The rise of social media technologies has created new ways to seek and share information for millions of users worldwide, but also has presented new challenges for libraries in meeting users where the
Genealogies document relationships between persons involved in historical events. Information about the events is parsed from communications from the past. This book explores a way to organize information from multiple communications into a trustworthy representation of a genealogical history of the modern world. The approach defines metrics for evaluating the consistency, correctness, closure, connectivity, completeness, and coherence of a genealogy. The metrics are evaluated using a 312,000-person research genealogy that explores the common ancestors of the royal families of Europe. A major result is that completeness is defined by a genealogy symmetry property driven by two exponential processes, the doubling of the number of potential ancestors each generation, and the rapid growth of lineage coalescence when the number of potential ancestors exceeds the available population. A genealogy expands from an initial root person to a large number of lineages, which then coalesce into a small number of progenitors. Using the research genealogy, candidate progenitors for persons of Western European descent are identified. A unifying ancestry is defined to which historically notable persons can be linked.
This chapter synthesizes the literature reviewed in Chaps. 3 and 4, comparing key issues relating to qualitative data reuse and big social research, and highlighting data curation practices that support epistemologically sound, ethical, and legal use and reuse of qualitative and big social data. The literature reviewed in Chaps. 3 and 4 reveals that issues in qualitative data reuse and big social research are similar, but their respective communities of practice are under-connected. Both types of data present the issues of context, data quality and trustworthiness, data comparability, informed consent, privacy and confidentiality, and intellectual property and data ownership. However, despite these similarities, big social research has not yet been widely framed as a form of qualitative data reuse, and qualitative data reuse has rarely been conducted on a large scale. Qualitative data reuse is a more established practice, and thus there are more developed data curation strategies to support responsible qualitative data sharing and reuse; even so, many issues in qualitative data reuse are still unresolved. In comparison, data curation for big social data is less well-developed, and there is little consensus about data curation strategies to support responsible big social research.
This chapter provides an overview of the history and benefits of big social research, then explores six key issues in big social research—context, data quality and trustworthiness, data comparability, informed consent, privacy and confidentiality, and intellectual property and data ownership. The chapter then outlines data curation implications of these issues, including data curation practices that could help alleviate some aspects of the issues.
This chapter reviews the results of semi-structured interviews with participants from three communities of practice: qualitative researchers who have shared or reused data, big social researchers, and data curators. By speaking directly to participants about their experiences and concerns, I aim to build conclusions about the similarities and differences in how each community of practice addresses the issues of context, data quality and trustworthiness, data comparability, informed consent, privacy and confidentiality, and intellectual property and data ownership. In this chapter, I also identify three new themes relevant to qualitative data reuse, big social research, and data curation: domain differences, strategies for responsible practice, and perspectives on data curation and data sharing.
This chapter discusses insights drawn from my interviews with qualitative researchers, big social researchers, and data curators, focusing on similarities and differences between communities of practice, and discussing implications for data curation. The initial discussion is organized around the six key issues that have structured this book—context, data quality and trustworthiness, data comparability, informed consent, privacy and confidentiality, and intellectual property and data ownership. I then discuss new ideas that emerged from the interviews about domain differences, strategies for responsible practice, and perceptions on data curation and sharing. The chapter concludes with a discussion of implications for data curation practice.
This book explores the connections between qualitative data reuse, big social research, and data curation the applications for research practices.
This book has explored the connections between qualitative data reuse, big social research, and data curation. I reviewed existing literature to identify the key issues of context, data quality and trustworthiness, data comparability, informed consent, privacy and confidentiality, and intellectual property and data ownership. Then I interviewed qualitative researchers, big social researchers, and data curators to further dive into each key issue and arrive at new insights about how domain differences affect each community of practice's viewpoints, different strategies that researchers and curators use to ensure responsible practice, and different perspectives on data curation. This chapter outlines key contributions of the research, ideas for future work, and closing thoughts about scaling up qualitative research.
This chapter provides an overview of the history and benefits of qualitative data reuse, then explores six key issues in qualitative data reuse—context, data quality and trustworthiness, data comparability, informed consent, privacy and confidentiality, and intellectual property and data ownership. The chapter then outlines data curation implications of these issues, including data curation practices that could help alleviate some aspects of the issues.
To build the foundation for the rest of the book, this chapter provides an overview of my general theoretical approach to this research—using a foundation of social constructivism, the book uses Communities of Practice Theory to understand qualitative researchers, big social researchers, and data curators. The chapter then provides a summary of my research methods—literature review and a qualitative content analysis. Finally, the chapter defines key terms that I use throughout the book: qualitative data, qualitative data reuse, big social data, and big social research.
The United Nations' (UN) call for the global community to support and contribute to the realization of the sustainable development goals (SDGs) has thrust universities and their units of research into the pool of stakeholders. Specifically, the universities have been encouraged to support and contribute to SDGs through the conduct of research, teaching, capacity building and governance. This Chapter focuses on one of the aforementioned ways of supporting SDGs, namely research, to assess the library and information science's (LIS) contribution to SDGs research between 2012 and 2021. The data was obtained from SciVal and analysed using a variety of tools to examine the trend of publication of research outputs that are linked to SDGs, determine LIS' percentage share of SDGs' publication outputs and impact, and assess the research focus areas in the LIS papers that are linked to SDGs. The findings show that LIS research on the SDGs has continued to increase over time, LIS contributes less than one percent of the SDGs publications (research), and that owing to its multidisciplinary, LIS contributes research and impact to all SDGs. The Chapter recommends the use of the SDGs (in addition to national and regional agenda) as a framework for LIS research in order to contribute to the realization of the UN's Agenda 2030.
This chapter sought to explore and explicate what the Diffusion of Innovations Theory is about. In addition, the chapter discusses the characteristics of innovators, the five-step process that an individual goes through when adopting a new idea or product, five adopter categories, the relevance of the theory to the LIS field, and concludes with the criticism of the theory. It cannot be denied that human beings do not routinely adopt new ideas or products. They make a conscious decision of whether to adopt or not. The Diffusion of Innovations theory outlines five characteristics that determine people's adoption of a new idea or innovation, namely: relative advantage; compatibility; complexity; trialability; and observability. This theory is used to explain how an idea or object is spread and adopted by many different individuals, be it in an organizational or societal context. The chapter presents the basic characteristics of individuals in a population and places them in one of the five adopter categories to determine the most effective way to appeal to that specific audience. Each category explains how a group of individuals assesses a new idea or technology and provides a five-step process that an individual goes through when adopting something new, namely: awareness, interest, evaluation, trial and adoption. Moreover, the chapter discusses how the Diffusion of Innovations theory describes the pattern and speed at which new ideas, practices, or products spread through a population. Thus, it groups individuals into five categories of how they adopt new ideas or technology, namely: innovators, early adopters, early majority, late majority, and laggards. The chapter also argues that the Diffusion of Innovations theory is relevant to the LIS field. It further argues that in general, the LIS field has adopted numerous innovations to automate a wide range of administrative and technical processes, build databases, and networks and provide better services to library users. Therefore, the diffusion and adoption of technology have become imperative for the efficient management of modern libraries and LIS as a field in general. The chapter concludes with a criticism of the theory.
Experience has shown that students largely struggle to produce acceptable dissertations because of omissions emanating from the planning/research proposal, execution, and reporting stages. This chapter aims to discuss the common errors experienced by examiners of library and information science (and allied disciplines) theses and dissertations by using my thirty years of experience as an examiner of more than one hundred and fifty theses and dissertations. The chapter is largely a phenomenological study. Errors occur at different stages of thesis development due to poor planning, execution, reporting, supervision, and insufficient student preparedness for a master's or doctoral level of work in terms of knowledge, skills, and attitude. The chapter recommends ways to alleviate the shortcomings. Although the chapter is informed by the lived experiences of one examiner and some qualitative content analysis, evidence of similar cases is reflected in related studies, some of which are reported in this chapter. The chapter adds to ongoing discussions on thesis writing that is likely to benefit both students and thesis supervisors and enable discussions on this important research domain.
Digital Humanities (DH) is growing area of interest among LIS and social sciences researchers. Scholars in LIS have conducted studies on DH programs and examined courses within the context of LIS. More recently a committee constituted by the Board of iSchools explored unique aspects of DH education within LIS. It is observed, however, that Digital Humanities (DH) research and education in Africa has not gained traction due to low visibility and lack of documented evidence. The chapter will, therefore, document evidence based on a study that examined the status of digital humanities in LIS Schools in selected universities in Africa. The study purposively selected twenty (20) universities based on predetermined criteria. Survey method utilizing mainly quantitative method was employed by the study. As a prelude to reporting the findings, the chapter will provide theoretical and historical development of DH in context of LIS education. The analysis of the pre-existing organizational, physical, technical, and social infrastructures and how these influence DH programs/initiatives in respective LIS schools will be reported. Leveraging the concept of "great scientific domains" (Rosenbloom in Int J Sci Soc 1:133-144, 2009) the study examined the convergence between LIS and schools of humanities and social sciences, with the aim of establishing potential areas of collaboration in DH research and curriculum.
Academic institution activities of teaching, learning, research, community engagement and academic citizenship has become more demanding due to increased users, workload of lecturers, changing workspace of library environment and multifarious tasks that librarians need to attend to on daily basis. This necessitates the study on the use of information and communication technologies (ICTs) by librarians for information and knowledge (IKM) in academic institutions in the Fourth Industrial Revolution (4IR). Librarians being the mouthpiece and gatekeeper of information and knowledge in academic institutions must become proactive in personal upskilling to use the ICTs for IKM in the academic institutions. The infiltration of digital technologies has also changed librarians' roles, available resources and services rendered in academic institutions. Three research objectives were used to guide the book chapter to include: examine the types of ICTs used by librarians for IKM in academic institutions; determine role of librarians in the use of ICTs for IKM in academic institutions and explore strategies to improve the use of ICTs by librarians for IKM in academic institutions. The qualitative research approach using interpretive content analysis of research articles harvested from online database of Google Scholar was adopted for this study. The author conceptualises and internalise key terms of ICTs and IKM in this book chapter. Findings reveals that there is a shift from the conventional ICTs of computer, printer, fax machine, scanners, photocopiers, digital camera, and microphones to digital technologies tools of webinar tools for teaching and learning, online educational resources, collaborative tools, social network sites, Web 2.0 technologies, online courseware, used for IKM in academic institutions in the Fourth Industrial Revolution. The roles of librarians have shifted from physical interaction and support to users to more of virtual where the users do not have to necessarily come to the physical library, with support of Internet connectivity. Finding further reveal strategies of continuous exposition and training of librarians to use recent ICTs in preparedness for IKM in academic institutions. The book chapter recommends proactive support by management of academic institutions to librarians for better and quality service delivery in the Fourth Industrial Revolution.
