Opinion paper: Data provenance challenges in
biomedical research

Baum, Benjamin; Bauer, Christian R.; Franke, Thomas; Kusch, Harald; Parciak, Marcel; Rottmann, Thorsten; Umbach, Nadine; Sax, Ulrich

doi:10.1515/itit-2016-0031

Cited by 5 publications

(6 citation statements)

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“…This can be used to identify potentially invalid processing steps, data quality degradation, or limitations for secondary use [ 3 , 4 , 6 ]. Terms such as data lineage and data pedigree can have slightly different meanings in some of the literature (eg, pedigree is sometimes understood as also capturing information about the quality or trustworthiness of data sources [ 3 , 4 ]) but are often also used interchangeably with provenance (eg, the studies by Simmhan et al [ 6 ] and Baum et al [ 7 ]), which is the approach we follow in this paper.…”

Section: Introductionmentioning

confidence: 99%

“…Although data provenance tracking is a common practice in some disciplines, such as physics, geoscience, geography (particularly in geographic information systems), material science, hydrologic science, and environmental modeling [ 15 - 19 ], it has yet to be widely adopted in many other data-driven research disciplines, including biomedical research [ 7 ]. Consequently, previous reviews either focused on provenance outside the biomedical context (eg, the studies by Simmhan et al [ 6 ] and Herschel et al [ 3 ]) or studied a larger spectrum of data generation and preparation activities of which provenance is just one aspect (eg, the study by de Lusignan et al [ 4 ]).…”

Section: Introductionmentioning

confidence: 99%

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Data Provenance in Biomedical Research: Scoping Review

Johns¹,

Meurers²,

Wirth³

et al. 2023

J Med Internet Res

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Background Data provenance refers to the origin, processing, and movement of data. Reliable and precise knowledge about data provenance has great potential to improve reproducibility as well as quality in biomedical research and, therefore, to foster good scientific practice. However, despite the increasing interest on data provenance technologies in the literature and their implementation in other disciplines, these technologies have not yet been widely adopted in biomedical research. Objective The aim of this scoping review was to provide a structured overview of the body of knowledge on provenance methods in biomedical research by systematizing articles covering data provenance technologies developed for or used in this application area; describing and comparing the functionalities as well as the design of the provenance technologies used; and identifying gaps in the literature, which could provide opportunities for future research on technologies that could receive more widespread adoption. Methods Following a methodological framework for scoping studies and the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines, articles were identified by searching the PubMed, IEEE Xplore, and Web of Science databases and subsequently screened for eligibility. We included original articles covering software-based provenance management for scientific research published between 2010 and 2021. A set of data items was defined along the following five axes: publication metadata, application scope, provenance aspects covered, data representation, and functionalities. The data items were extracted from the articles, stored in a charting spreadsheet, and summarized in tables and figures. Results We identified 44 original articles published between 2010 and 2021. We found that the solutions described were heterogeneous along all axes. We also identified relationships among motivations for the use of provenance information, feature sets (capture, storage, retrieval, visualization, and analysis), and implementation details such as the data models and technologies used. The important gap that we identified is that only a few publications address the analysis of provenance data or use established provenance standards, such as PROV. Conclusions The heterogeneity of provenance methods, models, and implementations found in the literature points to the lack of a unified understanding of provenance concepts for biomedical data. Providing a common framework, a biomedical reference, and benchmarking data sets could foster the development of more comprehensive provenance solutions.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Data Provenance in Biomedical Research: Scoping Review

Johns¹,

Meurers²,

Wirth³

et al. 2023

J Med Internet Res

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“…Because data collection can occur over years, obtaining accurate data provenance is a key challenge. [7] This is especially true for large multisite collaborations. Samples within SEEK are assumed to be immutable (ie, deposited only once), which does not allow for the ongoing collection and updating of information in a simple, coherent, and organized way.…”

Section: Introductionmentioning

confidence: 99%

NExtSEEK: Extending SEEK for Active Management of Interoperable Metadata

Pradhan¹,

Ding²,

Zhu³

et al. 2022

J Biomol Tech

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Data management is a critical challenge required to improve the rigor and reproducibility of large projects. Adhering to Findable, Accessible, Interoperable, and Reusable (FAIR) standards provides a baseline for meeting these requirements.Although many existing repositories handle data in a FAIR-compliant manner, there are limited tools in the public domain to handle the metadata burden required to connect data from multi-omic projects that span multiple institutions and are deposited in diverse repositories. One promising approach is the SEEK platform, which allows for diverse metadata and provides an established repository. SEEK is challenged by the assumption of single deposition events where a sample is immutable once entered in the database. This is structured for published data but presents a limitation for ongoing studies where multiple sequential events may occur in a single sample at different sites. To address this issue, we have created a modified wrapper around the SEEK platform that allows for active data management by establishing more discrete sample types that are mutable to permit the expansion of the types of metadata, allowing researchers to track additional information. The use of discrete nodes also converts assays from nodes to edges, creating a network model of the study and more accurately representing the experimental process. With these changes to SEEK, users are able to collect and organize the information that researchers need to improve reusability and reproducibility as well as make data and metadata available to the scientific community through public repositories.

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“…While systems for automated workflows and provenance capture have gained traction in specialised domains such as bioinformatics, the use, or indeed recognition of the need for provenance more generally, such as in the biomedical field as a whole remains "quite low" [4].…”

Section: Introductionmentioning

confidence: 99%

“…http://www.orchid.ac.uk/ 3 https://github.com/taverna/taverna-prov4 "PROV support" https://github.com/VisTrails/VisTrails/issues/1075 5 https://www.w3.org/TR/2013/REC-prov-o-20130430/#Entity…”

mentioning

confidence: 99%

Data Provenance for Sport

Simmons¹,

Barnett²,

Vajda³

et al. 2018

Preprint

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Data analysts often discover irregularities in their underlying dataset, which need to be traced back to the original source and corrected. Standards for representing data provenance (i.e. the origins of the data), such as the W3C PROV standard, can assist with this process, however require a mapping between abstract provenance concepts and the domain of use in order to apply them effectively. We propose a custom notation for expressing provenance of information in the sport performance analysis domain, and map our notation to concepts in the W3C PROV standard where possible. We evaluate the functionality of W3C PROV (without specialisations) and the VisTrails workflow manager (without extensions), and find that as is, neither are able to fully capture sport performance analysis workflows, notably due to limitations surrounding capture of automated and manual activities respectively. Furthermore, their notations suffer from ineffective use of visual design space, and present potential usability issues as their terminology is unlikely to match that of sport practitioners. Our findings suggest that one-size-fits-all provenance and workflow systems are a poor fit in practice, and that their notation and functionality need to be optimised for the domain of use.

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Opinion paper: Data provenance challenges in biomedical research

Cited by 5 publications

References 0 publications

Data Provenance in Biomedical Research: Scoping Review

Data Provenance in Biomedical Research: Scoping Review

NExtSEEK: Extending SEEK for Active Management of Interoperable Metadata

Data Provenance for Sport

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