñat Gutierrez, Alexandra Kühn, Sven Torstrick-von der Lieth, Christoph Herden, Erik Johannsen, Jan‐Uwe R. Schmidt, Felix Winkelmann, Andreas Schott, Daniel Schott   +19 more
wiley   +1 more source

The requirements of using provenance in e-Science experiments [PDF]

, 2006
In e-Science experiments, it is vital to record the experimental process for later use such as in interpreting results, verifying that the correct process took place or tracing where data came from.
Groth, Paul, Branco, Miguel, Miles, Simon, Moreau, Luc   +3 more
core   +2 more sources

Semantic Modeling in Materials Science and Engineering With Platform MaterialDigital Core Ontology 3.0

Advanced Engineering Materials, EarlyView.
The community‐driven Platform MaterialDigital Core Ontology (PMDco) 3.0 is introduced as a Basic Formal Ontology‐aligned semantic backbone for the processing–structure–properties paradigm in Materials Science and Engineering. Modular engineering, automated releases, and validation workflows are highlighted and key semantic patterns for materials ...
Markus Schilling, Philipp von Hartrott, Jörg Waitelonis, Thomas Hanke, Henk Birkholz, Hossein Beygi Nasrabadi, Khashayar Razghandi, Kamilla Zaripova, Felix Thonagel, Fabian Neuhaus, Martin Glauer, Lars Vogt, Harald Sack, Lutz Mädler, Bernd Bayerlein, Chris Eberl   +15 more
wiley   +1 more source