Browse Results

Select your format based upon: 1) how you want to read your book, and 2) compatibility with your reading tool. To learn more about using Bookshare with your device, visit the Help Center.

Here is an overview of the specialized formats that Bookshare offers its members with links that go to the Help Center for more information.

• Bookshare Web Reader - A customized reading tool for Bookshare members offering all the features of DAISY with a single click of the "Read Now" link.
• DAISY (Digital Accessible Information System) - A digital book file format. DAISY books from Bookshare are DAISY 3.0 text files that work with just about every type of access technology that reads text. Books that contain images will have the download option of ‘DAISY Text with Images’.
• BRF (Braille Refreshable Format) - Digital Braille for use with refreshable Braille devices and Braille embossers.
• MP3 (MPEG audio layer 3) - Provides audio only with no text. These books are created with a text-to-speech engine and spoken by Kendra, a high quality synthetic voice from Ivona. Any device that supports MP3 playback is compatible.
• DAISY Audio - Similar to the DAISY option above; however, this option uses MP3 files created with our text-to-speech engine that utilizes Ivonas Kendra voice. This format will work with Daisy Audio compatible players such as Victor Reader Stream and Read2Go.
• EPUB - A standard e-book format that can be read on many tools, such as iBooks, Readium or others.
• Word - Accessible Word output, which can be unzipped and opened in any tool that allows .docx files

Data-Driven Numerical Modelling in Geodynamics: Methods and Applications (SpringerBriefs in Earth Sciences)

This book describes the methods and numerical approaches for data assimilation in geodynamical models and presents several applications of the described methodology in relevant case studies. The book starts with a brief overview of the basic principles in data-driven geodynamic modelling, inverse problems, and data assimilation methods, which is then followed by methodological chapters on backward advection, variational (or adjoint), and quasi-reversibility methods. The chapters are accompanied by case studies presenting the applicability of the methods for solving geodynamic problems; namely, mantle plume evolution; lithosphere dynamics in and beneath two distinct geological domains – the south-eastern Carpathian Mountains and the Japanese Islands; salt diapirism in sedimentary basins; and volcanic lava flow. Applications of data-driven modelling are of interest to the industry and to experts dealing with geohazards and risk mitigation. Explanation of the sedimentary basin evolution complicated by deformations due to salt tectonics can help in oil and gas exploration; better understanding of the stress-strain evolution in the past and stress localization in the present can provide an insight into large earthquake preparation processes; volcanic lava flow assessments can advise on risk mitigation in the populated areas. The book is an essential tool for advanced courses on data assimilation and numerical modelling in geodynamics.

Data Driven Strategies: Theory and Applications

A key challenge in science and engineering is to provide a quantitative description of the systems under investigation, leveraging the noisy data collected. Such a description may be a complete mathematical model or a mechanism to return controllers corresponding to new, unseen inputs. Recent advances in the theories are described in detail, along with their applications in engineering. The book aims to develop model-free system analysis and control strategies, i.e., data-driven control from theoretical analysis and engineering applications based only on measured data. The study aims to develop system identification, and combination in advanced control theory, i.e., data-driven control strategy as system and controller are generated from measured data directly. The book reviews the development of system identification and its combination in advanced control theory, i.e., data-driven control strategy, as they all depend on measured data. Firstly, data-driven identification is developed for the closed-loop, nonlinear system and model validation, i.e., obtaining model descriptions from measured data. Secondly, the data-driven idea is combined with some control strategies to be considered data-driven control strategies, such as data-driven model predictive control, data-driven iterative tuning control, and data-driven subspace predictive control. Thirdly data-driven identification and data-driven control strategies are applied to interested engineering. In this context, the book provides algorithms to perform state estimation of dynamical systems from noisy data and some convex optimization algorithms through identification and control problems.

Data Driven Strategies: Theory and Applications

A key challenge in science and engineering is to provide a quantitative description of the systems under investigation, leveraging the noisy data collected. Such a description may be a complete mathematical model or a mechanism to return controllers corresponding to new, unseen inputs. Recent advances in the theories are described in detail, along with their applications in engineering. The book aims to develop model-free system analysis and control strategies, i.e., data-driven control from theoretical analysis and engineering applications based only on measured data. The study aims to develop system identification, and combination in advanced control theory, i.e., data-driven control strategy as system and controller are generated from measured data directly. The book reviews the development of system identification and its combination in advanced control theory, i.e., data-driven control strategy, as they all depend on measured data. Firstly, data-driven identification is developed for the closed-loop, nonlinear system and model validation, i.e., obtaining model descriptions from measured data. Secondly, the data-driven idea is combined with some control strategies to be considered data-driven control strategies, such as data-driven model predictive control, data-driven iterative tuning control, and data-driven subspace predictive control. Thirdly data-driven identification and data-driven control strategies are applied to interested engineering. In this context, the book provides algorithms to perform state estimation of dynamical systems from noisy data and some convex optimization algorithms through identification and control problems.

