<div>A minor update to Version 1.0: Lu Hf and O isotope data structure and delivery.</div><div><br></div><div>Isotopic data from rocks and minerals have the potential to yield unique insights into the composition and evolution of the Earth's crust and mantle. Time-integrated records of crust and mantle differentiation (as preserved by the U-Pb, Sm-Nd and Lu-Hf isotopic systems, for example) are important in a wide range of geological applications, especially when successfully integrated with other geological, geophysical, and geochemical datasets. However, such integration requires (i) compilation of comprehensive isotopic data coverages, (ii) unification of datasets in a consistent structure to facilitate inter-comparison, and (iii) easy public accessibility of the compiled and unified datasets in spatial and tabular formats useful and useable by a broad range of industry, government and academic users. This constitutes a considerable challenge, because although a wealth of isotopic information has been collected from the Australian continent over the last 40 years, the published record is fragmentary, and derived from numerous and disparate sources. Unlocking and harnessing the collective value of isotopic datasets will enable more comprehensive and powerful interpretations, and significantly broaden their applicability to Earth evolution studies and mineral exploration.</div><div><br></div><div>As part of the Exploring for the Future (EFTF) program (https://www.ga.gov.au/eftf), we have designed a new database structure and web service system to store and deliver full Lu-Hf isotope and associated O-isotope datasets, spanning new data collected during research programs conducted by Geoscience Australia (GA), as well as compiled literature data. Our approach emphasises the links between isotopic measurements and their spatial, geological, and data provenance information in order to support the widest possible range of uses. In particular, we build and store comprehensive links to the original sources of isotopic data so that (i) users can easily track down additional context and interpretation of datasets, and (ii) generators of isotopic data are appropriately acknowledged for their contributions.</div><div><br></div><div>This system delivers complete datasets including (i) full analytical and derived data as published by the original author, (ii) additional, normalised derived data recalculated specifically to maximise inter-comparability of data from disparate sources, (iii) metadata related to the analytical setup, (iv) a broad range of sample information including sampling location, rock type, geological province and stratigraphic unit information, and (v) descriptions of (and links to) source publications. The data is delivered through the Geoscience Australia web portal (www.portal.ga.gov.au), and can also be accessed through any web portal capable of consuming Open Geospatial Consortium (OGC)-compliant web services, or any GIS system capable of consuming Web Map Services (WMS) or Web Feature Services (WFS).</div><div><br></div><div>Version 1.0 of this Record (Waltenberg et al., 2021) described the database system and web service tables, and featured normalised Lu-Hf data that utilised CHondritic Uniform Reservoir (CHUR) parameters from Blichert-Toft and Albarède (1997). It also presented full tabulated datasets compiled from the North Australian Craton as part of the initial EFTF (2016–2020) program, comprising 5974 individual analyses from 149 unique rock samples. This update (version 1.1) enacts minor changes to some field names within the web services tables to ensure consistency with other web services offered by GA, and for normalised Lu-Hf data, it applies the CHUR parameters of Bouvier et al. (2008) to the entire dataset. The digital datasets presented by Waltenberg et al. (2021) have also been supplemented by more recent analyses collected as part of GA projects in Queensland and New South Wales, in collaboration with the relevant State geological surveys. Version 1.1 does not include an updated tabular data release; the digital dataset available via the web portal now comprises 7630 individual analyses from 180 unique rock samples.</div>

