It has become increasingly accepted that important digital data must be retained and shared in order to preserve and promote knowledge, advance research in and across all disciplines of scholarly endeavor, and maximize the return on investment of public funds. To meet this challenge, colleges and universities are adding data services to existing infrastructures by drawing on the expertise of information professionals who are already involved in the acquisition, management and preservation of data in their daily jobs. Data services include planning and implementing good data management practices, thereby increasing researchers' ability to compete for grant funding and ensuring that data collections with continuing value are preserved for reuse. This volume provides a framework to guide information professionals in academic libraries, presses, and data centers through the process of managing research data from the planning stages through the life of a grant project and beyond. It illustrates principles of good practice with use-case examples and illuminates promising data service models through case studies of innovative, successful projects and collaborations.
A comprehensive guide to everything scientists need to know about data management, this book is essential for researchers who need to learn how to organize, document and take care of their own data. Researchers in all disciplines are faced with the challenge of managing the growing amounts of digital data that are the foundation of their research. Kristin Briney offers practical advice and clearly explains policies and principles, in an accessible and in-depth text that will allow researchers to understand and achieve the goal of better research data management. Data Management for Researchers includes sections on: * The data problem – an introduction to the growing importance and challenges of using digital data in research. Covers both the inherent problems with managing digital information, as well as how the research landscape is changing to give more value to research datasets and code. * The data lifecycle – a framework for data’s place within the research process and how data’s role is changing. Greater emphasis on data sharing and data reuse will not only change the way we conduct research but also how we manage research data. * Planning for data management – covers the many aspects of data management and how to put them together in a data management plan. This section also includes sample data management plans. * Documenting your data – an often overlooked part of the data management process, but one that is critical to good management; data without documentation are frequently unusable. * Organizing your data – explains how to keep your data in order using organizational systems and file naming conventions. This section also covers using a database to organize and analyze content. * Improving data analysis – covers managing information through the analysis process. This section starts by comparing the management of raw and analyzed data and then describes ways to make analysis easier, such as spreadsheet best practices. It also examines practices for research code, including version control systems. * Managing secure and private data – many researchers are dealing with data that require extra security. This section outlines what data falls into this category and some of the policies that apply, before addressing the best practices for keeping data secure. * Short-term storage – deals with the practical matters of storage and backup and covers the many options available. This section also goes through the best practices to insure that data are not lost. * Preserving and archiving your data – digital data can have a long life if properly cared for. This section covers managing data in the long term including choosing good file formats and media, as well as determining who will manage the data after the end of the project. * Sharing/publishing your data – addresses how to make data sharing across research groups easier, as well as how and why to publicly share data. This section covers intellectual property and licenses for datasets, before ending with the altmetrics that measure the impact of publicly shared data. * Reusing data – as more data are shared, it becomes possible to use outside data in your research. This chapter discusses strategies for finding datasets and lays out how to cite data once you have found it. This book is designed for active scientific researchers but it is useful for anyone who wants to get more from their data: academics, educators, professionals or anyone who teaches data management, sharing and preservation. "An excellent practical treatise on the art and practice of data management, this book is essential to any researcher, regardless of subject or discipline." —Robert Buntrock, Chemical Information Bulletin
Research funders in the UK, USA and across Europe are implementing data management and sharing policies to maximize openness of data, transparency and accountability of the research they support. Written by experts from the UK Data Archive with over 20 years experience, this book gives post-graduate students, researchers and research support staff the data management skills required in today’s changing research environment. The book features guidance on: how to plan your research using a data management checklist how to format and organize data how to store and transfer data research ethics and privacy in data sharing and intellectual property rights data strategies for collaborative research how to publish and cite data how to make use of other people’s research data, illustrated with six real-life case studies of data use.
