Advertisement
| example of data management plan: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: Metadata Management with IBM InfoSphere Information Server Wei-Dong Zhu, Tuvia Alon, Gregory Arkus, Randy Duran, Marc Haber, Robert Liebke, Frank Morreale Jr., Itzhak Roth, Alan Sumano, IBM Redbooks, 2011-10-18 What do you know about your data? And how do you know what you know about your data? Information governance initiatives address corporate concerns about the quality and reliability of information in planning and decision-making processes. Metadata management refers to the tools, processes, and environment that are provided so that organizations can reliably and easily share, locate, and retrieve information from these systems. Enterprise-wide information integration projects integrate data from these systems to one location to generate required reports and analysis. During this type of implementation process, metadata management must be provided along each step to ensure that the final reports and analysis are from the right data sources, are complete, and have quality. This IBM® Redbooks® publication introduces the information governance initiative and highlights the immediate needs for metadata management. It explains how IBM InfoSphereTM Information Server provides a single unified platform and a collection of product modules and components so that organizations can understand, cleanse, transform, and deliver trustworthy and context-rich information. It describes a typical implementation process. It explains how InfoSphere Information Server provides the functions that are required to implement such a solution and, more importantly, to achieve metadata management. This book is for business leaders and IT architects with an overview of metadata management in information integration solution space. It also provides key technical details that IT professionals can use in a solution planning, design, and implementation process. |
| example of data management plan: 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: 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: 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: 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. |
| example of data management plan: 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: 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: 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: 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: 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: 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. |
| example of data management plan: How to Publish Data , 2008 |
| example of data management plan: 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: Practical Guide to Clinical Data Management, Third Edition Susanne Prokscha, 2011-10-26 The management of clinical data, from its collection during a trial to its extraction for analysis, has become a critical element in the steps to prepare a regulatory submission and to obtain approval to market a treatment. Groundbreaking on its initial publication nearly fourteen years ago, and evolving with the field in each iteration since then, the third edition of Practical Guide to Clinical Data Management includes important updates to all chapters to reflect the current industry approach to using electronic data capture (EDC) for most studies. See what’s new in the Third Edition: A chapter on the clinical trial process that explains the high level flow of a clinical trial from creation of the protocol through the study lock and provides the context for the clinical data management activities that follow Reorganized content reflects an industry trend that divides training and standard operating procedures for clinical data management into the categories of study startup, study conduct, and study closeout Coverage of current industry and Food and Drug Administration (FDA) approaches and concerns The book provides a comprehensive overview of the tasks involved in clinical data management and the computer systems used to perform those tasks. It also details the context of regulations that guide how those systems are used and how those regulations are applied to their installation and maintenance. Keeping the coverage practical rather than academic, the author hones in on the most critical information that impacts clinical trial conduct, providing a full end-to-end overview or introduction for clinical data managers. |
| example of data management plan: Data Driven Thomas C. Redman, 2008-09-22 Your company's data has the potential to add enormous value to every facet of the organization -- from marketing and new product development to strategy to financial management. Yet if your company is like most, it's not using its data to create strategic advantage. Data sits around unused -- or incorrect data fouls up operations and decision making. In Data Driven, Thomas Redman, the Data Doc, shows how to leverage and deploy data to sharpen your company's competitive edge and enhance its profitability. The author reveals: · The special properties that make data such a powerful asset · The hidden costs of flawed, outdated, or otherwise poor-quality data · How to improve data quality for competitive advantage · Strategies for exploiting your data to make better business decisions · The many ways to bring data to market · Ideas for dealing with political struggles over data and concerns about privacy rights Your company's data is a key business asset, and you need to manage it aggressively and professionally. Whether you're a top executive, an aspiring leader, or a product-line manager, this eye-opening book provides the tools and thinking you need to do that. |
