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What constitutes good intelligence is best understood by practitioners but has not been explored through empirical analysis and in the context of a digital age. This paper presents the first research inside all the agencies that form the Australian National Intelligence Community exploring how they are impacted by big data. Intelligence is often opaque to outsiders, yet understanding the characteristics of good intelligence is important to societies that rely on intelligence agencies for national security. This paper reflects the previously unheard perspectives of members of the agencies that form the Australian National Intelligence Community – where there is a significant empirical gap. Semi-structured interviews with 47 participants explored the impact of big data on intelligence and decision-making in Australia. This paper finds that intelligence must meet the following characteristics, many established in historical literature, in order to be considered good intelligence; (i) timely, (ii) purposeful, (iii) actionable, (iv) accurate, (v) provides value-add for an intended audience, and, (vi) is unbiased. This article explores and unpacks each of these characteristics of good intelligence and finds they remain critical in a big data era. [ABSTRACT FROM AUTHOR]

The article discusses the relationship between principal component analysis (PCA) and factor analysis, two data reduction techniques commonly used in big data analysis. The author suggests that factor analysis is an offshoot of PCA and questions whether the title of the paper should be "Vintage PCA with Varimax performing statistical inference." The author also praises the authors of the paper for their systematic study on the use of factor analysis and highlights the high citation count of a book on PCA by Professor Ian Jolliffe. The article is written by Kuldeep Kumar, a data analyst at Bond University in Australia. [Extracted from the article]

.Commercial platforms are being increasingly used in classrooms as teaching and learning tools. Beyond shifts in practice to accommodate this, the teacher becomes a tool of the platform – generating and collecting data for commercial entities. It is time to move from commercial platforms working through teachers to influence education, towards working with teachers in the ethical use of educational data. The purpose of this paper is twofold. Firstly, we draw on semi-structured interviews to present an exploratory typology of teacher influencer behaviour. Secondly, we argue that there is potential for teacher influencers to act as advocates. We introduce the conceptual notion of an 'educational data advocate'. We call for Teacher Influencers to be recognised as a source of expertise to understand the implications of the use of data-driven educational technologies. By doing so, a new pedagogical economy that promotes ethics and rights alongside educational and commercial outcomes may be generated. [ABSTRACT FROM AUTHOR]

This Special Issue addresses the use of linked data for research purposes and to carry out government functions such as child protection, allocation of resources, and debt recovery. Government investment in big data has the potential to change citizens' experience of the welfare state in a broad range of areas in both positive and negative ways. It is therefore important that the Australian social policy community understands and engages with the potential benefits and risks involved in the linkage and analysis of government datasets. Papers in this Special Issue discuss the technical challenges and institutional barriers involved in the construction and governance of linked government data assets and showcase the promise of big data for generating policy relevant insights. This Special Issue also features papers critically interrogating the potential for big data to produce social harms. We contextualise this collection of papers with a brief history of recent policy developments in regards to access to government held data. We also discuss ways of improving public trust and social licence for the use of big data and argue that the voices of First Nations and disadvantaged Australians must be given greater weight in discussions of how their data will be used. [ABSTRACT FROM AUTHOR]

In this paper, we discuss several problems with current Big data practices which, we claim, seriously erode the role of informed consent as it pertains to the use of personal information. To illustrate these problems, we consider how the notion of informed consent has been understood and operationalised in the ethical regulation of biomedical research (and medical practices, more broadly) and compare this with current Big data practices. We do so by first discussing three types of problems that can impede informed consent with respect to Big data use. First, we discuss the transparency (or explanation) problem. Second, we discuss the re-repurposed data problem. Third, we discuss the meaningful alternatives problem. In the final section of the paper, we suggest some solutions to these problems. In particular, we propose that the use of personal data for commercial and administrative objectives could be subject to a 'soft governance' ethical regulation, akin to the way that all projects involving human participants (e.g., social science projects, human medical data and tissue use) are regulated in Australia through the Human Research Ethics Committees (HRECs). We also consider alternatives to the standard consent forms, and privacy policies, that could make use of some of the latest research focussed on the usability of pictorial legal contracts. [ABSTRACT FROM AUTHOR]

Neoliberalism has wrought fundamental changes in the world of work, leading to rising inequality, substantial weakening of organised labour and a decline in industrial relations as a field, especially in relation to teaching. Drawing on historical 'big data' this paper argues that examining the history of worker mobilisation provides a better understanding of these developments, including the importance of considering diverse forms of organisation and action as well as multi-pronged methods built around a key set of issues. It can also inform efforts to address challenges posed by neoliberalism. We conclude by arguing that an historical perspective can better equip the field of industrial relations to meet challenges extending beyond the world of work. [ABSTRACT FROM AUTHOR]

Online social networks (OSNs) suffer from forged messages. Current studies have typically been focused on the detection of forged messages and do not provide the analysis of the behaviors of message publishers and network strategies to suppress forged messages. This paper carries out the analysis by taking a game theoretic approach, where infinitely repeated games are constructed to capture the interactions between a publisher and a network administrator and suppress forged messages in OSNs. Critical conditions, under which the publisher is disincentivized to publish any forged messages, are identified in the absence and presence of misclassification on genuine messages. Closed-form expressions are established for the maximum number of forged messages that a malicious publisher could publish. Confirmed by the numerical results, the proposed infinitely repeated games reveal that forged messages can be suppressed by improving the payoffs for genuine messages, increasing the cost of bots, and/or reducing the payoffs for forged messages. The increasing detection probability of forged messages or decreasing misclassification probability of genuine messages also has a strong impact on the suppression of forged messages. [ABSTRACT FROM AUTHOR]

