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In this new, authoritative textbook, internationally recognized metadata experts Zeng and Qin have created a comprehensive primer for advanced undergraduate, graduate, or continuing education courses in information organization, information technology, cataloging, digital libraries, electronic archives, and, of course, metadata.
What is metadata and what do I need to know about it? These are two key questions for the information professional operating in the digital age as more and more information resources are available in electronic format. This is a thought-provoking introduction to metadata written by one of its leading advocates. It assesses the current theory and practice of metadata and examines key developments - including global initiatives and multilingual issues - in terms of both policy and technology. Subjects discussed include: What is metadata? definitions and concepts Retrieval environments: web; library catalogues; documents and records management; GIS; e-Learning Using metadata to enhance retrieval: pointing to content; subject retrieval; language control and indexing Information management issues: interoperability; information security; authority control; authentication and legal admissibility of evidence; records management and document lifecyc≤ preservation issues Application of metadata to information management: document and records management; content management systems for the internet Managing metadata: how to develop a schema Standards development: Dublin Core; UK Government metadata standards (eGIF); IFLA FRBR Model for cataloguing resources Looking forward: the semantic web; the Web Ontology Working Group. Readership: This book will be essential reading for network-oriented librarians and information workers in all sectors and for LIS students. In addition, it will provide useful background reading for computer staff supporting information services. Publishers, policy makers and practitioners in other curatorial traditions such as museums work or archiving will also find much of relevance.
This book provides a practical introduction to metadata for the digital library, describing in detail how to implement a strategic approach which will enable complex digital objects to be discovered, delivered and preserved in the short- and long-term.
Since it was first published, LIS students and professionals everywhere have relied on Miller’s authoritative manual for clear instruction on the real-world practice of metadata design and creation. Now the author has given his text a top to bottom overhaul to bring it fully up to date, making it even easier for readers to acquire the knowledge and skills they need, whether they use the book on the job or in a classroom. By following this book’s guidance, with its inclusion of numerous practical examples that clarify common application issues and challenges, readers will learn about the concept of metadata and its functions for digital collections, why it’s essential to approach metadata specifically as data for machine processing, and how metadata can work in the rapidly developing Linked Data environment; know how to create high-quality resource descriptions using widely shared metadata standards, vocabularies, and elements commonly needed for digital collections; become thoroughly familiarized with Dublin Core (DC) through exploration of DCMI Metadata Terms, CONTENTdm best practices, and DC as Linked Data; discover what Linked Data is, how it is expressed in the Resource Description Framework (RDF), and how it works in relation to specific semantic models (typically called “ontologies”) such as BIBFRAME, comprised of properties and classes with “domain” and “range” specifications; get to know the MODS and VRA Core metadata schemes, along with recent developments related to their use in a Linked Data setting; understand the nuts and bolts of designing and documenting a metadata scheme; and gain knowledge of vital metadata interoperability and quality issues, including how to identify and clean inconsistent, missing, and messy metadata using innovative tools such as OpenRefine.
