cover of book
 

Metadata for Digital Collections
by Steven Jack Miller
American Library Association, 2022
eISBN: 978-0-8389-3801-0 | Paper: 978-0-8389-4748-7
Library of Congress Classification Z695.24
Dewey Decimal Classification 025.3

ABOUT THIS BOOK | AUTHOR BIOGRAPHY | REVIEWS | TOC
ABOUT THIS BOOK

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.

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