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Reusing existing data sets of health information for public health or medical research has much to recommend it. Much data repurposing in medical or public health research or practice involves information that has been stripped of individual identifiers but some does not. In some cases, there may have been consent to the reuse but in other cases consent may be absent and people may be entirely unaware of how the data about them are being used. Data sets are also being combined and may contain information with very different sources, consent histories, and individual identifiers. Much of the ethical and policy discussion about the permissibility of data reuse has centered on two questions: for identifiable data, the scope of the original consent and whether the reuse is permissible in light of that scope, and for de-identified data, whether there are unacceptable risks that the data will be reidentified in a manner that is harmful to any data subjects. Prioritizing these questions rests on a picture of the ethics of data use as primarily about respecting the choices of the data subject. We contend that this picture is mistaken; data repurposing, especially when data sets are combined, raises novel questions about the impacts of research on groups and their implications for individuals regarded as falling within these groups. These impacts suggest that the controversies about de-identification or reconsent for reuse are to some extent beside the point. Serious ethical questions are also raised by the inferences that may be drawn about individuals from the research and resulting risks of stigmatization. These risks may arise even when individuals were not part of the original data set being repurposed. Data reuse, repurposing, and recombination may have damaging effects on others not included within the original data sets. These issues of justice for individuals who might be regarded as indirect subjects of research are not even raised by approaches that consider only the implications for or agreement of the original data subject. This chapter argues that health information should be available for reuse, information should be available for use, but in a way that does not yield unexpected surprises, produce direct harm to individuals, or violate warranted trust.

In this chapter, I explore traditional notions of secondary data in qualitative research and consider the ways in which these are continually being reimagined in the digital age. I situate this discussion in respect to data typologies and, more reflexively, in relation to our need as researchers to make data real. I consider contemporary understandings of reuse in relation to secondary data, focusing particularly on qualitative interview data. Recognizing those who are already forging a path, I then suggest how we might move beyond notions of reuse and reimagine secondary data in the digital age. To illustrate these points, I highlight relevant studies drawing data from a range of online spaces, and finally summarize key considerations and challenges.

Open data are characterized by a number of economic, environmental, technological, innovative, and social benefits. They are seen as a significant contributor to the city’s transformation into smart city. This is all the more so when the society is on the border of Society 5.0, that is, shift from the information society to a super smart society or society of imagination takes place. However, the question constantly asked by open data experts is, what are the key factors to be met and satisfied in order to achieve promised benefits? The current trend of openness suggests that the principle of openness should be followed not only by data but also research, education, software, standard, hardware, etc., it should become a philosophy to be followed at different levels, in different domains. This should ensure greater transparency, eliminating inequalities, promoting, and achieving sustainable development goals (SDGs). Therefore, many agendas (sustainable development strategies, action plans) now have openness as a prerequisite. This chapter deals with concepts of open (government) data and Society 5.0 pointing to their common objectives, providing some success stories of open data use in smart cities or transformation of cities toward smart cities, mapping them to the features of the Society 5.0. We believe that this trend develops a new form of society, which we refer to as “open data-driven society.” It forms a bridge from Society 4.0 to Society 5.0. This chapter attempts to identify the role of openness in promoting human-centric smart society, smart city, and smart living.

The purpose of this chapter is to introduce the basic concepts and principles of linked data, discuss benefits that linked data provides in library environments, and present a short history of the development of library linked data.

The chapter is based on the literature review dealing with linked data, especially focusing on the library field.

In the library field, linked data is especially useful for expanding bibliographic data and authority data. Although diverse structured data is being produced by the library field, the lack of compatibility with the data from other fields currently limits the wider expansion and sharing of linked data.

The value of this chapter can be found in the potential use of linked data in the library field for improving bibliographic and authority data. Especially, this chapter will be useful for library professionals who have interests in the linked data regarding its applications in a library setting.

