The Surface Hydrology Points (Regional) dataset provides a set of related features classes to be used as the basis of the production of consistent hydrological information. This dataset contains a geometric representation of major hydrographic point elements - both natural and artificial. This dataset is the best available data supplied by Jurisdictions and aggregated by Geoscience Australia it is intended for defining hydrological features.

The `Inferred Isotopic Domain Boundaries of Australia data set is based on an interpretation of the recently released Neodymium depleted mantle model age map of Australia (GA Record 2013/44). The isotopic map of Australia was produced by gridding two-stage depleted mantle model ages calculated from Sm-Nd isotopic data for just over 1490 samples of felsic igneous rocks throughout Australia. The resultant isotopic map serves as a proxy for bulk crustal ages and accordingly allows the potential recognition of geological domains with differing geological histories. One of the major aims of the Neodymium depleted mantle model age map, therefore, was to use the isotopic map (and associated data) to aid in the recognition and definition of crustal blocks (geological terranes) at the continental and regional scale. Such boundaries are recognisable by regional changes in isotopic signature but are hindered by the variable and often low density of isotopic data points. Accordingly two major procedures have been adopted to locate the regional distribution of such boundaries across the geological continent. In areas of high data density (and high confidence), such as the Yilgarn Craton Western Australia, isotopic data alone was used to delineate crustal domains. In such regions it is evident that identified crustal blocks often but not universally approximate known geological terranes. In areas of moderate data density (and corresponding moderate confidence) (smoothed) boundaries of known geological provinces were used as a proxy for the isotopic boundary. For both high and moderate data densities identified crustal boundaries were extended (with corresponding less confidence) into regions of lower data density. In areas of low data density (and low confidence) boundaries were either based on other geological and/or geophysical data sets or were not attempted. The latter was particularly the case for regions covered by thick sedimentary successions. Two levels of confidence have been documented, namely the level of confidence in the location of the isotopic domain boundary, and the level of confidence that a boundary may actually exist. The `Inferred Isotopic Domain Boundaries of Australia map shows the locations of inferred boundaries of isotopic domains, which are assumed to represent the crustal blocks that comprise the Australia continent. The map therefore provides constraints on the three dimensional architecture of Australia, and allows a better understanding of how the Australian continent was constructed from the Mesoarchean through to the Phanerozoic. It is best viewed as a dynamic dataset, which will need to be refined and updated as new information, such as new isotopic data, becomes available.

the broad geological blocks from Archaean in the west, through Proterozoic in the centre, to Palaeozoic-Cainozoic in the east, are well presented in the 3-D electrical conductivity model as simple lower conductivity structures. In addition, the model shows conductivity contrast in the western craton, characteristic of enhanced conductivity structures which separate the cratonic blocks, and enhanced conductivity anomalies presented in eastern Australia.

An article on how to use Minecraft, the computer game, in the teaching of geology in the school classroom.

This dataset is the most current national compilation of catchment scale land use data for Australia (CLUM), as at March 2015. It is a seamless raster dataset that combines land use data for all state and territory jurisdictions, compiled at a resolution of 50 metres by 50 metres. It has been compiled from vector land use datasets collected as part of state and territory mapping programs through the Australian Collaborative Land Use and Management Program (ACLUMP). Catchment scale land use data was produced by combining land tenure and other types of land use information, fine-scale satellite data and information collected in the field. The date of mapping (1997 to 2014) and scale of mapping (1:20 000 to 1:250 000) vary, reflecting the source data capture date and scale. This information is provided in a supporting polygon dataset.

Satellite Earth observation data presents unique opportunities for society to respond to major challenges like climate change, food security and sustainable development. But significant technical challenges, including to enable different data streams to be integrated and the sheer volume of the data, are preventing that full value from being realised. The explosion in free, highresolution, global data from next-generation satellites, linked with the potential of new highperformance ICT infrastructure and architectures, positions us to meet this challenge. As the 2016 CEOS Chair, and as a sophisticated user of multiple EO satellite data streams, Australia is proposing that CEOS explore how these new technologies can ensure CEOS agency satellite data can be 'unlocked and put to work'.

