Understanding the legal implications of data sharing, access and reuse in the Australian research landscape

Researchers are increasingly involved in data-intensive research projects that cut across geographic and disciplinary borders. Quality research now often involves virtual communities of researchers participating in large-scale web-based collaborations, opening their earlystage research to the research community in order to encourage broader participation and accelerate discoveries. The result of such large-scale collaborations has been the production of ever-increasing amounts of data. In short, we are in the midst of a data deluge. Accompanying these developments has been a growing recognition that if the benefits of enhanced access to research are to be realised, it will be necessary to develop the systems and services that enable data to be managed and secured. It has also become apparent that to achieve seamless access to data it is necessary not only to adopt appropriate technical standards, practices and architecture, but also to develop legal frameworks that facilitate access to and use of research data. This chapter provides an overview of the current research landscape in Australia as it relates to the collection, management and sharing of research data. The chapter then explains the Australian legal regimes relevant to data, including copyright, patent, privacy, confidentiality and contract law. Finally, this chapter proposes the infrastructure elements that are required for the proper management of legal interests, ownership rights and rights to access and use data collected or generated by research projects.

Feasibility of using health data sources to inform product safety surveillance in Queensland : a report for the Queensland Injury Prevention Council

This report provides an evaluation of the current available evidence-base for identification and surveillance of product-related injuries in children in Queensland. While the focal population was children in Queensland, the identification of information needs and data sources for product safety surveillance has applicability nationally for all age groups. The report firstly summarises the data needs of product safety regulators regarding product-related injury in children, describing the current sources of information informing product safety policy and practice, and documenting the priority product surveillance areas affecting children which have been a focus over recent years in Queensland. Health data sources in Queensland which have the potential to inform product safety surveillance initiatives were evaluated in terms of their ability to address the information needs of product safety regulators. Patterns in product-related injuries in children were analysed using routinely available health data to identify areas for future intervention, and the patterns in product-related injuries in children identified in health data were compared to those identified by product safety regulators. Recommendations were made for information system improvements and improved access to and utilisation of health data for more proactive approaches to product safety surveillance in the future.

Hunters & gatherers : strategies for curriculum mapping and data collection for assurance of learning

Assurance of learning is a predominant feature in both quality enhancement and assurance in higher education. Assurance of learning is a process that articulates explicit program outcomes and standards, and systematically gathers evidence to determine the extent to which performance matches expectations. Benefits accrue to the institution through the systematic assessment of whole of program goals. Data may be used for continuous improvement, program development, and to inform external accreditation and evaluation bodies. Recent developments, including the introduction of the Tertiary Education and Quality Standards Agency (TEQSA) will require universities to review the methods they use to assure learning outcomes. This project investigates two critical elements of assurance of learning: 1. the mapping of graduate attributes throughout a program; and 2. the collection of assurance of learning data. An audit was conducted with 25 of the 39 Business Schools in Australian universities to identify current methods of mapping graduate attributes and for collecting assurance of learning data across degree programs, as well as a review of the key challenges faced in these areas. Our findings indicate that external drivers like professional body accreditation (for example: Association to Advance Collegiate Schools of Business (AACSB)) and TEQSA are important motivators for assuring learning, and those who were undertaking AACSB accreditation had more robust assurance of learning systems in place. It was reassuring to see that the majority of institutions (96%) had adopted an embedding approach to assuring learning rather than opting for independent standardised testing. The main challenges that were evident were the development of sustainable processes that were not considered a burden to academic staff, and obtainment of academic buy in to the benefits of assuring learning per se rather than assurance of learning being seen as a tick box exercise. This cultural change is the real challenge in assurance of learning practice.

