February 20, 2025 Software Closed Access

Ghosh, Tathagata

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    "language": "eng", 
    "title": "Data publication: DRACO DataMaster: A Metadata-Driven Approach Utilizing Ontologies and Knowledge Graphs for the Laser Particle Acceleration", 
    "creators": [
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        "affiliation": "Otto-von-Guericke University Magdeburg", 
        "orcid": "0009-0008-1545-8782", 
        "name": "Ghosh, Tathagata"
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    "version": "1.0", 
    "publication_date": "2025-02-20", 
    "contributors": [
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        "name": "Knodel, Oliver", 
        "affiliation": "HZDR", 
        "type": "ContactPerson", 
        "orcid": "0000-0001-8174-7795"
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      {
        "name": "Lokamani, Lokamani", 
        "affiliation": "HZDR", 
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        "orcid": "0000-0001-8679-5905"
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      {
        "name": "Pape, David", 
        "affiliation": "HZDR", 
        "type": "Supervisor", 
        "orcid": "0000-0002-3145-9880"
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      "Data management", 
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    "notes": "Master Thesis at Otto-von-Guericke University Magdeburg", 
    "description": "<p>DRACO (Dresden laser acceleration source) is a state-of-the-art highpower ultra-short pulse laser experiment at the Helmholtz-Zentrum Dresden-Rossendorf (HZDR), dedicated and optimized for investigating relativistic laserplasma physics. The software publication focuses on developing a <em>DRACO DataMaster</em> extension that would enable advanced data handling for the DRACO experiment at HZDR. The data generated in experimental runs at the DRACO experiment comprises integer, double, float, and array values stored in tabular formin the DRACO internal database. The <em>DRACO</em> <em>DataMaster</em> establishes an automated pipeline for creating knowledge graphs from unsorted tabular data that will be enriched with metadata in compliance with ontologies tailored for DRACO experiments. This approach, aligned with FAIR principles (Findable, Accessible, Interoperable, and Reusable), would enable deeper scientific insight and decision-making by enhancing data integration, structuring, and visualization. The proposed pipeline would significantly increase the efficiency and effectiveness of the data-driven research at HZDR, advancing experimental endeavors by providing a robust toolset for scientists.</p>", 
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