Data-Driven Techniques in Speech Synthesis (Telecommunications Technology & Applications Series)

This first review of a new field covers all areas of speech synthesis from text, ranging from text analysis to letter-to-sound conversion. At the leading edge of current research, the concise and accessible book is written by well respected experts in the field.

Data for the People: How to Make Our Post-Privacy Economy Work for You

A long-time chief data scientist at Amazon shows how open data can make everyone, not just corporations, richerEvery time we Google something, Facebook someone, Uber somewhere, or even just turn on a light, we create data that businesses collect and use to make decisions about us. In many ways this has improved our lives, yet, we as individuals do not benefit from this wealth of data as much as we could. Moreover, whether it is a bank evaluating our credit worthiness, an insurance company determining our risk level, or a potential employer deciding whether we get a job, it is likely that this data will be used against us rather than for us.In Data for the People, Andreas Weigend draws on his years as a consultant for commerce, education, healthcare, travel and finance companies to outline how Big Data can work better for all of us. As of today, how much we benefit from Big Data depends on how closely the interests of big companies align with our own. Too often, outdated standards of control and privacy force us into unfair contracts with data companies, but it doesn't have to be this way. Weigend makes a powerful argument that we need to take control of how our data is used to actually make it work for us. Only then can we the people get back more from Big Data than we give it.Big Data is here to stay. Now is the time to find out how we can be empowered by it.

Data Governance: A Guide

This book is a comprehensive resource designed to demystify the complex world of data governance for professionals across various sectors. This guide provides in-depth insights, methodologies, and best practices to help organizations manage their data effectively and securely. It covers essential topics such as data quality, privacy, security, and management ensuring that readers gain a holistic understanding of how to establish and maintain a robust data governance framework. Through a blend of theoretical knowledge and practical applications, this book addresses the challenges and benefits of data governance, equipping readers with the tools needed to navigate the evolving data landscape. In addition to foundational principles, this book explores real-world case studies that illustrate the tangible benefits and common pitfalls of implementing data governance. Emerging trends and technologies, including artificial intelligence, machine learning, and blockchain are also examined to prepare readers for future developments in the field. Whether you are a seasoned data management professional or new to the discipline, this book serves as an invaluable resource for mastering the intricacies of data governance and leveraging data as a strategic asset for organizational success. This resourceful guide targets data management professionals, IT managers, Compliance officers, Data Stewards, Data Owners Data Governance Managers and more. Business leaders, business executives academic researchers, students focused on computer science in data-related fields will also find this book a useful resource.

Data Governance in AI, FinTech and LegalTech: Law and Regulation in the Financial Sector

Advocating for more standardised data governance practices and promoting the digital economy, Data Governance in AI, FinTech and LegalTech investigates the rationale, legal base and tools of data governance in the financial sector. This timely book makes a significant contribution to the debate around how rapidly-evolving digital finance practices should be regulated.Contributions from leading researchers examine a range of financial services, offering a comprehensive assessment of the available tools for constructing multi-layered matrix systems for data governance in the financial services sector. Chapters explore data governance in the cryptocurrency market, crypto-asset providers, legal services for mergers and acquisitions, consumer insurance, consumer finance, digital platform services, securities exchanges and the green bond market. The book serves to define the legal contours of data governance, taking account of the influence of shifting business models, the views of multiple stakeholders and emerging issues surrounding data protection, privacy and cybersecurity.This is a crucial read for scholars of law and finance who are researching data regulation, data governance and financial market law. Exploring both the opportunities and risks arising from the digital transformation of financial markets, it will also be invaluable for practitioners and policy makers working in the financial sector, law, risk management and compliance.

The Data Handbook: A Guide to Understanding the Organization and Visualization of Technical Data

"What our teachers don't tell us in school is that we will spend most of our scientific or engineering career in front of computers, trying to beat them into submission." This extract from the Preface sets the style for this highly readable book. It is packed with information covering data representations, the pitfalls of computer arithmetic, and a variety of widely-used representations and standards. Each chapter begins with a detailed contents list and finishes with a brief summary of the topics presented and the whole is rounded off with a glossary and index.Novices will enjoy an occasionally lighthearted read from start to finish, while even the most experienced computer users who use the book as a reference will discover useful nuggets of information.A structured array of data sets are available online via the TELOS Web site, www.telospub.com, which will provide users with direct digital access to information they might need in working through the book.

