Introductory video to explaining Linked Data and DGGS practices and philosophies

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.

Final report on the backgrounds, collaboration structure, methods, and findings from the EIRAPSI project

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

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’.

Effective mineral, energy and groundwater resource management and exploration rely on accurate geological maps. While geological maps of the surface exist and increase in resolution, maps of the subsurface are sparse, and the underpinning geological and geophysical constraints are disordered or non-existent. The Estimates of Geological and Geophysical Surfaces (EGGS) database seeks to enable robust subsurface geological mapping by establishing an ordered collection of precious geological and geophysical interpretations of the subsurface. EGGS stores the depth to geological boundaries derived from boreholes as well as interpretations of depth to magnetic top assessments, airborne electromagnetics inversions and reflection seismic profiles. Since geological interpretation is iterative, links to geophysical datasets and processing streams used to image the subsurface are stored. These metadata allow interpretations to be readily associated with the datasets from which they are derived and re-examined. The geological basis for the interpretation is also recorded. Stratigraphic consistency is maintained by linking each interpretation to the Australian Stratigraphic Units Database. As part of the Exploring for the Future program, >170 000 points were entered into the EGGS database. These points underpin construction of cover thickness models and economic fairway assessments. <b>Citation:</b> Mathews, E.J., Czarnota, K., Meixner, A.J., Bonnardot, M.-A., Curtis, C., Wilford, J., Nicoll, M.G., Wong, S.C.T., Thorose, M. and Ley-Cooper, Y., 2020. Putting all your EGGS in one basket: the Estimates of Geological and Geophysical Surfaces database. 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

All commercially produced hydrogen worldwide is presently stored in salt caverns. In eastern Australia, the only known thick salt accumulations are found in the Boree Salt of the Adavale Basin in central Queensland. Although the number of wells penetrating the basin is limited, salt intervals up to 555 m thick have been encountered. The Boree Salt consists predominantly of halite and is considered to be suitable for hydrogen storage. Using well data and historical 2D seismic interpretations, we have developed a 3D model of the Adavale Basin, particularly focussing on the thicker sections of the Boree Salt. Most of the salt appears to be present at depths greater than 2000 m, but shallower sections are found in the main salt body adjacent to the Warrego Fault and to the south at the Dartmouth Dome. The preliminary 3D model developed for this study has identified three main salt bodies that may be suitable for salt cavern construction and hydrogen storage. These are the only known large salt bodies in eastern Australia and therefore represent potentially strategic assets for underground hydrogen storage. There are still many unknowns, with further work and data acquisition required to fully assess the suitability of these salt bodies for hydrogen storage. Recommendations for future work are provided. <b>Citation:</b> Paterson R., Feitz A. J., Wang L., Rees S. & Keetley J., 2022. From A preliminary 3D model of the Boree Salt in the Adavale Basin, Queensland. In: Czarnota, K. (ed.) Exploring for the Future: Extended Abstracts, Geoscience Australia, Canberra, https://dx.doi.org/10.26186/146935

<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 purpose of this document is to define an Emergency Management (EM) Metadata Profile Extension to the ISO 19115-1:2014/AMD 1:2018 to identify the metadata required to accurately describe EM resources. The EM Metadata Profile is designed to support the documentation and discovery of EM datasets, services, and other resources. This version of the Profile was developed to reflect extensions made to the current version of the international metadata standard: ISO 19115-1:2014/AMD 1:2018.