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| data management plan template: Data Management for Researchers Kristin Briney, 2015-09-01 A comprehensive guide to everything scientists need to know about data management, this book is essential for researchers who need to learn how to organize, document and take care of their own data. Researchers in all disciplines are faced with the challenge of managing the growing amounts of digital data that are the foundation of their research. Kristin Briney offers practical advice and clearly explains policies and principles, in an accessible and in-depth text that will allow researchers to understand and achieve the goal of better research data management. Data Management for Researchers includes sections on: * The data problem – an introduction to the growing importance and challenges of using digital data in research. Covers both the inherent problems with managing digital information, as well as how the research landscape is changing to give more value to research datasets and code. * The data lifecycle – a framework for data’s place within the research process and how data’s role is changing. Greater emphasis on data sharing and data reuse will not only change the way we conduct research but also how we manage research data. * Planning for data management – covers the many aspects of data management and how to put them together in a data management plan. This section also includes sample data management plans. * Documenting your data – an often overlooked part of the data management process, but one that is critical to good management; data without documentation are frequently unusable. * Organizing your data – explains how to keep your data in order using organizational systems and file naming conventions. This section also covers using a database to organize and analyze content. * Improving data analysis – covers managing information through the analysis process. This section starts by comparing the management of raw and analyzed data and then describes ways to make analysis easier, such as spreadsheet best practices. It also examines practices for research code, including version control systems. * Managing secure and private data – many researchers are dealing with data that require extra security. This section outlines what data falls into this category and some of the policies that apply, before addressing the best practices for keeping data secure. * Short-term storage – deals with the practical matters of storage and backup and covers the many options available. This section also goes through the best practices to insure that data are not lost. * Preserving and archiving your data – digital data can have a long life if properly cared for. This section covers managing data in the long term including choosing good file formats and media, as well as determining who will manage the data after the end of the project. * Sharing/publishing your data – addresses how to make data sharing across research groups easier, as well as how and why to publicly share data. This section covers intellectual property and licenses for datasets, before ending with the altmetrics that measure the impact of publicly shared data. * Reusing data – as more data are shared, it becomes possible to use outside data in your research. This chapter discusses strategies for finding datasets and lays out how to cite data once you have found it. This book is designed for active scientific researchers but it is useful for anyone who wants to get more from their data: academics, educators, professionals or anyone who teaches data management, sharing and preservation. An excellent practical treatise on the art and practice of data management, this book is essential to any researcher, regardless of subject or discipline. —Robert Buntrock, Chemical Information Bulletin |
| data management plan template: Practical Guide to Clinical Data Management Susanne Prokscha, 1999-01-31 Clinical data management (CDM) has changed from being an essentially clerical task in the late 1970s and early 1980s to a highly computerized, highly specialized field today. And clinical data manages have had to adapt their data management systems and processes accordingly. Practical Guide to Clinical Data Management steers you through a basic understanding of the role of data management in clinical trials and includes more advanced topics such as CDM systems, SOPs, and quality assurance. This book helps you ensure GCP, manage laboratory data, and deal with the kinds of clinical data that can cause difficulties in database applications. With the tools this book provides, you'll learn how to: Ensure that your DMB system is in compliance with federal regulations Build a strategic data management and databsing plan Track and record CRFs Deal with problem data, adverse event data, and legacy data Manage and store lab data Identify and manage discrepancies Ensure quality control over reports Choose a CDM system that is right for your company Create and implement a system validation plan and process Set up and enforce data collection standards Develop test plans and change control systems This book is your guide to finding the most successful and practical options for effective clinical data management. |
