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| data management and sharing: 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 and sharing: 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 and sharing: Principles of Data Management Keith Gordon, 2013-11-18 Data is a valuable corporate asset and its effective management can be vital to an organisation’s success. This professional guide covers all the key areas of data management, including database development and corporate data modelling. It is business-focused, providing the knowledge and techniques required to successfully implement the data management function. This new edition covers web technology and its relation to databases and includes material on the management of master data. |
| data management and sharing: 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 and sharing: Sharing Clinical Trial Data Institute of Medicine, Board on Health Sciences Policy, Committee on Strategies for Responsible Sharing of Clinical Trial Data, 2015-04-20 Data sharing can accelerate new discoveries by avoiding duplicative trials, stimulating new ideas for research, and enabling the maximal scientific knowledge and benefits to be gained from the efforts of clinical trial participants and investigators. At the same time, sharing clinical trial data presents risks, burdens, and challenges. These include the need to protect the privacy and honor the consent of clinical trial participants; safeguard the legitimate economic interests of sponsors; and guard against invalid secondary analyses, which could undermine trust in clinical trials or otherwise harm public health. Sharing Clinical Trial Data presents activities and strategies for the responsible sharing of clinical trial data. With the goal of increasing scientific knowledge to lead to better therapies for patients, this book identifies guiding principles and makes recommendations to maximize the benefits and minimize risks. This report offers guidance on the types of clinical trial data available at different points in the process, the points in the process at which each type of data should be shared, methods for sharing data, what groups should have access to data, and future knowledge and infrastructure needs. Responsible sharing of clinical trial data will allow other investigators to replicate published findings and carry out additional analyses, strengthen the evidence base for regulatory and clinical decisions, and increase the scientific knowledge gained from investments by the funders of clinical trials. The recommendations of Sharing Clinical Trial Data will be useful both now and well into the future as improved sharing of data leads to a stronger evidence base for treatment. This book will be of interest to stakeholders across the spectrum of research-from funders, to researchers, to journals, to physicians, and ultimately, to patients. |
| data management and sharing: Data Sharing Using A Common Data Architecture Michael H. Brackett, 1994-03-28 Data Sharing Using a Common Data Architecture Wouldn’t it be a pleasure to know and understand all the data in your organization? Wouldn’t it be great to easily identify and readily share those data to develop information that supports business strategies? Wouldn’t it be wonderful to have a formal data resource that provides just-in-time data for developing just-in-time information to support just-in-time decision making? Data Sharing Using a Common Data Architecture shows you how by: Defining a common data architecture, its contents, and its uses Refining data to a common data architecture Discussing disparate data, its structure, quality, and how to identify it Describing how Data Sharing Reality is achieved Focusing on the importance of people and creating a win-win situation Providing a data lexicon and extensive glossary Data Sharing Using a Common Data Architecture is must reading for data administrators, database administrators, MIS project leaders, application programmers, systems analysts, MIS trainers and instructors, and graduate students. |
| data management and sharing: 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. |
| data management and sharing: Principles of Data Management Keith Gordon, 2022-06-17 Data is a valuable corporate asset and its effective management can be vital to success. This professional guide covers all the key areas of data management, including database development and corporate data modelling. The new edition adds chapters on linked data, concept systems and big data and artificial intelligence. |
| data management and sharing: Farm data management, sharing and services for agriculture development Food and Agriculture Organization of the United Nations , 2021-02-26 This book aims to strengthen the skills of professionals who use, manage data for the benefit of farmers and farmers organizations by exposing them to the topics of importance of data in the agriculture value chain and how new and existing technologies, products and services can leverage farm level and global data to improve yield, reduce loss, add value and increase profitability and resilience. |
| data management and sharing: 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 and sharing: 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 and sharing: Sharing Research Data to Improve Public Health in Africa National Academies of Sciences, Engineering, and Medicine, Division of Behavioral and Social Sciences and Education, Committee on Population, 2015-09-18 Sharing research data on public health issues can promote expanded scientific inquiry and has the potential to advance improvements in public health. Although sharing data is the norm in some research fields, sharing of data in public health is not as firmly established. In March 2015, the National Research Council organized an international conference in Stellenbosch, South Africa, to explore the benefits of and barriers to sharing research data within the African context. The workshop brought together public health researchers and epidemiologists primarily from the African continent, along with selected international experts, to talk about the benefits and challenges of sharing data to improve public health, and to discuss potential actions to guide future work related to public health research data sharing. Sharing Research Data to Improve Public Health in Africa summarizes the presentations and discussions from this workshop. |