The aim of this exploratory research study is to obtain the views of relevant stakeholders in relation to the impact big data analytics (BDA), artificial intelligence (AI) and robotics have had in Australia on the audit and assurance services market. Views are also sought on the impact or potential for impact of blockchain technologies. This paper reports findings from interviews and/or focus groups with 20 individuals representing key stakeholders in the market for audit and assurance services in Australia. Via a synthesis of these stakeholder perceptions and a review of the relevant accounting literature, the study finds that, overall, the impact of BDA, robotics and AI on auditing is seen as positive. BDA takes auditors away from manual, intensive tasks, and gives them more time to apply their minds and skills to more critical evaluation‐type work or key audit judgements. Participants also highlighted the need for regulators and standard setters to keep on track with the fast‐paced IT, automation evolution in the auditing field. Finally, and surprisingly given the attention surrounding blockchain technologies, the participants in this study seemed unconvinced of the application of blockchain technologies in auditing practice. This paper reports findings from interviews and/or focus groups with 20 individuals representing key stakeholders in the market for audit and assurance services in Australia. Via a synthesis of these stakeholder perceptions and a review of the relevant accounting literature, the study finds overall, the impact of BDA, robotics and AI on auditing in Australia is seen as positive. [ABSTRACT FROM AUTHOR]

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The hospitality and tourism sector has long played a significant role in Australia's economy, especially in regional areas. Due to the onslaught of COVID-19, numerous businesses have experienced lockdowns, restrictions, and closures due to the fact that people's activity in restaurants, shopping centers, and recreational destinations was restricted, and many other places went into hibernation. After about 2 years since the outbreak, businesses in this sector are gradually starting to reopen and revitalize themselves, but in order to have better decision support about the future of this sector, thus being able to plan, businesses are suffering from an effective analytics solution due to the lack of broken data trends. Starting from fresh day-to-day real-time big data, the study aims to develop a new data analytics model, adopting the design science research methodology, which can provide invaluable options and techniques to make prediction easier from immediate past datasets. This study introduces an innovative design artifact as a big data solution for hospitality managers to utilize analytics for predictive strategic decision-making in post-COVID situation. The artifact can also be generalized for other sectors with tailoring aspects which are subject to further studies. The proposed artifact is then compared with other design artifacts related to big data solutions where it outperforms them in terms of comprehensiveness. The proposed artifact also shows promises for primarily available UGC in managers' decision support aids. [ABSTRACT FROM AUTHOR]

As the sophistication of genomic technologies increases and their cost continues to decrease, they are becoming routine tools in livestock breeding programs and production systems. The convergence of electronic measurement systems, cloud-based computing and fast internet enable the use of powerful decision support to help automate and manage livestock production, animal welfare and quality control. The complexity of livestock production systems, combined with the challenges of producing top-quality discretionary products for consumers, means that no single organisation has the range of expertise to support coordinated development of innovation in the relevant supply chains. Collaboration between a broad spectrum of scientists and industry partners is essential to ensure well integrated input to the design and implementation of programs to deliver improvements in efficiency, quality and profit. The need for collaboration among researchers, among research organisations and with end-users has never been more important. Collaboration brings together the skills needed to manage complex problems and enables the sharing of facilities and scarce resources within Australia and internationally. However, the most important component of effective collaboration is the early engagement of end-users to contribute to the design of programs of innovation, to ensure that investment is well targeted and that there is ownership of the problem as well as the solutions delivered through research. Although the potential benefits of effective collaboration are clear, it often takes more than logic to get individuals and organisations to work together. There needs to be a significant financial incentive combined with strong industry leadership and agreed common goals. Allocating resources to establish these foundations for effective collaboration should precede any major research and development funding initiative. The present paper argues that the new face of animal science in Australia should be structured around coordinated programs of research and development, on the basis of effective collaboration. Research leading to significant change in agricultural practice almost always involves collaboration between teams of researchers and multiple end-users as well as sharing resources across multiple organisations. Although benefits and efficiencies are easily recognised, effective multi-institutional teamwork is often difficult to achieve. The present paper draws on experience from the Sheep CRC, over a period of nearly 20 years, where collaboration among an increasing number of participants contributed to many significant changes in the Australian sheep industry. [ABSTRACT FROM AUTHOR]

The presence of randomly distributed measurement errors in scale scores such as those used in educational and behavioural assessments implies that careful adjustments are required to statistical model estimation procedures if inferences are required for 'true' as opposed to 'observed' relationships. In many cases this requires the use of external values for 'reliability' statistics or 'measurement error variances' which may be provided by a test constructor or else inferred or estimated by the data analyst. Popular measures are those described as 'internal consistency' estimates and sometimes other measures based on data grouping. All such measures, however, make particular assumptions that may be questionable but are often not examined. In this paper we focus on scaled scores derived from aggregating a set of indicators, and set out a general methodological framework for exploring different ways of estimating reliability statistics and measurement error variances, critiquing certain approaches and suggesting more satisfactory methods in the presence of longitudinal data. In particular, we explore the assumption of local (conditional) item response independence and show how a failure of this assumption can lead to biased estimates in statistical models using scaled scores as explanatory variables. We illustrate our methods using a large longitudinal data set of mathematics test scores from Queensland, Australia. [ABSTRACT FROM AUTHOR]

There have been several recent attempts to estimate the size of the Australian illicit tobacco market. The two most recent studies have provided significantly different results, with the Australian Taxation Office (ATO) arriving at an estimate of 5 per cent as the extent of the illicit component of the market, while a tobacco industry--funded approximation conducted by KPMG has suggested this figure to be 14.1 per cent. This paper investigates whether a new methodology for undertaking such an estimate could produce more accurate results and examines publicly available information to explore if there are datasets that could help determine which estimate of the illicit market may be more accurate. The resulting 'top-down' approach identifies an estimate lying between the ATO and KPMG estimates, although it is limited to the illicit cigarette market in Australia. Implications of the results are discussed, and suggestions for further research are presented. [ABSTRACT FROM AUTHOR]