| data management plan template: Research Data Management Joyce M. Ray, 2014 It has become increasingly accepted that important digital data must be retained and shared in order to preserve and promote knowledge, advance research in and across all disciplines of scholarly endeavor, and maximize the return on investment of public funds. To meet this challenge, colleges and universities are adding data services to existing infrastructures by drawing on the expertise of information professionals who are already involved in the acquisition, management and preservation of data in their daily jobs. Data services include planning and implementing good data management practices, thereby increasing researchers' ability to compete for grant funding and ensuring that data collections with continuing value are preserved for reuse. This volume provides a framework to guide information professionals in academic libraries, presses, and data centers through the process of managing research data from the planning stages through the life of a grant project and beyond. It illustrates principles of good practice with use-case examples and illuminates promising data service models through case studies of innovative, successful projects and collaborations. |
| data management plan template: DAMA-DMBOK Dama International, 2017 Defining a set of guiding principles for data management and describing how these principles can be applied within data management functional areas; Providing a functional framework for the implementation of enterprise data management practices; including widely adopted practices, methods and techniques, functions, roles, deliverables and metrics; Establishing a common vocabulary for data management concepts and serving as the basis for best practices for data management professionals. DAMA-DMBOK2 provides data management and IT professionals, executives, knowledge workers, educators, and researchers with a framework to manage their data and mature their information infrastructure, based on these principles: Data is an asset with unique properties; The value of data can be and should be expressed in economic terms; Managing data means managing the quality of data; It takes metadata to manage data; It takes planning to manage data; Data management is cross-functional and requires a range of skills and expertise; Data management requires an enterprise perspective; Data management must account for a range of perspectives; Data management is data lifecycle management; Different types of data have different lifecycle requirements; Managing data includes managing risks associated with data; Data management requirements must drive information technology decisions; Effective data management requires leadership commitment. |
| data management plan template: Data Management Margaret E. Henderson, 2016-10-25 Libraries organize information and data is information, so it is natural that librarians should help people who need to find, organize, use, or store data. Organizations need evidence for decision making; data provides that evidence. Inventors and creators build upon data collected by others. All around us, people need data. Librarians can help increase the relevance of their library to the research and education mission of their institution by learning more about data and how to manage it. Data Management will guide readers through: Understanding data management basics and best practices. Using the reference interview to help with data management Writing data management plans for grants. Starting and growing a data management service. Finding collaborators inside and outside the library. Collecting and using data in different disciplines. |
| data management plan template: 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. |
| data management plan template: 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. |
| data management plan template: 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. |
| data management plan template: 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. |
| data management plan template: Data Management in Large-Scale Education Research Crystal Lewis, 2024-07-09 Research data management is becoming more complicated. Researchers are collecting more data, using more complex technologies, all the while increasing the visibility of our work with the push for data sharing and open science practices. Ad hoc data management practices may have worked for us in the past, but now others need to understand our processes as well, requiring researchers to be more thoughtful in planning their data management routines. This book is for anyone involved in a research study involving original data collection. While the book focuses on quantitative data, typically collected from human participants, many of the practices covered can apply to other types of data as well. The book contains foundational context, instructions, and practical examples to help researchers in the field of education begin to understand how to create data management workflows for large-scale, typically federally funded, research studies. The book starts by describing the research life cycle and how data management fits within this larger picture. The remaining chapters are then organized by each phase of the life cycle, with examples of best practices provided for each phase. Finally, considerations on whether the reader should implement, and how to integrate those practices into a workflow, are discussed. Key Features: Provides a holistic approach to the research life cycle, showing how project management and data management processes work in parallel and collaboratively Can be read in its entirety, or referenced as needed throughout the life cycle Includes relatable examples specific to education research Includes a discussion on how to organize and document data in preparation for data sharing requirements Contains links to example documents as well as templates to help readers implement practices |
