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| example of data management plan 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 |
| example of data management plan 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. |
| example of data management plan in research: Data and Information in Online Environments Rogério Mugnaini, 2020-06-15 This book constitutes the refereed post-conference proceedings of the First International Conference on Data and Information in Online Environments, DIONE 2020, which took place in Florianópolis, Brazil, in March 2020. DIONE 2020 handles the growing interaction between the information sciences, communication sciences and computer sciences. The 18 revised full papers were carefully reviewed and selected from 37 submissions and focus on the production, dissemination and evaluation of contents in online environments. The goal is to improve cooperation between data science, natural language processing, data engineering, big data, research evaluation, network science, sociology of science and communication communities. |
| example of data management plan in research: Caring for Digital Data in Archaeology Archaeology Data Service, Digital Antiquity, 2013 A wide variety of organizations are both creating and retaining digital data from archaeological projects. While current methods for preservation and access to data vary widely, nearly all of these organizations agree that careful management of digital archaeological resources is an important aspect of responsible archaeological stewardship. The Archaeology Data Service and Digital Antiquity have produced this guide to provide information on the best way to create, manage, and document digital data files produced during the course of an archaeological project. This guide aims to improve the practice of depositing and preserving digital information safely within an archive for future use and is structured in three main parts: Digital Archiving - looks at the fundamentals of digital preservation and covers general preservation themes within the context of archaeological investigations, research, and resource management, with an overview of digital archiving practice and guidance.The Project Life cycle - looks at common project life cycle elements such as file naming, meta-data creation, and copyright and covers general, broad themes that should be considered at the outset of a project.Basic Components - looks at selected technique and file type-specific issues together with archive structuring and deposit. This section covers common file types that are frequently present in archaeological archives, irrespective of a project's primary technique or focus.The accompanying online Guides to Good Practice take these elements further and address the preservation of data resulting from common data collection, processing and analysis techniques such as aerial and geophysical survey, laser scanning, GIS and CAD. |
| example of data management plan in research: Fundamentals of Clinical Data Science Pieter Kubben, Michel Dumontier, Andre Dekker, 2018-12-21 This open access book comprehensively covers the fundamentals of clinical data science, focusing on data collection, modelling and clinical applications. Topics covered in the first section on data collection include: data sources, data at scale (big data), data stewardship (FAIR data) and related privacy concerns. Aspects of predictive modelling using techniques such as classification, regression or clustering, and prediction model validation will be covered in the second section. The third section covers aspects of (mobile) clinical decision support systems, operational excellence and value-based healthcare. Fundamentals of Clinical Data Science is an essential resource for healthcare professionals and IT consultants intending to develop and refine their skills in personalized medicine, using solutions based on large datasets from electronic health records or telemonitoring programmes. The book’s promise is “no math, no code”and will explain the topics in a style that is optimized for a healthcare audience. |
| example of data management plan 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. |
| example of data management plan in research: 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. |
| example of data management plan 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. |
| example of data management plan 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, |
| example of data management plan in research: Data Stewardship for Open Science Barend Mons, 2018-03-09 Data Stewardship for Open Science: Implementing FAIR Principles has been written with the intention of making scientists, funders, and innovators in all disciplines and stages of their professional activities broadly aware of the need, complexity, and challenges associated with open science, modern science communication, and data stewardship. The FAIR principles are used as a guide throughout the text, and this book should leave experimentalists consciously incompetent about data stewardship and motivated to respect data stewards as representatives of a new profession, while possibly motivating others to consider a career in the field. The ebook, avalable for no additional cost when you buy the paperback, will be updated every 6 months on average (providing that significant updates are needed or avaialble). Readers will have the opportunity to contribute material towards these updates, and to develop their own data management plans, via the free Data Stewardship Wizard. |
| example of data management plan in research: Opening Science Sönke Bartling, Sascha Friesike, 2013-12-16 Modern information and communication technologies, together with a cultural upheaval within the research community, have profoundly changed research in nearly every aspect. Ranging from sharing and discussing ideas in social networks for scientists to new collaborative environments and novel publication formats, knowledge creation and dissemination as we know it is experiencing a vigorous shift towards increased transparency, collaboration and accessibility. Many assume that research workflows will change more in the next 20 years than they have in the last 200. This book provides researchers, decision makers, and other scientific stakeholders with a snapshot of the basics, the tools, and the underlying visions that drive the current scientific (r)evolution, often called ‘Open Science.’ |