There is a growing appetite for large complex databases that integrate a range of personal, socio-demographic, health, genetic and financial information on individuals. It has been argued that 'Big Data' will provide the necessary catalyst to advance both biomedical research and health economics and outcomes research. However, it is important that we do not succumb to being data rich but information poor. This paper discusses the benefits and challenges of building Big Data, analysing Big Data and making appropriate inferences in order to advance cancer care, using Cancer 2015 (a prospective, longitudinal, genomic cohort study in Victoria, Australia) as a case study. Cancer 2015 has been linked to State and Commonwealth reimbursement databases that have known limitations. This partly reflects the funding arrangements in Australia, a country with both public and private provision, including public funding of private healthcare, and partly the legislative frameworks that govern data linkage. Additionally, linkage is not without time delays and, as such, achieving a contemporaneous database is challenging. Despite these limitations, there is clear value in using linked data and creating Big Data. This paper describes the linked Cancer 2015 dataset, discusses estimation issues given the nature of the data and presents panel regression results that allow us to make possible inferences regarding which patient, disease, genomic and treatment characteristics explain variation in health expenditure. [ABSTRACT FROM AUTHOR]

The map presented in this paper summarises the combined land- and airside accessibility within Australia. To this end, we calculate a bimodal accessibility index at the scale of statistical units by aggregating the (shortest) travel time for three route segments: (1) road travel from the origin to a departure airport, (2) air travel, and (3) road travel from an arrival airport to the destination. The average travel time from a statistical unit to all other statistical units is calculated for the units' population centroids, after which an accessibility surface is interpolated using kriging. The map shows that southeastern Australia is generally characterised by a high accessibility index with the most populated cities being hotspots of accessibility. Central and northern Australia are – with few exceptions – far less accessible. In addition to this largely-expected pattern, the map also reveals a number of specific and perhaps more surprising geographical patterns. [ABSTRACT FROM AUTHOR]

Human actions are causing widespread increases in fire size, frequency, and severity in diverse ecosystems globally. This alteration of fire regimes is considered a threat to numerous animal species, but empirical evidence of how fire regimes are shifting within both threatened species' ranges and protected areas is scarce, particularly at large spatial and temporal scales. We used a big data approach to quantify multidecadal changes in fire regimes in southern Australia from 1980 to 2021, spanning 415 reserves (21.5 million ha) and 129 threatened species' ranges including birds, mammals, reptiles, invertebrates, and frogs. Most reserves and threatened species' ranges within the region have experienced declines in unburnt vegetation (≥30 y without fire), increases in recently burnt vegetation (≤5 y since fire), and increases in fire frequency. The mean percentage of unburnt vegetation within reserves declined from 61 to 36% (1980 to 2021), whereas the mean percentage of recently burnt vegetation increased from 20 to 35%, and mean fire frequency increased by 32%, with the latter two trends primarily driven by the record- breaking 2019 to 2020 fire season. The strongest changes occurred for high- elevation threatened species, and reserves of high elevation, high productivity, and strong rainfall decline, particularly in the southeast of the continent. Our results provide evidence for the widely held but poorly tested assumption that threatened species are experiencing widespread declines in unburnt habitat and increases in fire frequency. This underscores the imperative for developing management strategies that conserve fire- threatened species in an increasingly fiery future. [ABSTRACT FROM AUTHOR]

The experiences of children are notoriously elusive in the sources on which historians traditionally rely. This paper will discuss several projects in which Trove has expanded the history of childhood in Australia, uncovering a nation-wide trade in children through adoption advertisements, and enriching our knowledge of out-of-home care, and the many inquiries into its failings. Trove has democratised the practice of history – allowing care leavers and abuse survivors to research their own histories – and enabled new avenues for identifying microhistories. However, this article also argues that Trove-focused history without an understanding of context can be misleading. [ABSTRACT FROM AUTHOR]

Competition assessment in Australia has traditionally been based on an evaluation of the market structure relying on five factors, namely the degree of market concentration, the height of barriers to entry, the extent of product differentiation, the extent of vertical integration, and the nature of arrangements between firms. These factors, known as the 'QCMA factors', are characteristic of competition in the manufacturing industries of the 'old economy'. Since the ascendancy of Chicago and Post-Chicago School thinking competition analysis in Australia has also taken into consideration non-structural factors. However, in light of the dominance of big tech companies in online markets, the so-called 'Neo-Brandeisian School' has advocated focusing on structural elements that are characteristic of online markets. This article examines to what extent the QCMA factors still a suitable structural framework for the assessment of competition in online markets. [ABSTRACT FROM AUTHOR]

Recent research undertaken revealed that a significant positive relationship exists between quality and safety performance. A major limitation of this research, however, was the nature of the sample; it was heterogeneous (i.e., a combination of U.S. and international projects) and the sample was restricted to 18 projects. Building upon initial research, this paper re-examines the association between quality and safety using a homogeneous sample of 569 projects, which were derived from an Australian construction company with an annual turnover in excess of $1 billion Australian dollars (AU$). A total of 19,314 nonconformances and 17,783 injuries were used to determine the validity and reliability of previous research. A weak association between quality and safety performance was found (p < 0.01). The p-values did not indicate any significant association between first aid and quality rates, except for the injury rate and rework frequency per million scope, which yielded an r-value of 0.307 and p-value 0.046 that is significant at 0.01 level. An association, however, between injuries and rework was identified (r² = 0.70). The discrepancy between this research's findings and that of previous work led to an examination of the issues of using ratios in correlation analysis. Thus, the statistical and arithmetic issues associated with the use of ratios are discussed, and it is recommended that estimating the relationships between quality and safety should be examined using regression techniques or analysis of covariance. Linear regression, therefore, was performed with the injury data as the dependent variable, and rework frequency and personnel hours as the independent variables. The regression results demonstrated that there is a significant association between injuries, and rework and personnel hours; it was revealed that both predictors accounted for 68.2% of the explained variability in injury frequency. The replication of the initial research has enabled a significant advancement in knowledge about relationship between quality and safety performance. [ABSTRACT FROM AUTHOR]

This paper examines recent trends in Australian personal bankruptcy by analysing a large data set obtained from the regulator, the Australian Financial Security Authority. It demonstrates a marked decline in Australian bankruptcy rates, since a peak in 2009, and a consistent rise in levels of unsecured debt among bankrupts. It identifies a number of distinct cohorts within the bankrupt population, and important differences between men and women, younger and older people, and professional and blue collar workers. We find that the debts of bankrupt individuals tend to fall into two distinct categories: a combination of taxation debts and legal liabilities; or a mixture of personal loan, overdraft and credit card debts, likely to be linked to consumer spending. These findings represent a significant contribution to Australian empirical bankruptcy research. They are also timely, in light of the Commonwealth Government's recent proposals to implement significant changes to Australian bankruptcy law. [ABSTRACT FROM AUTHOR]

