SEMar 19, 2018

Data provenance tracking as the basis for a biomedical virtual research environment

arXiv:1803.07433v13 citations
Originality Synthesis-oriented
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This addresses the need for traceable and reproducible analyses in collaborative biomedical research, though it is incremental as it builds on existing projects.

The paper tackles the challenge of capturing comprehensive provenance data in biomedical research to ensure reproducibility and collaboration, by extending a Virtual Laboratory into a generic Virtual Research Environment that integrates datasets, workflows, and provenance in the CRISTAL software.

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. 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.

Foundations

The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

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