| data management and sharing: DAMA-DMBOK Dama International, 2017 Defining a set of guiding principles for data management and describing how these principles can be applied within data management functional areas; Providing a functional framework for the implementation of enterprise data management practices; including widely adopted practices, methods and techniques, functions, roles, deliverables and metrics; Establishing a common vocabulary for data management concepts and serving as the basis for best practices for data management professionals. DAMA-DMBOK2 provides data management and IT professionals, executives, knowledge workers, educators, and researchers with a framework to manage their data and mature their information infrastructure, based on these principles: Data is an asset with unique properties; The value of data can be and should be expressed in economic terms; Managing data means managing the quality of data; It takes metadata to manage data; It takes planning to manage data; Data management is cross-functional and requires a range of skills and expertise; Data management requires an enterprise perspective; Data management must account for a range of perspectives; Data management is data lifecycle management; Different types of data have different lifecycle requirements; Managing data includes managing risks associated with data; Data management requirements must drive information technology decisions; Effective data management requires leadership commitment. |
| data management and sharing: Data Management at Scale Piethein Strengholt, 2020-07-29 As data management and integration continue to evolve rapidly, storing all your data in one place, such as a data warehouse, is no longer scalable. In the very near future, data will need to be distributed and available for several technological solutions. With this practical book, you’ll learnhow to migrate your enterprise from a complex and tightly coupled data landscape to a more flexible architecture ready for the modern world of data consumption. Executives, data architects, analytics teams, and compliance and governance staff will learn how to build a modern scalable data landscape using the Scaled Architecture, which you can introduce incrementally without a large upfront investment. Author Piethein Strengholt provides blueprints, principles, observations, best practices, and patterns to get you up to speed. Examine data management trends, including technological developments, regulatory requirements, and privacy concerns Go deep into the Scaled Architecture and learn how the pieces fit together Explore data governance and data security, master data management, self-service data marketplaces, and the importance of metadata |
| data management and sharing: Principles and Obstacles for Sharing Data from Environmental Health Research National Academies of Sciences, Engineering, and Medicine, Health and Medicine Division, Board on Population Health and Public Health Practice, Roundtable on Environmental Health Sciences, Research, and Medicine, 2016-05-29 On March 19, 2014, the National Academies of Sciences, Engineering, and Medicine held a workshop on the topic of the sharing of data from environmental health research. Experts in the field of environmental health agree that there are benefits to sharing research data, but questions remain regarding how to effectively make these data available. The sharing of data derived from human subjects-making them both transparent and accessible to others-raises a host of ethical, scientific, and process questions that are not always present in other areas of science, such as physics, geology, or chemistry. The workshop participants explored key concerns, principles, and obstacles to the responsible sharing of data used in support of environmental health research and policy making while focusing on protecting the privacy of human subjects and addressing the concerns of the research community. Principles and Obstacles for Sharing Data from Environmental Health Research summarizes the presentations and discussions from the workshop. |
| data management and sharing: Web Data Management Serge Abiteboul, Ioana Manolescu, Philippe Rigaux, Marie-Christine Rousset, Pierre Senellart, 2011-11-28 The Internet and World Wide Web have revolutionized access to information. Users now store information across multiple platforms from personal computers to smartphones and websites. As a consequence, data management concepts, methods and techniques are increasingly focused on distribution concerns. Now that information largely resides in the network, so do the tools that process this information. This book explains the foundations of XML with a focus on data distribution. It covers the many facets of distributed data management on the Web, such as description logics, that are already emerging in today's data integration applications and herald tomorrow's semantic Web. It also introduces the machinery used to manipulate the unprecedented amount of data collected on the Web. Several 'Putting into Practice' chapters describe detailed practical applications of the technologies and techniques. The book will serve as an introduction to the new, global, information systems for Web professionals and master's level courses. |
