Advertisement
| data management in research: Data Management for Researchers Kristin Briney, 2015-09-01 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 |
| data management in research: Exploring Research Data Management Andrew Cox, Eddy Verbaan, 2018-05-11 Research Data Management (RDM) has become a professional topic of great importance internationally following changes in scholarship and government policies about the sharing of research data. Exploring Research Data Management provides an accessible introduction and guide to RDM with engaging tasks for the reader to follow and develop their knowledge. Starting by exploring the world of research and the importance and complexity of data in the research process, the book considers how a multi-professional support service can be created then examines the decisions that need to be made in designing different types of research data service from local policy creation, training, through to creating a data repository. Coverage includes: A discussion of the drivers and barriers to RDM Institutional policy and making the case for Research Data Services Practical data management Data literacy and training researchers Ethics and research data services Case studies and practical advice from working in a Research Data Service. This book will be useful reading for librarians and other support professionals who are interested in learning more about RDM and developing Research Data Services in their own institution. It will also be of value to students on librarianship, archives, and information management courses studying topics such as RDM, digital curation, data literacies and open science. |
| data management in research: Research Data Management and Data Literacies Koltay Tibor, 2021-10-31 Research Data Management and Data Literacies help researchers familiarize themselves with RDM, and with the services increasingly offered by libraries. This new volume looks at data-intensive science, or 'Science 2.0' as it is sometimes termed in commentary, from a number of perspectives, including the tasks academic libraries need to fulfil, new services that will come online in the near future, data literacy and its relation to other literacies, research support and the need to connect researchers across the academy, and other key issues, such as 'data deluge,' the importance of citations, metadata and data repositories. This book presents a solid resource that contextualizes RDM, including good theory and practice for researchers and professionals who find themselves tasked with managing research data. - Gives guidance on organizing, storing, preserving and sharing research data using Research Data Management (RDM) - Contextualizes RDM within the global shift to data-intensive research - Helps researchers and information professionals understand and optimize data-intensive ways of working - Considers RDM in relation to varying needs of researchers across the sciences and humanities - Presents key issues surrounding RDM, including data literacy, citations, metadata and data repositories |
| data management in research: Engaging Researchers with Data Management Connie Clare, Maria J. Cruz, Elli Papadopoulou, James Savage (Research associate), Marta Teperek, Yan Wang, Iza Witkowska, Joanne Yeomans, 2019 Engaging Researchers with Data Management is an invaluable collection of 24 case studies, drawn from institutions across the globe, that demonstrate clearly and practically how to engage the research community with RDM. These case studies together illustrate the variety of innovative strategies research institutions have developed to engage with their researchers about managing research data. Each study is presented concisely and clearly, highlighting the essential ingredients that led to its success and challenges encountered along the way. By interviewing key staff about their experiences. |
| data management in research: Research Data Management Joyce M. Ray, 2014 It has become increasingly accepted that important digital data must be retained and shared in order to preserve and promote knowledge, advance research in and across all disciplines of scholarly endeavor, and maximize the return on investment of public funds. To meet this challenge, colleges and universities are adding data services to existing infrastructures by drawing on the expertise of information professionals who are already involved in the acquisition, management and preservation of data in their daily jobs. Data services include planning and implementing good data management practices, thereby increasing researchers' ability to compete for grant funding and ensuring that data collections with continuing value are preserved for reuse. This volume provides a framework to guide information professionals in academic libraries, presses, and data centers through the process of managing research data from the planning stages through the life of a grant project and beyond. It illustrates principles of good practice with use-case examples and illuminates promising data service models through case studies of innovative, successful projects and collaborations. |
| data management in research: The Data Book Meredith Zozus, 2017-07-12 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. |
| data management in research: Research Data Management - A European Perspective Filip Kruse, Jesper Boserup Thestrup, 2017-12-04 Based on case studies this book offers an insight in various European activities and practices in data management and their interaction with policies and programs. The latter form the background for the following case studies, provide the conceptual framework, at the same time giving an exhaustive understanding of the specific subjects. The case studies share common themes and give a concrete insight into vital issues such as web archiving, digitization of analog archives, researchers’ motivations for sharing data, and how libraries, archives and researchers can collaborate in creating research tools and services. |
