Discussion of available stratigraphic resources: the Australian Stratigraphic Units Database (ASUD); documentation of procedures for modifying existing units or establishing new ones; contact details for the Australian Stratigraphy Commission members and ASUD staff. Suggestions on ways of raising awareness through modern media such as a podcast or app, and a request for feedback on what sort of approach might appeal to a university student audience.

Short article about stratigraphy matters. ISSC, new ASUD State search tool, Timescale, comparisions with British geological practices. ISSN: 0312 4711

Part- page item on matters related to the Australian Stratigraphy Commission and the Australian Stratigraphic Units Database. This column discusses the usefulness and vulnerability of type sections and reference sections.

GeoSciML is the international standard for transfer of digital geological maps and relational database data. GeoSciML was developed over the past decade by the IUGS Commission for the Management and Application of Geoscience Information (CGI), and was adopted as an Open Geospatial Consortium (OGC) standard in June 2016. Ratification as an official OGC standard marked a coming of age for GeoSciML - it now meets the highest standards for documentation and current best practice for interoperable data transfer. GeoSciML is the preferred standard for geoscience data sharing initiatives worldwide, such as OneGeology, the European INSPIRE directive, the Australian Geoscience Portal, and the US Geoscience Information Network (USGIN). GeoSciML is also used by OGC's GroundwaterML data standard [1] and CGI's EarthResourceML standard [2]. Development of GeoSciML version 4 learnt considerably from user experiences with version 3.2, which was released in 2013 [3]. Although the GeoSciML v3 data model was conceptually sound, its XML schema implementation was considered overly complex for the general user. Version 4 developments focussed strongly on designing simpler XML schemas that allow data providers and users to interact with data at various levels of complexity. As a result, GeoSciML v4 provides three levels of user experience - 1. simple map portrayal, 2. GeoSciML-Basic for common age and lithology data for geological features, and 3. GeoSciML-Extended, which extends GeoSciML-Basic to deliver more detailed and complex relational data. Similar to GeoSciML v3, additional GeoSciML v4 schemas also extend the ISO Observations & Measurements standard to cover geological boreholes, sampling, and analytical measurements. The separate levels of GeoSciML also make it easier for software vendors to develop capabilities to consume relatively simple GeoSciML data without having to deal with the full range of complex GeoSciML schemas. Previously mandatory elements of GeoSciML, that were found to be overly taxing on users in version 3, are now optional in version 4. GeoSciML v4 comes with Schematron validation scripts which can be used by user communities to create profiles of GeoSciML to suit their particular community needs. For example, the European INSPIRE community has developed Schematrons for web service validation which require its users to populate otherwise-optional GeoSciML-Basic elements, and to use particular community vocabularies for geoscience terminology. Online assistance for data providers to use GeoSciML is now better than ever, with user communities such as OneGeology, INSPIRE, and USGIN providing user guides explaining how to create simple and complex GeoSciML web services. CGI also provides a range of standard vocabularies that can be used to populate GeoSciML data services. Full documentation and user guides are at www.geosciml.org.

Geoscience Australia is supporting the exploration and development of offshore oil and gas resources and establishment of Australia's national representative system of marine protected areas through provision of spatial information about the physical and biological character of the seabed. Central to this approach is prediction of Australia's seabed biodiversity from spatially continuous data of physical seabed properties. However, information for these properties is usually collected at sparsely-distributed discrete locations, particularly in the deep ocean. Thus, methods for generating spatially continuous information from point samples become essential tools. Such methods are, however, often data- or even variable- specific and it is difficult to select an appropriate method for any given dataset. Improving the accuracy of these physical data for biodiversity prediction, by searching for the most robust spatial interpolation methods to predict physical seabed properties, is essential to better inform resource management practises. In this regard, we conducted a simulation experiment to compare the performance of statistical and mathematical methods for spatial interpolation using samples of seabed mud content across the Australian margin. Five factors that affect the accuracy of spatial interpolation were considered: 1) region; 2) statistical method; 3) sample density; 4) searching neighbourhood; and 5) sample stratification by geomorphic provinces. Bathymetry, distance-to-coast and slope were used as secondary variables. In this study, we only report the results of the comparison of 14 methods (37 sub-methods) using samples of seabed mud content with five levels of sample density across the southwest Australian margin. The results of the simulation experiment can be applied to spatial data modelling of various physical parameters in different disciplines and have application to a variety of resource management applications for Australia's marine region.

