JMIR Publications

ABSTRACT

Background:

Data provenance is information about 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 increasing interest in the literature and implementation in other disciplines, data provenance 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 (1) systematizing articles covering data provenance technologies developed for or used in this application area, (2) describing and comparing the functionalities as well as the design of the provenance technologies utilized, and (3) identifying gaps in the literature that could provide opportunities for future research on technologies that could receive more widespread adoption.

Methods:

Following the PRISMA extension for scoping reviews, articles were identified by database searches in PubMed, IEEE Xplore and Web of Science 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 five axes: (1) publication metadata, (2) application scope, (3) provenance aspects covered, (4) data representation and (5) 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 are heterogeneous along all axes. We also identified relationships between motivations for the use of provenance information, as well as feature sets (capture, storage, retrieval, visualization, and analysis) and implementation details, such as the data models and technologies used. Important gaps that we identified are that 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 towards a lack of a unified understanding of provenance concepts for biomedical data. Providing a common framework as well as biomedical reference and benchmarking datasets could foster the development of more comprehensive provenance solutions.