The present work introduces HOMER (High Performance Measurement and Computing tool for Ontology-based Metadata Extraction and Re-use), 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. Through minimal user input, the crawler can be adapted to the user's needs and easily implemented within the workflow, enabling to retrieve relevant metadata. The obtained information can be further automatically post-processed. For example, strings may be trimmed by regular expressions or numerical values may be averaged. Currently, data can be collected from text-files and HDF5 files, as well as directly hardcoded by the user. However, the tool has been designed in a modular way, so that it allows straightforward extension of the supported file-types, the instruction processing routines and the post-processing operations.     «

The present work introduces HOMER (High Performance Measurement and Computing tool for Ontology-based Metadata Extraction and Re-use), 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. Through minimal user input, the crawler can be adapted to the user's needs and easily implemented wi...     »

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. Moreover, the authors gratefully acknowledge the Gauss Centre for Supercomputing e.V. (www.gauss-centre.eu) for funding this project by providing computing time on the GCS Supercomputer SuperMUC-NG at Leibniz Supercomputing Centre (www.lrz.de).     «

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. Moreover, the authors gratefully acknowledge the Gauss Centre for Supercomputing e.V. (www.gauss-centre.eu) for funding this project by providing computing time on the GCS Supercomputer SuperMUC-NG at...     »