| data management and sharing: Enterprise Master Data Management Allen Dreibelbis, Eberhard Hechler, Ivan Milman, Martin Oberhofer, Paul van Run, Dan Wolfson, 2008-06-05 The Only Complete Technical Primer for MDM Planners, Architects, and Implementers Companies moving toward flexible SOA architectures often face difficult information management and integration challenges. The master data they rely on is often stored and managed in ways that are redundant, inconsistent, inaccessible, non-standardized, and poorly governed. Using Master Data Management (MDM), organizations can regain control of their master data, improve corresponding business processes, and maximize its value in SOA environments. Enterprise Master Data Management provides an authoritative, vendor-independent MDM technical reference for practitioners: architects, technical analysts, consultants, solution designers, and senior IT decisionmakers. Written by the IBM ® data management innovators who are pioneering MDM, this book systematically introduces MDM’s key concepts and technical themes, explains its business case, and illuminates how it interrelates with and enables SOA. Drawing on their experience with cutting-edge projects, the authors introduce MDM patterns, blueprints, solutions, and best practices published nowhere else—everything you need to establish a consistent, manageable set of master data, and use it for competitive advantage. Coverage includes How MDM and SOA complement each other Using the MDM Reference Architecture to position and design MDM solutions within an enterprise Assessing the value and risks to master data and applying the right security controls Using PIM-MDM and CDI-MDM Solution Blueprints to address industry-specific information management challenges Explaining MDM patterns as enablers to accelerate consistent MDM deployments Incorporating MDM solutions into existing IT landscapes via MDM Integration Blueprints Leveraging master data as an enterprise asset—bringing people, processes, and technology together with MDM and data governance Best practices in MDM deployment, including data warehouse and SAP integration |
| data management and sharing: Handbook of Research on Social and Organizational Dynamics in the Digital Era Idemudia, Efosa C., 2019-08-30 Technology in the world today impacts every aspect of society and has infiltrated every industry, affecting communication, management, security, etc. With the emergence of such technologies as IoT, big data, cloud computing, AI, and virtual reality, organizations have had to adjust the way they conduct business to account for changing consumer behaviors and increasing data protection awareness. The Handbook of Research on Social and Organizational Dynamics in the Digital Era provides relevant theoretical frameworks and the latest empirical research findings on all aspects of social issues impacted by information technology in organizations and inter-organizational structures and presents the conceptualization of specific social issues and their associated constructs. Featuring coverage on a broad range of topics such as business management, knowledge management, and consumer behavior, this publication seeks to advance the practice and understanding of technology and the impacts of technology on social behaviors and norms in the workplace and society. It is intended for business professionals, executives, IT practitioners, policymakers, students, and researchers. |
| data management and sharing: Master Data Management David Loshin, 2010-07-28 The key to a successful MDM initiative isn't technology or methods, it's people: the stakeholders in the organization and their complex ownership of the data that the initiative will affect.Master Data Management equips you with a deeply practical, business-focused way of thinking about MDM—an understanding that will greatly enhance your ability to communicate with stakeholders and win their support. Moreover, it will help you deserve their support: you'll master all the details involved in planning and executing an MDM project that leads to measurable improvements in business productivity and effectiveness. - Presents a comprehensive roadmap that you can adapt to any MDM project - Emphasizes the critical goal of maintaining and improving data quality - Provides guidelines for determining which data to master. - Examines special issues relating to master data metadata - Considers a range of MDM architectural styles - Covers the synchronization of master data across the application infrastructure |
| data management and sharing: Big Data Fei Hu, 2016-04-27 Although there are already some books published on Big Data, most of them only cover basic concepts and society impacts and ignore the internal implementation details-making them unsuitable to R&D people. To fill such a need, Big Data: Storage, Sharing, and Security examines Big Data management from an R&D perspective. It covers the 3S desi |
| data management and sharing: Web Data Management Practices Athena Vakali, George Pallis, 2007-01-01 This book provides an understanding of major issues, current practices and the main ideas in the field of Web data management, helping readers to identify current and emerging issues, as well as future trends. The most important aspects are discussed: Web data mining, content management on the Web, Web applications and Web services--Provided by publisher. |
| data management and sharing: STEP for Data Management, Exchange and Sharing Julian Fowler, 1995 |