Data modelling in the beginning school years

This paper argues for a renewed focus on statistical reasoning in the beginning school years, with opportunities for children to engage in data modelling. Some of the core components of data modelling are addressed. A selection of results from the first data modelling activity implemented during the second year (2010; second grade) of a current longitudinal study are reported. Data modelling involves investigations of meaningful phenomena, deciding what is worthy of attention (identifying complex attributes), and then progressing to organising, structuring, visualising, and representing data. Reported here are children's abilities to identify diverse and complex attributes, sort and classify data in different ways, and create and interpret models to represent their data.

Ethical decision making and health care managers : developing managerial profiles based on ethical frameworks and other influencing factors

Whether the community is looking for “scapegoats” to blame, or seeking more radical and deeper causes, health care managers are in the firing line whenever there are woes in the health care sector. The public has a right to question whether ethics have much influence on the everyday decision making of health care managers. This thesis explores, through a series of published papers, the influence of ethics and other factors on the decision making of health care managers in Australia. Critical review of over 40 years of research on ethical decision making has revealed a large number of influencing factors, but there is a demonstrable lack of a multidimensional approach that measures the combined influences of these factors on managers. This thesis has developed an instrument, the Managerial Ethical Profile (MEP) scale, based on a multidimensional model combining a large number of influencing factors. The MEP scale measures the range of influences on individual managers, and describes the major tendencies by developing a number of empirical profiles derived from a hierarchical cluster analysis. The instrument was developed and refined through a process of pilot studies on academics and students (n=41) and small-business managers (n=41), and then was administered to the larger sample of health care managers (n=441). Results from this study indicate that Australian health care managers draw on a range of ethical frameworks in their everyday decision making, forming the basis of five MEPs (Knights, Guardian Angels, Duty Followers, Defenders, and Chameleons). Results from the study also indicate that the range of individual, organisational, and external factors that influence decision making can be grouped into three major clusters or functions. Cross referencing these functions and other demographic data to the MEPs provides analytical insight into the characteristics of the MEPs. These five profiles summarise existing strengths and weaknesses in managerial ethical decision making. Therefore identifying these profiles not only can contribute to increasing organisational knowledge and self-awareness, but also has clear implications for the design and implementation of ethics education and training in large scale organisations in the health care industry.

Data flow analysis of embedded program expressions

Data flow analysis techniques can be used to help assess threats to data confidentiality and integrity in security critical program code. However, a fundamental weakness of static analysis techniques is that they overestimate the ways in which data may propagate at run time. Discounting large numbers of these false-positive data flow paths wastes an information security evaluator's time and effort. Here we show how to automatically eliminate some false-positive data flow paths by precisely modelling how classified data is blocked by certain expressions in embedded C code. We present a library of detailed data flow models of individual expression elements and an algorithm for introducing these components into conventional data flow graphs. The resulting models can be used to accurately trace byte-level or even bit-level data flow through expressions that are normally treated as atomic. This allows us to identify expressions that safely downgrade their classified inputs and thereby eliminate false-positive data flow paths from the security evaluation process. To validate the approach we have implemented and tested it in an existing data flow analysis toolkit.

Identifying differences in safe roads and crash prone roads using clustering data mining

Road asset managers are overwhelmed with a high volume of raw data which they need to process and utilise in supporting their decision making. This paper presents a method that processes road-crash data of a whole road network and exposes hidden value inherent in the data by deploying the clustering data mining method. The goal of the method is to partition the road network into a set of groups (classes) based on common data and characterise the class crash types to produce a crash profiles for each cluster. By comparing similar road classes with differing crash types and rates, insight can be gained into these differences that are caused by the particular characteristics of their roads. These differences can be used as evidence in knowledge development and decision support.

Data modeling in elementary and middle school classes : a shared experience

This paper argues for a renewed focus on statistical reasoning in the elementary school years, with opportunities for children to engage in data modeling. Data modeling involves investigations of meaningful phenomena, deciding what is worthy of attention, and then progressing to organizing, structuring, visualizing, and representing data. Reported here are some findings from a two-part activity (Baxter Brown’s Picnic and Planning a Picnic) implemented at the end of the second year of a current three-year longitudinal study (grade levels 1-3). Planning a Picnic was also implemented in a grade 7 class to provide an opportunity for the different age groups to share their products. Addressed here are the grade 2 children’s predictions for missing data in Baxter Brown’s Picnic, the questions posed and representations created by both grade levels in Planning a Picnic, and the metarepresentational competence displayed in the grade levels’ sharing of their products for Planning a Picnic.

