For research in the fields of engineering asset management (EAM) and system health, relevant data resides in the information systems of the asset owners, typically industrial corporations or government bodies. For academics to access EAM data sets for research purposes can be a difficult and time-consuming task. To facilitate a more consistent approach toward releasing asset-related data, we have developed a data risk assessment tool (DRAT). This tool evaluates and suggests controls to manage, risks associated with the release of EAM datasets to academic entities for research purposes. Factors considered in developing the tool include issues such as where accountability for approval sits in organizations, what affects an individual manager’s willingness to approve release, and how trust between universities and industry can be established and damaged. This paper describes the design of the DRAT tool and demonstrates its use on case studies provided by EAM owners for past research projects. The DRAT tool is currently being used to manage the data release process in a government-industry-university research partnership.
<p>As open source geospatial mapping toolkits and platforms continue to develop and mature, the developers of web portals using these solutions need to regularly review and revaluate their technology choices in order to stay up to date and provide the best possible experience and functionality to their users. We are currently undergoing such a refresh with our AuScope Discovery Portal, Virtual Geophysics Laboratory, and the AuScope 3D Geological Models Portal. The task of deciding which solutions to utilise as part of the upgrade process is not to be underestimated. Our main evaluation criteria include the ability to support commonly used map layer formats and web service protocols, support for 3D display capabilities, community size and activity, ease of adding custom display and scientific workflow / processing widgets, cost and benefits of integration with existing components and maintainability into the future. We are beginning a journey to update and integrate our portals&#8217; functionality and will outline the decision process and conclusions of our investigations as well as the detailed evaluation of web based geospatial solutions against our functional and operational criteria.</p>
<p>The AuScope Virtual Research Environment (AVRE) program&#8217;s Engage activity was devised as a vehicle to promote low-barrier collaboration projects with Australian universities and publicly-funded research agencies and to provide an avenue for exploring new applications and technologies that could become part of the broader AuScope AVRE portfolio. In its second year, we developed two projects with another cohort of collaborative projects proponents from two Australian research institutions. Both projects have leveraged and extended upon previously developed open-source projects while tailoring them to clients&#8217; specific needs.</p><p>The latest projects developed under the AuScope AVRE Engage program were the AuScope Geochemistry Network (AGN) Lab Finder Application and the Magnetic Component Symmetry (MCS) Analysis application. The Lab Finder application fits within a broader ecosystem of AGN projects and is an online tool that provides an overview of participating laboratories, their equipment, techniques, contact information with a catalogue that sums up the possibilities of each analytical technique, and a user-friendly search and browsing interface. The MCS Analysis application implements the CSIRO Orthogonal Magnetic Component (OMC) analysis method for the detection of l variations in the magnetic field (i.e., anomalies) that are the result of subsurface magnetizations. Both applications were developed using free and open-source software (FOSS) and leveraged prior work and further expand on it. The AGN Lab Finder is an adaptation of the Technique Finder originally developed by Intersect for Microscopy Australia, which was redesigned to accommodate geochemistry-specific equipment and describe its analytical capabilities It provides an indexing mechanism and a search functionality allowing researchers to efficiently locate and identify laboratories with the equipment necessary to their research needs and that satisfies their analytical capability requirements. The MCS Analysis application is a derivative product based on Geophysical Processing Toolkit (GPT) that implements a user-centred approach to visual data analytics and modelling. It significantly improves user experience by integrating with open data services, adding complex interactivity and data visualisation functionality, and improving overall exploratory data analysis capability.</p><p>The Engage approach to running collaborative projects has proved successful over the last two years and produced low-maintenance tools that are made freely accessible to researchers. The approach to engage a wider audience and improve the speed of science delivery has influenced other projects within the CSIRO Mineral Resources business unit to implement similar programs.</p><p>This case study will demonstrate the social aspects of our experience in cross-institutional collaboration, showcase our learnings during the development of pilot projects, and outline our vision for future work.</p>
<p>Detecting and locating earthquakes relies on seismic events being recorded by a number of deployed seismometers. To detect earthquakes effectively and accurately, seismologists must design and install a network of seismometers that can capture small seismic events in the sub-surface.</p><p>A major challenge when deploying an array of seismometers (seismic array) is predicting the smallest earthquake that could be detected and located by that network. Varying the spacing and number of seismometers dramatically affects network sensitivity and location precision and is very important when researchers are investigating small-magnitude local earthquakes. For cost reasons, it is important to optimise network design before deploying seismometers in the field. In doing so, seismologists must accurately account for parameters such as station locations, site-specific noise levels, earthquake source parameters, seismic velocity and attenuation in the wave propagation medium, signal-to-noise ratios, and the minimum number of stations required to compute high-quality locations.</p><p>AuScope AVRE Engage Program team has worked with researchers from the seismology team at the University of Melbourne to better understand their solution for optimising seismic array design to date: an analytical method called SENSI that has been developed by Tramelli et al. (2013) to design seismic networks, including the GipNet array deployed to monitor seismicity in the Gippsland region in Victoria, Australia. The underlying physics and mechanics of the method are straightforward, and when applied sensibly, can be used as a basis for the design of seismic networks anywhere in the world. Our engineers have built an application leveraging a previously developed Geophysical Processing Toolkit (GPT) as an application platform and harnessed the scalability of a Cloud environment provided by the EASI Hub, which minimised the overall development time. The GPT application platform provided the groundwork for a web-based application interface and enabled interactive visualisations to facilitate human-computer interaction and experimentation.</p>
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