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dc.contributor.authorMcClatchey, Richard
dc.contributor.authorBranson, Andrew
dc.contributor.authorAnjum, Ashiq
dc.contributor.authorBloodsworth, Peter
dc.contributor.authorHabib, Irfan
dc.contributor.authorMunir, Kamran
dc.contributor.authorShamdasani, Jetendr
dc.contributor.authorSoomro, Kamran
dc.date.accessioned2018-10-15T14:43:37Z
dc.date.available2018-10-15T14:43:37Z
dc.date.issued2013-09
dc.identifier.citationMcClatchey, R. et al (2013) ‘Providing traceability for neuroimaging analyses’ International Journal of Medical Informatics, 82 (9):882-894. doi: 10.1016/j.ijmedinf.2013.05.005en
dc.identifier.issn1386-5056
dc.identifier.urihttp://hdl.handle.net/10545/623046
dc.description.abstractIntroduction With the increasingly digital nature of biomedical data and as the complexity of analyses in medical research increases, the need for accurate information capture, traceability and accessibility has become crucial to medical researchers in the pursuance of their research goals. Grid- or Cloud-based technologies, often based on so-called Service Oriented Architectures (SOA), are increasingly being seen as viable solutions for managing distributed data and algorithms in the bio-medical domain. For neuroscientific analyses, especially those centred on complex image analysis, traceability of processes and datasets is essential but up to now this has not been captured in a manner that facilitates collaborative study. Purpose and method Few examples exist, of deployed medical systems based on Grids that provide the traceability of research data needed to facilitate complex analyses and none have been evaluated in practice. Over the past decade, we have been working with mammographers, paediatricians and neuroscientists in three generations of projects to provide the data management and provenance services now required for 21st century medical research. This paper outlines the finding of a requirements study and a resulting system architecture for the production of services to support neuroscientific studies of biomarkers for Alzheimer's disease. Results The paper proposes a software infrastructure and services that provide the foundation for such support. It introduces the use of the CRISTAL software to provide provenance management as one of a number of services delivered on a SOA, deployed to manage neuroimaging projects that have been studying biomarkers for Alzheimer's disease. Conclusions In the neuGRID and N4U projects a Provenance Service has been delivered that captures and reconstructs the workflow information needed to facilitate researchers in conducting neuroimaging analyses. The software enables neuroscientists to track the evolution of workflows and datasets. It also tracks the outcomes of various analyses and provides provenance traceability throughout the lifecycle of their studies. As the Provenance Service has been designed to be generic it can be applied across the medical domain as a reusable tool for supporting medical researchers thus providing communities of researchers for the first time with the necessary tools to conduct widely distributed collaborative programmes of medical analysis.
dc.description.sponsorshipN/Aen
dc.language.isoenen
dc.publisherElsevieren
dc.relation.urlhttp://linkinghub.elsevier.com/retrieve/pii/S1386505613001111en
dc.rightsArchived with thanks to International Journal of Medical Informaticsen
dc.subjectProvenance dataen
dc.subjectAnalysis trackingen
dc.subjectNeuroimagingen
dc.subjectGrid computingen
dc.subjectService-oriented architecturesen
dc.titleProviding traceability for neuroimaging analyses.en
dc.typeArticleen
dc.contributor.departmentUniversity of the West of Englanden
dc.identifier.journalInternational Journal of Medical Informaticsen
dc.internal.reviewer-note06/10/2018 SER Publisher pdf attacheden
html.description.abstractIntroduction With the increasingly digital nature of biomedical data and as the complexity of analyses in medical research increases, the need for accurate information capture, traceability and accessibility has become crucial to medical researchers in the pursuance of their research goals. Grid- or Cloud-based technologies, often based on so-called Service Oriented Architectures (SOA), are increasingly being seen as viable solutions for managing distributed data and algorithms in the bio-medical domain. For neuroscientific analyses, especially those centred on complex image analysis, traceability of processes and datasets is essential but up to now this has not been captured in a manner that facilitates collaborative study. Purpose and method Few examples exist, of deployed medical systems based on Grids that provide the traceability of research data needed to facilitate complex analyses and none have been evaluated in practice. Over the past decade, we have been working with mammographers, paediatricians and neuroscientists in three generations of projects to provide the data management and provenance services now required for 21st century medical research. This paper outlines the finding of a requirements study and a resulting system architecture for the production of services to support neuroscientific studies of biomarkers for Alzheimer's disease. Results The paper proposes a software infrastructure and services that provide the foundation for such support. It introduces the use of the CRISTAL software to provide provenance management as one of a number of services delivered on a SOA, deployed to manage neuroimaging projects that have been studying biomarkers for Alzheimer's disease. Conclusions In the neuGRID and N4U projects a Provenance Service has been delivered that captures and reconstructs the workflow information needed to facilitate researchers in conducting neuroimaging analyses. The software enables neuroscientists to track the evolution of workflows and datasets. It also tracks the outcomes of various analyses and provides provenance traceability throughout the lifecycle of their studies. As the Provenance Service has been designed to be generic it can be applied across the medical domain as a reusable tool for supporting medical researchers thus providing communities of researchers for the first time with the necessary tools to conduct widely distributed collaborative programmes of medical analysis.


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