Download Free Curating Research Data Set Book in PDF and EPUB Free Download. You can read online Curating Research Data Set and write the review.

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.
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.
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
Development Research in Practice leads the reader through a complete empirical research project, providing links to continuously updated resources on the DIME Wiki as well as illustrative examples from the Demand for Safe Spaces study. The handbook is intended to train users of development data how to handle data effectively, efficiently, and ethically. “In the DIME Analytics Data Handbook, the DIME team has produced an extraordinary public good: a detailed, comprehensive, yet easy-to-read manual for how to manage a data-oriented research project from beginning to end. It offers everything from big-picture guidance on the determinants of high-quality empirical research, to specific practical guidance on how to implement specific workflows—and includes computer code! I think it will prove durably useful to a broad range of researchers in international development and beyond, and I learned new practices that I plan on adopting in my own research group.†? —Marshall Burke, Associate Professor, Department of Earth System Science, and Deputy Director, Center on Food Security and the Environment, Stanford University “Data are the essential ingredient in any research or evaluation project, yet there has been too little attention to standardized practices to ensure high-quality data collection, handling, documentation, and exchange. Development Research in Practice: The DIME Analytics Data Handbook seeks to fill that gap with practical guidance and tools, grounded in ethics and efficiency, for data management at every stage in a research project. This excellent resource sets a new standard for the field and is an essential reference for all empirical researchers.†? —Ruth E. Levine, PhD, CEO, IDinsight “Development Research in Practice: The DIME Analytics Data Handbook is an important resource and a must-read for all development economists, empirical social scientists, and public policy analysts. Based on decades of pioneering work at the World Bank on data collection, measurement, and analysis, the handbook provides valuable tools to allow research teams to more efficiently and transparently manage their work flows—yielding more credible analytical conclusions as a result.†? —Edward Miguel, Oxfam Professor in Environmental and Resource Economics and Faculty Director of the Center for Effective Global Action, University of California, Berkeley “The DIME Analytics Data Handbook is a must-read for any data-driven researcher looking to create credible research outcomes and policy advice. By meticulously describing detailed steps, from project planning via ethical and responsible code and data practices to the publication of research papers and associated replication packages, the DIME handbook makes the complexities of transparent and credible research easier.†? —Lars Vilhuber, Data Editor, American Economic Association, and Executive Director, Labor Dynamics Institute, Cornell University
This open access book summarises the latest developments on data management in the EU H2020 ENVRIplus project, which brought together more than 20 environmental and Earth science research infrastructures into a single community. It provides readers with a systematic overview of the common challenges faced by research infrastructures and how a ‘reference model guided’ engineering approach can be used to achieve greater interoperability among such infrastructures in the environmental and earth sciences. The 20 contributions in this book are structured in 5 parts on the design, development, deployment, operation and use of research infrastructures. Part one provides an overview of the state of the art of research infrastructure and relevant e-Infrastructure technologies, part two discusses the reference model guided engineering approach, the third part presents the software and tools developed for common data management challenges, the fourth part demonstrates the software via several use cases, and the last part discusses the sustainability and future directions.
This handbook is a comprehensive reference guide for researchers, funding agencies and organizations engaged in survey research. Drawing on research from a world-class team of experts, this collection addresses the challenges facing survey-based data collection today as well as the potential opportunities presented by new approaches to survey research, including in the development of policy. It examines innovations in survey methodology and how survey scholars and practitioners should think about survey data in the context of the explosion of new digital sources of data. The Handbook is divided into four key sections: the challenges faced in conventional survey research; opportunities to expand data collection; methods of linking survey data with external sources; and, improving research transparency and data dissemination, with a focus on data curation, evaluating the usability of survey project websites, and the credibility of survey-based social science. Chapter 23 of this book is open access under a CC BY 4.0 license at link.springer.com.
The relevance of research data today and for the future is well documented and discussed, in Germany as well as internationally. Ensuring that research data are accessible, sharable, and re-usable over time is increasingly becoming an essential task for researchers and research infrastructure institutions. Some reasons for this development include the following: - research data are documented and could therefore be validated - research data could be the basis for new research questions - research data could be re-analyzed by using innovative digital methods - research data could be used by other disciplines Therefore, it is essential that research data are curated, which means they are kept accessible and interpretable over time. In Germany, a baseline study was undertaken analyzing the situation in eleven research disciplines in 2012. The results were then published in a German-language edition. To address an international audience, the German-language edition of the study has been translated and abridged. The present publication provides a summary of the preconditions, results, and conclusions of the baseline study “Langzeitarchivierung von Forschungsdaten – Eine Bestandsaufnahme”. The editors of this edition hope that this publication will contribute to further discussion and increased awareness of this topic, resulting in expanded measures to improve the framework and the conditions for the digital curation of research data.
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.
This title defines what is required to achieve a culture of effective data management offering advice on the skills required, legal and contractual obligations, strategies and management plans and the data management infrastructure of specialists and services. Data management has become an essential requirement for information professionals over the last decade, particularly for those supporting the higher education research community, as more and more digital information is created and stored. As budgets shrink and funders of research demand evidence of value for money and demonstrable benefits for society, there is increasing pressure to provide plans for the sustainable management of data. Ensuring that important data remains discoverable, accessible and intelligible and is shared as part of a larger web of knowledge will mean that research has a life beyond its initial purpose and can offer real utility to the wider community. This edited collection, bringing together leading figures in the field from the UK and around the world, provides an introduction to all the key data issues facing the HE and information management communities. Each chapter covers a critical element of data management: • Why manage research data? • The lifecycle of data management • Research data policies: principles, requirements and trends • Sustainable research data • Data management plans and planning • Roles and responsibilities – libraries, librarians and data • Research data management: opportunities and challenges for HEIs • The national data centres • Contrasting national research data strategies: Australia and the USA • Emerging infrastructure and services for research data management and curation in the UK and Europe Readership: This is essential reading for librarians and information professionals working in the higher education sector, the research community, policy makers and university managers. It will also be a useful introduction for students taking courses in information management, archivists and national library services.
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.