Linked Data refers to a collection of interrelated datasets on the Web expressed in a standard structure. These Linked Data and relationships among them can be reached and managed by Semantic Web tools. Linked Data enables large scale integration of and reasoning on data on the Web. This cookbook is to documents the processes and workflows required to create a Linked Data API for a dataset in the Foundation Base Project in Geoscience Australia (GA) and further publish it on the AWS.

The pace, with which government agencies, researchers, industry, and the public need to react to national and international challenges of economic, environmental, and social natures, is constantly changing and rapidly increasing. Responses to the global COVID-19 pandemic event, the 2020 Australian bushfire and 2021 flood crisis situations are recent examples of these requirements. Decisions are no longer made on information or data coming from a single source or discipline or a solitary aspect of life: the issues of today are too complex. Solving complex issues requires seamless integration of data across multiple domains and understanding and consideration of potential impacts on businesses, the economy, and the environment. Modern technologies, easy access to information on the web, abundance of openly available data shifts is not enough to overcome previous limitations of dealing with data and information. Data and software have to be Findable, Accessible, Interoperable and Reusable (FAIR), processes have to be transparent, verifiable and trusted. The approaches toward data integration, analysis, evaluation, and access require rethinking to: - Support building flexible re-usable and re-purposeful data and information solutions serving multiple domains and communities. - Enable timely and effective delivery of complex solutions to enable effective decision and policy making. The unifying factor for these events is location: everything is happening somewhere at some time. Inconsistent representation of location (e.g. coordinates, statistical aggregations, and descriptions) and the use of multiple techniques to represent the same data creates difficulty in spatially integrating multiple data streams often from independent sources and providers. To use location for integration, location information needs to be embedded within the datasets and metadata, describing those datasets, so those datasets and metadata would become ‘spatially enabled’.

Introductory video to explaining Linked Data and DGGS practices and philosophies

One of the aims of the Exploring for the Future program is to promote the discovery of new mineral deposits in undercover frontiers. Iron oxide–copper–gold mineral systems are a desirable candidate for undercover exploration, because of their potential to generate large deposits with extensive alteration footprints. This mineral potential assessment uses the mineral systems concept: developing mappable proxies of required theoretical criteria, combined to demonstrate where conditions favourable for mineral deposit formation are spatially coincident. This assessment uses a 2D geographical information system workflow to map the favourability of the key mineral system components. Two outputs were created: a comprehensive assessment, using all available spatial data; and a coverage assessment, which is constrained to data that have no reliance on outcrop. The results of these assessment outputs were validated with spatial statistics, demonstrating how the assessment can predict the presence of known ore deposits. Both assessment outputs present new areas of interest with prospectivity in under-explored regions of undercover northern Australia. The intended aims are already being realised, as this tool has aided area selection for pre-competitive stratigraphic drilling as part of the MinEx CRC National Drilling Initiative. <b>Citation:</b> Murr, J., Skirrow, R.G., Schofield, A., Goodwin, J., Coghlan, R., Highet, L., Doublier, M.P., Duan, J. and Czarnota, K., 2020. Tennant Creek – Mount Isa IOCG mineral potential assessment. In: Czarnota, K., Roach, I., Abbott, S., Haynes, M., Kositcin, N., Ray, A. and Slatter, E. (eds.) Exploring for the Future: Extended Abstracts, Geoscience Australia, Canberra, 1–4.

This video demonstrates to viewers the importance and value on fit for purpose metadata, metadata standards, and metadata profiles.

A publicly available AGOL Dashboard that periodically updates to show the status of requests made to the Australian Exposure Information Platform (AEIP), categorised as Running, Queued and Completed (www.aeip.ga.gov.au)

<p>Digital Earth Australia manages a cloud based service that makes use of open source software and open standards to deliver satellite imagery to its clients. <p>In conjunction with Frontier SI and Commonwealth Scientific and Industrial Research Organisation, Geoscience Australia’s Digital Earth Australia project has developed a cloud architecture that utilizes the Open Data Cube (ODC) to deliver Earth Observation (EO) data through Open Geospatial Consortium (OGC) API standards, interactive Jupyter notebooks and direct file access.​ <p>This infrastructure enables EO data to be used to make decisions by industry and government partners, and reduces the time required to deliver new EO data products. ​ <p>To store the data, DEA utilises Amazon Web Services (AWS) Object store: Simple Storage Service (S3) to hold an archive of Cloud Optimised GeoTIFFs (COGs). ​ <p>This data is indexed by Open Data Cube (ODC) an open source python library. DEA deploy processing, visualisation and analysis applications that make use of the indexed data. This method reduces the duplication of code and effort and creates an extensible framework for delivering data.

The geosciences are a data-rich domain where Earth materials and processes are analysed from local to global scales. However, often we only have discrete measurements at specific locations, and a limited understanding of how these features vary across the landscape. Earth system processes are inherently complex, and trans-disciplinary science will likely become increasingly important in finding solutions to future challenges associated with the environment, mineral/petroleum resources and food security. Machine learning is an important approach to synthesise the increasing complexity and sheer volume of Earth science data, and is now widely used in prediction across many scientific disciplines. In this context, we have built a machine learning pipeline, called Uncover-ML, for both supervised and unsupervised learning, prediction and classification. The Uncover-ML pipeline was developed from a partnership between CSIRO and Geoscience Australia, and is largely built around the Python scikit-learn machine learning libraries. In this paper, we briefly describe the architecture and components of Uncover-ML for feature extraction, data scaling, sample selection, predictive mapping, estimating model performance, model optimisation and estimating model uncertainties. Links to download the source code and information on how to implement the algorithms are also provided. <b>Citation:</b> Wilford, J., Basak, S., Hassan, R., Moushall, B., McCalman, L., Steinberg, D. and Zhang, F, 2020. Uncover-ML: a machine learning pipeline for geoscience data analysis. In: Czarnota, K., Roach, I., Abbott, S., Haynes, M., Kositcin, N., Ray, A. and Slatter, E. (eds.) Exploring for the Future: Extended Abstracts, Geoscience Australia, Canberra, 1–4.

HiQGA is a general purpose software package for spatial statistical inference, geophysical forward modeling, Bayesian inference and inversion (both deterministic and probabilistic). It includes readily usable geophysical forward operators for airborne electromagnetics (AEM), controlled-source electromagnetics (CSEM) and magnetotellurics (MT). Physics-independent inversion frameworks are provided for probabilistic reversible-jump Markov chain Monte Carlo (rj-MCMC) inversions, with models parametrised by Gaussian processes (Ray and Myer, 2019), as well as deterministic inversions with an "Occam inversion" framework (Constable et al., 1987). In development software for EFTF since 2020

Since 2012, Geoscience Australia (GA) has been providing spatial support and advice to the National Situation Room (NSR) (formally the Crisis Coordination Centre (CCC)) within Emergency Management Australia (EMA) as part of GA’s collaboration with the Attorney-General’s Department. A key information requirement identified by EMA was the need to quickly understand what is in an event area. To address this requirement Geoscience Australia designed the Exposure Report which greatly simplifies the interpretation of exposure information for timely emergency response and recovery decision-making. The Exposure Report is generated by extracting the relevant attributes from the Geoscience Australia National Exposure information System (NEXIS) such as demographics, building, business, agriculture, institutions and infrastructure in an event footprint, geographical boundary or potentially threatened area. This automated process quickly presents the required information in a clear and easily accessible report detailing estimates of what exists in the event area. By improving the timeliness and accuracy of information used by the NSR, Geoscience Australia is enhancing the government’s ability to respond to disaster and activate appropriate financial assistance for recovery.