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Lightweight Distributed Provenance Model for Complex Real–world Environments.

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Title: 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
Subject Terms: DISTRIBUTED power generation, BIOMATERIALS, ECOLOGY, REPRODUCIBLE research
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]
Copyright of Scientific Data is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Lightweight Distributed Provenance Model for Complex Real–world Environments.
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  Data: <searchLink fieldCode="AR" term="%22Wittner%2C+Rudolf%22">Wittner, Rudolf</searchLink><br /><searchLink fieldCode="AR" term="%22Mascia%2C+Cecilia%22">Mascia, Cecilia</searchLink><br /><searchLink fieldCode="AR" term="%22Gallo%2C+Matej%22">Gallo, Matej</searchLink><br /><searchLink fieldCode="AR" term="%22Frexia%2C+Francesca%22">Frexia, Francesca</searchLink><br /><searchLink fieldCode="AR" term="%22Müller%2C+Heimo%22">Müller, Heimo</searchLink><br /><searchLink fieldCode="AR" term="%22Plass%2C+Markus%22">Plass, Markus</searchLink><br /><searchLink fieldCode="AR" term="%22Geiger%2C+Jörg%22">Geiger, Jörg</searchLink><br /><searchLink fieldCode="AR" term="%22Holub%2C+Petr%22">Holub, Petr</searchLink>
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  Data: Scientific Data; 8/17/2022, Vol. 9 Issue 1, p1-19, 19p
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  Data: <searchLink fieldCode="DE" term="%22DISTRIBUTED+power+generation%22">DISTRIBUTED power generation</searchLink><br /><searchLink fieldCode="DE" term="%22BIOMATERIALS%22">BIOMATERIALS</searchLink><br /><searchLink fieldCode="DE" term="%22ECOLOGY%22">ECOLOGY</searchLink><br /><searchLink fieldCode="DE" term="%22REPRODUCIBLE+research%22">REPRODUCIBLE research</searchLink>
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  Data: 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]
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  Data: <i>Copyright of Scientific Data is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: 8/17/2022
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