• We work on multiple wild animals detection and pose estimation. • We constructed a large wild animal surveillance dataset. • We proposed a ratio based data distillation framework. Monitoring wildlife populations and activities have significance for biology and ecology. With the rapid development of computer vision and deep learning techniques, it is possible to employ state-of-the-art convoluntional neural network (CNN) based detectors to process the big data from field surveillance cameras and assist in the following studies. However, data labelling during the training stage is a very time-consuming, labour intensive and expensive task. In this paper, we detect multiple animals (Kangaroo, emu, dingo, bird and wildcat) in the wild in an Omni-supervised learning setting. The unlabeled data from the surveillance cameras will be filtered and used for training via data distillation approach. Moreover, we also perform joint pose estimation and detection for Kangaroo which has the most samples in the dataset. To study the feasibility, we also built a large full high definition (HD) wild animal surveillance image dataset from collected data from several national parks across the State of Queensland in Australia and this dataset will be made publicly available. Extensive experiments show that the detection and pose estimation results can be further improved by using unlabeled data wisely. [ABSTRACT FROM AUTHOR]

Digitalization and the use of Smart Farming Technologies are considered a major opportunity for the future of agriculture. However, realisation of full benefits is constrained by: (1) farmers' interest in and use of big data to improve farm decision making; (2) issues of data sovereignty and trust between providers and users of data and technology; (3) institutional arrangements associated with the governance of data platforms. This paper examines the case of Australia's dairy herd milk recording system, arguably one of agriculture's first cases of 'big data' use, which collects, analyses and uses farm-level data (milk production, lactation and breeding records) to provide individual cow and herd performance information, used by individual farmers for farm management decisions. The aim of this study was to 1) examine the use of big data to add value to farm decision making; and 2) explore factors and processes, including institutional arrangements, which influence farmer engagement with and use of big data. This paper traces the Australian history of the organisation of dairy herd recording (established in 1912 and digitalized in late 1970s) and then uses findings from a longitudinal study of 7 case study dairy farms, which were incentivised to become involved in herd recording in 2015. Applying a conceptual framework linking path dependency in farm decision making and collaborative governance capacity, we find three new important dimensions of the farm user context influencing farmer demand for big data applications: 1) the transition to a new business stage; 2) the additionality farmers seek from data generated in one component of the farm system to other subsystems, and 3) the use of data in long term or strategic decision making. Further, we identified critical attributes of support services in addressing digital literacy, capacity and capability issues at farm level, including diversity in data presentation formats and facilitation of the on-farm transition process through intermediary herd test organisations. The role of farmers as governance actors, or citizens in the decisions of the trajectory of big data applications, adds to understanding of the nature of collaborative governance arrangements that support farm engagement. • Farmer interest in data relates to business stage and the decision's importance. • Cooperative governance structures facilitate farmer trust and use of big data. • Support services reduce the impact of low digital literacy, capacity and capability. • Inter-dependence occurs between existing data processes and optimising data use. [ABSTRACT FROM AUTHOR]

Online behavioural advertising (OBA) comes to consumers at a price. Often unknowingly, people deliver up commercially-valuable personal information as a condition of online user experience, functionality and access. Websites are increasingly tracking user behaviours for commercial purposes and social media derives its income largely from data collection and advertising targeted to the personal disclosures and behavioural attributes which are its data-production mainstay. In this context, consumers face a plethora of information collection practices, all designed to generate data analytics including inferential and predictive profiling to create a ‘digital identity’ for OBA purposes. In this subterranean exchange, consumers are economically redefined as data subjects and advertising targets; a reframing which is perhaps why the OBA industry faces a crisis in consumer concern, both as to privacy and trust. This paper proposes that the regulatory control of OBA in Australia is in disarray. Consumer ignorance of online privacy management and OBA practices is demonstrable. Industry transparency, disclosure, consent processes and compliance practices are questionable. Regulator interest is minimal, industry self-regulation is weak and consumer technical ability and personal responsibility is a last fragile line of defence. Data breaches are ubiquitous in a crowded and poorly-audited supply chain, and entail significant adverse consumer consequences. Yet despite these serious concerns, Australian regulators are failing to respond to OBA issues, either through mandating greater industry disclosure or through regulatory action. The author seeks to expose these weaknesses in calling for consumer and privacy regulators to take more meaningful action to better protect consumers' interests online. [ABSTRACT FROM AUTHOR]

This paper describes the current emphasis on collecting and using quantitative data that encourages the 'quantification of everything' (Mayer-Schönberger & Cukier, 2013, p. 78). The author argues that qualitative data provides more complex and nuanced understandings of how young people engage with literacy teaching and learning opportunities in classrooms. Some useful examples of methods that teachers can use to collect and analyse qualitative data in their literacy classrooms are provided. [ABSTRACT FROM AUTHOR]

Understanding and being able to measure, analyze, compare, and contrast the image of a tourism destination, also known as tourism destination image (TDI), is critical in tourism management and destination marketing. Although various methodologies have been developed, a consistent, reliable, and scalable method for measuring TDI is still unavailable. This study aims to address the challenge by proposing a framework for a holistic measure of TDI in four dimensions, including popularity, sentiment, time, and location. A structural model for TDI measurement that covers various aspects of a tourism destination is developed. TDI is then measured by a comprehensive computational framework that can analyze complex textual and visual data on a large scale. A case study using more than 30,000 images, and 10,000 comments in relation to three tourism destinations in Australia demonstrates the effectiveness of the proposed framework. [ABSTRACT FROM AUTHOR]