| data management in research: Managing and Sharing Research Data Louise Corti, Veerle Van den Eynden, Libby Bishop, Matthew Woollard, 2014-02-04 Research funders in the UK, USA and across Europe are implementing data management and sharing policies to maximize openness of data, transparency and accountability of the research they support. Written by experts from the UK Data Archive with over 20 years experience, this book gives post-graduate students, researchers and research support staff the data management skills required in today’s changing research environment. The book features guidance on: how to plan your research using a data management checklist how to format and organize data how to store and transfer data research ethics and privacy in data sharing and intellectual property rights data strategies for collaborative research how to publish and cite data how to make use of other people’s research data, illustrated with six real-life case studies of data use. |
| data management in research: Teaching Research Data Management Julia Bauder, 2022-01-03 Armed with this guide's strategies and concrete examples, subject librarians, data services librarians, and scholarly communication librarians will be inspired to roll up their sleeves and get involved with teaching research data management competencies to students and faculty. 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. You will learn about such topics as integrating research data management into information literacy instruction; threshold concepts for novice learners of data management; four key competencies that are entry points for library-faculty collaboration in data instruction; an 8-step plan for outreach to faculty and grad students in engineering and the sciences; using RStudio to teach data management, data visualization, and research reproducibility; expanding data management instruction with adaptable modules for remote learning; designing a data management workshop series; developing a research guide on data types, open data repositories, and data storage; creating a data management plan assignment for STEM undergraduates; and data management training to ensure compliance with grant requirements. |
| data management in research: Research Data Access and Management in Modern Libraries Bhardwaj, Raj Kumar, Banks, Paul, 2019-05-15 Handling and archiving data should be done in a highly professional and quality-controlled manner. For academic and research libraries, it is required to know how to document data and support traceability, as well as to make it reusable and productive. However, these institutions have different requirements relating to the archiving and reusability of data. Therefore, a comprehensive source of information is required to understand data access and management within these organizations. Research Data Access and Management in Modern Libraries is a critical scholarly resource that delves into innovative data management strategies and strategy implementation in library settings and provides best practices to stakeholders using the latest tools and technology. It further explores concepts such as research data management, data access, data preservation, building document and data institutional repositories, applications of Web 2.0 tools, mobile technology applications in data access, and conducting information literacy programs. This book is ideal for librarians, information specialists, research scholars, students, IT managers, computer scientists, policymakers, educators, and academic administrators. |
| data management in research: How to Manage, Analyze, and Interpret Survey Data Arlene Fink, 2003 Shows how to manage survey data and become better users of statistical and qualitative survey information. This book explains the basic vocabulary of data management and statistics, and demonstrates the principles and logic behind the selection and interpretation of commonly used statistical and qualitative methods to analyze survey data. |
| data management in research: Handbook of Research on Information and Records Management in the Fourth Industrial Revolution Chigwada, Josiline Phiri, Tsvuura, Godfrey, 2021-06-25 Information and records management has been an important part of society for establishing procedures to effectively manage information. As technology has increased in society, this essential function has been impacted as well. With the onset of technological tools brought upon by the fourth industrial revolution, technologies such as artificial intelligence, the internet of things, big data, and more have changed the face of information and records management. These technologies and tools have paved new ways for security, efficiency in timely processes, new ways to create and process records, and other beneficial traits. Along with these advancements come new contemporary issues, leading to the need for research on how exactly information records management is functioning in modern times, the technologies brought on by the fourth industrial revolution, and both the benefits and challenges to this transition. The Handbook of Research on Information and Records Management in the Fourth Industrial Revolution showcases contemporary issues and demonstrates the value of information and records management in the fourth industrial revolution. The book provides a summary of the key activities undertaken by information and records managers as they seek to make records and information management more visible in the modern knowledge-driven society. The chapters highlight innovation, the use of information and communication technology in information and records management, best practices, challenges encountered, and how they are overcome. The target audience of this book will be composed of professionals, librarians, archivists, lecturers, and researchers working in the field of library and information science, along with practitioners, academicians, and students interested in information and records management in the 21st century. |
