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| data science for librarians: Data Science for Librarians Yunfei Du, Hammad Rauf Khan, 2020-03-26 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. |
| data science for librarians: Data Science for Librarians Yunfei Du, Hammad Rauf Khan, 2020-03-26 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. |
| data science for librarians: Data Science for Librarians Yunfei Du, Hammad Rauf Khan, 2020-03-26 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. |
| data science for librarians: Hands-On Data Science for Librarians Sarah Lin, Dorris Scott, 2023-05-09 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 |
| data science for librarians: Data Science in the Library Joel Herndon, 2021-08-26 This book explores the rapid expansion of data sources, visualizations, and analytics created in the last decade and explores the strategies, tools, and approaches that educators and information specialists are employing to train a new generation of data professionals. |
| data science for librarians: The Data Librarian’s Handbook Robin Rice, John Southall, 2016-12-20 An insider’s guide to data librarianship packed full of practical examples and advice for any library and information professional learning to deal with data. Interest in data has been growing in recent years. Support for this peculiar class of digital information – its use, preservation and curation, and how to support researchers’ production and consumption of it in ever greater volumes to create new knowledge, is needed more than ever. Many librarians and information professionals are finding their working life is pulling them toward data support or research data management but lack the skills required. The Data Librarian’s Handbook, written by two data librarians with over 30 years’ combined experience, unpicks the everyday role of the data librarian and offers practical guidance on how to collect, curate and crunch data for economic, social and scientific purposes. With contemporary case studies from a range of institutions and disciplines, tips for best practice, study aids and links to key resources, this book is a must-read for all new entrants to the field, library and information students and working professionals. Key topics covered include: • the evolution of data libraries and data archives • handling data compared to other forms of information • managing and curating data to ensure effective use and longevity • how to incorporate data literacy into mainstream library instruction and information literacy training • how to develop an effective institutional research data management (RDM) policy and infrastructure • how to support and review a data management plan (DMP) for a project, a key requirement for most research funders • approaches for developing, managing and promoting data repositories • handling and sharing confidential or sensitive data • supporting open scholarship and open science, ensuring data are discoverable, accessible, intelligible and assessable. This title is for the practising data librarian, possibly new in their post with little experience of providing data support. It is also for managers and policy-makers, public service librarians, research data management coordinators and data support staff. It will also appeal to students and lecturers in iSchools and other library and information degree programmes where academic research support is taught. |
| data science for librarians: Data Science for Librarians Jason Miller, 2024-05-07 Discover the transformative potential of data science in the world of libraries with this comprehensive guide tailored specifically for librarians seeking to enhance their professional expertise. Delving into the intersection of information science and cutting-edge data analytics, this book equips readers with the knowledge and skills needed to harness the power of data for informed decision-making and innovative service delivery. From understanding the fundamentals of data science to implementing advanced techniques like machine learning and text mining, each chapter offers practical insights and real-world examples that illuminate the path forward. Readers will learn how to collect, clean, and analyze data effectively, uncovering valuable insights that can drive strategic initiatives and optimize library resources. But this book is more than just a technical manual-it's a roadmap for librarians navigating the complexities of the digital age. With a focus on ethical considerations, privacy protection, and staying ahead of emerging trends, it empowers librarians to leverage data responsibly and ethically, ensuring that their practices uphold the core values of librarianship. Whether you're a seasoned professional looking to expand your skill set or a newcomer eager to explore the possibilities of data science, this book is your indispensable companion on the journey to unlocking the full potential of libraries in the 21st century. |
| data science for librarians: Practical Data Science for Information Professionals David Stuart, 2020-07-24 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. |