| example of data management plan: EPA-R5 , 1972 |
| example of data management plan: Performance Dashboards Wayne W. Eckerson, 2005-10-27 Tips, techniques, and trends on how to use dashboard technology to optimize business performance Business performance management is a hot new management discipline that delivers tremendous value when supported by information technology. Through case studies and industry research, this book shows how leading companies are using performance dashboards to execute strategy, optimize business processes, and improve performance. Wayne W. Eckerson (Hingham, MA) is the Director of Research for The Data Warehousing Institute (TDWI), the leading association of business intelligence and data warehousing professionals worldwide that provide high-quality, in-depth education, training, and research. He is a columnist for SearchCIO.com, DM Review, Application Development Trends, the Business Intelligence Journal, and TDWI Case Studies & Solution. |
| example of data management plan: Anthropological Data in the Digital Age Jerome W. Crowder, Mike Fortun, Rachel Besara, Lindsay Poirier, 2019-11-01 For more than two decades, anthropologists have wrestled with new digital technologies and their impacts on how their data are collected, managed, and ultimately presented. Anthropological Data in the Digital Age compiles a range of academics in anthropology and the information sciences, archivists, and librarians to offer in-depth discussions of the issues raised by digital scholarship. The volume covers the technical aspects of data management—retrieval, metadata, dissemination, presentation, and preservation—while at once engaging with case studies written by cultural anthropologists and archaeologists returning from the field to grapple with the implications of producing data digitally. Concluding with thoughts on the new considerations and ethics of digital data, Anthropological Data in the Digital Age is a multi-faceted meditation on anthropological practice in a technologically mediated world. |
| example of data management plan: 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: 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: Digital Curation, Second Edition Gillian Oliver , Ross Harvey, 2017-11-21 As an in-depth explanation of the entire digital curation lifecycle, from creation to appraisal to preservation to organization/access to transformation, the first edition of this text set a benchmark for both thoroughness and clarity. Boasting the expert guidance of international authorities Oliver and Harvey, this revamped and expanded edition widens the scope to address continuing developments in the strategies, technological approaches, and activities that are part of this rapidly changing field. In addition to current practitioners, those pursuing a career as librarian, archivist, or records manager will find this definitive survey invaluable. Filled with up-to-date best practices, it covers such important topics as the scope and incentives of digital curation, detailing Digital Curation Centre’s (DCC) lifecycle model as well as the Data Curation Continuum; key requirements for digital curation, from description and representation to planning and collaboration;the value and utility of metadata;considering the needs of producers and consumers when creating an appraisal and selection policy for digital objects;the paradigm shift by institutions towards cloud computing and its impact on costs, storage, and other key aspects of digital curation;the quality and security of data;new and emerging data curation resources, including innovative digital repository software and digital forensics tools;mechanisms for sharing and reusing data, with expanded sections on open access, open data, and open standards initiatives; and processes to ensure that data are preserved and remain usable over time.Useful as both a teaching text and day-to-day working guide, this book outlines the essential concepts and techniques that are crucial to preserving the longevity of digital resources. |
| example of data management plan: Ecological Informatics Friedrich Recknagel, William K. Michener, 2017-09-21 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: ORI Introduction to the Responsible Conduct of Research Nicholas Hans Steneck, 2003 |
| example of data management plan: 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 |
| example of data management plan: 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 of data management plan: Digitalization of Medicine in Low- and Middle-Income Countries Zisis Kozlakidis, |
