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Approaches and Criteria for Provenance in Biomedical Data Sets and Workflows: Protocol for a Scoping Review

Journal

JMIR RESEARCH PROTOCOLS
Volume 10, Issue 11, Pages -

Publisher

JMIR PUBLICATIONS, INC
DOI: 10.2196/31750

Keywords

provenance; biomedical; workflow; data sharing; lineage; scoping review; data genesis; scientific data; digital objects; healthcare data

Funding

  1. German Federal Ministry of Education and Research within the German Medical Informatics Initiative [01ZZ1801E]
  2. Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) [SFB 1270/1-99150580]
  3. National Research Data Infrastructure for Personal Health Data (NFDI4Health) DFG [442326535]

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This study aims to investigate existing evidence on approaches and criteria for provenance tracking, as well as identifying current knowledge gaps in the biomedical domain. The research covers modeling aspects and metadata frameworks for meaningful and usable provenance information during the creation and processing of (sensitive) scientific biomedical data.
Background: Provenance supports the understanding of data genesis, and it is a key factor to ensure the trustworthiness of digital objects containing (sensitive) scientific data. Provenance information contributes to a better understanding of scientific results and fosters collaboration on existing data as well as data sharing. This encompasses defining comprehensive concepts and standards for transparency and traceability, reproducibility, validity, and quality assurance during clinical and scientific data workflows and research. Objective: The aim of this scoping review is to investigate existing evidence regarding approaches and criteria for provenance tracking as well as disclosing current knowledge gaps in the biomedical domain. This review covers modeling aspects as well as metadata frameworks for meaningful and usable provenance information during creation, collection, and processing of (sensitive) scientific biomedical data. This review also covers the examination of quality aspects of provenance criteria. Methods: This scoping review will follow the methodological framework by Arksey and O'Malley. Relevant publications will be obtained by querying PubMed and Web of Science. All papers in English language will be included, published between January 1, 2006 and March 23, 2021. Data retrieval will be accompanied by manual search for grey literature. Potential publications will then be exported into a reference management software, and duplicates will be removed. Afterwards, the obtained set of papers will be transferred into a systematic review management tool. All publications will be screened, extracted, and analyzed: title and abstract screening will be carried out by 4 independent reviewers. Majority vote is required for consent to eligibility of papers based on the defined inclusion and exclusion criteria. Full-text reading will be performed independently by 2 reviewers and in the last step, key information will be extracted on a pretested template. If agreement cannot be reached, the conflict will be resolved by a domain expert. Charted data will be analyzed by categorizing and summarizing the individual data items based on the research questions. Tabular or graphical overviews will be given, if applicable. Results: The reporting follows the extension of the Preferred Reporting Items for Systematic reviews and Meta-Analyses statements for Scoping Reviews. Electronic database searches in PubMed and Web of Science resulted in 469 matches after deduplication. As of September 2021, the scoping review is in the full-text screening stage. The data extraction using the pretested charting template will follow the full-text screening stage. We expect the scoping review report to be completed by February 2022. Conclusions: Information about the origin of healthcare data has a major impact on the quality and the reusability of scientific results as well as follow-up activities. This protocol outlines plans for a scoping review that will provide information about current approaches, challenges, or knowledge gaps with provenance tracking in biomedical sciences.

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