The improvement of the agri-food supply chain sustainability plays pivotal role in the planet’s survival and in overcoming of climate disasters. Digital technologies that support the collection of Big Data produced along the agri-food supply chain (SC) emerge as powerful tools to accelerate the ecological transition of the sector. Digital technologies can support the implementation of circular business models by sharing data across the SC, monitoring in real time the materials flow, automatizing some agricultural practices and improving the decision-making through the development of decision support systems. Despite the relevance of these arguments, there is a lack of shared frameworks and guidelines for the effective development of a “data-driven circular economy” in the agri-food SC. In this scenario, this chapter examines how scholars investigate data-oriented strategies to accelerate the ecological transition and the adoption of circular economy (CE) models in the agri-food sector (AFS). To this end, a systematic literature review (SLR) was performed. Twenty-nine papers were selected following a rigorous sampling process. Both bibliometric and descriptive results are provided in the first part of this chapter. According to the analytical framework developed, the selected papers were examined in light of the “reduce, reuse and recycle” (3R) paradigm. Moreover, an additional R was retrieved from the systematic review (i.e., redesign), broadening the analytical perspective. The results indicate that scholars have predominantly provided theoretical contributions concerning the role of digital technologies and big data for the agri-food circular transition from a macro-perspective. The findings are useful for policy-makers and managers, who can promote and implement the big data-oriented approach to facilitate the circular transition. Limitations and future research directions are also provided.

Over the past decade, the number of people engaging with social media has grown rapidly. This means that social media platforms such as Twitter and Facebook are potentially good sources of rich, naturally occurring data. As a result, a growing number of researchers are utilizing these platforms for the collection of data on any number of topics. To date, no consistent approach to the ethics of using social media data has been provided to researchers in this sphere. This chapter presents research that has developed an ethics framework for the use of researchers working with social media data. The chapter also presents the framework itself and guidance on how to use the framework when conducting social media research. A full report can be accessed on: http://www.abdn.ac.uk/socsci/research/new-europe-centre/information-societies-projects-225.php

The library metadata of the twenty-first century is moving toward a linked data model. BIBFRAME, which stands for Bibliographic Framework Initiative, was launched in 2011 with the goal to make bibliographic descriptions sharable and interoperable on the web. Since its inception, BIBFRAME development has made remarkable progress. The focus of BIBFRAME discussions has now shifted from experimentation to implementation. The library community is collaborating with all stakeholders to build the infrastructure for BIBFRAME production in order to provide the environment where BIBFRAME data can be easily created, reused, and shared. This chapter addresses library community's BIBFRAME endeavors, with the focus on Library of Congress, Program for Cooperative Program, Linked Data for Production Phase 2, and OCLC. This chapter discusses BIBFRAME's major differences from the MARC standard with the hope of helping metadata practitioners get a general understanding of the future metadata activity. While the BIBFRAME landscape is beginning to take shape and its practical implications are beginning to develop, it is anticipated that MARC records will continue to be circulated for the foreseeable future. Upcoming multistandard metadata environments will bring new challenges to metadata practitioners, and this chapter addresses the required knowledge and skills for this transitional and multistandard metadata landscape. Finally, this chapter explores BIBFRAME's remaining challenges to realize the BIBFRAME production environment and asserts that BIBFRAME's ultimate goal is to deliver a value-added next-web search experience to our users.

The chapter deliberates on research ethics and the unanticipated side effects that technological developments have brought in the past decades. It looks at data protection and privacy through the prism of ethics and focuses on the need for safeguarding the fundamental rights of the research participants in the new digital era. Acknowledging the benefits of data analytics for boosting scientific process, the chapter reflects on the main principles and specific research derogations, introduced by the EU General Data Protection Regulation. Further on, it discusses some of the most pressing ethics concerns, related to the use, reuse, and misuse of data; the distinction between publicly available and open data; ethics challenges in online recruitment of research participants; and the potential bias and representativeness problems of Big Data research. The chapter underscores that all challenges should be properly addressed at the outset of research design. Highlighting the power asymmetries between Big Data studies and individuals’ rights to data protection, human dignity, and respect for private and family life, the chapter argues that anonymization may be reasonable, yet not the ultimate ethics solution. It asserts that while anonymization techniques may protect individual data protection rights, the former may not be sufficient to prevent discrimination and stigmatization of entire groups of populations. Finally, the chapter suggests some approaches for ensuring ethics compliance in the digital era.