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 stewards in business and IT are the backbone of a successful data governance implementation because they do the work to make a company's data trusted, dependable, and high quality. Data Stewardship explains everything you need to know to successfully implement the stewardship portion of data governance, including how to organize, train, and work with data stewards, get high-quality business definitions and other metadata, and perform the day-to-day tasks using a minimum of the steward's time and effort. David Plotkin has loaded this book with practical advice on stewardship so you can get right to work, have early successes, and measure and communicate those successes, gaining more support for this critical effort. - Provides clear and concise practical advice on implementing and running data stewardship, including guidelines on how to organize based on company structure, business functions, and data ownership - Shows how to gain support for your stewardship effort, maintain that support over the long-term, and measure the success of the data stewardship effort and report back to management - Includes detailed lists of responsibilities for each type of data steward and strategies to help the Data Governance Program Office work effectively with the data stewards

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.

As digital technologies are expanding the power and reach of research, they are also raising complex issues. These include complications in ensuring the validity of research data; standards that do not keep pace with the high rate of innovation; restrictions on data sharing that reduce the ability of researchers to verify results and build on previous research; and huge increases in the amount of data being generated, creating severe challenges in preserving that data for long-term use. Ensuring the Integrity, Accessibility, and Stewardship of Research Data in the Digital Age examines the consequences of the changes affecting research data with respect to three issues - integrity, accessibility, and stewardship-and finds a need for a new approach to the design and the management of research projects. The report recommends that all researchers receive appropriate training in the management of research data, and calls on researchers to make all research data, methods, and other information underlying results publicly accessible in a timely manner. The book also sees the stewardship of research data as a critical long-term task for the research enterprise and its stakeholders. Individual researchers, research institutions, research sponsors, professional societies, and journals involved in scientific, engineering, and medical research will find this book an essential guide to the principles affecting research data in the digital age.

Multi-Domain Master Data Management delivers practical guidance and specific instruction to help guide planners and practitioners through the challenges of a multi-domain master data management (MDM) implementation. Authors Mark Allen and Dalton Cervo bring their expertise to you in the only reference you need to help your organization take master data management to the next level by incorporating it across multiple domains. Written in a business friendly style with sufficient program planning guidance, this book covers a comprehensive set of topics and advanced strategies centered on the key MDM disciplines of Data Governance, Data Stewardship, Data Quality Management, Metadata Management, and Data Integration. - Provides a logical order toward planning, implementation, and ongoing management of multi-domain MDM from a program manager and data steward perspective. - Provides detailed guidance, examples and illustrations for MDM practitioners to apply these insights to their strategies, plans, and processes. - Covers advanced MDM strategy and instruction aimed at improving data quality management, lowering data maintenance costs, and reducing corporate risks by applying consistent enterprise-wide practices for the management and control of master data.

This open access book provides a broad context for the understanding of current problems of science and of the different movements aiming to improve the societal impact of science and research. The author offers insights with regard to ideas, old and new, about science, and their historical origins in philosophy and sociology of science, which is of interest to a broad readership. The book shows that scientifically grounded knowledge is required and helpful in understanding intellectual and political positions in various discussions on the grand challenges of our time and how science makes impact on society. The book reveals why interventions that look good or even obvious, are often met with resistance and are hard to realize in practice. Based on a thorough analysis, as well as personal experiences in aids research, university administration and as a science observer, the author provides - while being totally open regarding science's limitations- a realistic narrative about how research is conducted, and how reliable ‘objective’ knowledge is produced. His idea of science, which draws heavily on American pragmatism, fits in with the global Open Science movement. It is argued that Open Science is a truly and historically unique movement in that it translates the analysis of the problems of science into major institutional actions of system change in order to improve academic culture and the impact of science, engaging all actors in the field of science and academia.

As we move into an era of unprecedented volumes of data and computing power, the benefits aren't for business alone. Data can help citizens access government, hold it accountable and build new services to help themselves. Simply making data available is not sufficient. The use of data for the public good is being driven by a distributed community of media, nonprofits, academics and civic advocates. This report from O'Reilly Radar highlights the principles of data in the public good, and surveys areas where data is already being used to great effect, covering: Consumer finance Transit data Government transparency Data journalism Aid and development Crisis and emergency response Healthcare

Citizen science enlists members of the public to make and record useful observations, such as counting birds in their backyards, watching for the first budding leaf in spring, or measuring local snowfall. The large numbers of volunteers who participate in projects such as Project FeederWatch or Project BudBurst collect valuable research data, which, when pooled together, create an enormous body of scientific data on a vast geographic scale. In return, such projects aim to increase participants' connections to science, place, and nature, while supporting science literacy and environmental stewardship. In Citizen Science, experts from a variety of disciplines—including scientists and education specialists working at the Cornell Lab of Ornithology, where many large citizen science programs use birds as proxies for biodiversity—share their experiences of creating and implementing successful citizen science projects, primarily those that use massive data sets gathered by citizen scientists to better understand the impact of environmental change. This first and foundational book for this developing field of inquiry addresses basic aspects of how to conduct citizen science projects, including goal-setting, program design, and evaluation, as well as the nuances of creating a robust digital infrastructure and recruiting a large participant base through communications and marketing. An overview of the types of research approaches and techniques demonstrates how to make use of large data sets arising from citizen science projects. A final section focuses on citizen science's impacts and its broad connections to understanding the human dimensions and educational aspects of participation. Citizen Science teaches teams of program developers and researchers how to cross the bridge from success at public engagement to using citizen science data to understand patterns and trends or to test hypotheses about how ecological processes respond to change at large geographic scales. Intended as a resource for a broad audience of experts and practitioners in natural sciences, information science, and social sciences, this book can be used to better understand how to improve existing programs, develop new ones, and make better use of the data resources that have accumulated from citizen science efforts. Its focus on harnessing the impact of "crowdsourcing" for scientific and educational endeavors is applicable to a wide range of fields, especially those that touch on the importance of massive collaboration aimed at understanding and conserving what we can of the natural world.

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.