Providing traceability for neuroimaging analyses

McClatchey, Richard; Branson, Andrew; Anjum, Ashiq; Bloodsworth, Peter; Habib, Irfan; Munir, Kamran; Shamdasani, Jetendr; Soomro, Kamran

doi:10.1016/j.ijmedinf.2013.05.005

Cited by 25 publications

(19 citation statements)

References 18 publications

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“…On the other hand, standards designers need to avoid the feature creep that often arises which can result in frameworks which are overly rigid and cannot adapt as new information becomes available and need to be included. Nevertheless, when fully mature, these modern schema will significantly improve the description of neuroimaging data sets, encode the provenace associated with data processing (Mackenzie-Graham, Van Horn et al 2008; McClatchey, Branson et al 2013), and help to populate large-scale archives prospectively, thereby encouraging common analysis frameworks. Such practical standards will be essential for multi-site trials and major neuroimaging initiatives, where data sharing has been expressly mandated by funding agencies.…”

Section: Standards Are Frequently Non-standardmentioning

confidence: 99%

Human neuroimaging as a “Big Data” science

Horn

Toga

2013

Brain Imaging and Behavior

127

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The maturation of in vivo neuroimaging has lead to incredible quantities of digital information about the human brain. While much is made of the data deluge in science, neuroimaging represents the leading edge of this onslaught of “big data”. A range of neuroimaging databasing approaches has streamlined the transmission, storage, and dissemination of data from such brain imaging studies. Yet few, if any, common solutions exist to support the science of neuroimaging. In this article, we discuss how modern neuroimaging research represents a mutifactorial and broad ranging data challenge, involving the growing size of the data being acquired; sociologial and logistical sharing issues; infrastructural challenges for multi-site, multi-datatype archiving; and the means by which to explore and mine these data. As neuroimaging advances further, e.g. aging, genetics, and age-related disease, new vision is needed to manage and process this information while marshalling of these resources into novel results. Thus, “big data” can become “big” brain science.

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Section: Standards Are Frequently Non-standardmentioning

confidence: 99%

Human neuroimaging as a “Big Data” science

Horn

Toga

2013

Brain Imaging and Behavior

127

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“…Given its successful record for supporting data analytics at CERN, CRISTAL was selected as the basis of a VRE to support medical image analyses in the EC Framework 7 projects neuGRID and neuGRID for Users (N4U) in studies of Alzheimer's disease. The full details of these studies are beyond the scope of the current paper (details can be found in [24]); they serve to illustrate the functions of a DDS as used for tracing scientific workflows and supporting biomedical data analytics in a VRE. In the N4U project we have delivered a Virtual Laboratory (VL [25]) or VRE as the platform for their analyses offering neuroscientists tracked access to a wide range of Grid-or Cloud-resident big data sets and services, and support for their studies of biomarkers for identifying the onset of Alzheimer's disease.…”

Section: Medical Analytics and Traceability Using Cristal In A Vrementioning

confidence: 99%

Data Provenance Tracking as the Basis for a Biomedical Virtual Research Environment

McClatchey¹

2017

Proceedings of International Symposium on Grids and Clouds (ISGC) 2017 — PoS(ISGC2017)

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In complex data analyses it is increasingly important to capture information about the usage of data sets in addition to their preservation over time to ensure reproducibility of results, to verify the work of others and to ensure appropriate conditions data have been used for specific analyses. Scientific workflow based studies are beginning to realize the benefit of capturing this provenance of data and the activities used to process, transform and carry out studies on those data. This is especially true in biomedicine where the collection of data through experiment is costly and/or difficult to reproduce and where that data needs to be preserved over time. One way to support the development of workflows and their use in (collaborative) biomedical analyses is through the use of a Virtual Research Environment. The dynamic and distributed nature of Grid/Cloud computing, however, makes the capture and processing of provenance information a major research challenge. Furthermore most workflow provenance management services are designed only for data-flow oriented workflows and researchers are now realising that tracking data or workflows alone or separately is insufficient to support the scientific process. What is required for collaborative research is traceable and reproducible provenance support in a full orchestrated Virtual Research Environment (VRE) that enables researchers to define their studies in terms of the datasets and processes used, to monitor and visualize the outcome of their analyses and to log their results so that others users can call upon that acquired knowledge to support subsequent studies. We have extended the work carried out in the neuGRID and N4U projects in providing a so-called Virtual Laboratory to provide the foundation for a generic VRE in which sets of biomedical data (images, laboratory test results, patient records, epidemiological analyses etc.) and the workflows (pipelines) used to process those data, together with their provenance data and results sets are captured in the CRISTAL software. This paper outlines the functionality provided for a VRE by the Open Source CRISTAL software and examines how that can provide the foundations for a practice-based knowledge base for biomedicine and, potentially, for a wider research community.

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“…There is a one-to-one mapping between a Pipeline and a Data Element (which is a single file/image from a dataset); this information is captured in the Analysis Element Item which is later sent to the Grid for processing, which generates a set of Results for the end user. For further description of this model and its usage in the project please see [10].…”

Section: Experimental Analysis In N4umentioning

confidence: 99%

Analysis Traceability and Provenance for HEP

Shamdasani

McClatchey

Branson

et al. 2015

J. Phys.: Conf. Ser.

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This paper presents the use of the CRISTAL software in the N4U project. CRISTAL was used to create a set of provenance aware analysis tools for the Neuroscience domain. This paper advocates that the approach taken in N4U to build the analysis suite is sufficiently generic to be able to be applied to the HEP domain. A mapping to the PROV model for provenance interoperability is also presented and how this can be applied to the HEP domain for the interoperability of HEP analyses.

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Providing traceability for neuroimaging analyses

Cited by 25 publications

References 18 publications

Human neuroimaging as a “Big Data” science

Human neuroimaging as a “Big Data” science

Data Provenance Tracking as the Basis for a Biomedical Virtual Research Environment

Analysis Traceability and Provenance for HEP

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