| data management and sharing: 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 and sharing: 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 and sharing: Discussion Framework for Clinical Trial Data Sharing Committee on Strategies for Responsible Sharing of Clinical Trial Data, Institute of Medicine, Board on Health Sciences Policy, 2014 Sharing data generated through the conduct of clinical trials offers the promise of placing evidence about the safety and efficacy of therapies and clinical interventions on a firmer basis and enhancing the benefits of clinical trials. Ultimately, such data sharing - if carried out appropriately - could lead to improved clinical care and greater public trust in clinical research and health care. Discussion Framework for Clinical Trial Data Sharing: Guiding Principles, Elements, and Activities is part of a study of how data from clinical trials might best be shared. This document is designed as a framework for discussion and public comment. This framework is being released to stimulate reactions and comments from stakeholders and the public. The framework summarizes the committee's initial thoughts on guiding principles that underpin responsible sharing of clinical trial data, defines key elements of clinical trial data and data sharing, and describes a selected set of clinical trial data sharing activities. |
| data management and sharing: The Silent Shore Charles L. Chavis Jr., 2022-01-11 The definitive account of the lynching of twenty-three-year-old Matthew Williams in Maryland, the subsequent investigation, and the legacy of modern-day lynchings. On December 4, 1931, a mob of white men in Salisbury, Maryland, lynched and set ablaze a twenty-three-year-old Black man named Matthew Williams. His gruesome murder was part of a wave of silent white terrorism in the wake of the stock market crash of 1929, which exposed Black laborers to white rage in response to economic anxieties. For nearly a century, the lynching of Matthew Williams has lived in the shadows of the more well-known incidents of racial terror in the deep South, haunting both the Eastern Shore and the state of Maryland as a whole. In The Silent Shore, author Charles L. Chavis Jr. draws on his discovery of previously unreleased investigative documents to meticulously reconstruct the full story of one of the last lynchings in Maryland. Bringing the painful truth of anti-Black violence to light, Chavis breaks the silence that surrounded Williams's death. Though Maryland lacked the notoriety for racial violence of Alabama or Mississippi, he writes, it nonetheless was the site of at least 40 spectacle lynchings after the abolition of slavery in 1864. Families of lynching victims rarely obtained any form of actual justice, but Williams's death would have a curious afterlife: Maryland's politically ambitious governor Albert C. Ritchie would, in an attempt to position himself as a viable challenger to FDR, become one of the first governors in the United States to investigate the lynching death of a Black person. Ritchie tasked Patsy Johnson, a member of the Pinkerton detective agency and a former prizefighter, with going undercover in Salisbury and infiltrating the mob that murdered Williams. Johnson would eventually befriend a young local who admitted to participating in the lynching and who also named several local law enforcement officers as ringleaders. Despite this, a grand jury, after hearing 124 witness statements, declined to indict the perpetrators. But this denial of justice galvanized Governor Ritchie's Interracial Commission, which would become one of the pioneering forces in the early civil rights movement in Maryland. Complicating historical narratives associated with the history of lynching in the city of Salisbury, The Silent Shore explores the immediate and lingering effect of Williams's death on the politics of racism in the United States, the Black community in Salisbury, the broader Eastern Shore, the state of Maryland, and the legacy of modern-day lynchings. |
| data management and sharing: The DAMA Dictionary of Data Management Dama International, 2011 A glossary of over 2,000 terms which provides a common data management vocabulary for IT and Business professionals, and is a companion to the DAMA Data Management Body of Knowledge (DAMA-DMBOK). Topics include: Analytics & Data Mining Architecture Artificial Intelligence Business Analysis DAMA & Professional Development Databases & Database Design Database Administration Data Governance & Stewardship Data Management Data Modeling Data Movement & Integration Data Quality Management Data Security Management Data Warehousing & Business Intelligence Document, Record & Content Management Finance & Accounting Geospatial Data Knowledge Management Marketing & Customer Relationship Management Meta-Data Management Multi-dimensional & OLAP Normalization Object-Orientation Parallel Database Processing Planning Process Management Project Management Reference & Master Data Management Semantic Modeling Software Development Standards Organizations Structured Query Language (SQL) XML Development |
| data management and sharing: Data Management Margaret E. Henderson, 2016-10-25 Libraries organize information and data is information, so it is natural that librarians should help people who need to find, organize, use, or store data. Organizations need evidence for decision making; data provides that evidence. Inventors and creators build upon data collected by others. All around us, people need data. Librarians can help increase the relevance of their library to the research and education mission of their institution by learning more about data and how to manage it. Data Management will guide readers through: Understanding data management basics and best practices. Using the reference interview to help with data management Writing data management plans for grants. Starting and growing a data management service. Finding collaborators inside and outside the library. Collecting and using data in different disciplines. |
