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Using Schema-based Metadata for Image Labels accessed with FAIR Digital Objects

Blumenröhr, Nicolas ORCID iD icon 1; Aversa, Rossella ORCID iD icon 1
1 Scientific Computing Center (SCC), Karlsruher Institut für Technologie (KIT)


Scientific image data sets can be continuously enriched by labels describing new features which are relevant for some specific task. This process can be automated by means of Machine Learning (ML) techniques. Although such an approach shows clear advantages, especially when it is applied to large datasets, it also poses an important challenge:
Relabeling image data sets curated by different scientists, in order to collectively use them for ML, requires a common agreement on the labels which can be used. This can be achieved thanks to the use of a standardized way to describe the label information: a metadata schema including vocabularies. Furthermore, machine-actionable decisions on the label information for relabeling can be enabled by the representation of images and schema-based metadata as FAIR Digital Objects (DOs).
We introduce a metadata schema including vocabularies to describe ML image data represented as FAIR DOs that can be accessed for relabeling. The specifications of the metadata schema are presented. The relevance of a standardized metadata description including vocabularies for relabeling ML image data is emphasized. ... mehr

Volltext §
DOI: 10.5445/IR/1000155009
Veröffentlicht am 23.01.2023
DOI: 10.5281/zenodo.7243871
Cover der Publikation
Zugehörige Institution(en) am KIT Scientific Computing Center (SCC)
Publikationstyp Poster
Publikationsdatum 05.10.2022
Sprache Englisch
Identifikator KITopen-ID: 1000155009
HGF-Programm 46.21.05 (POF IV, LK 01) HMC
Weitere HGF-Programme 46.21.01 (POF IV, LK 01) Domain-Specific Simulation & SDLs and Research Groups
Veranstaltung 1st Helmholtz Metadata Collaboration Konferenz 2023 (HMC 2022), Online, 05.10.2022 – 06.10.2022
Projektinformation NFFA-Europe (EU, H2020, 654360)
Schlagwörter FAIR Digital Objects, Metadata, Schemas, Vocabularies, Machine Learning
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