| data management in research: Cases on Research Support Services in Academic Libraries Fernández-Marcial, Viviana, González-Solar, Llarina, 2020-08-28 Academic libraries have traditionally had two key functions, to support teaching and to support research. In an evolving and competitive university environment, along with the emergence of various technologies and substantial changes in scientific communication, university management has reached a turning point. Academic libraries are facing a paradigm shift in the role they need to play to achieve the research objectives of universities. Research support services in academic libraries have evolved as a response to these changes. They are heterogeneous, adapt to their university culture, adopt different points of view, take different approaches in their organizational structures, and include a diverse catalog of activities. Having an overview of different experiences will allow libraries to adopt best practices, redefine services, and even establish new management and collaboration models. Cases on Research Support Services in Academic Libraries is a critical scholarly resource that uses case studies to systematize the experiences of research support services in academic libraries for the support of higher education faculty. The cases focus on such items as the role of technology and its impact as well as how these services help to improve the excellence of universities. Featuring a wide range of topics such as library services, data management, and open science, this book is ideal for librarians, academicians, professionals, researchers, and students. |
| data management in research: Doing Management Research Raymond-Alain Thietart, 2001-04-18 `This book provides refreshing and powerful insights on the challenges of conducting management research from a European perspective. Particulalrly for someone embarking on a managment research career this book will provide valuable guidelines.′ -- Ian MacMillan, Wharton School of Business, University of Pennsylvania `This comprehensive volume is distinguished by its balance and pragmatism. The authors who present the various research methods are not proponents but researchers who have applied these methods. The authors who discuss philosophical and strategic issues are not advocates but researchers who have had to confront these issues in their research′ - Bill Starbuck, New York University `Doing Management Research is a fabulous contribution to our field. Thietart and his colleagues have put together a unique and valuable guide to help management scholars more deeply understand the issues, dynamics and contradictions of executing first class managerial research. This book will hold an important place on the researcher′s desk for years to come′ - Michael Tushman, Harvard Business School ′This is an excellent in-depth examination of the conduct of management research. It will serve as a valuable resource for management scholars and researchers and is a must read for Ph.D. students in management.′ -- Michael Hitt, Arizona State University `This book will prove to be an excellent guide for those engaged in management research for the first time and an excellent refresher for more experienced scholars. Raymond Thietart and his colleagues should be thanked roundly for this comprehensive volume′ - Gordon Walker, Southern Methodist University, Cox Business School `This textbook makes an outstanding contribution to texts on management research. For researchers considering management research it offers an extensive guide to the research process′ - Paula Roberts, Nurse Researcher Doing Management Research, a major new textbook, provides answers to questions and problems which researchers invariably encounter when embarking on management research, be it quantitative or qualitative. This book will carefully guide the reader through the research process from beginning to end. An excellent tool for academics and students, it enables the reader to acquire and build upon empirical evidence, and to decide what tools to use to understand and describe what is being observed, and then, which methods of analysis to adopt. There is an entire section dedicated to writing up and communicating the research findings. Written in an accessible and easy-to-use style, this book can be read from cover to cover or dipped into, to clarify particular issues during the research process. Doing Management Research results from the ′hands-on′ experience of a large group of researchers who have all had to address the different issues raised when undertaking management research. It is anchored in real methodological problems that researchers face in their work. This work will also become one of the most useful reference tools for senior researchers who are looking for answers to epistemological or methodological problems. |
| data management in research: Maturity and Innovation in Digital Libraries Milena Dobreva, Annika Hinze, Maja Žumer, 2018-11-14 This book constitutes the refereed proceedings of the 20th International Conference on Asia-Pacific Digital Libraries, ICADL 2018, held in Hamilton, New Zealand, in November 2018. The 20 full, 6 short, and 11 work in progress papers presented in this volume were carefully reviewed and selected from 77 submissions. The papers were organized in topical sections named: topic modeling and semantic analysis; social media, web, and news; heritage and localization; user experience; digital library technology; and use cases and digital librarianship. |
| data management in research: Data Management for Social Scientists Nils B. Weidmann, 2023-03-09 Equips social scientists with the tools and techniques to conduct quantitative research in the age of big data. |
| data management in research: Managing Scientific Information and Research Data Svetla Baykoucheva, 2015-07-14 Innovative technologies are changing the way research is performed, preserved, and communicated. Managing Scientific Information and Research Data explores how these technologies are used and provides detailed analysis of the approaches and tools developed to manage scientific information and data. Following an introduction, the book is then divided into 15 chapters discussing the changes in scientific communication; new models of publishing and peer review; ethics in scientific communication; preservation of data; discovery tools; discipline-specific practices of researchers for gathering and using scientific information; academic social networks; bibliographic management tools; information literacy and the information needs of students and researchers; the involvement of academic libraries in eScience and the new opportunities it presents to librarians; and interviews with experts in scientific information and publishing. - Promotes innovative technologies for creating, sharing and managing scientific content - Presents new models of scientific publishing, peer review, and dissemination of information - Serves as a practical guide for researchers, students, and librarians on how to discover, filter, and manage scientific information - Advocates for the adoption of unique author identifiers such as ORCID and ResearcherID - Looks into new tools that make scientific information easy to discover and manage - Shows what eScience is and why it is becoming a priority for academic libraries - Demonstrates how Electronic Laboratory Notebooks can be used to record, store, share, and manage research data - Shows how social media and the new area of Altmetrics increase researchers' visibility and measure attention to their research - Directs to sources for datasets - Provides directions on choosing and using bibliographic management tools - Critically examines the metrics used to evaluate research impact - Aids strategic thinking and informs decision making |