Data in Medicine: A Physical-Technical Introduction for Physicians and Biologists (Instrumentation and Techniques in Clinical Medicine #1)

Nowadays clinical medicine is to a great extent dependent on techniques and instrumentation. Not infrequently, instrumentation is so complicated that technical specialists are required to perform the measurements and to process the data. Interpretation of the results, however, generally has to be done by physicians. For proper interpretation of data and good com­ munication with technical specialists, knowledge of, among other things, principle, advantages, limitations and applicability of the used techniques is necessary. Besides, this knowledge is required for critical comparison of systems to measure a certain variable. Critical evaluation as well as com­ parison of techniques and instruments ought to be an essential component of medical practice. In general, basic techniques and instrumentation are not taught in medi­ cal schools nor during residencies. Therefore, physicians themselves have to collect practical information about principle, advantages and limitations of techniques and instruments when using them in clinical medicine. This practical information, focussed on the specific techniques used in the various disciplines, is usually difficult to obtain from handbooks and manufacturers' manuals. Hence a new series of books is started on instru­ mentation and techniques in clinical medicine.

Data, Instruments, and Theory: A Dialectical Approach to Understanding Science (PDF)

Robert John Ackermann deals decisively with the problem of relativism that has plagued post-empiricist philosophy of science. Recognizing that theory and data are mediated by data domains (bordered data sets produced by scientific instruments), he argues that the use of instruments breaks the dependency of observation on theory and thus creates a reasoned basis for scientific objectivity.Originally published in 1985.The Princeton Legacy Library uses the latest print-on-demand technology to again make available previously out-of-print books from the distinguished backlist of Princeton University Press. These editions preserve the original texts of these important books while presenting them in durable paperback and hardcover editions. The goal of the Princeton Legacy Library is to vastly increase access to the rich scholarly heritage found in the thousands of books published by Princeton University Press since its founding in 1905.

Data Integration in the Life Sciences: 4th International Workshop, DILS 2007, Philadelphia, PA, USA, June 27-29, 2007, Proceedings (Lecture Notes in Computer Science #4544)

This book constitutes the refereed proceedings of the 4th International Workshop on Data Integration in the Life Sciences, DILS 2007, held in Philadelphia, PA, USA in July 2007. It covers new architectures and experience on using systems, managing and designing scientific workflows, mapping and matching techniques, modeling of life science data, and annotation in data integration.

Data Integration in the Life Sciences: 7th International Conference, DILS 2010, Gothenburg, Sweden, August 25-27, 2010. Proceedings (Lecture Notes in Computer Science #6254)

The development and increasingly widespread deployment of high-throughput experimental methods in the life sciences is giving rise to numerous large, c- plex and valuable data resources. This foundation of experimental data und- pins the systematic study of organismsand diseases, which increasinglydepends on the development of models of biological systems. The development of these models often requires integration of diverse experimental data resources; once constructed, the models themselves become data and present new integration challenges for tasks such as interpretation, validation and comparison. The Data Integration in the Life Sciences (DILS) Conference series brings together data and knowledge management researchers from the computer s- ence research community with bioinformaticians and computational biologists, to improve the understanding of how emerging data integration techniques can address requirements identi?ed in the life sciences. DILS 2010 was the seventh event in the series and was held in Goth- burg, Sweden during August 25–27, 2010. The associated proceedings contain 14 peer-reviewed papers and 2 invited papers. The sessions addressed ontology engineering, and in particular, evolution, matching and debugging of ontologies, akeycomponentforsemanticintegration;Web servicesasanimportanttechn- ogy for data integration in the life sciences; data and text mining techniques for discovering and recognizing biomedical entities and relationships between these entities; and information management, introducing data integration solutions for di?erent types of applications related to cancer, systems biology and - croarray experimental data, and an approach for integrating ranked data in the life sciences.

Data Integration in the Life Sciences: Third International Workshop, DILS 2006, Hinxton, UK, July 20-22, 2006, Proceedings (Lecture Notes in Computer Science #4075)

This book constitutes the refereed proceedings of the Third International Workshop on Data Integration in the Life Sciences, DILS 2006, held in Hinxton, UK in July 2006. Presents 19 revised full papers and 4 revised short papers together with 2 keynote talks, addressing current issues in data integration from the life science point of view. The papers are organized in topical sections on data integration, text mining, systems, and workflow.