Technology is empowering advances in health care, extending beyond the clinical interface to the collection, collation and use of personal data. While this advance has the potential for population-wide benefits, there are legal and ethical challenges which carry the risk of both individual and collective harms. This section critically appraises the existing approach to the governance of health data in Australia. This approach is grounded in the principles of autonomy, privacy and respect for individual choice. This section then identifies the broader imperatives of social good, public health, improvement of outcomes and advancement of knowledge and the importance of balancing individual and collective interests. Central to this discussion are the concepts of social licence and avoiding ethical debt. A significant challenge to the appropriate sharing and use of health data are the existing regulatory barriers (both perceived and actual) and these are explored in some detail. [ABSTRACT FROM AUTHOR]

The transparency of local government decision-making commonly depends on the discovery and use of information by a wide range of participants acting within the surrounding local integrity system. The roles of external organizations (including the media, academics, and non-government institutions) in using information to perform oversight roles is an important characteristic of local integrity systems. However, even in a relative deluge of document-based local government information, pernicious forms of unethical decision-making can evade immediate detection, hidden in plain view within archives of pages which are too expansive to be manually scrutinized. Responding to numerous examples of failed internal governance in local governments in Australia, we profile the experimental development of a prototype system intended to systematically extract information from meeting minutes. We identify how elementary differences in document expression constrains many innovative uses of these records to support the identification of potentially unethical decision-making. Drawing together the integrity systems and information accessibility literature, we conclude that improved integration of information standards in local government documents holds immense potential in expanding how local integrity systems act to foster ethical decision-making among elected local government officials. [ABSTRACT FROM AUTHOR]

Background: The use of government health data for secondary purposes, such as monitoring the quality of hospital services, researching the health needs of populations, and testing how well new treatments work, is increasing. This increase in the secondary uses of health data has led to increased interest in what the public thinks about data sharing, in particular, the possibilities of sharing with the private sector for research and development. Although international evidence demonstrates broad public support for the secondary use of health data, this support does not extend to sharing health data with the private sector. If governments intend to share health data with the private sector, knowing what the public thinks will be important. This paper reports a national survey to explore public attitudes in Australia toward sharing health data with private companies for research on and development of therapeutic drugs and medical devices.Objective: This study aims to explore public attitudes in Australia toward sharing government health data with the private sector.Methods: A web-based survey tool was developed to assess attitudes about sharing government health data with the private sector. A market research company was employed to administer the web-based survey in June 2019.Results: The survey was completed by 2537 individuals residing in Australia. Between 51.8% and 57.98% of all participants were willing to share their data, with slightly fewer in favor of sharing to improve health services (51.99%) and a slightly higher proportion in favor of sharing for research and development (57.98%). There was a preference for opt-in consent (53.44%) and broad support for placing conditions on sharing health information with private companies (62% to 91.99%). Wide variability was also observed in participants' views about the extent to which the private sector could be trusted and how well they would behave if entrusted with people's health information. In their qualitative responses, the participants noted concerns about private sector corporate interests, corruption, and profit making and expressed doubt about the Australian government's capacity to manage data sharing safely. The percentages presented are adjusted against the Australian population.Conclusions: This nationally representative survey provides preliminary evidence that Australians are uncertain about sharing their health data with the private sector. Although just over half of all the respondents supported sharing health data with the private sector, there was also strong support for strict conditions on sharing data and for opt-in consent and significant concerns about how well the private sector would manage government health data. Addressing public concern about sharing government health data with the private sector will require more and better engagement to build community understanding about how agencies can collect, share, protect, and use their personal data. [ABSTRACT FROM AUTHOR]

Developments in tools powered by Artificial Intelligence (‘AI’) and Big Data — both existing and emerging — are predicted to have a revolutionary effect on the insurance industry in the near future. These technological advances have begun materialising at challenging times for the insurance industry in Australia. Various inquiries, including the Royal Commission into Misconduct in the Banking, Superannuation and Financial Services Industry, have uncovered evidence of insurers’ unethical, and often unlawful, practices, which adversely affect consumers. The focus of this article is harm that may be caused to consumers arising out of use of AI- and Big Data-powered analytics in terms of discrimination, exclusion, and unfair prices. We analyse insurance-specific rules currently in place, including recent reforms. We focus on anti-discrimination laws, insureds’ duty of disclosure, and insurers’ obligations (including the duty of utmost good faith), and consider whether they sufficiently address the potential harms that using decision-making models may cause to consumers. [ABSTRACT FROM AUTHOR]

Fauna surveys are traditionally manual, and hence limited in scale, expensive and labour‐intensive. Low‐cost hardware and storage mean that acoustic recording now has the potential to efficiently build scale in terrestrial fauna surveys, both spatially and temporally.With this aim, we have constructed the Australian Acoustic Observatory. It provides a direct and permanent record of terrestrial soundscapes through continuous recording across Australian ecoregions, including those periodically subject to fire and flood, when manual surveys are dangerous or impossible.The observatory comprises 360 permanent listening stations deployed across Australia. Groups of four sensors are deployed at each of 90 sites, placed strategically across ecoregions, to provide representative datasets of soundscapes. Each station continuously records sound, resulting in year‐round data collection. All data are made freely available under an open access licence.The Australian Acoustic Observatory is the world's first terrestrial acoustic observatory of this size. It provides continental‐scale environmental monitoring of unparalleled spatial extent, temporal resolution and archival stability. It enables new approaches to understanding ecosystems, long‐term environmental change, data visualization and acoustic science that will only increase in scientific value over time, particularly as others replicate the design in other parts of the world. [ABSTRACT FROM AUTHOR]

Background: There is good evidence of both community support for sharing public sector administrative health data in the public interest and concern about data security, misuse and loss of control over health information, particularly if private sector organizations are the data recipients. To date, there is little research describing the perspectives of informed community members on private sector use of public health data and, particularly, on the conditions under which that use might be justified. Methods: Two citizens' juries were held in February 2020 in two locations close to Sydney, Australia. Jurors considered the charge: 'Under what circumstances is it permissible for governments to share health data with private industry for research and development?' Results: All jurors, bar one, in principle supported sharing government administrative health data with private industry for research and development. The support was conditional and the juries' recommendations specifying these conditions related closely to the concerns they identified in deliberation. Conclusion: The outcomes of the deliberative processes suggest that informed Australian citizens are willing to accept sharing their administrative health data, including with private industry, providing the intended purpose is clearly of public benefit, sharing occurs responsibly in a framework of accountability, and the data are securely held. Patient and Public Contribution: The design of the jury was guided by an Advisory Group including representatives from a health consumer organization. The jurors themselves were selected to be descriptively representative of their communities and with independent facilitation wrote the recommendations. [ABSTRACT FROM AUTHOR]