| example of data management plan in research: Statistical Confidentiality George T. Duncan, Mark Elliot, Gonzalez Juan Jose Salazar, 2011-03-22 Because statistical confidentiality embraces the responsibility for both protecting data and ensuring its beneficial use for statistical purposes, those working with personal and proprietary data can benefit from the principles and practices this book presents. Researchers can understand why an agency holding statistical data does not respond well to the demand, “Just give me the data; I’m only going to do good things with it.” Statisticians can incorporate the requirements of statistical confidentiality into their methodologies for data collection and analysis. Data stewards, caught between those eager for data and those who worry about confidentiality, can use the tools of statistical confidentiality toward satisfying both groups. The eight chapters lay out the dilemma of data stewardship organizations (such as statistical agencies) in resolving the tension between protecting data from snoopers while providing data to legitimate users, explain disclosure risk and explore the types of attack that a data snooper might mount, present the methods of disclosure risk assessment, give techniques for statistical disclosure limitation of both tabular data and microdata, identify measures of the impact of disclosure limitation on data utility, provide restricted access methods as administrative procedures for disclosure control, and finally explore the future of statistical confidentiality. |
| example of data management plan 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. |
| example of data management plan in research: Registries for Evaluating Patient Outcomes Agency for Healthcare Research and Quality/AHRQ, 2014-04-01 This User’s Guide is intended to support the design, implementation, analysis, interpretation, and quality evaluation of registries created to increase understanding of patient outcomes. For the purposes of this guide, a patient registry is an organized system that uses observational study methods to collect uniform data (clinical and other) to evaluate specified outcomes for a population defined by a particular disease, condition, or exposure, and that serves one or more predetermined scientific, clinical, or policy purposes. A registry database is a file (or files) derived from the registry. Although registries can serve many purposes, this guide focuses on registries created for one or more of the following purposes: to describe the natural history of disease, to determine clinical effectiveness or cost-effectiveness of health care products and services, to measure or monitor safety and harm, and/or to measure quality of care. Registries are classified according to how their populations are defined. For example, product registries include patients who have been exposed to biopharmaceutical products or medical devices. Health services registries consist of patients who have had a common procedure, clinical encounter, or hospitalization. Disease or condition registries are defined by patients having the same diagnosis, such as cystic fibrosis or heart failure. The User’s Guide was created by researchers affiliated with AHRQ’s Effective Health Care Program, particularly those who participated in AHRQ’s DEcIDE (Developing Evidence to Inform Decisions About Effectiveness) program. Chapters were subject to multiple internal and external independent reviews. |
| example of data management plan in research: Digital Libraries for Open Knowledge Eva Méndez, Fabio Crestani, Cristina Ribeiro, Gabriel David, João Correia Lopes, 2018-09-04 This book constitutes the proceedings of the 22nd International Conference on Theory and Practice of Digital Libraries, TPDL 2018, held in Porto, Portugal, in September 2018. The 51 full papers, 17 short papers, and 13 poster and tutorial papers presented in this volume were carefully reviewed and selected from 81 submissions. The general theme of TPDL 2018 was Digital Libraries for Open Knowledge. The papers present a wide range of the following topics: Metadata, Entity Disambiguation, Data Management, Scholarly Communication, Digital Humanities, User Interaction, Resources, Information Extraction, Information Retrieval, Recommendation. |
| example of data management plan in research: ADKAR Jeff Hiatt, 2006 In his first complete text on the ADKAR model, Jeff Hiatt explains the origin of the model and explores what drives each building block of ADKAR. Learn how to build awareness, create desire, develop knowledge, foster ability and reinforce changes in your organization. The ADKAR Model is changing how we think about managing the people side of change, and provides a powerful foundation to help you succeed at change. |
| example of data management plan in research: Ecological Informatics Friedrich Recknagel, William K. Michener, 2018-08-14 This book introduces readers to ecological informatics as an emerging discipline that takes into account the data-intensive nature of ecology, the valuable information to be found in ecological data, and the need to communicate results and inform decisions, including those related to research, conservation and resource management. At its core, ecological informatics combines developments in information technology and ecological theory with applications that facilitate ecological research and the dissemination of results to scientists and the public. Its conceptual framework links ecological entities (genomes, organisms, populations, communities, ecosystems, landscapes) with data management, analysis and synthesis, and communicates new findings to inform decisions by following the course of a loop. In comparison to the 2nd edition published in 2006, the 3rd edition of Ecological Informatics has been completely restructured on the basis of the generic conceptual f ramework provided in Figure 1. It reflects the significant advances in data management, analysis and synthesis that have been made over the past 10 years, including new remote and in situ sensing techniques, the emergence of ecological and environmental observatories, novel evolutionary computations for knowledge discovery and forecasting, and new approaches to communicating results and informing decisions. |