Although critical minerals (CMs) are currently produced from existing mines, their distributions in many mineral systems are, in many cases, poorly known, raising the possibility that CMs are not fully recovered from some ores. The Critical Minerals in Ores (CMiO) database, compiled by Geoscience Australia, United States Geological Survey, Geological Survey of Canada, and Geological Survey of Queensland as part of the Critical Minerals Mapping Initiative, contains high-quality geochemical data from global ore deposits classified using a common framework, enabling global comparison. Using CMiO and other data, we have undertaken preliminary investigations on distributions of CMs in mineral systems including porphyry Cu (PCu), iron oxide-Cu-Au (IOCG), iron oxide-apatite (IOA), rare earth element (REE), and Zn-dominated systems. The PCu systems are enriched in Re, Pt, Pd, Se, and Te relative to the continental crust. At the Pebble (USA) PCu deposit, Re and Se are enriched in Cu ore zones; whereas Te is enriched immediately outside these zones. Although generally not recovered, alkalic PCu deposits (e.g., Galore Creek, Canada; Cadia, Australia) can be enriched in Pd and Pt. Cobalt and some REEs occur in IOCG systems, with Co enriched in magnetite-dominant IOCG systems (e.g., Ernest Henry, Australia; Kwyjibo, Canada), and REEs enriched in IOA (e.g., Pea Ridge, USA) and hematite-dominant IOCG systems (e.g., Olympic Dam, Australia). The enrichment of individual REEs depends strongly on mineral system type. In magmatic and metasomatic systems, light REEs (Ce to Sm) and Y are enriched in hematite-rich IOCG, IOA and carbonatite (e.g., Saint-Honoré, Canada) deposits, whereas heavy REEs (Eu to Lu) are enriched in deposits associated with peralkaline magmatism (e.g., Strange Lake, Canada). Unconformity-related REE (e.g., Maw, Canada; Wolverine, Australia) and ionic clay (e.g., Koopamurra, Australia) deposits also tend to be heavy REE-rich, whereas shale-hosted (e.g., SBH, Canada) and phosphorite (e.g., Ardmore, Australia) deposits can be enriched in heavy and/or light REEs. Zinc deposits are important sources of Ga, Ge, and In. Assessment of the distribution of these CMs in Zn deposits suggest that Ge is concentrated in deposits formed from low temperature, oxidized fluids (Mississippi Valley-type: Tres Marias, Mexico; sediment-hosted massive sulfides: Red Dog, USA), whereas In is enriched in deposits formed from higher temperature, reduced fluids (volcanic-hosted massive sulfide: Kidd Creek, Canada; skarn: Isabel, Australia). These examples demonstrate the utility of the CMiO and other datasets for characterizing CMs distribution in individual ore deposit and predicting CMs potentials of mineral systems. This abstract was presented at the Joint Annual Meeting of the Geological Association of Canada (GAC), Mineralogical Association of Canada (MAC) and Society for Geology Applied to Mineral Deposits, Sudbury, Canada May 2023

<div>GeoInsight was an 18-month pilot project developed in the latter part of Geoscience Australia’s Exploring for the Future Program (2016–2024). The aim of this pilot was to develop a new approach to communicating geological information to non-technical audiences, that is, non-geoscience professionals. The pilot was developed using a human-centred design approach in which user needs were forefront considerations. Interviews and testing found that users wanted a simple and fast, plain-language experience which provided basic information and provided pathways for further research. GeoInsight’s vision is to be an accessible experience that curates information and data from across the Geoscience Australia ecosystem, helping users make decisions and refine their research approach, quickly and confidently.</div><div><br></div><div>Geoscience Australia hosts a wealth of geoscientific data, and the quantity of data available in the geosciences is expanding rapidly. This requires newly developed applications such as the GeoInsight pilot to be adaptable and malleable to changes and updates within this data. As such, utilising the existing Oracle databases, web service publication and platform development workflows currently employed within Geoscience Australia (GA) were optimal choices for data delivery for the GeoInsight pilot.&nbsp;This record is intended to give an overview of the how and why of the technical infrastructure of this project. It aims to summarise how the underlying databases were used for both existing and new data, as well as development of web services to supply the data to the pilot application.&nbsp;</div>

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

<div>Geoscience Australia's geoscientific relational databases use look-up tables to describe the data stored within. These look-ups contain, but are not limited to, information about boreholes, field geology, inorganic and organic geochemistry, hydrochemistry, geophysics, rock properties, samples and other general geological terms. These terms have then been compiled into a vocabulary of terms for publication via GA's vocabulary service. Within this vocabulary, GA references where sourced terms are published in external vocabularies with a source vocabulary URI (Uniform Resource Identifier). </div><div><br></div><div>All vocabularies, collections of concepts within vocabularies and individual concepts are identified with URI persistent identifiers of the form:</div><div>http://pid.geoscience.gov.au/def/voc/ga/{VOCABULARY-KEY}/{COLLECTION-OR-CONCEPT-NAME}</div>