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.

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

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.

<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 magnetotelluric (MT) method is increasingly being applied to map tectonic architecture and mineral systems. Under the Exploring for the Future (EFTF) program, Geoscience Australia has invested significantly in the collection of new MT data. The science outputs from these data are underpinned by an open-source data analysis and visualisation software package called MTPy. MTPy started at the University of Adelaide as a means to share academic code among the MT community. Under EFTF, we have applied software engineering best practices to the code base, including adding automated documentation and unit testing, code refactoring, workshop tutorial materials and detailed installation instructions. New functionality has been developed, targeted to support EFTF-related products, and includes data analysis and visualisation. Significant development has focused on modules to work with 3D MT inversions, including capability to export to commonly used software such as Gocad and ArcGIS. This export capability has been particularly important in supporting integration of resistivity models with other EFTF datasets. The increased functionality, and improvements to code quality and usability, have directly supported the EFTF program and assisted with uptake of MTPy among the international MT community. <b>Citation:</b> Kirkby, A.L., Zhang, F., Peacock, J., Hassan, R. and Duan, J., 2020. Development of the open-source MTPy package for magnetotelluric data analysis and visualisation. 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)

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.

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.