| example of data management plan 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. |
| example of data management plan in research: How to Publish Data , 2008 |
| example of data management plan in research: Metadata and Semantic Research Emmanouel Garoufallou, María-Antonia Ovalle-Perandones, 2021-03-17 This book constitutes the thoroughly refereed proceedings of the 14th International Conference on Metadata and Semantic Research, MTSR 2020, held in Madrid, Spain, in December 2020. Due to the COVID-19 pandemic the conference was held online. The 24 full and 13 short papers presented were carefully reviewed and selected from 82 submissions. The papers are organized in the following tracks: metadata, linked data, semantics and ontologies; metadata and semantics for digital libraries, information retrieval, big, linked, social and open data; metadata and semantics for agriculture, food, and environment, AgroSEM 2020; metadata and semantics for open repositories, research information systems and data infrastructures; digital humanities and digital curation, DHC 2020; metadata and semantics for cultural collections and applications; european and national projects; knowledge IT artifacts (KITA) in professional communities and aggregations, KITA 2020. |
| example of data management plan in research: ORI Introduction to the Responsible Conduct of Research Nicholas Hans Steneck, 2003 |
| example of data management plan in research: Leading Change John P. Kotter, 2012 From the ill-fated dot-com bubble to unprecedented merger and acquisition activity to scandal, greed, and, ultimately, recession -- we've learned that widespread and difficult change is no longer the exception. By outlining the process organizations have used to achieve transformational goals and by identifying where and how even top performers derail during the change process, Kotter provides a practical resource for leaders and managers charged with making change initiatives work. |
| example of data management plan 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. |
| example of data management plan in research: 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. |
| example of data management plan in research: Understanding Metadata , 2004 |
| example of data management plan 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 |
| example of data management plan in research: 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. |
| example of data management plan in research: The Pig Book Citizens Against Government Waste, 2013-09-17 The federal government wastes your tax dollars worse than a drunken sailor on shore leave. The 1984 Grace Commission uncovered that the Department of Defense spent $640 for a toilet seat and $436 for a hammer. Twenty years later things weren't much better. In 2004, Congress spent a record-breaking $22.9 billion dollars of your money on 10,656 of their pork-barrel projects. The war on terror has a lot to do with the record $413 billion in deficit spending, but it's also the result of pork over the last 18 years the likes of: - $50 million for an indoor rain forest in Iowa - $102 million to study screwworms which were long ago eradicated from American soil - $273,000 to combat goth culture in Missouri - $2.2 million to renovate the North Pole (Lucky for Santa!) - $50,000 for a tattoo removal program in California - $1 million for ornamental fish research Funny in some instances and jaw-droppingly stupid and wasteful in others, The Pig Book proves one thing about Capitol Hill: pork is king! |
| example of data management plan in research: 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. |
| example of data management plan in research: Research Methods Kirsty Williamson, Graeme Johanson, 2017-11-27 Research Methods: Information, Systems, and Contexts, Second Edition, presents up-to-date guidance on how to teach research methods to graduate students and professionals working in information management, information science, librarianship, archives, and records and information systems. It provides a coherent and precise account of current research themes and structures, giving students guidance, appreciation of the scope of research paradigms, and the consequences of specific courses of action. Each of these valuable sections will help users determine the relevance of particular approaches to their own questions. The book presents academics who teach research and information professionals who carry out research with new resources and guidance on lesser-known research paradigms. - Provides up-to-date knowledge of research methods and their applications - Provides a coherent and precise account of current research themes and structures through chapters written by authors who are experts in their fields - Helps students and researchers understand the range of quantitative and qualitative approaches available for research, as well as how to make practical use of them - Provides many illustrations from projects in which authors have been involved, to enhance understanding - Emphasises the nexus between formulation of research question and choice of research methodology - Enables new researchers to understand the implications of their planning decisions |
| example of data management plan in research: Engaging Researchers with Data Management: The Cookbook Connie Clare, Maria Cruz, Elli Papadopoulou, James Savage, Marta Teperek, Yan Wang, Iza Witkowska, Joanne Yeomans, 2019-10-09 Effective Research Data Management (RDM) is a key component of research integrity and reproducible research, and its importance is increasingly emphasised by funding bodies, governments, and research institutions around the world. However, many researchers are unfamiliar with RDM best practices, and research support staff are faced with the difficult task of delivering support to researchers across different disciplines and career stages. What strategies can institutions use to solve these problems? 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 and the organisational context, the authors of this book have created an essential resource for organisations looking to increase engagement with their research communities. This handbook is a collaboration by research institutions, for research institutions. It aims not only to inspire and engage, but also to help drive cultural change towards better data management. It has been written for anyone interested in RDM, or simply, good research practice. |
