The Murray Darling Basin Elevation Project (MDBEP) data covers the areas of the Murray Floodplain (Wakool and Edward Rivers) and Darling catchment areas (Balonne, Barwon, Bokhara, Boomi, Culgoa, Gwydir, Namoi, Macintyre, Macquarie, Mooni and Narran rivers).

The ANZLIC Metadata tool uses the ISO 19139 (2005) metadata standard which is the current supported metadata standard used in Australia in its ArcGIS Desktop version 10.4 software package. This metadata tool is intended to be used through the ArcGIS Desktop version 10.4 software accessed via the description tab in ArcCatalog. The tool was previously developed and supported by Esri Australia but this was formally handed over to ANZLIC in 2017. Geoscience Australia in support of ANZLIC has developed this new metadata tool based on the previous work of Esri Australia and the new Esri Inc ArcGIS Desktop 10.4 Metadata toolkit. The tool is intended for users of Esri Inc ArcGIS Desktop version 10.4 software to create ANZLIC compliant ISO 19139 metadata associated with datasets.

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.

This video demonstrates to viewers the importance and value on fit for purpose metadata, metadata standards, and metadata profiles.

Modern magnetotellurics (MT) offers a multiscale capability to image the electrical properties of Earth’s crust and upper mantle. The data it provides and the models derived from it are important geophysical contributions to understanding Earth’s geology and resource potential. In Australia, MT data is acquired by the resource exploration industry, university-based research groups, and Federal, State and Territory geological surveys. To ensure this data can be used to its full potential, including by groups and individuals who may not have been responsible for its acquisition, it is important that community-agreed standards be adopted for the acquired data and its associated metadata. <b>Citation: </b>Jingming Duan, Alison Kirkby, Darren Kyi, Wenping Jiang, Marina Costelloe & Adrian Hitchman (2021) Metadata standards for magnetotelluric time-series data, <i>Preview</i>, 2021:215, 61-63. DOI: 10.1080/14432471.2021.2012035

The pace, with which government agencies, researchers, industry, and the public need to react to national and international challenges of economic, environmental, and social natures, is constantly changing and rapidly increasing. Responses to the global COVID-19 pandemic event, the 2020 Australian bushfire and 2021 flood crisis situations are recent examples of these requirements. Decisions are no longer made on information or data coming from a single source or discipline or a solitary aspect of life: the issues of today are too complex. Solving complex issues requires seamless integration of data across multiple domains and understanding and consideration of potential impacts on businesses, the economy, and the environment. Modern technologies, easy access to information on the web, abundance of openly available data shifts is not enough to overcome previous limitations of dealing with data and information. Data and software have to be Findable, Accessible, Interoperable and Reusable (FAIR), processes have to be transparent, verifiable and trusted. The approaches toward data integration, analysis, evaluation, and access require rethinking to: - Support building flexible re-usable and re-purposeful data and information solutions serving multiple domains and communities. - Enable timely and effective delivery of complex solutions to enable effective decision and policy making. The unifying factor for these events is location: everything is happening somewhere at some time. Inconsistent representation of location (e.g. coordinates, statistical aggregations, and descriptions) and the use of multiple techniques to represent the same data creates difficulty in spatially integrating multiple data streams often from independent sources and providers. To use location for integration, location information needs to be embedded within the datasets and metadata, describing those datasets, so those datasets and metadata would become ‘spatially enabled’.