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Lightweight Distributed Provenance Model for Complex Real–world Environments.

Authors :
Wittner, Rudolf
Mascia, Cecilia
Gallo, Matej
Frexia, Francesca
Müller, Heimo
Plass, Markus
Geiger, Jörg
Holub, Petr
Source :
Scientific Data; 8/17/2022, Vol. 9 Issue 1, p1-19, 19p
Publication Year :
2022

Abstract

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. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
20524463
Volume :
9
Issue :
1
Database :
Complementary Index
Journal :
Scientific Data
Publication Type :
Academic Journal
Accession number :
158563760
Full Text :
https://doi.org/10.1038/s41597-022-01537-6