| example of data management plan in research: Cochrane Handbook for Systematic Reviews of Interventions Julian P. T. Higgins, Sally Green, 2008-11-24 Healthcare providers, consumers, researchers and policy makers are inundated with unmanageable amounts of information, including evidence from healthcare research. It has become impossible for all to have the time and resources to find, appraise and interpret this evidence and incorporate it into healthcare decisions. Cochrane Reviews respond to this challenge by identifying, appraising and synthesizing research-based evidence and presenting it in a standardized format, published in The Cochrane Library (www.thecochranelibrary.com). The Cochrane Handbook for Systematic Reviews of Interventions contains methodological guidance for the preparation and maintenance of Cochrane intervention reviews. Written in a clear and accessible format, it is the essential manual for all those preparing, maintaining and reading Cochrane reviews. Many of the principles and methods described here are appropriate for systematic reviews applied to other types of research and to systematic reviews of interventions undertaken by others. It is hoped therefore that this book will be invaluable to all those who want to understand the role of systematic reviews, critically appraise published reviews or perform reviews themselves. |
| example of data management plan in research: Databrarianship Lynda M. Kellam, Kristi Thompson, 2016 With the appearance of big data, open data, and particularly research data curation on many libraries' radar screens, data service has become a critically important topic for academic libraries. Drawing on the expertise of a diverse community of practitioners, this collection of case studies, original research, survey chapters, and theoretical explorations presents a wide-ranging look at the field of academic data librarianship. By covering the data lifecycle from collection development to preservation, examining the challenges of working with different forms of data, and exploring service models suited to a variety of library types, this volume provides a toolbox of strategies that will allow librarians and administrators to respond creatively and effectively to the data deluge. Edited by Kristi Thompson and Lynda Kellam, Databrarianship: The Academic Data Librarian in Theory and Practice provides advice and insight on data services for all types of academic libraries and will be of interest to library educators--Publisher's website. |
| example of data management plan in research: The Capability Maturity Model Mark C. Paulk, 1995 Principal Contributors and Editors: Mark C. Paulk, Charles V. Weber, Bill Curtis, Mary Beth Chrissis In every sense, the CMM represents the best thinking in the field today... this book is targeted at anyone involved in improving the software process, including members of assessment or evaluation teams, members of software engineering process groups, software managers, and software practitioners... From the Foreword by Watts Humphrey The Capability Maturity Model for Software (CMM) is a framework that demonstrates the key elements of an effective software process. The CMM describes an evolutionary improvement path for software development from an ad hoc, immature process to a mature, disciplined process, in a path laid out in five levels. When using the CMM, software professionals in government and industry can develop and improve their ability to identify, adopt, and use sound management and technical practices for delivering quality software on schedule and at a reasonable cost. This book provides a description and technical overview of the CMM, along with guidelines for improving software process management overall. It is a sequel to Watts Humphrey's important work, Managing the Software Process, in that it structures the maturity framework presented in that book more formally. Features: Compares the CMM with ISO 9001 Provides an overview of ISO's SPICE project, which is developing international standards for software process improvement and capability determination Presents a case study of IBM Houston's Space Shuttle project, which is frequently referred to as being at Level 5 0201546647B04062001 |
| example of data management plan 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. |
| example of data management plan in research: 1641 Depositions Aidan Clarke, 2014 The 1641 Depositions are witness testimonies, mainly by Protestants, but also by some Catholics, from all social backgrounds, concerning their experiences of the 1641 Irish rebellion. The testimonies document the loss of goods, military activity, and the alleged crimes committed by the Irish insurgents. This body of material is unparalleled anywhere in early modern Europe. It provides a unique source of information for the causes and events surrounding the 1641 rebellion and for the social, economic, cultural, religious, and political history of seventeenth- century Ireland, England and Scotland. In total, 19,010 manuscript pages in 31 bound volumes held at Trinity College Dublin have been transcribed and are arranged for publication in 12 volumes from 2014 onwards. The depositions are available online at www.1641.tcd.ie .--Provided by publisher. |
| example of data management plan in research: Practical Guide to Clinical Data Management Susanne Prokscha, 2006-08-01 The management of clinical data, from its collection 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. As its importance has grown, clinical data management (CDM) has changed from an essentially clerical task in the late 1970s and early 1980s t |
| example of data management plan 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. |
| example of data management plan 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 |
| example of data management plan 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. |
EXAMPLE Definition & Meaning - Merriam-Webster
The meaning of EXAMPLE is one that serves as a pattern to be imitated or not to be imitated. How to use example in a sentence. Synonym Discussion of Example.