• Developing a novel approach for using trip sequences instead of separate trips. • Developing a unique similarity measure for the trip sequences. • Focusing only on the temporal attributes of the trip sequences. • Running the method on real-world case studies. • Evaluating the performance of the developed method. • Comparing the outcome with the existing literature. • Achieving a considerable improvement in inferring the trip purpose. Smart card transactions are known as a rich and continuous source of public transit data, but they miss some important attributes about trips and passengers. One of these missing attributes is the trip purpose attribute. This paper proposes a novel method to infer the trip purpose attribute from the sequences of trips of passengers instead of separate trips. The proposed method infers the trip purpose attribute (a missing attribute in the smart card data) from the temporal attributes (available attributes in the smart card data). First, the relation between the temporal attributes and the trip purpose attribute is learnt by discovering clusters of passengers in the Household Travel Survey dataset while each passenger is represented by one sequence of trips. Then, the discovered clusters are utilized to infer the trip purpose of smart card transactions by allocating each passenger to the closest clusters. The proposed method is implemented on the smart card and HTS datasets from southeast Queensland, Australia. The evaluation results showed a considerable improvement in inferring the trip purpose compared to the results published in the literature. Notably, the effect of considering the trip sequence was more significant than considering land use variables. [ABSTRACT FROM AUTHOR]

In Australia, as in other places in the world, we accumulate and curate Big Data – in the public and private spheres; as consumers and citizens; for profit, for proof and for the promises it holds. Yet, data are only ever a partial snapshot of the lives of humans, communities, environments and places. Data has its place, but in order to use data to effect change, we need to understand which questions to ask, which assumptions to suspend, which data to collect and how to interpret them, we need knowledge of the broader context. Taking a systems‐level view of data allows us to ask questions about it as a technical and sense making apparatus, as well as about the consequences of data collection, storage and use. This article advocates for a cybernetic approach to thinking about and using data. Cybernetics can be broadly understood as the study of feedback, communication and control in a system. A cybernetic approach to Big Data means de‐centring data to focus on a broader system of cultural, technological and environmental dimensions. In this approach, the system itself is the core unit of analysis, and also a vantage point or an approach through which to encounter and re/structure such systems. Employing a cybernetic approach allows us to move beyond attempts to order and reorder information, and instead see it as making implicit knowledge explicit, and then knowing that the hard work is what to do with that knowledge. Through this we are able to conceptualise more complex inputs, outputs and interactions, than simply those captured in a dataset. The insights drawn from a particular dataset or datasets, or from a particular model or algorithm (however well parameterised) never represent the whole truth, and a cybernetics framing enables practitioners to say, if that is the case, what now? A cybernetic approach suggests we should preference deeper understanding, not simply more truth. [ABSTRACT FROM AUTHOR]

Surveillance and governance of the poor have been key foci for the critical social policy literature for some time, although scholarship regarding the role of big data in social policy – and how this might differ from previous forms of surveillance – is continuing to emerge. This article contributes to this literature by exploring the use of big data under Australia and Aotearoa/New Zealand's social investment approaches. Both seek to replace social policy spending with "investment" expected to produce future fiscal returns; in the words of a former Aotearoa/New Zealand Prime Minister, they aim to do "more with less". We argue that big data often provides a poor evidence base for social investment and can, instead, be better understood as part of a panoptic toolkit for poverty governance, mediating continuous surveillance, examination and normalisation of the body politic by enabling the discursive (re)construction of people's identities and subjectivities. This construction serves to exacerbate social inequalities and undermine the rights of ordinary citizens. [ABSTRACT FROM AUTHOR]

In 2018 NAPLAN (National Assessment Program – Literacy and Numeracy) reached a 10-year milestone. Introduced in 2008 by the Australian Curriculum, Assessment and Reporting Authority (ACARA), NAPLAN assesses student literacy and numeracy in Years 3, 5, 7 and 9. As a key education reform in Australia, NAPLAN has provoked critical debate across public, political and academic fora. This study synthesises themes emerging from a quantitative systematic review of NAPLAN-related literature to identify the main discourses that have emerged in response to this large-scale standardised assessment programme. The dominant discourses coalesce around 'datafication', 'social justice', 'affect and emotion', and 'accountability and performativity'. The stakes are high in these discursive struggles around NAPLAN as large-scale standardised testing is reconstituting the purposes of education, the professionalism and professional identities of educators, and affecting parents and students' engagement in schooling. [ABSTRACT FROM AUTHOR]

Traditional methods applied in electricity demand forecasting have been challenged by the course of dimensionality arisen with a growing number of distributed or decentralized energy systems are employing. Without manually operated data preprocessing, classic models are not well-calibrated for their robustness when dealing with the disruptive elements (e.g., demand changes in holidays and extreme weather). Based on the application of big data driven analytics, we propose a novel machine learning method originating from the parallel neural networks for robust monitoring and forecasting power demand to enhance supervisory control and data acquisition for new industrial tendency such as Industry 4.0 and Energy IoT. Through our approach, we generalize the implementation of machine learning by using classic feed-forward neural networks, for parallelization in order to let the proposed method achieve superior performance when dealing with high dimensionality and disruptiveness. With the high-frequency data of consumption in Australia from January 2009 to December 2015, the overall empirical results confirm that our proposed method performs significantly better for dynamic monitoring and forecasting of power demand comparing with the classic methods. [ABSTRACT FROM AUTHOR]

