Research Data Management

This 4th edition, Responsible Conduct of Research, provides an overview of ethical, legal, and social issues in scientific research. Giving an in-depth analysis of a wide array of topics, this new edition will give insights into ethical theory and decision-making, misconduct, questionable research practices, data sharing, data auditing, reproducibility, authorship, publication, peer review, intellectual property, conflict of interest, mentoring, safe research environment, animal experimentation, research with human subjects, and social responsibility.

Recently, social science has had numerous episodes of influential research that was found invalid when placed under rigorous scrutiny. The growing sense that many published results are potentially erroneous has made those conducting social science research more determined to ensure the underlying research is sound. Transparent and Reproducible Social Science Research is the first book to summarize and synthesize new approaches to combat false positives and non-reproducible findings in social science research, document the underlying problems in research practices, and teach a new generation of students and scholars how to overcome them.

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.

Numerous controversies have arisen, especially since 2010, in a wide array of disciplines that stem from the failure to reproduce studies or their findings:biology, biomedical and preclinical research, business and organizational studies, computational sciences, drug discovery, economics, education, epidemiology and statistics, genetics, immunology, policy research, political science, psychology, and sociology.This monograph defines terms and constructs related to reproducible research, weighs key considerations and challenges in reproducing or replicating studies, and discusses transparency in publications that can support reproducible researchgoals.

While there has been rapid growth in the creation and use of 3D data over the last decade, the ongoing development and evolving usage of these data have left many unresolved questions about their stability, durability, and long-term accessibility.   3D Data Creation to Curation: Community Standards for 3D Data Preservation collects the efforts of the Community Standards for 3D Data Preservation (CS3DP) initiative--a large practicing community of librarians, researchers, engineers, and designers--to move toward establishment of shared guidelines, practices, and standards.

Create good data from the start, rather than fixing it after it is collected. By following the guidelines in this book, you will be able to conduct more effective analyses and produce timely presentations of research data.

As an in-depth explanation of the entire digital curation lifecycle, from creation to appraisal to preservation to organization/access to transformation, the first edition of this text set a benchmark for both thoroughness and clarity.

This book collects core facts for the state of the art in provenance approaches and techniques, complemented by a critical review of existing approaches. New research directions are also provided that combine data science and knowledge graphs, for an increasingly important research topic.

There is growing recognition of the need to address the fragility of digital information, on which our society heavily depends for smooth operation in all aspects of daily life. There are clearly many types of digital objects that may need to be preserved, such as databases, scientific data and software itself. David Giaretta, Director of the Alliance for Permanent Access,  and his contributors explain why the tools and techniques used for preserving rendered objects are inadequate for all these other types of digital objects, and they provide the concepts, techniques and tools that are needed.

Legal Data and Information in Practice provides readers with an understanding of how to facilitate the acquisition, management, and use of legal data in organizations such as libraries, courts, governments, universities, and start-ups.

The traditional academic imperative to "publish or perish" is increasingly coupled with the newer necessity of "impact or perish"--the requirement that a publication have "impact," as measured by a variety of metrics, including citations, views, and downloads. Gaming the Metrics examines how the increasing reliance on metrics to evaluate scholarly publications has produced radically new forms of academic fraud and misconduct. The contributors show that the metrics-based "audit culture" has changed the ecology of research, fostering the gaming and manipulation of quantitative indicators, which lead to the invention of such novel forms of misconduct as citation rings and variously rigged peer reviews. The chapters, written by both scholars and those in the trenches of academic publication, provide a map of academic fraud and misconduct today. They consider such topics as the shortcomings of metrics, the gaming of impact factors, the emergence of so-called predatory journals, the "salami slicing" of scientific findings, the rigging of global university rankings, and the creation of new watchdogs and forensic practices. The open access edition of this book was made possible by generous funding from the UC Davis Library TOME Grant.

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.

This book is about data cleaning, which is used to refer to all kinds of tasks and activities to detect and repair errors in the data. Rather than focus on a particular data cleaning task, we give an overview of the end-to-end data cleaning process, describing various error detection and repair methods, and attempt to anchor these proposals with multiple taxonomies and views. Specifically, we cover four of the most common and important data cleaning tasks, namely, outlier detection, data transformation, error repair (including imputing missing values), and data deduplication. Furthermore, due to the increasing popularity and applicability of machine learning techniques, we include a chapter that specifically explores how machine learning techniques are used for data cleaning, and how data cleaning is used to improve machine learning models.

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.