Data Integration in the Life Sciences: Second International Workshop, DILS 2005, San Diego, CA, USA, July 20-22, 2005, Proceedings (Lecture Notes in Computer Science #3615)

Data Integration in the Life Sciences: 6th International Workshop, DILS 2009, Manchester, UK, July 20-22, 2009, Proceedings (Lecture Notes in Computer Science #5647)

Data integration in the life sciences continues to be important but challe- ing. The ongoing development of new experimental methods gives rise to an increasingly wide range of data sets, which in turn must be combined to allow more integrative views of biological systems. Indeed, the growing prominence of systems biology, where mathematical models characterize behaviors observed in experiments of di?erent types, emphasizes the importance of data integration to the life sciences. In this context, the representation of models of biological behavior as data in turn gives rise to challenges relating to provenance, data quality, annotation, etc., all of which are associated with signi?cant research activities within computer science. The Data Integration in the Life Sciences (DILS) Workshop Series brings together data and knowledge management researchers from the computer s- ence research community with bioinformaticians and computational biologists, to improve the understanding of how emerging data integration techniques can address requirements identi?ed in the life sciences.

Data Integration in the Life Sciences: First International Workshop, DILS 2004, Leipzig, Germany, March 25-26, 2004, proceedings (Lecture Notes in Computer Science #2994)

Data Journeys in the Sciences

This groundbreaking, open access volume analyses and compares data practices across several fields through the analysis of specific cases of data journeys. It brings together leading scholars in the philosophy, history and social studies of science to achieve two goals: tracking the travel of data across different spaces, times and domains of research practice; and documenting how such journeys affect the use of data as evidence and the knowledge being produced. The volume captures the opportunities, challenges and concerns involved in making data move from the sites in which they are originally produced to sites where they can be integrated with other data, analysed and re-used for a variety of purposes. The in-depth study of data journeys provides the necessary ground to examine disciplinary, geographical and historical differences and similarities in data management, processing and interpretation, thus identifying the key conditions of possibility for the widespread data sharing associated with Big and Open Data. The chapters are ordered in sections that broadly correspond to different stages of the journeys of data, from their generation to the legitimisation of their use for specific purposes. Additionally, the preface to the volume provides a variety of alternative “roadmaps” aimed to serve the different interests and entry points of readers; and the introduction provides a substantive overview of what data journeys can teach about the methods and epistemology of research.

Data Mining and Applications in Genomics (Lecture Notes in Electrical Engineering #25)

Data Mining and Applications in Genomics contains the data mining algorithms and their applications in genomics, with frontier case studies based on the recent and current works at the University of Hong Kong and the Oxford University Computing Laboratory, University of Oxford. It provides a systematic introduction to the use of data mining algorithms as an investigative tool for applications in genomics. Data Mining and Applications in Genomics offers state of the art of tremendous advances in data mining algorithms and applications in genomics and also serves as an excellent reference work for researchers and graduate students working on data mining algorithms and applications in genomics.

Data Mining and Exploration: From Traditional Statistics to Modern Data Science

This book introduces both conceptual and procedural aspects of cutting-edge data science methods, such as dynamic data visualization, artificial neural networks, ensemble methods, and text mining. There are at least two unique elements that can set the book apart from its rivals. First, most students in social sciences, engineering, and business took at least one class in introductory statistics before learning data science. However, usually these courses do not discuss the similarities and differences between traditional statistics and modern data science; as a result learners are disoriented by this seemingly drastic paradigm shift. In reaction, some traditionalists reject data science altogether while some beginning data analysts employ data mining tools as a “black box”, without a comprehensive view of the foundational differences between traditional and modern methods (e.g., dichotomous thinking vs. pattern recognition, confirmation vs. exploration, single method vs. triangulation, single sample vs. cross-validation etc.). This book delineates the transition between classical methods and data science (e.g. from p value to Log Worth, from resampling to ensemble methods, from content analysis to text mining etc.). Second, this book aims to widen the learner's horizon by covering a plethora of software tools. When a technician has a hammer, every problem seems to be a nail. By the same token, many textbooks focus on a single software package only, and consequently the learner tends to fit the problem with the tool, but not the other way around. To rectify the situation, a competent analyst should be equipped with a tool set, rather than a single tool. For example, when the analyst works with crucial data in a highly regulated industry, such as pharmaceutical and banking, commercial software modules (e.g., SAS) are indispensable. For a mid-size and small company, open-source packages such as Python would come in handy. If the research goal is to create an executive summary quickly, the logical choice is rapid model comparison. If the analyst would like to explore the data by asking what-if questions, then dynamic graphing in JMP Pro is a better option. This book uses concrete examples to explain the pros and cons of various software applications.