| example of data management plan: Delivering Research Data Management Services Graham Pryor, Sarah Jones, Angus Whyte, 2013-12-10 Step-by-step guidance to setting up and running effective institutional research data management services to support researchers and networks. The research landscape is changing, with key global research funders now requiring institutions to demonstrate how they will preserve and share research data. However, the practice of structured research data management is very new, and the construction of services remains experimental and in need of models and standards of approach. This groundbreaking guide will lead researchers, institutions and policy makers through the processes needed to set up and run effective institutional research data management services. This ‘how to’ guide provides a step-by-step explanation of the components for an institutional service. Case studies from the newly emerging service infrastructures in the UK, USA and Australia draw out the lessons learnt. Different approaches are highlighted and compared; for example, a researcher-focused strategy from Australia is contrasted with a national, top-down approach, and a national research data management service is discussed as an alternative to institutional services. Key topics covered: • Research data provision • Options and approaches to research data management service provision • A spectrum of roles, responsibilities and competences • A pathway to sustainable research data services: from scoping to sustainability • The range and components of RDM infrastructure and services Case studies: • Johns Hopkins University • University of Southampton • Monash University • The UK Data Service • Jisc Managing Research Data programmes. Readership: This book will be an invaluable guide to those entering a new and untried enterprise. It will be particularly relevant to heads of libraries, information technology managers, research support office staff and research directors planning for these types of services. It will also be of interest to researchers, funders and policy makers as a reference tool for understanding how shifts in policy will have a range of ramifications within institutions. Library and information science students will find it an informative window on an emerging area of practice. |
| example of data management plan: The Conterminous United States Mineral Assessment Program Joseph F. Rinella, Pixie A. Hamilton, Stuart W. McKenzie, 1992 |
| example of data management plan: Sampling and Surveying Radiological Environments Mark E. Byrnes, 2000-09-19 Private landowners or Federal Agencies responsible for cleaning up radiological environments are faced with the challenge of clearly defining the nature and extent of radiological contamination, implementing remedial alternatives, then statistically verifying that cleanup objectives have been met. Sampling and Surveying Radiological Environments pr |
Complete Guide to Writing Data Management Plans
Nov 3, 2017 · Developing a plan is an excellent way to identify useful and important records, optimize your data handling process, and anticipate issues that may arise in publishing, …
Data Management Plan - Open University
Our data management plan (DMP) aims to ensure that the data generated through this project is created, stored and made accessible in a shareable format. This will enhance the quality and …
Data Management Plan - ACDM
This plan is a summary representing how the data management processes will be conducted from the set-up of the required systems and apply them to deliver complete, clean and consistent …
University of Pittsburgh -- NSF Data Management Plan – …
University of Pittsburgh -- NSF Data Management Plan – Example 1 Data Management Main research results will be shared with the academic community and general public through the …
Example Data Management and Sharing Plan - National …
This is an example of a Data Management and Sharing (DMS) Plan for the collection of EHR data for a new study with plans to be shared in a Repository for Sharing Scientific Data (RSSD).
Data Management Plan – EXAMPLE - Oregon State University …
The data management plan in this document addresses how the Principal and Co- principal investigators will conform to NSF policy on the dissemination and sharing of research results.
Writing an Effective Data Management Plan - Rice University
1. Discuss challenges in developing data management plans (DMPs) 2. Review examples of agency guidelines 3. Highlight best practices for data management 4. Evaluate a sample plan 5. …
Guide to writing a Research Data Management Plan
guidance on appropriate tools and technologies. By the end of this guide, you will have the knowledge and tools necessary to create a thorough and effective DMP that not only meets the …
What data will you collect or create? - Colorado State University
Data Management Plan Template (rev. 2023-08-25) Adapted from DMPTool.org • See DataCite’s Repository Finder tool, re3data.org, or NIH-Supported Data Sharing Resources. • For information …
DATA MANAGEMENT AND SHARING PLAN - National …
The National Institute on Aging (NIA) Division of Social and Behavioral Research (BSR) provides the following sample Data Management and Sharing Plan for a project involving collection of social …
DATA MANAGEMENT PLAN Project Information - Montclair …
This Data Management Plan (DMP) covers the data that will be collected by a team at Montclair State University and on the design, development, and analysis of a set of animated contrasting …
Writing Data Management Plans - Michigan State University
What is a data management plan? A data management plan is a supplementary document, no longer than two pages, that describes the kinds of data to be produced, how they will be managed, and …
DATA MANAGEMENT PLAN GUIDELINES - Cape Peninsula …
• Data management plan (DMP): A document describing the manner in which research data will be treated during as well as after the completion of research projects.