| data management in research: Collecting Qualitative Data Greg Guest, Emily E. Namey, Marilyn L. Mitchell, 2013 Provides a very practical and step-by-step guide to collecting and managing qualitative data, |
| data management in research: Effective Big Data Management and Opportunities for Implementation Singh, Manoj Kumar, G., Dileep Kumar, 2016-06-20 “Big data” has become a commonly used term to describe large-scale and complex data sets which are difficult to manage and analyze using standard data management methodologies. With applications across sectors and fields of study, the implementation and possible uses of big data are limitless. Effective Big Data Management and Opportunities for Implementation explores emerging research on the ever-growing field of big data and facilitates further knowledge development on methods for handling and interpreting large data sets. Providing multi-disciplinary perspectives fueled by international research, this publication is designed for use by data analysts, IT professionals, researchers, and graduate-level students interested in learning about the latest trends and concepts in big data. |
| data management in research: Data Collection and Management Magda Stouthamer-Loeber, Welmoet Bok van Kammen, 1995-08-08 Tired of a trial-and-error approach to collecting and managing data? Data Collection and Management offers helpful information on managing research projects. By stressing how to use good standards for data collecting and processing, the authors cover such important how-tos as planning research activities; making budgetary decisions and keeping the budget under control; hiring, training, and supervising field interviewing staff; establishing whether interviewers are ready to start interviewing; and ensuring high participant acquisition and retention rates. The book also covers using computerized information systems for tracking data collected and the data management process. Proposal writers, principal investigators, graduate research students, and project coordinators of research requiring large-scale field data collection will find the book to be an indispensable tool. |
| data management in research: Implementing Information Technology Governance: Models, Practices and Cases Van Grembergen, Wim, De Haes, Steven, 2007-09-30 In many organizations, information technology (IT) has become crucial in the support, sustainability, and growth of the business. This pervasive use of technology has created a critical dependency on IT that calls for a specific focus on IT governance. Implementing Information Technology Governance: Models, Practices and Cases presents insight gained through literature reviews and case studies to provide practical guidance for organizations who want to start implementing IT governance or improving existing governance models, and provides a detailed set of IT governance structures, processes, and relational mechanisms that can be leveraged to implement IT governance in practice. |
| data management in research: Handbook of Research on Connecting Research Methods for Information Science Research Ngulube, Patrick, 2019-12-13 In today’s globalized world, viable and reliable research is fundamental for the development of information. Innovative methods of research have begun to shed light on notable issues and concerns that affect the advancement of knowledge within information science. Building on previous literature and exploring these new research techniques are necessary to understand the future of information and knowledge. The Handbook of Research on Connecting Research Methods for Information Science Research is a collection of innovative research on the methods and application of study methods within library and information science. While highlighting topics including data management, philosophical foundations, and quantitative methodology, this book is ideally designed for librarians, information science professionals, policymakers, advanced-level students, researchers, and academicians seeking current research on transformative methods of research within information science. |
| data management in research: Ensuring Research Integrity and the Ethical Management of Data Sibinga, Cees Th. Smit, 2018-01-31 Data management technology is rapidly progressing, and with it comes the need for stricter rules that ensure the information being collected is handled appropriately. Ensuring Research Integrity and the Ethical Management of Data is an essential resource that examines the best approaches for providing quality research, as well as how to effectively manage that information in a reputable way. Featuring extensive research on relevant topics such as qualitative data collection, data sharing, data misinterpretation, and intellectual property, this scholarly publication is an ideal reference source for academicians, students, and researchers interested in current trends and techniques in ethical research and data management. |