A clear trend has now emerged to integrate spatial data with mainstream corporate data management systems, and the technology to do this is now largely to hand. GIS is becoming less of a specialist field and more closely tied to general data management. These advances apply not just to 2-dimensional data but to 3 or more dimensions - going beyond the bounds of conventional Euclidean space. The technology to handle n-dimensional space that is widely used in business data-warehousing applications can now be put to use handling the complexities of geochrono-logical, geochemical and geophysical space. This is extremely good news for geoscience, which has never sat very comfortably within the 2-dimensional confines of traditional GIS systems. Now we can expect to see the beginnings of true geoscience information systems that can be applied to space-time chunks of the earth's crust in the search for minerals and oil.

NOTE: removed on request: 25 May 2016 by Sundaram Baskaran GWATER is a corporate database designed to accommodate a number of existing project groundwater and surface water data sets in AGSO. One of the aims in developing the database as a corporate repository is to enable sharing between AGSO projects allowing re-use of data sets derived from various sources such as the State and Territory water authorities. The database would also facilitate an easier exchange of data between AGSO and these authorities. This document presents an overview of the current structure of the database, and describes the present data entry and retrieval forms in some detail. Definitions of all tables and data fields contained within them are listed in an appendix. The database structure will not remain static. Future developments, such as the integration of data directly out of the database into geographic information systems, are expected to lead to modifications in the database structure with possible addition of new tables or fields. Use of GWATER by a range of project areas will undoubtedly lead to different needs in accessing the data, resulting in the request for further development of the data access tools.

The national mineral deposits dataset covers 60 commodities and more than 1050 of Australia's most significant mineral deposits - current and historic mines and undeveloped deposits. This release adds more than 100 new deposits to the previous release of OZMIN plus upgraded resource and production figures.

The Alkaline Rocks of Australia OZCHEM database subset is comprised of 927 wholerock analyses derived from AGSO field work and the literature. AGSO's complete OZCHEM database contains approximately 50000 analyses, mainly from Australia but some are also from Papua New Guinea, Antarctica, Solomon Islands and New Zealand. Approximately 32000 analyses of Australian rocks of all ages and some New Zealand Tertiary volcanics are available for sale. The location is stored with each analysis along with geological descriptions, including the host stratigraphic unit and lithology. Most samples have been collected by AGSO field parties.OZCHEM is stored in an ORACLE relational database and is available in Oracle export, comma-delimited relational ASCII, and Microsoft Access formats.

Geoscience Australia is supporting the exploration and development of offshore oil and gas resources and establishment of Australia's national representative system of marine protected areas through provision of spatial information about the physical and biological character of the seabed. Central to this approach is prediction of Australia's seabed biodiversity from spatially continuous data of physical seabed properties. However, information for these properties is usually collected at sparsely-distributed discrete locations, particularly in the deep ocean. Thus, methods for generating spatially continuous information from point samples become essential tools. Such methods are, however, often data- or even variable- specific and it is difficult to select an appropriate method for any given dataset. Improving the accuracy of these physical data for biodiversity prediction, by searching for the most robust spatial interpolation methods to predict physical seabed properties, is essential to better inform resource management practises. In this regard, we conducted a simulation experiment to compare the performance of statistical and mathematical methods for spatial interpolation using samples of seabed mud content across the Australian margin. Five factors that affect the accuracy of spatial interpolation were considered: 1) region; 2) statistical method; 3) sample density; 4) searching neighbourhood; and 5) sample stratification by geomorphic provinces. Bathymetry, distance-to-coast and slope were used as secondary variables. In this study, we only report the results of the comparison of 14 methods (37 sub-methods) using samples of seabed mud content with five levels of sample density across the southwest Australian margin. The results of the simulation experiment can be applied to spatial data modelling of various physical parameters in different disciplines and have application to a variety of resource management applications for Australia's marine region.