Research Data Management

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?

Between the Spreadsheets: Classifying and Fixing Dirty Data draws on classification expert Susan Walsh's decade of experience in data classification to present a fool-proof method for cleaning and classifying your data. The book covers everything from the very basics of data classification to normalisation, taxonomies and presents the author's proven COAT methodology, helping ensure an organisation's data is Consistent, Organised, Accurate and Trustworthy.

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.

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.

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

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.

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?

A comprehensive guide for scientific researchers providing everything they need to know about data management and how to organize, document, use and reuse their data.

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.

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.

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.

The Data Literacy Cookbook includes a variety of approaches to and lesson plans for teaching data literacy, from simple activities to self-paced learning modules to for-credit and discipline-specific courses.

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.

Fay and Brittain present statistical hypothesis testing and compatible confidence intervals, focusing on application and proper interpretation. The emphasis is on equipping applied statisticians with enough tools - and advice on choosing among them - to find reasonable methods for almost any problem and enough theory to tackle new problems by modifying existing methods. After covering the basic mathematical theory and scientific principles, tests and confidence intervals are developed for specific types of data. Essential methods for applications are covered, such as general procedures for creating tests (e.g., likelihood ratio, bootstrap, permutation, testing from models), adjustments for multiple testing, clustering, stratification, causality, censoring, missing data, group sequential tests, and non-inferiority tests. New methods developed by the authors are included throughout, such as melded confidence intervals for comparing two samples and confidence intervals associated with Wilcoxon-Mann-Whitney tests and Kaplan-Meier estimates. Examples, exercises, and the R package asht support practical use.