Understanding MARC Bibliographic


Book Description




The MARC II Format


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Practical Cataloguing


Book Description

This essential new textbook provides cataloguers with the skills needed for transition to Resource Description and Access (RDA). The book builds on John Bowman's highly regarded Essential Cataloguing and gives an introduction to Functional Requirements for Bibliographic Records (FRBR), which provides the conceptual basis for RDA; discusses the differences between AACR2 and RDA; and shows the current state of play in MARC 21. Key topics are: introduction to catalogues and cataloguing standards the FRBRization of the catalogue bibliographic elements access points and headings RDA: the new standard, its development, structure and features AACR and RDA: the similarities and differences between the two standards the MARC21 record bringing it all together the birth of RDA and the death of MARC. The final chapter includes ten records displayed in AACR2 level 1, AACR2 level 2, RDA and MARC 21, making it easy to see the differences at a glance. There is also a fully explained worked example based on RDA Appendix M. Readership: Written at a time of transition in international cataloguing, this book provides cataloguers and students with a background in general cataloguing principles, the current code (AACR2) and format (MARC 21) and the new standard (RDA). The contextual chapters provide library managers with an up-to-date overview of the development of RDA in order to equip them to make the transition. The book will be essential reading for students of library and information studies and practising library and information professionals in all sectors. It will also be of great interest to the archives sector.




Bibliographic Formats and Standards


Book Description

Describes the manual, Bibliographic Formats and Standards, 2nd. ed., a revised guide to machine-readable cataloging records in the WorldCat. Describes conventions. Describes and provides an example of input standards tables. Addresses revisions of the manual as well as ordering and distribution. Includes acknowledgements. Provides a link to the table of contents.




AACR2-e


Book Description

Contains complete text of the Anglo-American Cataloging Rules, 2d ed., 1998 rev., including all amendments, all appendices, a fully searchable table of contents and index, a tutorial, and Folio Views Infobase.




The MARC II Format, Supplement One


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MARC 21 for Everyone


Book Description

Provides an introduction to MARC21, including quizzes, tables, and examples, to explain the shared language of tags, subfields, indicators, and codes.




MARC Manual


Book Description

If you are in the process-beginning, middle, or end-of automating your catalog, you will welcome the wealth of information in this concise, easy-to-use handbook. Created for librarians new to MARC and for those accustomed to using MARC data, it explains all three types of MARC records, and it gives considerations and specifications for MARC database processing, MARC products, and online systems. Byrne addresses MARC format integration in a separate chapter new to this edition and thoroughly explains the new and changed MARC codes that resulted from MARC format integration. In another new chapter she covers the MARC Format for Community Information. All information has been updated- including that on MARC authority records and holdings records.




Mathematics for Machine Learning


Book Description

The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.