| data management plan template: Exploring Research Data Management Andrew Cox, Eddy Verbaan, 2018-05-11 Research Data Management (RDM) has become a professional topic of great importance internationally following changes in scholarship and government policies about the sharing of research data. Exploring Research Data Management provides an accessible introduction and guide to RDM with engaging tasks for the reader to follow and develop their knowledge. Starting by exploring the world of research and the importance and complexity of data in the research process, the book considers how a multi-professional support service can be created then examines the decisions that need to be made in designing different types of research data service from local policy creation, training, through to creating a data repository. Coverage includes: A discussion of the drivers and barriers to RDM Institutional policy and making the case for Research Data Services Practical data management Data literacy and training researchers Ethics and research data services Case studies and practical advice from working in a Research Data Service. This book will be useful reading for librarians and other support professionals who are interested in learning more about RDM and developing Research Data Services in their own institution. It will also be of value to students on librarianship, archives, and information management courses studying topics such as RDM, digital curation, data literacies and open science. |
| data management plan template: Managing and Sharing Research Data Louise Corti, Veerle Van den Eynden, Libby Bishop, Matthew Woollard, 2014-02-04 Research funders in the UK, USA and across Europe are implementing data management and sharing policies to maximize openness of data, transparency and accountability of the research they support. Written by experts from the UK Data Archive with over 20 years experience, this book gives post-graduate students, researchers and research support staff the data management skills required in today’s changing research environment. The book features guidance on: how to plan your research using a data management checklist how to format and organize data how to store and transfer data research ethics and privacy in data sharing and intellectual property rights data strategies for collaborative research how to publish and cite data how to make use of other people’s research data, illustrated with six real-life case studies of data use. |
| data management plan template: How to be FAIR with Your Data Claudia Engelhardt, Raisa Barthauer, Katarzyna Biernacka, Aoife Coffey, Ronald Cornet, Alina Danciu, Yuri Demchenko, Stephen Downes, Christopher Erdmann, Federica Garbuglia, Kerstin Germer, Kerstin Helbig, Margareta Hellström, Kristina Hettne, Dawn Hibbert, Mijke Jetten, Yulia Karimova, Karsten Kryger Hansen, Mari Elisa Kuusniemi, Viviana Letizia, Valerie McCutcheon, Barbara McGillivray, Jenny Ostrop, Britta Petersen, Ana Petrus, Stefan Reichmann, Najla Rettberg, Carmen Reverté, Nick Rochlin, Bregt Saenen, Birgit Schmidt, Jolien Scholten, Hugh Shanahan, Armin Straube, Veerle Van den Eynden, Justine Vandendorpe, Shanmugasundaram Venkataram, André Vieira, Cord Wiljes, Ulrike Wuttke, Joanne Yeomans, Biru Zhou, 2022 This handbook was written and edited by a group of about 40 collaborators in a series of six book sprints that took place between 1 and 10 June 2021. It aims to support higher education institutions with the practical implementation of content relating to the FAIR principles in their curricula, while also aiding teaching by providing practical material, such as competence profiles, learning outcomes, lesson plans, and supporting information. It incorporates community feedback received during the public consultation which ran from 27 July to 12 September 2021. |
| data management plan template: 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. |
| data management plan template: 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. |
| data management plan template: The Medical Library Association Guide to Data Management for Librarians Lisa Federer, 2016-09-15 Technological advances and the rise of collaborative, interdisciplinary approaches have changed the practice of research. The 21st century researcher not only faces the challenge of managing increasingly complex datasets, but also new data sharing requirements from funders and journals. Success in today’s research enterprise requires an understanding of how to work effectively with data, yet most researchers have never had any formal training in data management. Libraries have begun developing services and programs to help researchers meet the demands of the data-driven research enterprise, giving librarians exciting new opportunities to use their expertise and skills. The Medical Library Association Guide to Data Management for Librarians highlights the many ways that librarians are addressing researchers’ changing needs at a variety of institutions, including academic, hospital, and government libraries. Each chapter ends with “pearls of wisdom,” a bulleted list of 5-10 takeaway messages from the chapter that will help readers quickly put the ideas from the chapter into practice. From theoretical foundations to practical applications, this book provides a background for librarians who are new to data management as well as new ideas and approaches for experienced data librarians. |
| data management plan template: Target-setting Methods and Data Management to Support Performance-based Resource Allocation by Transportation Agencies National Cooperative Highway Research Program, 2010 TRB's National Cooperative Highway Research Program (NCHRP) Report 666: Target Setting Methods and Data Management to Support Performance-Based Resource Allocation by Transportation Agencies - Volume I: Research Report, and Volume II: Guide for Target-Setting and Data Management provides a framework and specific guidance for setting performance targets and for ensuring that appropriate data are available to support performance-based decision-making. Volume III to this report was published separately in an electronic-only format as NCHRP Web-Only Document 154. Volume III includes case studies of organizations investigated in the research used to develop NCHRP Report 666. |