Multi-sensor data fusion for UAV navigation during landing operations

This paper presents a practical framework to synthesize multi-sensor navigation information for localization of a rotary-wing unmanned aerial vehicle (RUAV) and estimation of unknown ship positions when the RUAV approaches the landing deck. The estimation performance of the visual tracking sensor can also be improved through integrated navigation. Three different sensors (inertial navigation, Global Positioning System, and visual tracking sensor) are utilized complementarily to perform the navigation tasks for the purpose of an automatic landing. An extended Kalman filter (EKF) is developed to fuse data from various navigation sensors to provide the reliable navigation information. The performance of the fusion algorithm has been evaluated using real ship motion data. Simulation results suggest that the proposed method can be used to construct a practical navigation system for a UAV-ship landing system.

The conceptual and operational compatibility of data breach notification and information privacy laws

Mandatory data breach notification laws are a novel and potentially important legal instrument regarding organisational protection of personal information. These laws require organisations that have suffered a data breach involving personal information to notify those persons that may be affected, and potentially government authorities, about the breach. The Australian Law Reform Commission (ALRC) has proposed the creation of a mandatory data breach notification scheme, implemented via amendments to the Privacy Act 1988 (Cth). However, the conceptual differences between data breach notification law and information privacy law are such that it is questionable whether a data breach notification scheme can be solely implemented via an information privacy law. Accordingly, this thesis by publications investigated, through six journal articles, the extent to which data breach notification law was conceptually and operationally compatible with information privacy law. The assessment of compatibility began with the identification of key issues related to data breach notification law. The first article, Stakeholder Perspectives Regarding the Mandatory Notification of Australian Data Breaches started this stage of the research which concluded in the second article, The Mandatory Notification of Data Breaches: Issues Arising for Australian and EU Legal Developments (‘Mandatory Notification‘). A key issue that emerged was whether data breach notification was itself an information privacy issue. This notion guided the remaining research and focused attention towards the next stage of research, an examination of the conceptual and operational foundations of both laws. The second article, Mandatory Notification and the third article, Encryption Safe Harbours and Data Breach Notification Laws did so from the perspective of data breach notification law. The fourth article, The Conceptual Basis of Personal Information in Australian Privacy Law and the fifth article, Privacy Invasive Geo-Mashups: Privacy 2.0 and the Limits of First Generation Information Privacy Laws did so for information privacy law. The final article, Contextualizing the Tensions and Weaknesses of Information Privacy and Data Breach Notification Laws synthesised previous research findings within the framework of contextualisation, principally developed by Nissenbaum. The examination of conceptual and operational foundations revealed tensions between both laws and shared weaknesses within both laws. First, the distinction between sectoral and comprehensive information privacy legal regimes was important as it shaped the development of US data breach notification laws and their subsequent implementable scope in other jurisdictions. Second, the sectoral versus comprehensive distinction produced different emphases in relation to data breach notification thus leading to different forms of remedy. The prime example is the distinction between market-based initiatives found in US data breach notification laws compared to rights-based protections found in the EU and Australia. Third, both laws are predicated on the regulation of personal information exchange processes even though both laws regulate this process from different perspectives, namely, a context independent or context dependent approach. Fourth, both laws have limited notions of harm that is further constrained by restrictive accountability frameworks. The findings of the research suggest that data breach notification is more compatible with information privacy law in some respects than others. Apparent compatibilities clearly exist as both laws have an interest in the protection of personal information. However, this thesis revealed that ostensible similarities are founded on some significant differences. Data breach notification law is either a comprehensive facet to a sectoral approach or a sectoral adjunct to a comprehensive regime. However, whilst there are fundamental differences between both laws they are not so great to make them incompatible with each other. The similarities between both laws are sufficient to forge compatibilities but it is likely that the distinctions between them will produce anomalies particularly if both laws are applied from a perspective that negates contextualisation.

