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Biobanks are collections of biological samples (e.g. tissue samples and body fluids) and their associated data intended for various approaches in medical research. The field of biobanking evolves rapidly as an interdisciplinary branch of research and requires educational efforts to provide skilled experts in Europe and beyond. New ways in research and research education play a pivotal role in the future of biobanking.

The increasing of requests and potential uses of biospecimens from biobanks necessitates an international and national intensified transfer of forward looking knowledge and know-how. In Austria, this could be realized by special trainings as well as a postgraduate education. Furthermore, the forward looking research and further development of infrastructure will play a pivotal role in biobanks in the future.

Few opportunities are available for specific education on biobanking in Europe. This could be remedied by the creation networks of ISO-certified biobanks and co-operation with interested parties.

The current research focuses on the situation of information transfer in the field of biobanking in Europe. A wider investigation in better harmonization and standardization of methods in other parts of the world would be beneficial.

The value of biomolecular resources such as biobanks has previously been discussed in detail, e.g. by the Time magazine. The paper focuses on demonstrating the importance for education in the future of biobanking in general.

The paper contributes to the discussion on valuation in organization studies and strategic management literature. The nascent literature on valuation practices has examined established markets where producers and consumers are known and rivalry in the market is a given. Furthermore, previous research has operated with a narrow meaning of value as either a financial profit or a subjective consumer preference. Such a narrow view on value is problematic and insufficient for studying the interlacing of innovation and value creation in emerging technoscientific business domains.

The authors present an empirical study about value creation in an emerging technoscience business domain formed around personalized medicine and digital health data.

The results of this analysis show that in a technoscientific domain, valuation of innovations is multiple and malleable, entails pursuing attractiveness in collaboration and partnerships and is performative, and due to emphatic future orientation, values are indefinite and promissory.

As research implications, this study shows that valuation practices in an emerging technoscience business domain focus on defining the potential economic value in the future and attracting partners as probable future beneficiaries. Commercial value upon innovation in an embryonic business milieu is created and situated in valuation practices that constitute the prospective market, the prevalent economic discourse, and rationale. This is in contrast to an established market, where valuation practices are determined at the intersection of customer preferences and competitive arenas where suppliers, producers, service providers and new entrants to the market present value propositions.

The study findings extend discussion on valuation from established business domains to emerging technoscience business domains which are in a “pre-competition” phase where suppliers, customers, producers and their collaborative and competitive relations are not yet established.

As managerial implications, this study provides insights into health innovation stakeholders, including stakeholders in the public, private and academic sectors, about the ecosystem dynamics in a technoscientific innovation. Such insight is useful in strategic decision-making about ecosystem strategy and ecosystem business model for value proposition, value creation and value capture in an emerging innovation domain characterized by collaborative and competitive relations among stakeholders. To business managers, the findings of this study about valuation practices are useful in strategic decision-making about ecosystem strategy and ecosystem business model for value proposition, value creation and value capture in an emerging innovation domain characterized by collaborative and competitive relations among stakeholders. To policy makers, this study provides an in-depth analysis of an overall business ecosystem in an emerging technoscience business that can be propelled to increase the financial investments in the field. As a policy implication, this study provides insights into the various dimensions of valuation in technoscience business to policy makers, who make governance decisions to guide and control the development of medical innovation using digital health data.

This study's results expand previous theorizing on valuation by showing that in technoscientific innovation all types of value created – scientific, clinical, social or economic – are predominantly promissory. This study complements the nascent theorizing on value creation and valuation practices of technoscientific innovation.

Medical research requires biological material and data collected through biobanks in reliable processes with quality assurance. Medical studies based on data with unknown or questionable quality are useless or even dangerous, as evidenced by recent examples of withdrawn studies. Medical data sets consist of highly sensitive personal data, which has to be protected carefully and is available for research only after the approval of ethics committees. The purpose of this research is to propose an architecture to support researchers to efficiently and effectively identify relevant collections of material and data with documented quality for their research projects while observing strict privacy rules.

Following a design science approach, this paper develops a conceptual model for capturing and relating metadata of medical data in biobanks to support medical research.

This study describes the landscape of biobanks as federated medical data lakes such as the collections of samples and their annotations in the European federation of biobanks (Biobanking and Biomolecular Resources Research Infrastructure – European Research Infrastructure Consortium, BBMRI-ERIC) and develops a conceptual model capturing schema information with quality annotation. This paper discusses the quality dimensions for data sets for medical research in-depth and proposes representations of both the metadata and data quality documentation with the aim to support researchers to effectively and efficiently identify suitable data sets for medical studies.

This novel conceptual model for metadata for medical data lakes has a unique focus on the high privacy requirements of the data sets contained in medical data lakes and also stands out in the detailed representation of data quality and metadata quality of medical data sets.

The purpose of this paper is to examine the framework, operation and prospects of two innovative planning tools – biobanking and biodiversity certification – to protect biodiversity in the context of urbanisation.

Using the Australian State of New South Wales, and its capital Sydney, as a focus, the paper reviews the history and performance of the two planning tools selected for examination.

