The Location Index (Loc-I) project (http://locationindex.org) aims to enable government agencies to geospatially-integrate and analyse data reliably, effectively and efficiently across portfolios and information domains. Loc-I is part of the Data Integration Partnership for Australia (DIPA) initiative, which seeks to maximise government data to improve policy advice. This poster presents the current Linked Data approaches that provide general solutions for data integration of location-based data and its application in the Australian context. This poster was presented at the Earth Science Information Partners (ESIP) Winter Meeting in January 2020 in Bethesda, MD.

The history of a piece of information is known as "provenance". From extensive interactions with hydro-and geo-scientists in Australian science agencies we found both widespread demand for provenance and widespread confusion about how to manage it and how to develop requirements for managing it. We take inspiration from the well-known software development Capability Maturity Model to design a Maturity Model for provenance management that we call the PMM. The PMM can be used to assess the state of existing practices within an organisation or project, to benchmark practices and existing tools, to develop requirements for new provenance projects, and to track improvements in provenance management across an organisational unit. We present the PMM and evaluate it through application in a workshop of scientists across three data-intensive science projects. We find that scientists recognise the value of a structured approach to requirements elicitation that ensures that aspects are not overlooked.

For the first time in Tasmania, federated real-time water resources data are being used by a community of irrigators for managing water availability for irrigation and environmental requirements, i.e. in the Ringarooma River catchment, north-east Tasmania. Data include observed weather, soil water, stream flow, and water quality, and daily forecasts of rainfall and stream flow. These data are accessed via a customised website that provides near-real-time information. Irrigators cooperate through a water users group, and aim to avoid flows decreasing below an environmental threshold that would trigger a 'cease-to-take' regulation that may occur during the dry season when irrigation demands are high. At times when there is high risk of a cease-to-take declaration occurring, irrigators coordinate within and outside the group, and with the regulator, to reduce extractions of water from the river and coordinate releases of stored water. Adopting data-driven real-time management has contributed to the avoidance of cease-to-take declarations during the past two years, despite increased irrigation. Several novel aspects of this project include: federating irrigator-relevant data from multiple agencies, daily stream flow forecasts (from the eWater 'Source' model), localised weather forecasts (from a national meteorological model), and a strong community spirit of cooperation in managing water resources and environmental values. This sets a basis for more sophisticated water management, including: sub-catchment water management, and flow predictions that potentially include daily extractions and releases.

Scale precipitation is a major issue in the petroleum industry limiting production and increasing maintenance costs. Scale composition and structure can vary considerably between different fields and over the life of a well. It is vitally important to understand the mechanisms of scale formation in order to prevent or treat them. In this study, three scale samples were selected for investigation from 2 wells, designated B2, DN12 and DN12B. The mineralogy of the samples was characterised using optical microscopy and scanning electron microscopy methods. Sample DN12 was dominantly barite and showed evidence of incompatible fluid mixing, which caused redox changes in the components. Possible dissolution structures also indicated the movement of saline fluids through parts of the sample. DN12B, a latter sample from the same well, was composed mainly of calcite and silica with isolated barite crystals. Multiple phases were also exhibited representing decreasing pH conditions resulting in reduced calcite and increased silica precipitation. The change in the dominant mineral indicated significant changes in production fluid composition which is normally considered stable. The washed sample B2 was predominantly silica and iron oxide with siderite, barite and pyrite and abundant halite before washing. The multiple observed phases indicated multiple redox changes caused by periodic fluid mixing. Stages of iron and silica precipitation may have also been caused by pH, Eh and compositional variations. The presence of pyrite, globular silica and iron oxide structures indicated the influence of bacteria in the mineral precipitation as a live bacterial slime accompanied the sample when collected.

Sustainability science has been viewed as a new discipline which focuses on the complex interactions between nature and society. It demands intensive integration of data from different sources within different domains. Governments collect and generate huge amounts of scientific data and thus are in a unique position to support sustainability research. However, there are many challenges in discovering and re-using government data. In this chapter, first, we survey the sustainability related datasets published by the Australian government. We believe this is the critical first step to identifying the opportunities and issues and advancing the Australian Government 2.0 agenda. Second, we investigate the role of Linked Data in integrating a selection of Australian government datasets to generate sustainability science hypotheses and support the data analysis. We discuss the challenges based on our survey experience and present some recommendations for data publishing and analysis.

As data capture technology improves and computer platforms continue to increase in storage capacity, data management becomes increasingly important. A data management system helps ensure that data has appropriate security, is logically structured, and properly archived with demonstrable integrity. Over the past 2 years CSIRO has taken on some high-profile projects with a high expectation for auditing of final results by various interested parties. A key to the success of these projects has been the implementation of a data management system.In early 2007, the National Water Commission (NWC) comissioned CSIRO to develop a whole of basin assessment of water availability for the Murray-Darling Basin. This became known as the Murray-Darling Basin Sustainable Yields (MDBSY) project. Following the successful completion of the MDBSY project, CSIRO was contracted to conduct three additional Sustainable Yields (SY) projects across Northern Australia, Tasmania, and south-west Western Australia. The system established for the MDBSY project formed a 'blueprint' for use in these projects.The MDBSY and subsequent SY projects have involved large volumes of data across various disciplinary project teams. The high public profile and associated level of scrutiny of the results, presented in the final reports, required the establishment of a data audit trail for all project results, so that all values could be traced to their origins. In order to achieve this, a high degree of discipline was required when managing project data. The data management framework developed to support these projects was built on a set of protocols, processes, and standards.The SY projects have been structured with four disciplinary teams as well as a team focused on data management. The data management team was comprised of a data team leader, a project data manager, and a data coordinator from each of the disciplinary teams. The data team leader and data manager were responsible for developing the protocols and procedures, and for organising the development/acquisition of tools required for organising and managing all of the project data. The key responsibility for the data coordinators was to ensure that their team's data were migrated into the project archive and catalogued appropriately with metadata descriptions.This paper will outline the development of the data management protocols, with emphasis on the establishment of a data audit trail, and associated tools. It will also highlight how the developments within the SY projects have provided a framework for propagating such a system across all future projects, and how the 'seeds' of a data management culture have begun to 'grow' within CSIRO. Finally it will share some of the learnings from this activity.