Cataloguing

Most of you probably know what the CIP [Cataloging in Publication] data block is, even if you do not realize it. A Cataloging in Publication record (aka CIP data) is a bibliographic record prepared by the Library of Congress for a book that has not yet been published. When the book is published, the publisher includes the CIP data on the copyright page (usually the verso of the title page of print books), thereby facilitating book processing for libraries and book dealers.

The Bodleian Libraries has launched an ambitious project to create a new web-based research tool that will allow scholars and members of the public to view and search the complete photographic works of British photographic pioneer William Henry Fox Talbot.
This online catalogue raisonné will include images of thousands of photographs and negatives by Talbot and his close circle. It will shed new light on Talbot’s photographic discoveries and will invite academics and the public to help fill in the blanks about mystery images.

The Earth may not be flat, but the web certainly is.

“There is no ‘top’ to the World-Wide Web,” declared a 1992 foundational document from the World Wide Web Consortium—meaning that there is no central server or organizational authority to determine what does or does not get published. It is, like Borges’ famous Library of Babel, theoretically infinite, stitched together with hyperlinks rather than top-down, Dewey Decimal-style categories.1 It is also famously open—built atop a set of publicly available industry standards.

The Online Audiovisual Catalogers Cataloging and Policy Committee (OLAC CAPC) is pleased to announce the publication of two Best Practice Guides – “Best Practices for Streaming Media Using RDA and MARC21” and “Best Practices for Cataloging DVD-Video and Blu-ray Discs Using RDA and MARC21.”

In addition to the set of best practices, both documents include many in-line and full MARC record examples illustrating the best practices. The documents are accessible at the OLAC website.
Direct links to each document:

Libraries, archives, museums, and other cultural heritage organizations collect, create, and steward a rapidly increasing volume of digital content. Both research conclusions and professionals’ real-life experiences expose the inherent fragility of this content. The cultural heritage and information science communities have developed guidelines, best practices, policies, procedures, and processes that can enable an organization to achieve high levels of digital preservation.

People can summarize a complex scene in a few words without thinking twice. It’s much more difficult for computers. But we’ve just gotten a bit closer -- we’ve developed a machine-learning system that can automatically produce captions (like the three above) to accurately describe images the first time it sees them. This kind of system could eventually help visually impaired people understand pictures, provide alternate text for images in parts of the world where mobile connections are slow, and make it easier for everyone to search on Google for images.

We present a model that generates free-form natural language descriptions of image regions. Our model leverages datasets of images and their sentence descriptions to learn about the inter-modal correspondences between text and visual data. Our approach is based on a novel combination of Convolutional Neural Networks over image regions, bidirectional Recurrent Neural Networks over sentences, and a structured objective that aligns the two modalities through a multimodal embedding.

The Cataloging in Publication (CIP) Program is now accepting e-books from major U.S. publishers to help build the Library of Congress’ collections. This is a major step forward in management of e-book collections at the Library of Congress, as the process for ingesting CIP e-books will be used as a model for acquiring e-books from other acquisitions sources.

Abstract
DCAT is an RDF vocabulary designed to facilitate interoperability between data catalogs published on the Web. This document defines the schema and provides examples for its use.
By using DCAT to describe datasets in data catalogs, publishers increase discoverability and enable applications easily to consume metadata from multiple catalogs. It further enables decentralized publishing of catalogs and facilitates federated dataset search across sites. Aggregated DCAT metadata can serve as a manifest file to facilitate digital preservation.

The Library of Congress and the Association of College and Research Libraries have updated the cataloging guidelines for describing pictures, and they are now available in a free, online book, “Descriptive Cataloging of Rare Materials (Graphics).”