| data management in research: The Encyclopedia of Research Methods in Criminology and Criminal Justice, 2 Volume Set J. C. Barnes, David R. Forde, 2021-09-08 The Encyclopedia of RESEARCH METHODS IN CRIMINOLOGY & CRIMINAL JUSTICE The most comprehensive reference work on research designs and methods in criminology and criminal justice This Encyclopedia of Research Methods in Criminology and Criminal Justice offers a comprehensive survey of research methodologies and statistical techniques that are popular in criminology and criminal justice systems across the globe. With contributions from leading scholars and practitioners in the field, it offers a clear insight into the techniques that are currently in use to answer the pressing questions in criminology and criminal justice. The Encyclopedia contains essential information from a diverse pool of authors about research designs grounded in both qualitative and quantitative approaches. It includes information on popular datasets and leading resources of government statistics. In addition, the contributors cover a wide range of topics such as: the most current research on the link between guns and crime, rational choice theory, and the use of technology like geospatial mapping as a crime reduction tool. This invaluable reference work: Offers a comprehensive survey of international research designs, methods, and statistical techniques Includes contributions from leading figures in the field Contains data on criminology and criminal justice from Cambridge to Chicago Presents information on capital punishment, domestic violence, crime science, and much more Helps us to better understand, explain, and prevent crime Written for undergraduate students, graduate students, and researchers, The Encyclopedia of Research Methods in Criminology and Criminal Justice is the first reference work of its kind to offer a comprehensive review of this important topic. |
| data management in research: Data Management in R Martin Elff, 2021-01-09 An invaluable step-by-step, pedagogically engaging guide to data management in R for social science researchers, this book shows students how to recode and document data, as well as how to combine data from different sources or import from statistical packages other than R. |
| data management in research: The Data Librarian’s Handbook Robin Rice, John Southall, 2016-12-20 An insider’s guide to data librarianship packed full of practical examples and advice for any library and information professional learning to deal with data. Interest in data has been growing in recent years. Support for this peculiar class of digital information – its use, preservation and curation, and how to support researchers’ production and consumption of it in ever greater volumes to create new knowledge, is needed more than ever. Many librarians and information professionals are finding their working life is pulling them toward data support or research data management but lack the skills required. The Data Librarian’s Handbook, written by two data librarians with over 30 years’ combined experience, unpicks the everyday role of the data librarian and offers practical guidance on how to collect, curate and crunch data for economic, social and scientific purposes. With contemporary case studies from a range of institutions and disciplines, tips for best practice, study aids and links to key resources, this book is a must-read for all new entrants to the field, library and information students and working professionals. Key topics covered include: • the evolution of data libraries and data archives • handling data compared to other forms of information • managing and curating data to ensure effective use and longevity • how to incorporate data literacy into mainstream library instruction and information literacy training • how to develop an effective institutional research data management (RDM) policy and infrastructure • how to support and review a data management plan (DMP) for a project, a key requirement for most research funders • approaches for developing, managing and promoting data repositories • handling and sharing confidential or sensitive data • supporting open scholarship and open science, ensuring data are discoverable, accessible, intelligible and assessable. This title is for the practising data librarian, possibly new in their post with little experience of providing data support. It is also for managers and policy-makers, public service librarians, research data management coordinators and data support staff. It will also appeal to students and lecturers in iSchools and other library and information degree programmes where academic research support is taught. |
| data management in research: Using R for Data Management, Statistical Analysis, and Graphics Nicholas J. Horton, Ken Kleinman, 2010-07-28 Quick and Easy Access to Key Elements of Documentation Includes worked examples across a wide variety of applications, tasks, and graphicsUsing R for Data Management, Statistical Analysis, and Graphics presents an easy way to learn how to perform an analytical task in R, without having to navigate through the extensive, idiosyncratic, and sometimes |
| data management in research: Stream Data Management Nauman Chaudhry, Kevin Shaw, 2005-04-14 Researchers in data management have recently recognized the importance of a new class of data-intensive applications that requires managing data streams, i.e., data composed of continuous, real-time sequence of items. Streaming applications pose new and interesting challenges for data management systems. Such application domains require queries to be evaluated continuously as opposed to the one time evaluation of a query for traditional applications. Streaming data sets grow continuously and queries must be evaluated on such unbounded data sets. These, as well as other challenges, require a major rethink of almost all aspects of traditional database management systems to support streaming applications. Stream Data Management comprises eight invited chapters by researchers active in stream data management. The collected chapters provide exposition of algorithms, languages, as well as systems proposed and implemented for managing streaming data. Stream Data Management is designed to appeal to researchers or practitioners already involved in stream data management, as well as to those starting out in this area. This book is also suitable for graduate students in computer science interested in learning about stream data management. |
| data management in research: Practical Guide to Clinical Data Management Susanne Prokscha, 2011-10-26 The management of clinical data, from its collection during a trial to its extraction for analysis, has become a critical element in the steps to prepare a regulatory submission and to obtain approval to market a treatment. Groundbreaking on its initial publication nearly fourteen years ago, and evolving with the field in each iteration since then, |
