Provenance Information for Biomedical Data and Workflows: Scoping Review (Preprint)

BACKGROUND The record of the origin and the history of data, known as provenance, holds importance. Provenance information leads to higher interpretability of scientific results and enables reliable collaboration and data sharing. However, the lack of comprehensive evidence on provenance approaches hinders the uptake of good scientific practice in clinical research. OBJECTIVE This scoping review aims to identify approaches and criteria for provenance tracking in the biomedical domain. We reviewed the state-of-the-art frameworks, associated artifacts, and methodologies for provenance tracking. METHODS This scoping review followed the methodological framework developed by Arksey and O’Malley. We searched the PubMed and Web of Science databases for English-language articles published from 2006 to 2022. Title and abstract screening were carried out by 4 independent reviewers using the Rayyan screening tool. A majority vote was required for consent on the eligibility of papers based on the defined inclusion and exclusion criteria. Full-text reading and screening were performed independently by 2 reviewers, and information was extracted into a pretested template for the 5 research questions. Disagreements were resolved by a domain expert. The study protocol has previously been published. RESULTS The search resulted in a total of 764 papers. Of 624 identified, deduplicated papers, 66 (10.6%) studies fulfilled the inclusion criteria. We identified diverse provenance-tracking approaches ranging from practical provenance processing and managing to theoretical frameworks distinguishing diverse concepts and details of data and metadata models, provenance components, and notations. A substantial majority investigated underlying requirements to varying extents and validation intensities but lacked completeness in provenance coverage. Mostly, cited requirements concerned the knowledge about data integrity and reproducibility. Moreover, these revolved around robust data quality assessments, consistent policies for sensitive data protection, improved user interfaces, and automated ontology development. We found that different stakeholder groups benefit from the availability of provenance information. Thereby, we recognized that the term <i>provenance</i> is subjected to an evolutionary and technical process with multifaceted meanings and roles. Challenges included organizational and technical issues linked to data annotation, provenance modeling, and performance, amplified by subsequent matters such as enhanced provenance information and quality principles. CONCLUSIONS As data volumes grow and computing power increases, the challenge of scaling provenance systems to handle data efficiently and assist complex queries intensifies, necessitating automated and scalable solutions. With rising legal and scientific demands, there is an urgent need for greater transparency in implementing provenance systems in research projects, despite the challenges of unresolved granularity and knowledge bottlenecks. We believe that our recommendations enable quality and guide the implementation of auditable and measurable provenance approaches as well as solutions in the daily tasks of biomedical scientists. INTERNATIONAL REGISTERED REPORT RR2-10.2196/31750