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@article{2212026,
author = {Wittner, Rudolf and Mascia, Cecilia and Gallo, Matej and Frexia, Francesca and Müller, Heimo and Plass, Markus and Geiger, Jörg and Holub, Petr},
article_location = {Berlin},
article_number = {9},
doi = {http://dx.doi.org/10.1038/s41597-022-01537-6},
keywords = {provenance;reproducibility;traceability;PROV},
language = {eng},
issn = {2052-4463},
journal = {Scientific Data},
title = {Lightweight Distributed Provenance Model for Complex Real–world Environments},
url = {https://doi.org/10.1038/s41597-022-01537-6},
volume = {2022},
year = {2022}
}
TY - JOUR
ID - 2212026
AU - Wittner, Rudolf - Mascia, Cecilia - Gallo, Matej - Frexia, Francesca - Müller, Heimo - Plass, Markus - Geiger, Jörg - Holub, Petr
PY - 2022
TI - Lightweight Distributed Provenance Model for Complex Real–world Environments
JF - Scientific Data
VL - 2022
IS - 9
SP - 1-19
EP - 1-19
PB - Nature Research
SN - 20524463
KW - provenance;reproducibility;traceability;PROV
UR - https://doi.org/10.1038/s41597-022-01537-6
N2 - 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.
ER -
WITTNER, Rudolf, Cecilia MASCIA, Matej GALLO, Francesca FREXIA, Heimo MÜLLER, Markus PLASS, Jörg GEIGER and Petr HOLUB. Lightweight Distributed Provenance Model for Complex Real–world Environments. \textit{Scientific Data}. Berlin: Nature Research, 2022, vol.~2022, No~9, p.~1-19. ISSN~2052-4463. Available from: https://dx.doi.org/10.1038/s41597-022-01537-6.
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