| data management plan template: The DAMA Dictionary of Data Management Dama International, 2011 A glossary of over 2,000 terms which provides a common data management vocabulary for IT and Business professionals, and is a companion to the DAMA Data Management Body of Knowledge (DAMA-DMBOK). Topics include: Analytics & Data Mining Architecture Artificial Intelligence Business Analysis DAMA & Professional Development Databases & Database Design Database Administration Data Governance & Stewardship Data Management Data Modeling Data Movement & Integration Data Quality Management Data Security Management Data Warehousing & Business Intelligence Document, Record & Content Management Finance & Accounting Geospatial Data Knowledge Management Marketing & Customer Relationship Management Meta-Data Management Multi-dimensional & OLAP Normalization Object-Orientation Parallel Database Processing Planning Process Management Project Management Reference & Master Data Management Semantic Modeling Software Development Standards Organizations Structured Query Language (SQL) XML Development |
| data management plan template: 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. |
| data management plan template: Managing and Sharing Research Data Louise Corti, Veerle Van den Eynden, Libby Bishop, Matthew Woollard, 2014-03-01 Research funders in the UK, USA and across Europe are implementing data management and sharing policies to maximize openness of data, transparency and accountability of the research they support. Written by experts from the UK Data Archive with over 20 years experience, this book gives post-graduate students, researchers and research support staff the data management skills required in today's changing research environment. The book features guidance on: how to plan your research using a data management checklist how to format and organize data how to store and transfer data research ethics and privacy in data sharing and intellectual property rights data strategies for collaborative research how to publish and cite data how to make use of other people's research data, illustrated with six real-life case studies of data use. |
| data management plan template: The Data Management Toolkit: A Step-By-Step Implementation Guide for the Pioneers of Data Management Irina Steenbeek, 2019-03-09 Eight years ago, I joined a new company. My first challenge was to develop an automated management accounting reporting system. A deep analysis of the existing reports showed us the high necessity to implement a singular reporting platform, and we opted to implement a data warehouse. At the time, one of the consultants came to me and said, I heard that we might need data management. I don't know what it is. Check it out. So I started Googling Data management...This book is for professionals who are now in the same position I found myself in eight years ago and for those who want to become a data management pro of a medium sized company.It is a collection of hands-on knowledge, experience and observations on how to implement data management in an effective, feasible and to-the-point way. |
| data management plan template: 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. |
| data management plan template: Big Data Infrastructure Technologies for Data Analytics Yuri Demchenko, |
| data management plan template: The Open Handbook of Linguistic Data Management Andrea L. Berez-Kroeker, Bradley McDonnell, Eve Koller, Lauren B. Collister, 2022-01-18 A guide to principles and methods for the management, archiving, sharing, and citing of linguistic research data, especially digital data. Doing language science depends on collecting, transcribing, annotating, analyzing, storing, and sharing linguistic research data. This volume offers a guide to linguistic data management, engaging with current trends toward the transformation of linguistics into a more data-driven and reproducible scientific endeavor. It offers both principles and methods, presenting the conceptual foundations of linguistic data management and a series of case studies, each of which demonstrates a concrete application of abstract principles in a current practice. In part 1, contributors bring together knowledge from information science, archiving, and data stewardship relevant to linguistic data management. Topics covered include implementation principles, archiving data, finding and using datasets, and the valuation of time and effort involved in data management. Part 2 presents snapshots of practices across various subfields, with each chapter presenting a unique data management project with generalizable guidance for researchers. The Open Handbook of Linguistic Data Management is an essential addition to the toolkit of every linguist, guiding researchers toward making their data FAIR: Findable, Accessible, Interoperable, and Reusable. |
| data management plan template: Writing and Managing SOPs for GCP Susanne Prokscha, 2015-07-29 Writing and Managing SOPs for GCP is the first book to discuss managing Standard Operating Procedures (SOPs) for Good Clinical Practice (GCP) from conception to retirement. It recommends approaches that have a direct impact on improving SOP and regulatory compliance. Throughout the text, the book provides a user's point of view to keep topics focus |