Data Mining and Exploration: From Traditional Statistics to Modern Data Science

This book introduces both conceptual and procedural aspects of cutting-edge data science methods, such as dynamic data visualization, artificial neural networks, ensemble methods, and text mining. There are at least two unique elements that can set the book apart from its rivals. First, most students in social sciences, engineering, and business took at least one class in introductory statistics before learning data science. However, usually these courses do not discuss the similarities and differences between traditional statistics and modern data science; as a result learners are disoriented by this seemingly drastic paradigm shift. In reaction, some traditionalists reject data science altogether while some beginning data analysts employ data mining tools as a “black box”, without a comprehensive view of the foundational differences between traditional and modern methods (e.g., dichotomous thinking vs. pattern recognition, confirmation vs. exploration, single method vs. triangulation, single sample vs. cross-validation etc.). This book delineates the transition between classical methods and data science (e.g. from p value to Log Worth, from resampling to ensemble methods, from content analysis to text mining etc.). Second, this book aims to widen the learner's horizon by covering a plethora of software tools. When a technician has a hammer, every problem seems to be a nail. By the same token, many textbooks focus on a single software package only, and consequently the learner tends to fit the problem with the tool, but not the other way around. To rectify the situation, a competent analyst should be equipped with a tool set, rather than a single tool. For example, when the analyst works with crucial data in a highly regulated industry, such as pharmaceutical and banking, commercial software modules (e.g., SAS) are indispensable. For a mid-size and small company, open-source packages such as Python would come in handy. If the research goal is to create an executive summary quickly, the logical choice is rapid model comparison. If the analyst would like to explore the data by asking what-if questions, then dynamic graphing in JMP Pro is a better option. This book uses concrete examples to explain the pros and cons of various software applications.

Data Mining and Knowledge Discovery for Process Monitoring and Control (Advances in Industrial Control)

Modern computer-based control systems are able to collect a large amount of information, display it to operators and store it in databases but the interpretation of the data and the subsequent decision making relies mainly on operators with little computer support. This book introduces developments in automatic analysis and interpretation of process-operational data both in real-time and over the operational history, and describes new concepts and methodologies for developing intelligent, state space-based systems for process monitoring, control and diagnosis. The book brings together new methods and algorithms from process monitoring and control, data mining and knowledge discovery, artificial intelligence, pattern recognition, and causal relationship discovery, as well as signal processing. It also provides a framework for integrating plant operators and supervisors into the design of process monitoring and control systems.

Data Mining for Bioinformatics

Covering theory, algorithms, and methodologies, as well as data mining technologies, Data Mining for Bioinformatics provides a comprehensive discussion of data-intensive computations used in data mining with applications in bioinformatics. It supplies a broad, yet in-depth, overview of the application domains of data mining for bioinformatics to he

Data Mining for Bioinformatics

Covering theory, algorithms, and methodologies, as well as data mining technologies, Data Mining for Bioinformatics provides a comprehensive discussion of data-intensive computations used in data mining with applications in bioinformatics. It supplies a broad, yet in-depth, overview of the application domains of data mining for bioinformatics to he

Data Mining for Biomarker Discovery (Springer Optimization and Its Applications #65)

Biomarker discovery is an important area of biomedical research that may lead to significant breakthroughs in disease analysis and targeted therapy. Biomarkers are biological entities whose alterations are measurable and are characteristic of a particular biological condition. Discovering, managing, and interpreting knowledge of new biomarkers are challenging and attractive problems in the emerging field of biomedical informatics.This volume is a collection of state-of-the-art research into the application of data mining to the discovery and analysis of new biomarkers. Presenting new results, models and algorithms, the included contributions focus on biomarker data integration, information retrieval methods, and statistical machine learning techniques.This volume is intended for students, and researchers in bioinformatics, proteomics, and genomics, as well engineers and applied scientists interested in the interdisciplinary application of data mining techniques.

Data Mining for Biomedical Applications: PAKDD 2006 Workshop, BioDM 2006, Singapore, April 9, 2006, Proceedings (Lecture Notes in Computer Science #3916)

This book constitutes the refereed proceedings of the International Workshop on Data Mining for Biomedical Applications, BioDM 2006, held in Singapore in conjunction with the 10th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2006). The 14 revised full papers presented together with one keynote talk were carefully reviewed and selected from 35 submissions. The papers are organized in topical sections