Epidemiological trends for major causes of death and disability, such as stroke, may be monitored using administrative data to guide public health initiatives and service delivery. We calculated admissions rates for ischaemic stroke, intracerebral haemorrhage and subarachnoid haemorrhage between 1 January 2005 and December 31st, 2013 and rates of 30-day mortality and 365-day mortality in 30-day survivors to 31 December 2014 for patients aged 15 years or older from New South Wales, Australia. Annual Average Percentage Change in rates was estimated using negative binomial regression. Of 81,703 eligible admissions, 64,047 (78.4%) were ischaemic strokes and 13,302 (16.3%) and 4,778 (5.8%) were intracerebral and subarachnoid haemorrhages, respectively. Intracerebral haemorrhage admissions significantly declined by an average of 2.2% annually (95% Confidence Interval = −3.5% to −0.9%) (p < 0.001). Thirty-day mortality rates significantly declined for ischaemic stroke (Average Percentage Change −2.9%, 95% Confidence Interval = −5.2% to −1.0%) (p = 0.004) and subarachnoid haemorrhage (Average Percentage Change = −2.6%, 95% Confidence Interval = −4.8% to −0.2%) (p = 0.04). Mortality at 365-days amongst 30-day survivors of ischaemic stroke and intracerebral haemorrhage was stable over time and increased in subarachnoid haemorrhage (Annual Percentage Change 6.2%, 95% Confidence Interval = −0.1% to 12.8%), although not significantly (p = 0.05). Improved prevention may have underpinned declining intracerebral haemorrhage rates while survival gains suggest that innovations in care are being successfully translated. Mortality in patients surviving the acute period is unchanged and may be increasing for subarachnoid haemorrhage warranting investment in post-discharge care and secondary prevention. [ABSTRACT FROM AUTHOR]

Aims: Internationally, there has recently been growing interest in the use of neoadjuvant pertuzumab and trastuzumab in patients with non‐metastatic HER‐2 positive breast cancer following the NEOSPHERE trial in 2012. However, pertuzumab is currently not funded by the Pharmaceutical Benefits Scheme (PBS) in Australia for use in this setting. The authors sought to assess the clinical and pathological response rates at the time of surgery in patients who received neoadjuvant dual anti‐HER2 and taxane therapy in a multidisciplinary breast cancer unit. Methods: A retrospective case series of all patients treated with the neoadjuvant therapy, and who had definitive surgery was conducted. Demographic data, size, grade, tumor type, receptor status prior to neoadjuvant treatment, pathological complete response (pCR) rates, and adverse effects were analyzed. Results: Nineteen patients were included in the study. Sixty‐eight percent of all patients achieved pCR, of which 54% further demonstrated no residual ductal carcinoma in situ. Eight patients (42%) had N1 disease pretreatment, of these 88% demonstrated total pCR in the axilla and the breast. Most adverse effects to treatment were manageable grade 1–2 side effects. Conclusion: This is the first reported Australian experience using neoadjuvant dual anti‐HER2 and taxane therapy for HER‐2 positive nonmetastatic breast cancer. The authors have demonstrated favorable pCR rates for invasive disease compared to the NEOSPHERE trial (68% vs 46%), with reasonable patient tolerability. Larger collaborative data sets are required to fully evaluate correlation of pCR with survival outcomes, and cost‐effectiveness. National funding models need to be considered. [ABSTRACT FROM AUTHOR]

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With the increasing use of machine learning for big data analytics, several methods have been implemented for the purpose of exploration targeting using mineral potential mapping in a GIS environment. Random forests (RF) have been successfully applied to data-driven mineral potential mapping using relatively small numbers of input maps that have typically been pre-classified by a geologist familiar with the mineral system being targeted. However, it is useful to understand how well RF perform for mineral potential mapping when a large number of multi-class categorical or non-thresholded numeric input maps are used in the classification or when weighted or ranked training data are used. Four different implementations of RF are presented to examine how the results vary depending on the degree of intervention from an expert in the modeling process. A case study has been devised using data from the eastern Lachlan Orogen in New South Wales (Australia) for the purposes of targeting porphyry Cu–Au mineralization related to the Macquarie Arc. The results demonstrate that the use of a large number of multi-class categorical or non-thresholded numeric predictive input maps results in a poor mineral potential map outcome. An expert review to determine reclassifications or thresholds that produce geologically meaningful maps as proxies for the mineral system being targeted results in more effective RF-based mineral potential maps being produced. Weighting or ranking the deposits used as training data produces more narrowly defined prospective areas that may assist with targeting tier-one economic deposits. Comparison of the RF results to a standard weights of evidence analysis highlighted some significant differences in which predictive maps should be considered important for modeling, and in the extent of prospective area delineated from each output mineral potential map. [ABSTRACT FROM AUTHOR]

The article discusses study on farm management by researchers Bill Malcolm and Vic Wright. Topics discussed include influence of microeconomics on farm management work, impact of big data on the agricultural sector and need for farms to participate in the design of supply chain linkages along with impact of technology on allocation of costs and benefits to food supply chains.

The advancement of information technology in today's technologically driven era has had a significant impact on the way corporate organisations are conducting their business, especially in a developed country such as Australia. Consequently, it is now almost impossible to conduct effective and efficient audits without the use of technology‐based tools in control environments that are dominated by big data and increasing volumes of electronic audit evidence. Generalised Audit Software (GAS) is one of the most frequently used technology‐based tools available for the internal audit function for tests of controls purposes. The objective of this article is to explore the maturity of the use of GAS by internal audit functions in Australia. The literature review reveals that the use of GAS by internal audit functions globally is still at a relatively low level of maturity, despite the increased adoption of information technology and the generation of big data within organisations. Similarly, the empirical results also confirm the low level of maturity in the use of GAS by internal audit functions in Australia. Only 17.4% of the respondents displayed a high level of maturity with regard to the use of GAS. This article assesses the maturity level of Generalised Audit Software (GAS) usage by internal audit functions in Australia. [ABSTRACT FROM AUTHOR]

Surface albedo is one of the essential climate variables as it influences the radiation budget and the energy balance. Because it is used in a variety of scientific fields, from local to global scale, spatially and temporally disaggregated albedo data are required, which can be derived from satellites. Satellite observations have led to directional-hemispherical (black-sky) and bi-hemispherical (white-sky) albedo products, but time series of high spatial resolution true (blue-sky) albedo estimations at global level are not available. Here, we exploit the capabilities of Google Earth Engine (GEE) for big data analysis to derive global snow-free land surface albedo estimations and trends at a 500-m scale, using satellite observations from 2000 to 2015. Our study reveals negative albedo trends mainly in Mediterranean, India, south-western Africa and Eastern Australia, whereas positive trends mainly in Ukraine, South Russia and Eastern Kazakhstan, Eastern Asia, Brazil, Central and Eastern Africa and Central Australia. The bulk of these trends can be attributed to rainfall, changes in agricultural practices and snow cover duration. Our study also confirms that at local scale, albedo changes are consistent with land cover/use changes that are driven by anthropogenic activities. [ABSTRACT FROM AUTHOR]

Background: Scarcity of prospective medication non-adherence cost measurements for the Australian population with no directly measured estimates makes determining the burden medication non-adherence places on the Australian health care system difficult. This study aims to indirectly estimate the national cost of medication non-adherence in Australia comparing the cost prior to and following a community pharmacy-led intervention. Methods: Retrospective observational study. A de-identified database of dispensing data from 20,335 patients (n=11,257 on rosuvastatin, n=6,797 on irbesartan and n=2,281 on desvenlafaxine) was analyzed and average adherence rate determined through calculation of PDC. Included patients received a pharmacist-led medication adherence intervention and had twelve months dispensing records; six months before and six months after the intervention. The national cost estimate of medication non-adherence in hypertension, dyslipidemia and depression pre- and post-intervention was determined through utilization of disease prevalence and comorbidity, non-adherence rates and per patient disease-specific adherence-related costs. Results: The total national cost of medication non-adherence across three prevalent conditions, hypertension, dyslipidemia and depression was $10.4 billion equating to $517 per adult. Following enrollment in the pharmacist-led intervention medication non-adherence costs per adult decreased $95 saving the Australian health care system and patients $1.9 billion annually. Conclusion: In the absence of a directly measured national cost of medication non-adherence, this estimate demonstrates that pharmacists are ideally placed to improve patient adherence and reduce financial burden placed on the health care system due to non-adherence. Funding of medication adherence programs should be considered by policy and decision makers to ease the current burden and improve patient health outcomes moving forward. [ABSTRACT FROM AUTHOR]

The value of plant ecological datasets with hundreds or thousands of species is principally determined by the taxonomic accuracy of their plant names. However, combining existing lists of species to assemble a harmonized dataset that is clean of taxonomic errors can be a difficult task for non-taxonomists. Here, we describe the range of taxonomic difficulties likely to be encountered during dataset assembly and present an easy-to-use taxonomic cleaning protocol aimed at assisting researchers not familiar with the finer details of taxonomic cleaning. The protocol produces a final dataset (FD) linked to a companion dataset (CD), providing clear details of the path from existing lists to the FD taken by each cleaned taxon. Taxa are checked off against ten categories in the CD that succinctly summarize all taxonomic modifications required. Two older, publicly-available lists of naturalized Asteraceae in Australia were merged into a harmonized dataset as a case study to quantify the impacts of ignoring the critical process of taxonomic cleaning in invasion ecology. Our FD of naturalized Asteraceae contained 257 species and infra-species. Without implementation of the full cleaning protocol, the dataset would have contained 328 taxa, a 28% overestimate of taxon richness by 71 taxa. Our naturalized Asteraceae CD described the exclusion of 88 names due to nomenclatural issues (e.g. synonymy), the inclusion of 26 updated currently accepted names and four taxa newly naturalized since the production of the source datasets, and the exclusion of 13 taxa that were either found not to be in Australia or were in fact doubtfully naturalized. This study also supports the notion that automated processes alone will not be enough to ensure taxonomically clean datasets, and that manual scrutiny of data is essential. In the long term, this will best be supported by increased investment in taxonomy and botany in university curricula. [ABSTRACT FROM AUTHOR]

ABSTRACT We present a homogenized, monthly, upper-air temperature data set comprising 22 stations across the Australian continent, as well as five Australian remote island sites. The data set is based on 2300 UTC radiosonde soundings, with most records starting in 1958. The data are mostly complete since that time, with missing data more frequent at higher levels. The quality control process involved the examination of station metadata and an objective statistical test to detect discontinuities in the data, for which monthly temperature series were adjusted. Homogenized temperature data were analysed at standard levels. The all-Australian annual mean temperature shows statistically significant trends over the 1958-2011 period, larger than the trends in surface temperature. Over this period, the annual 850 and 700 hPa mean temperatures show positive trends of 0.21 and 0.16 °C decade−1, respectively, more than the surface temperature which shows positive trend of 0.12 °C decade−1. Mid-troposphere (the 500, 400 and 300 hPa levels) also shows positive trends of 0.13, 0.15 and 0.20 °C decade−1, respectively. Trends are small and of reverse sign at levels that intercept the tropopause: +0.08 °C decade−1 at 200 hPa and −0.14 °C decade−1 at 150 hPa. In the lower stratosphere (at 100 hPa), temperature trend is strongly negative, −0.39 °C decade−1. A similar vertical structure in the trends was found for the remote islands. The homogenized upper-air radiosonde records for the Australian region are an independent confirmation of the expected physical response to increased atmospheric greenhouse gas concentrations: (1) a general warming in the troposphere, (2) a cooling in the lower stratosphere and (3) a faster warming of the troposphere than the surface. [ABSTRACT FROM AUTHOR]

Big Data technologies hold great promise for improved efficiency and effectiveness for law enforcement and national security. This article explores the potential impact of Big Data on the production of security in society. Building on a Bourdesian framework for analysing police and new technologies, the article draws on empirical data from an Australian study to examine how security agents made sense of the capability and value of Big Data and developed technological frames that envisaged how this new technology could enhance or change their practices. The analysis reveals the expectations and anxieties regarding Big Data among stakeholders and concludes that the community should take a more active role in understanding Big Data and influencing the governance of its usage. [ABSTRACT FROM AUTHOR]