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Titel:

HOMER - HPMC tool for Ontology-based Metadata Extraction and Re-use.

Dokumenttyp:
Forschungsdaten
Veröffentlichungsdatum:
06.02.2025
Verantwortlich:
Farnbacher, Benjamin
Autorinnen / Autoren:
Hoppe, Nils; Chiapparino, Giuseppe; Ulrich, Friedrich; Jiao, Yu; Farnbacher, Benjamin; Ralev, Radoslav; Fang, Donghao; Rosche, Eric; Wunderlich, Florian; Sosa Rodriguez, Angel; Stemmer, Christian
Institutionszugehörigkeit:
TUM
Herausgeber:
TUM
Identifikator:
doi:10.14459/2022mp1694401.002
Konzept-DOI:
doi:10.14459/2022mp1694401
Enddatum der Datenerzeugung:
20.01.2025
Fachgebiet:
DAT Datenverarbeitung, Informatik; MAS Maschinenbau; MTA Technische Mechanik, Technische Thermodynamik, Technische Akustik
zusätzliche Fachgebiete:
Research Data Management, High Performance Computing
Quellen der Daten:
Logfiles und Nutzungsdaten / log files and usage data
Andere Quellen der Daten:
Python code + documentation
Datentyp:
Programme und Anwendungen / software and applications
Methode der Datenerhebung:
Versioned entity of the research software developed by the Chair of Aerodynamics and Fluid Mechanics at Technical University of Munich. Updates are continuously released at https://gitlab.lrz.de/nfdi4ing/crawler
Beschreibung:
HOMER is a python-written metadata crawler that allows to automatically retrieve relevant research metadata from script-based workflows on HPC systems. The tool offers a flexible approach to metadata collection, as the metadata scheme can be read out from an ontology file.
Links:
Updates are continuously released at https://gitlab.lrz.de/nfdi4ing/crawler
The corresponding article can be found here: https://www.inggrid.org/article/id/3983/
Schlagworte:
Research Data Management, Metadata, HPMC, Ontology
Technische Hinweise:
View and download (5.9 MB total, 112 Files)
The data server also offers downloads with FTP
The data server also offers downloads with rsync (password m1694401.002):
rsync rsync://m1694401.002@dataserv.ub.tum.de/m1694401.002/
Sprache:
en
Andere Rechte:
GNU GPLv3 (or newer)
Text: CC-BY-SA (4.0)
Note:
The authors would like to thank the Federal Government and the Heads of Government of the Länder, as well as the Joint Science Conference (GWK), for their funding and support within the framework of the NFDI4Ing consortium. Funded by the German Research Foundation (DFG) project number 442146713.
The authors would also thank the Competence Network for Scientific High Performance Computing in Bavaria (KONWIHR) for their funding and support within a short term project.
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