Data management plans - Monash University
Why do I need a data management plan? The carrot: improvements to efficiency, protection, quality and exposure. Data management in some form is an important part of research.
How to write a data management plan (DMP) - TU Wien
producing a DMP for the first time, the Center for Research Data Management provides TU Wien specific guidance on how to write a DMP. This “how to” is particularly useful if your funder or …
Edinburgh Data Management Plan Template - University of …
Consider the types of data your research will generate, for example qualitative survey data, computational models, statistics, measurements, text, images, audio visual data, or samples. Also …
Writing an Effective Data Management Plan - Rice University
Mar 11, 2016 · 1. Discuss challenges in developing data management plans (DMPs) 2. Review examples of agency guidelines 3. Highlight best practices for data management 4. Evaluate a …
Data Management Plan - MIT Mathematics
Data Management Plan Examples: Example#1: All papers obtained will be promptly submitted for publication and posted in .pdf format on the PIs website. All course materials created will also be …
NSF Data Management Plan Instructions and Template - Utah …
Apr 16, 2019 · DATA MANAGEMENT PLAN TEMPLATE* 1. Types of data, samples, physical collections, software, curriculum materials, and other materials to be produced in the course of …
Data management plans - Monash University
What are data management plans? • Who will be responsible for each of these activities. Many Australian Universities have Data Management Plan tools available for use by researchers …
Open Research Data and Data Management Plans
a Data Management Plan (DMP) within six months after the start of their grant. Grantees are ... This could be, for example, as soon as possible after the data collection, or at the end of the project. …
NIH Data Management and Sharing: Budget Guidance
Sep 12, 2023 · Example DMS Budgets with Justifications . Estimating Data Management and Sharing Costs . Stage Description (NIH Language) How to Estimate Costs Proposal Budget …
Tips for Writing a Data Management and Sharing (DMS) …
“data accessibility”) to leave sufficient time for the data repository to process the data and work with your data team to address any quality assurance/quality control questions. Do not wait …
Data Management Plan Guidelines and Template 1.
The Data Management Plan should help researchers manage their data during the full life cycle of a research project. This data management plan should be updated before, during and after the …
NSF Data Management Plan Instructions and Template
Apr 16, 2019 · The Data Management Plan (DMP) addresses the Merit Review Criteria of the proposal by indicating how the results and products of the project will be collected and …
Edinburgh Data Management Plan Template - University of …
The data in the Research Data Management (RDM) file-store is automatically replicated to an off-site disaster facility and also backed up with a 60-day retention period, with 10 days of file …
EXAMPLE DATA MANAGEMENT AND SHARING PLAN (in …
Feb 10, 2023 · EXAMPLE DATA MANAGEMENT AND SHARING PLAN (in compliance with SF-424 Forms H) EXAMPLE FOR HUMAN GENOMIC DATA ELEMENT 1: DATA TYPE ...