| data science for librarians: Library Improvement Through Data Analytics Lesley S. J. Farmer, Alan M. Safer, 2016-07-27 This book shows how to act on and make sense of data in libraries. Using a range of techniques, tools and methodologies it explains how data can be used to help inform decision making at every level.Sound data analytics is the foundation for making an evidence-based case for libraries, in addition to guiding myriad organizational decisions, from optimizing operations for efficiency to responding to community needs. Designed to be useful for beginners as well as those with a background in data, this book introduces the basics of a six point framework that can be applied to a variety of library settings for effective system based, data-driven management. Library Improvement Through Data Analytics includes:- the basics of statistical concepts- recommended data sources for various library functions and processes, and guidance for using census, university, or - - government data in analysis- techniques for cleaning data- matching data to appropriate data analysis methods- how to make descriptive statistics more powerful by spotlighting relationships- 14 practical case studies, covering topics such as access and retrieval, digitization, e-book collection development, staffing, facilities, and instruction.This book's clear, concise coverage will enable librarians, archivists, curators and technologists of every experience level to gain a better understanding of statistics in order to facilitate library improvement. |
| data science for librarians: A Librarian's Guide to Graphs, Data and the Semantic Web James Powell, 2015-07-09 Graphs are about connections, and are an important part of our connected and data-driven world. A Librarian's Guide to Graphs, Data and the Semantic Web is geared toward library and information science professionals, including librarians, software developers and information systems architects who want to understand the fundamentals of graph theory, how it is used to represent and explore data, and how it relates to the semantic web. This title provides a firm grounding in the field at a level suitable for a broad audience, with an emphasis on open source solutions and what problems these tools solve at a conceptual level, with minimal emphasis on algorithms or mathematics. The text will also be of special interest to data science librarians and data professionals, since it introduces many graph theory concepts by exploring data-driven networks from various scientific disciplines. The first two chapters consider graphs in theory and the science of networks, before the following chapters cover networks in various disciplines. Remaining chapters move on to library networks, graph tools, graph analysis libraries, information problems and network solutions, and semantic graphs and the semantic web. - Provides an accessible introduction to network science that is suitable for a broad audience - Devotes several chapters to a survey of how graph theory has been used in a number of scientific data-driven disciplines - Explores how graph theory could aid library and information scientists |
| data science for librarians: Handbook of Research on Knowledge and Organization Systems in Library and Information Science Holland, Barbara Jane, 2021-06-25 Due to changes in the learning and research environment, changes in the behavior of library users, and unique global disruptions such as the COVID-19 pandemic, libraries have had to adapt and evolve to remain up-to-date and responsive to their users. Thus, libraries are adding new, digital resources and services while maintaining most of the old, traditional resources and services. New areas of research and inquiry in the field of library and information science explore the applications of machine learning, artificial intelligence, and other technologies to better serve and expand the library community. The Handbook of Research on Knowledge and Organization Systems in Library and Information Science examines new technologies and systems and their application and adoption within libraries. This handbook provides a global perspective on current and future trends concerning library and information science. Covering topics such as machine learning, library management, ICTs, blockchain technology, social media, and augmented reality, this book is essential for librarians, library directors, library technicians, media specialists, data specialists, catalogers, information resource officers, administrators, IT consultants and specialists, academicians, and students. |
| data science for librarians: Databrarianship Lynda M. Kellam, Kristi Thompson, 2016 With the appearance of big data, open data, and particularly research data curation on many libraries' radar screens, data service has become a critically important topic for academic libraries. Drawing on the expertise of a diverse community of practitioners, this collection of case studies, original research, survey chapters, and theoretical explorations presents a wide-ranging look at the field of academic data librarianship. By covering the data lifecycle from collection development to preservation, examining the challenges of working with different forms of data, and exploring service models suited to a variety of library types, this volume provides a toolbox of strategies that will allow librarians and administrators to respond creatively and effectively to the data deluge. Edited by Kristi Thompson and Lynda Kellam, Databrarianship: The Academic Data Librarian in Theory and Practice provides advice and insight on data services for all types of academic libraries and will be of interest to library educators--Publisher's website. |