| data management and sharing: Principles and Practice of Clinical Trials Steven Piantadosi, Curtis L. Meinert, 2022-07-19 This is a comprehensive major reference work for our SpringerReference program covering clinical trials. Although the core of the Work will focus on the design, analysis, and interpretation of scientific data from clinical trials, a broad spectrum of clinical trial application areas will be covered in detail. This is an important time to develop such a Work, as drug safety and efficacy emphasizes the Clinical Trials process. Because of an immense and growing international disease burden, pharmaceutical and biotechnology companies continue to develop new drugs. Clinical trials have also become extremely globalized in the past 15 years, with over 225,000 international trials ongoing at this point in time. Principles in Practice of Clinical Trials is truly an interdisciplinary that will be divided into the following areas: 1) Clinical Trials Basic Perspectives 2) Regulation and Oversight 3) Basic Trial Designs 4) Advanced Trial Designs 5) Analysis 6) Trial Publication 7) Topics Related Specific Populations and Legal Aspects of Clinical Trials The Work is designed to be comprised of 175 chapters and approximately 2500 pages. The Work will be oriented like many of our SpringerReference Handbooks, presenting detailed and comprehensive expository chapters on broad subjects. The Editors are major figures in the field of clinical trials, and both have written textbooks on the topic. There will also be a slate of 7-8 renowned associate editors that will edit individual sections of the Reference. |
| data management and sharing: 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 and sharing: Data Management for Libraries Laura Krier, Carly A. Strasser, 2014 Since the National Science Foundation joined the National Institutes of Health in requiring that grant proposals include a data management plan, academic librarians have been inundated with related requests from faculty and campus-based grant consulting offices. Data management is a new service area for many library staff, requiring careful planning and implementation. This guide offers a start-to-finish primer on understanding, building, and maintaining a data management service, showing another way the academic library can be invaluable to researchers. Krier and Strasser of the California Digital Library guide readers through every step of a data management plan by Offering convincing arguments to persuade researchers to create a data management plan, with advice on collaborating with them Laying out all the foundations of starting a service, complete with sample data librarian job descriptions and data management plans Providing tips for conducting successful data management interviews Leading readers through making decisions about repositories and other infrastructure Addressing sensitive questions such as ownership, intellectual property, sharing and access, metadata, and preservation This LITA guide will help academic librarians work with researchers, faculty, and other stakeholders to effectively organize, preserve, and provide access to research data. |
| data management and sharing: Data Matters National Academies of Sciences, Engineering, and Medicine, Policy and Global Affairs, Government-University-Industry Research Roundtable, Planning Committee for the Workshop on Ethics, Data, and International Research Collaboration in a Changing World, 2019-01-28 In an increasingly interconnected world, perhaps it should come as no surprise that international collaboration in science and technology research is growing at a remarkable rate. As science and technology capabilities grow around the world, U.S.-based organizations are finding that international collaborations and partnerships provide unique opportunities to enhance research and training. International research agreements can serve many purposes, but data are always involved in these collaborations. The kinds of data in play within international research agreements varies widely and may range from financial and consumer data, to Earth and space data, to population behavior and health data, to specific project-generated dataâ€this is just a narrow set of examples of research data but illustrates the breadth of possibilities. The uses of these data are various and require accounting for the effects of data access, use, and sharing on many different parties. Cultural, legal, policy, and technical concerns are also important determinants of what can be done in the realms of maintaining privacy, confidentiality, and security, and ethics is a lens through which the issues of data, data sharing, and research agreements can be viewed as well. A workshop held on March 14-16, 2018, in Washington, DC explored the changing opportunities and risks of data management and use across disciplinary domains. The third workshop in a series, participants gathered to examine advisory principles for consideration when developing international research agreements, in the pursuit of highlighting promising practices for sustaining and enabling international research collaborations at the highest ethical level possible. The intent of the workshop was to explore, through an ethical lens, the changing opportunities and risks associated with data management and use across disciplinary domainsâ€all within the context of international research agreements. This publication summarizes the presentations and discussions from the workshop. |