| data management in research: Data Management for Researchers Kristin Briney, 2015 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 |
| data management in research: Data Governance and Data Management Rupa Mahanti, 2021-09-08 This book delves into the concept of data as a critical enterprise asset needed for informed decision making, compliance, regulatory reporting and insights into trends, behaviors, performance and patterns. With good data being key to staying ahead in a competitive market, enterprises capture and store exponential volumes of data. Considering the business impact of data, there needs to be adequate management around it to derive the best value. Data governance is one of the core data management related functions. However, it is often overlooked, misunderstood or confused with other terminologies and data management functions. Given the pervasiveness of data and the importance of data, this book provides comprehensive understanding of the business drivers for data governance and benefits of data governance, the interactions of data governance function with other data management functions and various components and aspects of data governance that can be facilitated by technology and tools, the distinction between data management tools and data governance tools, the readiness checks to perform before exploring the market to purchase a data governance tool, the different aspects that must be considered when comparing and selecting the appropriate data governance technologies and tools from large number of options available in the marketplace and the different market players that provide tools for supporting data governance. This book combines the data and data governance knowledge that the author has gained over years of working in different industrial and research programs and projects associated with data, processes and technologies with unique perspectives gained through interviews with thought leaders and data experts. This book is highly beneficial for IT students, academicians, information management and business professionals and researchers to enhance their knowledge and get guidance on implementing data governance in their own data initiatives. |
| data management in research: Clinical Data Management Richard K. Rondel, Sheila A. Varley, Colin F. Webb, 2000-02-03 Extensively revised and updated, with the addition of new chapters and authors, this long-awaited second edition covers all aspects of clinical data management. Giving details of the efficient clinical data management procedures required to satisfy both corporate objectives and quality audits by regulatory authorities, this text is timely and an important contribution to the literature. The volume: * is written by well-known and experienced authors in this area * provides new approaches to major topics in clinical data management * contains new chapters on systems software validation, database design and performance measures. It will be invaluable to anyone in the field within the pharmaceutical industry, and to all biomedical professionals working in clinical research. |
| data management in research: Managing Research Data Graham Pryor, 2012-01-20 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. |
| data management in research: Big Data Management Fausto Pedro García Márquez, Benjamin Lev, 2016-11-15 This book focuses on the analytic principles of business practice and big data. Specifically, it provides an interface between the main disciplines of engineering/technology and the organizational and administrative aspects of management, serving as a complement to books in other disciplines such as economics, finance, marketing and risk analysis. The contributors present their areas of expertise, together with essential case studies that illustrate the successful application of engineering management theories in real-life examples. |
| data management in research: Quality Management and Accreditation in Hematopoietic Stem Cell Transplantation and Cellular Therapy Mahmoud Aljurf, John A. Snowden, Patrick Hayden, Kim H. Orchard, Eoin McGrath, 2021-02-19 This open access book provides a concise yet comprehensive overview on how to build a quality management program for hematopoietic stem cell transplantation (HSCT) and cellular therapy. The text reviews all the essential steps and elements necessary for establishing a quality management program and achieving accreditation in HSCT and cellular therapy. Specific areas of focus include document development and implementation, audits and validation, performance measurement, writing a quality management plan, the accreditation process, data management, and maintaining a quality management program. Written by experts in the field, Quality Management and Accreditation in Hematopoietic Stem Cell Transplantation and Cellular Therapy: A Practical Guide is a valuable resource for physicians, healthcare professionals, and laboratory staff involved in the creation and maintenance of a state-of-the-art HSCT and cellular therapy program. |
| data management in research: Academic Library Development and Administration in China Lian J. Ruan, Qiang Zhu, Ying Ye, 2017 This book provides a resource to promote Sino-U.S. communication and collaboration between their academic libraries, reflecting on the expanding, global field of information science-- |
| data management in research: Scientific Data Management Arie Shoshani, Doron Rotem, 2019-08-30 Dealing with the volume, complexity, and diversity of data currently being generated by scientific experiments and simulations often causes scientists to waste productive time. Scientific Data Management: Challenges, Technology, and Deployment describes cutting-edge technologies and solutions for managing and analyzing vast amounts of data, helping scientists focus on their scientific goals. The book begins with coverage of efficient storage systems, discussing how to write and read large volumes of data without slowing the simulation, analysis, or visualization processes. It then focuses on the efficient data movement and management of storage spaces and explores emerging database systems for scientific data. The book also addresses how to best organize data for analysis purposes, how to effectively conduct searches over large datasets, how to successfully automate multistep scientific process workflows, and how to automatically collect metadata and lineage information. This book provides a comprehensive understanding of the latest techniques for managing data during scientific exploration processes, from data generation to data analysis. Enhanced by numerous detailed color images, it includes real-world examples of applications drawn from biology, ecology, geology, climatology, and more. Check out Dr. Shoshani discuss the book during an interview with International Science Grid This Week (iSGTW): http: //www.isgtw.org/?pid=1002259 |