| data science for librarians: Data Science John D. Kelleher, Brendan Tierney, 2018-04-13 A concise introduction to the emerging field of data science, explaining its evolution, relation to machine learning, current uses, data infrastructure issues, and ethical challenges. The goal of data science is to improve decision making through the analysis of data. Today data science determines the ads we see online, the books and movies that are recommended to us online, which emails are filtered into our spam folders, and even how much we pay for health insurance. This volume in the MIT Press Essential Knowledge series offers a concise introduction to the emerging field of data science, explaining its evolution, current uses, data infrastructure issues, and ethical challenges. It has never been easier for organizations to gather, store, and process data. Use of data science is driven by the rise of big data and social media, the development of high-performance computing, and the emergence of such powerful methods for data analysis and modeling as deep learning. Data science encompasses a set of principles, problem definitions, algorithms, and processes for extracting non-obvious and useful patterns from large datasets. It is closely related to the fields of data mining and machine learning, but broader in scope. This book offers a brief history of the field, introduces fundamental data concepts, and describes the stages in a data science project. It considers data infrastructure and the challenges posed by integrating data from multiple sources, introduces the basics of machine learning, and discusses how to link machine learning expertise with real-world problems. The book also reviews ethical and legal issues, developments in data regulation, and computational approaches to preserving privacy. Finally, it considers the future impact of data science and offers principles for success in data science projects. |
| data science for librarians: Data Management Margaret E. Henderson, 2016-10-25 Libraries organize information and data is information, so it is natural that librarians should help people who need to find, organize, use, or store data. Organizations need evidence for decision making; data provides that evidence. Inventors and creators build upon data collected by others. All around us, people need data. Librarians can help increase the relevance of their library to the research and education mission of their institution by learning more about data and how to manage it. Data Management will guide readers through: Understanding data management basics and best practices. Using the reference interview to help with data management Writing data management plans for grants. Starting and growing a data management service. Finding collaborators inside and outside the library. Collecting and using data in different disciplines. |
| data science for librarians: Big Data Shocks Andrew Weiss, 2018 Big Data Shocks examines the roots of big data, the current climate and rising stars in this world. The book explores the issues raised by big data and discusses theoretical as well as practical approaches to managing information whose scope exists beyond the human scale. |
| data science for librarians: Data Science for Economics and Finance Sergio Consoli, Diego Reforgiato Recupero, Michaela Saisana, 2021 This open access book covers the use of data science, including advanced machine learning, big data analytics, Semantic Web technologies, natural language processing, social media analysis, time series analysis, among others, for applications in economics and finance. In addition, it shows some successful applications of advanced data science solutions used to extract new knowledge from data in order to improve economic forecasting models. The book starts with an introduction on the use of data science technologies in economics and finance and is followed by thirteen chapters showing success stories of the application of specific data science methodologies, touching on particular topics related to novel big data sources and technologies for economic analysis (e.g. social media and news); big data models leveraging on supervised/unsupervised (deep) machine learning; natural language processing to build economic and financial indicators; and forecasting and nowcasting of economic variables through time series analysis. This book is relevant to all stakeholders involved in digital and data-intensive research in economics and finance, helping them to understand the main opportunities and challenges, become familiar with the latest methodological findings, and learn how to use and evaluate the performances of novel tools and frameworks. It primarily targets data scientists and business analysts exploiting data science technologies, and it will also be a useful resource to research students in disciplines and courses related to these topics. Overall, readers will learn modern and effective data science solutions to create tangible innovations for economic and financial applications. |
| data science for librarians: Handbook of Research on Academic Libraries as Partners in Data Science Ecosystems Mani, Nandita S., Cawley, Michelle A., 2022-05-06 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. |
| data science for librarians: A Hands-On Introduction to Data Science Chirag Shah, 2020-04-02 An introductory textbook offering a low barrier entry to data science; the hands-on approach will appeal to students from a range of disciplines. |