Template for NSF Data Management Plan. In general, the …
Template for NSF Data Management Plan. In general, the data management plan should answer these two questions: 1) What data is generated by your project? 2) What is your plan for …
Data Management Plan (DMP) Template - IITA
A data management plan 1is a formal statement describing how research data will be managed and documented throughout a research project and the terms regarding the subsequent …
Writing Data Management Plans - Michigan State University
A data management plan is a supplementary document, no longer than two pages, that describes the kinds of data to be produced, how ... researcher would need to be able to use the data. For …
Data Management Plan (DMP) template - Agence nationale …
Outline the roles and responsibilities for data management/stewardship activities for example data capture, metadata production, data quality, storage and backup, data archiving, and data …
Data Security for Data Management Plans - Marquette …
Data Management Plan Guidance Data Security Page 1 of 3. Data Security for Data Management Plans . Does this apply to me? Data security applies to every researcher. The impacts may …
Template for a Data Management Plan - TU Dublin
Data Management Plan Template . This is information about what needs to be in a Data Management Plan and a generic template for a plan is at the end of the document. If you are …
Data Management Plan Sample Unidata NSF Project
About the Project Data This is an example of a Data Management Plan based on the NSF Division of Atmospheric and Geospace Sciences (AGS) template. Here we provide a sample …
Guidelines for Effective Data Management Plans - tntech.edu
Guidelines for Effective Data Management Plans Data Management Plans Federal funding agencies are increasingly recommending or requiring formal data management plans with all …
Data Management Plan for Surveysshould be completed for …
Will the data include identifying information that may connect the data to a person or group? Describe how access to identifiable data will be maintained and how it will be used only for the …
Data and Safety Monitoring Plan (DSMP) Guideline - Mayo …
Data and Safety Monitoring Plan (DSMP) Template Definitions Data Safety Monitoring Plan (DSMP): A DSMP is a quality assurance plan for a research study. A Data and Safety …
Data Management for Proposals and Awards in the …
The data management plan (DMP) should be short (no more than two pages) and should be submitted ... For example, the data produced by an archeologist might be quite different from …
Guidance for the preparation of data management plans for …
Contents Acknowledgements ..... 1
Data Management Plans for Archaeological Research – 2017
data management plan (DMP) as part of proposals seeking funding (e.g., National Science Foundation 2010). ... The DMP template provided in this guide will help you complete your data …
Guidelines for Effective Data Management Plans
Guidelines for Effective Data Management Plans Data Management Plans Federal funding agencies are increasingly recommending or requiring formal data management plans with all …
EXAMPLE OF A DATA MANAGEMENT POLICY - Dutch …
Copying a data management plan from another project is impossible, but a good procedure to make a data management plan may be copied. Such a procedure, made by Rob van …
D7.9 Data Management Plan v2 - clarity-h2020.eu
This report is the third deliverable of Task 7.3 “Data Management ” and describes the final Data Management Plan (DMP) for the CLARITY project, funded by the EU’s Horizon 2020 …
Data Management Plan – EXAMPLE - Oregon State …
Data Management Plan – EXAMPLE . NSF Proposal: ## Proposal Title. Oregon State University is committed to ensuring excellent data management in their research with NSF. The data …
DATA MANAGEMENT PLAN (DMP) guide - University of …
DATA MANAGEMENT PLAN (DMP) guide Data Management Planning, especially the considerations, conversations and documentation ... It is an example of a PID. E2 Licencing If …
Suggested Elements to Cover in a Data Management Plan
Data Management Plan [TEMPLATE with Examples] . 1. Data description (Highly Recommended) . Example 1: This project will produce public-use nationally representative survey data for the …
Supplemental DRAFT Guidance: Elements of a NIH
Data Management and Sharing Plan (Plan) for public comment. A Plan should describe in two pages or less the proposed approach to data management and sharing that the specific …
DATA MANAGEMENT PLAN - Maryland
MD iMap Data Management Plan 5 1 PURPOSE MD iMap is Maryland’s statewide enterprise GIS system. The standards and specifications within this Data Management Plan will improve data …
Data Management and Sharing Plans (DMSP) NSF, NIH
A formal written data management plan is a required component of a grant proposal for most federal funding agencies. Investigators should follow instruction from the sponsor for ... For …
