Medizinische Universität Graz Austria/Österreich - Forschungsportal - Medical University of Graz

Logo MUG-Forschungsportal

Gewählte Publikation:

SHR Neuro Krebs Kardio Lipid Stoffw Microb

Wittner, R; Mascia, C; Gallo, M; Frexia, F; Müller, H; Plass, M; Geiger, J; Holub, P.
Lightweight Distributed Provenance Model for Complex Real-world Environments.
Sci Data. 2022; 9(1): 503 Doi: 10.1038/s41597-022-01537-6 [OPEN ACCESS]
Web of Science PubMed PUBMED Central FullText FullText_MUG


Co-Autor*innen der Med Uni Graz
Müller Heimo
Plass Markus

Dimensions Citations:

Plum Analytics:

Scite (citation analytics):

Provenance is information describing the lineage of an object, such as a dataset or biological material. Since these objects can be passed between organizations, each organization can document only parts of the objects life cycle. As a result, interconnection of distributed provenance parts forms distributed provenance chains. Dependant on the actual provenance content, complete provenance chains can provide traceability and contribute to reproducibility and FAIRness of research objects. In this paper, we define a lightweight provenance model based on W3C PROV that enables generation of distributed provenance chains in complex, multi-organizational environments. The application of the model is demonstrated with a use case spanning several steps of a real-world research pipeline - starting with the acquisition of a specimen, its processing and storage, histological examination, and the generation/collection of associated data (images, annotations, clinical data), ending with training an AI model for the detection of tumor in the images. The proposed model has become an open conceptual foundation of the currently developed ISO 23494 standard on provenance for biotechnology domain.
Find related publications in this database (using NLM MeSH Indexing)
Reproducibility of Results - administration & dosage

© Med Uni Graz Impressum