| data management and sharing: 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 and sharing: Frontiers in Massive Data Analysis National Research Council, Division on Engineering and Physical Sciences, Board on Mathematical Sciences and Their Applications, Committee on Applied and Theoretical Statistics, Committee on the Analysis of Massive Data, 2013-09-03 Data mining of massive data sets is transforming the way we think about crisis response, marketing, entertainment, cybersecurity and national intelligence. Collections of documents, images, videos, and networks are being thought of not merely as bit strings to be stored, indexed, and retrieved, but as potential sources of discovery and knowledge, requiring sophisticated analysis techniques that go far beyond classical indexing and keyword counting, aiming to find relational and semantic interpretations of the phenomena underlying the data. Frontiers in Massive Data Analysis examines the frontier of analyzing massive amounts of data, whether in a static database or streaming through a system. Data at that scale-terabytes and petabytes-is increasingly common in science (e.g., particle physics, remote sensing, genomics), Internet commerce, business analytics, national security, communications, and elsewhere. The tools that work to infer knowledge from data at smaller scales do not necessarily work, or work well, at such massive scale. New tools, skills, and approaches are necessary, and this report identifies many of them, plus promising research directions to explore. Frontiers in Massive Data Analysis discusses pitfalls in trying to infer knowledge from massive data, and it characterizes seven major classes of computation that are common in the analysis of massive data. Overall, this report illustrates the cross-disciplinary knowledge-from computer science, statistics, machine learning, and application disciplines-that must be brought to bear to make useful inferences from massive data. |
| data management and sharing: Visualizing Health and Healthcare Data Katherine Rowell, Lindsay Betzendahl, Cambria Brown, 2020-11-10 The only data visualization book written by and for health and healthcare professionals. In health and healthcare, data and information are coming at organizations faster than they can consume and interpret it. Health providers, payers, public health departments, researchers, and health information technology groups know the ability to analyze and communicate this vast array of data in a clear and compelling manner is paramount to success. However, they simply cannot find experienced people with the necessary qualifications. The quickest (and often the only) route to meeting this challenge is to hire smart people and train them. Visualizing Health and Healthcare Data: Creating Clear and Compelling Visualizations to See how You're Doing is a one-of-a-kind book for health and healthcare professionals to learn the best practices of data visualization specific to their field. It provides a high-level summary of health and healthcare data, an overview of relevant visual intelligence research, strategies and techniques to gather requirements, and how to build strong teams with the expertise required to create dashboards and reports that people love to use. Clear and detailed explanations of data visualization best practices will help you understand the how and the why. Learn how to build beautiful and useful data products that deliver powerful insights for the end user Follow along with examples of data visualization best practices, including table and graph design for health and healthcare data Learn the difference between dashboards, reports, multidimensional exploratory displays and infographics (and why it matters) Avoid common mistakes in data visualization by learning why they do not work and better ways to display the data Written by a top leader in the field of health and healthcare data visualization, this book is an excellent resource for top management in healthcare, as well as entry-level to experienced data analysts in any health-related organization. |
| data management and sharing: Handbook on Using Administrative Data for Research and Evidence-based Policy Shawn Cole, Iqbal Dhaliwal, Anja Sautmann, 2021 This Handbook intends to inform Data Providers and researchers on how to provide privacy-protected access to, handle, and analyze administrative data, and to link them with existing resources, such as a database of data use agreements (DUA) and templates. Available publicly, the Handbook will provide guidance on data access requirements and procedures, data privacy, data security, property rights, regulations for public data use, data architecture, data use and storage, cost structure and recovery, ethics and privacy-protection, making data accessible for research, and dissemination for restricted access use. The knowledge base will serve as a resource for all researchers looking to work with administrative data and for Data Providers looking to make such data available. |
| data management and sharing: Target-setting Methods and Data Management to Support Performance-based Resource Allocation by Transportation Agencies National Cooperative Highway Research Program, 2010 TRB's National Cooperative Highway Research Program (NCHRP) Report 666: Target Setting Methods and Data Management to Support Performance-Based Resource Allocation by Transportation Agencies - Volume I: Research Report, and Volume II: Guide for Target-Setting and Data Management provides a framework and specific guidance for setting performance targets and for ensuring that appropriate data are available to support performance-based decision-making. Volume III to this report was published separately in an electronic-only format as NCHRP Web-Only Document 154. Volume III includes case studies of organizations investigated in the research used to develop NCHRP Report 666. |