| data science for librarians: Data Science in Chemistry Thorsten Gressling, 2020-11-23 The ever-growing wealth of information has led to the emergence of a fourth paradigm of science. This new field of activity – data science – includes computer science, mathematics and a given specialist domain. This book focuses on chemistry, explaining how to use data science for deep insights and take chemical research and engineering to the next level. It covers modern aspects like Big Data, Artificial Intelligence and Quantum computing. |
| data science for librarians: Basic Research Methods for Librarians Ronald R. Powell, Lynn Silipigni Connaway, 2004 Any library that does not have a copy of Basic Research Methods for Librarians ought to acquire this edition, and many library schools will want to put it on the list of required readings. It remains the best book on its subject. |
| data science for librarians: Rethinking Information Work G. Kim Dority, 2016-02-22 A state-of-the-art guide to the world of library and information science that gives readers valuable insights into the field and practical tools to succeed in it. As the field of information science continues to evolve, professional-level opportunities in traditional librarianship—especially in school and public libraries—have stalled and contracted, while at the same time information-related opportunities in non-library settings continue to expand. These two coinciding trends are opening up many new job opportunities for LIS professionals, but the challenge lies in helping them (and LIS students) understand how to align their skills and mindsets with these new opportunities.The new edition of G. Kim Dority's Rethinking Information Work: A Career Guide for Librarians and Other Information Professionals gives readers helpful information on self-development, including learning to thrive on change, using key career skills like professional networking and brand-building, and how to make wise professional choices. Taking readers through a planning process that starts with self-examination and ends in creating an actionable career path, the book presents an expansive approach that considers all LIS career possibilities and introduces readers to new opportunities. This guide is appropriate for those embarking on careers in library and information science as well as those looking to make a change, providing career design strategies that can be used to build a lifetime of career opportunity. |
| data science for librarians: Data Information Literacy Jake Carlson, Lisa R. Johnston, 2015-01-15 Given the increasing attention to managing, publishing, and preserving research datasets as scholarly assets, what competencies in working with research data will graduate students in STEM disciplines need to be successful in their fields? And what role can librarians play in helping students attain these competencies? In addressing these questions, this book articulates a new area of opportunity for librarians and other information professionals, developing educational programs that introduce graduate students to the knowledge and skills needed to work with research data. The term data information literacy has been adopted with the deliberate intent of tying two emerging roles for librarians together. By viewing information literacy and data services as complementary rather than separate activities, the contributors seek to leverage the progress made and the lessons learned in each service area. The intent of the publication is to help librarians cultivate strategies and approaches for developing data information literacy programs of their own using the work done in the multiyear, IMLS-supported Data Information Literacy (DIL) project as real-world case studies. The initial chapters introduce the concepts and ideas behind data information literacy, such as the twelve data competencies. The middle chapters describe five case studies in data information literacy conducted at different institutions (Cornell, Purdue, Minnesota, Oregon), each focused on a different disciplinary area in science and engineering. They detail the approaches taken, how the programs were implemented, and the assessment metrics used to evaluate their impact. The later chapters include the DIL Toolkit, a distillation of the lessons learned, which is presented as a handbook for librarians interested in developing their own DIL programs. The book concludes with recommendations for future directions and growth of data information literacy. More information about the DIL project can be found on the project's website: datainfolit.org. |
| data science for librarians: Artificial Intelligence and Machine Learning in Libraries Jason Griffey, 2019-01-01 This issue of Library Technology Reports argues that the near future of library work will be enormously impacted and perhaps forever changed as a result of artificial intelligence (AI) and machine learning systems becoming commonplace. |
| data science for librarians: Reference and Information Services Kay Ann Cassell, Uma Hiremath, 2013 Search skills of today bear little resemblance to searches through print publications. Reference service has become much more complex than in the past, and is in a constant state of flux. Learning the skill sets of a worthy reference librarian can be challenging, unending, rewarding, and-- yes, fun. |
