Curating Research Data Set

Data are becoming the proverbial coin of the digital realm: a research commodity that might purchase reputation credit in a disciplinary culture of data sharing, or buy transparency when faced with funding agency mandates or publisher scrutiny. Unlike most monetary systems, however, digital data can flow in all too great an abundance. Not only does this currency actually grow on trees, but it comes from animals, books, thoughts, and each of us! And that is what makes data curation so essential. The abundance of digital research data challenges library and information science professionals to harness this flow of information streaming from research discovery and scholarly pursuit and preserve the unique evidence for future use. Volume One of Curating Research Data explores the variety of reasons, motivations, and drivers for why data curation services are needed in the context of academic and disciplinary data repository efforts. Twelve chapters, divided into three parts, take an in-depth look at the complex practice of data curation as it emerges around us. Part I sets the stage for data curation by describing current policies, data sharing cultures, and collaborative efforts currently underway that impact potential services. Part II brings several key issues, such as cost recovery and marketing strategy, into focus for practitioners when considering how to put data curation services in action. Finally, Part III describes the full lifecycle of data by examining the ethical and practical reuse issues that data curation practitioners must consider as we strive to prepare data for the future. Digital data is ubiquitous and rapidly reshaping how scholarship progresses now and into the future. The information expertise of librarians can help ensure the resiliency of digital data, and the information it represents, by addressing how the meaning, integrity, and provenance of digital data generated by researchers today will be captured and conveyed to future researchers.

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

In this book readers will find technological discussions on the existing and emerging technologies across the different stages of the big data value chain. They will learn about legal aspects of big data, the social impact, and about education needs and requirements. And they will discover the business perspective and how big data technology can be exploited to deliver value within different sectors of the economy. The book is structured in four parts: Part I “The Big Data Opportunity” explores the value potential of big data with a particular focus on the European context. It also describes the legal, business and social dimensions that need to be addressed, and briefly introduces the European Commission’s BIG project. Part II “The Big Data Value Chain” details the complete big data lifecycle from a technical point of view, ranging from data acquisition, analysis, curation and storage, to data usage and exploitation. Next, Part III “Usage and Exploitation of Big Data” illustrates the value creation possibilities of big data applications in various sectors, including industry, healthcare, finance, energy, media and public services. Finally, Part IV “A Roadmap for Big Data Research” identifies and prioritizes the cross-sectorial requirements for big data research, and outlines the most urgent and challenging technological, economic, political and societal issues for big data in Europe. This compendium summarizes more than two years of work performed by a leading group of major European research centers and industries in the context of the BIG project. It brings together research findings, forecasts and estimates related to this challenging technological context that is becoming the major axis of the new digitally transformed business environment.

This book constitutes the refereed proceedings of the 13th European Conference on Research and Advanced Technology for Digital Libraries, ECDL 2009, held in Corfu, Greece, in September/October 2009. The 28 revised full papers and 6 revised short papers presented together with 2 panel description, the extended abstracts of 20 revised poster and 16 demo papers were carefully reviewed and selected from a total of 181 submissions. The papers are organized in topical sections on services, infrastructures, interaction, knowledge organization systems, interfaces, resource discovery, architectures, information retrieval, preservation, and evaluation.

An up-to-date examination of the evolving field of digital curation and its important place in libraries, covering the major technical, social, and organizational issues surrounding curation for libraries, archives, and other information-based organizations. This book addresses the evolving field of digital curation and its important place in libraries, covering the myriad issues surrounding curation for libraries, archives, and other information-based organizations. Balancing research, theory, and practice in curation, this book is a valuable resource for students, librarians, and archivists that will help them understand the technology infrastructure that supports curation, develop effective curation plans, and make the best choices when digitizing collections that aid in the long term preservation and curation of their materials. The book can serve as a textbook for graduate courses in digital curation, digital libraries, and informatics as well as be useful to librarians and archivists for individual continuing professional education.

Big data and machine learning are driving the Fourth Industrial Revolution. With the age of big data upon us, we risk drowning in a flood of digital data. Big data has now become a critical part of both the business world and daily life, as the synthesis and synergy of machine learning and big data has enormous potential. Big data and machine learning are projected to not only maximize citizen wealth, but also promote societal health. As big data continues to evolve and the demand for professionals in the field increases, access to the most current information about the concepts, issues, trends, and technologies in this interdisciplinary area is needed. The Encyclopedia of Data Science and Machine Learning examines current, state-of-the-art research in the areas of data science, machine learning, data mining, and more. It provides an international forum for experts within these fields to advance the knowledge and practice in all facets of big data and machine learning, emphasizing emerging theories, principals, models, processes, and applications to inspire and circulate innovative findings into research, business, and communities. Covering topics such as benefit management, recommendation system analysis, and global software development, this expansive reference provides a dynamic resource for data scientists, data analysts, computer scientists, technical managers, corporate executives, students and educators of higher education, government officials, researchers, and academicians.

The massive increase in digital information in the last decade has created new requirements for institutional and technological structures and workforce skills. Preparing the Workforce for Digital Curation focuses on education and training needs to meet the demands for access to and meaningful use of digital information, now and in the future. This study identifies the various practices and spectrum of skill sets that comprise digital curation, looking in particular at human versus automated tasks. Additionally, the report examines the possible career path demands and options for professionals working in digital curation activities, and analyzes the economic benefits and societal importance of digital curation for competitiveness, innovation, and scientific advancement. Preparing the Workforce for Digital Curation considers the evolving roles and models of digital curation functions in research organizations, and their effects on employment opportunities and requirements. The recommendations of this report will help to advance digital curation and meet the demand for a trained workforce.