Research Data Management and Data Literacies help researchers familiarize themselves with RDM, and with the services increasingly offered by libraries. This new volume looks at data-intensive science, or 'Science 2.0' as it is sometimes termed in commentary, from a number of perspectives, including the tasks academic libraries need to fulfil, new services that will come online in the near future, data literacy and its relation to other literacies, research support and the need to connect researchers across the academy, and other key issues, such as 'data deluge,' the importance of citations, metadata and data repositories. This book presents a solid resource that contextualizes RDM, including good theory and practice for researchers and professionals who find themselves tasked with managing research data. - Gives guidance on organizing, storing, preserving and sharing research data using Research Data Management (RDM) - Contextualizes RDM within the global shift to data-intensive research - Helps researchers and information professionals understand and optimize data-intensive ways of working - Considers RDM in relation to varying needs of researchers across the sciences and humanities - Presents key issues surrounding RDM, including data literacy, citations, metadata and data repositories
Innovative technologies are changing the way research is performed, preserved, and communicated. Managing Scientific Information and Research Data explores how these technologies are used and provides detailed analysis of the approaches and tools developed to manage scientific information and data. Following an introduction, the book is then divided into 15 chapters discussing the changes in scientific communication; new models of publishing and peer review; ethics in scientific communication; preservation of data; discovery tools; discipline-specific practices of researchers for gathering and using scientific information; academic social networks; bibliographic management tools; information literacy and the information needs of students and researchers; the involvement of academic libraries in eScience and the new opportunities it presents to librarians; and interviews with experts in scientific information and publishing. - Promotes innovative technologies for creating, sharing and managing scientific content - Presents new models of scientific publishing, peer review, and dissemination of information - Serves as a practical guide for researchers, students, and librarians on how to discover, filter, and manage scientific information - Advocates for the adoption of unique author identifiers such as ORCID and ResearcherID - Looks into new tools that make scientific information easy to discover and manage - Shows what eScience is and why it is becoming a priority for academic libraries - Demonstrates how Electronic Laboratory Notebooks can be used to record, store, share, and manage research data - Shows how social media and the new area of Altmetrics increase researchers' visibility and measure attention to their research - Directs to sources for datasets - Provides directions on choosing and using bibliographic management tools - Critically examines the metrics used to evaluate research impact - Aids strategic thinking and informs decision making
Research Data Management (RDM) has become a professional topic of great importance internationally following changes in scholarship and government policies about the sharing of research data. Exploring Research Data Management provides an accessible introduction and guide to RDM with engaging tasks for the reader to follow and develop their knowledge. Starting by exploring the world of research and the importance and complexity of data in the research process, the book considers how a multi-professional support service can be created then examines the decisions that need to be made in designing different types of research data service from local policy creation, training, through to creating a data repository. Coverage includes: A discussion of the drivers and barriers to RDM Institutional policy and making the case for Research Data Services Practical data management Data literacy and training researchers Ethics and research data services Case studies and practical advice from working in a Research Data Service. This book will be useful reading for librarians and other support professionals who are interested in learning more about RDM and developing Research Data Services in their own institution. It will also be of value to students on librarianship, archives, and information management courses studying topics such as RDM, digital curation, data literacies and open science.
The Data Book: Collection and Management of Research Data is the first practical book written for researchers and research team members covering how to collect and manage data for research. The book covers basic types of data and fundamentals of how data grow, move and change over time. Focusing on pre-publication data collection and handling, the text illustrates use of these key concepts to match data collection and management methods to a particular study, in essence, making good decisions about data. The first section of the book defines data, introduces fundamental types of data that bear on methodology to collect and manage them, and covers data management planning and research reproducibility. The second section covers basic principles of and options for data collection and processing emphasizing error resistance and traceability. The third section focuses on managing the data collection and processing stages of research such that quality is consistent and ultimately capable of supporting conclusions drawn from data. The final section of the book covers principles of data security, sharing, and archival. This book will help graduate students and researchers systematically identify and implement appropriate data collection and handling methods.
Engaging Researchers with Data Management is an invaluable collection of 24 case studies, drawn from institutions across the globe, that demonstrate clearly and practically how to engage the research community with RDM. These case studies together illustrate the variety of innovative strategies research institutions have developed to engage with their researchers about managing research data. Each study is presented concisely and clearly, highlighting the essential ingredients that led to its success and challenges encountered along the way. By interviewing key staff about their experiences.
Handling and archiving data should be done in a highly professional and quality-controlled manner. For academic and research libraries, it is required to know how to document data and support traceability, as well as to make it reusable and productive. However, these institutions have different requirements relating to the archiving and reusability of data. Therefore, a comprehensive source of information is required to understand data access and management within these organizations. Research Data Access and Management in Modern Libraries is a critical scholarly resource that delves into innovative data management strategies and strategy implementation in library settings and provides best practices to stakeholders using the latest tools and technology. It further explores concepts such as research data management, data access, data preservation, building document and data institutional repositories, applications of Web 2.0 tools, mobile technology applications in data access, and conducting information literacy programs. This book is ideal for librarians, information specialists, research scholars, students, IT managers, computer scientists, policymakers, educators, and academic administrators.
Armed with this guide's strategies and concrete examples, subject librarians, data services librarians, and scholarly communication librarians will be inspired to roll up their sleeves and get involved with teaching research data management competencies to students and faculty. The usefulness of research data management skills bridges numerous activities, from data-driven scholarship and open research by faculty to documentation for grant reporting. And undergrads need a solid foundation in data management for future academic success. This collection gathers practitioners from a broad range of academic libraries to describe their services and instruction around research data. You will learn about such topics as integrating research data management into information literacy instruction; threshold concepts for novice learners of data management; four key competencies that are entry points for library-faculty collaboration in data instruction; an 8-step plan for outreach to faculty and grad students in engineering and the sciences; using RStudio to teach data management, data visualization, and research reproducibility; expanding data management instruction with adaptable modules for remote learning; designing a data management workshop series; developing a research guide on data types, open data repositories, and data storage; creating a data management plan assignment for STEM undergraduates; and data management training to ensure compliance with grant requirements.