| data science for librarians: Human-Centered Data Science Cecilia Aragon, Shion Guha, Marina Kogan, Michael Muller, Gina Neff, 2022-03-01 Best practices for addressing the bias and inequality that may result from the automated collection, analysis, and distribution of large datasets. Human-centered data science is a new interdisciplinary field that draws from human-computer interaction, social science, statistics, and computational techniques. This book, written by founders of the field, introduces best practices for addressing the bias and inequality that may result from the automated collection, analysis, and distribution of very large datasets. It offers a brief and accessible overview of many common statistical and algorithmic data science techniques, explains human-centered approaches to data science problems, and presents practical guidelines and real-world case studies to help readers apply these methods. The authors explain how data scientists’ choices are involved at every stage of the data science workflow—and show how a human-centered approach can enhance each one, by making the process more transparent, asking questions, and considering the social context of the data. They describe how tools from social science might be incorporated into data science practices, discuss different types of collaboration, and consider data storytelling through visualization. The book shows that data science practitioners can build rigorous and ethical algorithms and design projects that use cutting-edge computational tools and address social concerns. |
| data science for librarians: The Accidental Data Scientist Amy L. Affelt, 2015 Amy Affelt, author of The Accidental Data Scientist, notes that Librarians and information professionals have always worked with data in order to meet the information needs of their constituents, thus 'Big Data' is not a new concept for them. With The Accidental Data Scientist, Amy Affelt shows information professionals how to leverage their skills and training to master emerging tools, techniques, and vocabulary; create mission-critical Big Data research deliverables; and discover rewarding new career opportunities by embracing their inner Data Scientist. |
| data science for librarians: Data Feminism Catherine D'Ignazio, Lauren F. Klein, 2020-03-31 A new way of thinking about data science and data ethics that is informed by the ideas of intersectional feminism. Today, data science is a form of power. It has been used to expose injustice, improve health outcomes, and topple governments. But it has also been used to discriminate, police, and surveil. This potential for good, on the one hand, and harm, on the other, makes it essential to ask: Data science by whom? Data science for whom? Data science with whose interests in mind? The narratives around big data and data science are overwhelmingly white, male, and techno-heroic. In Data Feminism, Catherine D'Ignazio and Lauren Klein present a new way of thinking about data science and data ethics—one that is informed by intersectional feminist thought. Illustrating data feminism in action, D'Ignazio and Klein show how challenges to the male/female binary can help challenge other hierarchical (and empirically wrong) classification systems. They explain how, for example, an understanding of emotion can expand our ideas about effective data visualization, and how the concept of invisible labor can expose the significant human efforts required by our automated systems. And they show why the data never, ever “speak for themselves.” Data Feminism offers strategies for data scientists seeking to learn how feminism can help them work toward justice, and for feminists who want to focus their efforts on the growing field of data science. But Data Feminism is about much more than gender. It is about power, about who has it and who doesn't, and about how those differentials of power can be challenged and changed. |
| data science for librarians: Using Qualitative Methods in Action Research Douglas Cook, Lesley S. J. Farmer, 2011 |
| data science for librarians: Data Science in Practice Alan Said, Vicenç Torra, 2018-09-19 This book approaches big data, artificial intelligence, machine learning, and business intelligence through the lens of Data Science. We have grown accustomed to seeing these terms mentioned time and time again in the mainstream media. However, our understanding of what they actually mean often remains limited. This book provides a general overview of the terms and approaches used broadly in data science, and provides detailed information on the underlying theories, models, and application scenarios. Divided into three main parts, it addresses what data science is; how and where it is used; and how it can be implemented using modern open source software. The book offers an essential guide to modern data science for all students, practitioners, developers and managers seeking a deeper understanding of how various aspects of data science work, and of how they can be employed to gain a competitive advantage. |
| data science for librarians: The Medical Library Association Guide to Data Management for Librarians Lisa Federer, 2016-09-15 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. |
| data science for librarians: ALA Glossary of Library and Information Science, Fourth Edition Michael Levine-Clark, Toni M. Carter, 2013-05-21 The only things librarians seem to encounter more often than acronyms are strings of jargon and arcane technical phrases—and there are so many floating around that even just reading an article in a professional journal can bewilder experienced librarians, to say nothing of those new to the profession! Featuring thousands of revised and brand new entries, the fourth edition of ALA Glossary of Library and Information Science presents a thorough yet concise guide to the specific words that describe the materials, processes and systems relevant to the field of librarianship. A panel of experts from across the LIS world have thoroughly updated the glossary to include the latest technology- and internet-related terms, covering metadata, licensing, electronic resources, instruction, assessment, readers’ advisory, and electronic workflow. This book will become an essential part of every library’s and librarian’s reference collection and will also be a blessing for LIS students and recent graduates. |
