Responsible Conduct of Research & Scholarly Activity

Written by experts at the UK Data Archive, with over thirty years of experience in working with and teaching people to work with data, this book is the globally-reaching guide for any postgraduate student or researcher looking to build their data management skills. Focused on both primary and secondary data and packed with checklists and templates, it contains everything readers need to know for managing all types data before, during, and after the research process. Building on foundational data management techniques, it offers practical advice and insight into the unique skills needed to work with newer forms of data, like social media and big data. It also demonstrates how to: - Identify quality data that is credible, ethically-sound, and available for use - Choose and collect data suitable for particular research questions and project scopes - Work with personal, communal, administrative, and other sensitive and public data - Make the most of metadata - Visualise and share data using innovative platforms like blogs, infographics, and podcasts.   

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.  

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. Contributors include: James L. Mullins, Purdue University; MacKenzie Smith, University of California at Davis; Sherry Lake, University of Virginia; John Kunze, University of California; Bernard Reilly, Center for Research Libraries; Jacob Carlson, Purdue University; Melissa Levine, University of Michigan; Jenn Riley, University of North Carolina at Chapel Hill; Jan Brase, German National Library of Science and Technology; Seamus Ross, University of Toronto; Sarah Shreeves, University of Illinois at Urbana-Champaign; Jared Lyle, University of Michigan; Michele Kimpton, DuraSpace; Brian Schottlaender, University of California San Diego; Suzie Allard, University of Tennessee; Angus Whyte, Digital Curation Centre; Scott Brandt, Purdue University; Brian Westra, University of Oregon; Geneva Henry, Rice University; Gail Steinhart, Cornell University; and Cliff Lynch, Coalition for Networked Information. Charleston Insights in Library, Information, and Archival Sciences is a new series produced as a collaboration between the organizers of the Charleston Library Conference and Purdue University Press. Volumes in the series focus on important topics in library and information science, presenting the issues in a relatively jargon-free way that is accessible to all types of information professionals.