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NeuroProv: Provenance data visualisation for neuroimaging analyses

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


Bilal Arshad

Jetendr Shamdasani

Zaheer Khan
Professor in Computer Science


© 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.


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.

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
Keywords scientific workflows, biomedical analysis, neuroimaging, visualisation
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Additional Information Additional Information : This is the author's accepted manuscript. The final published version is available here:


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