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  • This strategy outlines the opportunities for Geoscience Australia to continue to apply first class geoscience to the most important challenges in the future by embracing Digital Science. The pace of change in technology is transforming just about every part of our lives, including the way we work, communicate, access services and do science. Science, in particular, has seen a dramatic increase on digital technology, so is itself becoming digital. Digital Science is a radical transformation of science due to technical and cultural changes which is more open, global, collaborative, creative and closer to society. Digital science is about the interplay among scientific data, scientific computing, platforms and people and is driven by the following trends: ● Science advice is expected to be available on demand ● Scientific questions are becoming increasingly complex. ● Scientific data is growing exponentially. ● Digital technologies have reached unprecedented capabilities. As science is rapidly changing, the way Geoscience Australia operationalises science must therefore also change and continue to evolve for the agency to fulfil its potential: Geoscience Australia needs to embrace digital methods in areas of numeric literacy, scientific computing, programming, data modelling, machine learning and mathematics. In other words science has to embrace quantitative methods for it to be contestable and evidence based.

  • <p>The Australian Geoscience Information Network (AusGIN, www.geoscience.gov.au) provides geoscience data and information from all Australian state, territory and federal Geological Survey organisations, under the governance of the Australia/NZ Government Geoscience Information Committee (GGIC). The information includes geological and geophysical maps, data, and publications; minerals and energy legislative and regulatory guidelines in all Australian jurisdictions; and links to the standards used in Australian resources data and resource exploration reporting. <p>The flagship of AusGIN is the AusGIN Geoscience Portal - an online mapping portal built on open source standards and applications. The Portal provides access to geoscience data through web services which use agreed international and national standards. These include the IUGS CGI and Open Geospatial Consortium (OGC) geoscience data standards GeoSciML and EarthResourceML, the ISO19115 metadata standard, GGIC's MineralTenementML, and OGC web service standards (Web Map Service - WMS, Web Feature Service - WFS, Catalog Service for the Web - CSW). <p>Among the many datasets featured in the Geoscience Portal, each of the Geological Surveys deliver featured datasets from their own data holdings using GeoSciML (boreholes), EarthResourceML (mineral occurrences, mines), and MineralTenementML (mineral exploration and mining tenements). Because all these services comply to shared data structures, the Portal is able to apply common filter queries and symbolisation to all the services using standard protocols. The Portal also makes use of CGI and GGIC vocabulary services to construct common filter queries on the data services. <p>The Geoscience Portal also enables users to search ISO19115 metadata services to find more datasets and publications from the Geological Surveys. The Portal can query live CSW services (such as from Geoscience Australia), as well as using its own GeoNetwork catalog to periodically harvest metadata from those Surveys where live CSW services are not available.

  • <div><strong>Output type: </strong>Exploring for the Future Extended Abstract <strong> </strong></div><div><br></div><div><strong>Short abstract: </strong>There is an increased international focus on achieving high environmental, socio-economic, and governance (ESG) outcomes within mineral supply chains, in addition to delivering positive economic results. Mineral exploration and development projects must balance these disparate objectives to the satisfaction of separate stakeholders. However, the challenge of reconciling distinct preferences can obscure viable outcomes and confound project selection, particularly in the early stages of project development. Here, we discuss how such investment decisions can be treated as multicriteria optimization problems. In appraising the pre-competitive potential for nickel sulphide developments, we show how this approach can be used to effectively evaluate competing objectives and to locate regions that perform best under a range of different metrics. We outline a mapping framework that identifies Australian regions that optimally balance geological potential, economic value, and environmental impact. Our workflow creates a new capability within Australia to incorporate high-level, holistic information into the earliest stages of exploration. While this abstract focuses on mineral exploration, the modelling could be extended to other Australian resource development applications. Importantly, our results further underscore the need to compile baseline ESG datasets across Australia to help drive sustainable exploration decisions.</div><div><br></div><div><strong>Citation:</strong> Walsh S.D.C., Haynes M.W. &amp; Wang C., 2024. Multicriteria resource potential mapping: balancing geological, economic &amp; environmental factors. In: Czarnota, K. (ed.) Exploring for the Future: Extended Abstracts. Geoscience Australia, Canberra. https://doi.org/10.26186/149250</div>

  • Geoscience Australia has a leading role in the Australian Government's Open Data Network. Over the past few years Geoscience Australia has, in partnership with agencies across the Australian Government, including the Department of Communications and the Department of Finance, delivered a series of projects that have cemented Geoscience Australia's role as a leading exemplar of Open Data in the Australian Government. These projects have included the recent release of the National Map; FIND - a spatial search engine across government data providers; providing over 80 per cent of the data that is published on data.gov.au; implementation of Creative Commons licensing for the majority of our data; and Geoscience Australia's sponsorship of GovHack.

