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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 User’s Guide is intended to support the design, implementation, analysis, interpretation, and quality evaluation of registries created to increase understanding of patient outcomes. For the purposes of this guide, a patient registry is an organized system that uses observational study methods to collect uniform data (clinical and other) to evaluate specified outcomes for a population defined by a particular disease, condition, or exposure, and that serves one or more predetermined scientific, clinical, or policy purposes. A registry database is a file (or files) derived from the registry. Although registries can serve many purposes, this guide focuses on registries created for one or more of the following purposes: to describe the natural history of disease, to determine clinical effectiveness or cost-effectiveness of health care products and services, to measure or monitor safety and harm, and/or to measure quality of care. Registries are classified according to how their populations are defined. For example, product registries include patients who have been exposed to biopharmaceutical products or medical devices. Health services registries consist of patients who have had a common procedure, clinical encounter, or hospitalization. Disease or condition registries are defined by patients having the same diagnosis, such as cystic fibrosis or heart failure. The User’s Guide was created by researchers affiliated with AHRQ’s Effective Health Care Program, particularly those who participated in AHRQ’s DEcIDE (Developing Evidence to Inform Decisions About Effectiveness) program. Chapters were subject to multiple internal and external independent reviews.
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.
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.
This benchmark text is back in a new edition thoroughly updated to incorporate developments and changes in metadata and related domains. Zeng and Qin provide a solid grounding in the variety and interrelationships among different metadata types, offering a comprehensive look at the metadata schemas that exist in the world of library and information science and beyond. Readers will gain knowledge and an understanding of key topics such as the fundamentals of metadata, including principles of metadata, structures of metadata vocabularies, and metadata descriptions; metadata building blocks, from modeling to defining properties, from designing application profiles to implementing value vocabularies, and from specification generating to schema encoding, illustrated with new examples; best practices for metadata as linked data, the new functionality brought by implementing the linked data principles, and the importance of knowledge organization systems; resource metadata services, quality measurement, and interoperability approaches; research data management concepts like the FAIR principles, metadata publishing on the web and the recommendations by the W3C in 2017, related Open Science metadata standards such as Data Catalog Vocabulary (DCAT) version 2, and metadata-enabled reproducibility and replicability of research data; standards used in libraries, archives, museums, and other information institutions, plus existing metadata standards’ new versions, such as the EAD 3, LIDO 1.1, MODS 3.7, DC Terms 2020 release coordinating its ISO 15396-2:2019, and Schema.org’s update in responding to the pandemic; and newer, trending forces that are impacting the metadata domain, including entity management, semantic enrichment for the existing metadata, mashup culture such as enhanced Wikimedia contents, knowledge graphs and related processes, semantic annotations and analysis for unstructured data, and supporting digital humanities (DH) through smart data. A supplementary website provides additional resources, including examples, exercises, main takeaways, and editable files for educators and trainers.
The first-ever WHO Report on Patient Safety, the "Global Patient Safety Report 2024", offers a comprehensive overview of patient safety implementation worldwide. Aligned with the Global Patient Safety Action Plan 2021–2030, this report explores policies, strategies, and initiatives shaping safety in health care. From analyses of country actions to in-depth summaries of burden of unsafe care, it provides crucial insights for policy-makers, health care leaders, researchers, and patient safety advocates. Explore how nations address challenges, learn from case studies and feature stories, and gain deeper understanding in priority areas for action. This report serves as a vital resource for fostering global collaboration and advancing patient safety in health care. The contents of this report encompass: - An analysis that compiles and describes actions taken by countries, including the summary of these actions across different WHO regions and income levels based on Member State survey. - An in-depth summary presenting evidence on the overall burden of unsafe health care practices, viewed broadly as well as within specific population groups, clinical domains, and according to major sources of harm. - Case studies showcasing how different countries are learning and developing patient safety solutions within their unique contexts, along with feature stories highlighting key global initiatives and interventions in patient safety. - Comparative analyses offering deeper insights into crucial areas such as patient safety policies, legal frameworks, patient involvement, educational initiatives, reporting and learning systems, and the involvement of various stakeholders.
As data management and integration continue to evolve rapidly, storing all your data in one place, such as a data warehouse, is no longer scalable. In the very near future, data will need to be distributed and available for several technological solutions. With this practical book, you’ll learnhow to migrate your enterprise from a complex and tightly coupled data landscape to a more flexible architecture ready for the modern world of data consumption. Executives, data architects, analytics teams, and compliance and governance staff will learn how to build a modern scalable data landscape using the Scaled Architecture, which you can introduce incrementally without a large upfront investment. Author Piethein Strengholt provides blueprints, principles, observations, best practices, and patterns to get you up to speed. Examine data management trends, including technological developments, regulatory requirements, and privacy concerns Go deep into the Scaled Architecture and learn how the pieces fit together Explore data governance and data security, master data management, self-service data marketplaces, and the importance of metadata
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
Every enterprise application creates data, whether it’s log messages, metrics, user activity, outgoing messages, or something else. And how to move all of this data becomes nearly as important as the data itself. If you’re an application architect, developer, or production engineer new to Apache Kafka, this practical guide shows you how to use this open source streaming platform to handle real-time data feeds. Engineers from Confluent and LinkedIn who are responsible for developing Kafka explain how to deploy production Kafka clusters, write reliable event-driven microservices, and build scalable stream-processing applications with this platform. Through detailed examples, you’ll learn Kafka’s design principles, reliability guarantees, key APIs, and architecture details, including the replication protocol, the controller, and the storage layer. Understand publish-subscribe messaging and how it fits in the big data ecosystem. Explore Kafka producers and consumers for writing and reading messages Understand Kafka patterns and use-case requirements to ensure reliable data delivery Get best practices for building data pipelines and applications with Kafka Manage Kafka in production, and learn to perform monitoring, tuning, and maintenance tasks Learn the most critical metrics among Kafka’s operational measurements Explore how Kafka’s stream delivery capabilities make it a perfect source for stream processing systems
Africa is not on track to meeting the Sustainable Development Goal (SDG) 2 targets to end hunger and ensure access by all people to safe, nutritious and sufficient food all year round and to end all forms of malnutrition. The number of hungry people on the continent has risen by 47.9 million since 2014 and now stands at 250.3 million, or nearly one-fifth of the population. The 2017, 2018 and 2019 editions of this report explain that this gradual deterioration of food security was due to conflict, weather extremes, and economic slowdowns and downturns, often overlapping. A continued worsening of food security is expected also for 2020 as a result of the COVID-19 pandemic. In addition to hunger, across all countries in Africa millions of people suffer from widespread micronutrient deficiencies, and overweight and obesity are emerging as significant health concerns in many countries. This report shows that the food system in Africa does not provide food at a cost that makes nutritious food affordable to a majority of the population, and this is reflected in the high disease burden associated with maternal and child malnutrition, high body-mass, micronutrient deficiencies and dietary risk factors. The report also shows that current food consumption patterns impose high health and environmental costs, which are not reflected in food prices. The findings presented in this report highlight the importance of prioritizing the transformation of food systems to ensure access to affordable and healthy diets for all, produced in a sustainable manner.