Results 71 to 80 of about 413,957 (241)

AutoWIG: Automatic Generation of Python Bindings for C++ Libraries [PDF]

arXiv, 2017
Most of Python and R scientific packages incorporate compiled scientific libraries to speed up the code and reuse legacy libraries. While several semi-automatic solutions exist to wrap these compiled libraries, the process of wrapping a large library is cumbersome and time consuming.
arxiv  

Semantic Orchestration and Exploitation of Material Data: A Dataspace Solution Demonstrated on Steel and Copper Applications

Advanced Engineering Materials, EarlyView.
This article introduces the Dataspace Management System (DSMS), a methodological framework realized in software, designed as a technology stack to power dataspaces with a focus on advanced knowledge management in materials science and manufacturing. DSMS leverages heterogeneous data through semantic integration, linkage, and visualization, aligned with
Yoav Nahshon, Lukas Morand, Matthias Büschelberger, Dirk Helm, Kiran Kumaraswamy, Paul Zierep, Matthias Weber, Pablo de Andrés   +7 more
wiley   +1 more source

Seamless Science: Lifting Experimental Mechanical Testing Lab Data to an Interoperable Semantic Representation

Advanced Engineering Materials, EarlyView.
In pursuit of modern data management techniques, this study presents an in‐lab pipeline combining electronic laboratory notebooks (eLabFTW) and Python scripts for creating semantically enriched, interoperable, machine‐actionable data. Automating data mapping enhances usability, collaboration, and unified knowledge representation.
Markus Schilling, Sebastian Bruns, Bernd Bayerlein, Jehona Kryeziu, Jörg Schaarschmidt, Jörg Waitelonis, Pedro Dolabella Portella, Karsten Durst   +7 more
wiley   +1 more source

SimpleSBML: A Python package for creating, editing, and interrogating SBML models: Version 2.0 [PDF]

arXiv, 2020
In this technical report, we describe a new version of SimpleSBML which provides an easier to use interface to python-libSBML allowing users of Python to more easily construct, edit, and inspect SBML based models. The most commonly used package for constructing SBML models in Python is python-libSBML based on the C/C++ library libSBML.
arxiv  

Source code: automatic C library wrapping - Ctypes from the trenches [PDF]

, 2009
At some point of time many Python developers at least in computational science will face the situation that they want to interface some natively compiled library from Python.
Kloss, Guy K.
core  

Ontologies for FAIR Data in Additive Manufacturing: A Use Case‐Based Evaluation

Advanced Engineering Materials, EarlyView.
An ontology‐based approach for generating findable, accessible, interoperable, reusable data in additive manufacturing is explored, focusing on powder bed fusion. The article highlights the benefits of enhanced data findability and digital twin enablement, while addressing challenges like data integration complexity and the need for specialized ...
Thomas Bjarsch, Klaus Drechsler, Johannes Schilp   +2 more
wiley   +1 more source

Pantry: A Macro Library for Python [PDF]

, 2018
Python lacks a simple way to create custom syntax and constructs that goes outside of its own syntax rules. A paradigm that allows for these possibilities to exist within languages is macros.
Pang, Derek
core   +1 more source

Performance Evaluation of Upper‐Level Ontologies in Developing Materials Science Ontologies and Knowledge Graphs

Advanced Engineering Materials, EarlyView.
This article provides examples of ontology development in the materials science domain (use‐case of Brinell hardness testing) and gives ontology developers an overview for selecting their desired top‐level ontologies (e.g., BFO, EMMO, PROVO) by considering different evaluation parameters like semantic richness, domain coverage, extensibility ...
Hossein Beygi Nasrabadi, Ebrahim Norouzi, Harald Sack, Birgit Skrotzki   +3 more
wiley   +1 more source

OMB-Py: Python Micro-Benchmarks for Evaluating Performance of MPI Libraries on HPC Systems [PDF]

arXiv, 2021
Python has become a dominant programming language for emerging areas like Machine Learning (ML), Deep Learning (DL), and Data Science (DS). An attractive feature of Python is that it provides easy-to-use programming interface while allowing library developers to enhance performance of their applications by harnessing the computing power offered by High
arxiv  

Ontology‐Based Digital Infrastructure for Data‐Driven Glass Development

Advanced Engineering Materials, EarlyView.
This work addresses the inefficiencies in traditional glass development by implementing an ontology‐based digital infrastructure coupled with a high‐throughput robotic melting system. The approach integrates machine learning models, predictive tools, and a semantic database.
Ya‐Fan Chen, Felix Arendt, Hansjörg Bornhöft, Andréa S. S. de Camargo, Joachim Deubener, Andreas Diegeler, Shravya Gogula, Altair T. Contreras Jaimes, Sebastian Kempf, Martin Kilo, René Limbach, Ralf Müller, Rick Niebergall, Zhiwen Pan, Frank Puppe, Stefan Reinsch, Gerhard Schottner, Simon Stier, Tina Waurischk, Lothar Wondraczek, Marek Sierka   +20 more
wiley   +1 more source