| data management and sharing: 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 and sharing: Proceedings of the 2023 3rd International Conference on Social Development and Media Communication (SDMC 2023) Sikandar Ali Qalati, Mohd Farid Mohd Sharif, Mohd. Khairie Ahmad, Dragana Ostic, 2024-01-29 This is an open access book. As a leading role in the global megatrend of scientific innovation, China has been creating a more and more open environment for scientific innovation, increasing the depth and breadth of academic cooperation, and building a community of innovation that benefits all. Such endeavors are making new contributions to the globalization and creating a community of shared future. To adapt to this changing world and China's fast development in the new era, 2023 3rd International Conference on Social Development and Media Communication (SDMC 2023) to be held in November 2023. This conference takes bringing together global wisdom in scientific innovation to promote high-quality development as the theme and focuses on cutting-edge research fields including Social Development and Media Communication. SDMC 2023 encourages the exchange of information at the forefront of research in different fields, connects the most advanced academic resources in China and the world, transforms research results into industrial solutions, and brings together talent, technology and capital to drive development. The conference sincerely invites experts, scholars, business people and other relevant personnel from universities, scientific research institutions at home and abroad to attend and exchange! 2023 3rd International Conference on Social Development and Media Communication (SDMC 2023) will conduct in-depth discussions on the impact of social development on media communication and the impact of media communication on social development. Scholars in relevant fields are cordially invited to participate. We warmly invite you to participate in SDMC 2023 and look forward to seeing you in Xishuang Banna,China. |
| data management and sharing: Sharing Clinical Research Data Institute of Medicine, Board on Health Care Services, Board on Health Sciences Policy, Roundtable on Translating Genomic-Based Research for Health, National Cancer Policy Forum, Forum on Neuroscience and Nervous System Disorders, Forum on Drug Discovery, Development, and Translation, 2013-06-07 Pharmaceutical companies, academic researchers, and government agencies such as the Food and Drug Administration and the National Institutes of Health all possess large quantities of clinical research data. If these data were shared more widely within and across sectors, the resulting research advances derived from data pooling and analysis could improve public health, enhance patient safety, and spur drug development. Data sharing can also increase public trust in clinical trials and conclusions derived from them by lending transparency to the clinical research process. Much of this information, however, is never shared. Retention of clinical research data by investigators and within organizations may represent lost opportunities in biomedical research. Despite the potential benefits that could be accrued from pooling and analysis of shared data, barriers to data sharing faced by researchers in industry include concerns about data mining, erroneous secondary analyses of data, and unwarranted litigation, as well as a desire to protect confidential commercial information. Academic partners face significant cultural barriers to sharing data and participating in longer term collaborative efforts that stem from a desire to protect intellectual autonomy and a career advancement system built on priority of publication and citation requirements. Some barriers, like the need to protect patient privacy, pre- sent challenges for both sectors. Looking ahead, there are also a number of technical challenges to be faced in analyzing potentially large and heterogeneous datasets. This public workshop focused on strategies to facilitate sharing of clinical research data in order to advance scientific knowledge and public health. While the workshop focused on sharing of data from preplanned interventional studies of human subjects, models and projects involving sharing of other clinical data types were considered to the extent that they provided lessons learned and best practices. The workshop objectives were to examine the benefits of sharing of clinical research data from all sectors and among these sectors, including, for example: benefits to the research and development enterprise and benefits to the analysis of safety and efficacy. Sharing Clinical Research Data: Workshop Summary identifies barriers and challenges to sharing clinical research data, explores strategies to address these barriers and challenges, including identifying priority actions and low-hanging fruit opportunities, and discusses strategies for using these potentially large datasets to facilitate scientific and public health advances. |
Data Management and Sharing Policy
Under the NIH Data Management & Sharing Policy, investigators are empowered to choose the most appropriate methods for sharing scientific data. Learn more about methods for data …
Writing a Data Management & Sharing Plan | Data Sharing
Jan 25, 2023 · Under the 2023 Data Management and Sharing (DMS) Policy, NIH expects researchers to maximize the appropriate sharing of scientific data, taking into account factors …
Home Page | Data Sharing
Find answers to frequently asked questions on the 2023 Data Management & Sharing Policy. Topics include budget, policy scope, compliance, and more. View DMS FAQs
DATA MANAGEMENT AND SHARING PLAN - National …
NIH expects that in drafting Plans, researchers maximize the appropriate sharing of scientific data. Describe and justify any applicable factors or data use limitations affecting subsequent access, …
Research Covered Under the Data Management & Sharing Policy
Jan 25, 2023 · Learn what types of research projects fall under NIH’s Data Management and Sharing Policy, and how NIH handles sharing of proprietary data.
