Research Data Management

The Practice of Reproducible Research presents concrete examples of how researchers in the data-intensive sciences are working to improve the reproducibility of their research projects. In each of the thirty-one case studies in this volume, the author or team describes the workflow that they used to complete a real-world research project.

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. 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.

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?

While there is a lot of talk about how we now live in a knowledge society, the reality has been less impressive: we have yet to truly transition to a knowledge society--in part, this book argues, because discussion mostly focuses on a knowledge economy and information society rather than on ways to mobilize to create an actual knowledge society. That all may change, however, with the rise of open data and big data. This book considers the role of the open data movement in fostering transformation, showing that at the heart of any successful mobilization will be an emerging open data ecosystem and new ways for societal actors to effectively produce and use data.

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: 1.Understanding data management basics and best practices. 2.Using the reference interview to help with data management 3.Writing data management plans for grants. 4.Starting and growing a data management service. 5.Finding collaborators inside and outside the library. 6.Collecting and using data in different disciplines.

In the behavioral sciences today, there is increasing emphasis on transparency, and the need for research studies to be made replicable. This book presents a straightforward approach to managing and documenting one's data so that other researchers can repeat the study. While data management may seem intimidating to new researchers, this book shows how easy it can (and should!) be. The first chapter presents a basic structure of folders and subfolders for organizing data files, and then each subsequent chapter delves into details for a specific folder. Step by step, readers learn to label and archive different kinds of project documents and data files, including original, processed, and working data. Readers also learn to write command codes showing exactly how the original data are analyzed. Examples illustrate how to document the most common types of research (an online survey, a paper questionnaire, and a multiple-trial experiment). Since major research funders now require recipients to meet strict standards for data handling, this book will foster a vital career skill for students and promote transparency and replicability of research.

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.

Using straightforward examples, the book takes you through an entire reproducible research workflow. This practical workflow enables you to gather and analyze data as well as dynamically present results in print and on the web.

Useful book on data management, especially for social scientists. Includes examples with Stata, SPSS, SAS statistical software

Recent events have vividly underscored the societal importance of science, yet the majority of the public are unaware that a large proportion of published scientific results are simply wrong. The Problem with Science is an exploration of the manifestations and causes of this scientific crisis, accompanied by a description of the very promising corrective initiatives largely developed over the past decade to stem the spate of irreproducible results that have come to characterize many of our sciences.