Impact of a data warehouse model for improved decision-making process in healthcare

The health system is one sector dealing with a deluge of complex data. Many healthcare organisations struggle to utilise these volumes of health data effectively and efficiently. Also, there are many healthcare organisations, which still have stand-alone systems, not integrated for management of information and decision-making. This shows, there is a need for an effective system to capture, collate and distribute this health data. Therefore, implementing the data warehouse concept in healthcare is potentially one of the solutions to integrate health data. Data warehousing has been used to support business intelligence and decision-making in many other sectors such as the engineering, defence and retail sectors. The research problem that is going to be addressed is, "how can data warehousing assist the decision-making process in healthcare". To address this problem the researcher has narrowed an investigation focusing on a cardiac surgery unit. This research used the cardiac surgery unit at the Prince Charles Hospital (TPCH) as the case study. The cardiac surgery unit at TPCH uses a stand-alone database of patient clinical data, which supports clinical audit, service management and research functions. However, much of the time, the interaction between the cardiac surgery unit information system with other units is minimal. There is a limited and basic two-way interaction with other clinical and administrative databases at TPCH which support decision-making processes. The aims of this research are to investigate what decision-making issues are faced by the healthcare professionals with the current information systems and how decision-making might be improved within this healthcare setting by implementing an aligned data warehouse model or models. As a part of the research the researcher will propose and develop a suitable data warehouse prototype based on the cardiac surgery unit needs and integrating the Intensive Care Unit database, Clinical Costing unit database (Transition II) and Quality and Safety unit database [electronic discharge summary (e-DS)]. The goal is to improve the current decision-making processes. The main objectives of this research are to improve access to integrated clinical and financial data, providing potentially better information for decision-making for both improved from the questionnaire and by referring to the literature, the results indicate a centralised data warehouse model for the cardiac surgery unit at this stage. A centralised data warehouse model addresses current needs and can also be upgraded to an enterprise wide warehouse model or federated data warehouse model as discussed in the many consulted publications. The data warehouse prototype was able to be developed using SAS enterprise data integration studio 4.2 and the data was analysed using SAS enterprise edition 4.3. In the final stage, the data warehouse prototype was evaluated by collecting feedback from the end users. This was achieved by using output created from the data warehouse prototype as examples of the data desired and possible in a data warehouse environment. According to the feedback collected from the end users, implementation of a data warehouse was seen to be a useful tool to inform management options, provide a more complete representation of factors related to a decision scenario and potentially reduce information product development time. However, there are many constraints exist in this research. For example the technical issues such as data incompatibilities, integration of the cardiac surgery database and e-DS database servers and also, Queensland Health information restrictions (Queensland Health information related policies, patient data confidentiality and ethics requirements), limited availability of support from IT technical staff and time restrictions. These factors have influenced the process for the warehouse model development, necessitating an incremental approach. This highlights the presence of many practical barriers to data warehousing and integration at the clinical service level. Limitations included the use of a small convenience sample of survey respondents, and a single site case report study design. As mentioned previously, the proposed data warehouse is a prototype and was developed using only four database repositories. Despite this constraint, the research demonstrates that by implementing a data warehouse at the service level, decision-making is supported and data quality issues related to access and availability can be reduced, providing many benefits. Output reports produced from the data warehouse prototype demonstrated usefulness for the improvement of decision-making in the management of clinical services, and quality and safety monitoring for better clinical care. However, in the future, the centralised model selected can be upgraded to an enterprise wide architecture by integrating with additional hospital units’ databases.