| data science for librarians: New Roles for Research Librarians Hilde Daland, Kari-Mette Walmann Hidle, 2016-05-20 New Roles for Research Librarians: Meeting the Expectations for Research Support presents strategies librarians can use to adapt to the new conditions and growing expectations that are emerging from students and researchers. Even if they have never completed a PhD, or even been engaged in independent research themselves, this book will provide a new roadmap on how to deal with the new work environment. The book provides different approaches that include the library in the research process, an area that is often neglected by researchers during their planning and strategic work on research projects. Users will find content that offers tactics on how to create a new dialogue between the librarian and the postgraduate student, along with comprehensive discussions on different starting points, and how communication and collaboration can help reach the best of both worlds. - Explores the new roles available for research librarians and how they can be integral parts of research - Provides a new roadmap on how to deal with the new work environment that now exists between librarians and researchers - Discusses the development and systemizing of research support services and strategies - Offers insights into the collaboration between the librarian and PhD-candidates |
| data science for librarians: The Librarian's Career Guidebook Priscilla K. Shontz, 2004 Sage advice and career guidance is offered by sixty-four information professionals from diverse positions and workplaces. This practical guide addresses a wide variety of career issues. The advice is aimed at librarians in various stages of a career: prospective librarians, M.L.S. students, and entry-level librarians, as well as experienced information professionals. Covers: - Career options - Education - The job search - On-the-job experience - Professional development - Essential skills and strategies for enjoying your career |
| data science for librarians: The Library Book Susan Orlean, 2019-10-01 Susan Orlean’s bestseller and New York Times Notable Book is “a sheer delight…as rich in insight and as varied as the treasures contained on the shelves in any local library” (USA TODAY)—a dazzling love letter to a beloved institution and an investigation into one of its greatest mysteries. “Everybody who loves books should check out The Library Book” (The Washington Post). On the morning of April 28, 1986, a fire alarm sounded in the Los Angeles Public Library. The fire was disastrous: it reached two thousand degrees and burned for more than seven hours. By the time it was extinguished, it had consumed four hundred thousand books and damaged seven hundred thousand more. Investigators descended on the scene, but more than thirty years later, the mystery remains: Did someone purposefully set fire to the library—and if so, who? Weaving her lifelong love of books and reading into an investigation of the fire, award-winning New Yorker reporter and New York Times bestselling author Susan Orlean delivers a “delightful…reflection on the past, present, and future of libraries in America” (New York magazine) that manages to tell the broader story of libraries and librarians in a way that has never been done before. In the “exquisitely written, consistently entertaining” (The New York Times) The Library Book, Orlean chronicles the LAPL fire and its aftermath to showcase the larger, crucial role that libraries play in our lives; delves into the evolution of libraries; brings each department of the library to vivid life; studies arson and attempts to burn a copy of a book herself; and reexamines the case of Harry Peak, the blond-haired actor long suspected of setting fire to the LAPL more than thirty years ago. “A book lover’s dream…an ambitiously researched, elegantly written book that serves as a portal into a place of history, drama, culture, and stories” (Star Tribune, Minneapolis), Susan Orlean’s thrilling journey through the stacks reveals how these beloved institutions provide much more than just books—and why they remain an essential part of the heart, mind, and soul of our country. |
| data science for librarians: Encyclopedia of Data Science and Machine Learning Wang, John, 2023-01-20 Big data and machine learning are driving the Fourth Industrial Revolution. With the age of big data upon us, we risk drowning in a flood of digital data. Big data has now become a critical part of both the business world and daily life, as the synthesis and synergy of machine learning and big data has enormous potential. Big data and machine learning are projected to not only maximize citizen wealth, but also promote societal health. As big data continues to evolve and the demand for professionals in the field increases, access to the most current information about the concepts, issues, trends, and technologies in this interdisciplinary area is needed. The Encyclopedia of Data Science and Machine Learning examines current, state-of-the-art research in the areas of data science, machine learning, data mining, and more. It provides an international forum for experts within these fields to advance the knowledge and practice in all facets of big data and machine learning, emphasizing emerging theories, principals, models, processes, and applications to inspire and circulate innovative findings into research, business, and communities. Covering topics such as benefit management, recommendation system analysis, and global software development, this expansive reference provides a dynamic resource for data scientists, data analysts, computer scientists, technical managers, corporate executives, students and educators of higher education, government officials, researchers, and academicians. |