  • The fourth paradigm of data intensive science is upon us: a new fundamental scientific methodology has emerged which is underpinned by the capability to analyse large volumes of data using advanced computational capacities. This combination is enabling earth and space scientists to respond to decadal challenges on issues such as the sustainable development of our natural resources, impacts of climate change and protection from national hazards. Fundamental to the data intensive paradigm is data that are readily accessible and capable of being integrated and amalgamated with other data often from multiple sources. For many years Earth and Space science practitioners have been drowning in a data deluge. In many cases, either lacking confidence in their capability and/or not having the time or capacity to manage these data assets they have called in the data professionals. However, such people rarely had domain knowledge of the data they were dealing with and before long it emerged that although the 'containers' of data were now much better managed and documented, in reality the content was locked up and difficult to access, particularly for HPC environments where national to global scale problems were being addressed. Geoscience Australia (GA) is the custodian of over 4 PB of Geoscientific data and is a key provider of evidence-based, scientific advice to government on national issues. Since 2011, in collaboration with CSIRO Minerals Down Under Program, and the National Computational Infrastructure, GA has begun a series of data intensive scientific research pilots that focussed on applying advanced ICT tools and technologies to enhance scientific outcomes for the agency, in particular, national scale analysis of data sets that can be up to 500 TB in size. As in any change program, a small group of innovators and early adopters took up the challenge of data intensive science and quickly showed that GA was able to use new ICT technologies to exploit an information-rich world to undertake applied research and to deliver new business outcomes in ways that current technologies do not allow. The innovators clearly had the necessary skills to rapidly adapt to data intensive techniques. However, if we were to scale out to the rest of the organisation, we needed to quantify these skills. The Strategic People Development Section of GA agreed to: - Conduct a capability analysis of the scientific staff that participated in the pilot projects including a review of university training and post graduate training; and - Conduct capability analysis of the technical groups involved in the pilot projects. The analysis identified the need for multi-disciplinary teams across the spectrum from pure scientists to pure ICT staff along with a key hybrid role - the Data Scientist, who has a greater capacity in mathematical, numerical modelling, statistics, computational skills, software engineering and spatial skills and the ability to integrate data across multiple domains. To fill the emerging gap, GA is asking the questions; how do we find or develop this capability, can we successfully transform the Scientist or the ICT Professional, are our educational facilities modifying their training but it is certainly leading GA to acknowledge, formalise, and promote a continuum of skills and roles, changing our recruitment, re-assignment and Learning and Development strategic decisions.

  • The National Computational Infrastructure (NCI) at the Australian National University (ANU) has co-located a priority set of over 10 PetaBytes (PBytes) of national data collections within a HPC research facility. The facility provides an integrated high-performance computational and storage platform, or a High Performance Data (HPD) platform, to serve and analyse the massive amounts of data across the spectrum of environmental collections in particular from the climate, environmental and geoscientific domains. The data is managed in concert with the government agencies, major academic research communities and collaborating overseas organisations. By co-locating the vast data collections with high performance computing environments and harmonising these large valuable data assets, new opportunities have arisen for Data-Intensive interdisciplinary science at scales and resolutions not hitherto possible.

  • <div>Steelmaking value chains are economically important to Australia, but the need to decarbonize traditional steel-making processes could disrupt existing supply lines. Hydrogen-based iron and steel production offers one pathway for reducing the carbon intensity of steel. The opportunities and challenges presented by this technology, for Australia, are obscured as its cost competitiveness depends on the interaction between multiple industrial processes, including feedstock requirements, storage options, and the availability of infrastructure. To address these problems, we have developed the Green Steel Economic Fairways Mapper. This mapping tool enables user-driven assessments of the green iron or steel resource potential across Australia. The tool optimizes system capacities for renewable energy generation, battery storage, hydrogen electrolysis, and hydrogen storage to estimate the levelized costs of green steel and how these costs vary regionally. Here, we present examples of analysis and integration with other geospatial datasets. Our model compares favourably to previously published cost estimates while also providing granular, spatial considerations of resource potential. Examples demonstrate that the tool that can be used to inform decision-making in the development of actions to de-risk green steel development within Australia.</div>

  • <div><strong>Output Type: </strong>Exploring for the Future Extended Abstract</div><div><br></div><div><strong>Short Abstract: </strong>The global push towards decarbonisation may disrupt existing steelmaking supply chains. While this presents opportunities for Australia, it remains an open question as to what the country’s role should be within new international value chains. Here, we examine green steel production and export strategies between Australia and Japan, comparing different exports from raw material feedstocks to end products. We assess five scenarios in 2030, involving Australian exports of green steel, hot briquetted iron, green ammonia, liquid hydrogen, and/or iron ore pellets. The export of iron ore pellets for Japanese processing using offshore wind is most expensive (~AU$1500/tonne). Although, direct steel production is most economical (~AU$1000/tonne) due to lower energy costs from holistic system optimisation, exporting hot-briquetted iron or HBI (~AU$1032/tonne) balances Australia’s resources with Japan’s steel manufacturing expertise. The liquid hydrogen and ammonia pathways incur substantial energy losses from conversion and reconversion processes, making them less competitive. Trade partnerships across the value chain enhance sustainability and economic feasibility of international green steel manufacturing.</div><div><br></div><div><strong>Citation: </strong>Wang, C., Walsh, S.D.C., Haynes, M.W., Weng, Z. & Feitz, A., 2024. Green steel supply chain options between Australia and Japan. In: Czarnota, K. (ed.) Exploring for the Future: Extended Abstracts, Geoscience Australia, Canberra, https://doi.org/10.26186/149413</div>

  • This web service contains marine geospatial data held by Geoscience Australia. It includes bathymetry and backscatter gridded data plus derived layers, bathymetry coverage information, bathmetry collection priority and planning areas, marine sediment data and other derived products. It also contains the 150 m and optimal resolution bathymetry, 5 m sidescan sonar (SSS) and synthetic aperture sonar (SAS) data collected during phase 1 and 2 marine surveys conducted by the Governments of Australia, Malaysia and the People's Republic of China for the search of Malaysian Airlines Flight MH370 in the Indian Ocean. This web service allows exploration of the seafloor topography through the compilation of multibeam sonar and other marine datasets acquired.