| data management plan template: Data Science Landscape Usha Mujoo Munshi, Neeta Verma, 2018-03-01 The edited volume deals with different contours of data science with special reference to data management for the research innovation landscape. The data is becoming pervasive in all spheres of human, economic and development activity. In this context, it is important to take stock of what is being done in the data management area and begin to prioritize, consider and formulate adoption of a formal data management system including citation protocols for use by research communities in different disciplines and also address various technical research issues. The volume, thus, focuses on some of these issues drawing typical examples from various domains. The idea of this work germinated from the two day workshop on “Big and Open Data – Evolving Data Science Standards and Citation Attribution Practices”, an international workshop, led by the ICSU-CODATA and attended by over 300 domain experts. The Workshop focused on two priority areas (i) Big and Open Data: Prioritizing, Addressing and Establishing Standards and Good Practices and (ii) Big and Open Data: Data Attribution and Citation Practices. This important international event was part of a worldwide initiative led by ICSU, and the CODATA-Data Citation Task Group. In all, there are 21 chapters (with 21st Chapter addressing four different core aspects) written by eminent researchers in the field which deal with key issues of S&T, institutional, financial, sustainability, legal, IPR, data protocols, community norms and others, that need attention related to data management practices and protocols, coordinate area activities, and promote common practices and standards of the research community globally. In addition to the aspects touched above, the national / international perspectives of data and its various contours have also been portrayed through case studies in this volume. |
| data management plan template: Data Strategy Bernard Marr, 2017-04-03 BRONZE RUNNER UP: Axiom Awards 2018 - Business Technology Category Less than 0.5 per cent of all data is currently analyzed and used. However, business leaders and managers cannot afford to be unconcerned or sceptical about data. Data is revolutionizing the way we work and it is the companies that view data as a strategic asset that will survive and thrive. Data Strategy is a must-have guide to creating a robust data strategy. Explaining how to identify your strategic data needs, what methods to use to collect the data and, most importantly, how to translate your data into organizational insights for improved business decision-making and performance, this is essential reading for anyone aiming to leverage the value of their business data and gain competitive advantage. Packed with case studies and real-world examples, advice on how to build data competencies in an organization and crucial coverage of how to ensure your data doesn't become a liability, Data Strategy will equip any organization with the tools and strategies it needs to profit from Big Data, analytics and the Internet of Things (IoT). |
| data management plan template: Marine Analytical Chemistry Julián Blasco, Antonio Tovar-Sánchez, 2022-11-03 This textbook offers a comprehensive and authoritative introduction to the latest analytical methods, tools and techniques used in the marine environment, bringing together the two fields of chemical oceanography and analytical chemistry. Divided into 11 chapters, the book starts with an overview of the main parameters of the marine carbon system, and it covers different sampling strategies used by the marine scientific community, and the different chemical analyses to measure trace metals, radionuclides and organic matter in the marine environment. Particular attention is given to the identification and quantification of marine persistent organic pollutants, emerging organic contaminants and microplastics. Readers will also find accessible explanations and real life examples of the application of remote sensing and in-situ sensing technologies to monitor the marine environment. The textbook finishes with a chapter on data treatment that outlines the relevant statistical approaches, uncertainty estimation and quality assurance of marine chemical measurements. This textbook provides both students and professionals alike with a transdisciplinary and comprehensive foundation for the chemical analysis of our oceans and seas. |
| data management plan template: Technology, Change and the Academic Library Jeremy Atkinson, 2020-09-22 Massive technological change has been impacting universities and university libraries in recent years. Such change has manifested in technological developments impacting all areas of academic library activity, including systems, services, collections, the physical library environment, marketing, and support for university teaching, learning, research, and administration. Many books and papers have examined these changes from a technical perspective. However, there is little substantive reflection on what technological change means, and how best to get out in front of it, for the academic library. Technology, Change and the Academic Library systematically reflects on technological innovation, the successes, failures and lessons learned, the nature, process and culture of change, and key aspects including impacts on library staff and users, roles and responsibilities, and skills and capabilities. The book takes an international perspective on the massive change currently affecting academic libraries. The title gives an overview and literature review, considers technological innovation and change management, future technologies and future change, and provides information on further reading. Case studies describe the rationale, aims, and objectives for particular technological innovations, and consider methods, outcomes, and recommendations for the future. Finally, the book reflects back on how technological change can best be wrought in academic libraries. - Gives library managers and librarians insight into how best to identify, plan, and implement technological innovation - Provides a wide-ranging overview, literature review, and a series of reflective case studies on technological innovation in libraries - Emphasises current trends, lessons, and critical issues for putting technological innovation into place - Offers an international perspective on technological innovation in the academic library - Uses a critical methodology to reflect on what works, what does not, and how managers can apply lessons from real cases worldwide |
