An AusPIX Integration Table for major Australian geographies. Based on the standard AusPIX DGGS 2.4408 hectare plot, this dataset integrates each major geography with all others in the table. It is a core dataset that describes all the AusPIX DGGS level 10 cells in Australia and extends them into a cross-walk that references point, line, polygon, or grid data to those AusPIX DGGS cells. This table can be easily linked to further integrate and reference any other AusPIX enabled dataset available. The Integration set is designed to allow a wide variety of statistical enquiry, as well as visualisation of data and outputs. For example Python Pandas can consume csv downloads of selected parts of this database to allow employment of all Pandas functions. For visualisation, QGIS can connect (and visualise on the desktop), your SQL queries on the database for example. This table is a 430 million row PostgreSQL database provisioned on AWS. It can be filtered and searched using SQL, and results can be downloaded to CSV. It is a pre-calculated dataset using the 'AusPIX framework for data integration, statistics and visualisation by DGGS Location (linked in "Associations" in the panel on the right). An API over the top of this dataset is available at: https://api.dggs.ga.gov.au/docs

Understanding the character of Australia's extensive regolith cover is crucial to the continuing success of mineral exploration. We hypothesize that the regolith contains geochemical fingerprints of processes related to the development and preservation of mineral systems at a range of scales. We test this hypothesis by analysing the composition of surface sediments within greenfield regional (southern Thomson Orogen) and continental (Australia) study areas. In the southern Thomson Orogen area, the first principal component (PC1) derived in our study (Ca, Sr, Cu, Mg, Au, and Mo at one end; rare earth elements (REEs) and Th at the other) is very similar to the empirical vector successfully used by a local company exploring for Cu-Au mineralisation (enrichment in Sr, Ca and Au concomitant with depletion in REEs). Mapping the spatial distribution of PC1 in the region reveals several areas of elevated values and possible mineralisation potential. One of the strongest targets in the PC1 map is located between Brewarrina and Bourke in northern New South Wales. Here both historical and recent exploration drilling has intersected mineralisation with up to 1 % Cu, 0.1 g/t Au, and 717 ppm Zn, purportedly related to a volcanic arc setting. The analysis of a comparable geochemical dataset at the continental scale yields a similar PC1 (Ca, Sr, Mg, Cu, Au, and Mo at one end; REEs and Th at the other) to the regional study. Mapping PC1 at the continental scale shows patterns that (1) are compatible with the regional study, and (2) reveal several geological regions possibly with an enhanced potential for this style of Cu-Au mineralisation. These include well-endowed mineral provinces such as the Curnamona, southern Pilbara, and Capricorn regions, but also some greenfield regions such as the Albany-Fraser/western Eucla, western Murray, and Eromanga geological regions. We conclude that the geochemical composition of Australia's regolith may hold critical information pertaining to mineralisation within/beneath it.

Multi-element geochemical surveys of rocks, soils, stream/lake/floodplain sediments, and regolith are typically carried out at continental, regional and local scales. The chemistry of these materials is defined by their primary mineral assemblages and their subsequent modification by comminution and weathering. Modern geochemical datasets represent a multi-dimensional geochemical space that can be studied using multivariate statistical methods from which patterns reflecting geochemical/geological processes are described (process discovery). These patterns form the basis from which probabilistic predictive maps are created (process validation). Processing geochemical survey data requires a systematic approach to effectively interpret the multi-dimensional data in a meaningful way. Problems that are typically associated with geochemical data include closure, missing values, censoring, merging, levelling different datasets, and adequate spatial sample design. Recent developments in advanced multivariate analytics, geospatial analysis and mapping provide an effective framework to analyze and interpret geochemical datasets. Geochemical and geological processes can often be recognized through the use of data discovery procedures such as the application of principal component analysis. Classification and predictive procedures can be used to confirm lithological variability, alteration, and mineralization. Geochemical survey data of lake/till sediments from Canada and of floodplain sediments from Australia show that predictive maps of bedrock and regolith processes can be generated. Upscaling a multivariate statistics-based prospectivity analysis for arc related Cu-Au mineralization from a regional survey in the southern Thomson Orogen in Australia to the continental scale, reveals a number of regions with similar (or stronger) multivariate response and hence potentially similar (or higher) mineral potential throughout Australia. <b>Citation:</b> E. C. Grunsky, P. de Caritat; State-of-the-art analysis of geochemical data for mineral exploration. <i>Geochemistry: Exploration, Environment, Analysis</i> 2019; 20 (2): 217–232. doi: https://doi.org/10.1144/geochem2019-031 This article appears in multiple journals (Lyell Collection & GeoScienceWorld)

Data is currently being used, and reused, in ecological research at unprecedented rates. To ensure appropriate reuse however, we need to ask the question: “Are aggregated databases currently providing the right information to enable effective and unbiased reuse?” We investigate this question, with a focus on designs that purposefully bias the selection of sampling locations (upweighting the probability of selection of some locations). These designs are common and examples are those that have unequal inclusion probabilities or are stratified. We perform a simulation experiment by creating datasets with progressively more bias, and examine the resulting statistical estimates. The effect of ignoring the survey design can be profound, with biases of up to 250% when naive analytical methods are used. The bias is not reduced by adding more data. Fortunately, the bias can be mitigated by using an appropriate estimator or an appropriate model. These are only applicable however, when essential information about the survey design is available: the randomisation structure (e.g. inclusion probabilities or stratification), and/or covariates used in the randomisation process. The results suggest that such information must be stored and served with the data to support inference and reuse. <b>Citation: </b>S.D. Foster, J. Vanhatalo, V.M. Trenkel, T. Schulz, E. Lawrence, R. Przeslawski, and G.R. Hosack. 2021. Effects of ignoring survey design information for data reuse. Ecological Applications 31(6): e02360. 10.1002/eap.2360