NeuroProv: Provenance data visualisation for neuroimaging analyses

Munir, Kamran; Arshad, Bilal; McClatchey, Richard; Shamdasani, Jetendr; Khan, Zaheer

Abstract

© 2019 Elsevier Ltd Visualisation underpins the understanding of scientific data both through exploration and explanation of analysed data. Provenance strengthens the understanding of data by showing the process of how a result has been achieved. With the significant increase in data volumes and algorithm complexity, clinical researchers are struggling with information tracking, analysis reproducibility and the verification of scientific output. In addition, data coming from various heterogeneous sources with varying levels of trust in a collaborative environment adds to the uncertainty of the scientific outputs. This provides the motivation for provenance data capture and visualisation support for analyses. In this paper a system, NeuroProv is presented, to visualise provenance data in order to aid in the process of verification of scientific outputs, comparison of analyses, progression and evolution of results for neuroimaging analyses. The experimental results show the effectiveness of visualising provenance data for neuroimaging analyses.

Citation

Munir, K., Arshad, B., McClatchey, R., Shamdasani, J., & Khan, Z. (2019). NeuroProv: Provenance data visualisation for neuroimaging analyses. Journal of Computer Languages, 52, 72-87. https://doi.org/10.1016/j.cola.2019.04.004

• Journal Article Type: Article
• Acceptance Date: Apr 15, 2019
• Online Publication Date: Apr 26, 2019
• Publication Date: Jun 1, 2019
• Deposit Date: Apr 26, 2019
• Publicly Available Date: Apr 27, 2020
• Journal: Journal of Computer Languages
• Print ISSN: 2590-1184
• Electronic ISSN: 2590-1184
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 52
• Pages: 72-87
• DOI: https://doi.org/10.1016/j.cola.2019.04.004
• Keywords: scientific workflows, biomedical analysis, neuroimaging, visualisation
• Public URL: https://uwe-repository.worktribe.com/output/846014
• Publisher URL: https://doi.org/10.1016/j.cola.2019.04.004
• Related Public URLs: https://www.elsevier.com/journals/institutional/journal-of-computer-languages/2590-1184
• Additional Information: Additional Information : This is the author's accepted manuscript. The final published version is available here: https://doi.org/10.1016/j.cola.2019.04.004