EXAMPLE | English meaning - Cambridge Dictionary
EXAMPLE definition: 1. something that is typical of the group of things that it is a member of: 2. a way of helping…. Learn more.
EXAMPLE Definition & Meaning | Dictionary.com
one of a number of things, or a part of something, taken to show the character of the whole. This painting is an example of his early work. a pattern or model, as of something to be imitated or …
Example - definition of example by The Free Dictionary
1. one of a number of things, or a part of something, taken to show the character of the whole. 2. a pattern or model, as of something to be imitated or avoided: to set a good example. 3. an …
Example Definition & Meaning - YourDictionary
To be illustrated or exemplified (by). Wear something simple; for example, a skirt and blouse.
EXAMPLE - Meaning & Translations | Collins English Dictionary
An example of something is a particular situation, object, or person which shows that what is being claimed is true. 2. An example of a particular class of objects or styles is something that …
example noun - Definition, pictures, pronunciation and usage …
used to emphasize something that explains or supports what you are saying; used to give an example of what you are saying. There is a similar word in many languages, for example in …
Example - Definition, Meaning & Synonyms - Vocabulary.com
An example is a particular instance of something that is representative of a group, or an illustration of something that's been generally described. Example comes from the Latin word …
example - definition and meaning - Wordnik
noun Something that serves as a pattern of behaviour to be imitated (a good example) or not to be imitated (a bad example). noun A person punished as a warning to others. noun A parallel …
EXAMPLE Synonyms: 20 Similar Words - Merriam-Webster
Some common synonyms of example are case, illustration, instance, sample, and specimen. While all these words mean "something that exhibits distinguishing characteristics in its …
EXAMPLE Definition & Meaning - Merriam-Webster
The meaning of EXAMPLE is one that serves as a pattern to be imitated or not to be imitated. How to use example in a sentence. Synonym Discussion of Example.
EXAMPLE | English meaning - Cambridge Dictionary
EXAMPLE definition: 1. something that is typical of the group of things that it is a member of: 2. a way of helping…. Learn more.
EXAMPLE Definition & Meaning | Dictionary.com
one of a number of things, or a part of something, taken to show the character of the whole. This painting is an example of his early work. a pattern or model, as of something to be imitated or …
Example - definition of example by The Free Dictionary
1. one of a number of things, or a part of something, taken to show the character of the whole. 2. a pattern or model, as of something to be imitated or avoided: to set a good example. 3. an …
Example Definition & Meaning - YourDictionary
To be illustrated or exemplified (by). Wear something simple; for example, a skirt and blouse.
EXAMPLE - Meaning & Translations | Collins English Dictionary
An example of something is a particular situation, object, or person which shows that what is being claimed is true. 2. An example of a particular class of objects or styles is something that …
example noun - Definition, pictures, pronunciation and usage …
used to emphasize something that explains or supports what you are saying; used to give an example of what you are saying. There is a similar word in many languages, for example in …
Example - Definition, Meaning & Synonyms - Vocabulary.com
An example is a particular instance of something that is representative of a group, or an illustration of something that's been generally described. Example comes from the Latin word …
example - definition and meaning - Wordnik
noun Something that serves as a pattern of behaviour to be imitated (a good example) or not to be imitated (a bad example). noun A person punished as a warning to others. noun A parallel …
EXAMPLE Synonyms: 20 Similar Words - Merriam-Webster
Some common synonyms of example are case, illustration, instance, sample, and specimen. While all these words mean "something that exhibits distinguishing characteristics in its …