About Data Management & Sharing Policies | Data Sharing
NIH encourages the sharing of data whenever possible. Learn about the 2003 NIH Data Sharing policy and the 2023 NIH Data Management and Sharing policy as well as how they apply to …
Frequently Asked Questions (FAQs) | Data Sharing
Data Management & Sharing Policy Overview; Research Covered Under the Data Management & Sharing Policy
Data Management | Data Sharing - National Institutes of Health
Data management is the process of validating, organizing, protecting, maintaining, and processing scientific data to ensure the accessibility, reliability, and quality of the data for its users. Proper …
Planning & Budgeting for Data Management and Sharing
Writing a Data Management & Sharing Plan. Learn what NIH expects Data Management & Sharing Plans to address, as well as how to submit your Plan.
Data Management & Sharing Policy Overview | Data Sharing
Jan 25, 2023 · NIH has issued the Data Management and Sharing (DMS) policy (effective January 25, 2023) to promote the sharing of scientific data. Sharing scientific data accelerates …
Data Management and Sharing Policy
Under the NIH Data Management & Sharing Policy, investigators are empowered to choose the most appropriate methods for sharing scientific data. Learn more about methods for data …
Writing a Data Management & Sharing Plan | Data Sharing
Jan 25, 2023 · Under the 2023 Data Management and Sharing (DMS) Policy, NIH expects researchers to maximize the appropriate sharing of scientific data, taking into account factors …
Home Page | Data Sharing
Find answers to frequently asked questions on the 2023 Data Management & Sharing Policy. Topics include budget, policy scope, compliance, and more. View DMS FAQs
DATA MANAGEMENT AND SHARING PLAN - National …
NIH expects that in drafting Plans, researchers maximize the appropriate sharing of scientific data. Describe and justify any applicable factors or data use limitations affecting subsequent access, …
Research Covered Under the Data Management & Sharing Policy
Jan 25, 2023 · Learn what types of research projects fall under NIH’s Data Management and Sharing Policy, and how NIH handles sharing of proprietary data.
About Data Management & Sharing Policies | Data Sharing
NIH encourages the sharing of data whenever possible. Learn about the 2003 NIH Data Sharing policy and the 2023 NIH Data Management and Sharing policy as well as how they apply to …
Frequently Asked Questions (FAQs) | Data Sharing
Data Management & Sharing Policy Overview; Research Covered Under the Data Management & Sharing Policy
Data Management | Data Sharing - National Institutes of Health
Data management is the process of validating, organizing, protecting, maintaining, and processing scientific data to ensure the accessibility, reliability, and quality of the data for its users. Proper …
Planning & Budgeting for Data Management and Sharing
Writing a Data Management & Sharing Plan. Learn what NIH expects Data Management & Sharing Plans to address, as well as how to submit your Plan.
Data Management & Sharing Policy Overview | Data Sharing
Jan 25, 2023 · NIH has issued the Data Management and Sharing (DMS) policy (effective January 25, 2023) to promote the sharing of scientific data. Sharing scientific data accelerates …