| data management in research: Data Management in Large-Scale Education Research Crystal Lewis, 2024-07-09 Research data management is becoming more complicated. Researchers are collecting more data, using more complex technologies, all the while increasing the visibility of our work with the push for data sharing and open science practices. Ad hoc data management practices may have worked for us in the past, but now others need to understand our processes as well, requiring researchers to be more thoughtful in planning their data management routines. This book is for anyone involved in a research study involving original data collection. While the book focuses on quantitative data, typically collected from human participants, many of the practices covered can apply to other types of data as well. The book contains foundational context, instructions, and practical examples to help researchers in the field of education begin to understand how to create data management workflows for large-scale, typically federally funded, research studies. The book starts by describing the research life cycle and how data management fits within this larger picture. The remaining chapters are then organized by each phase of the life cycle, with examples of best practices provided for each phase. Finally, considerations on whether the reader should implement, and how to integrate those practices into a workflow, are discussed. Key Features: Provides a holistic approach to the research life cycle, showing how project management and data management processes work in parallel and collaboratively Can be read in its entirety, or referenced as needed throughout the life cycle Includes relatable examples specific to education research Includes a discussion on how to organize and document data in preparation for data sharing requirements Contains links to example documents as well as templates to help readers implement practices |
| data management in research: Open Scientific Data Vera J. Lipton, 2020 Public science is critical to the economy and to society. However, much of the beneficial impact of scientific research only occurs when scientific knowledge is disseminated broadly and is used by others. This book examines the emerging policy, law and practice of facilitating open access to scientific research data. One particular focus is to examine the open data policies recently introduced by research funders and publishers, and the potential in these for driving the practice of open scientific data into the future. This study identifies five major stumbling blocks to sustainable open scientific data. Firstly, it is the prevailing mindset that facilitating open access to data is analogous to facilitating open access to publications and, therefore, research data can easily be shared, with research funders and librarians effectively leading the process. Secondly, it is the unclear meaning of the term data which causes confusion among stakeholders. Thirdly, it is the misunderstood incentives for data sharing and the additional inputs required from researchers. Fourthly, data privacy—an issue that only applies to selected research datasets, and yet appears to dominate the discussion about open research data. Finally, there is a copyright law, which poses challenges at different stages of data release and reuse. In this book, it is argued that the above problems can be addressed using a staged model for open scientific data. I draw specifically on the practice with open scientific data at CERN (the European Organization for Nuclear Research) and the practice of sharing clinical trial data to argue that open data can be shared at various stages of processing and diversification. This model is supplemented by recommendations proposing changes to existing open data mandates and the introduction of a text and data mining exemption into Australian copyright law. |
| data management in research: Managing and Sharing Research Data Louise Corti, Veerle Van den Eynden, Libby Bishop, Matthew Woollard, 2014-03-01 Research funders in the UK, USA and across Europe are implementing data management and sharing policies to maximize openness of data, transparency and accountability of the research they support. Written by experts from the UK Data Archive with over 20 years experience, this book gives post-graduate students, researchers and research support staff the data management skills required in today's changing research environment. The book features guidance on: how to plan your research using a data management checklist how to format and organize data how to store and transfer data research ethics and privacy in data sharing and intellectual property rights data strategies for collaborative research how to publish and cite data how to make use of other people's research data, illustrated with six real-life case studies of data use. |
Support Your Data: A Research Data Management Guide for …
Research data management (RDM), a term that encompassess activities related to the storage, organization, documentation, and dissemination of data*1, is central to efforts aimed at …
RESEARCH DATA MANAGEMENT BEST PRACTICES - UVA …
This workshop provides an overview of research data management best practices. The emphasis is on strategies researchers can implement to make their data more findable, accessible, …
Research Data Management Best Practices - Smithsonian …
These best practices are designed to improve overall management of data at each point in the lifecycle, resulting in published data that are not only easy to care for long after the project is …
Essentials of data management: an overview - Nature
Data management is a multistep process that involves obtaining, cleaning, and storing data to allow accurate analysis and produce meaningful results.