Autodesk Maya 2020 is a powerful, integrated 3D modeling, animation, visual effects, and rendering software developed by Autodesk Inc. This integrated node based 3D software finds its application in the development of films, games, and design projects. The intuitive user interface and workflow tools of Maya 2020 have made the job of design visualization specialists a lot easier. Autodesk Maya 2020: A Comprehensive Guide covers all features of Autodesk Maya 2020 software in a simple, lucid, and comprehensive manner. It will unleash your creativity, thus helping you create realistic 3D models, animation, and visual effects. In this edition, new tools and enhancements in modeling, animation, rigging as well as performance improvements in bifrost are covered. Additionally, the newly introduced Mash module, which is used for creating motion graphics, is also covered in the book. Salient Features: Consists of 17 chapters that are organized in a pedagogical sequence covering a wide range of topics such as Maya interface, Polygon modeling, NURBS modeling, texturing, lighting, cameras, animation, Paint Effects, Rendering, nHair, Fur, Fluids, Particles, nParticles and Bullet Physics in Autodesk Maya 2020. The first page of every chapter summarizes the topics that are covered in it. Consists of hundreds of illustrations and a comprehensive coverage of Autodesk Maya 2020 concepts & commands. Real-world 3D models and examples focusing on industry experience. Step-by-step instructions that guide the user through the learning process. Additional information is provided throughout the book in the form of tips and notes. Self-Evaluation test, Review Questions, and Exercises are given at the end of each chapter so that the users can assess their knowledge. Table of Contents Chapter 1: Exploring Maya Interface Chapter 2: Polygon Modeling Chapter 3: NURBS Curves and Surfaces Chapter 4: NURBS Modeling Chapter 5: UV Mapping Chapter 6: Shading and Texturing Chapter 7: Lighting Chapter 8: Animation Chapter 9: Rigging, Constraints, and Deformers Chapter 10: Paint Effects Chapter 11: Rendering Chapter 12: Particle System Chapter 13: Introduction to nParticles Chapter 14: Fluids Chapter 15: nHair Chapter 16: Bifrost Chapter 17: Bullet Physics and Motion Graphics Index
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
Metadata (also known as structured data) plays a growing role in how customers and other online audiences get information. Well-defined metadata ensures that digital content is ease-to-locate, is up-to-date, can be targeted to specific needs, and can be re-used for multiple purposes by both the publishers and consumers of the content. Metadata plays a key role in SEO, content licensing, content marketing, social media visibility, analytics, and mobile app design. Metadata is most powerful when it is designed and developed in an integrated manner, where all these roles support each other. Metadata Basics for Web Content is the first comprehensive survey discussing the various kinds of metadata available to support the creation, management, delivery, and assessment of web content. The book is designed to help publishers of web content understand the many benefits of metadata, and identify what they need to do to realize these benefits.Metadata may sound like a specialized technical topic, but it affects everyone who is involved with publishing content online. Effective metadata requires the collaboration of various members of a web team. The book provides insights about metadata will be useful for web team members with different responsibilities, whether they are authors, content strategists, SEOs, web analytics professionals, user experience designers, front-end developers, or marketing experts. The book provides a foundation for publishers to develop integrated requirements relating to web metadata, so that their content can be successful in supporting a diverse range of business goals.Book features: Extensive diagrams explaining key conceptsGlossary of over 75 important termsOver 200 footnotes providing additional details and links to tutorialsSimple code examples illustrating concepts discussed. Links to resources such as important industry standards and software toolsAbout the AuthorMichael C Andrews is an American IT consultant currently based in Hyderabad, India. He started working with online metadata as a technical information specialist at the US Commerce Department in the 1980s, and was among the first wave of people whose full-time job responsibilities focused on using the Internet to access and manage published content. For the past 15 years he has worked as a consultant in the fields of user experience and content strategy. He's worked as a senior manager for content strategy with one of the world's largest digital consultancies, and has advised clients such the National Institutes of Health, Verizon and the World Bank. He has lived and worked in the US, UK, New Zealand, Italy, as well as India.Andrews has an MSc in human computer interaction from the University of Sussex in England, and a Masters with a specialization in international finance from Columbia University in New York. He also has a certificate in XML and RDF Technologies from the Library Juice Academy.
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Perform fast interactive analytics against different data sources using the Presto high-performance, distributed SQL query engine. With this practical guide, you’ll learn how to conduct analytics on data where it lives, whether it’s Hive, Cassandra, a relational database, or a proprietary data store. Analysts, software engineers, and production engineers will learn how to manage, use, and even develop with Presto. Initially developed by Facebook, open source Presto is now used by Netflix, Airbnb, LinkedIn, Twitter, Uber, and many other companies. Matt Fuller, Manfred Moser, and Martin Traverso show you how a single Presto query can combine data from multiple sources to allow for analytics across your entire organization. Get started: Explore Presto’s use cases and learn about tools that will help you connect to Presto and query data Go deeper: Learn Presto’s internal workings, including how to connect to and query data sources with support for SQL statements, operators, functions, and more Put Presto in production: Secure Presto, monitor workloads, tune queries, and connect more applications; learn how other organizations apply Presto