Through its analysis, this paper supports the use of biobanking and biodiversity certification as tools at the disposal of planners which should be employed to augment the array of tools to manage the impacts on biodiversity of urban development. Indeed, these tools have the potential to increase the level of conserved biodiversity if used appropriately.

Biobanking and biodiversity certification are, respectively, examples of market-based and strategic-based statutory planning mechanisms that can be used to protect biodiversity in an urban development context. Creative adaption of environmental planning laws is required to ensure effective implementation of these innovative planning tools. The relevant contemporary statutory enactments that have given effect to these mechanisms are examined in this paper.

The purpose of this paper is to examine the regulatory, policy and market‐based approaches taken to incorporate biodiversity conservation in the management of urban growth in Sydney and more broadly in New South Wales, Australia's most populous state. Problems associated with managing Sydney's growth – particularly from the intersection of dealing with perceived property rights and the protection of natural resources such as biodiversity – are identified, and the scope for hybrid “smart regulation” is examined.

The relevant issues are illustrated through significant State Government development decisions relating to the retention of biodiversity in the new growth areas of Sydney.

The paper argues that to better integrate biodiversity conservation in Australian cities a mixed approach be adopted in which a number of tools are utilised – and that this needs to occur in the context of a sound overarching strategic planning framework. This constitutes a hybrid approach involving a “fixed” strategic spatial plan informing statutory‐based regulation primarily through zoning and other development controls, augmented by a range of market based tools implemented through statute and common law measures such as conservation covenants.

Singular reliance on traditional “command and control” regulatory approaches as both a cause and ineffectual solution to the problems faced in biodiversity conservation is highlighted. Newer “market based” mechanisms which are being introduced (e.g. biobanking), or should be adopted (e.g. transferable development rights), and management at the strategic level (e.g. biodiversity certification), are examined.

Big Data analysis is one of the key challenges to the provision of health care to emerge in the last few years. This challenge has been spearheaded by the huge interest in the “4Ps” of health care (predictive, preventive, personalized, and participatory). Big Data offers striking development opportunities in health care and life sciences. Healthcare research is already using Big Data to analyze the spatial distribution of diseases such as diabetes mellitus at detailed geographic levels. Big Data is also being used to assess location-specific risk factors based on data of health insurance claims. Other studies in systems medicine utilize bioinformatics approaches to human biology which necessitate Big Data statistical analysis and medical informatics tools. Big Data is also being used to develop electronic algorithms to forecast clinical events in real time, with the intent to improve patient outcomes and thus reduce costs.

Yet, this Big Data era also poses critically difficult ethical challenges, since it is breaking down the traditional divisions between what belongs to public and private domains in health care and health research. Big Data in health care raises complex ethical concerns due to use of huge datasets obtained from different sources for varying reasons. The clinical translation of this Big Data is thus resulting in key ethical and epistemological challenges for those who use these data to generate new knowledge and the clinicians who eventually apply it to improve patient care.

Underlying this challenge is the fact that patient consent often cannot be collected for the use of individuals’ personal data which then forms part of this Big Data. There is also the added dichotomy of healthcare providers which use such Big Data in attempts to reduce healthcare costs, and the negative impact this may have on the individual with respect to privacy issues and potential discrimination.

Big Data thus challenges societal norms of privacy and consent. Many questions are being raised on how these huge masses of data can be managed into valuable information and meaningful knowledge, while still maintaining ethical norms. Maintaining ethical integrity may lack behind in such a fast-changing sphere of knowledge. There is also an urgent need for international cooperation and standards when considering the ethical implications of the use of Big Data-intensive information.

This chapter will consider some of the main ethical aspects of this fast-developing field in the provision of health care, health research, and public health. It will use examples to concretize the discussion, such as the ethical aspects of the applications of Big Data obtained from clinical trials, and the use of Big Data obtained from the increasing popularity of health mobile apps and social media sites.

Patients having ventricular arrhythmias and atrial fibrillation, that are early markers of stroke and sudden cardiac death, as well as benign subjects are all studied using the electrocardiogram (ECG). In order to identify cardiac anomalies, ECG signals analyse the heart's electrical activity and show output in the form of waveforms. Patients with these disorders must be identified as soon as possible. ECG signals can be difficult, time-consuming and subject to inter-observer variability when inspected manually.

There are various forms of arrhythmias that are difficult to distinguish in complicated non-linear ECG data. It may be beneficial to use computer-aided decision support systems (CAD). It is possible to classify arrhythmias in a rapid, accurate, repeatable and objective manner using the CAD, which use machine learning algorithms to identify the tiny changes in cardiac rhythms. Cardiac infractions can be classified and detected using this method. The authors want to categorize the arrhythmia with better accurate findings in even less computational time as the primary objective. Using signal and axis characteristics and their association n-grams as features, this paper makes a significant addition to the field. Using a benchmark dataset as input to multi-label multi-fold cross-validation, an experimental investigation was conducted.

This dataset was used as input for cross-validation on contemporary models and the resulting cross-validation metrics have been weighed against the performance metrics of other contemporary models. There have been few false alarms with the suggested model's high sensitivity and specificity.

The results of cross validation are significant. In terms of specificity, sensitivity, and decision accuracy, the proposed model outperforms other contemporary models.