Guidelines for Data Management Plans (max 2 pages) …
Example Data Management Plan: The Fisheries Telemetry Project, implemented by Best Fisherman Group (P.I. James Best), will generate environmental information, including the …
Deliverable 7.4: Data Management Plan adhering to the …
This document describes the initial Data Management Plan (DMP), as Deliverable 7.4 on Month 6, customized for the PERFORM project, funded by the SPIRE program (Sustainable Process …
NIDDK Example Data Management and Sharing Plan …
NIDDK Example Data Management and Sharing Plan – Secondary Data Analysis Element 1: Data Type: A. Types and amount of scientific data expected to be generated in the project: This …
Data management plan template
%PDF-1.7 %âãÏÓ 1095 0 obj > endobj 1107 0 obj >/Filter/FlateDecode/ID[987372DAF6C39751A1C6CF9183794080>8856B04B0762CF45ACD2EEB82573207E>]/Index[1095 …
Data Management Plans - FFG
What is a Data Management Plan (DMP)? “… Data Management Plans (DMPs) detailing what data the project will generate, whether and how it will be exploited or made accessible for …
DATA MANAGEMENT AND SHARING PLAN - National …
data management and sharing plan An example from an application proposing to collect genomic, phenotypic, and clinical data from human subjects. If any of the proposed research in the …
Research Data Management Plan
Research Data Management Plan - Guidelines 4 data with varying levels of access, including open data, restricted data, and closed data Appendix A lists several example research …
TEMPLATE HORIZON 2020 DATA MANAGEMENT PLAN (DMP)
H2020 templates: Data management plan v2.0 – 15.02.2018 2 Project1 Number: [insert project reference number] Project Acronym: [insert acronym] Project title: [insert project title] DATA …
DATA MANAGEMENT AND SHARING PLAN - National …
Management and Sharing Plan for a project involving secondary data analysis using data obtained from an existing repository. In this case, the Health and Retirement Study (HRS) is …
D8.5 Data Management Plan - imi-conception.eu
D8.5 Data Management Plan Lead contributor Florian van der Nolle (1. UMC Utrecht) f.l.vandernolle-raven@umcutrecht.nl Other contributors Pieter Stolk (1. UMCU) ... purposed for …
Data Management Plan - MIT Mathematics
Data Management Plan Examples: Example#1: All papers obtained will be promptly submitted for publication and posted in .pdf format on the PIs website. All course materials created will also …
Data Management Standard Operating Procedure DMSOP) …
separate project-specific data management plans as well to sufficiently describe some of the project specific data management processes. SECTION 3: DATA SHARING a) Discuss data …
How to Write a Data Management Plan for a National …
Data Management and Cybersecurity The National Science Foundation (NSF) has made good the announcement in last May’s press release to require a data management plan with every NSF …
Research Data Management - Dublin City University
Research Data Management Plan: Guidance and Resources is a short guide to assist you with creating a Research Data Management Plan (DMP). Research Data Management ... For …
Data Management Plan - irsjd.org
researchers in creating their Data Management Plan (DMP). It is specifically aimed at projects financed under the EU's Horizon Europe programme to create a FAIR data management plan, …
Guidelines for Data Management Plans Templates and …
The Data Management Plan you submit may be posted to the internet by NOAA, and serves to document how the recipient intends to comply with their award conditions related to collecting …
Data Governance Plan - Volume 1 Data Governance Primer
1.2 data Governance and StewardShIp proGram plan The FHWA data governance and stewardship strategic goals address the issues and challenges currently confronting the …
Writing an NIH Data Management and Sharing Plan - Johns …
Jan 25, 2023 · NIH Data Management and Sharing Policy: Effect On Jan. 25 •Policy Goals: Advance rigorous and reproducible research Promote public trust in research •Requirement to …
Guidelines for Effective Data Management Plans - Mentor …
Guidelines for Effective Data Management Plans Data Management Plans Federal funding agencies are increasingly recommending or requiring formal data management plans with all …
Planning for Data Management - USGS
For example, limits on data sharing can be an issue if the applicant has policy conflicting with the funding entity. The “why” should be covered for each of the components listed. For ... A data …
Data Management Planning NERC funding applicants
• A one-page (or less) Outline Data Management Plan (ODMP) is required at the application stage.1 • A fuller Data Management Plan (DMP) must be provided to NERC within three …
NCIPC Data Management Plan Template - Centers for …
reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. An agency may not …
