NeuroProv: Provenance data visualisation for neuroimaging analyses

© 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

Utilizing Provenance in Reusable Research Objects

Science is conducted collaboratively, often requiring the sharing of knowledge about computational experiments. When experiments include only datasets, they can be shared using Uniform Resource Identifiers (URIs) or Digital Object Identifiers (DOIs). An experiment, however, seldom includes only datasets, but more often includes software, its past execution, provenance, and associated documentation. The Research Object has recently emerged as a comprehensive and systematic method for aggregation and identification of diverse elements of computational experiments. While a necessary method, mere aggregation is not sufficient for the sharing of computational experiments. Other users must be able to easily recompute on these shared research objects. Computational provenance is often the key to enable such reuse. In this paper, we show how reusable research objects can utilize provenance to correctly repeat a previous reference execution, to construct a subset of a research object for partial reuse, and to reuse existing contents of a research object for modified reuse. We describe two methods to summarize provenance that aid in understanding the contents and past executions of a research object. The first method obtains a process-view by collapsing low-level system information, and the second method obtains a summary graph by grouping related nodes and edges with the goal to obtain a graph view similar to application workflow. Through detailed experiments, we show the efficacy and efficiency of our algorithms.Comment: 25 page

Towards Interoperable Research Infrastructures for Environmental and Earth Sciences

This open access book summarises the latest developments on data management in the EU H2020 ENVRIplus project, which brought together more than 20 environmental and Earth science research infrastructures into a single community. It provides readers with a systematic overview of the common challenges faced by research infrastructures and how a ‘reference model guided’ engineering approach can be used to achieve greater interoperability among such infrastructures in the environmental and earth sciences. The 20 contributions in this book are structured in 5 parts on the design, development, deployment, operation and use of research infrastructures. Part one provides an overview of the state of the art of research infrastructure and relevant e-Infrastructure technologies, part two discusses the reference model guided engineering approach, the third part presents the software and tools developed for common data management challenges, the fourth part demonstrates the software via several use cases, and the last part discusses the sustainability and future directions