Capturing provenance information for biomedical data and workflows: A scoping review

Abstract

Background: Provenance enriched scientific results ensure their reproducibility and trustworthiness, particularly when containing sensitive data. 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. Our scoping review identifies evidence regarding approaches and criteria for provenance tracking in the biomedical domain. We investigate the state-of-the-art frameworks, associated artifacts, and methodologies for provenance tracking. Methods: This scoping review followed the methodological framework by Arksey and O'Malley. PubMed and Web of Science databases were searched for English-language articles published from January 1, 2006, to March 23, 2021. Title and abstract screening were carried out by four 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 two reviewers, and information was extracted into a pre-tested template for the five research questions. Disagreements were resolved by a domain expert. The study protocol has previously been published. Results: The search resulted in a total of 564 papers. Of 469 identified, de-duplicated papers, 54 studies fulfilled the inclusion criteria and were subjected to five research questions. The review identified the heterogeneous tracking approaches, their artifacts, and varying degrees of fulfillment of the research questions. Based on this, we developed a roadmap for a tailor-made provenance framework considering the software life cycle. Conclusions: In this paper we investigate the state-of-the-art frameworks, associated artifacts, and methodologies for provenance tracking including real-life applications. We observe that most authors imply ideal conditions for provenance tracking. However, our analysis discloses several gaps for which we illustrate future steps toward a systematic provenance strategy. We believe the recommendations enforce quality and guide the implementation of auditable and measurable provenance approaches as well as solutions in the daily routine of biomedical scientists.

FAIR health data in the national and international data space

“…Die Lizenz ist Teil der Metadaten. Sie klärt eindeutig die Bedingungen zur Nachnutzung eines Datensatzes [ 16 ] R1.2: (Meta‑)Daten enthalten detaillierte Provenienzinformationen – Wünschenswert wäre die Implementierung eines W3C-Standards für Provenienzinformation über die KDS-Elemente [ 22 ] R1.3: (Meta‑)Daten entsprechen fachgebietsrelevanten Standards Repräsentation des MII-KDS als FHIR-Ressourcen Die Verwendung des internationalen HL7-FHIR-Standards erhöht die Interoperabilität der MII-Ressourcen mit Software, aber beispielsweise auch mit dem MDS der NFDI4Health, welches ebenfalls mit FHIR-Ressourcen a abgebildet wurde [ 23 ] NFDI4Health Nationale Forschungsdateninfrastruktur für personenbezogene Gesundheitsdaten, MDS Metadatenschema, FDPG Forschungsdatenportal für Gesundheit, KDS Kerndatensatz, DZD Deutsches Zentrum für Diabetesforschung, MDM Medical Data Model, MII Medizininformatik-Initiative, FHIR Fast Healthcare Interoperability Resources, LOINC Logical Observation Identifiers Names and Codes, HL7 Health Level 7, ICD-10-GM International Classification of Diagnoses 10. Revision German Modification, W3C World Wide Web Consortium a https://simplifier.net/NFDI4Health-Metadata-Schema/ , Zugegriffen: 26.…”