Research Data Management Procedure - University of Pretoria
This procedure describes and guides all role-players during the sequential stages of the Research Data Management (RDM) process, distinguishes between different kinds of data, and presents a …
Research Data Management Principles and Guidance Yale …
Jun 7, 2018 · l of data management before, during, and subsequent to the active research period. “Research data management” refers to the actions researchers take before, during, and after a …
RESEARCH DATA MANAGEMENT FUNDAMENTALS
Following best practices in research data management can help you secure grant funding and create a data output that becomes part of the scholarly record. This document walks you …
Foundational Practices of Research Data Management - IU
By implementing foundational practices of data management, researchers set themselves up for success by formalizing processes and reducing common errors in data handling, which can free …
Management of Data and Information in Research
The responsibilities of researchers with respect to management of data and information in research should be clear from the beginning of a research project. The development of a data …
1. Principles of Data Management 2020 - UMass
“Data Management is the process of providing the appropriate labeling, storage, and access for data at all stages of a research project. Here you can find best practices, resources, and support …
Guidelines for Responsible Data Management in Scientific …
effort by the Principal Investigator (PI) of a research project. For oversight to be thorough and correct, PIs must understand the basic concepts of data management and ensure that every …
Research Data Management - Harvard Business School
"Research data management concerns the organization of data, from its entry to the research cycle through the dissemination and archiving of valuable results. It aims to ensure reliable verification …
Data Management, Analysis Tools, and Analysis Mechanics
This chapter discusses how to combine and manage data streams, and how to use data management tools to produce analytical results that are error free and reproducible, once useful …
Research Data Management Guide - Imperial College London
Research data management is the planning, organisation and preservation of the evidence that underpins all research conclusions. Good data management ensures data is safely stored, …
Research Data Management - CLIR
esearch data management. The aims of the project were (1) to study and document trends in the data management plans and associated institutional policies of research institutions in response …
Smithsonian Data Management Best Practices
Mar 1, 2021 · i A data management plan is a formal document that outlines what you will do with your data during and after a research project. DMPs help articulate who has responsibility for …
Guidance on best practice in the management of research data
Systematic management and sharing of research data has many benefits for the research community and the public. These include reinforcing open, transparent and robust academic …
Module I: Introduction to research data management
What is research data management (RDM)? "Research data management concerns the organisation of data, from its entry to the research cycle through to the dissemination and archiving of …
Data Management Plans: Stages, Components, and Activities
Dec 30, 2011 · The purpose of this paper is to describe the stages, components, and strategies of successful data management using a longitudinal data set. Data management involves …
Data management - epidemiolog
The data management system includes the overview of the flow of data from research subjects to data analysts. Before it can be analyzed, data must be collected, reviewed, coded, computerized, …
Essentials of Data Management: An Overview - PMC
What is data management? Data management is a multi-step process that involves obtaining, cleaning, and storing data to allow accurate analysis and produce meaningful results.
What is Research Data Management | Data Management
Research Data Management is the process of providing the appropriate labeling, storage, and access for data at all stages of a research project
What Is Research Data Management (RDM) for 2025?
May 23, 2025 · Research data management describes a way to organize and store the data that a research project has accumulated in the most efficient way possible. It manages data gathered …
Research Data Management - Science Europe
Jan 27, 2021 · Research data management refers to the handling of research data (collection, organisation, storage, and documentation) during and after a research activity. Good data …
Support Your Data: A Research Data Management Guide for …
Research data management (RDM), a term that encompassess activities related to the storage, organization, documentation, and dissemination of data*1, is central to efforts aimed at …
Research Data Management: Home - University of Washington
Jun 6, 2025 · Guide of resources related to the many aspects of research data management. Data management encompasses the processes surrounding collecting, organizing, describing, …
Understanding and Implementing Research Data Management
May 1, 2018 · Based on this introduction, we discuss different objectives of research data management, such as ensuring replicability or sharing of research data generated in a …
Home - Research Data Management - University of Utah
Apr 25, 2025 · Whatever their form, data are the evidence for your research findings and proper data management is critical for good research practice and compliance. This subject guide is …
RESEARCH DATA MANAGEMENT BEST PRACTICES - UVA …
What is the Data Life Cycle? The life cycle illustrates steps through which well managed data moves from creation to conclusion in a research project. A DMP should describe how you will …
WELCOME - Research Data Management - New York University
May 28, 2025 · This guide is meant to help you navigate some common best practices when managing your research. Research data management (RDM) is the process of managing the …