Seabed hardness is an important character of seabed substrate as it may influence the nature of attachment of an organism to the seabed. Hence spatially continuous predictions of seabed hardness are important baseline environmental information for sustainable management of Australia's marine jurisdiction. Seabed hardness is usually inferred from multibeam backscatter data with unknown accuracy and can be inferred from underwater video footage or directly measured at limited locations. It can also be predicted based on two-class hardness data using environmental predictors, but no study has been undertaken for predicting multiple-class hardness data. In this study, we classified the seabed hardness into four classes based on underwater video images that were extracted from the underwater video footage. We developed an optimal predictive model to predict the spatial distribution of seabed hardness using random forest (RF) based on the point data of hardness classes and spatially continuous multibeam bathymetry, backscatter and other derived predictors. A novel model selection method that is the averaged variable importance (AVI) was used based on predictive accuracy that was acquired from averaging the results of 100 times replication of 10-fold cross validation. Finally, the spatial predictions generated using the most accurate model was visually examined and analyzed in comparison with previously published predictions based on two-class hardness data. This study confirmed that: 1) seabed hardness of four classes can be predicted into a spatially continuous layer with a high degree of accuracy; 2) model selection for RF is essential for identifying an optimal predictive model in environmental sciences and AVI select the most accurate predictive model(s) instead of the most parsimonious ones, and is recommended for future studies; 3) the typical approach used in pre-selecting predictors by excluding correlated variables (i.e. r 0.95 or the inflation factor 20) needs to be re-examined for identifying predictive models using machine learning methods, at least for the application of random forest in marine environmental sciences; 4) RF is an effective modelling method with high predictive accuracy for multi-level categorical data and can be applied to `small p and large n problems in the environmental sciences; and 5) the spatial predictions for four-class hardness data were similar with the predictions based on two hardness classes, with a high match rates. RF and AVI are recommended for generating spatially continuous predictions of categorical variables in future studies. In summary, AVI shows its effectiveness in searching for the most accurate predictive models and are recommended for future studies. This study further confirms the superior performance of RF in marine environmental sciences. RF is an effective modelling method with high predictive accuracy not only for presence/absence data and but also for multi-level categorical data. RF and AVI are recommended for generating spatially continuous predictions of categorical variables in future studies.

The Sun's gravitational field deflects the apparent positions of close objects in accordance with the formulae of general relativity. Optical astrometry is used to test the prediction, but only with the stars close to the Sun and only during total Solar eclipses. Nowadays, more advanced technique, geodetic Very Long Baseline Interferometry (VLBI) is applied for testing of general relativity with precision about 0.01 percent. The geodetic VLBI is capable of measuring the gravitational delay based on the differential Shapiro's delay. By reason, the gravitational delay is equivalent to the deflection of the light from distant radio sources and could be measured at any time and across the whole sky. In accordance with the theory, all celestial objects display annual circular motion with the magnitude proportional to their ecliptic latitude due to the Earth orbital motion. In particular, the objects near the ecliptic pole draw an annual circle with magnitude of 4 millisecond of arc. In contrast to a single optical telescope, a single ground-based VLBI interferometer is made of two radio telescopes separated by several thousand kilometers. This provides an additional advantage to detect a secondary light deflection angle caused by the parallactic shift of the Sun as observed from both ends of the interferometer. This effect is proportional to the baseline length and is about 0".01 for grazing light at baseline of 8000 km. It could be used in future space interplanetary VLBI missions with baseline length of one billion kilometers (comparable to the Jupiter orbit size) for direct detection of invisible mass from extragalactic objects.

A video created for the Australia Minerals booth at the China Mining 2015conference. The video has key information translated into Mandarin.

Spatially continuous predictions of seabed hardness are important baseline environmental information for sustainable management of Australia's marine jurisdiction. Seabed hardness is often inferred from multibeam backscatter data with unknown accuracy, can be inferred based on underwater video footage at limited locations. It can also be predicted to two classes. In this study, we classified the seabed into four classes based on two new seabed hardness classification schemes (i.e. hard90 and hard70) for seabed video footage by. We developed optimal predictive models to predict the spatial distribution of seabed hardness using random forest (RF) based on point data of hardness classes and spatially continuous multibeam backscatter data. Five feature selection (FS) methods that are variable importance (VI), averaged variable importance (AVI), the combined, Boruta, and RRF were tested. Effects of highly correlated, important and unimportant predictors on the accuracy of RF predictive models were also examined. Finally, the most accurate models were used to predict the spatial distribution of the hardness classes and the predictions were visually examined and compared with the predictions based on two-class hardness classification. This study confirms that: 1) hard90 and hard70 are effective seabed hardness classification schemes; 2) seabed hardness can be predicted into a spatially continuous layer with a high degree of accuracy; 3) the typical approach used to pre-select predictors by excluding highly correlated predictors needs to be re-examined when using machine learning methods, at least, for RF, in the environmental sciences; 4) the identification of the important and unimportant predictors provides useful guidelines for further improving the predictive models; 5) FS is essential for identifying an optimal RF predictive model and the RF methods select the most accurate predictive model(s) instead of the most parsimonious ones, and AVI and Boruta are recommended for future studies; and 6) RF is an effective modelling method with high predictive accuracy for multi-level categorical data, can be applied to `small p and large n problems in environmental sciences, and is recommended for future studies. In addition, automated computational programs for AVI need be developed to improve its computational efficiency and caution should be taken when applying filter FS method in selecting predictive models in future studies.