| data management plan template: Having Success with NSF Ping Li, Karen Marrongelle, 2012-11-27 This book is designed to help researchers achieve success in funding their National Science Foundation (NSF) research proposals. The book discusses aspects of the proposal submission and review process that are not typically communicated to the research community. Written by authors with successful track records in grant writing and years of experience as NSF Program Directors, this book provides an insider’s view of successful grantsmanship. Written in a practical approach, this book offers tips that will not be found in official paperwork and provides answers to questions frequently asked of NSF Program Directors. The purpose of the book is to improve your NSF grant-writing skills and improve your chances of funding. |
| data management plan template: Working as a Data Librarian Eric O. Johnson, 2018-11-16 Many librarians' job responsibilities increasingly require them to understand and handle data. Learn how to be an effective data librarian—even if you never expected to need data skills. The field of data librarianship is rapidly growing, and some librarians may feel that their training and experience does not cover data questions asked by patrons seeking advice. With this gentle guide for librarians moving—sometimes unexpectedly—into the world of data librarianship, all you need is a willingness to learn the skills required for the rapidly growing number of jobs requiring data librarianship. Working as a Data Librarian focuses on transferable skills and understanding and does not assume extensive knowledge. It introduces tasks and concepts needed to be an effective data librarian, such as best practices for data reference interviewing, finding data sources, data visualization, data literacy, the data lifecycle, metadata design, database design, understanding data management, and preparing data management plans. Additional sections focus on supporting creativity (Makerspaces and Fablabs, 3-D modeling), supporting analysis (GIS, data visualization, text mining, statistical methods), supporting research (digital scholarship, digital preservation, institutional data repositories, scholarly communication), and outreach (data librarian liaisonship, hackathons, developing outreach programs). |
| data management plan template: Department of Homeland Security Appropriations for 2010 United States. Congress. House. Committee on Appropriations. Subcommittee on Homeland Security, 2009 |
| data management plan template: 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. |
| data management plan template: Piecing Together Systematic Reviews and Other Evidence Syntheses Sarah T. Jewell, 2022-10-17 Systematic reviews and other evidence syntheses have a vital role in summarizing the literature, exploring gaps in research, prioritizing new research, and providing literature to support decision-making and evidence-based practices. Librarians adapt their practices as members of the higher education and research community. If they consult and teach with researchers, faculty, and students, review methods will likely be a part of their work. Piecing Together Systematic Reviews and Other Evidence Syntheses: A Guide for Librarians aims to be the definitive text on systematic reviews for librarians, information professionals, and expert searchers. Starting with an introduction to evidence syntheses, the book follows the acronym PIECCESS, a framework for the 8 phases which flow through 8 processes. The 8 phases are (1) Proposal of scope; (2) Protocol registration; (3) Preliminary findings; (4) Paper completion; (5) Preserve project; (6) Promote to stakeholders; (7) Impact compilation; (8) Updating the review. The 8 processes are Plan, Identify, Evaluate, Collect, Combine, Explain, Summarize, and Share. After the processes of a review project are covered, guidance for developing and running a service is provided as well as teaching reviews and training for librarians. The intended audience for this book is any librarian interested in consulting, collaborating, completing, or teaching reviews. It has several applications: for training librarians new to reviews, for those developing a new review service, for those wanting to establish policies for current service, and as a reference for those conducting reviews or running a service. Participating in reviews is a new frontier of librarianship, with expanded opportunities for new service, research areas, and professional activities. This book is part of the effort to standardize best practices when engaging in evidence syntheses. |
