PUMA publications for /group/researchcode/metadata%20hpc-computingWed Nov 06 08:56:02 CET 2024[000][29*][000]A metadata crawler for simulation data management2021hpc-computing metadata crawler NFDI4ING extraction HOMER (HPMC tool for Ontology-based Metadata Extraction and Re-use).Tue Feb 09 17:31:47 CET 2021The Journal of SupercomputingLike a rainbow in the dark: metadata annotation for HPC applications in the age of dark data2021forschungsdaten hpc-computing metadata tools automated The deluge of dark data is about to happen. Lacking data management capabilities, especially in the field of supercomputing, and missing data documentation (i.e., missing metadata annotation) constitute a major source of dark data. The present work contributes to addressing this challenge by presenting ExtractIng, a generic automated metadata extraction toolkit. Existing metadata information of simulation output files scattered through the file system, can be aggregated, parsed and converted to the EngMeta metadata model. Use cases from computational engineering are considered to demonstrate the viability of ExtractIng. The evaluation results show that the metadata extraction is simulation-code independent in the sense that it can handle data outputs from various fields of science, is easy to integrate into simulation workflows and compatible with a multitude of computational environments.Mon Dec 07 15:02:15 CET 2020Journal of Chemical {\&} Engineering Data1231313--1329Semantic Interoperability and Characterization of Data Provenance in Computational Molecular Engineering652019forschungsdaten hpc-computing metadata ontologie molecularSimulation Tue Sep 12 15:52:49 CEST 2017Computational Materials Science 218 - 230AiiDA: automated interactive infrastructure and database for computational science 1112016framework forschungsdaten hpc-computing metadata software reproducibility softwareLizenzMotivation repository Abstract Computational science has seen in the last decades a spectacular rise in the scope, breadth, and depth of its efforts. Notwithstanding this prevalence and impact, it is often still performed using the renaissance model of individual artisans gathered in a workshop, under the guidance of an established practitioner. Great benefits could follow instead from adopting concepts and tools coming from computer science to manage, preserve, and share these computational efforts. We illustrate here our paradigm sustaining such vision, based around the four pillars of Automation, Data, Environment, and Sharing. We then discuss its implementation in the open-source AiiDA platform (http://www.aiida.net), that has been tuned first to the demands of computational materials science. AiiDA’s design is based on directed acyclic graphs to track the provenance of data and calculations, and ensure preservation and searchability. Remote computational resources are managed transparently, and automation is coupled with data storage to ensure reproducibility. Last, complex sequences of calculations can be encoded into scientific workflows. We believe that AiiDA’s design and its sharing capabilities will encourage the creation of social ecosystems to disseminate codes, data, and scientific workflows.