Provenance Levels for Biomedical In-vitro Experiments

Auge, Tanja; Genehr, Sascha; Klettke, Meike; Krüger, Frank; Schröder, Max

doi:10.5281/zenodo.16736028

Provenance Levels for Biomedical In-vitro Experiments

Auge, Tanja; Genehr, Sascha; Klettke, Meike; Krüger, Frank; Schröder, Max

Publisher: Zenodo
DOI: 10.5281/zenodo.16736028
Repository: zenodo.org
Language: en

Abstract

Provenance data are an essential information source of scientific experiments and thus for maintaining the credibility of research findings [1]. There are several approaches for capturing provenance information, that either focus on the process (workflow provenance) or the actual data transformations (data provenance) [2]. The first is mainly used for describing activities including agents and their interdependencies by means of ontologies, such as PROV-O [3], [4], to answer the W7-questions [5]. The latter is grounded in database theory and employs a tuple based reconstruction to answer the questions why, how or where a data item/query result was created [6]. Thus, workflow provenance fails to answer questions about particular atomic data tuples, such as "Which parts of the concrete dataset were analyzed statistically?". On the other hand, the data provenance fails at incorporating the procedural view, such as who was involved in the analysis or relation between several datasets. We propose to combine these two provenance types into a unified framework enabling an integrated view on different granularity levels of the scientific experiments. In particular, we propose to include the modeling of data provenance similar to the approach of Ives et al. [7] in the PROV-O workflow provenance approach. This unified framework enables the reasoning over W7+1-questions [8] and their combinations in the particular level of detail needed, e.g. to identify the necessary parts of data sets for a particular analysis and the involved workflow including responsibilities and experimental settings. In order to illustrate the conceptual framework, we employ the use case of in-vitro experiments from the biomedical domain. We concentrate on in-vitro experiments, i.e. performing lab experiments including measuring characteristics followed by analyzing the data. In the biomedical domain these results are often used in simulations and the simulation findings than proven in lab experiments again. An excerpt of our concrete use case is shown in Figure 1. The upper part encodes the analyzation step of the raw data in terms of workflow provenance. While the raw data (prov:entity) are provided by the lab scientist (prov:agent), the analysis is performed (prov:activity) by the data analyst (prov:agent). The resulting data (prov:entity) are also attributed to the data analyst. The lower part of Figure 1 encodes the data provenances graph using our proposed modeling mechanism. In particular, the activities relate to database actions such as join (*) and union (+) of tuples. Additional comments specify the corresponding witnesses [6], which answers typical why-questions in data provenance. For the modeling of the different levels of details [8] including also workflow provenance and data provenance, we propose to employ the BFO [9] relations bfo:hasPart and bfo:partOf. These provenance levels are linked by dotted edges. In the example, the five data tuples on the data provenance level are bfo:partOf the verified data entity on the workflow level. In addition, we propose to employ bfo:hasPart between the levels of activities such as ∗ and the activity analysis. Summarizing this conceptual framework, it provides a unified view. Thus, it allows to reason over all W7+1-questions [8] including all necessary details without limiting provenance on either to process or tuple parts.

Full text

Conference on Research Data Infrastructure 2025 RDM in Context eventual DOI © Authors. This work is licensed under a Creative Commons Attribution 4.0 International License Provenance Levels for Biomedical In-vitro Experiments Tanja Auge1, Sascha Genehr2,3, Meike Klettke1, Frank Kr¨ uger4, and Max Schr¨ oder3 1University of Regensburg, Germany 2University of Rostock, Germany 3Rostock University Library, Germany 4Wismar University of Applied Sciences, Germany *Correspondence: Tanja Auge, [email protected] Abstract Provenance data are an essential information source of scientific experiments and thus for maintaining the credibility of research findings [1]. There are several approaches for capturing provenance information, that either focus on the process (workflow provenance) or the actual data transformations (data provenance) [2]. The first is mainly used for describing activities including agents and their interdependencies by means of ontologies, such as PROV-O [3], [4], to answer the W7-questions [5]. The latter is grounded in database theory and employs a tuple based reconstruction to answer the questions why,how or where a data item/query result was created [6]. Thus, workflow provenance fails to answer questions about particular atomic data tuples, such as “Which parts of the concrete dataset were analyzed statistically?”. On the other hand, the data provenance fails at incorporating the procedural view, such as who was involved in the analysis or relation between several datasets. We propose to combine these two provenance types into a unified framework enabling an integrated view on different granularity levels of the scientific experiments. In particular, we propose to include the modeling of data provenance similar to the approach of Ives et al. [7] in the PROV-O workflow provenance approach. This unified framework enables the reasoning over W7+1-questions [8] and their combinations in the particular level of detail needed, e.g. to identify the necessary parts of data sets for a particular analysis and the involved workflow including responsibilities and experimental settings. In order to illustrate the conceptual framework, we employ the use case of in-vitro experiments from the biomedical domain. We concentrate on in-vitro experiments, i.e. performing lab experiments including measuring characteristics followed by analyzing the data. In the biomedical domain these results are often used in simulations and the simulation findings than proven in lab experiments again. An excerpt of our concrete use case is shown in Figure 1. The upper part encodes the analyzation step of the raw data in terms of workflow provenance. While the raw data (prov:Entity) are provided by the lab scientist (prov:Agent), the analysis is performed (prov:Activity) by the data analyst (prov:Agent). The resulting data (prov:Entity) are also attributed Auge et al. |CoRDI 2025 data: verified data tuple: R_1 tuple: S_1 tuple: R_2 tuple: S_2 tuple: R_3 organisation: universitypeople: lab scientist people: analyst used data: result wasGeneratedBy wasAttributedTo actedOnBehalfOf wasAttributedTo wasAssociatedWith bfo:partOf * * + used used used used witness: {r_1, s_1} witness: {r_3, s_2} witness: {{r_1, s_1},{r_3, s_2}} rdfs:comment rdfs:comment rdfs:comment wasInformedBy wasInformedBy tuple: TwasGeneratedBy bfo:hasPart workflow provenance data provenance prov:Entity prov:Agent prov:Activity xsd:string bfo:hasPart bfo:hasPart bfo:hasPart Legend: activity: analysis Figure 1. Provenance graph for a biomedical in-vitro experiment. Proposed connections between workflow and data provenance are highlighted in bold font and dotted edges. All relations without prefix are from the PROV ontology [4]. to the data analyst. The lower part of Figure 1 encodes the data provenances graph using our proposed modeling mechanism. In particular, the activities relate to database actions such as join (*) and union (+) of tuples. Additional comments specify the corresponding witnesses [6], which answers typical why-questions in data provenance. For the modeling of the different levels of details [8] including also workflow provenance and data provenance, we propose to employ the BFO [9] relations bfo:hasPart and bfo:partOf. These provenance levels are linked by dotted edges. In the example, the five data tuples on the data provenance level are bfo:partOf the verified data entity on the workflow level. In addition, we propose to employ bfo:hasPart between the levels of activities such as *and the activity analysis. Summarizing this conceptual framework, it provides a unified view. Thus, it allows to reason over all W7+1-questions [8] including all necessary details without limiting provenance on either to process or tuple parts. Author contributions TA Conceptualization, Visualization, Writing - Original Draft. SG Conceptualization, Visualization, Writing - Original Draft. MK Writing - Review & Editing. FK Funding acquisition, Writing - Review & Editing. MS Conceptualization, Visualization, Writing - Original Draft. All authors read and approved the final manuscript. Auge et al. |CoRDI 2025 Competing interests The authors declare that they have no competing interests. Funding SG is funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - SFB 1270/2 – 299150580. References [1] K. Gierend, F. Kr¨ uger, S. Genehr, et al., “Provenance information for biomedical data and workflows: Scoping review,” Journal of Medical Internet Research, vol. 26, e51297, Aug. 2024, ISSN: 1438-8871. DOI:10.2196/51297. [2] M. Herschel, R. Diestelk¨ amper, and H. Ben Lahmar, “A survey on provenance: What for? what form? what from?” VLDB J., vol. 26, no. 6, pp. 881–906, 2017. DOI:10.1007/s00778017-0486-1. [3] L. Moreau, P. Groth, J. Cheney, T. Lebo, and S. Miles, “The rationale of prov,” Journal of Web Semantics, vol. 35, pp. 235–257, Dec. 2015, ISSN: 1570-8268. DOI:10.1016/j. websem.2015.04.001. [4] T. Lebo, S. Sahoo, D. McGuinness, et al. “PROV-O: The PROV Ontology.” (2013), [Online]. Available: http://www.w3.org/TR/2013/REC-prov-o-20130430/. [5] S. Ram and J. Liu, “Understanding the Semantics of Data Provenance to Support Active Conceptual Modeling,” in LNCS, Springer Berlin Heidelberg, 2007, pp. 17–29. DOI:10. 1007/978-3-540-77503-4_3. [6] J. Cheney, L. Chiticariu, and W. C. Tan, “Provenance in databases: Why, how, and where,” Found. Trends Databases, vol. 1, no. 4, pp. 379–474, 2009. DOI:10.1561/1900000006. [7] Z. Ives, V. Tannen, O. Biton, et al. “ORCHESTRA: Managing the Collaborative Sharing of Evolving Data.” (2007), [Online]. Available: https://www.cis.upenn.edu/~zives/ orchestra/. [8] T. Auge, S. Genehr, M. Klettke, F. Kr¨ uger, and M. Schr¨ oder. “Towards dimensions and granularity in a unified workflow and data provenance framework.” arXiv: 2504.11278 [cs.DB]. (2025). [9] J. N. Otte, J. Beverley, and A. Ruttenberg, “Bfo: Basic formal ontology1,” Applied Ontology, vol. 17, no. 1, pp. 17–43, 2022. DOI:10.3233/AO-220262.