| data management plan template: Good practices guidance handbook for national TB surveys , 2021-04-01 The purpose of this document is to describe and explain how to apply the principles of good clinical practices (GCP) and good data management practices (GDMP) in the context of national tuberculosis (TB) surveys, namely national population-based surveys of TB prevalence, anti-TB drug resistance surveys, and surveys of costs faced by TB patients and their households. The primary target audience for this handbook includes national TB programmes (NTPs) and partners involved in the planning, design, conduct, oversight, analysis and reporting of a national TB survey. The in-country national survey coordination team is responsible for ensuring that all survey team members conform to this guidance document. |
| data management plan template: Scientific and Technical Aerospace Reports , 1995 |
| data management plan template: Trends and Innovations in Information Systems and Technologies Álvaro Rocha, Hojjat Adeli, Luís Paulo Reis, Sandra Costanzo, Irena Orovic, Fernando Moreira, 2020-05-18 This book gathers selected papers presented at the 2020 World Conference on Information Systems and Technologies (WorldCIST’20), held in Budva, Montenegro, from April 7 to 10, 2020. WorldCIST provides a global forum for researchers and practitioners to present and discuss recent results and innovations, current trends, professional experiences with and challenges regarding various aspects of modern information systems and technologies. The main topics covered are A) Information and Knowledge Management; B) Organizational Models and Information Systems; C) Software and Systems Modeling; D) Software Systems, Architectures, Applications and Tools; E) Multimedia Systems and Applications; F) Computer Networks, Mobility and Pervasive Systems; G) Intelligent and Decision Support Systems; H) Big Data Analytics and Applications; I) Human–Computer Interaction; J) Ethics, Computers & Security; K) Health Informatics; L) Information Technologies in Education; M) Information Technologies in Radiocommunications; and N) Technologies for Biomedical Applications. |
| data management plan template: Open Source Software Policy Options for NASA Earth and Space Sciences National Academies of Sciences, Engineering, and Medicine, Division on Engineering and Physical Sciences, Space Studies Board, Committee on Best Practices for a Future Open Code Policy for NASA Space Science, 2019-01-27 Modern science is ever more driven by computations and simulations. In particular, the state of the art in space and Earth science often arises from complex simulations of climate, space weather, and astronomical phenomena. At the same time, scientific work requires data processing, presentation, and analysis through broadly available proprietary and community software.1 Implicitly or explicitly, software is central to science. Scientific discovery, understanding, validation, and interpretation are all enhanced by access to the source code of the software used by scientists. This report investigates and recommends options for NASA's Science Mission Directorate (SMD) as it considers how to establish a policy regarding open source software to complement its existing policy on open data. In particular, the report reviews existing data and software policies and the lessons learned from the implementation of those policies, summarizes community perspectives, and presents policy options and recommendations for implementing an open source software policy for NASA SMD. |
| data management plan template: Advance Concepts of Clinical Research Guidance for Industry Dr. Gayatri Ganu, Book is useful for the industrial experts who engage in clinical trials, also for students and research scholar who come in contact with clinical terms. |
| data management plan template: Managing Research Data Graham Pryor, 2012-01-20 This title defines what is required to achieve a culture of effective data management offering advice on the skills required, legal and contractual obligations, strategies and management plans and the data management infrastructure of specialists and services. Data management has become an essential requirement for information professionals over the last decade, particularly for those supporting the higher education research community, as more and more digital information is created and stored. As budgets shrink and funders of research demand evidence of value for money and demonstrable benefits for society, there is increasing pressure to provide plans for the sustainable management of data. Ensuring that important data remains discoverable, accessible and intelligible and is shared as part of a larger web of knowledge will mean that research has a life beyond its initial purpose and can offer real utility to the wider community. This edited collection, bringing together leading figures in the field from the UK and around the world, provides an introduction to all the key data issues facing the HE and information management communities. Each chapter covers a critical element of data management: • Why manage research data? • The lifecycle of data management • Research data policies: principles, requirements and trends • Sustainable research data • Data management plans and planning • Roles and responsibilities – libraries, librarians and data • Research data management: opportunities and challenges for HEIs • The national data centres • Contrasting national research data strategies: Australia and the USA • Emerging infrastructure and services for research data management and curation in the UK and Europe Readership: This is essential reading for librarians and information professionals working in the higher education sector, the research community, policy makers and university managers. It will also be a useful introduction for students taking courses in information management, archivists and national library services. |
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …
