This unique textbook intersects traditional library science with data science principles that readers will find useful in implementing or improving data services within their libraries. Data Science for Librarians introduces data science to students and practitioners in library services. Writing for academic, public, and school library managers; library science students; and library and information science educators, authors Yunfei Du and Hammad Rauf Khan provide a thorough overview of conceptual and practical tools for data librarian practice. Partially due to how quickly data science evolves, libraries have yet to recognize core competencies and skills required to perform the job duties of a data librarian. As society transitions from the information age into the era of big data, librarians and information professionals require new knowledge and skills to stay current and take on new job roles, such as data librarianship. Such skills as data curation, research data management, statistical analysis, business analytics, visualization, smart city data, and learning analytics are relevant in library services today and will become increasingly so in the near future. This text serves as a tool for library and information science students and educators working on data science curriculum design.

More data, more problems -- A new strand of librarianship -- Data creation and collection -- Data for the academic librarian -- Research data services and the library ecosystem -- Data sources -- Data curation (archiving/preservation) -- Data storage, management, and retrieval -- Data analysis and visualization -- Data ethics and policies -- Data for public libraries and special libraries -- Conclusion: library, information, and data science.

Librarians understand the need to store, use and analyze data related to their collection, patrons and institution, and there has been consistent interest over the last 10 years to improve data management, analysis, and visualization skills within the profession. However, librarians find it difficult to move from out-of-the-box proprietary software applications to the skills necessary to perform the range of data science actions in code. This book will focus on teaching R through relevant examples and skills that librarians need in their day-to-day lives that includes visualizations but goes much further to include web scraping, working with maps, creating interactive reports, machine learning, and others. While there’s a place for theory, ethics, and statistical methods, librarians need a tool to help them acquire enough facility with R to utilize data science skills in their daily work, no matter what type of library they work at (academic, public or special). By walking through each skill and its application to library work before walking the reader through each line of code, this book will support librarians who want to apply data science in their daily work. Hands-On Data Science for Librarians is intended for librarians (and other information professionals) in any library type (public, academic or special) as well as graduate students in library and information science (LIS). Key Features: Only data science book available geared toward librarians that includes step-by-step code examples Examples include all library types (public, academic, special) Relevant datasets Accessible to non-technical professionals Focused on job skills and their applications

Beyond providing space for data science activities, academic libraries are often overlooked in the data science landscape that is emerging at academic research institutions. Although some academic libraries are collaborating in specific ways in a small subset of institutions, there is much untapped potential for developing partnerships. As library and information science roles continue to evolve to be more data-centric and interdisciplinary, and as research using a variety of data types continues to proliferate, it is imperative to further explore the dynamics between libraries and the data science ecosystems in which they are a part. The Handbook of Research on Academic Libraries as Partners in Data Science Ecosystems provides a global perspective on current and future trends concerning the integration of data science in libraries. It provides both a foundational base of knowledge around data science and explores numerous ways academicians can reskill their staff, engage in the research enterprise, contribute to curriculum development, and help build a stronger ecosystem where libraries are part of data science. Covering topics such as data science initiatives, digital humanities, and student engagement, this book is an indispensable resource for librarians, information professionals, academic institutions, researchers, academic libraries, and academicians.

Technological advances and the rise of collaborative, interdisciplinary approaches have changed the practice of research. The 21st century researcher not only faces the challenge of managing increasingly complex datasets, but also new data sharing requirements from funders and journals. Success in today’s research enterprise requires an understanding of how to work effectively with data, yet most researchers have never had any formal training in data management. Libraries have begun developing services and programs to help researchers meet the demands of the data-driven research enterprise, giving librarians exciting new opportunities to use their expertise and skills. The Medical Library Association Guide to Data Management for Librarians highlights the many ways that librarians are addressing researchers’ changing needs at a variety of institutions, including academic, hospital, and government libraries. Each chapter ends with “pearls of wisdom,” a bulleted list of 5-10 takeaway messages from the chapter that will help readers quickly put the ideas from the chapter into practice. From theoretical foundations to practical applications, this book provides a background for librarians who are new to data management as well as new ideas and approaches for experienced data librarians.

This book constitutes the thoroughly refereed proceedings of the 16th Italian Research Conference on Digital Libraries, IRCDL 2020, held in Bari, Italy, in January 2020. The 12 full papers and 6 short papers presented were carefully selected from 26 submissions. The papers are organized in topical sections on information retrieval, bid data and data science in DL; cultural heritage; open science.

As new technology and opportunities emerge through the revolutionary impacts of the digital age, the function of libraries and librarians and how they provide services to constituents is rapidly changing. The impact of new technology touches everything from libraries' organizational structures, business models, and workflow processes, to position descriptions and the creation of new positions. As libraries are required to make operational adjustments to meet the growing technological demands of libraries' customer bases and provide these services, librarians must be flexible in adapting to this fast-moving environment. This volume shares the unique perspectives and experiences of librarians on the front lines of this technological transformation. The essays within provide details of both the practical applications of surviving, adapting, and growing when confronted with changing roles and responsibilities, as well as a big picture perspective of the changing roles impacting libraries and librarians. This book strives to be a valuable tool for librarians involved in public and technical services, digital humanities, virtual and augmented reality, government documents, information technology, and scholarly communication.

In recent years, there has been an increased urgency and appeal to examine the impacts of systemic racism in all parts of society, and the field of library and information science is no exception. To actively combat enabling and perpetuating structural racism and white supremacy, libraries across the globe are addressing justice, equity, diversity, and inclusion (JEDI) by investing resources, creating initiatives, and engaging in reflection and deep questioning. Perspectives on Justice, Equity, Diversity, and Inclusion in Libraries examines how JEDI initiatives and actions have been incorporated into all aspects of librarianship and various types of libraries. The book serves as a collection of exemplary cases across all settings of librarianship to showcase how this work is being implemented and to provide commentary on implications and future opportunities for growth. Covering key topics such as community, ethics, and inclusive spaces, this premier reference source is ideal for administrators, policymakers, academicians, researchers, scholars, practitioners, librarians, instructors, and students.

The seventh edition of this frequently adopted textbook features new or expanded sections on social justice research, data analysis software, scholarly identity research, social networking, data science, and data visualization, among other topics. It continues to include discipline experts' voices. The revised seventh edition of this popular text provides instruction and guidance for professionals and students in library and information science who want to conduct research and publish findings, as well as for practicing professionals who want a broad overview of the current literature. Providing a broad introduction to research design, the authors include principles, data collection techniques, and analyses of quantitative and qualitative methods, as well as advantages and limitations of each method and updated bibliographies. Chapters cover the scientific method, sampling, validity, reliability, and ethical concerns along with quantitative and qualitative methods. LIS students and professionals will consult this text not only for instruction on conducting research but also for guidance in critically reading and evaluating research publications, proposals, and reports. As in the previous edition, discipline experts provide advice, tips, and strategies for completing research projects, dissertations, and theses; writing grants; overcoming writer's block; collaborating with colleagues; and working with outside consultants. Journal and book editors discuss how to publish and identify best practices and understudied topics, as well as what they look for in submissions.

Practical Data Science for Information Professionals provides an accessible introduction to a potentially complex field, providing readers with an overview of data science and a framework for its application. It provides detailed examples and analysis on real data sets to explore the basics of the subject in three principle areas: clustering and social network analysis; predictions and forecasts; and text analysis and mining. As well as highlighting a wealth of user-friendly data science tools, the book also includes some example code in two of the most popular programming languages (R and Python) to demonstrate the ease with which the information professional can move beyond the graphical user interface and achieve significant analysis with just a few lines of code. After reading, readers will understand: · the growing importance of data science · the role of the information professional in data science · some of the most important tools and methods that information professionals can use. Bringing together the growing importance of data science and the increasing role of information professionals in the management and use of data, Practical Data Science for Information Professionals will provide a practical introduction to the topic specifically designed for the information community. It will appeal to librarians and information professionals all around the world, from large academic libraries to small research libraries. By focusing on the application of open source software, it aims to reduce barriers for readers to use the lessons learned within.