| data science for librarians: Big Data Applications for Improving Library Services Sangeeta N. Dhamdhere, 2020 This book explores the application of big data in library services-- |
| data science for librarians: Data Science for Public Policy Jeffrey C. Chen, Edward A. Rubin, Gary J. Cornwall, 2021-09-01 This textbook presents the essential tools and core concepts of data science to public officials, policy analysts, and economists among others in order to further their application in the public sector. An expansion of the quantitative economics frameworks presented in policy and business schools, this book emphasizes the process of asking relevant questions to inform public policy. Its techniques and approaches emphasize data-driven practices, beginning with the basic programming paradigms that occupy the majority of an analyst’s time and advancing to the practical applications of statistical learning and machine learning. The text considers two divergent, competing perspectives to support its applications, incorporating techniques from both causal inference and prediction. Additionally, the book includes open-sourced data as well as live code, written in R and presented in notebook form, which readers can use and modify to practice working with data. |
| data science for librarians: Zotero Jason Puckett, 2011 Zotero is a reference manager program. It exists either as an add-on for the Firefox web browser, a separate program, or both. It allows researchers to save references from library catalogs, research databases and other websites with a single click. |
| data science for librarians: Data Science Thinking Longbing Cao, 2018-08-17 This book explores answers to the fundamental questions driving the research, innovation and practices of the latest revolution in scientific, technological and economic development: how does data science transform existing science, technology, industry, economy, profession and education? How does one remain competitive in the data science field? What is responsible for shaping the mindset and skillset of data scientists? Data Science Thinking paints a comprehensive picture of data science as a new scientific paradigm from the scientific evolution perspective, as data science thinking from the scientific-thinking perspective, as a trans-disciplinary science from the disciplinary perspective, and as a new profession and economy from the business perspective. |
| data science for librarians: Big Data Shocks Andrew Weiss, 2018-03-15 Big data, as it has become known in business and information technology circles, has the potential to improve our knowledge about human behavior, and to help us gain insight into the ways in which we organize ourselves, our cultures, and our external and internal lives. Libraries stand at the center of the information world, both facilitating and contributing to this flood as well as helping to shape and channel it to specific purposes. But all technologies come with a price. Where the tool can serve a purpose, it can also change the user's behavior to fit the purposes of the tool. Big Data Shocks: An Introduction to Big Data for Librarians and Information Professionals examines the roots of big data, the current climate and rising stars in this world. The book explores the issues raised by big data and discusses theoretical as well as practical approaches to managing information whose scope exists beyond the human scale. What’s at stake ultimately is the privacy of the people who support and use our libraries and the temptation for us to examine their behaviors. Such tension lies deep in the heart of our great library institutions. This book addresses these issues and many of the questions that arise from them, including: What is our role as librarians within this new era of big data? What are the impacts of new powerful technologies that track and analyze our behavior? Do data aggregators know more about us and our patrons than we do? How can librarians ethically balance the need to demonstrate learning and knowledge creation and privacy? Do we become less private merely because we use a tool or is it because the tool has changed us? What's in store for us with the internet of things combining with data mining techniques? All of these questions and more are explored in this book |
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues …
Belmont Forum Adopts Open Data Principles for Environme…
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to …
Belmont Forum Data Accessibility Statement an…
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their …
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will enable a …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels to …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …
