Lightweight Distributed Provenance Model for Complex Real–world
Environments

J 2022

Lightweight Distributed Provenance Model for Complex Real–world Environments

WITTNER, Rudolf, Cecilia MASCIA, Matej GALLO, Francesca FREXIA, Heimo MÜLLER et. al.

Basic information

Original name

Lightweight Distributed Provenance Model for Complex Real–world Environments

Authors

WITTNER, Rudolf (703 Slovakia, guarantor, belonging to the institution), Cecilia MASCIA (380 Italy), Matej GALLO (703 Slovakia, belonging to the institution), Francesca FREXIA (380 Italy), Heimo MÜLLER (40 Austria), Markus PLASS (40 Austria), Jörg GEIGER (276 Germany) and Petr HOLUB (203 Czech Republic, belonging to the institution)

Edition

Scientific Data, Berlin, Nature Research, 2022, 2052-4463

Other information

Language

English

Type of outcome

Článek v odborném periodiku

Field of Study

10201 Computer sciences, information science, bioinformatics

Country of publisher

Germany

Confidentiality degree

není předmětem státního či obchodního tajemství

References:

URL

Impact factor

Impact factor: 9.800

RIV identification code

RIV/00216224:14610/22:00126457

Organization unit

Institute of Computer Science

DOI

http://dx.doi.org/10.1038/s41597-022-01537-6

UT WoS

000842397500003

Keywords (in Czech)

provenance;reprodukovatelnost;dohledatelnost;PROV

Keywords in English

provenance;reproducibility;traceability;PROV

Abstract

V originále

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.

Links

90140, large research infrastructures

Name: e-INFRA CZ

Citovat

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. 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.

@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.

Detailed Information on Publication Record